diff --git a/KalmanFilter.py b/KalmanFilter.py
new file mode 100644
index 0000000..db8857c
--- /dev/null
+++ b/KalmanFilter.py
@@ -0,0 +1,179 @@
+import numpy as np
+import scipy.linalg
+
+class KalmanFilter(object):
+
+    def __init__(self):
+        ndim, dt = 2, 1.
+
+        # Create Kalman filter model matrices.
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        self._std_position = 0.200 
+        self._std_velocity = 0.0350
+
+    def initiate(self, measurement):
+        """Create track from unassociated measurement.
+
+        Parameters
+        ----------
+        measurement : ndarray
+            world coordinates of a box (x,y)
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector (4 dimensional) and covariance matrix (4x4
+            dimensional) of the new track. Unobserved velocities are initialized
+            to 0 mean.
+
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            self._std_position,
+            self._std_position,
+            self._std_velocity,
+            self._std_velocity
+        ]
+
+        covariance = np.diag(np.square(std))
+
+        return mean, covariance
+
+    def predict(self, mean, covariance):
+        """Run Kalman filter prediction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The 4 dimensional mean vector of the object state at the previous time step.
+        covariance : ndarray
+            The 4x4 dimensional covariance matrix of the object state at the previous time step.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+        """
+
+        std_pos = [
+            self._std_position,
+            self._std_position
+        ]
+
+        std_vel = [
+            self._std_velocity,
+            self._std_velocity
+        ]
+
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+        mean = np.dot(self._motion_mat, mean)
+
+        covariance = np.linalg.multi_dot((self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance):
+        """Project state distribution to measurement space.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The state's mean vector (4 dimensional array).
+        covariance : ndarray
+            The state's covariance matrix (4x4 dimensional).
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the projected mean and covariance matrix of the given state
+            estimate.
+
+        """
+
+        std = [
+            self._std_position,
+            self._std_position
+        ]
+
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._update_mat, covariance, self._update_mat.T))
+            
+        return mean, covariance + innovation_cov
+        
+    def update(self, mean, covariance, measurement):
+        """Run Kalman filter correction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The predicted state's mean vector (4 dimensional).
+        covariance : ndarray
+            The state's covariance matrix (4x4 dimensional).
+        measurement : ndarray
+            world coordinates of a box (x, y)
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the measurement-corrected state distribution.
+        """
+
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(
+            projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
+            check_finite=False).T
+
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        new_covariance = covariance - np.linalg.multi_dot((
+            kalman_gain, projected_cov, kalman_gain.T))
+
+        return new_mean, new_covariance
+
+    def gating_distance(self, mean, covariance, pos):
+        """Compute gating distance between state distribution and measurements.
+
+        A suitable distance threshold can be obtained from `chi2inv95`. If
+        `only_position` is False, the chi-square distribution has 4 degrees of
+        freedom, otherwise 2.
+
+        Parameters
+        ----------
+        mean : ndarray
+            Mean vector over the state distribution (4 dimensional).
+        covariance : ndarray
+            Covariance of the state distribution (4x4 dimensional).
+        pos : ndarray
+
+        Returns
+        -------
+        ndarray
+        """
+
+        mean, covariance = self.project(mean, covariance)
+        
+        cholesky_factor = np.linalg.cholesky(covariance)
+        d = pos - mean
+        z = scipy.linalg.solve_triangular(
+            cholesky_factor, d.T, lower=True, check_finite=False,
+            overwrite_b=True)
+        squared_maha = np.sum(z * z, axis=0)
+        
+        return squared_maha
\ No newline at end of file
diff --git a/Track.py b/Track.py
new file mode 100644
index 0000000..aa58303
--- /dev/null
+++ b/Track.py
@@ -0,0 +1,23 @@
+class OnlineTrack:
+    def __init__(self, id, mean, covariance, kf):
+        """
+            each Track object has mean [x,y,dx,dy] and covariance matrix
+            and receives Kalman Filter object
+        """
+        self.id = id
+        self.mean = mean
+        self.covariance = covariance
+        self.kf = kf
+
+    def update(self, detection):
+        """
+            updates corresponding Kalman Filter object's parameters & mean and covariance of the track
+            detection - is a box in x,y,w,h format
+        """
+        self.mean, self.covariance = self.kf.update(self.mean, self.covariance, detection)
+
+    def predict(self):
+        """
+            updates track's mean and covariance
+        """
+        self.mean, self.covariance = self.kf.predict(self.mean, self.covariance)
diff --git a/Tracker.py b/Tracker.py
new file mode 100644
index 0000000..6bd4bc5
--- /dev/null
+++ b/Tracker.py
@@ -0,0 +1,71 @@
+from Track import OnlineTrack
+import numpy as np
+import KalmanFilter
+
+from scipy.optimize import linear_sum_assignment
+import utils
+
+class OnlineTracker:
+
+    def __init__(self):
+        self.tracks = []
+        self._next_id = 1
+
+    def onlineTrackerInit(self, boxes_init):
+
+        for box in boxes_init:
+            new_kf = KalmanFilter.KalmanFilter()
+            mean, cov = new_kf.initiate(np.asarray(box.to_world()))
+            self.tracks.append(OnlineTrack(self._next_id, mean, cov, new_kf))
+            self._next_id += 1
+
+        # print ('%d tracks created' % (len(self.tracks)))
+
+        return
+
+    def onlineTrackerAssign(self, measurements, fnum, thresh=5.9915, inf=1000000):
+
+        for track in self.tracks:
+            track.predict() # updates track.mean and track.covariance
+        
+        cost_matrix = np.zeros((len(self.tracks), len(measurements)))
+
+        for i, track in enumerate(self.tracks):
+            for j, box in enumerate(measurements):
+                sq_mah_dist = track.kf.gating_distance(track.mean, track.covariance, box.to_world())
+                cost_matrix[i][j] = inf if sq_mah_dist >= thresh else sq_mah_dist
+
+        row_indices, col_indices = linear_sum_assignment(cost_matrix)
+        
+        matches = []
+        unmatched_tracks = []
+        unmatched_dets = []
+
+        for row, col in zip(row_indices, col_indices):
+            if cost_matrix[row][col] == inf:
+                # print ('f:%d id:%d cost:%f' % (fnum, self.tracks[row].id, cost_matrix[row][col]))
+                unmatched_tracks.append(row)
+                unmatched_dets.append(col)
+            else:
+                matches.append((row, col))
+
+        for i, _ in enumerate(self.tracks):
+            if i not in row_indices:
+                unmatched_tracks.append(i)
+
+        for j, _ in enumerate(measurements):
+            if j not in col_indices:
+                unmatched_dets.append(j)
+
+        # for tidx in unmatched_tracks:
+        #     print ('f:%d id:%d unmatched' % (fnum, self.tracks[tidx].id))
+
+        return matches, unmatched_tracks, unmatched_dets
+
+    def onlineTrackerUpdate(self, matches, measurements):
+        """
+            tracks.update - new_measurements
+        """
+        
+        for match in matches:
+            self.tracks[match[0]].update(measurements[match[1]].to_world())
diff --git a/TrackletNode.py b/TrackletNode.py
new file mode 100644
index 0000000..3e14f8e
--- /dev/null
+++ b/TrackletNode.py
@@ -0,0 +1,7 @@
+class TrackletNode:
+    def __init__(self, idx, sfIdx, s3dc, efIdx, e3dc):
+        self._id = idx + 1
+        self._sfIdx = sfIdx
+        self._efIdx = efIdx
+        self._s3dc = s3dc
+        self._e3dc = e3dc
diff --git a/bbox.py b/bbox.py
new file mode 100644
index 0000000..03c1bf8
--- /dev/null
+++ b/bbox.py
@@ -0,0 +1,30 @@
+import numpy as np
+
+class Box(object):
+    def __init__(self, tlbr, confidence, transform, imsize):
+        self.tlbr = np.asarray(tlbr, dtype=np.float)
+        self.confidence = float(confidence)
+        self.transform = transform
+        self.size = imsize
+
+    def to_tlwh(self):
+        tlbr = self.tlbr.copy()
+        w = tlbr[2]-tlbr[0]
+        h = tlbr[3]-tlbr[1]
+        x, y = tlbr[0], tlbr[1]
+        tlwh = [x,y,w,h]
+        return tlwh
+
+    def box2midpoint_normalised(self, box, iw, ih):
+        w = box[2]-box[0]
+        x, y = box[0] + w/2, box[3]
+        return (x/iw, y/ih)
+
+    def to_world(self):
+        p = self.box2midpoint_normalised(self.tlbr, self.size[1], self.size[0])
+        cx, cy = self.transform.video_to_ground(p[0], p[1])
+        cx, cy = cx*self.transform.parameter.get("ground_width"), cy*self.transform.parameter.get("ground_height")
+        return np.asarray([cx,cy], dtype=np.float)
+
+
+
diff --git a/data/first_half.txt b/data/first_half.txt
new file mode 100644
index 0000000..9ad808b
--- /dev/null
+++ b/data/first_half.txt
@@ -0,0 +1,129558 @@
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+1126,2331.5151,144.17108,2351.017,184.41757,0.99718297
+1126,3269.2085,366.79175,3301.1836,412.05048,0.99541736
+1126,3844.5935,757.91284,3872.14,819.8993,0.98978734
+1126,2019.2678,926.6494,2055.521,1021.952,0.9794871
+1126,3824.7424,779.5843,3859.344,844.2724,0.94205767
+1126,3732.214,789.64594,3771.0378,855.54767,0.43828908
+1126,3648.8582,806.1027,3693.0637,871.7963,0.113940135
+1127,2184.862,520.4344,2220.7922,592.0225,0.99955493
+1127,1974.1373,281.52966,1995.076,337.79218,0.99952984
+1127,2927.7515,393.3025,2951.8008,446.3994,0.9994572
+1127,3077.3918,576.73364,3106.7239,642.2212,0.9994004
+1127,2815.1096,314.20526,2838.2502,361.49347,0.9993856
+1127,2547.782,375.52124,2580.7302,432.50824,0.99933285
+1127,2348.9133,212.66629,2374.0334,260.1929,0.9992361
+1127,1161.9092,321.05453,1181.2107,376.31473,0.9991315
+1127,3062.721,735.6859,3104.14,814.10486,0.99904245
+1127,1917.8486,325.63934,1939.7883,379.09674,0.9990196
+1127,2003.0316,222.60638,2020.0533,270.80328,0.99892896
+1127,2525.7427,547.3662,2571.1392,618.6566,0.9988745
+1127,2693.255,395.91455,2717.1235,454.37323,0.99886924
+1127,3058.8076,253.95596,3080.6016,296.75998,0.9987577
+1127,2950.1738,343.36276,2971.5884,394.75974,0.99873465
+1127,3433.916,471.8894,3454.4946,521.1462,0.99873084
+1127,2719.2632,228.48228,2739.4546,270.59476,0.998684
+1127,2019.0616,353.907,2042.3832,411.20566,0.9984497
+1127,2039.0706,204.35086,2061.26,248.1348,0.99842703
+1127,3272.8271,366.58118,3300.3682,413.0094,0.9979656
+1127,2726.3816,306.2259,2755.3923,352.02557,0.9978479
+1127,1956.1697,123.09384,1975.185,159.86343,0.9978198
+1127,2331.3367,144.09302,2350.7488,184.58533,0.99762124
+1127,3843.2522,757.9212,3870.986,819.8254,0.9855757
+1127,2019.2524,926.65826,2055.4812,1021.92926,0.97880006
+1127,3002.349,411.51794,3029.5845,461.31158,0.9575184
+1127,3824.7046,779.53827,3860.187,844.16925,0.93752813
+1127,3732.2644,789.8979,3771.6487,855.39874,0.34555104
+1127,3648.8987,806.31195,3693.007,871.3934,0.29685125
+1128,1974.2987,281.3347,1995.171,337.46182,0.9995573
+1128,2186.0596,520.9227,2221.8628,592.0447,0.9995454
+1128,2815.1292,314.28516,2838.387,361.44855,0.9994265
+1128,3075.5627,576.0523,3106.1677,641.9631,0.99937075
+1128,2547.0547,375.80157,2580.6074,432.4784,0.9993243
+1128,2927.0376,393.52393,2951.2627,446.38733,0.99931
+1128,1917.7535,325.47653,1939.3043,379.1481,0.9991517
+1128,2348.5745,212.26837,2373.39,260.37134,0.9991505
+1128,2693.556,396.3167,2716.9246,454.65466,0.99903935
+1128,1161.8671,320.76837,1180.4388,376.43842,0.99890447
+1128,2719.9358,228.46535,2739.701,270.39212,0.99889284
+1128,3059.2922,254.41112,3080.843,296.6942,0.9988451
+1128,2522.713,548.39703,2570.2021,620.10187,0.99882466
+1128,2001.7721,222.6176,2019.6564,270.90283,0.9987662
+1128,3062.4307,736.3824,3103.0005,814.483,0.9987453
+1128,2950.0789,343.42136,2971.182,394.8561,0.9986821
+1128,3433.3086,471.99573,3454.1084,521.0853,0.9986621
+1128,2039.2998,203.89468,2061.2642,248.39761,0.99846315
+1128,2018.5284,354.01947,2042.0358,411.08368,0.9982596
+1128,2331.018,142.55661,2349.7983,183.49033,0.9982174
+1128,3275.603,366.36972,3299.687,411.3892,0.9981269
+1128,2725.718,306.8895,2753.1604,351.76553,0.9980763
+1128,1956.6714,123.380516,1975.845,159.29504,0.99790996
+1128,3000.0828,411.3663,3029.4124,462.32846,0.9970354
+1128,2019.3853,926.639,2055.3694,1021.99884,0.97833246
+1128,3842.882,757.9651,3870.1697,819.7057,0.9758807
+1128,3824.688,780.01404,3860.6768,844.1826,0.95783985
+1128,3732.1921,790.06964,3772.3347,855.4504,0.47283217
+1128,3648.9658,806.85925,3692.9375,871.21387,0.26730275
+1129,1974.3236,281.19232,1995.8197,337.3213,0.9996216
+1129,3073.9446,575.945,3105.1638,642.14386,0.99952054
+1129,2815.288,314.48557,2838.544,362.2383,0.99946433
+1129,2188.0688,521.4886,2223.189,591.83673,0.9994247
+1129,2546.5198,375.96432,2579.869,431.9623,0.99933606
+1129,2926.4658,393.74,2950.4575,446.36713,0.9992836
+1129,2693.658,396.48273,2716.6697,455.05365,0.9992249
+1129,2520.338,549.4519,2568.9692,620.64246,0.9992065
+1129,1917.3865,325.54794,1938.8906,379.10977,0.99914765
+1129,2724.3896,306.81088,2751.3115,351.756,0.99911755
+1129,3059.4473,254.32208,3081.3545,296.70642,0.9989249
+1129,2720.136,228.4133,2740.3357,270.7287,0.9988752
+1129,1161.7471,320.60587,1180.0591,376.65732,0.9988739
+1129,2000.6648,222.8534,2018.9912,271.35916,0.9987809
+1129,2950.0588,343.49463,2971.0188,394.8026,0.9987625
+1129,2348.1055,212.35437,2373.505,260.01257,0.99872524
+1129,3433.7668,472.0188,3453.9148,521.0603,0.9986469
+1129,3275.3613,366.31168,3299.4072,411.1575,0.9985293
+1129,2017.825,354.1577,2041.8899,411.04236,0.99845624
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+1129,3061.9492,736.3806,3101.961,814.4341,0.9980586
+1129,2039.1785,204.18353,2060.555,248.1936,0.9979274
+1129,1957.0942,123.710815,1975.9434,159.34042,0.99779546
+1129,2019.3821,926.6749,2055.07,1022.0254,0.9770618
+1129,3824.663,780.2456,3861.4106,844.2107,0.96166295
+1129,3841.9316,757.6902,3869.3887,819.81165,0.9515879
+1129,3732.2336,790.2373,3772.8748,855.2821,0.6880566
+1129,3648.9773,806.86896,3692.8352,871.27386,0.37027702
+1130,1974.2881,280.88614,1995.6685,336.63425,0.9997248
+1130,3071.039,576.1169,3103.745,642.0169,0.9995009
+1130,2815.5068,314.74527,2838.5781,362.19882,0.99947697
+1130,2190.2744,522.52997,2224.2026,591.8974,0.99946684
+1130,2693.5715,396.84213,2715.867,455.83078,0.9992736
+1130,2925.6226,393.74637,2950.2764,446.50302,0.9992126
+1130,1917.1304,325.554,1938.9031,379.20804,0.99916494
+1130,2519.6538,550.3029,2565.9043,621.1326,0.9991399
+1130,2546.338,377.82306,2578.9346,431.41333,0.9991341
+1130,2720.855,228.53745,2740.9688,270.82996,0.9990933
+1130,2724.2727,305.74625,2748.5378,351.55515,0.99896693
+1130,2949.9094,343.434,2970.1477,394.88882,0.9989512
+1130,3059.2727,254.5142,3081.7185,296.7871,0.99881256
+1130,1999.9707,222.76736,2018.3113,271.28033,0.9987778
+1130,2347.3225,212.22037,2372.9827,260.1184,0.9985936
+1130,3434.0295,471.38794,3453.7615,521.24005,0.9985526
+1130,2038.6193,204.33337,2059.244,247.72794,0.9985238
+1130,1161.3113,320.45673,1179.706,376.54614,0.9984962
+1130,2017.3721,354.4013,2041.8066,411.07452,0.99844944
+1130,2330.4878,140.57182,2349.5132,183.32884,0.9982462
+1130,3274.7295,365.15714,3299.352,412.1301,0.9981008
+1130,3060.9087,736.1222,3101.1787,814.39966,0.9979962
+1130,2997.228,413.58496,3028.9004,465.02454,0.9979798
+1130,1957.4727,123.68341,1976.2551,159.12613,0.99772924
+1130,2019.3226,926.6896,2054.9148,1021.9928,0.96761435
+1130,3841.1902,757.59595,3868.7615,819.67957,0.9250632
+1130,3824.604,780.3817,3861.5059,844.22,0.9217538
+1130,3732.1538,790.6865,3773.336,855.427,0.37355936
+1130,3649.0803,807.4263,3692.6697,871.177,0.35958043
+1131,1974.2219,280.4569,1995.9468,336.59308,0.99966085
+1131,2815.4255,315.55078,2838.7463,362.42377,0.99947983
+1131,3071.0784,575.81824,3102.7463,641.92883,0.9994331
+1131,2192.6082,522.9325,2225.113,591.8959,0.9994265
+1131,2924.7307,393.69537,2950.053,446.49286,0.99919873
+1131,2545.701,378.4715,2578.0603,431.1203,0.9991881
+1131,2693.1812,396.87915,2715.143,456.28534,0.9991611
+1131,2721.7993,228.53214,2741.1353,270.82196,0.99913365
+1131,1916.908,325.59857,1938.9163,379.26752,0.9991309
+1131,2949.976,343.4892,2970.0513,394.96796,0.9990038
+1131,2519.0593,550.3819,2565.3562,621.25555,0.9989078
+1131,3060.1091,254.7147,3082.0012,296.81,0.9988846
+1131,1998.9237,222.85913,2017.6517,271.08792,0.99872226
+1131,2016.6284,355.36133,2041.7493,411.25458,0.99869907
+1131,3434.23,471.09543,3453.8574,521.115,0.9986804
+1131,2724.2896,304.60315,2745.0366,351.38458,0.99860686
+1131,1161.1696,320.37833,1179.711,376.55905,0.9984066
+1131,2996.4465,414.81116,3029.285,466.18677,0.9983918
+1131,2330.1484,140.499,2348.8506,183.28604,0.99838567
+1131,2347.1191,212.08249,2372.794,259.97757,0.99837625
+1131,3059.3289,735.14465,3097.9734,814.6459,0.99825794
+1131,2038.765,204.40816,2057.2922,247.66454,0.9981675
+1131,3273.5945,364.4751,3299.3171,412.61462,0.99758196
+1131,1957.8866,123.5906,1976.1312,159.09003,0.99746346
+1131,2019.2654,926.78925,2054.8752,1021.9871,0.9647767
+1131,3842.036,757.57916,3868.0896,819.0146,0.9474983
+1131,3824.6006,780.8702,3861.1035,844.17377,0.93727756
+1131,3649.1218,807.47644,3692.512,871.1123,0.43468654
+1131,3732.1326,790.6911,3774.078,855.30707,0.4029087
+1132,1974.1752,280.15546,1995.6544,336.06268,0.99967015
+1132,2815.6204,315.5072,2838.8464,362.86005,0.9994485
+1132,3070.8035,575.8617,3101.284,641.4093,0.9993742
+1132,2196.7515,522.857,2226.3442,591.96173,0.999268
+1132,2722.0544,228.49411,2741.319,270.83667,0.9992093
+1132,2724.0,303.869,2742.4004,351.53873,0.99918765
+1132,2545.519,379.1948,2577.151,430.74857,0.9991335
+1132,2949.9497,343.69046,2969.8062,395.45523,0.99912447
+1132,1916.8737,325.57642,1938.9203,379.23242,0.99911267
+1132,2515.6602,549.95074,2563.4849,621.1025,0.9991008
+1132,2924.4443,393.7237,2949.7524,446.2141,0.9990362
+1132,2346.396,212.52588,2369.1738,259.5154,0.99900985
+1132,2693.291,397.1667,2714.4556,456.70355,0.9989396
+1132,1998.4393,222.9669,2017.4596,270.90613,0.9989053
+1132,3435.0303,471.2544,3454.0322,520.479,0.99879706
+1132,2016.4302,355.77036,2041.4976,411.43716,0.99878126
+1132,3060.6782,254.84343,3082.1187,296.71906,0.998747
+1132,2996.2551,415.50647,3028.9622,467.20068,0.9986767
+1132,1161.1917,320.4401,1179.3821,376.595,0.9983967
+1132,2038.3892,204.60574,2055.41,247.85278,0.99828535
+1132,2330.0913,139.94441,2347.2637,182.96846,0.99827886
+1132,3273.1262,364.48456,3299.4934,412.46533,0.9979988
+1132,1958.0063,123.56839,1976.24,159.74518,0.99781764
+1132,3059.426,734.983,3095.491,814.1895,0.9973009
+1132,2019.2284,926.8025,2054.8215,1021.98267,0.96352845
+1132,3824.5977,780.8368,3860.1987,844.2355,0.939823
+1132,3840.785,757.4718,3867.405,819.0375,0.9243412
+1132,3649.2307,807.4466,3692.222,871.0804,0.59377944
+1132,3732.1458,791.1935,3774.426,855.36975,0.4391572
+1133,1973.9604,279.76233,1996.3997,335.29523,0.99953663
+1133,2199.8198,523.277,2227.919,591.86816,0.9994554
+1133,3070.2505,576.0979,3099.5234,641.2672,0.9993918
+1133,2815.3528,316.103,2838.765,364.30923,0.9993581
+1133,2722.4211,228.60536,2741.6023,271.28058,0.99924904
+1133,2924.036,394.03378,2948.14,446.5206,0.9992454
+1133,2545.365,380.08496,2576.5256,430.6222,0.9992112
+1133,1997.6375,223.06796,2016.6614,271.18024,0.9991535
+1133,1916.784,325.60742,1938.9777,379.11603,0.9991334
+1133,2722.5754,303.3565,2740.7356,351.0636,0.99897206
+1133,3058.4229,735.2407,3092.5977,814.2844,0.99896896
+1133,2950.0796,343.6937,2969.7383,395.52634,0.99895954
+1133,2515.4958,549.6171,2560.0793,620.8814,0.99895835
+1133,3433.8264,471.2635,3453.8342,520.9548,0.99891245
+1133,2015.8634,355.92377,2041.3776,411.43524,0.99890924
+1133,2996.412,415.7007,3029.954,467.22836,0.9988305
+1133,2346.0469,213.58417,2368.8364,259.25793,0.9987136
+1133,2692.7441,397.1964,2714.0747,457.4093,0.998415
+1133,1161.1509,320.45203,1179.2742,376.63666,0.9983657
+1133,3272.0947,364.46146,3300.232,411.67203,0.99822026
+1133,3061.7053,255.08308,3082.988,296.56735,0.9981579
+1133,2037.8756,203.98291,2054.1794,248.78125,0.99762815
+1133,1958.6251,123.68933,1976.2911,158.99469,0.99719125
+1133,2329.793,139.1279,2345.787,182.78662,0.99696714
+1133,2019.1547,926.80396,2054.7886,1021.9774,0.95844
+1133,3824.5632,780.8079,3859.7532,844.258,0.945738
+1133,3839.5225,757.59985,3866.211,819.31934,0.90631
+1133,3649.1858,806.91943,3691.9294,871.17554,0.7935811
+1133,3732.1917,791.20557,3774.6018,855.28516,0.6840875
+1133,3832.4,759.0117,3865.9644,838.6227,0.20734237
+1134,1973.167,279.72668,1996.1323,335.01935,0.9995963
+1134,2200.2627,524.24384,2229.623,591.9633,0.99930584
+1134,3052.2512,735.0436,3089.1892,814.2328,0.9992982
+1134,1996.0767,222.66116,2016.1887,271.18103,0.99929714
+1134,2545.2822,380.85437,2576.1636,430.60553,0.99927527
+1134,2815.5396,317.345,2838.8862,364.2537,0.99919134
+1134,2722.4075,228.97716,2741.8948,271.59573,0.99912816
+1134,1916.7515,325.63895,1939.0645,378.96085,0.9991049
+1134,2345.797,214.44424,2367.0876,259.22275,0.9990778
+1134,2923.1755,394.42545,2947.158,446.416,0.9990478
+1134,3068.373,575.4745,3097.9941,640.80225,0.9989692
+1134,2016.0137,355.8898,2040.2942,411.44583,0.9989188
+1134,2719.9644,302.95248,2738.2568,350.55722,0.9989182
+1134,3063.4521,255.19484,3083.272,296.60645,0.99877363
+1134,3433.0737,471.82422,3453.7578,521.4396,0.9987735
+1134,2950.0732,343.59805,2969.669,395.73532,0.9987556
+1134,2692.4934,398.19647,2713.825,457.47906,0.9987024
+1134,2515.7288,549.55005,2555.596,620.78564,0.99864537
+1134,2997.3147,415.46683,3030.6125,467.25748,0.99848664
+1134,1161.1005,320.4309,1179.2562,376.65942,0.99840313
+1134,3271.251,364.93872,3301.081,411.60754,0.99781215
+1134,1958.7496,123.6153,1976.4379,159.03975,0.99696463
+1134,2329.938,139.92203,2345.2197,182.59726,0.9969043
+1134,2036.7758,204.07533,2053.1138,248.4869,0.99596137
+1134,3824.6252,780.86475,3859.0984,844.21643,0.9623423
+1134,2019.0547,926.717,2054.7754,1021.99664,0.95457906
+1134,3838.0798,757.4925,3865.2678,817.6431,0.92499864
+1134,3649.0342,807.02576,3691.895,871.2611,0.78802484
+1134,3732.2693,791.46497,3774.8987,854.999,0.78391844
+1134,3830.391,758.7497,3864.5894,837.0717,0.47679982
+1135,1972.5144,279.72516,1996.0222,334.91602,0.99966514
+1135,3068.5186,575.0826,3096.441,640.4399,0.9993999
+1135,1994.9767,222.91605,2015.5616,271.39825,0.9993789
+1135,2815.4468,317.45975,2838.9844,364.47934,0.99925786
+1135,2545.275,381.8211,2576.7769,430.59412,0.9992378
+1135,2200.6128,524.3601,2230.79,592.1415,0.99922836
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+1140,1960.3503,123.31552,1976.2078,158.93597,0.9972773
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+1140,2016.9325,926.88824,2054.7627,1022.3996,0.97492003
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+1141,1960.4619,123.230606,1976.2703,158.90715,0.997324
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+1142,1992.4716,222.77393,2013.8214,270.85583,0.99951327
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+1142,2015.7708,926.9035,2054.8252,1022.4194,0.97810674
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+1143,1992.5409,223.11539,2013.8925,271.2732,0.9994229
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+1221,1953.2167,297.21567,1973.8197,354.65695,0.9988732
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+1221,2015.775,925.74146,2055.0098,1022.2871,0.9854542
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+1222,1953.1162,297.29028,1973.4705,354.67993,0.9990928
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+1222,2015.9299,925.6889,2055.0146,1022.27826,0.98317
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+1226,2015.868,925.7048,2055.1523,1022.30084,0.98300654
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+1227,1992.8953,360.71915,2023.7959,418.07822,0.99897754
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+1227,2015.8003,925.7347,2055.2214,1022.3057,0.982644
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+1228,1992.9426,361.39056,2024.6816,417.96198,0.99870634
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+1228,2015.6592,925.7292,2055.3462,1022.29364,0.98296726
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+2067,2201.3628,876.4857,2249.7788,962.44275,0.9992563
+2067,1969.4303,287.43585,2002.0162,343.84277,0.999148
+2067,1142.8458,223.13329,1161.5009,266.36768,0.9991129
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+2069,1972.7673,286.67563,2002.6716,343.42325,0.999212
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+2074,1990.4126,285.30936,2007.3176,343.3679,0.99836904
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+2076,1970.8632,412.19046,2003.5282,475.77823,0.99767345
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+2833,3154.9294,448.78558,3183.512,503.20422,0.9989209
+2833,1985.356,886.858,2020.4634,981.0368,0.9988996
+2833,2290.4136,199.79,2315.5986,246.44981,0.99870837
+2833,2894.8994,350.5978,2924.666,399.91183,0.99866676
+2833,2735.3076,214.76294,2770.1597,257.09116,0.9985697
+2833,2269.2996,280.11505,2302.3997,329.16534,0.99853563
+2833,2566.8005,191.60864,2592.661,232.009,0.99839133
+2833,2495.1423,194.40663,2514.3289,236.03606,0.99838114
+2833,2169.4734,172.55905,2200.1604,215.45944,0.9982817
+2833,2696.1514,270.39505,2728.3784,317.30795,0.99820375
+2833,2200.8381,362.4449,2236.3767,421.2141,0.9981694
+2833,2772.3296,275.4878,2798.4214,319.08734,0.99802727
+2833,2687.0933,195.43784,2713.7603,234.55222,0.99783427
+2833,3009.0527,347.5468,3027.2627,397.40332,0.99772495
+2833,3117.007,396.23663,3139.0896,455.72095,0.99757993
+2833,1901.9779,250.02254,1926.9222,297.0178,0.99757224
+2833,2723.2595,173.93039,2745.1218,206.14621,0.99713373
+2833,3261.4382,351.07568,3283.7102,395.14276,0.995867
+2833,2746.2178,184.37054,2771.5483,217.24179,0.99493307
+2833,3640.705,780.8994,3684.567,843.94727,0.9938975
+2833,3656.928,803.2239,3698.215,874.31836,0.9409635
+2833,3693.7126,799.5053,3724.2405,870.81635,0.28004944
+2833,3719.3953,792.1662,3758.0413,856.23395,0.10125199
+2834,2204.9282,364.19894,2247.2104,423.49283,0.9994535
+2834,1950.1139,302.16846,1975.6923,355.2398,0.9994393
+2834,1083.6925,305.19675,1111.9003,353.90347,0.9992747
+2834,1918.4801,121.6363,1940.2377,160.33847,0.9992403
+2834,1986.6312,229.9953,2020.4269,280.79266,0.9991072
+2834,1985.1014,885.60645,2019.7284,980.49805,0.99898654
+2834,2290.8625,199.439,2318.5403,246.93222,0.998979
+2834,2739.8352,307.42352,2774.6785,359.61237,0.99887174
+2834,2897.167,350.2907,2929.397,402.4806,0.9988381
+2834,1908.456,250.67146,1930.8743,297.2693,0.9987368
+2834,2323.9338,409.7477,2362.3245,472.55692,0.99869734
+2834,2572.7993,191.54907,2596.8413,231.65903,0.99867284
+2834,3158.308,448.60278,3185.2798,501.69684,0.99864703
+2834,2700.6235,271.15784,2733.3403,318.0852,0.998422
+2834,2174.189,172.7996,2202.712,216.51378,0.99825007
+2834,2276.587,281.77295,2306.2295,329.26874,0.99815327
+2834,2495.7878,193.86887,2517.1838,235.51617,0.9980792
+2834,3122.8472,395.07968,3142.129,457.7863,0.9979121
+2834,2691.484,195.71202,2719.2207,235.88417,0.99766636
+2834,2722.4575,173.80829,2747.7134,206.35382,0.9973168
+2834,3266.0803,351.18317,3284.4177,395.20197,0.99718285
+2834,3010.1895,347.46753,3034.1025,397.99652,0.9968831
+2834,2742.683,215.99873,2774.981,258.8352,0.99499714
+2834,2748.1848,184.87247,2772.3855,218.05249,0.994358
+2834,2780.077,276.48856,2802.2405,319.07748,0.99418974
+2834,3643.1372,779.9378,3687.6548,843.24335,0.9792853
+2834,3656.639,802.87317,3697.804,873.8738,0.93242437
+2834,3693.4653,800.14594,3724.5571,871.2491,0.31320217
+2834,3719.6567,792.14594,3759.0146,856.6396,0.2288871
+2834,2706.8125,884.0827,2751.4204,972.3713,0.12603964
+2835,2326.9243,408.54633,2365.4517,471.18616,0.9995005
+2835,1950.9103,301.09143,1980.515,354.4972,0.9992555
+2835,3162.5986,448.8386,3187.6836,501.42087,0.999175
+2835,2209.6265,364.075,2257.7847,423.29297,0.999131
+2835,2293.807,199.07312,2323.4968,246.48978,0.99907047
+2835,1984.4324,885.3664,2019.4966,979.44794,0.99896026
+2835,3126.1294,395.2808,3147.3691,459.6519,0.9989209
+2835,1990.5106,231.09053,2020.8632,280.55637,0.9987453
+2835,1084.4182,305.0565,1113.9978,354.46555,0.9986964
+2835,2707.171,273.02716,2737.379,319.07538,0.99851424
+2835,2900.8418,350.71472,2935.8428,402.755,0.9984971
+2835,1919.5411,121.48128,1942.5658,160.00926,0.99839646
+2835,2744.3765,309.59604,2778.375,360.7559,0.99835217
+2835,3268.1191,351.33673,3285.315,395.27118,0.9982784
+2835,2497.6667,193.2332,2520.32,233.77965,0.9979492
+2835,2700.5044,196.53421,2722.8145,237.1394,0.99792045
+2835,2180.1152,173.64078,2205.3203,216.77435,0.9977704
+2835,1913.8907,250.11328,1935.4379,297.31708,0.99772173
+2835,2573.0928,190.67403,2600.442,231.3908,0.9975268
+2835,3009.881,347.53186,3039.7395,397.85944,0.99750525
+2835,2784.4783,276.96436,2806.805,319.3802,0.9974831
+2835,2724.18,173.4527,2751.0676,206.58734,0.9971307
+2835,2282.965,282.16174,2309.385,329.2417,0.9965559
+2835,2752.1943,185.45331,2774.0415,218.28314,0.9954314
+2835,2754.6326,216.67062,2780.0403,259.5515,0.99419594
+2835,3646.1392,777.973,3690.4194,844.22144,0.9875995
+2835,3655.9514,802.8105,3698.4714,873.9556,0.90293914
+2835,3722.873,792.1147,3759.9678,856.84467,0.17783548
+2835,3693.4172,800.515,3724.7664,870.9005,0.12611404
+2836,2329.7915,408.3705,2369.6167,470.20444,0.999493
+2836,1952.1249,301.17484,1986.3373,354.44772,0.9994746
+2836,2217.4214,365.19275,2262.4707,425.27808,0.99933976
+2836,3165.8271,449.57434,3189.2056,500.57013,0.9992889
+2836,1995.6364,232.07153,2020.9476,280.78552,0.99920005
+2836,2750.3447,311.32886,2782.8306,360.82202,0.9990025
+2836,2499.2488,192.8544,2523.452,235.05374,0.9988949
+2836,2295.4172,200.00829,2327.4832,245.96376,0.9986307
+2836,2903.3083,351.15213,2939.5217,403.97525,0.99862087
+2836,1984.1501,884.84546,2019.2012,979.17444,0.99848664
+2836,2290.287,281.7583,2313.7783,329.0514,0.9984566
+2836,1922.5219,122.34847,1943.8812,160.31604,0.998447
+2836,1915.6917,249.45845,1938.3074,296.47818,0.9984005
+2836,2785.5654,277.05453,2812.0571,319.7175,0.9982331
+2836,2574.996,190.3129,2602.794,230.55228,0.99815816
+2836,2713.8665,274.73105,2741.5276,319.14938,0.99809366
+2836,3127.0068,395.18002,3152.1577,461.81815,0.9980416
+2836,3269.1123,350.96716,3287.5806,395.71088,0.9980223
+2836,2762.4976,218.21884,2784.253,259.53973,0.99788815
+2836,2186.65,174.62491,2208.669,216.66882,0.99718875
+2836,3013.204,347.83368,3044.437,397.65906,0.9969798
+2836,2708.3865,197.44557,2727.8982,237.16884,0.9968726
+2836,1086.0504,305.65448,1114.3483,355.16113,0.9966629
+2836,2726.0464,173.73227,2753.2998,206.3616,0.99538815
+2836,3649.3,777.69556,3693.6638,843.09875,0.99328035
+2836,2757.4358,185.4818,2776.5813,219.32143,0.9932295
+2836,3656.6023,802.7286,3698.1194,873.8321,0.95651424
+2836,3723.311,792.33655,3760.4292,857.82996,0.28478047
+2836,2706.7805,882.3665,2757.161,968.55475,0.22910719
+2837,1954.789,301.6752,1990.0626,355.0823,0.99951863
+2837,3167.974,449.5656,3190.3135,500.61664,0.9994972
+2837,2334.7368,408.89136,2372.934,467.2674,0.9994054
+2837,1917.4685,248.71689,1941.4976,296.647,0.99891555
+2837,2225.3674,366.44504,2263.8083,425.2062,0.998841
+2837,2299.7373,200.49266,2331.1187,247.20955,0.9987882
+2837,2500.0493,193.27608,2526.746,235.16774,0.99875665
+2837,1999.8131,231.08575,2023.1403,280.64008,0.9986578
+2837,2577.6086,190.24055,2605.8728,230.19722,0.9986343
+2837,2722.3352,275.26056,2745.238,319.06506,0.998466
+2837,2755.722,311.79977,2785.8123,360.78915,0.9983828
+2837,2907.9756,351.23718,2942.3228,405.52252,0.9983296
+2837,2786.7524,278.19852,2817.5073,322.668,0.99823534
+2837,1984.0637,884.2551,2019.0642,979.02466,0.9981912
+2837,2290.5442,281.26605,2319.7039,328.95142,0.9980478
+2837,3126.6638,396.5278,3156.551,462.10275,0.9978794
+2837,3019.3928,349.6653,3047.236,399.34525,0.99787223
+2837,3270.1873,351.06348,3289.6116,396.07764,0.99782073
+2837,2191.8652,174.68198,2212.1387,216.83224,0.99774015
+2837,2714.0793,197.85365,2733.3542,236.72652,0.9975763
+2837,1089.4457,305.7022,1114.8993,354.97418,0.99690574
+2837,2765.7898,219.37445,2789.9343,260.08005,0.99659914
+2837,2729.5537,173.77055,2755.6948,206.94774,0.9965497
+2837,1926.5548,123.88714,1945.6068,160.41916,0.99525934
+2837,2758.8975,185.70029,2778.876,219.15094,0.9931538
+2837,3653.8794,776.50525,3696.6265,842.79944,0.9892314
+2837,3656.8787,801.8308,3697.965,873.9906,0.98290676
+2837,3725.6233,792.5727,3761.4363,857.79706,0.34421584
+2838,3168.6313,449.24765,3192.1084,500.61362,0.99941874
+2838,2340.4102,409.3293,2377.27,468.23544,0.99924976
+2838,2005.6896,230.5891,2027.7667,280.23712,0.99900997
+2838,2304.4236,201.39874,2334.9192,247.24033,0.9989471
+2838,1958.8191,302.47415,1990.6296,355.72745,0.9989059
+2838,3028.9553,350.66455,3050.1765,400.3562,0.99883264
+2838,1918.244,248.60153,1947.1205,296.69986,0.998796
+2838,2723.6262,275.46622,2748.9138,319.29575,0.9986732
+2838,1093.8672,305.4659,1116.5762,355.20297,0.9986457
+2838,2763.128,311.85852,2789.9897,361.06256,0.9986272
+2838,2914.5874,352.97977,2946.793,405.58188,0.9984251
+2838,2292.47,280.98602,2323.5447,328.93286,0.99841833
+2838,2501.6414,194.08423,2529.09,235.41254,0.99841046
+2838,2237.3484,366.84866,2269.4197,425.03763,0.9983058
+2838,2735.8987,173.7154,2757.89,206.78937,0.9979882
+2838,2193.2031,174.24498,2216.2349,216.37566,0.997851
+2838,1983.8307,883.5219,2019.1361,979.2452,0.9977041
+2838,2790.1414,278.61624,2822.7405,323.5046,0.9976361
+2838,2766.7534,219.70116,2796.1514,259.37595,0.99752635
+2838,3127.4475,398.15558,3159.5261,462.4599,0.99720424
+2838,3270.569,351.14343,3291.0193,396.41486,0.9971124
+2838,2581.0679,190.21165,2610.1104,230.80354,0.9969092
+2838,2714.9597,199.18034,2739.049,236.54402,0.9944706
+2838,1930.2849,123.979706,1948.1941,160.1094,0.99040353
+2838,2760.584,185.77669,2780.1528,217.81725,0.9882423
+2838,3657.2522,775.1375,3699.3806,837.86224,0.98605806
+2838,3656.8376,801.50824,3698.7087,874.11566,0.9550179
+2838,3729.5488,792.78174,3762.498,856.80176,0.77239937
+2839,2009.0544,230.41017,2028.8411,280.38464,0.99944335
+2839,3169.2627,448.0213,3193.7456,500.36694,0.9993488
+2839,2345.291,409.66132,2380.2046,468.6858,0.99918836
+2839,1964.9897,302.72565,1991.7363,355.8105,0.9991291
+2839,2294.8174,281.2187,2328.7358,330.55927,0.9988514
+2839,1098.2255,305.3477,1118.8558,355.4733,0.9988418
+2839,1919.6705,248.74799,1952.7499,296.5218,0.9988194
+2839,2194.446,174.03786,2220.6233,216.80675,0.99873906
+2839,2247.899,366.4095,2276.314,424.64117,0.9985545
+2839,2504.6843,194.30554,2532.1902,236.58148,0.99848384
+2839,3033.9282,351.82635,3051.9585,401.71375,0.9981477
+2839,2310.079,200.94499,2339.314,247.24126,0.99810237
+2839,2741.8591,173.57895,2761.303,207.39822,0.99791783
+2839,2769.1465,220.191,2802.9438,260.3669,0.99790096
+2839,2586.0547,191.02777,2613.4473,231.73087,0.9978941
+2839,2766.2104,311.86874,2793.6943,360.29813,0.9978231
+2839,2718.2627,199.51439,2743.9497,235.14656,0.9976508
+2839,2921.102,354.67978,2951.6777,405.37668,0.99750704
+2839,3271.134,351.63663,3292.6194,396.8989,0.99684525
+2839,2796.4343,279.50674,2826.5852,324.45676,0.99683446
+2839,3127.453,399.5822,3160.7737,462.70996,0.9967361
+2839,2725.7993,275.9181,2753.746,319.08795,0.9966074
+2839,1983.7634,883.07166,2019.3396,979.10034,0.99588567
+2839,1934.3549,123.5171,1951.4161,160.22998,0.9947212
+2839,3659.9917,775.0321,3701.921,833.5315,0.9931149
+2839,2761.7317,185.57797,2781.1868,217.8313,0.9910069
+2839,3657.041,802.37616,3698.6392,873.8503,0.8936791
+2839,3732.416,793.2955,3763.2349,857.1506,0.6913935
+2840,1971.5125,302.00125,1994.7661,354.25882,0.9993585
+2840,2010.1996,229.47453,2031.1254,280.57986,0.99932826
+2840,3170.7432,447.11978,3195.921,499.15994,0.9992206
+2840,2929.1838,354.60626,2954.9407,405.13672,0.9991923
+2840,2352.6716,409.5102,2382.5872,469.0235,0.99913126
+2840,2297.2827,280.75424,2336.3555,330.1619,0.99911004
+2840,2196.6882,173.70117,2224.5872,217.49841,0.9989932
+2840,1101.2981,304.76645,1121.4844,354.77847,0.9989072
+2840,3038.7458,352.28552,3055.8123,402.11072,0.99879575
+2840,2317.2515,200.31114,2342.0146,246.50377,0.9987184
+2840,2766.7712,311.1779,2799.215,361.11026,0.9986828
+2840,2251.4094,366.10678,2283.113,424.72403,0.99856335
+2840,1922.0878,249.828,1955.4303,297.10138,0.9979394
+2840,2727.9028,275.3191,2758.3584,320.93524,0.9977938
+2840,3271.8508,352.08463,3293.0085,397.47653,0.99777466
+2840,2507.8218,195.47772,2533.7363,236.33545,0.9977068
+2840,2802.6199,280.61365,2832.05,324.5661,0.99760616
+2840,2591.324,191.80768,2616.5793,232.23187,0.9973109
+2840,1983.8195,882.273,2019.1654,979.23456,0.996977
+2840,2743.8745,174.10468,2762.9443,207.33963,0.99667907
+2840,1936.5773,122.982765,1952.9667,160.07365,0.996431
+2840,2774.8923,220.29831,2809.214,262.23163,0.99633056
+2840,2761.232,185.86002,2780.5713,218.15607,0.9955707
+2840,2722.8892,198.52861,2750.4683,238.55061,0.99527186
+2840,3128.724,402.13083,3161.5488,463.53934,0.9950113
+2840,3662.9714,776.23254,3704.7327,833.65015,0.8949249
+2840,3657.1438,803.1047,3698.9084,873.50995,0.7864754
+2840,3733.16,793.3384,3763.681,857.7556,0.5912435
+2841,2011.4309,228.88625,2034.8936,280.46448,0.99960345
+2841,1103.2499,303.92307,1123.1571,354.80167,0.999215
+2841,2767.8535,311.59494,2803.4893,363.21933,0.99914896
+2841,1977.067,303.04028,1997.8165,354.88672,0.9990501
+2841,2358.695,408.41455,2385.9841,469.00415,0.99901366
+2841,3170.4722,445.5093,3198.6309,500.74924,0.99892825
+2841,2302.2703,281.92554,2341.157,330.52283,0.99876785
+2841,2199.7285,173.94447,2229.564,217.38844,0.99875224
+2841,2320.0493,199.59076,2345.0483,245.52124,0.9987411
+2841,2254.3938,365.36914,2290.5168,423.40747,0.9986766
+2841,2931.8486,354.19135,2958.1255,405.14667,0.99863404
+2841,3040.144,352.36844,3061.392,402.72025,0.99845016
+2841,2810.749,281.4046,2837.0776,324.43524,0.9980317
+2841,2513.239,195.74504,2536.6687,236.1819,0.9980107
+2841,2731.2659,276.12534,2764.4714,322.3265,0.9978362
+2841,1937.3939,122.40402,1954.3029,160.41422,0.9976579
+2841,2760.124,186.23326,2781.9028,219.26363,0.99756855
+2841,3132.6926,408.87912,3163.7747,463.11496,0.99748755
+2841,2598.8062,191.99017,2619.6719,232.0185,0.99746525
+2841,2784.7402,221.67119,2816.4565,263.54984,0.9974089
+2841,3272.3428,351.73056,3294.7168,397.48282,0.9968828
+2841,2730.3499,199.85173,2754.6438,239.17906,0.9964563
+2841,1926.4684,250.95103,1955.8759,297.71002,0.9963792
+2841,1983.6387,881.6881,2019.0769,979.0145,0.99611723
+2841,2747.9634,174.46983,2765.0122,209.45161,0.985193
+2841,3657.0854,803.0027,3700.045,873.37036,0.5309464
+2841,3733.578,793.4007,3764.5056,857.79083,0.42776638
+2841,3667.7278,776.2307,3708.8054,854.0563,0.29092836
+2841,3656.6658,777.3341,3709.8425,875.2975,0.2518412
+2842,2012.5627,229.6174,2040.6401,280.16388,0.9995894
+2842,1105.1593,303.89108,1125.8932,354.9534,0.9994235
+2842,2770.2585,312.4957,2807.1462,365.50046,0.9993611
+2842,3171.3867,445.38315,3200.7485,500.4636,0.9992562
+2842,2257.9043,365.27502,2297.13,422.32025,0.99913883
+2842,2359.9192,407.0179,2391.55,468.58078,0.99911875
+2842,1979.4108,302.6761,2001.7418,354.4569,0.9989604
+2842,3040.212,352.67892,3068.0454,402.64493,0.99884206
+2842,2322.801,199.91943,2348.0442,244.2117,0.99881953
+2842,2308.787,283.27594,2342.7388,331.9701,0.9983615
+2842,2518.1997,195.32434,2541.415,236.33325,0.99835217
+2842,2816.8872,281.33264,2841.9736,324.06555,0.99834955
+2842,3137.1902,409.6498,3166.7424,462.95602,0.99829334
+2842,2735.4668,276.4595,2769.8535,323.07535,0.9981061
+2842,1983.6469,881.0715,2019.0026,979.3222,0.9980184
+2842,2203.5842,174.14255,2232.801,218.1369,0.99782795
+2842,2790.8188,221.99368,2822.7485,263.71793,0.9977507
+2842,2760.0742,186.4957,2783.7344,218.72617,0.9976441
+2842,2602.0554,191.9857,2623.2585,231.92961,0.9976018
+2842,1938.8711,121.88461,1956.3062,160.19519,0.99703264
+2842,2933.8953,353.66873,2961.571,404.46982,0.99675167
+2842,3273.0283,351.99783,3294.7295,398.17844,0.996639
+2842,2739.3271,201.98834,2760.3271,239.65884,0.9963582
+2842,1930.9935,251.33519,1957.5634,298.39127,0.9959967
+2842,2749.6191,175.41493,2767.5894,211.50682,0.9789221
+2842,3657.3235,802.7245,3701.615,873.46265,0.52730924
+2842,3656.6736,782.8807,3714.2,874.41583,0.36825043
+2842,3733.8652,793.40533,3765.0322,857.45966,0.29328793
+2842,3670.046,776.5538,3712.4585,853.71564,0.23693168
+2843,1980.9181,300.88443,2005.8383,355.92227,0.9994166
+2843,2015.004,230.16284,2041.8578,280.69403,0.999406
+2843,1106.244,303.24023,1131.1376,354.9489,0.9993356
+2843,2262.7083,365.67957,2307.0754,423.92664,0.9992888
+2843,2360.7803,405.98532,2395.775,468.47305,0.99922764
+2843,3173.3647,444.1378,3203.7354,498.56345,0.999154
+2843,2773.3992,313.37436,2811.1497,365.25906,0.9987748
+2843,2935.5566,353.60452,2965.0762,404.95773,0.9987287
+2843,2801.3723,222.22218,2827.576,263.25946,0.99853885
+2843,2324.7573,199.43562,2351.045,243.84853,0.99847037
+2843,2523.081,194.25122,2544.561,235.70395,0.99835986
+2843,2208.2014,175.28278,2234.6443,219.09485,0.99816364
+2843,2315.5476,284.00314,2346.786,331.97256,0.9980579
+2843,2740.32,277.54453,2772.547,324.25137,0.9980015
+2843,1983.4985,880.37646,2019.1965,979.19434,0.99788815
+2843,1939.5367,121.51334,1958.6981,160.05722,0.9976865
+2843,2818.976,281.89233,2846.1855,324.64227,0.9976793
+2843,3042.8154,353.9517,3073.3682,402.96518,0.99741864
+2843,3141.4822,408.37585,3169.9182,462.61694,0.9973889
+2843,1937.1243,251.38226,1960.1357,298.32684,0.9972785
+2843,2745.6035,201.38466,2766.1025,239.95573,0.9963511
+2843,2603.203,191.07367,2627.196,231.79251,0.9960531
+2843,2763.0532,187.348,2787.3628,218.45634,0.9959824
+2843,3273.4375,352.27612,3294.3647,398.0016,0.99575543
+2843,2749.4907,177.19109,2770.5586,208.8126,0.9828887
+2843,3657.2627,803.5812,3701.4502,873.65295,0.5684589
+2843,3656.7048,782.8301,3718.7312,874.2134,0.43918547
+2843,3734.3745,794.0734,3765.395,857.12054,0.25283962
+2843,3668.457,776.63336,3715.4497,857.4161,0.24362908
+2844,2363.293,405.02078,2399.1948,467.56107,0.99963343
+2844,1107.4989,303.9456,1134.9825,354.75534,0.99960154
+2844,2018.8076,230.56758,2043.3767,281.05902,0.9995302
+2844,2268.0369,366.62183,2315.513,425.3418,0.999468
+2844,1981.2673,301.75098,2010.7668,356.35913,0.9994241
+2844,2777.0022,314.72318,2814.7341,366.43332,0.9989636
+2844,3173.6685,444.3489,3206.2354,498.2402,0.99887425
+2844,1940.828,122.1671,1962.108,159.53691,0.9988293
+2844,2524.3284,192.98265,2545.7273,235.46992,0.9988166
+2844,3050.208,355.26392,3077.4058,403.367,0.99875295
+2844,2322.571,284.3757,2351.6624,331.8503,0.9985902
+2844,3148.6636,407.27054,3172.88,462.5961,0.9985776
+2844,2938.415,353.72128,2970.0278,404.1829,0.998536
+2844,1983.759,879.76874,2019.5249,979.05597,0.9984933
+2844,2747.5305,200.77385,2771.9285,238.30913,0.99846494
+2844,2823.1624,282.69083,2850.2312,326.00546,0.99846363
+2844,1943.0234,251.34734,1964.6155,298.15256,0.99800617
+2844,2747.5552,278.46625,2776.5737,323.96606,0.99784684
+2844,2605.2317,190.85545,2631.842,231.3863,0.99762386
+2844,2214.8398,175.21762,2239.3027,219.22473,0.9976131
+2844,2327.0005,199.10205,2354.8467,243.69406,0.99756
+2844,3273.8408,352.0923,3294.4512,397.79865,0.99646384
+2844,2804.368,222.06306,2831.3826,262.48276,0.9963947
+2844,2749.3975,176.90865,2774.5913,208.23238,0.9956261
+2844,2766.1414,186.489,2789.6003,218.78346,0.98285276
+2844,3657.1165,803.3111,3700.595,873.97723,0.7823846
+2844,3676.5684,775.2299,3717.287,847.5566,0.26518866
+2844,3658.3794,782.7735,3722.6704,872.1838,0.19274533
+2844,3734.3374,794.399,3765.9512,858.3017,0.11148115
+2844,3693.2642,801.41235,3726.4556,870.0094,0.10508714
+2845,1982.6084,302.3051,2017.7839,355.8023,0.99959356
+2845,2365.364,404.89703,2404.89,465.5255,0.9995735
+2845,2022.2051,231.59097,2044.1729,281.1429,0.99942976
+2845,1109.997,304.23904,1136.8561,355.09457,0.99914443
+2845,3173.7246,446.22842,3208.854,499.24887,0.9989882
+2845,2328.912,284.28168,2356.2397,331.8139,0.99898666
+2845,3055.7551,356.04654,3079.0603,404.3002,0.9989806
+2845,2607.604,191.64677,2635.0254,232.15614,0.9989157
+2845,2221.7417,175.59222,2244.0356,219.0156,0.9989103
+2845,2781.918,315.5424,2818.4346,366.5192,0.99887913
+2845,2276.3433,368.06384,2319.1455,426.8698,0.99885964
+2845,2941.4744,354.16522,2974.7102,405.0739,0.9988104
+2845,1941.1887,121.996056,1963.5264,160.18417,0.998743
+2845,2828.975,283.76898,2854.2002,327.21252,0.9987307
+2845,2526.2139,192.29869,2548.5078,233.90083,0.9986506
+2845,3154.4248,407.62344,3175.4087,463.3082,0.9985796
+2845,2805.9075,222.3808,2836.5178,263.61017,0.9985214
+2845,2750.5151,201.02313,2776.622,238.34741,0.99849737
+2845,1983.4587,879.4321,2019.5356,977.5851,0.998237
+2845,1946.8857,251.04568,1967.6233,297.98325,0.9979121
+2845,2329.602,199.43309,2358.1274,244.11906,0.9976406
+2845,2754.8408,279.12988,2782.6543,324.1054,0.9973092
+2845,3274.0376,352.4626,3294.6865,397.70538,0.99707544
+2845,2749.1724,177.51381,2778.724,208.54549,0.9932656
+2845,2769.3027,185.6906,2790.4106,220.22935,0.97041106
+2845,3657.0903,802.73615,3701.2017,874.1298,0.7357409
+2845,3681.8806,774.9594,3721.3362,822.9798,0.4189907
+2845,3681.3281,774.2172,3721.5688,854.37836,0.2210832
+2845,3689.4043,796.54834,3725.421,869.7484,0.16697128
+2845,3657.855,798.748,3727.399,872.4556,0.1277902
+2845,3734.3364,795.1567,3766.6094,859.217,0.12556969
+2845,2760.381,184.01006,2784.6892,213.83424,0.10058218
+2846,2368.2456,405.8388,2409.0308,465.81787,0.99962354
+2846,1985.1704,302.98367,2021.874,356.09622,0.9995505
+2846,1947.3251,250.33116,1972.4781,298.8985,0.99904495
+2846,1112.96,305.25458,1137.5208,355.86383,0.99901617
+2846,2223.4937,174.66757,2247.7046,220.12146,0.99897623
+2846,2835.1147,284.77563,2858.0498,328.15155,0.9989716
+2846,3174.8335,447.0142,3211.0503,499.5029,0.9989594
+2846,2330.958,282.7724,2360.2295,331.53125,0.998841
+2846,2026.3877,232.28578,2045.5735,281.1941,0.9988399
+2846,2286.0623,369.67975,2322.3098,427.3321,0.9987962
+2846,2526.897,191.97485,2550.5498,233.8826,0.99874437
+2846,2334.9885,200.07957,2361.7766,245.19148,0.9986198
+2846,1983.1812,878.80524,2019.7725,977.67145,0.9985868
+2846,3159.1746,408.75922,3177.951,463.56805,0.99854517
+2846,2761.1733,279.726,2787.8843,324.03003,0.99831283
+2846,2612.067,191.8042,2640.0908,232.4765,0.9981254
+2846,2789.3142,315.23456,2821.6077,366.42023,0.9980367
+2846,2945.8936,355.0767,2979.8398,407.00027,0.9976909
+2846,3274.657,352.602,3294.6726,397.41925,0.9975153
+2846,3060.1619,356.81897,3080.319,405.19043,0.9973556
+2846,2755.25,201.97949,2781.6616,238.0257,0.99733293
+2846,2809.1902,222.51047,2841.7527,264.39786,0.9968342
+2846,2751.0825,177.9245,2782.4253,209.22083,0.99443406
+2846,1943.2003,122.64866,1965.0468,161.2162,0.9921103
+2846,2772.8047,185.90158,2791.288,220.6676,0.9398429
+2846,3657.162,803.1711,3702.0269,874.0258,0.8612419
+2846,3688.067,773.1609,3724.606,865.2106,0.50884825
+2846,3734.1763,795.14355,3767.732,860.2517,0.19983315
+2846,3692.088,773.41895,3725.1084,833.8926,0.15954646
+2846,2753.7837,183.62343,2790.276,219.28627,0.10818565
+2847,2372.1528,405.99554,2412.6973,465.94775,0.99929893
+2847,1988.1183,303.52335,2022.7145,357.49857,0.9992724
+2847,1116.5193,305.7198,1137.9282,355.9051,0.9991198
+2847,2332.5254,282.01453,2363.7085,331.64514,0.9990675
+2847,3178.1082,447.12595,3213.446,499.35904,0.9990458
+2847,2225.5662,173.92796,2251.3289,219.11833,0.9990079
+2847,2340.8528,200.97949,2364.715,245.60794,0.9989221
+2847,2530.3071,191.99803,2553.1738,233.67708,0.99887234
+2847,1949.0701,249.77026,1975.4375,298.9032,0.9988642
+2847,3161.0269,409.45505,3182.4883,464.38657,0.9988405
+2847,3063.6443,358.0371,3083.3171,405.04602,0.99879265
+2847,2617.265,191.69289,2644.0168,233.12675,0.99860555
+2847,1983.1554,877.6602,2020.0653,976.87115,0.99836963
+2847,2796.5288,315.225,2823.8184,366.6393,0.9982254
+2847,2759.9385,201.5462,2785.7173,239.50043,0.99796355
+2847,2030.2009,232.45653,2047.9255,281.32532,0.9979044
+2847,2843.0356,285.98563,2862.2803,328.71756,0.9978892
+2847,2951.7324,357.22116,2983.81,407.1423,0.99788046
+2847,2298.1023,370.3917,2328.1536,427.3375,0.9977229
+2847,2763.058,278.65848,2791.102,323.77744,0.99753904
+2847,3275.0234,352.8604,3295.401,396.9532,0.99698395
+2847,2756.8394,178.57457,2785.2783,209.94878,0.99491596
+2847,1945.6497,123.61938,1967.2578,161.52496,0.9934283
+2847,2826.1543,223.30444,2845.5566,262.98242,0.9891711
+2847,2777.2961,186.42307,2793.411,220.8003,0.9384893
+2847,3657.0142,802.8388,3708.3906,873.92633,0.7875161
+2847,3697.148,772.8896,3726.4604,832.018,0.72098434
+2847,3734.4177,795.62085,3768.3826,860.8323,0.36160976
+2847,3692.2173,776.51166,3725.9224,868.6369,0.2884452
+2847,2772.4888,184.09836,2791.085,209.67307,0.21067983
+2847,2766.902,185.97958,2793.2307,220.09354,0.10433638
+2848,2376.8699,406.29333,2418.1086,466.30334,0.9994783
+2848,2336.0566,282.074,2367.7397,332.17935,0.99922884
+2848,1992.6613,303.79578,2023.1007,357.51257,0.99891794
+2848,2531.3489,192.7616,2555.6428,234.00177,0.99888164
+2848,3182.026,446.5035,3214.1797,499.95334,0.99884856
+2848,2226.0295,173.78386,2255.096,218.27272,0.9987249
+2848,3161.9998,410.54398,3186.2903,464.76315,0.9987054
+2848,1983.437,876.5419,2020.1152,975.9844,0.9987023
+2848,1120.3966,305.59436,1138.3007,355.83527,0.99840957
+2848,2802.3796,314.98303,2826.7112,366.90265,0.9983955
+2848,2345.8455,200.8154,2368.32,245.66052,0.99831545
+2848,2764.2915,278.05753,2795.2979,324.23178,0.9981735
+2848,2032.1042,231.63431,2050.594,281.53665,0.99812263
+2848,1950.1285,251.02348,1979.7308,298.1391,0.99796224
+2848,2622.7786,192.82623,2649.0422,233.43494,0.997837
+2848,3066.206,358.1914,3089.5469,405.38818,0.99773335
+2848,2958.494,358.22968,2987.339,406.5218,0.9976788
+2848,3275.066,352.6341,3296.1172,396.8911,0.99762446
+2848,2306.1287,369.8758,2334.4314,426.761,0.99758756
+2848,2844.4883,286.97833,2866.5146,330.6148,0.996786
+2848,2766.4026,201.73068,2791.1633,239.8128,0.9961423
+2848,1950.4673,124.15219,1970.2803,160.74225,0.99523866
+2848,2829.935,223.79555,2849.3096,263.87204,0.99170387
+2848,2762.965,180.36143,2787.6453,209.96577,0.9910336
+2848,3657.1572,803.1447,3704.6729,874.19867,0.8208883
+2848,2781.819,185.01112,2794.3904,217.17647,0.7925798
+2848,3696.637,772.6404,3728.825,830.64185,0.7187784
+2848,3734.6013,796.0596,3769.297,861.01013,0.33086586
+2848,3689.6704,782.37726,3727.4146,869.5766,0.26165798
+2848,3674.83,800.03235,3725.3516,867.6157,0.10647267
+2849,2385.1997,406.58307,2423.8325,466.15656,0.9993156
+2849,3187.3208,444.3623,3215.416,499.1737,0.9993026
+2849,2803.6082,315.46414,2829.0447,368.0217,0.99916625
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+2849,3068.7195,358.50546,3095.2473,407.35812,0.9989814
+2849,2963.6323,357.99933,2989.0068,407.41254,0.9987643
+2849,2032.7964,230.85616,2052.8992,281.8195,0.9987464
+2849,3161.8374,411.84344,3190.1167,464.8401,0.9987135
+2849,2767.7593,278.36105,2799.727,325.38815,0.99863094
+2849,2630.253,192.8356,2653.8516,233.14606,0.99859756
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+2849,1983.5887,876.144,2020.3434,974.79376,0.99835736
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+2849,1953.7588,123.789795,1971.8496,160.56116,0.9975446
+2849,3275.229,353.05884,3296.8179,396.74823,0.99754274
+2849,2308.509,369.6055,2341.7415,425.48856,0.99173635
+2849,2767.948,180.38965,2790.798,211.72171,0.99107754
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+2849,3657.245,803.52246,3700.9731,874.05347,0.78135365
+2849,2779.5085,185.75562,2796.8538,216.25226,0.7593056
+2849,3734.8044,796.3883,3769.7903,860.7206,0.41450897
+2849,3700.365,773.07776,3730.7078,829.5814,0.3865946
+2849,3686.7598,781.0407,3728.7012,869.915,0.13016269
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+2850,2003.294,303.63193,2028.6438,357.13492,0.9989042
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+2850,1983.3691,875.43097,2020.8115,972.6508,0.99752396
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+2851,1983.443,874.31384,2020.6173,971.46326,0.9965281
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+2852,1983.6134,873.5771,2020.5675,970.36725,0.99746764
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+2853,3166.876,412.27237,3198.4766,465.55948,0.99863964
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+2853,1959.0127,122.61194,1978.1362,160.27924,0.99836725
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+2853,1983.442,872.9208,2020.9286,967.66833,0.99805236
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+2859,1983.9888,870.1171,2021.5251,961.0164,0.99908304
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+2859,2663.394,193.27362,2687.6562,232.44815,0.9974247
+2859,1981.0696,252.89687,2010.6089,300.76056,0.99684995
+2859,1972.1101,122.75499,1986.6692,160.49437,0.99645764
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+2859,2813.0227,186.32532,2831.8694,211.72968,0.24759655
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+2860,3097.4678,363.7628,3125.2158,412.12857,0.9991061
+2860,1984.5197,869.84515,2021.3932,960.9401,0.9990859
+2860,2828.5867,325.1147,2861.4734,376.08972,0.9990207
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+2860,1983.674,253.42116,2011.8693,301.11642,0.99815863
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+2860,2382.106,200.0117,2408.6665,244.54262,0.99752766
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+2860,3724.227,771.169,3761.0674,830.60345,0.75997895
+2860,3744.257,796.6678,3775.508,861.80096,0.7507289
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+2861,2384.3064,200.13208,2411.2961,243.94778,0.9993711
+2861,1984.1544,869.12744,2021.2711,960.56665,0.9993358
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+2861,1973.6444,122.5414,1988.9703,160.44551,0.9970931
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+2861,3743.9946,796.9077,3776.143,861.6124,0.7054338
+2861,3657.193,803.3861,3700.4128,874.1776,0.6818997
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+2862,1994.03,254.31364,2014.5874,301.18152,0.99718285
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+2862,2812.8928,189.93202,2842.096,220.67796,0.97683495
+2862,3726.477,771.3627,3766.226,833.04675,0.74798447
+2862,3744.008,797.02814,3777.2644,861.2968,0.7282038
+2862,3657.1763,803.33,3700.4858,874.20856,0.6493089
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+2862,2758.2231,874.4594,2789.4468,955.9655,0.33907732
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+2863,2037.4855,302.6133,2065.5388,358.09506,0.99945647
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+2863,2402.9387,287.48468,2431.5193,337.07703,0.9993236
+2863,1982.6105,868.47107,2020.7189,960.8102,0.9993129
+2863,2841.8271,324.81955,2865.5396,376.2502,0.9990902
+2863,3006.4414,357.9899,3033.3394,407.83957,0.9988954
+2863,2052.3745,235.4684,2081.9736,286.68872,0.99884605
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+2863,2284.492,179.16951,2310.5408,222.5681,0.9984528
+2863,2392.2087,376.51654,2426.9324,433.97693,0.9983847
+2863,2902.083,296.5206,2930.2002,342.74844,0.9981439
+2863,2569.1025,196.38992,2598.371,234.79407,0.9979582
+2863,2676.1653,194.41443,2703.0286,234.23999,0.99791306
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+2863,2827.6577,291.13135,2863.3086,330.19287,0.9967501
+2863,1998.5729,254.35115,2017.0306,300.92365,0.9964122
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+2863,3731.0889,770.7944,3769.7964,824.7778,0.66935873
+2863,3657.1543,803.2704,3700.4507,874.3367,0.48135716
+2863,2827.8384,189.81013,2856.2656,214.31938,0.4153229
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+2864,1982.54,867.96606,2020.6885,961.1206,0.9992623
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+2864,2571.566,196.33742,2600.6206,234.97368,0.998626
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+2864,2401.6753,377.5479,2432.501,433.85962,0.9985238
+2864,2285.838,178.75543,2314.314,222.28754,0.9983041
+2864,2001.9431,254.88069,2019.9551,301.11957,0.99809915
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+2864,3657.0486,803.3753,3700.4753,874.4279,0.5498782
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+2865,3016.1372,359.25775,3039.3516,408.06128,0.9993086
+2865,1982.5916,867.64935,2020.3628,961.2098,0.99929
+2865,2406.4688,286.44092,2439.0088,336.48926,0.9991246
+2865,2287.4182,178.89905,2318.0095,221.53589,0.99908423
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+2865,2000.9534,254.62936,2023.169,302.16678,0.9986632
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+2865,2038.7797,302.73102,2071.0225,358.2478,0.9968695
+2865,2688.047,195.90112,2711.5042,234.54407,0.99660444
+2865,3101.2622,364.57028,3128.395,412.80112,0.9953838
+2865,3284.794,356.3401,3305.7124,399.73596,0.99509263
+2865,2833.4636,291.5068,2871.0764,333.6177,0.9945016
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+2865,3736.1323,769.0879,3776.9497,828.2578,0.9903184
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+2866,1982.7053,866.873,2019.9521,960.7056,0.9993498
+2866,2408.8787,286.22696,2443.3591,336.57993,0.9991499
+2866,3210.2073,440.66525,3242.0955,491.6188,0.9990465
+2866,2057.18,235.33105,2085.702,287.67444,0.9988851
+2866,2289.639,178.94037,2321.5667,223.12897,0.99872345
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+2866,2577.4204,196.75748,2605.0522,235.16388,0.9984633
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+2866,2913.5723,299.79443,2941.795,344.08453,0.99835324
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+2866,2694.1401,195.53265,2716.0776,234.6618,0.9981527
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+2866,3016.8284,358.81134,3042.034,408.15118,0.99804014
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+2866,2413.3223,377.7402,2445.937,433.34818,0.99478585
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+2866,3741.5847,768.53613,3779.1511,826.3196,0.9933634
+2866,1978.8007,125.305786,1999.257,161.90134,0.99189055
+2866,2849.8076,208.7502,2869.916,241.7321,0.99178934
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+2866,3744.4424,780.64484,3780.3633,859.4621,0.5316226
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+2867,1983.5972,866.25885,2019.3169,960.70734,0.99939656
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+2884,1980.9462,859.1451,2010.7706,956.81635,0.99943644
+2884,2080.0884,300.4996,2100.3726,355.23465,0.9993253
+2884,2089.2268,238.95978,2112.6887,291.75745,0.99932516
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+2884,2512.8044,397.05542,2552.6287,454.8968,0.9991955
+2884,2895.0933,331.41724,2915.8662,384.5103,0.99916935
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+2884,2480.6697,289.4704,2514.068,339.09924,0.9990251
+2884,2355.7659,184.86409,2386.719,229.46025,0.99872714
+2884,3796.8665,756.7963,3836.9216,811.7327,0.9986291
+2884,2997.0095,313.63004,3020.196,353.0234,0.99840146
+2884,2755.1746,200.42267,2778.6033,239.54263,0.99834216
+2884,3070.4014,356.43823,3099.877,403.76782,0.9982151
+2884,2044.4736,254.92715,2061.0664,303.40353,0.99780625
+2884,3240.5127,436.15042,3273.3506,486.01718,0.99759066
+2884,2878.2185,205.3265,2892.285,243.75107,0.9974667
+2884,2461.3577,204.6334,2484.259,247.31633,0.99740297
+2884,2646.6204,204.77612,2673.2673,239.19507,0.9970938
+2884,2981.83,239.11313,3009.1118,274.11343,0.9964013
+2884,1998.7917,124.80978,2012.4436,162.90382,0.9923505
+2884,2513.5215,383.6978,2548.9678,444.02448,0.9888859
+2884,3293.7976,356.8379,3314.1301,401.63428,0.98454314
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+2884,2933.5571,208.76082,2955.651,243.23872,0.9813976
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+2884,2915.3486,209.29546,2943.2515,241.85863,0.8638725
+2884,3656.8577,803.7408,3703.4055,874.5429,0.39127007
+2884,3694.4949,801.91425,3727.3162,870.3869,0.2280094
+2884,3246.7102,423.26065,3277.5066,476.63205,0.13614948
+2885,1200.2233,305.0467,1223.162,356.86957,0.9995353
+2885,2081.3635,300.6032,2101.1863,355.75842,0.9994941
+2885,1980.8113,859.1814,2010.5356,956.6853,0.9994436
+2885,2896.3176,332.95306,2917.7395,385.18457,0.9994325
+2885,2089.4053,239.50862,2114.3687,292.21655,0.9993406
+2885,3800.8635,755.82324,3840.2927,811.71204,0.99928147
+2885,2483.919,290.35983,2518.8662,340.2228,0.9988851
+2885,2360.83,186.32123,2388.6553,231.10834,0.99878055
+2885,3072.042,356.4058,3103.026,402.52396,0.99872357
+2885,2756.7961,200.05862,2781.9558,239.45477,0.99866676
+2885,3240.8965,436.87305,3274.518,486.33728,0.998643
+2885,2517.3374,397.62735,2556.8613,455.611,0.9984029
+2885,3151.8293,362.57806,3173.5427,408.40222,0.9983797
+2885,2649.2205,205.17252,2678.0266,239.9606,0.99815434
+2885,2878.9512,205.30382,2892.5474,243.3799,0.9973909
+2885,2997.2197,315.3515,3024.7378,355.74298,0.99721044
+2885,2983.2644,238.95717,3011.8728,275.36844,0.9971311
+2885,2462.2498,203.96072,2486.9504,246.30072,0.9969909
+2885,1999.8193,124.731766,2013.1892,162.82585,0.99284005
+2885,2517.2144,383.8072,2553.749,442.30298,0.99279517
+2885,2047.4717,255.10622,2062.5146,302.0925,0.9806277
+2885,2937.192,210.15599,2959.065,243.92183,0.9801703
+2885,3293.954,357.00388,3314.912,401.6337,0.9793985
+2885,2942.6367,310.219,2975.0186,355.8958,0.97295547
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+2885,2924.7466,211.65782,2948.2427,240.51964,0.91587746
+2885,3656.8396,803.77985,3703.4612,874.591,0.3931572
+2885,3694.3325,801.82587,3727.3364,870.37836,0.29200134
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+2886,2082.0305,301.82986,2103.8557,355.76187,0.9996358
+2886,2089.6687,240.21419,2116.9094,292.71854,0.99940836
+2886,1980.7825,859.08466,2010.0723,956.5278,0.9993554
+2886,2896.7153,334.0218,2919.3599,385.91284,0.99931777
+2886,1203.2788,304.8568,1223.7822,356.80774,0.99931264
+2886,2758.3088,200.40205,2784.3293,239.40611,0.999042
+2886,3805.4502,754.0,3843.063,810.55896,0.9990268
+2886,3076.5276,356.25452,3106.6155,402.6814,0.9989197
+2886,2522.817,397.84134,2563.2026,456.59488,0.99864453
+2886,2987.0542,240.00752,3015.4893,277.7647,0.99857116
+2886,3243.2942,437.23346,3276.3508,486.1452,0.9984981
+2886,2366.5486,188.3141,2391.555,230.95616,0.99830234
+2886,2488.443,290.97815,2521.132,341.20673,0.99826247
+2886,2048.4233,256.13998,2067.3599,304.70383,0.9982601
+2886,2463.4429,204.05385,2490.337,247.42717,0.99814355
+2886,2520.4392,383.7132,2562.9895,442.2937,0.99791354
+2886,2651.8936,204.89847,2682.48,242.26239,0.99778724
+2886,2879.55,205.08168,2892.7332,243.25899,0.99751437
+2886,2997.9038,315.94012,3029.463,357.25226,0.9974443
+2886,3155.0142,362.5255,3175.2651,408.38763,0.9966035
+2886,2000.3435,124.63217,2014.147,162.75433,0.9921957
+2886,2946.9666,311.25082,2979.9055,355.83896,0.9903803
+2886,2938.599,212.29515,2960.4868,243.60756,0.9822379
+2886,3255.7148,414.52478,3283.4678,461.9574,0.95919496
+2886,2934.105,212.8339,2951.1865,243.7225,0.92031854
+2886,3294.9795,357.45172,3314.8315,400.4099,0.91268504
+2886,3293.8367,368.161,3310.2678,402.70056,0.43373108
+2886,3694.3652,801.9326,3727.398,870.2777,0.324325
+2886,3656.813,803.7287,3703.6255,874.67523,0.32411864
+2886,3753.7236,789.85,3784.2656,854.3943,0.27624613
+2887,2082.2554,302.2846,2108.1538,355.9926,0.99959785
+2887,3806.4146,752.2819,3846.5376,810.8476,0.9995042
+2887,1980.7772,859.0784,2009.8793,956.43207,0.99935526
+2887,2048.843,255.76756,2071.4797,304.8595,0.9993241
+2887,2896.4019,335.45178,2922.8062,387.10675,0.99927336
+2887,2760.4988,200.25093,2787.9749,239.86818,0.9991074
+2887,2090.6528,240.95459,2118.2085,292.84644,0.99905914
+2887,3081.1265,356.5977,3110.4414,404.499,0.99872845
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+2887,2493.7942,292.34448,2524.7292,341.93085,0.9986527
+2887,2465.2444,204.31442,2493.4915,248.42123,0.99859256
+2887,2372.2307,188.87212,2396.5447,231.05238,0.99841356
+2887,2879.7993,204.4495,2893.0483,243.34586,0.9982816
+2887,3247.8171,436.28238,3278.2825,485.87668,0.9981786
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+2887,2528.98,397.76413,2567.5322,455.96365,0.99667007
+2887,2000.8158,124.35292,2015.9391,162.6256,0.9958305
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+2887,2525.9055,384.8676,2568.1482,442.14722,0.9935929
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+2887,3258.369,413.45636,3285.366,461.4709,0.9556237
+2887,2950.9473,312.74985,2987.733,357.6346,0.94328225
+2887,3294.9507,357.69507,3314.9067,400.51416,0.91832733
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+2887,3293.7446,365.72168,3310.9702,402.52734,0.41750443
+2887,3694.3862,802.0122,3727.3926,870.2688,0.33827242
+2887,3656.833,803.72125,3703.6338,874.6579,0.33191913
+2887,3753.8665,789.8808,3784.3743,851.91364,0.17267264
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+2888,3808.5647,752.09,3849.5535,809.7247,0.99942696
+2888,1980.8031,859.07733,2009.8983,956.3732,0.9993604
+2888,2896.3552,336.17117,2925.8093,387.54373,0.9991906
+2888,2091.026,241.92513,2119.4888,293.32864,0.9990158
+2888,3085.9355,356.86374,3113.245,405.45444,0.9989328
+2888,2049.0686,255.83093,2074.633,304.41425,0.99890697
+2888,1209.3138,304.16385,1226.8873,357.0717,0.9988462
+2888,2376.9436,189.0981,2400.2659,230.92549,0.9987691
+2888,2500.0872,292.38638,2527.715,342.34244,0.9985115
+2888,3006.2876,317.93762,3039.251,360.71997,0.9984863
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+2888,2467.8462,204.7923,2496.584,248.9249,0.99821436
+2888,3251.1838,435.36163,3280.2039,486.45044,0.9981018
+2888,2880.128,204.38924,2893.1792,243.37929,0.9980884
+2888,3156.5508,364.11398,3180.3296,409.4,0.9977825
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+2888,2659.2544,205.61494,2690.5596,246.45992,0.99735814
+2888,2997.0283,242.24088,3020.5146,279.8226,0.9970817
+2888,2000.6771,124.32765,2017.0548,162.37483,0.9951631
+2888,2957.5354,314.65005,2991.8313,360.46347,0.9942731
+2888,2940.1829,214.39418,2966.757,246.41777,0.98552114
+2888,3260.1562,412.34604,3286.8218,461.05405,0.9571307
+2888,3295.4844,357.67227,3314.4727,400.54013,0.94623226
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+2888,3656.8457,803.8874,3703.7178,874.61127,0.30805716
+2888,3694.281,802.05115,3727.2893,870.31274,0.30550963
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+2889,2091.8206,241.98206,2120.1072,294.54205,0.99952924
+2889,3811.3748,750.6554,3852.3323,807.23975,0.9993917
+2889,1980.9669,859.1937,2009.8444,956.27234,0.99934
+2889,2896.6873,336.6169,2928.1257,387.75223,0.9992219
+2889,3090.4822,358.05505,3116.9314,406.0564,0.9991166
+2889,2471.1348,205.695,2499.793,249.64005,0.99869895
+2889,1211.0758,303.7254,1229.2225,356.6441,0.9986687
+2889,2049.1665,256.17917,2078.2993,303.7789,0.99850464
+2889,2538.3535,396.6331,2573.5737,455.69254,0.99843234
+2889,2377.7634,188.89142,2403.7488,230.84052,0.9983613
+2889,3014.444,319.51724,3044.3142,360.97598,0.9980179
+2889,2666.8232,206.95319,2694.1,246.05869,0.9979722
+2889,2767.9573,202.13612,2793.6917,242.72197,0.9978371
+2889,3253.8213,433.38403,3280.226,486.85986,0.997593
+2889,2880.435,204.71373,2893.7275,243.33696,0.9973998
+2889,2507.261,292.50696,2532.068,342.46124,0.99733365
+2889,3157.6917,363.8211,3184.575,408.92035,0.99711585
+2889,3004.463,242.8059,3022.667,279.5958,0.9970156
+2889,2001.0042,124.67326,2016.9614,162.52916,0.9959699
+2889,2965.3628,315.7181,2993.9111,360.9373,0.99306905
+2889,3269.939,413.14233,3289.3252,462.35742,0.9862125
+2889,2946.0874,214.7446,2973.8413,248.47855,0.97297287
+2889,3295.1494,357.628,3315.0615,400.97833,0.9628481
+2889,2545.562,387.56723,2575.1865,445.13675,0.80850554
+2889,3656.8723,803.9299,3703.697,874.50055,0.35707033
+2889,3694.3918,802.06067,3727.3845,870.3329,0.34473178
+2889,3753.8838,789.31476,3784.621,851.7304,0.17826505
+2890,2082.419,302.31085,2115.2275,356.2403,0.999608
+2890,1981.2555,859.2626,2009.8492,956.2645,0.99930954
+2890,2093.8328,241.85303,2120.2522,294.18237,0.99929035
+2890,2380.186,189.21715,2406.5884,233.67375,0.99916565
+2890,3094.5005,358.23285,3120.1123,406.6624,0.99916387
+2890,1212.7133,303.60754,1233.4991,356.77698,0.9990256
+2890,2540.2102,394.60684,2578.2664,455.6555,0.99891675
+2890,3814.228,751.2317,3855.1128,806.2095,0.9987262
+2890,3024.7683,319.71344,3048.967,361.11932,0.99854267
+2890,2897.61,336.73764,2929.2449,388.76645,0.99840254
+2890,2049.3577,256.95898,2080.4246,304.25513,0.99797446
+2890,3256.7332,432.79987,3280.987,482.01117,0.9978587
+2890,2880.5823,204.41075,2894.6492,243.35612,0.9977254
+2890,2509.503,292.30563,2535.142,342.0425,0.9976266
+2890,2475.135,206.32877,2502.133,250.17993,0.9975228
+2890,2001.4254,124.25535,2017.7311,162.60661,0.99654716
+2890,2673.8528,207.46211,2698.7346,246.23375,0.9964965
+2890,2972.9875,317.2662,2998.2175,360.6627,0.99648345
+2890,3153.9995,364.01642,3189.046,408.87073,0.9961957
+2890,2772.9402,203.89636,2796.927,242.63565,0.99568176
+2890,3006.4917,243.6595,3025.6987,280.55225,0.9922023
+2890,3271.4016,413.6841,3290.6301,461.025,0.9772019
+2890,3295.148,357.5266,3314.8672,401.1695,0.9732185
+2890,2958.945,215.5689,2978.7883,250.17552,0.9550865
+2890,2950.0981,214.9204,2973.586,245.56313,0.893219
+2890,3656.8848,804.07947,3704.0034,874.48425,0.43537432
+2890,3694.3398,802.07465,3727.3525,870.3271,0.40239444
+2890,3753.9587,789.2825,3784.3376,851.07336,0.13421914
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+2891,2082.8748,302.8725,2116.464,356.76978,0.99944025
+2891,2095.3489,241.97047,2120.4553,294.30017,0.99941134
+2891,2542.8394,394.7608,2580.106,454.9264,0.9993357
+2891,3095.8545,358.11365,3123.9136,405.72137,0.9993293
+2891,1981.4149,859.2946,2009.8522,956.23456,0.9992735
+2891,3817.4302,751.56866,3858.6519,807.2768,0.99919623
+2891,2680.1255,207.88756,2702.3643,246.21114,0.9987025
+2891,2898.7583,337.0247,2931.9668,388.53384,0.99863285
+2891,2381.987,189.36949,2410.2625,234.16066,0.9983271
+2891,2049.842,257.02026,2081.8665,304.8374,0.99825805
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+2891,2510.0215,292.44208,2539.0098,340.9828,0.9980355
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+2891,2880.778,204.3584,2895.302,243.37076,0.9978928
+2891,2002.1035,124.402664,2017.9263,162.5367,0.9974159
+2891,2779.0037,204.29944,2799.8357,242.4581,0.9970264
+2891,2480.0347,207.13788,2505.935,250.33356,0.9967018
+2891,2981.7551,317.66272,3003.6086,361.91504,0.9960884
+2891,3153.8735,363.74973,3192.3457,408.6689,0.99554527
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+2891,2962.1787,215.30542,2983.9424,249.99002,0.9659837
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+2891,2953.4573,214.06451,2975.909,247.17456,0.84807837
+2891,3656.9038,804.07837,3703.9854,874.4779,0.45659348
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+2892,1981.4181,859.4151,2009.9645,956.24164,0.99937683
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+2892,3820.3044,752.7226,3860.9265,807.4025,0.99917114
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+2892,3097.9287,357.39124,3127.0195,405.6822,0.9990101
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+2892,2002.3639,124.71183,2017.9506,162.5184,0.9976019
+2892,2880.7256,204.70825,2895.6704,243.32559,0.99758995
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+2893,2549.304,395.68604,2588.2605,453.72388,0.9993569
+2893,1981.4614,859.73364,2010.2263,956.2811,0.99933976
+2893,3015.474,245.7038,3038.7358,287.565,0.9992291
+2893,3825.2476,752.5918,3864.8125,807.63965,0.9992161
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+2893,2002.9048,124.891655,2018.5947,162.56566,0.997682
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+2894,1981.6268,859.7856,2010.4152,956.28937,0.9993331
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+2894,2003.4409,124.655304,2019.0557,162.33548,0.99719304
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+2895,1981.7556,859.9741,2010.5515,956.291,0.99930227
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+2895,2003.9473,124.10078,2019.4253,162.25275,0.9977277
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+2896,1981.8265,859.9922,2010.5873,956.2793,0.9992555
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+2896,2004.4127,123.75397,2019.5675,162.08002,0.9971685
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+2897,1982.113,860.2519,2010.7549,956.27997,0.99923027
+2897,3263.1448,431.9278,3285.156,481.34094,0.9991768
+2897,2098.063,302.4586,2117.5176,356.6741,0.9991265
+2897,2496.4348,207.94865,2524.5774,251.79082,0.99911004
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+2897,2793.688,207.35492,2819.4512,248.05966,0.99866843
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+2897,2881.0938,203.3548,2897.0142,242.4667,0.99764234
+2897,2005.0563,123.624405,2019.5433,162.12596,0.99746025
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+2897,3694.1008,802.0982,3727.5076,870.44525,0.49314433
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+2898,1226.031,303.85828,1244.3076,356.763,0.9993106
+2898,1982.3527,861.3436,2011.1139,956.37244,0.999244
+2898,3263.7876,431.73102,3285.2617,481.0808,0.9992436
+2898,3836.1084,747.5333,3878.5957,806.00073,0.9991278
+2898,2541.2925,295.58542,2566.9365,345.40213,0.99906415
+2898,2499.0264,208.60712,2527.0679,253.3917,0.9990074
+2898,2407.9946,191.80789,2434.5288,236.0875,0.9989128
+2898,2101.4038,302.74326,2118.5308,356.82492,0.99887675
+2898,3010.5935,327.27905,3036.2673,372.85535,0.9986731
+2898,3063.8274,324.35724,3086.9402,367.8681,0.9983784
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+2898,2005.4275,122.83916,2020.2686,162.4605,0.99695385
+2898,2068.8855,256.13037,2085.1785,306.29987,0.99677545
+2898,2798.2964,207.68497,2822.8184,248.20929,0.996185
+2898,3280.679,410.44577,3308.3787,457.01297,0.9960574
+2898,3035.736,252.23482,3056.6292,291.50574,0.99592704
+2898,2922.0496,338.06674,2950.492,388.07993,0.9937138
+2898,3295.013,357.86932,3314.7502,400.8191,0.9812262
+2898,2595.6394,391.19012,2618.222,447.3051,0.9770477
+2898,2985.761,222.13876,3002.1028,256.0665,0.96319777
+2898,2980.0063,217.50314,2996.8726,252.91644,0.8394299
+2898,3657.0886,804.1137,3704.6052,874.3443,0.5640255
+2898,3694.107,802.1014,3727.5122,870.439,0.51456124
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+2899,2107.3752,242.9195,2129.697,296.13702,0.9995759
+2899,1982.462,861.63226,2011.4012,956.3359,0.9993
+2899,3263.7217,431.67673,3286.438,480.9501,0.9991967
+2899,1227.3484,303.84332,1245.8164,357.2143,0.9991391
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+2899,3123.688,358.36465,3147.2212,405.19144,0.99891293
+2899,2409.331,191.83011,2437.728,237.26276,0.9988978
+2899,3069.7673,325.15576,3092.654,368.4806,0.9987668
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+2899,2069.3867,256.1816,2087.9473,306.0826,0.9983163
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+2899,2881.45,203.48302,2897.0208,241.83644,0.9978405
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+2899,2989.2617,220.2438,3006.0464,254.96204,0.96821636
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+2900,2108.1108,243.22739,2130.4976,296.47714,0.99964714
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+2900,2410.6716,192.01192,2441.2742,238.38756,0.9986847
+2900,3077.5115,326.8753,3096.5999,368.80206,0.99868137
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+2900,3841.1824,748.37683,3881.9475,801.96216,0.99811953
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+2900,2005.5763,122.986015,2019.8226,162.31882,0.99709845
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+3278,2807.738,297.8728,2825.868,342.69757,0.9987404
+3278,2046.1844,354.96475,2078.1863,411.61295,0.9986878
+3278,2001.0695,124.168594,2020.1376,162.28517,0.9983261
+3278,3052.5728,250.17464,3069.3784,292.08154,0.99827945
+3278,2450.1262,147.4232,2464.173,189.24376,0.9982231
+3278,2395.78,176.87653,2423.2024,219.45111,0.99807453
+3278,3862.2874,746.4007,3892.9568,803.4843,0.9978878
+3278,2049.8303,275.37567,2074.6467,330.16248,0.9978849
+3278,2910.277,304.76398,2928.4666,351.72046,0.99730057
+3278,3253.8494,368.83752,3284.4744,406.36,0.9925982
+3278,3678.314,792.6078,3710.4688,858.657,0.8350578
+3278,148.52849,585.5324,242.21657,658.22186,0.75335735
+3278,143.4397,604.62,210.44604,668.22217,0.21608631
+3278,3947.0906,757.16785,3987.6995,819.8601,0.1334841
+3279,1974.2622,859.7426,2007.48,956.9837,0.9995092
+3279,2538.0854,343.84167,2560.9805,397.66565,0.99932694
+3279,2988.7664,571.22296,3021.7415,636.65326,0.99931407
+3279,2338.7273,470.455,2370.5286,535.90533,0.9992912
+3279,2102.3328,314.31158,2126.8972,367.01917,0.9992893
+3279,2436.159,214.4499,2461.2727,261.67157,0.99928755
+3279,2556.1606,484.95175,2592.895,551.4078,0.9992423
+3279,3049.8662,416.29822,3073.3096,471.07666,0.9991653
+3279,1251.2799,318.7496,1270.5753,374.80777,0.99915814
+3279,3416.6335,503.9532,3440.1755,557.94257,0.9991068
+3279,2811.7266,228.07927,2831.0083,268.19037,0.99900985
+3279,2501.3926,353.69788,2523.546,408.9303,0.9989925
+3279,3169.6196,629.2158,3199.8179,699.2986,0.99894446
+3279,2299.6235,204.6911,2321.0713,249.43918,0.99882215
+3279,2934.223,375.4115,2958.49,425.18817,0.9988024
+3279,2045.9829,355.8649,2078.2798,412.8951,0.9986669
+3279,3114.0261,317.17703,3136.7166,362.79575,0.9986364
+3279,2049.524,275.25363,2072.7422,330.90268,0.9985341
+3279,2447.6787,147.89497,2462.4927,189.2003,0.9984743
+3279,2805.1892,297.71832,2824.5168,341.9865,0.9984629
+3279,2000.8717,124.05541,2019.963,162.29361,0.9984553
+3279,3052.382,250.18526,3069.4744,292.15518,0.99821717
+3279,3862.1707,746.51227,3892.0588,804.1298,0.9981548
+3279,2395.0308,177.18437,2417.3384,219.61195,0.9978333
+3279,2908.6433,304.77222,2927.4333,351.85315,0.99734515
+3279,3254.029,370.7498,3284.012,405.94846,0.9602056
+3279,148.5347,585.53735,242.23059,658.2517,0.7520128
+3279,3679.3538,792.49,3721.1277,858.09143,0.4756194
+3279,143.22339,604.88586,210.23526,668.3644,0.2171755
+3280,2335.7983,471.70914,2370.4033,536.4379,0.99955684
+3280,1974.296,859.7555,2007.4794,956.9877,0.99951744
+3280,2436.0906,214.51233,2460.6028,260.94965,0.99948645
+3280,2537.4055,343.3548,2560.1243,397.92603,0.99935335
+3280,3168.5098,629.727,3198.1118,698.8072,0.9993445
+3280,2988.8672,571.17145,3022.3145,636.4522,0.999215
+3280,2101.9976,314.15433,2124.9004,367.18643,0.99920124
+3280,3049.9832,416.43744,3073.5183,471.11584,0.99918085
+3280,2551.9495,485.52734,2592.1843,551.2849,0.9991047
+3280,1250.3788,318.70508,1270.5599,374.9536,0.99903667
+3280,3110.3748,317.74078,3135.6204,362.7846,0.99901044
+3280,2297.0657,205.22513,2317.8367,250.15527,0.9989711
+3280,2500.7773,354.20743,2522.7153,410.8453,0.998909
+3280,2049.0886,275.48187,2070.0603,330.99182,0.9988971
+3280,2934.38,375.15228,2960.018,427.87567,0.99883693
+3280,2810.714,227.63126,2829.677,268.76627,0.99881893
+3280,2045.6572,356.15707,2078.267,413.4031,0.99872214
+3280,2000.5023,124.06229,2019.5038,162.34872,0.99870324
+3280,2443.9312,148.06662,2460.9634,188.85068,0.9986156
+3280,2800.9023,297.70953,2823.1436,342.07617,0.99858356
+3280,3420.7417,504.99228,3441.6943,558.69354,0.99858165
+3280,3052.229,250.10478,3069.7124,292.13376,0.9982394
+3280,3861.8918,746.5912,3891.0198,803.45496,0.9978865
+3280,2906.5642,304.80618,2926.986,350.82846,0.99770564
+3280,2392.9553,176.87903,2412.5676,219.66345,0.9972817
+3280,3253.4229,372.80325,3284.6997,406.3688,0.96752083
+3280,148.90793,585.57697,242.21149,657.91144,0.75169885
+3280,3679.0396,791.7385,3728.1143,857.3727,0.62221575
+3280,139.36847,599.26587,232.2438,667.50916,0.22487228
+3280,138.76636,613.3647,202.3183,668.5865,0.10089261
+3281,2334.6543,472.17758,2370.599,536.9081,0.99950516
+3281,1974.3308,859.7403,2007.4893,956.97565,0.9995024
+3281,2536.7852,343.54456,2558.0527,398.1718,0.99935085
+3281,2435.3577,214.09613,2460.2937,260.74548,0.9992429
+3281,1249.0623,319.12198,1270.5464,375.6252,0.99921083
+3281,2101.7732,314.2035,2122.703,367.2171,0.99919516
+3281,2550.3977,486.2526,2591.8835,551.5242,0.99917704
+3281,2988.674,571.0761,3021.5623,636.5522,0.9991352
+3281,3163.3193,630.25775,3196.6108,699.39496,0.99913496
+3281,3050.0093,416.68787,3073.7798,471.1255,0.9990765
+3281,2295.406,206.2432,2314.6277,250.07907,0.9990652
+3281,2500.0833,354.98825,2522.238,411.54794,0.99880815
+3281,2440.595,148.33636,2460.5208,189.00443,0.9987709
+3281,2047.4017,275.70285,2068.189,330.82877,0.9987708
+3281,2389.066,176.56662,2405.8833,219.93332,0.998767
+3281,2810.0981,227.78659,2828.5347,268.77094,0.998689
+3281,2045.2502,356.34384,2078.2268,414.3278,0.99868447
+3281,3421.4492,505.6921,3443.4888,558.74805,0.9986778
+3281,3109.7888,317.61905,3134.345,362.9393,0.99867284
+3281,2934.4458,375.33408,2961.1294,428.43668,0.99865496
+3281,2000.1981,124.14509,2019.3344,162.30873,0.99849343
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+3281,3052.1997,250.07104,3069.8228,292.1239,0.9979876
+3281,3861.9539,746.5562,3890.9822,804.5678,0.9979767
+3281,2797.7332,297.48984,2822.0872,342.2902,0.99783736
+3281,3253.2886,372.97092,3284.6475,406.5471,0.9586368
+3281,3679.3384,789.9319,3730.5205,855.4263,0.92875415
+3281,146.22551,585.1941,242.5525,661.1078,0.7161408
+3281,140.465,601.53595,225.72882,668.06903,0.3165531
+3281,169.47217,583.72345,242.32117,646.1539,0.145077
+3281,138.74118,613.8315,204.76785,668.8743,0.11709485
+3282,2333.5132,472.47913,2370.2734,537.0895,0.99954224
+3282,1974.5306,859.5502,2007.4525,956.80945,0.9995054
+3282,1248.272,319.28656,1270.1975,374.9165,0.99937075
+3282,2534.098,343.3433,2556.3025,398.0176,0.9993243
+3282,2045.3407,275.63333,2065.4941,331.1868,0.99926645
+3282,2436.807,148.5615,2458.8865,188.8306,0.9991875
+3282,2986.4087,571.4433,3018.391,636.36346,0.999181
+3282,2550.1145,486.4875,2591.5095,551.7504,0.99906594
+3282,2435.5774,213.63623,2458.4128,260.65463,0.999035
+3282,3050.0251,417.1211,3073.9495,471.11145,0.9990257
+3282,2100.3125,313.89404,2120.3374,367.245,0.9989937
+3282,3158.6116,631.35895,3194.51,700.38055,0.9989526
+3282,2293.2627,206.2716,2311.77,249.9119,0.99889225
+3282,2808.0312,227.6145,2827.8975,268.47052,0.9988856
+3282,2044.4287,356.4856,2078.1177,415.10962,0.9987464
+3282,3109.0005,317.9136,3133.0513,363.1636,0.9987117
+3282,2933.9277,376.58984,2962.099,429.74786,0.9985399
+3282,1999.9465,124.29913,2019.6011,162.26865,0.9985355
+3282,2385.4185,175.978,2405.0625,219.70685,0.9984629
+3282,3861.7744,746.55597,3890.7979,805.571,0.99841547
+3282,2499.6143,354.77625,2521.3604,411.8274,0.998403
+3282,3421.203,506.5184,3445.033,559.84265,0.99834657
+3282,3052.6838,250.10521,3069.909,291.94095,0.99825054
+3282,2795.2651,298.12875,2821.245,342.34006,0.9975858
+3282,2902.9995,305.15857,2925.9136,352.22534,0.9974298
+3282,3254.0457,373.10214,3285.9202,407.308,0.98593485
+3282,3681.0688,789.54565,3733.0244,855.4071,0.98058355
+3282,146.24985,585.1624,242.57553,661.23737,0.7098127
+3282,139.38037,600.5229,228.18033,667.951,0.31649476
+3282,169.95093,583.7086,242.31561,645.6493,0.13999934
+3282,138.72807,613.87024,204.82756,668.90955,0.12286363
+3283,2333.3418,472.5949,2369.6738,537.0452,0.9995766
+3283,1974.5306,859.5502,2007.4525,956.80945,0.9995054
+3283,1248.0298,318.98352,1270.2063,374.49695,0.99945813
+3283,2098.6804,313.74295,2119.054,367.44827,0.9993407
+3283,2983.732,571.1451,3014.2593,636.2913,0.9992719
+3283,3154.9087,632.2365,3192.8008,700.8636,0.99920565
+3283,2042.258,275.8077,2063.89,331.40903,0.999129
+3283,2530.5088,343.56528,2554.1318,398.01077,0.99908197
+3283,2435.3386,213.07097,2456.5203,260.34268,0.9990268
+3283,2434.6057,148.52643,2457.8162,188.63605,0.9990163
+3283,2549.887,486.23334,2590.1716,551.6484,0.99901533
+3283,2382.3499,175.28522,2403.4128,219.2811,0.99898523
+3283,3050.1628,417.45978,3074.0784,471.16278,0.9989679
+3283,2290.701,206.41719,2309.014,250.03441,0.99866605
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+3283,3108.9539,318.7068,3132.1545,363.25006,0.9985678
+3283,2934.6604,378.39743,2963.2312,431.15903,0.99853265
+3283,3861.5967,746.663,3890.8096,806.51324,0.9985234
+3283,2044.3328,356.44946,2078.257,416.50452,0.9984666
+3283,3421.1113,507.09073,3446.5703,561.53564,0.9984451
+3283,1999.6195,124.3573,2018.9457,162.3223,0.99841285
+3283,3052.7695,250.211,3070.0308,291.97485,0.99823004
+3283,2498.8987,354.77997,2520.8684,411.8205,0.9977464
+3283,2902.2217,305.99945,2925.3027,352.5072,0.9974928
+3283,2794.079,297.74582,2820.5952,342.1451,0.99709475
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+3283,3684.727,789.75665,3736.87,855.23517,0.9781927
+3283,146.37434,585.0432,242.68477,661.1196,0.7154633
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+3284,1974.5305,859.5499,2007.4517,956.80994,0.9995053
+3284,2333.3052,472.83212,2368.8433,537.3598,0.9994567
+3284,3152.638,633.5359,3190.7493,702.07227,0.99931896
+3284,1247.9004,319.16254,1270.0908,374.63934,0.99930143
+3284,2979.5698,570.9792,3011.1323,636.26886,0.9992949
+3284,2435.8042,212.76636,2454.1533,260.5331,0.99929273
+3284,2527.9114,344.40814,2552.9011,398.20966,0.99911934
+3284,2433.3096,148.27493,2456.9805,188.5538,0.99905056
+3284,2377.4495,175.02042,2402.2146,216.83658,0.9990361
+3284,2040.0519,275.8672,2062.8936,331.5857,0.99903524
+3284,2550.2761,485.8911,2587.889,550.99854,0.9989723
+3284,3050.2454,417.52615,3074.3162,471.17545,0.9989643
+3284,2935.7375,380.02997,2964.696,434.0039,0.9988871
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+3284,2498.637,355.04105,2520.3074,412.15323,0.99873465
+3284,3861.4353,746.7953,3890.985,806.96265,0.9986003
+3284,1999.4916,124.47363,2018.607,162.3136,0.99851996
+3284,3109.6052,318.73172,3131.4695,363.21213,0.99851793
+3284,2044.2798,356.7484,2078.2495,417.37073,0.9984034
+3284,2287.7695,205.98236,2306.77,250.78256,0.99836487
+3284,3052.8752,250.2007,3070.0593,291.95428,0.9982399
+3284,3421.119,507.78976,3448.738,561.9494,0.9982309
+3284,2901.2546,306.65567,2924.777,352.6774,0.9977452
+3284,2794.348,298.32095,2819.5486,342.5081,0.9976547
+3284,3257.0698,372.968,3288.2017,408.68564,0.9948319
+3284,3691.4976,789.66754,3739.2856,856.2325,0.8659152
+3284,141.69969,586.48553,241.68059,665.5124,0.73419446
+3284,165.5455,583.31085,242.40771,649.9134,0.35388565
+3284,141.69025,610.68463,209.09924,668.68317,0.2742188
+3284,3954.337,738.34375,4003.1245,818.32837,0.20176588
+3285,2976.728,572.266,3009.0776,636.59424,0.9995278
+3285,2333.2976,472.81418,2367.384,537.5338,0.999516
+3285,1974.5306,859.5502,2007.4525,956.80945,0.9995054
+3285,1247.5483,319.9339,1270.1748,375.95575,0.99939525
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+3285,2525.4192,344.7916,2551.2952,398.22263,0.9993154
+3285,2433.2463,147.91599,2456.72,188.29205,0.99920875
+3285,2371.5786,175.05247,2400.1582,216.52953,0.9991961
+3285,2094.1777,315.04205,2118.1304,369.13873,0.9991799
+3285,2498.074,356.10913,2519.4563,412.40765,0.99908555
+3285,3050.2678,417.5705,3074.5447,471.16492,0.9990202
+3285,2285.6865,204.56128,2304.8838,248.70767,0.99880874
+3285,2435.6409,212.78502,2453.0452,260.27966,0.9987257
+3285,2038.1691,276.05374,2062.6768,331.78476,0.9987105
+3285,3861.3484,746.97363,3891.3567,806.8568,0.99864167
+3285,2801.6465,227.57793,2825.1797,268.82562,0.9985935
+3285,1999.064,124.57242,2018.331,162.20251,0.9985262
+3285,2937.0662,381.767,2965.923,434.23282,0.99836403
+3285,2550.2183,485.88803,2584.5044,550.5315,0.9983563
+3285,3052.9146,250.18356,3070.2183,291.8574,0.9983393
+3285,3109.3342,318.48587,3129.6912,363.13876,0.99824584
+3285,2043.4103,357.57385,2078.2336,417.43,0.9980672
+3285,2794.0962,298.25027,2819.2373,342.6451,0.9979931
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+3285,2900.9426,307.1496,2924.2302,352.75934,0.99782455
+3285,3261.0537,373.3908,3288.9233,408.38757,0.99490535
+3285,3696.588,790.04297,3741.018,857.85474,0.83265495
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+3286,2333.2522,472.60187,2364.9631,537.6795,0.99958533
+3286,1974.5359,859.51794,2007.4622,956.82263,0.9995127
+3286,2282.7546,204.57964,2303.5447,249.07161,0.9994054
+3286,1247.4175,319.86847,1270.0679,375.96454,0.9993808
+3286,3152.4368,633.94385,3186.9011,702.813,0.99933064
+3286,2092.3506,315.58676,2116.921,370.3821,0.99923646
+3286,2432.9487,147.19104,2456.1465,188.31326,0.9991555
+3286,2521.9297,344.92136,2550.4106,398.17166,0.9991449
+3286,3050.2646,417.6825,3074.7983,471.1836,0.9990086
+3286,2496.7222,355.8997,2519.0952,412.1274,0.9989398
+3286,2434.061,212.72261,2451.7998,259.5365,0.9987502
+3286,2550.1887,485.66888,2580.9207,549.84454,0.9986809
+3286,2037.5133,277.1635,2061.9873,331.74744,0.99866176
+3286,1998.9547,124.80448,2018.0785,162.13574,0.99862754
+3286,3053.0503,250.12663,3070.4727,291.8219,0.99844426
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+3286,3108.8564,318.06873,3128.7144,363.44092,0.99832255
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+3330,1981.4297,871.6125,2013.0195,964.3713,0.9990484
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+3369,3032.159,417.46417,3066.8137,467.7912,0.9988299
+3369,1977.61,124.19192,1995.3292,162.2301,0.99882644
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+3369,3865.2446,761.55566,3901.954,824.8103,0.89526176
+3369,3853.2107,770.2286,3888.964,832.11554,0.6493114
+3369,3970.7842,729.7114,3995.6953,785.5886,0.5125858
+3369,118.015854,618.01196,186.48051,682.46606,0.36040717
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+3370,2024.5684,364.05444,2047.845,423.16077,0.9995894
+3370,2258.3455,493.01172,2284.7688,560.59973,0.9994823
+3370,2891.3,599.26855,2922.2747,666.10376,0.999423
+3370,2467.4104,531.84924,2497.197,602.8186,0.99941325
+3370,1241.7894,321.28568,1265.4039,376.5018,0.9993463
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+3370,3509.9824,522.3777,3530.269,575.2495,0.99894255
+3370,2945.913,408.66382,2973.0723,461.39233,0.9988914
+3370,3084.234,261.35388,3104.9238,302.90625,0.9988557
+3370,1977.24,123.991104,1994.9458,162.20409,0.99884194
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+3370,2017.9889,887.9124,2053.5725,985.7703,0.998684
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+3370,2003.7495,290.68204,2021.8523,347.84756,0.9982241
+3370,1989.8026,324.5697,2010.2382,376.92072,0.9981895
+3370,2352.198,203.33015,2369.5657,249.76512,0.9981432
+3370,2508.821,399.07346,2530.6692,462.433,0.99808264
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+3370,3873.4092,761.16296,3900.667,822.3103,0.88946164
+3370,3857.8118,761.1931,3899.7136,829.902,0.8119732
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+3371,2256.9634,493.4734,2285.0415,561.048,0.9995499
+3371,2891.1897,599.562,2921.803,666.2689,0.99943537
+3371,1241.8553,321.41602,1265.4386,376.53143,0.999361
+3371,2466.8936,533.1024,2495.4678,603.02893,0.99935836
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+3371,2780.3613,230.69296,2804.2583,272.03186,0.9990458
+3371,2712.783,303.61646,2732.568,351.117,0.99893004
+3371,1976.982,123.82808,1994.6981,162.1696,0.9988834
+3371,3509.7368,521.91644,3530.5283,574.6436,0.9988638
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+3371,2018.245,888.60345,2053.969,985.69037,0.99865973
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+3371,2003.0543,290.69363,2021.3473,347.40054,0.9985863
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+3371,1987.078,324.5969,2009.2733,376.8088,0.99779654
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+3371,2307.396,133.02264,2324.4653,174.80298,0.99594045
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+3371,3852.7693,760.81165,3900.2664,832.72266,0.93864834
+3371,132.79272,608.3704,209.20999,669.0903,0.2546062
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+3372,2024.3173,363.89368,2046.4379,423.07678,0.9996364
+3372,2255.7126,494.4127,2284.8992,562.1234,0.9994804
+3372,1241.8882,321.385,1265.4321,376.4964,0.999383
+3372,3509.722,521.1044,3530.7756,573.8236,0.99935645
+3372,2891.2334,599.46326,2921.6172,666.4397,0.999355
+3372,2712.5962,303.59653,2731.666,351.07773,0.9991854
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+3372,3097.8137,694.5062,3134.3625,767.6544,0.9988865
+3372,1976.6775,123.912315,1994.4756,162.03223,0.9988753
+3372,1984.2906,324.4831,2009.277,377.09314,0.9988557
+3372,2018.4446,889.17145,2054.1116,985.6789,0.99884367
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+3372,2002.3091,290.7546,2020.8252,346.95206,0.9983901
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+3372,2164.6829,172.83958,2182.739,214.79832,0.99799794
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+3373,2018.6654,890.0741,2054.644,985.7079,0.9990507
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+3373,1976.0371,124.07376,1994.2295,162.00833,0.9989458
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+3373,1981.7847,324.4915,2009.0625,377.25717,0.998755
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+3373,2001.4586,290.86017,2020.4218,346.83856,0.99814975
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+3374,2891.1633,599.4725,2921.3357,666.867,0.9993481
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+3374,2486.4182,378.25473,2509.9065,434.59726,0.99924123
+3374,2023.7994,363.50842,2044.3353,423.0567,0.9992372
+3374,1975.5085,123.94686,1993.8948,162.3051,0.9992155
+3374,3510.0757,518.9012,3530.7563,573.92487,0.99914
+3374,2105.2896,201.92705,2120.3345,247.94298,0.99896514
+3374,2018.9222,890.33374,2055.2974,985.8478,0.9989221
+3374,2941.8455,408.00458,2971.091,461.81995,0.99886656
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+3374,2348.7454,202.73735,2366.8704,248.23662,0.9988354
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+3374,2709.4492,303.729,2729.5898,351.1657,0.9981698
+3374,2000.6216,291.11905,2020.1831,346.66397,0.9980597
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+3374,3974.889,726.25665,3992.911,756.71686,0.57414824
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+3375,2891.0654,599.4716,2921.4165,667.0007,0.9993761
+3375,2023.635,363.34662,2043.1995,423.0431,0.99928504
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+3375,2486.5205,378.55203,2509.5088,434.5981,0.9991222
+3375,1975.3031,123.85637,1993.5575,162.29588,0.9991042
+3375,2462.0842,540.3362,2490.34,608.90015,0.99905163
+3375,3509.8381,518.4359,3531.303,573.6437,0.9990158
+3375,1979.295,324.5609,2008.7789,377.3255,0.9990144
+3375,3092.7422,698.59796,3130.2622,772.9963,0.9989405
+3375,2941.1658,407.95337,2970.5037,461.80713,0.99889165
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+3375,2346.4646,202.89337,2366.1804,248.59134,0.9987149
+3375,2511.9446,402.12088,2535.9167,466.17056,0.9986437
+3375,1999.4978,291.23666,2019.7402,347.21292,0.9986345
+3375,2865.0947,314.44003,2886.272,362.08658,0.998587
+3375,2019.1404,890.9936,2056.7063,985.9384,0.99856585
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+3375,3038.3638,422.53577,3071.1606,467.52142,0.9981035
+3375,2161.4495,172.02876,2176.303,215.03902,0.9979202
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+3375,3864.4453,759.07654,3896.186,815.24097,0.9027916
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+3375,3974.6038,725.66565,3993.1086,757.4663,0.46669853
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+3376,2254.7131,496.01385,2280.9265,564.46857,0.99931395
+3376,2486.5955,378.8901,2509.0896,436.0095,0.9992366
+3376,2785.2886,231.04486,2808.335,272.26166,0.9991731
+3376,1979.2894,324.80902,2008.6469,377.24255,0.99907446
+3376,2023.2854,363.08896,2042.6606,423.12912,0.99907047
+3376,2158.0708,171.97955,2175.456,215.14557,0.9990501
+3376,1998.8032,291.55246,2019.7346,347.6793,0.99902403
+3376,1975.1677,123.84544,1993.3457,162.23293,0.999017
+3376,2940.9822,408.12558,2969.9622,461.8466,0.9989052
+3376,2511.92,402.61066,2535.1768,466.4049,0.9988655
+3376,3508.9814,517.9587,3531.2915,571.7968,0.9988335
+3376,2460.954,541.1645,2489.3481,611.9567,0.99879336
+3376,2345.3,202.82654,2365.7083,248.73767,0.9986499
+3376,3006.6748,322.5559,3025.357,371.03174,0.9986395
+3376,3092.547,699.0897,3127.949,774.83923,0.998544
+3376,3090.1165,262.51956,3108.1223,305.08334,0.99852306
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+3376,2099.7114,201.60664,2117.9243,247.5496,0.9983632
+3376,2019.3335,891.5028,2058.1067,986.074,0.9980919
+3376,3038.27,424.05203,3072.5503,468.5975,0.9980172
+3376,2705.336,303.56134,2728.0972,350.82562,0.99530965
+3376,3270.1501,367.2163,3298.5598,406.935,0.99429095
+3376,2302.446,134.05017,2317.5334,174.2398,0.9915759
+3376,3863.035,758.7948,3896.048,815.1146,0.8972735
+3376,149.24086,619.1495,188.67302,663.4372,0.5658757
+3376,3865.8135,764.5857,3902.211,826.4125,0.5182082
+3376,3871.4517,774.28424,3908.332,832.8433,0.48935086
+3376,3963.8215,732.8718,3988.7986,785.6024,0.32758498
+3376,3972.9941,726.6158,3992.9385,755.9162,0.30183357
+3376,130.30603,619.15643,189.04056,668.7924,0.19977282
+3377,2890.795,600.2779,2921.6377,668.4389,0.99960035
+3377,2254.6116,496.3414,2279.9597,564.339,0.99951434
+3377,1242.1517,321.5498,1265.4364,376.4682,0.9993711
+3377,1997.4801,291.92755,2019.4171,348.66785,0.9992538
+3377,2486.5623,379.22894,2509.0046,436.82367,0.99918634
+3377,2023.0353,363.3182,2042.0375,423.09024,0.9991698
+3377,2097.6572,201.7148,2118.0771,247.54411,0.99913484
+3377,3508.6494,517.98584,3531.3398,571.5891,0.99910647
+3377,2940.826,408.13736,2969.242,461.8679,0.99909306
+3377,1978.8921,324.62527,2008.2156,377.05753,0.9990644
+3377,3004.25,322.1723,3024.3027,370.9314,0.9990388
+3377,1974.9784,123.82528,1992.9896,162.19296,0.9990094
+3377,3092.672,699.92505,3126.7722,774.6012,0.9988901
+3377,2512.0005,402.9104,2534.5728,466.7337,0.9988889
+3377,2460.7449,542.65546,2487.9968,613.79364,0.99880695
+3377,2785.8594,231.16498,2808.8848,272.4389,0.99876475
+3377,2153.997,172.04425,2175.02,214.87604,0.9986437
+3377,3038.2698,425.56042,3074.3079,470.66357,0.9986039
+3377,2343.712,203.01953,2365.3428,248.70633,0.9983833
+3377,3089.6707,262.3528,3108.4802,305.475,0.9983474
+3377,2864.766,314.4349,2885.7437,361.93576,0.9983203
+3377,2019.5763,891.78455,2059.256,985.9901,0.9976388
+3377,2703.3743,304.05612,2727.1897,351.23898,0.99622726
+3377,2300.6094,133.69633,2317.0127,174.10246,0.99370533
+3377,3269.7202,367.26968,3298.085,407.01846,0.99190795
+3377,3861.6565,758.4994,3895.486,816.4431,0.93104064
+3377,3963.8645,731.6856,3986.9866,785.48065,0.5666114
+3377,3874.4944,774.6579,3912.6487,833.5855,0.21706888
+3377,120.41147,619.88226,185.87997,670.0889,0.19515851
+3378,2890.7205,600.5063,2921.9788,669.2933,0.9995788
+3378,2254.5903,497.09436,2279.2168,564.3065,0.9995648
+3378,1997.4279,292.34677,2019.2848,348.37857,0.9994017
+3378,1242.2334,321.55374,1265.6309,376.43375,0.9993456
+3378,2022.1381,363.0087,2041.1434,423.25406,0.99922156
+3378,2486.52,380.02048,2508.8862,438.49393,0.9991949
+3378,1978.592,324.17847,2007.2676,377.0982,0.99916375
+3378,3508.4558,517.9647,3531.2615,571.0387,0.99911433
+3378,2940.6484,408.02704,2968.9014,461.82172,0.99910164
+3378,3038.0125,427.77408,3076.159,471.7532,0.99892956
+3378,3003.2402,322.0771,3022.8232,370.48718,0.998928
+3378,1974.3004,123.680275,1992.8419,162.04039,0.9989215
+3378,2511.8591,403.18317,2534.2278,466.79663,0.99890363
+3378,2150.477,172.16064,2174.687,215.30042,0.9987872
+3378,2788.201,231.44588,2809.0706,272.8917,0.99873966
+3378,3092.4292,700.34393,3126.2173,775.08417,0.99873275
+3378,2460.1204,543.4555,2487.6462,614.5621,0.9987124
+3378,2094.6975,202.2702,2118.1624,247.58295,0.9984382
+3378,2864.7397,314.5031,2885.7236,362.0056,0.9982652
+3378,2342.8955,202.93509,2365.2534,248.71988,0.9982212
+3378,2702.4438,304.48813,2726.5615,351.29355,0.99819213
+3378,3089.8022,262.35968,3108.356,305.58026,0.998181
+3378,2020.8408,892.69006,2060.1855,986.0039,0.9978052
+3378,2297.8525,135.02449,2316.3477,174.52638,0.99504036
+3378,3269.6604,367.58124,3296.0637,406.74707,0.9927858
+3378,3861.4194,758.3573,3894.754,816.15405,0.8198014
+3378,3963.8542,731.5695,3986.827,787.2723,0.6728168
+3378,138.53441,625.09686,180.38002,668.4102,0.34567535
+3378,61.56408,640.16016,84.37359,688.40405,0.25530338
+3378,3974.1946,726.7482,3992.758,754.4305,0.11393188
+3379,2890.7483,600.697,2922.2263,670.2893,0.9995913
+3379,2254.516,497.4465,2279.2217,564.9242,0.9994881
+3379,1996.5093,292.4148,2018.8118,348.5813,0.99938595
+3379,1242.2335,321.61792,1265.5997,376.4414,0.9993247
+3379,2940.1719,408.03424,2968.2446,461.83008,0.99927396
+3379,2020.4585,362.8891,2040.7742,423.1524,0.9991698
+3379,1978.7025,324.0277,2005.0897,377.1129,0.99911684
+3379,3000.015,322.5732,3021.9143,370.35428,0.99909914
+3379,2486.5247,380.9661,2508.9573,439.7236,0.999064
+3379,2789.2715,231.91985,2809.1719,273.33942,0.99905545
+3379,2092.0303,202.40814,2118.122,247.85135,0.9990403
+3379,2511.649,403.28262,2534.002,466.84293,0.9988913
+3379,3092.557,700.33105,3125.6038,774.9536,0.9988832
+3379,1974.2432,123.751045,1992.7212,161.85278,0.99882704
+3379,3507.5315,517.80023,3529.5393,571.6536,0.9987803
+3379,2148.5776,172.87744,2174.1543,215.41568,0.99856263
+3379,3038.519,427.8445,3078.791,472.91165,0.99848217
+3379,2459.789,543.7191,2487.0938,614.4973,0.99841046
+3379,3089.9607,262.34247,3108.5457,306.5724,0.9983937
+3379,2701.9563,304.50183,2725.5525,351.3778,0.9982487
+3379,2021.2242,892.9917,2060.7014,985.99316,0.99812394
+3379,2864.7002,314.60272,2885.7144,362.04535,0.9978872
+3379,2342.5676,203.36195,2364.864,248.67003,0.99765044
+3379,2296.6924,135.5378,2315.9844,174.14314,0.9961389
+3379,3269.7378,367.6242,3294.6362,406.6662,0.99280655
+3379,3861.0708,758.12067,3893.956,815.53033,0.8425766
+3379,3962.8376,731.0467,3987.2893,787.4428,0.76085234
+3379,139.91101,605.13153,182.36716,671.45685,0.37961248
+3379,3875.5188,772.6964,3920.4583,832.9885,0.20817861
+3379,3973.7825,726.5883,3992.8484,754.58344,0.16208659
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+3380,2940.0046,407.74625,2967.7693,461.848,0.99953926
+3380,2254.3823,498.2666,2279.2334,565.99023,0.9995136
+3380,1242.3137,321.68805,1265.5859,376.43988,0.9992993
+3380,1996.5033,292.57516,2018.5336,348.67435,0.99924004
+3380,2486.5847,381.3935,2508.867,440.01752,0.999153
+3380,3507.0415,516.58167,3529.1484,568.77405,0.9991511
+3380,2789.825,232.19505,2809.3015,274.0069,0.99914885
+3380,1978.8339,323.97598,2001.7333,376.8705,0.9990627
+3380,3092.6152,700.3652,3125.6997,774.80975,0.9990169
+3380,2147.6716,173.0228,2173.779,215.42654,0.99898845
+3380,2459.8577,543.7895,2486.8372,614.3283,0.9989077
+3380,1974.1432,123.77906,1992.3951,161.8425,0.9987841
+3380,2995.5688,322.18463,3020.5938,370.68042,0.9987544
+3380,2019.161,362.77045,2040.4227,423.10162,0.99870574
+3380,2090.6821,202.43054,2117.982,247.58939,0.9984363
+3380,3090.1372,262.83722,3108.8306,306.7552,0.9983876
+3380,2021.5978,893.08417,2061.4958,986.0618,0.9982468
+3380,2341.9253,202.97845,2364.829,248.72162,0.9981546
+3380,3039.121,429.17453,3081.6855,473.11887,0.9980787
+3380,2511.3267,403.56418,2533.4219,466.8881,0.99803084
+3380,2701.9558,304.55615,2724.8093,351.1817,0.9978003
+3380,2864.74,314.80475,2885.7913,362.10126,0.9977946
+3380,2296.1099,135.07274,2316.2725,174.16649,0.9960033
+3380,3269.7605,367.69006,3293.3997,406.9897,0.9925925
+3380,3860.8723,757.7008,3893.2961,815.8213,0.8570411
+3380,3959.9243,731.6778,3985.9932,788.92474,0.72010964
+3380,139.33379,604.39465,181.06941,671.2948,0.3770505
+3380,3875.897,773.06104,3921.75,832.0039,0.26091093
+3380,3973.767,726.58264,3992.917,757.1874,0.14338966
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+3381,2253.977,499.26367,2280.7422,566.9573,0.99946934
+3381,1242.2974,321.7008,1265.6201,376.46033,0.9992774
+3381,1995.6636,292.7219,2018.3857,348.99997,0.99927145
+3381,2459.784,543.7456,2486.6282,614.06165,0.9991966
+3381,2939.7378,407.60532,2966.9634,461.74173,0.99918026
+3381,2790.108,232.31604,2809.2876,274.07272,0.9991326
+3381,3092.339,700.4564,3124.7122,774.93066,0.99908507
+3381,2991.6328,322.44995,3019.185,370.6997,0.99898416
+3381,3506.163,515.36176,3528.7256,568.5616,0.9989679
+3381,1979.0035,323.8468,1999.0739,377.08447,0.99896777
+3381,2486.5486,382.31372,2509.203,440.739,0.9989403
+3381,1974.0206,123.80037,1992.2284,161.835,0.99874425
+3381,2017.9105,362.6909,2039.9252,423.15488,0.99861634
+3381,2022.0706,893.2361,2061.9197,985.94116,0.9985024
+3381,3090.4915,262.8883,3109.5657,306.97156,0.9983998
+3381,2090.495,202.451,2116.6436,247.38136,0.9982389
+3381,2341.788,202.74603,2364.581,248.65643,0.9982205
+3381,2146.7698,172.96454,2173.073,215.52832,0.9982066
+3381,2701.7764,304.7026,2723.1274,351.55954,0.99811316
+3381,2511.248,403.95898,2532.7666,466.94373,0.99757427
+3381,2864.7,315.17984,2886.0256,361.97202,0.99733776
+3381,3041.1445,429.1267,3084.1567,472.9926,0.9973273
+3381,2295.6633,133.74918,2316.0671,174.00479,0.99706274
+3381,3269.9624,366.66144,3292.9316,406.77435,0.9950323
+3381,3861.2227,757.4809,3892.4683,814.82477,0.81464046
+3381,3958.9204,732.35205,3985.2559,789.3843,0.43337202
+3381,3880.5383,774.7605,3924.115,834.68567,0.4012338
+3381,138.6085,603.1176,184.23503,671.3027,0.39462417
+3381,3970.2478,727.59814,3991.9626,756.10376,0.18634623
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+3382,2253.735,500.63486,2281.3254,568.78546,0.9994537
+3382,1995.4998,292.95782,2018.1545,349.1631,0.9992724
+3382,1242.3081,321.7001,1265.71,376.48538,0.9992623
+3382,3506.0386,514.88995,3528.0376,567.64667,0.99924755
+3382,2147.6052,173.85255,2171.715,215.3846,0.9991135
+3382,2790.3228,232.54622,2809.3691,274.1986,0.99907696
+3382,2939.6765,407.6377,2966.2053,461.71722,0.99906033
+3382,2459.2954,543.9791,2485.941,614.2838,0.9990441
+3382,3092.1355,701.1918,3124.0715,775.4253,0.998939
+3382,2486.5264,382.97995,2509.4321,441.67258,0.9989243
+3382,2017.0573,362.25937,2039.5131,423.03824,0.99874264
+3382,1973.9307,123.623,1992.094,161.86327,0.9987123
+3382,2991.075,323.10333,3018.201,370.75323,0.9986168
+3382,2090.311,202.1081,2115.5332,247.02289,0.99853134
+3382,3090.5474,263.16794,3110.3306,307.01407,0.99834025
+3382,1978.6449,323.842,1996.7037,377.14444,0.99827516
+3382,2341.4583,203.18616,2364.2405,248.6206,0.99804616
+3382,3044.3079,428.637,3086.3997,473.27792,0.9978115
+3382,2510.8423,404.09726,2532.352,466.90836,0.99765944
+3382,3270.0615,365.5655,3292.6094,407.00482,0.9975369
+3382,2022.4774,893.5366,2062.3628,985.92395,0.9974038
+3382,2864.9727,315.65173,2886.331,362.0854,0.99724686
+3382,2295.375,134.1512,2315.773,173.5753,0.99681866
+3382,2701.8545,304.45676,2722.3203,351.40604,0.9967314
+3382,3861.0757,757.32025,3892.0322,814.96893,0.9394047
+3382,3958.4788,733.6407,3983.158,789.4709,0.7477038
+3382,3884.3179,774.3775,3926.9727,834.0616,0.7022719
+3383,2890.9768,603.0617,2925.507,673.16144,0.99964917
+3383,2253.394,501.42096,2282.0664,570.1285,0.99943167
+3383,1242.3105,321.71323,1265.6843,376.4918,0.99927086
+3383,1995.3813,293.07004,2017.4011,349.124,0.99923897
+3383,2458.6863,544.7973,2485.7698,615.082,0.9992378
+3383,3503.2688,514.2559,3527.4446,567.3177,0.9991447
+3383,2939.9153,407.76855,2965.8752,461.55646,0.9991239
+3383,2790.3262,232.84247,2809.3813,274.22675,0.99901867
+3383,3091.9844,702.021,3122.5723,776.56226,0.9989792
+3383,2991.394,323.453,3017.1865,370.9262,0.9989159
+3383,2341.079,202.8413,2363.775,248.66905,0.99882334
+3383,2090.481,201.52089,2113.5356,247.21046,0.99877745
+3383,2016.2593,362.28284,2039.1426,423.02563,0.9987093
+3383,2486.6436,383.5577,2509.711,442.15903,0.998701
+3383,1973.8378,123.62194,1991.933,161.83925,0.99866545
+3383,3090.5696,263.1276,3110.9749,307.1011,0.9986634
+3383,2147.0237,172.99956,2169.6868,215.28859,0.99859565
+3383,3049.5808,428.13022,3088.9841,473.53336,0.99806494
+3383,1978.2997,323.96338,1994.7391,377.17194,0.99800605
+3383,2701.8398,304.08173,2721.0845,351.36316,0.9978866
+3383,2510.1838,404.44794,2531.5354,466.81683,0.9977447
+3383,2294.9988,133.10631,2315.1008,173.35997,0.99766374
+3383,2865.073,316.0509,2886.572,361.8767,0.9973098
+3383,2023.3604,893.7707,2062.7434,985.8516,0.99698895
+3383,3270.1394,365.26575,3292.7,406.60718,0.99671835
+3383,3861.1667,757.0841,3891.032,814.33545,0.97669244
+3383,3957.1953,735.0637,3981.8643,789.32715,0.709275
+3383,3887.5027,772.4878,3929.0696,834.1604,0.67987114
+3383,139.00835,611.05475,179.89522,669.5395,0.15873033
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+3384,2891.2603,603.71063,2926.1084,673.28314,0.9996692
+3384,2253.1777,502.29736,2283.1978,571.2499,0.9993699
+3384,1242.3047,321.71564,1265.7861,376.511,0.99927276
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+3384,2147.267,172.85602,2167.0645,214.96136,0.99917704
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+3384,1995.176,293.3623,2016.7314,349.4441,0.99908686
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+3384,2939.9238,407.2158,2965.4238,461.40512,0.9989654
+3384,2016.2091,362.19797,2038.9052,423.048,0.9989367
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+3384,2486.664,384.02805,2509.8667,442.6857,0.9987741
+3384,1973.8135,123.5261,1991.6321,161.72678,0.99868375
+3384,3090.1904,263.30655,3111.606,307.28476,0.9985771
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+3384,3054.7974,426.87912,3090.8164,473.22037,0.9984333
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+3472,1992.3031,363.96893,2019.591,422.84613,0.999703
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+3473,1956.1139,122.526344,1971.3314,158.88695,0.9992624
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+3474,1961.9569,293.1037,1984.621,348.6958,0.9995528
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+3474,1955.7676,122.65355,1971.2312,159.06526,0.9991997
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+3474,2038.948,181.37605,2058.2961,225.0671,0.99908173
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+3474,3273.2512,363.11563,3304.1843,407.82455,0.99715036
+3474,3495.7893,512.30914,3523.5305,561.9748,0.9968451
+3474,2260.6147,131.79123,2277.1294,170.72498,0.9966743
+3474,3823.6116,750.46594,3851.2673,810.2031,0.9961055
+3474,2030.9387,913.44354,2070.8542,1005.43915,0.9847149
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+3475,2227.711,551.8154,2260.3315,624.56067,0.99958724
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+3475,1992.2726,364.38263,2013.3751,423.40674,0.9994905
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+3475,2855.2764,737.5988,2902.998,818.1912,0.9992372
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+3475,2260.5356,131.9025,2277.8315,170.68295,0.9972167
+3475,3495.816,512.3701,3523.7207,561.97095,0.9970675
+3475,3273.6196,362.90466,3306.1895,407.61914,0.9965138
+3475,3823.4429,750.2707,3851.1064,810.3901,0.99553114
+3475,3853.481,760.03955,3878.9214,817.7506,0.9882434
+3475,2030.9384,913.4438,2070.851,1005.4372,0.98466927
+3475,141.29482,608.645,182.61806,668.4408,0.20610937
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+3476,1209.5756,321.47412,1230.2731,376.11115,0.9995803
+3476,2227.438,552.806,2261.6992,627.29553,0.9994566
+3476,2627.3064,308.70944,2647.3914,355.967,0.9993674
+3476,1963.1031,293.16196,1985.556,349.0403,0.9993666
+3476,2855.5188,737.6222,2897.2654,818.5665,0.99933904
+3476,1991.7023,363.9534,2011.9095,423.88986,0.99926394
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+3476,2038.3146,181.2611,2055.1755,225.23384,0.9990619
+3476,1955.1202,122.693405,1970.3568,159.2033,0.9990212
+3476,2921.41,411.2165,2946.2415,464.68726,0.9989392
+3476,2831.1978,245.63364,2853.163,287.13132,0.99888164
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+3476,2594.9407,430.843,2637.6335,481.24533,0.99869114
+3476,2003.8705,203.06927,2020.6888,248.48904,0.9986563
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+3477,2855.6375,736.85547,2892.804,817.708,0.9992022
+3477,2933.7024,335.68488,2955.55,386.8208,0.9991721
+3477,2597.5132,431.62573,2642.3145,481.14874,0.999019
+3477,2364.7073,607.43054,2406.7869,681.81934,0.9990165
+3477,1990.8357,364.23627,2010.9412,424.25635,0.9989711
+3477,1954.7034,122.61629,1970.1562,159.40697,0.9989479
+3477,2921.4895,411.09323,2945.698,465.00528,0.9987674
+3477,2038.3297,181.50313,2054.2114,224.86702,0.99875927
+3477,2831.0354,246.23788,2853.6736,287.60452,0.9986772
+3477,2002.7007,203.23744,2020.208,248.373,0.99866605
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+3477,2820.0205,329.4077,2855.7827,371.47913,0.9982053
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+3477,2521.8867,398.84314,2553.7964,450.13165,0.9977507
+3477,2260.9868,131.80124,2278.2368,170.70663,0.9976173
+3477,3274.5369,363.21228,3306.7532,406.0578,0.99671704
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+3477,3848.0332,762.48517,3875.2559,821.13763,0.9942213
+3477,2030.9457,913.44586,2070.8638,1005.4643,0.9840629
+3477,3113.5164,450.44064,3148.535,494.30447,0.9790947
+3477,141.26039,608.8216,182.99065,668.43805,0.20861009
+3477,3128.3127,450.97934,3146.6614,480.71402,0.12768084
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+3478,2782.532,616.7034,2818.4353,685.02704,0.9995239
+3478,1963.1631,293.4083,1986.2947,349.1359,0.9994929
+3478,1208.1823,321.39706,1227.6942,376.56912,0.99945337
+3478,2933.7178,336.1902,2955.1074,387.4347,0.999246
+3478,2854.8186,736.3553,2890.1707,818.6346,0.999196
+3478,2626.5552,309.41867,2646.5454,356.09164,0.999185
+3478,2357.2053,213.66179,2380.2908,256.74728,0.9991565
+3478,1954.6102,123.00722,1969.8663,159.01833,0.9990916
+3478,2364.7332,608.11005,2403.425,681.77313,0.9989083
+3478,2831.6033,246.40903,2854.3323,287.95505,0.9989034
+3478,2521.9517,400.76932,2555.811,451.82938,0.99890053
+3478,2597.8416,431.2267,2645.9495,481.3212,0.99887663
+3478,1989.2618,364.94247,2009.9225,424.5059,0.99885297
+3478,2921.2737,411.17307,2945.2961,465.3821,0.9986823
+3478,2037.4141,181.53888,2053.7122,224.99582,0.9986811
+3478,2001.8136,203.27524,2019.7875,248.42035,0.9986125
+3478,3097.2358,267.51096,3119.6108,310.23306,0.99852234
+3478,2823.6025,330.2765,2857.891,371.84717,0.99791366
+3478,2261.5537,131.72958,2278.2637,170.51785,0.99756014
+3478,1870.8246,328.35678,1893.5443,383.7321,0.99736565
+3478,3274.2458,364.02982,3307.4143,406.27707,0.9972792
+3478,3495.4736,512.5051,3524.4097,562.282,0.99573934
+3478,3825.0005,749.8705,3851.0322,810.7335,0.995676
+3478,3114.2305,451.22723,3152.4414,493.24054,0.9938345
+3478,3846.5076,763.6796,3873.9246,820.9958,0.98942393
+3478,2030.9374,913.485,2070.692,1005.4708,0.98517543
+3478,141.34846,608.92645,182.78792,668.3316,0.20472649
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+3479,2228.2764,556.202,2263.7769,629.3447,0.9995497
+3479,1963.1232,293.63858,1986.4784,348.8937,0.9994849
+3479,2599.0554,431.50415,2649.9626,482.0443,0.99930716
+3479,2831.819,246.81448,2855.2034,288.1904,0.9993025
+3479,2933.714,336.17004,2954.8884,387.5345,0.9992706
+3479,2625.5872,309.21207,2645.842,355.65482,0.9992518
+3479,2852.0354,735.9314,2887.6863,818.5193,0.9992218
+3479,1207.9458,321.6098,1225.7493,376.90582,0.99913603
+3479,2521.629,402.58713,2558.5952,453.1357,0.9991321
+3479,1954.263,122.86897,1969.4246,159.49837,0.99900913
+3479,2364.409,608.60223,2402.3342,681.98724,0.99872977
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+3479,2035.4331,181.89995,2052.7214,224.81789,0.99866855
+3479,2001.1746,203.21884,2019.1487,249.42398,0.9985721
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+3479,2921.0396,411.17465,2945.151,466.18588,0.99848205
+3479,1868.7572,328.4339,1893.3861,383.87024,0.99837434
+3479,1986.864,365.50735,2008.8699,425.00546,0.99830025
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+3479,2030.9048,913.4986,2070.6333,1005.46783,0.98300505
+3479,3846.3608,765.1099,3872.4565,821.2796,0.97109544
+3479,141.4341,609.41864,182.24321,668.4875,0.2107624
+3480,2777.5942,618.8495,2814.2207,690.1504,0.9996057
+3480,2845.125,735.9189,2885.252,817.16486,0.9995301
+3480,2229.4092,556.1088,2262.9775,629.4666,0.99951637
+3480,1963.233,293.85153,1986.5944,348.78616,0.9995023
+3480,2601.4404,431.7697,2652.5508,484.70667,0.99938524
+3480,2522.007,404.23364,2561.1438,453.66962,0.9992656
+3480,2933.6387,336.42688,2954.688,387.82385,0.9991924
+3480,2831.984,247.18716,2856.1177,288.30106,0.99911875
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+3480,2623.7214,309.53735,2645.1663,355.9052,0.9990257
+3480,2359.8635,213.83885,2383.2449,258.94168,0.9990049
+3480,2033.7733,182.45547,2051.9985,225.17957,0.9988403
+3480,1953.9808,122.56503,1968.9279,159.81494,0.9988379
+3480,2920.8083,411.09888,2944.9998,466.73456,0.99877995
+3480,2362.9568,609.605,2398.3206,682.4719,0.9985733
+3480,3097.3293,267.59576,3119.578,310.20074,0.99852234
+3480,3116.8943,452.182,3160.904,501.2625,0.9984617
+3480,1985.4716,366.2616,2007.8441,426.88208,0.9984604
+3480,2000.8281,203.55495,2018.7842,249.44518,0.99845743
+3480,1867.0079,329.62973,1892.6324,383.86667,0.9984566
+3480,2827.3696,330.36258,2861.293,372.06516,0.9982054
+3480,2262.3843,131.68608,2278.7212,170.50575,0.9975921
+3480,3495.6985,512.6347,3525.1199,562.4534,0.99571556
+3480,3276.455,364.22986,3306.8232,405.11584,0.9950795
+3480,3824.7097,749.83,3851.4797,811.1945,0.98942006
+3480,2030.8761,913.5891,2070.3967,1005.50903,0.9804261
+3480,3845.558,766.3229,3869.1787,821.01697,0.9144091
+3480,141.43735,609.4573,181.95786,668.57776,0.21044743
+3480,61.6351,645.31744,85.045,684.68976,0.1048925
+3481,2775.9883,619.8372,2812.4922,690.59406,0.9996718
+3481,2229.675,556.07153,2262.1467,629.4153,0.99960726
+3481,2604.62,432.42126,2655.0415,486.40997,0.9995185
+3481,2837.5938,735.8187,2883.412,816.84814,0.99951386
+3481,1963.4364,294.28546,1986.6547,348.90417,0.99943954
+3481,2521.7249,405.448,2563.66,455.48083,0.99941874
+3481,1207.1154,321.8342,1225.4056,377.55197,0.99918705
+3481,2933.7305,336.86554,2954.5142,387.8853,0.99909055
+3481,2361.828,214.4945,2384.5593,259.69736,0.99905163
+3481,3120.8835,453.72107,3164.943,502.52106,0.99903667
+3481,2031.5306,182.97476,2051.5193,225.27783,0.9989397
+3481,2360.9102,610.20966,2392.4712,682.84735,0.9989003
+3481,2622.174,310.02115,2644.5398,355.87192,0.9988451
+3481,1865.6495,329.9778,1892.8756,383.63492,0.99872345
+3481,1953.3134,122.43988,1968.463,159.77005,0.9986475
+3481,2920.546,410.9612,2944.6304,466.98972,0.9985775
+3481,2000.6306,203.63945,2018.4326,249.33052,0.99857545
+3481,3097.4302,267.59067,3119.599,310.1876,0.9984909
+3481,1983.8934,366.75778,2006.6967,427.07535,0.99838483
+3481,2827.7456,330.27252,2863.6162,372.1018,0.99827814
+3481,2833.675,247.3327,2856.4382,288.16968,0.99810684
+3481,2262.636,131.57976,2278.7068,170.84468,0.99747854
+3481,3279.172,363.34225,3307.7986,405.0712,0.9971738
+3481,3495.8635,512.55634,3525.093,562.38885,0.9960459
+3481,3824.8967,750.0939,3851.652,811.0343,0.9885168
+3481,2030.8622,913.62555,2070.3699,1005.43585,0.978211
+3481,3844.3108,766.915,3867.387,820.8337,0.8748989
+3481,141.41562,609.5951,181.79959,668.5836,0.20092903
+3481,3825.4207,750.9864,3863.2024,819.2277,0.15001476
+3481,61.63987,645.31714,85.10825,684.68335,0.11110395
+3482,2230.6257,555.6446,2261.882,629.4537,0.99969256
+3482,2775.4114,620.4389,2811.326,690.48175,0.9996729
+3482,2831.4082,735.73975,2880.7603,816.5337,0.99956614
+3482,1963.5332,294.61746,1986.626,349.3127,0.9993988
+3482,2620.0312,310.25485,2643.6157,356.33347,0.9993044
+3482,2609.6577,433.7054,2657.1328,487.29916,0.9992623
+3482,2030.0029,183.47421,2050.5051,225.3906,0.99925023
+3482,2363.8813,215.49365,2385.6191,260.2611,0.999138
+3482,2933.4656,337.41687,2954.4524,387.41656,0.9990489
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+3482,2523.5989,407.4439,2566.3489,455.84888,0.9989083
+3482,1952.4811,122.54079,1968.2081,159.90837,0.9987539
+3482,3126.2898,455.22235,3169.5588,503.84332,0.9986759
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+3482,3097.2527,267.5655,3119.555,310.09937,0.99858814
+3482,2920.3848,411.23566,2944.332,467.0182,0.9985532
+3482,2000.4395,203.9294,2018.2649,249.51146,0.998514
+3482,2829.2686,330.25998,2866.4463,372.30338,0.99829954
+3482,1983.5475,367.3646,2006.4696,426.7714,0.99829894
+3482,2358.307,611.1107,2391.47,684.7837,0.99794084
+3482,2833.4514,247.64503,2856.9915,288.379,0.9971565
+3482,3281.4453,362.09766,3308.7866,405.09204,0.9970335
+3482,2263.3423,131.65547,2278.851,170.65825,0.99681324
+3482,3496.3901,513.27045,3525.2437,562.0264,0.99560577
+3482,2030.8932,913.7462,2070.347,1005.41077,0.9801199
+3482,3824.8796,750.2113,3857.029,815.61633,0.90945894
+3482,141.37323,609.413,181.91498,668.63934,0.20589578
+3482,61.708347,645.2661,85.111435,684.822,0.12995347
+3482,3836.9912,749.4659,3865.0425,792.87213,0.115959056
+3482,3938.5203,744.54626,3972.0393,800.9209,0.10830723
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+3483,2827.9358,735.5711,2878.9773,816.73944,0.99950206
+3483,1963.4647,295.03378,1986.6005,350.4072,0.99937373
+3483,1206.3563,322.22946,1225.5048,378.4094,0.9993057
+3483,2029.9413,183.94571,2049.9688,226.20364,0.99927086
+3483,2616.6365,435.58252,2661.9402,488.11737,0.9990307
+3483,2933.5835,337.83707,2954.3188,388.53275,0.99899703
+3483,2365.5728,216.79483,2386.247,260.57352,0.9989513
+3483,2618.6292,310.69647,2642.5515,356.4588,0.9988254
+3483,1951.7665,122.66375,1967.756,160.01857,0.9987147
+3483,3097.169,267.56567,3119.4976,310.14056,0.9986473
+3483,1864.2832,331.5293,1893.1226,383.51923,0.9985991
+3483,2527.6895,408.18777,2569.6685,456.2004,0.9985777
+3483,2356.9492,611.6492,2389.8232,686.37854,0.9985545
+3483,2920.4714,411.47226,2943.5647,467.7925,0.99854976
+3483,2000.2435,204.02654,2017.9882,249.59859,0.9984864
+3483,2830.586,330.43567,2867.914,373.48584,0.99834347
+3483,1983.2654,367.75012,2006.3792,426.86273,0.9978661
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+3483,3135.031,456.5913,3172.3708,504.17023,0.99687356
+3483,2263.8848,131.9472,2278.7896,170.5326,0.9965335
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+3483,3497.9565,513.62427,3525.311,561.82275,0.99517906
+3483,2030.884,913.801,2070.3176,1005.4487,0.9794351
+3483,3825.566,750.5982,3861.0366,819.82,0.9325464
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+3484,1963.7417,295.70886,1986.3271,351.93384,0.9995421
+3484,2231.1772,555.14954,2262.4478,629.35,0.9994961
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+3484,2029.5925,184.06772,2049.4363,226.61957,0.9991302
+3484,1205.7815,322.56012,1225.6663,378.8011,0.99910885
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+3484,2367.921,218.26027,2387.3467,261.04755,0.9988703
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+3484,2616.9773,311.36188,2641.367,356.964,0.9987465
+3484,2000.1241,204.32492,2017.5804,249.56177,0.9987281
+3484,1864.084,331.45822,1893.1572,383.49875,0.99872774
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+3484,1951.0248,122.37205,1967.3966,159.94814,0.9986952
+3484,2533.294,409.39838,2572.085,456.83447,0.9986786
+3484,2920.4692,412.10062,2943.2412,468.35098,0.9984888
+3484,2831.9373,331.01108,2869.6848,375.08884,0.9984378
+3484,3141.8691,457.52203,3176.9844,504.79596,0.99835783
+3484,1983.1162,368.0835,2006.6667,427.02338,0.99825794
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+3484,3498.9739,513.74805,3525.3914,562.3638,0.9969543
+3484,2264.4258,131.9307,2279.295,170.21552,0.9961914
+3484,2835.102,247.51282,2858.7305,288.4837,0.9946732
+3484,2030.8969,913.8259,2070.1663,1005.5497,0.97706693
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+3484,3835.6045,750.06995,3860.9639,793.4242,0.28295222
+3484,141.40506,609.7323,181.19504,668.6588,0.20878905
+3484,61.800293,645.256,85.11508,684.7859,0.14436264
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+3485,2826.2043,737.0892,2875.7913,816.95917,0.99939144
+3485,2231.4736,555.3232,2262.5903,630.2575,0.9993592
+3485,2353.6226,613.8925,2387.4238,688.6547,0.99922657
+3485,2932.7776,337.6053,2954.3777,390.20844,0.99911696
+3485,1205.1085,322.34027,1225.5853,379.0927,0.9990735
+3485,2919.6924,412.6665,2942.6274,468.09998,0.9989147
+3485,2029.1978,183.78683,2048.952,226.51321,0.9988939
+3485,2631.2576,438.268,2672.3303,487.59705,0.9988919
+3485,2000.0952,204.24709,2017.44,249.51491,0.9988901
+3485,1983.0457,368.51105,2007.3442,427.1078,0.99887615
+3485,1951.036,122.156456,1967.1088,159.6958,0.99866617
+3485,1864.0614,331.45496,1893.2506,383.59644,0.9986533
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+3485,2617.5667,311.44742,2640.9602,356.86978,0.9985808
+3485,2539.517,410.1865,2575.3525,457.05655,0.99852306
+3485,2834.298,331.5438,2871.8225,376.173,0.9983942
+3485,2369.6284,219.00664,2388.462,260.84262,0.9983778
+3485,3501.3364,513.8062,3525.5098,563.13995,0.99813735
+3485,3288.1213,359.03082,3311.6497,405.737,0.9978625
+3485,2265.0066,131.54019,2279.444,170.52045,0.9963438
+3485,3142.736,458.125,3182.051,504.9168,0.99580044
+3485,2837.0154,247.9062,2859.5134,288.58838,0.99227434
+3485,2030.8613,913.90125,2070.1787,1005.5022,0.974963
+3485,3826.811,750.1595,3858.832,820.47406,0.8843393
+3485,3938.455,744.23035,3969.6812,804.8098,0.2213303
+3485,141.40831,609.85,181.25426,668.7114,0.18805054
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+3486,2774.0522,619.91864,2809.043,690.3189,0.9995902
+3486,1963.6649,297.50067,1986.1676,353.13104,0.9995146
+3486,2231.5493,556.1305,2263.1616,632.4575,0.999411
+3486,2826.4043,738.1959,2872.6123,817.37134,0.9992225
+3486,2932.4897,338.34143,2953.9702,391.26196,0.9991001
+3486,2028.8007,183.5785,2048.0818,226.4918,0.9990753
+3486,1864.4606,331.53214,1892.9452,383.7507,0.9989502
+3486,1950.2134,122.275925,1966.6958,159.33739,0.9989498
+3486,1204.3918,322.47476,1225.4861,379.39133,0.9989477
+3486,2617.7417,311.78592,2640.4307,356.8428,0.9989347
+3486,2354.2117,615.08405,2386.4749,690.2646,0.9989249
+3486,1999.8138,204.15616,2017.444,249.72153,0.99891615
+3486,1983.0315,368.33426,2006.971,427.3273,0.99883777
+3486,2631.8164,438.86664,2677.3271,487.3664,0.9987016
+3486,2370.5327,218.96068,2389.956,262.17,0.9986493
+3486,3097.615,267.73944,3118.8235,309.91705,0.9985196
+3486,2919.4202,413.36072,2941.764,468.92627,0.99850744
+3486,2837.1106,332.02704,2873.2903,376.22125,0.9984194
+3486,2540.0515,409.97806,2580.785,456.90213,0.9981487
+3486,3501.6094,514.3293,3525.5513,564.1392,0.99781716
+3486,2839.7996,248.44223,2859.9573,288.97525,0.99666387
+3486,3144.037,458.47357,3185.8232,505.6969,0.99610376
+3486,3290.2498,359.40396,3312.821,405.0347,0.9960853
+3486,2265.3774,131.77135,2279.7246,170.74043,0.9949917
+3486,2030.8907,913.9909,2070.057,1005.5573,0.9749131
+3486,3825.7917,750.18164,3857.4905,822.02856,0.8620507
+3486,141.32285,609.81555,181.74918,668.7787,0.16924849
+3486,61.87013,645.2238,85.28618,684.7596,0.16356945
+3486,3938.1697,744.96216,3970.8425,807.21436,0.15686691
+3487,2771.0159,619.5255,2808.964,690.29236,0.99966776
+3487,2633.56,439.0853,2682.5444,490.39914,0.99951124
+3487,1963.5856,299.0735,1986.1385,353.37012,0.99950314
+3487,2230.2676,558.48724,2263.171,634.7496,0.9994863
+3487,2826.5063,737.7628,2870.9072,817.0714,0.9993793
+3487,3145.803,458.62323,3190.9094,506.26855,0.99925727
+3487,1864.6552,331.4734,1892.63,384.4253,0.9991435
+3487,1949.9004,122.49382,1966.4426,159.6591,0.99906725
+3487,2539.7456,409.07126,2585.191,456.40836,0.99902236
+3487,2028.7134,183.64151,2046.928,226.93253,0.9989618
+3487,2932.0354,338.30493,2953.862,391.56384,0.9989593
+3487,2617.5017,311.80484,2640.1365,356.7593,0.9989459
+3487,1203.6165,322.83658,1225.4131,380.118,0.9989371
+3487,1999.6365,204.1854,2017.4712,249.71289,0.9989343
+3487,1982.8617,369.28656,2007.1954,426.9007,0.9989303
+3487,2370.6736,218.99149,2391.5007,263.81665,0.9988342
+3487,3098.261,267.49417,3118.5715,309.9767,0.9986078
+3487,2919.0413,414.11188,2941.637,468.33496,0.9985083
+3487,2840.8723,332.56598,2875.3875,376.61823,0.99841297
+3487,2354.0713,615.53613,2385.8257,691.05005,0.9982381
+3487,3501.6985,514.4259,3525.7332,565.9785,0.9974201
+3487,3293.7622,359.42035,3314.4253,405.28607,0.9967434
+3487,2840.1382,248.4166,2860.9478,290.5541,0.99640214
+3487,2265.9304,131.88312,2279.8137,170.99734,0.99411434
+3487,2030.8502,914.04443,2069.8738,1005.58594,0.96872
+3487,3824.0962,749.7358,3856.0522,823.54095,0.8119167
+3487,3935.7847,746.0017,3967.6475,807.8638,0.39327776
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+3487,141.19571,609.7205,181.93405,668.8433,0.17656307
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+3488,1963.5527,299.32462,1986.1377,354.28525,0.9995788
+3488,2229.431,560.5985,2263.3967,637.042,0.99951243
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+3488,2617.369,311.56018,2639.2493,356.8609,0.9992488
+3488,2826.659,738.05634,2868.536,817.28375,0.99911946
+3488,1949.0942,122.592125,1966.1418,159.48097,0.99909425
+3488,1982.7034,369.92123,2006.9827,427.14145,0.99906725
+3488,1203.0406,323.05325,1225.3788,380.18344,0.9989478
+3488,3149.56,459.36578,3195.6665,508.24323,0.998941
+3488,1999.5126,204.22552,2017.3099,249.77728,0.9988682
+3488,2931.7615,338.37903,2953.8264,392.11902,0.9988422
+3488,1864.8427,331.45496,1892.9886,384.46906,0.99884063
+3488,2845.7273,332.5591,2876.6165,376.57944,0.9986671
+3488,2918.8975,414.31555,2941.3286,467.99506,0.998664
+3488,3098.2976,267.2969,3118.328,310.0007,0.9985089
+3488,3294.4292,360.34726,3315.1958,404.36234,0.9985067
+3488,2371.053,219.19717,2393.1145,264.74097,0.9983925
+3488,2840.9329,248.87987,2861.467,290.86533,0.99818796
+3488,2028.1666,183.67278,2045.5812,226.93956,0.99800223
+3488,3501.6785,514.6041,3526.3743,566.9966,0.99798894
+3488,2540.897,408.73492,2588.5474,457.54675,0.99791557
+3488,2353.9202,615.7912,2385.6277,691.38727,0.997843
+3488,2266.2449,131.75519,2280.2205,171.02121,0.9936646
+3488,2030.8903,914.1415,2069.8188,1005.55493,0.96867406
+3488,3823.0664,749.7496,3854.5615,825.23444,0.70256716
+3488,3931.8699,746.2883,3972.4387,808.98694,0.29758355
+3488,61.88031,645.138,85.327194,684.762,0.2004219
+3488,141.46042,610.50793,181.04843,669.0842,0.1976857
+3489,2765.0774,620.3367,2805.3206,690.499,0.99970883
+3489,1963.4033,299.61743,1986.2241,354.34113,0.99958163
+3489,2228.8655,562.5051,2263.7024,639.4115,0.9994155
+3489,2640.758,440.7248,2690.012,496.7293,0.99940145
+3489,2617.3604,311.0987,2638.2266,356.94043,0.99911135
+3489,1202.2684,324.00436,1225.268,380.26212,0.99911135
+3489,1982.5969,369.71466,2006.998,427.9267,0.9990767
+3489,2826.6794,737.7898,2866.2385,817.42615,0.99907184
+3489,1948.4875,122.60495,1965.8242,159.52792,0.9990464
+3489,1865.2871,331.6317,1893.2258,384.4334,0.9989813
+3489,2841.534,249.27438,2863.4553,291.12515,0.99891865
+3489,2371.881,220.58627,2395.1208,265.0838,0.9989039
+3489,2931.8816,338.5787,2953.6033,391.93063,0.9988432
+3489,2543.4995,409.6225,2591.959,458.5133,0.9987962
+3489,1999.2845,204.26591,2017.2174,249.85051,0.9987795
+3489,3154.7322,460.37787,3199.655,511.53192,0.9987231
+3489,3098.3447,267.01068,3118.1372,309.97345,0.9985279
+3489,2918.6301,414.5307,2941.2957,469.1402,0.9983735
+3489,2353.9495,616.15,2385.7102,691.2925,0.9981933
+3489,2849.9895,333.0149,2878.943,377.14276,0.9981135
+3489,2027.5813,183.4981,2045.1306,226.71053,0.9977424
+3489,3502.1487,514.6908,3526.8372,565.2859,0.9975268
+3489,3294.659,361.7109,3316.028,404.69034,0.99485576
+3489,2266.189,131.91875,2280.6367,171.11577,0.99443024
+3489,2030.96,914.186,2069.5454,1005.52936,0.96504396
+3489,3822.6128,749.70105,3854.1172,825.3334,0.96451825
+3489,3930.46,747.54266,3971.1323,809.4793,0.24492839
+3489,141.45581,610.5129,180.97778,669.08673,0.18680792
+3489,61.874695,645.14136,85.465836,684.694,0.18418443
+3490,2762.3767,620.7857,2802.3606,690.3587,0.9995745
+3490,1963.3193,299.602,1986.1414,354.45215,0.9995542
+3490,2228.5818,563.78644,2263.8606,641.1314,0.9992399
+3490,2646.4724,442.60712,2694.6912,499.6195,0.99923384
+3490,2826.6272,737.532,2864.1267,817.5836,0.9992273
+3490,1201.855,324.16147,1225.1208,380.3585,0.99914503
+3490,1982.7471,370.33966,2007.012,429.146,0.99913543
+3490,1948.3497,122.73455,1965.3014,159.50131,0.9990445
+3490,2372.9568,221.43521,2396.2996,267.40686,0.9990085
+3490,1865.7894,332.02542,1894.1566,384.5092,0.9989863
+3490,1999.1934,204.26413,2017.178,249.88284,0.99884295
+3490,2931.8813,338.95065,2953.5942,392.306,0.9988171
+3490,2616.5684,310.64944,2637.6733,356.88345,0.9988036
+3490,2841.7146,249.94768,2863.8298,291.67856,0.9986657
+3490,3098.0178,267.18524,3118.0735,309.9923,0.9986298
+3490,2026.4736,183.59467,2045.0391,226.78513,0.9986285
+3490,2548.3992,410.67188,2596.323,460.80316,0.99857616
+3490,2918.4153,414.72534,2941.1526,469.3385,0.99850816
+3490,2353.9795,616.3302,2385.4053,691.14514,0.99849975
+3490,2850.5354,332.9456,2880.7288,378.01486,0.99823964
+3490,3294.2092,361.96878,3316.5403,405.78738,0.9977685
+3490,3502.506,514.8307,3527.6316,563.4457,0.99745995
+3490,2266.5684,132.06194,2280.8994,171.32884,0.9944754
+3490,3822.6575,749.74915,3854.1404,825.2666,0.99050456
+3490,3176.9507,461.8205,3203.7056,513.36316,0.98702466
+3490,2031.0507,914.22577,2069.745,1005.6464,0.97355574
+3490,3929.7925,747.9712,3971.1255,809.27734,0.32171232
+3490,61.850048,645.10315,85.46049,684.5918,0.19929236
+3490,141.48456,611.0257,180.40924,669.1608,0.19162074
+3491,1963.3654,299.62833,1985.8219,354.44955,0.9994875
+3491,2826.2578,737.2792,2862.2686,817.8165,0.9994252
+3491,2228.873,564.9097,2263.9712,641.74066,0.9993761
+3491,2760.1072,622.23157,2799.173,690.37976,0.99929476
+3491,1201.0981,324.34955,1224.8782,380.44086,0.999204
+3491,1982.6804,371.10977,2006.646,430.09103,0.99912924
+3491,2552.876,411.47162,2600.1592,462.9422,0.9990693
+3491,1948.1904,122.76173,1964.9231,159.55173,0.99902606
+3491,2353.928,616.98285,2385.5994,691.08795,0.9990126
+3491,1866.5463,332.3992,1894.7699,384.493,0.9990086
+3491,2656.8638,444.43558,2699.642,499.64267,0.99895823
+3491,1999.1447,204.19417,2017.2357,249.88808,0.9988416
+3491,2025.4081,183.5454,2045.5624,227.00185,0.9987513
+3491,2851.1543,333.1745,2883.0303,379.80212,0.99868804
+3491,2918.4954,415.08456,2941.077,468.8558,0.9986627
+3491,2931.0059,339.28513,2953.6191,392.33224,0.9986559
+3491,2616.0164,310.42395,2637.4172,357.17316,0.9985935
+3491,3098.1045,267.1693,3117.933,310.04034,0.9984376
+3491,3502.0432,515.04767,3528.0964,564.0369,0.99843425
+3491,2841.5906,250.41219,2864.402,292.4629,0.9983866
+3491,2375.2551,222.96722,2397.9983,267.7546,0.9983209
+3491,3294.3596,363.91913,3317.6326,406.02496,0.99660444
+3491,2267.3071,132.00978,2281.1812,171.36192,0.9946569
+3491,2030.9774,914.1511,2069.7852,1005.75085,0.9776953
+3491,3821.771,749.73517,3854.3286,825.3377,0.9442002
+3491,3177.8906,464.093,3209.3086,510.75626,0.84086245
+3491,3177.5588,475.36798,3202.6028,513.3717,0.74945706
+3491,3186.228,464.30652,3207.9165,499.7292,0.4491706
+3491,3929.8484,749.25244,3963.8938,809.14404,0.22543593
+3491,141.54796,610.95715,180.11668,669.261,0.20482975
+3491,61.836983,645.064,85.47474,684.51263,0.195127
+3492,2757.4417,623.0134,2798.616,690.9514,0.99963856
+3492,1868.1067,332.80823,1895.3745,384.58887,0.99951136
+3492,1963.002,299.82385,1985.5698,354.56903,0.99950886
+3492,2229.3784,565.321,2265.1094,641.95026,0.9993691
+3492,2824.5132,737.0805,2860.955,818.11774,0.99932885
+3492,1200.0374,324.30093,1224.5964,380.49893,0.9992453
+3492,2354.1738,617.72766,2385.9463,691.3572,0.9991504
+3492,2667.5933,446.505,2704.31,499.29352,0.9991229
+3492,1982.6893,372.09363,2005.9935,431.44342,0.9990766
+3492,1947.9348,122.46561,1964.4194,159.4809,0.99900705
+3492,2024.1624,183.64922,2045.635,228.02614,0.9989837
+3492,1999.1211,204.28285,2017.3425,250.06773,0.99886703
+3492,2560.2827,412.4339,2604.4263,463.3031,0.99884653
+3492,2918.3533,415.25626,2941.0925,468.8616,0.9987777
+3492,2377.4185,223.91264,2399.77,267.44025,0.998749
+3492,2930.4197,339.4847,2952.8982,391.9479,0.99868375
+3492,3177.9944,464.3738,3218.4373,513.67883,0.9985917
+3492,2615.5017,310.38782,2637.1746,357.64868,0.9985852
+3492,3501.6177,515.02545,3528.9517,564.43744,0.9985746
+3492,2852.6292,333.52087,2885.9924,380.99518,0.998524
+3492,3098.1658,267.2136,3117.7073,310.0294,0.9981831
+3492,2841.855,250.729,2864.5679,292.93018,0.99812156
+3492,3294.384,365.2571,3319.1604,406.18735,0.9961791
+3492,2267.2712,131.96841,2281.735,171.50262,0.9944112
+3492,2031.1351,914.386,2069.3977,1005.8323,0.98474205
+3492,3821.0315,749.7052,3854.8474,825.54785,0.96554184
+3492,3929.3945,748.835,3970.2046,809.51056,0.21725546
+3492,141.5643,611.0631,179.88736,669.2611,0.2103392
+3492,61.90799,645.0314,85.48073,684.558,0.18107826
+3493,2756.2969,624.29816,2798.872,692.3311,0.9996517
+3493,1962.4878,299.96448,1985.5183,354.54987,0.99955827
+3493,2231.1904,565.42865,2266.2979,642.0169,0.99945945
+3493,1199.5865,324.9999,1224.243,380.62228,0.9992275
+3493,2671.5793,446.9709,2710.8123,499.51904,0.9992155
+3493,1947.8956,122.29443,1964.0511,159.48703,0.99914825
+3493,1870.3752,332.79004,1896.315,384.36377,0.99905497
+3493,2822.109,737.4309,2859.4365,817.69666,0.99902785
+3493,1982.6484,373.0195,2006.0869,433.49014,0.99898726
+3493,2614.8909,310.57916,2636.7898,358.53033,0.9989779
+3493,3179.5742,463.8095,3224.5557,513.9127,0.99895287
+3493,2354.1987,618.60767,2386.0078,692.558,0.9989172
+3493,2380.2788,224.51837,2400.898,267.63586,0.99885535
+3493,2023.6493,183.88899,2045.7565,228.03587,0.99877185
+3493,2929.885,340.04553,2952.0984,391.76495,0.99876815
+3493,1999.1006,204.40436,2017.646,250.34412,0.9986803
+3493,2918.19,415.4876,2941.1372,469.0028,0.9986344
+3493,2855.0774,334.5011,2887.5706,381.98846,0.99833316
+3493,2842.6436,251.13715,2865.2197,293.23108,0.9982644
+3493,3098.243,267.11612,3117.6096,309.69943,0.9982414
+3493,3501.5977,515.25824,3530.2588,566.1901,0.9979432
+3493,3294.6362,364.85776,3321.2705,406.62167,0.99716467
+3493,2568.7751,414.15497,2608.882,464.0912,0.996382
+3493,2267.3665,131.61548,2282.1467,171.54333,0.9948171
+3493,2030.9272,914.341,2069.1426,1005.89594,0.97987753
+3493,3819.5896,749.5408,3855.4905,825.01465,0.9793361
+3493,141.59273,611.1458,179.95436,669.15485,0.20654546
+3493,61.907135,645.016,85.43254,684.5239,0.1624374
+3493,3929.3198,748.93555,3969.2637,809.35144,0.16121177
+3493,1904.7701,95.51452,1917.0153,123.89692,0.10447525
+3494,2755.73,624.6271,2797.4468,692.9287,0.9996929
+3494,1962.3951,300.02005,1985.0482,354.6199,0.99959797
+3494,2233.4595,565.691,2267.0996,642.3537,0.99950635
+3494,2671.9463,446.7818,2716.1353,498.97687,0.9992768
+3494,1199.0654,325.03577,1223.3442,380.6156,0.9992724
+3494,2573.6707,415.6095,2614.7356,464.05493,0.9992623
+3494,1947.619,122.57794,1963.6869,159.48026,0.9992322
+3494,2614.0085,311.3556,2636.2742,358.78485,0.9991602
+3494,2382.2117,224.83871,2402.486,268.04413,0.9991074
+3494,2354.2058,619.2547,2386.0754,693.77045,0.9990983
+3494,3181.5027,465.25552,3229.4846,514.2724,0.99902487
+3494,1873.5121,332.64453,1896.9154,384.6383,0.99898976
+3494,2918.3071,415.6564,2940.9658,469.36862,0.9989838
+3494,1982.6122,373.4,2005.7958,434.21848,0.9988732
+3494,2819.7651,737.9224,2858.3667,820.94464,0.99885654
+3494,1999.4148,204.45284,2017.6035,250.8137,0.9987465
+3494,2022.9958,184.37051,2045.7417,228.56754,0.9986916
+3494,2928.1353,340.5011,2951.0498,391.83838,0.99854636
+3494,2858.0703,335.94388,2890.1948,382.55582,0.99853957
+3494,2842.668,251.30164,2865.7598,293.2544,0.9984022
+3494,3501.859,515.4361,3530.3516,567.53815,0.99819934
+3494,3295.0002,366.0071,3323.4216,407.42502,0.9980332
+3494,3098.4326,267.34158,3117.4927,309.6235,0.9980069
+3494,2267.8784,131.60576,2283.0288,171.2889,0.99539447
+3494,2030.6469,914.1639,2068.8186,1006.0306,0.976071
+3494,3819.3164,749.7436,3855.686,813.53156,0.95823765
+3494,3815.118,768.9634,3842.2898,825.8269,0.85207653
+3494,141.60005,610.9652,179.96458,669.17004,0.21860932
+3494,61.91079,645.02295,85.39449,684.51953,0.14481908
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+3495,2236.7085,566.539,2267.7144,642.22064,0.9996445
+3495,1961.488,300.0599,1984.7966,354.3038,0.99959034
+3495,2673.9229,446.98834,2721.901,500.55615,0.9995209
+3495,2754.9448,624.63617,2795.705,693.9484,0.9994784
+3495,1198.8364,325.03204,1223.0217,380.5406,0.99947304
+3495,1947.1621,122.756134,1963.5295,159.51694,0.99939144
+3495,2612.135,311.90295,2635.5198,359.5821,0.9993563
+3495,1875.9136,333.0856,1897.772,384.79,0.9992747
+3495,2918.0847,416.06012,2941.0657,469.6919,0.9991303
+3495,2574.7446,416.0914,2619.713,464.14908,0.99902034
+3495,2817.3892,738.8619,2856.879,821.89923,0.9990157
+3495,2383.0173,224.42679,2404.2283,269.99484,0.9989936
+3495,2354.069,620.5399,2386.322,696.3113,0.9989492
+3495,3188.2917,465.9773,3232.5037,515.70593,0.99886215
+3495,2844.0957,251.22134,2867.108,293.30173,0.99879897
+3495,1999.5577,204.57835,2017.6012,251.10631,0.99877614
+3495,2022.7755,185.29524,2045.7042,228.94887,0.9987557
+3495,1982.424,373.86395,2004.6832,434.86572,0.9985781
+3495,2862.5615,337.18076,2892.0112,382.9783,0.998451
+3495,2926.5503,341.1378,2950.6758,392.5754,0.9983588
+3495,3294.7566,365.98132,3326.008,407.63464,0.9981407
+3495,3501.58,515.99744,3530.7896,568.01196,0.998005
+3495,3098.5476,267.37814,3117.2039,309.95316,0.9979235
+3495,2268.0303,131.37064,2283.6729,171.36234,0.99569404
+3495,2030.3666,914.0612,2068.2983,1006.04474,0.9684626
+3495,3824.3018,749.9035,3856.0205,808.903,0.94162685
+3495,3809.7617,768.45013,3841.271,828.8912,0.85167235
+3495,3810.1873,789.9528,3840.0579,827.48737,0.30139422
+3495,3929.938,748.00696,3956.8818,808.48584,0.21962854
+3495,141.57108,610.735,180.11874,669.16174,0.21759713
+3495,61.913845,645.00397,85.426926,684.5705,0.14094709
+3495,1904.778,95.45926,1916.9716,123.7236,0.11607575
+3496,2239.948,567.47314,2269.429,642.66016,0.9997298
+3496,1961.1805,301.04523,1984.713,355.175,0.9996886
+3496,2384.7544,224.49878,2406.4126,271.72205,0.9995136
+3496,2577.2725,416.94003,2623.0576,467.9639,0.99947435
+3496,1198.5157,325.13828,1222.2538,380.509,0.9994734
+3496,2677.5613,447.12622,2727.4548,502.48126,0.9994703
+3496,3194.115,467.86823,3237.7449,518.72925,0.99946743
+3496,2754.6533,624.68646,2793.15,694.492,0.9994666
+3496,1946.6176,122.591194,1963.214,159.43677,0.9993649
+3496,1878.0726,333.18533,1899.2828,385.29404,0.9993437
+3496,2918.3098,417.4328,2941.2415,470.4621,0.9991968
+3496,2867.0933,337.8892,2895.9321,383.15628,0.9990772
+3496,2815.4763,739.747,2855.7825,822.53436,0.9990138
+3496,2610.89,313.61774,2634.8054,359.9879,0.99893004
+3496,1999.6268,204.97272,2017.2343,251.20163,0.9987117
+3496,2022.1145,185.61143,2045.925,230.3455,0.99857235
+3496,1982.3187,374.20532,2004.3114,435.2801,0.9985538
+3496,3295.4011,366.55936,3327.3123,407.79062,0.9983942
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+3496,2353.8853,621.8888,2386.9932,699.1406,0.998278
+3496,2846.6418,251.28989,2867.9954,293.0237,0.9982699
+3496,3098.4502,267.13904,3117.2393,309.5699,0.9976458
+3496,3501.3018,516.37756,3530.912,567.78467,0.99702126
+3496,2267.855,131.52373,2283.892,171.26765,0.9957521
+3496,3823.3994,750.00836,3856.585,809.9107,0.9562101
+3496,2030.4158,914.3022,2068.053,1006.3128,0.95127106
+3496,3805.8015,769.1928,3840.9119,829.6198,0.5759428
+3496,3929.9424,747.3574,3957.2969,808.51587,0.34119114
+3496,141.5329,610.5424,180.46329,669.10187,0.22466782
+3496,61.938908,645.0002,85.44716,684.6617,0.13899975
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+3497,2242.3064,568.5218,2270.7576,642.35504,0.9995753
+3497,1198.3765,325.0475,1221.7334,380.6399,0.99951696
+3497,1959.6554,301.4248,1984.6156,356.20288,0.9995152
+3497,2580.0042,418.1312,2626.2063,469.95877,0.9994837
+3497,2386.5867,226.00458,2407.757,273.214,0.9993925
+3497,2753.9607,624.95874,2790.9675,695.12354,0.9993919
+3497,2814.0188,740.5666,2854.932,821.3769,0.99933064
+3497,1946.6318,122.36136,1962.9258,159.39096,0.9993019
+3497,2918.2744,417.81036,2941.316,471.93713,0.9992945
+3497,1878.0156,333.9174,1901.6362,387.16214,0.99900913
+3497,3201.9504,469.051,3240.4802,520.6334,0.99877733
+3497,2610.2249,314.63992,2634.4275,360.2562,0.99871707
+3497,2353.5935,623.39923,2388.0623,700.3918,0.99871206
+3497,1982.2269,375.0309,2005.5167,435.07278,0.998601
+3497,2021.9333,186.26349,2045.7568,232.42892,0.9986008
+3497,2848.354,251.72783,2868.526,293.14,0.99850875
+3497,1999.5308,205.92311,2017.0903,251.4298,0.9984817
+3497,2922.0322,340.93307,2949.608,394.0685,0.99836296
+3497,2867.1787,337.9585,2899.38,383.38043,0.9983449
+3497,3097.3762,266.9915,3117.1404,309.88535,0.9982272
+3497,3501.6345,516.631,3532.2073,568.0582,0.9975898
+3497,3297.4578,366.32092,3329.7156,408.04663,0.99751866
+3497,2267.6096,131.45723,2284.6526,171.2469,0.9967705
+3497,3822.8545,750.21375,3856.9082,809.2821,0.9721634
+3497,2029.9628,914.3181,2067.5508,1006.395,0.9348032
+3497,3803.6414,770.46344,3841.1409,831.3953,0.546618
+3497,3930.015,746.6369,3957.6326,808.36884,0.45799905
+3497,141.48868,610.5109,180.51157,669.17584,0.21114263
+3497,61.918922,645.04584,85.47828,684.674,0.13408165
+3497,1904.7994,95.37572,1916.9467,123.57715,0.1314996
+3498,2687.6055,450.52298,2738.1338,507.64297,0.99960774
+3498,2753.91,624.88727,2787.4846,695.2973,0.99960536
+3498,2243.1248,568.7904,2272.4949,642.5747,0.99956864
+3498,1959.3851,301.8674,1984.0118,357.28452,0.99956185
+3498,1198.2626,325.12274,1221.7303,380.78662,0.9994253
+3498,2583.3833,419.29895,2630.7632,472.07654,0.9993754
+3498,1877.9622,334.4314,1903.2739,387.59192,0.9992938
+3498,2388.6675,226.86708,2408.7998,275.39844,0.99927217
+3498,1946.3844,122.49994,1962.8317,159.32935,0.9992679
+3498,2918.306,418.5327,2941.345,473.16443,0.9991223
+3498,2812.3843,742.21063,2853.9854,824.491,0.9989286
+3498,3210.3242,471.19904,3244.941,523.6321,0.99880004
+3498,1982.2859,375.21234,2007.6252,435.028,0.99872893
+3498,2610.2742,315.13416,2634.0256,360.28217,0.9985128
+3498,3097.1494,267.0198,3117.0444,310.41702,0.99850357
+3498,1999.7896,206.16107,2017.1079,251.50458,0.9984546
+3498,2021.7578,186.3995,2045.761,232.66241,0.9983714
+3498,2920.7783,341.43408,2949.3555,393.9452,0.9983462
+3498,2850.1597,251.81429,2869.1123,293.35504,0.998319
+3498,2867.8723,337.61548,2902.594,383.4806,0.99831617
+3498,2353.684,625.0901,2391.1226,703.21716,0.9982287
+3498,3300.581,366.90884,3330.4443,407.5972,0.99783176
+3498,3502.0042,516.88916,3533.2385,567.9231,0.99767965
+3498,2267.893,132.06729,2285.4165,171.3316,0.9962638
+3498,3826.82,750.2905,3857.3367,808.3623,0.9670711
+3498,2029.4976,914.2059,2066.9043,1006.66437,0.9412973
+3498,3802.6685,772.15753,3834.225,832.59235,0.6097968
+3498,3929.1313,746.1859,3957.1655,809.6655,0.5655573
+3498,141.49019,610.455,180.53685,669.1851,0.21363215
+3498,61.918915,645.04584,85.47827,684.674,0.13408463
+3498,1904.8015,95.3684,1916.9436,123.5613,0.13322721
+3499,2754.0564,624.99884,2784.3953,695.6379,0.99966383
+3499,1958.6962,302.93707,1983.527,358.1134,0.9995301
+3499,1198.1079,325.02338,1221.1416,380.62665,0.9994816
+3499,2244.461,569.2106,2273.4897,641.9398,0.99944156
+3499,1877.6149,335.23703,1904.7931,387.94095,0.9993837
+3499,2390.3977,228.21169,2409.9377,276.1039,0.9993291
+3499,1945.9148,122.5545,1962.7571,159.31847,0.9993099
+3499,2698.8555,451.9427,2743.524,508.85803,0.9992312
+3499,2590.9353,421.3475,2635.8108,473.47403,0.9992131
+3499,2918.3052,419.04117,2941.643,474.22208,0.9991598
+3499,2920.3523,341.8834,2947.9465,393.98734,0.99913377
+3499,1982.2577,375.34695,2007.3483,434.8892,0.99912614
+3499,2869.4827,337.41534,2904.5872,383.59705,0.99873763
+3499,2353.796,626.3891,2393.6177,704.0893,0.9986487
+3499,3095.9568,267.0184,3116.9397,310.4606,0.998607
+3499,3503.964,516.6966,3533.4568,567.7944,0.9985582
+3499,2811.1252,743.0816,2852.757,825.0384,0.99849415
+3499,2850.8562,252.14493,2870.0852,293.65616,0.99841857
+3499,3303.9194,366.67596,3333.0605,407.91156,0.99840325
+3499,1999.9448,206.4193,2017.3235,252.25444,0.9983956
+3499,2610.2422,315.14944,2633.7764,360.28854,0.99835867
+3499,2021.702,186.55658,2044.9613,232.815,0.9980667
+3499,3221.0051,474.21118,3250.8186,525.23425,0.99760276
+3499,2267.9019,132.08565,2286.0674,171.32213,0.99613404
+3499,3823.299,750.3219,3857.6853,808.7999,0.9546233
+3499,2028.5671,914.7435,2066.382,1007.14087,0.7389825
+3499,3802.5051,774.04443,3829.3362,832.7173,0.7030408
+3499,3928.1821,747.44476,3955.5444,810.7109,0.5263356
+3499,141.48282,610.3714,180.61972,669.19403,0.2135462
+3499,61.91893,645.04584,85.47826,684.674,0.13401647
+3499,1904.797,95.39313,1916.9396,123.54555,0.13191795
+3500,1198.0035,325.4775,1220.2723,380.5554,0.9995939
+3500,2244.9504,570.41077,2274.3264,642.095,0.99954444
+3500,1958.423,303.4062,1982.8446,359.1684,0.9994771
+3500,2754.243,625.3072,2782.1956,696.2155,0.9994355
+3500,2391.864,229.22644,2411.1877,276.78903,0.99938333
+3500,1945.876,122.411575,1962.7681,159.29152,0.99934596
+3500,2918.1514,419.82193,2941.7847,474.6877,0.9992144
+3500,2852.8174,252.24286,2870.662,294.66174,0.99907815
+3500,2354.1028,628.27234,2395.3928,704.10046,0.99899536
+3500,2870.4167,336.89825,2907.4084,383.86945,0.99896014
+3500,2809.7979,743.65845,2852.3018,826.3273,0.9989498
+3500,2920.3738,341.91086,2948.533,394.27927,0.9989473
+3500,1981.7816,375.67603,2008.9381,434.80334,0.9988643
+3500,1877.2323,335.97836,1906.8156,386.822,0.99881315
+3500,2600.8965,423.0975,2640.6123,473.59805,0.99880135
+3500,2610.4346,314.51483,2633.609,360.29645,0.9986957
+3500,2709.267,453.61896,2749.542,509.2746,0.9985612
+3500,2000.1272,206.50946,2017.2227,252.18945,0.9984779
+3500,3095.6804,267.06042,3116.7952,310.60565,0.9984322
+3500,3307.3103,367.5617,3333.9705,408.19965,0.9983551
+3500,2021.55,186.88062,2043.7434,233.0958,0.9981893
+3500,3505.648,516.90155,3533.9893,567.67096,0.9975727
+3500,3223.179,476.10318,3255.3538,526.7872,0.99723226
+3500,2267.9072,132.41081,2286.6465,171.16035,0.9940268
+3500,3820.8167,750.38654,3857.9011,808.56,0.96987444
+3500,2028.47,914.8189,2066.321,1007.633,0.86149627
+3500,3927.2021,748.74744,3957.2817,812.8928,0.30571386
+3500,3801.2485,774.5154,3826.0425,833.0133,0.2505604
+3500,141.48828,610.4271,180.58005,669.1909,0.2132973
+3500,1904.7969,95.3607,1916.9241,123.48213,0.1387781
+3500,61.91893,645.04584,85.47827,684.674,0.13403262
+3501,2245.0608,571.1905,2275.7654,643.767,0.9996325
+3501,1958.1439,304.47665,1982.2538,360.63382,0.99954957
+3501,2753.7869,626.0525,2780.7473,697.9215,0.9994461
+3501,2918.1482,420.40787,2942.1238,475.72348,0.9993181
+3501,1198.0812,326.25037,1220.149,381.18005,0.99929833
+3501,2920.5544,342.4887,2947.8381,394.15765,0.99921167
+3501,2808.9006,744.1121,2850.657,826.3005,0.9992079
+3501,1945.6759,122.52887,1962.5294,159.36603,0.9991819
+3501,2354.496,628.7696,2399.3052,704.26654,0.99916244
+3501,2392.7432,230.1666,2412.3994,276.51736,0.9991115
+3501,2873.2114,337.7664,2909.646,383.59256,0.9989543
+3501,2610.4563,313.64444,2633.6829,360.14743,0.99885094
+3501,2852.6992,252.5823,2871.4878,295.40237,0.998836
+3501,3095.4688,267.3778,3116.3232,310.78186,0.9988337
+3501,3311.54,365.1333,3335.5513,408.07544,0.99880993
+3501,1981.2844,376.44977,2009.8696,435.27606,0.99878234
+3501,2717.0767,454.95023,2755.6113,508.8998,0.99873525
+3501,3507.938,518.2512,3534.2808,567.66943,0.998588
+3501,1877.833,337.42606,1908.6626,387.44888,0.9985808
+3501,2022.0392,186.94675,2042.3226,232.60208,0.9985228
+3501,2611.142,424.49976,2646.2944,473.72852,0.99847955
+3501,2000.2828,207.12505,2017.2035,252.60274,0.99840003
+3501,3223.509,478.29453,3261.281,526.2254,0.9978302
+3501,2268.1138,132.5264,2287.7998,171.07208,0.99323213
+3501,3827.8416,750.3351,3857.991,808.60315,0.9679951
+3501,2027.9829,915.1338,2064.2961,1008.0834,0.93216926
+3501,3925.9062,749.73206,3957.0469,814.60815,0.41333318
+3501,3797.125,774.6513,3822.686,832.82806,0.17840056
+3501,140.57074,608.5379,186.87708,669.51276,0.16632846
+3501,1904.7273,95.23079,1916.9158,123.1494,0.104279615
+3502,2752.1436,626.8552,2779.0254,699.2666,0.9996518
+3502,2245.2761,572.40607,2278.6648,645.8543,0.9995838
+3502,1958.0729,304.8301,1981.9662,361.04916,0.99945015
+3502,1198.074,326.47678,1219.9717,381.4103,0.9993247
+3502,2920.6362,342.23773,2947.6304,394.05615,0.9992472
+3502,2917.8975,421.27866,2942.6719,476.2346,0.99922633
+3502,1945.5927,122.497574,1962.5289,159.32594,0.9991985
+3502,2393.7163,230.04681,2413.665,276.22992,0.999171
+3502,3225.0635,479.88733,3267.2402,527.91376,0.99912363
+3502,1979.7668,376.7165,2011.104,437.62317,0.99902165
+3502,2610.5457,312.40723,2633.9382,360.34192,0.9990103
+3502,3314.3281,364.95367,3336.683,407.8844,0.9989893
+3502,2807.755,744.5727,2850.669,826.46295,0.99896955
+3502,3095.2083,267.47076,3116.3127,310.50244,0.99894744
+3502,2356.7092,628.614,2399.8704,704.1382,0.9989434
+3502,2613.4324,424.86066,2651.4924,473.77917,0.99881184
+3502,2717.6826,455.47696,2762.6,508.38687,0.9987374
+3502,2877.7095,338.10052,2911.9487,383.80347,0.9985911
+3502,1878.8745,338.66177,1909.7156,389.29025,0.9985863
+3502,2852.5837,252.93146,2872.0193,295.7287,0.9985474
+3502,2000.5221,207.38326,2017.4916,252.68086,0.9984952
+3502,3510.0864,518.62115,3534.87,567.59393,0.9984257
+3502,2022.0975,186.96936,2041.8251,232.21469,0.9981287
+3502,2268.121,134.00685,2288.1245,171.09929,0.9943709
+3502,3828.0405,750.34436,3858.3174,808.6437,0.9899226
+3502,2027.8206,915.12067,2063.425,1008.36835,0.9479685
+3502,3792.2134,773.82275,3819.796,832.95105,0.47068733
+3502,3924.2485,751.2244,3957.1357,815.762,0.37134823
+3502,140.63167,608.59717,186.41173,669.4435,0.1703904
+3502,2614.1218,861.0925,2650.3054,943.8428,0.14599647
+3502,1904.7266,95.21802,1916.9111,123.13617,0.106762856
+3503,1958.0634,305.20776,1981.4808,361.11053,0.9995596
+3503,2245.4185,573.5327,2281.1455,647.4225,0.9995534
+3503,2747.8806,627.886,2778.617,699.89624,0.9994808
+3503,2720.3032,456.0919,2768.5415,511.5043,0.99946886
+3503,1198.0548,326.87946,1220.0509,381.66437,0.9992932
+3503,2920.888,342.26575,2947.2366,394.22064,0.99923635
+3503,3229.5857,480.58163,3272.3625,529.66144,0.99921465
+3503,1945.4904,122.463455,1962.4965,159.34093,0.9991891
+3503,2917.7922,421.80997,2943.0012,476.749,0.99917054
+3503,2394.051,230.35876,2415.258,276.546,0.9990777
+3503,1977.1489,377.8163,2011.8809,440.56216,0.9990258
+3503,2610.0576,312.24698,2634.3687,360.2172,0.9989336
+3503,2614.5444,424.83047,2657.1514,473.75943,0.99890876
+3503,2359.4827,628.0373,2398.723,704.1204,0.9988187
+3503,1879.7552,338.8986,1910.0387,390.86313,0.9987791
+3503,3095.05,267.7146,3116.3206,310.6076,0.9987575
+3503,2000.6902,207.37115,2017.7512,252.60889,0.9983853
+3503,3316.0342,365.13348,3337.809,409.16644,0.99837744
+3503,2807.1636,745.00354,2850.3018,826.25867,0.9983601
+3503,2852.8809,253.77325,2872.9238,296.70868,0.99816954
+3503,2022.0099,187.03035,2040.9967,232.35756,0.9980902
+3503,2882.6838,339.76004,2915.2522,384.63974,0.99779296
+3503,3509.6328,520.0075,3535.787,569.1806,0.99637884
+3503,2268.5527,134.13531,2288.2651,171.04169,0.99608934
+3503,3827.1487,750.3145,3858.533,808.1889,0.9940578
+3503,2027.2311,915.37213,2062.6829,1008.7841,0.94960034
+3503,3790.8628,772.82007,3817.8345,832.28357,0.733217
+3503,3922.782,753.1528,3950.7473,816.68146,0.29029426
+3503,140.68404,608.67676,185.94728,669.41895,0.17495544
+3503,1904.726,95.21721,1916.9088,123.1326,0.10746064
+3504,2245.5483,575.68524,2282.6816,648.5389,0.99964285
+3504,1958.0852,305.16867,1981.1252,361.2698,0.9995016
+3504,2743.4648,628.55634,2778.101,700.1479,0.9994437
+3504,2725.0876,458.54007,2774.6824,514.67883,0.9994398
+3504,1945.4916,122.49116,1962.4635,159.37979,0.9991837
+3504,1198.0931,327.21588,1220.0712,382.32227,0.9991767
+3504,2918.2395,422.0926,2943.6868,477.23785,0.99916685
+3504,2394.569,230.33223,2417.1917,279.2685,0.9991196
+3504,1974.3718,378.92694,2010.8284,442.11078,0.99910957
+3504,2616.272,424.81143,2662.998,476.07028,0.9989893
+3504,2920.925,342.51575,2946.072,394.19946,0.9989592
+3504,1880.8263,338.58362,1910.1005,391.0982,0.9988846
+3504,2609.8018,312.29324,2635.4019,360.34177,0.998872
+3504,3094.6162,267.95056,3116.0244,310.60663,0.99884075
+3504,2806.452,745.0482,2849.0286,826.40015,0.9986425
+3504,2853.5107,253.94746,2873.0723,297.60748,0.99859816
+3504,2000.898,207.48264,2017.7957,252.63242,0.9984579
+3504,2021.6948,186.91646,2040.6091,232.37444,0.9983083
+3504,2361.2163,627.00165,2398.9097,704.0268,0.9981279
+3504,3238.1174,482.73547,3278.157,531.14294,0.9976688
+3504,2887.746,340.6514,2918.0435,384.71335,0.9974769
+3504,3318.7546,366.23355,3338.556,409.9122,0.9970374
+3504,3826.8606,750.4204,3858.804,808.56116,0.99633753
+3504,2269.2366,133.30368,2288.5842,171.12848,0.9963193
+3504,3513.6484,521.7108,3537.2954,570.4479,0.9891375
+3504,2026.8267,915.3296,2061.9885,1009.26196,0.95780855
+3504,3786.8767,772.3063,3814.8904,831.5582,0.7241563
+3504,3920.6423,754.2975,3948.3552,818.2239,0.5042005
+3504,140.80824,608.711,185.04988,669.41754,0.17461008
+3504,1904.7269,95.20489,1916.9034,123.12156,0.110387295
+3505,2739.3955,629.441,2777.706,700.88837,0.99964845
+3505,2245.7947,577.40674,2282.551,650.29626,0.9995982
+3505,1958.0332,305.247,1980.5815,361.3841,0.9995146
+3505,2729.8794,458.64096,2781.5874,517.1493,0.9994437
+3505,2395.0012,231.13719,2418.4514,280.22455,0.99942017
+3505,1972.2533,380.77124,2010.3873,442.7268,0.9992762
+3505,1198.1506,328.22385,1220.4397,382.9352,0.99924135
+3505,1945.4983,122.51458,1962.4182,159.401,0.9991829
+3505,2805.9663,745.6304,2848.2559,826.3445,0.999166
+3505,2918.5237,422.22723,2944.485,477.47314,0.9991048
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+3506,1957.4298,305.2707,1980.3021,361.53223,0.9995832
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+3506,1970.4229,382.09256,2010.095,443.98343,0.99926096
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+3507,1956.6693,305.24286,1980.0443,361.5716,0.99966955
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+3507,1970.4667,382.61084,2008.4276,444.9453,0.99929774
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+3507,2364.3677,627.8304,2401.4053,705.2038,0.99926096
+3507,1945.9789,123.199455,1962.309,159.30466,0.99923885
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+3507,2001.5981,208.00702,2018.3074,252.79138,0.9985856
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+3507,3771.2295,773.699,3807.8584,834.4905,0.9390448
+3507,3919.0608,756.0214,3944.5676,821.2597,0.85769236
+3507,2025.4492,915.04504,2060.9858,1010.5005,0.7283271
+3508,2731.782,632.93353,2777.1218,703.0878,0.99960333
+3508,1956.0126,305.4077,1979.7118,361.33014,0.9995939
+3508,2247.048,580.8112,2287.9695,652.0201,0.9994584
+3508,2920.3738,422.66217,2945.426,477.8977,0.99929345
+3508,1970.6263,382.69427,2007.4564,445.07166,0.9992906
+3508,1946.12,123.18728,1962.5028,159.17442,0.9992711
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+3508,2364.1858,629.32587,2403.2434,707.7497,0.9991696
+3508,3321.4817,371.45743,3346.387,414.20786,0.99910176
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+3508,2002.0594,208.18542,2018.7125,252.98499,0.99863404
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+3508,3918.9885,755.8496,3944.5447,821.2771,0.8939558
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+3509,1955.5917,305.4059,1979.5067,361.6426,0.9995913
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+3509,2247.7927,581.7694,2291.473,652.00867,0.99947697
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+3509,1946.4933,123.02525,1963.1644,159.09981,0.9988111
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+3509,2655.9104,432.28214,2690.9607,482.5732,0.9986228
+3509,2002.228,208.22711,2018.8091,253.19952,0.99856216
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+3509,2023.6364,915.4972,2060.551,1010.53015,0.93818474
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+3509,3770.881,799.0959,3798.887,834.0507,0.3689453
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+3510,2732.0767,633.1673,2776.0083,703.17804,0.9996215
+3510,1954.9926,305.74652,1979.2423,362.3686,0.9995121
+3510,1198.1685,328.21518,1219.7217,384.49124,0.99940646
+3510,2405.176,238.55556,2427.8875,282.81305,0.9993193
+3510,2361.4941,635.0646,2406.815,713.2755,0.999231
+3510,1891.4166,340.18143,1912.5465,391.1982,0.9992211
+3510,2769.2976,466.15115,2813.469,520.2048,0.99920136
+3510,1946.717,122.988945,1963.4292,159.55377,0.9991211
+3510,2921.254,423.45566,2945.9482,477.50027,0.99911386
+3510,1973.4855,383.04712,2006.3827,444.99115,0.99908745
+3510,2856.9775,255.80678,2875.6406,298.63123,0.9988506
+3510,2607.4373,318.18497,2640.7986,361.80508,0.9987319
+3510,2800.2078,749.3957,2839.0408,830.25446,0.99870694
+3510,2002.4205,208.3001,2018.9523,253.54625,0.9986773
+3510,3094.5361,268.33337,3113.2583,310.85278,0.99854904
+3510,3321.8396,372.16864,3349.8127,415.54718,0.998505
+3510,3826.9658,750.8775,3862.0361,809.4637,0.99847823
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+3510,3517.8477,529.4042,3543.4253,580.0929,0.99820936
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+3510,2021.8016,190.22327,2038.191,234.55865,0.9976942
+3510,2276.1213,131.89754,2293.0984,172.91103,0.99729735
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+3510,2904.28,343.82574,2939.6072,383.18793,0.9582716
+3510,2023.5653,915.5983,2060.4365,1010.6148,0.94679815
+3510,3918.7776,755.5423,3942.7341,821.3493,0.89247215
+3510,2925.5015,340.89163,2944.4883,388.9227,0.59883654
+3510,3770.381,776.13367,3797.8264,835.05676,0.32904202
+3510,3995.7432,746.0819,4024.2783,803.00183,0.10039853
+3511,2732.3596,632.1379,2775.0056,703.6164,0.9996635
+3511,1891.6284,340.57373,1916.2366,391.8543,0.99966335
+3511,2251.2139,585.0593,2295.8765,652.70935,0.99962914
+3511,1954.5636,306.5997,1978.9279,364.1423,0.999509
+3511,2771.152,466.82767,2821.0066,521.8562,0.99943954
+3511,2360.3416,639.46606,2408.449,716.54895,0.9994105
+3511,1198.1611,328.26056,1219.8123,384.54358,0.9993229
+3511,1975.5656,383.15414,2005.3588,444.92892,0.99924517
+3511,2405.879,238.50998,2429.7456,283.10184,0.9991893
+3511,2921.2786,423.9268,2945.9788,478.3187,0.9991424
+3511,1946.5769,123.26241,1963.575,159.93231,0.9990916
+3511,2660.616,434.87424,2703.4607,484.87375,0.9989548
+3511,2857.406,256.23602,2876.1384,298.7621,0.99889225
+3511,2608.0593,317.02332,2642.3342,361.71222,0.99887246
+3511,2798.3115,750.1598,2837.8345,834.7505,0.9987159
+3511,3826.92,751.63525,3862.521,809.6781,0.99857306
+3511,2002.5822,208.34634,2019.1898,253.94757,0.9985713
+3511,3094.56,268.44232,3112.9395,310.66156,0.99843997
+3511,3322.4653,372.31458,3349.9888,416.37775,0.9982834
+3511,2277.3323,131.73068,2293.592,173.25818,0.9982344
+3511,3518.701,530.89355,3544.4827,579.306,0.9976803
+3511,3288.0735,494.25204,3320.889,542.8119,0.99740714
+3511,2021.6082,190.59723,2037.9951,234.77667,0.997127
+3511,2920.5422,346.6236,2943.695,397.79614,0.9757937
+3511,2022.5482,915.93335,2059.981,1010.6555,0.9685101
+3511,2909.4573,345.2315,2943.1516,392.26868,0.9606229
+3511,3919.001,755.0853,3942.3184,821.04724,0.90369856
+3511,3770.0676,776.442,3796.2644,834.7595,0.8868645
+3511,2929.0168,344.9532,2943.721,387.52753,0.38946268
+3511,3996.0332,746.05383,4024.21,803.5659,0.20080614
+3512,2776.307,467.7273,2827.197,525.04205,0.99970394
+3512,2733.6838,631.74426,2774.4177,703.3253,0.99958426
+3512,1891.9227,341.32126,1919.64,392.49942,0.9995695
+3512,1954.456,307.3222,1978.6492,364.5547,0.99952054
+3512,2407.3596,238.56091,2431.2292,286.65295,0.999471
+3512,2359.1797,642.57166,2411.3813,718.7925,0.9994265
+3512,2254.5452,586.6358,2298.385,652.66693,0.99940956
+3512,2663.2205,435.62234,2708.9368,487.72397,0.9993272
+3512,1198.283,328.43744,1219.4924,384.41064,0.9993073
+3512,1977.595,383.48685,2004.6658,444.9648,0.99927264
+3512,2921.4253,425.49768,2946.0615,480.0307,0.9991948
+3512,3324.068,372.73175,3350.886,416.45374,0.9990681
+3512,1946.6277,123.26378,1963.7087,160.02144,0.9989881
+3512,2857.5757,256.11966,2876.524,299.269,0.998917
+3512,2797.3179,751.8093,2835.7559,835.6627,0.9987903
+3512,2609.154,316.0052,2644.5662,361.67847,0.9987857
+3512,2002.771,208.63092,2019.4795,253.88995,0.99852884
+3512,3300.6094,496.1984,3325.234,542.7041,0.9984909
+3512,3094.4019,268.87054,3112.6094,310.7743,0.99832994
+3512,3520.4167,533.11255,3544.86,579.2958,0.99827766
+3512,3828.0488,751.95917,3863.0132,809.7022,0.9981974
+3512,2020.8484,190.52374,2037.7405,235.00244,0.9973979
+3512,2277.8323,131.58572,2293.202,173.47421,0.9972037
+3512,2920.071,347.69794,2944.5496,398.47052,0.98762834
+3512,2022.4304,915.6714,2059.7742,1010.8894,0.9841552
+3512,3918.7395,754.63495,3941.784,820.86224,0.8499514
+3512,3768.2192,775.06433,3794.4927,834.5603,0.73377395
+3512,3995.3772,746.2211,4024.091,804.0572,0.5369286
+3512,2927.2366,345.13577,2944.4583,388.22147,0.48461336
+3513,2734.389,631.8898,2772.4954,703.1924,0.99969673
+3513,2359.5288,644.4257,2414.2974,721.01117,0.9994778
+3513,1953.257,308.16724,1978.445,365.72668,0.99945754
+3513,2669.4097,437.19138,2715.1245,491.0637,0.99943453
+3513,1979.3307,384.7063,2004.0199,444.97192,0.9993862
+3513,1198.2264,328.39575,1219.5607,384.21082,0.9993544
+3513,2259.0967,587.78735,2300.9243,652.1957,0.9993537
+3513,2409.2778,239.20511,2432.9487,288.88864,0.9993303
+3513,2782.04,469.1466,2833.1147,528.3121,0.999326
+3513,2921.7463,426.3014,2946.2356,481.88104,0.9990828
+3513,1946.8832,122.998856,1963.7064,159.99608,0.9989753
+3513,2857.328,256.26562,2876.8186,299.80585,0.99895906
+3513,1892.1877,342.363,1921.2664,393.1909,0.9989248
+3513,3827.6006,752.1148,3863.2915,809.9494,0.9988661
+3513,3326.2266,371.70203,3353.0425,416.6654,0.998823
+3513,2797.1196,752.8823,2834.7285,837.63965,0.9988217
+3513,2002.9044,208.93842,2019.3636,254.4208,0.99858475
+3513,2611.8923,315.48645,2646.2214,361.28027,0.99851006
+3513,3521.52,535.52203,3546.1675,579.64264,0.998259
+3513,3093.9124,268.988,3112.4084,310.83542,0.99812657
+3513,2020.293,190.50964,2037.7327,235.15399,0.99688005
+3513,2277.928,134.34659,2293.7654,174.2727,0.99647385
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+3523,2021.566,194.58421,2042.424,239.08257,0.99669707
+3523,2017.5968,915.7123,2058.3499,1011.99304,0.98247886
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+3524,1954.783,312.57275,1979.0735,371.15784,0.99948597
+3524,1983.1022,393.07645,2007.003,454.3467,0.9994777
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+3524,2789.7693,760.99524,2830.9285,847.6376,0.9990283
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+3524,2634.5894,319.9713,2667.2017,364.21704,0.9988078
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+3524,2002.2722,210.85385,2021.6709,256.26846,0.9986563
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+3524,2959.1401,358.58014,2981.2134,402.96362,0.9983614
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+3524,3376.101,524.7488,3397.1504,571.568,0.9979234
+3524,1948.4319,122.96542,1965.9097,160.03842,0.99782854
+3524,3342.322,378.66974,3369.528,425.00568,0.99779725
+3524,3834.491,752.2474,3864.241,810.10956,0.99729925
+3524,2921.883,353.60638,2951.9036,405.1593,0.997297
+3524,3529.8757,552.4755,3552.9846,596.00714,0.99723536
+3524,2280.2278,135.24742,2296.5603,175.30717,0.9970282
+3524,2021.5311,194.78447,2042.7189,240.22072,0.9961856
+3524,2017.3365,915.87964,2058.393,1011.90466,0.9845375
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+3525,1954.8307,313.99786,1978.9066,371.47473,0.9995272
+3525,1983.4294,393.37283,2008.3506,454.97263,0.99938035
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+3525,2882.6045,490.32706,2916.0615,545.1366,0.9986284
+3525,2001.9476,211.32167,2021.2787,256.24857,0.9985446
+3525,2752.4006,456.5977,2791.4285,505.16818,0.9984798
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+3525,2922.557,354.05,2953.678,405.73016,0.9981153
+3525,3088.4236,271.84158,3105.4607,312.33176,0.99809736
+3525,1948.3179,122.90961,1966.259,160.0408,0.99805427
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+3525,1911.0188,346.99194,1939.7192,398.5639,0.9975606
+3525,2280.4858,135.56958,2296.546,175.80383,0.9972926
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+3525,3379.0815,526.0119,3403.9087,571.5822,0.9969169
+3525,2021.8785,195.28006,2042.4432,240.27362,0.99679416
+3525,2017.2733,916.08185,2058.2173,1012.02386,0.9858974
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+3526,1954.972,314.93042,1978.8722,372.79126,0.999503
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+3526,1982.6661,394.64383,2009.6725,456.6359,0.99922824
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+3526,2001.6721,211.52887,2021.0632,256.58435,0.99848586
+3526,2924.4482,354.32922,2955.1714,405.5935,0.9984143
+3526,1948.4423,123.25179,1966.4137,160.01607,0.99827135
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+3526,2884.6045,490.98444,2922.2588,544.86395,0.9982664
+3526,1911.9441,348.24463,1941.5852,401.7624,0.99819034
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+3526,2281.2292,136.04247,2296.8743,175.60152,0.9972229
+3526,3834.5603,752.236,3863.82,809.6794,0.99712735
+3526,2022.1418,195.29242,2041.9456,240.46799,0.9967397
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+3526,2017.2214,915.99207,2058.1145,1012.6124,0.98255825
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+3527,1955.0037,315.44086,1978.6323,374.3042,0.9994916
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+3527,1980.9581,395.9084,2010.8304,458.6303,0.99933845
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+3527,2886.4631,491.6815,2928.7278,545.1714,0.9987669
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+3527,2002.1915,211.86964,2021.0328,257.19748,0.9986051
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+3527,3382.4468,528.8672,3417.2837,578.9197,0.9983461
+3527,2927.084,354.3222,2956.5361,405.47888,0.9982522
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+3527,1912.9896,349.2152,1942.3444,402.5522,0.99806124
+3527,3088.014,271.7073,3104.883,312.94382,0.9978752
+3527,2648.39,319.86163,2673.514,364.28253,0.9974486
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+3527,2022.2253,195.1481,2041.7524,240.47192,0.9961539
+3527,2017.1017,916.00946,2058.325,1012.6612,0.9880029
+3527,3719.8135,780.68774,3754.6382,841.86975,0.8937244
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+3528,1979.9526,396.89807,2012.2625,460.00385,0.999329
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+3528,2446.6333,648.0204,2483.7632,723.57556,0.9993013
+3528,1954.627,315.6756,1978.2925,375.18225,0.99926823
+3528,2786.2544,764.3343,2831.8906,854.5113,0.9992493
+3528,2741.0222,645.03326,2775.3655,720.1152,0.99924856
+3528,3386.4958,531.63824,3424.9988,583.7652,0.9988937
+3528,2868.424,263.2759,2888.2878,305.45145,0.998872
+3528,2765.2766,461.83176,2812.596,516.98303,0.9987686
+3528,2971.2922,364.70007,2989.3445,407.37183,0.99871993
+3528,2002.276,212.37164,2020.9252,257.45056,0.9986713
+3528,1948.8545,123.40029,1966.6392,160.10258,0.99866676
+3528,1915.2036,349.9808,1943.7778,402.56784,0.9983973
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+3528,2016.7556,916.04517,2057.973,1012.5084,0.98884016
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+3529,2741.926,645.07,2776.3152,719.8376,0.9993656
+3529,1954.7018,316.16187,1977.9628,375.21362,0.9993298
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+3529,1916.2368,350.6537,1944.0076,403.28314,0.9991665
+3529,2438.4172,255.63004,2460.14,304.7515,0.99916184
+3529,1980.0496,397.98914,2013.2458,460.58716,0.9991167
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+3529,2774.3235,464.49188,2818.1824,520.5139,0.99874085
+3529,2001.9095,212.71518,2020.5555,258.57462,0.9986809
+3529,1948.6567,123.35504,1966.6418,160.04984,0.9986204
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+3529,3353.072,380.38037,3370.8606,424.78693,0.9982508
+3529,2022.2625,194.99243,2039.4121,241.34799,0.9979564
+3529,2650.889,319.1093,2678.3376,364.15845,0.9977818
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+3529,2281.983,136.89838,2297.271,175.96707,0.9967062
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+3529,3527.916,559.999,3555.6616,604.49414,0.9963504
+3529,2016.31,915.983,2058.1396,1012.7066,0.9903709
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+3529,3719.4639,781.3337,3748.1099,842.1064,0.60403174
+3530,2315.9397,602.8217,2366.8362,674.7973,0.9997782
+3530,2448.8672,645.28064,2497.6304,722.19446,0.99954444
+3530,1954.3561,316.4611,1977.3251,375.6965,0.9993749
+3530,2743.7312,645.73645,2777.2102,720.105,0.99936885
+3530,2440.0593,257.72482,2461.4587,307.36246,0.9992711
+3530,2902.3716,497.97824,2951.6406,558.55273,0.99921274
+3530,1919.523,350.7546,1943.7319,403.67282,0.9991277
+3530,1980.1411,399.44098,2014.5005,461.52753,0.9990651
+3530,2924.075,434.94522,2947.3777,491.1992,0.99906164
+3530,1196.2042,333.92477,1218.5421,390.2225,0.9988865
+3530,2001.8525,213.07346,2020.1599,258.94904,0.99886715
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+3530,1949.1985,123.43437,1966.6311,160.0699,0.9985207
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+3530,2282.4067,138.58978,2297.3105,176.63449,0.99597883
+3530,3834.348,751.76,3862.8513,810.1698,0.9959688
+3530,2016.2883,916.10626,2058.1135,1012.80023,0.99357045
+3530,3526.0469,561.2064,3555.371,614.4192,0.99268085
+3530,3719.2166,780.68866,3746.407,842.189,0.9131531
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+3531,2326.148,604.485,2372.0005,674.9227,0.99961406
+3531,1953.9816,316.8455,1977.0934,375.60757,0.9994991
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+3531,2441.534,259.83615,2462.285,307.78366,0.9994149
+3531,1922.3912,351.36807,1943.9818,403.89438,0.9992748
+3531,1980.6315,400.7064,2015.1442,462.69156,0.99921393
+3531,2001.7135,213.71295,2019.7306,259.3744,0.9989498
+3531,2910.193,500.9637,2953.6687,562.02594,0.998752
+3531,1195.9153,334.04623,1218.3713,390.23953,0.9987423
+3531,2868.6829,263.59586,2889.0105,307.1077,0.9986921
+3531,3413.0686,540.4553,3439.4617,593.95044,0.99863654
+3531,2021.9623,195.96115,2038.7015,242.1373,0.9986179
+3531,1949.5708,123.55717,1966.6785,159.91373,0.99860126
+3531,2652.29,320.02896,2683.4482,365.35812,0.99841285
+3531,2976.4395,366.3722,2993.7705,408.5686,0.9983962
+3531,2937.3877,355.89914,2961.0396,407.16165,0.9983851
+3531,3084.1226,271.97888,3105.0708,313.47614,0.9981122
+3531,2786.0405,765.7002,2831.581,854.6974,0.9977977
+3531,3354.2964,381.29788,3372.1465,427.8713,0.996967
+3531,3833.885,752.18195,3862.556,810.58624,0.99654645
+3531,2795.968,469.57147,2825.4133,522.6388,0.99648523
+3531,2282.6543,139.62825,2297.496,177.00117,0.9961665
+3531,2015.9725,916.13855,2058.3792,1013.32117,0.9954184
+3531,3526.6438,562.0606,3555.0603,616.40216,0.9945825
+3531,3718.2742,779.88544,3744.5076,842.63116,0.9628419
+3531,3892.0115,764.1299,3920.7131,825.9862,0.35951033
+3532,2328.3926,606.26044,2377.0273,678.675,0.9997353
+3532,2454.7058,642.6949,2508.8376,716.72003,0.9996068
+3532,2747.5134,649.03436,2778.3645,724.6586,0.9995782
+3532,1953.5907,316.99402,1976.9967,375.505,0.9994728
+3532,2923.8733,436.113,2947.4226,492.2108,0.9994276
+3532,2442.6113,261.26898,2463.1777,307.88007,0.999231
+3532,1981.6438,401.66943,2015.8796,464.36072,0.9991047
+3532,1924.2318,351.77133,1944.2164,403.59964,0.9989568
+3532,2001.7125,214.23247,2019.2633,260.31516,0.99895585
+3532,2977.533,366.92773,2994.9006,412.06073,0.9989135
+3532,3420.8313,543.65704,3445.8835,594.96674,0.998895
+3532,2869.0674,263.6319,2888.915,307.08185,0.9988392
+3532,2022.1897,196.32349,2038.5923,242.19699,0.99879766
+3532,2654.1567,321.31952,2685.7432,365.97742,0.99859196
+3532,1195.6373,334.31998,1218.1969,390.26102,0.9985897
+3532,1949.5444,123.573,1966.7063,159.86191,0.9985783
+3532,2921.2524,502.37454,2959.3403,562.19073,0.998442
+3532,2785.8398,765.8106,2831.0757,854.37726,0.9984176
+3532,3354.087,382.08633,3373.9282,429.03964,0.99784756
+3532,2937.9976,357.08035,2962.046,408.2537,0.99780506
+3532,3083.377,272.21667,3105.1743,313.55695,0.9977829
+3532,2804.0222,471.8399,2830.9573,522.5416,0.9977564
+3532,3833.6348,752.30365,3862.144,810.862,0.9966259
+3532,2282.6628,139.86864,2297.6672,177.03017,0.9961461
+3532,2015.9451,916.1349,2058.3,1013.6216,0.99509466
+3532,3527.5398,562.88666,3554.1125,610.37384,0.99329996
+3532,3715.4868,779.59235,3742.8418,844.59564,0.93826973
+3532,3889.9875,764.6621,3918.6829,826.0242,0.50468576
+3533,2328.5623,607.09143,2385.0745,678.9619,0.99988544
+3533,1952.8501,317.19897,1977.0444,375.39563,0.99951386
+3533,2461.995,641.5533,2512.748,716.05597,0.9995061
+3533,2750.3843,651.45386,2778.349,727.1172,0.99944097
+3533,2923.7542,436.39124,2947.3333,492.43597,0.999373
+3533,1983.3877,402.76385,2016.2859,464.4505,0.9992124
+3533,3423.5034,545.35864,3450.646,599.07947,0.9992085
+3533,2443.3787,262.70837,2464.263,307.91675,0.9991166
+3533,2937.929,357.79114,2963.318,410.15808,0.99900764
+3533,2001.7107,214.88069,2019.2305,260.8259,0.9989274
+3533,1925.4442,352.5197,1945.7684,404.04224,0.9989121
+3533,2869.428,263.49454,2888.8245,307.02878,0.9987822
+3533,1949.6464,123.60483,1966.7096,159.76248,0.99855417
+3533,2021.8461,197.18365,2038.836,242.13956,0.99855036
+3533,2978.7227,367.13165,2995.9595,414.1624,0.99848074
+3533,1195.35,334.54044,1218.0426,390.24948,0.9984471
+3533,2656.354,322.69604,2687.706,366.3631,0.9983388
+3533,3353.8967,382.11646,3375.0032,429.48865,0.99827766
+3533,2808.2605,472.40494,2837.7908,523.13824,0.998038
+3533,2785.7607,766.1637,2830.5215,854.3773,0.9979717
+3533,3082.723,272.50845,3105.1877,313.00058,0.9973284
+3533,3528.2698,564.1384,3554.0217,611.8004,0.99725276
+3533,3833.2258,752.446,3861.777,810.37787,0.99718374
+3533,2933.5264,505.35217,2965.6294,562.15576,0.9969137
+3533,2282.8604,140.18625,2297.957,177.01062,0.99624205
+3533,2015.9222,916.2956,2058.4624,1013.6857,0.99441296
+3533,3709.0542,779.8457,3741.0547,844.8966,0.9263315
+3533,3888.0745,765.54376,3917.8157,828.51276,0.69952697
+3534,2331.558,607.8459,2391.3384,679.58514,0.9997919
+3534,2752.421,652.3875,2778.7754,728.2891,0.9995498
+3534,1952.2156,317.60162,1976.8599,375.49506,0.9994966
+3534,2923.6077,436.45804,2947.445,492.4689,0.9993861
+3534,2469.2644,641.68066,2519.486,716.3268,0.9993212
+3534,1984.2972,403.21616,2016.7001,465.01706,0.9992835
+3534,3427.1116,547.4181,3452.7483,603.41125,0.99921334
+3534,2001.7668,215.02008,2019.4539,260.83328,0.9989365
+3534,1925.725,354.41278,1949.1532,405.1574,0.99888414
+3534,2869.5388,263.44925,2888.6848,307.1201,0.9988543
+3534,2937.9033,358.9349,2964.7886,411.03854,0.99883133
+3534,2979.6045,367.7082,2996.8447,414.9702,0.9987753
+3534,2443.7234,263.52786,2465.0818,308.84427,0.998733
+3534,1195.4147,334.7486,1217.5067,390.2063,0.99859613
+3534,1949.6476,123.64048,1966.7452,159.80281,0.99855703
+3534,2785.2605,766.0076,2829.8635,854.57416,0.9984842
+3534,3354.1833,382.7111,3375.3367,430.09482,0.9983632
+3534,2021.767,197.27261,2038.5851,243.61923,0.99809474
+3534,2661.0654,323.8307,2690.037,366.5431,0.9979377
+3534,2941.261,505.8836,2973.3723,562.06146,0.99753296
+3534,3081.6755,272.21585,3104.9094,313.6553,0.997514
+3534,3832.1243,752.6849,3861.5837,811.1336,0.9972953
+3534,2809.911,473.74902,2843.9475,523.13367,0.9963636
+3534,2283.133,140.19685,2298.0115,176.95253,0.9959836
+3534,2015.7915,916.1869,2058.4568,1013.829,0.99588996
+3534,3526.6187,566.6333,3556.062,616.9061,0.9955722
+3534,3703.7937,780.4888,3738.5203,845.6947,0.90856236
+3534,3887.9646,767.6285,3916.3967,830.18036,0.7725412
+3535,2334.601,608.7372,2396.5493,679.6273,0.99981874
+3535,2752.3923,653.7045,2778.9343,729.561,0.99955374
+3535,1951.8138,318.5037,1977.0687,376.67087,0.9995066
+3535,2923.0342,436.60806,2947.4668,492.61758,0.9993685
+3535,3432.252,549.83124,3458.187,606.7746,0.9993426
+3535,2444.9539,264.11426,2465.8147,312.0528,0.9993007
+3535,1986.5752,403.48886,2017.011,464.9448,0.99923074
+3535,2784.6624,766.41327,2828.552,854.4882,0.99913675
+3535,2813.2744,475.58026,2851.3262,529.5899,0.9990688
+3535,2980.723,369.64633,2997.9446,417.10318,0.99900705
+3535,2869.4727,263.42688,2888.5376,307.04633,0.9989912
+3535,2478.861,641.5325,2521.714,716.3335,0.99898297
+3535,1925.8141,355.53528,1953.3678,406.22607,0.99896574
+3535,2001.5026,215.16907,2019.5516,260.85,0.9989146
+3535,1195.3835,335.05933,1217.4932,390.18488,0.9987618
+3535,2943.591,507.27625,2981.0955,565.2615,0.9987488
+3535,2938.568,360.1255,2965.6199,412.15137,0.9985801
+3535,1949.649,123.64364,1966.744,159.80365,0.9985588
+3535,3080.269,272.4277,3104.4116,313.6866,0.9985555
+3535,3354.095,383.13303,3376.3357,430.95718,0.9983486
+3535,2021.2516,197.43144,2038.3876,243.79375,0.9981055
+3535,3830.1106,753.4471,3860.9236,812.3614,0.9979709
+3535,2665.5579,323.92154,2693.2898,366.83548,0.9970162
+3535,2282.983,139.9598,2298.232,176.96986,0.9961308
+3535,2016.2767,916.1701,2058.7625,1013.98517,0.9958775
+3535,3522.8489,568.60626,3558.4104,621.10345,0.9913933
+3535,3699.8455,780.9949,3735.8508,846.609,0.8792904
+3535,3888.1301,768.5381,3915.3567,829.69885,0.62032866
+3536,2342.3875,609.2805,2400.7659,680.37976,0.9997873
+3536,2751.5024,654.2402,2778.774,730.47516,0.99949515
+3536,1951.6508,319.69662,1976.9371,377.8983,0.9994696
+3536,2445.8582,264.90344,2466.6746,314.19763,0.9993754
+3536,2922.0803,437.24088,2947.2903,492.7499,0.9993612
+3536,2782.6548,767.64545,2827.1367,854.87164,0.99931943
+3536,3437.6526,553.6457,3462.761,610.4151,0.99917233
+3536,1988.8953,403.9616,2017.1028,464.87866,0.9991704
+3536,2001.455,215.11269,2019.2692,260.9116,0.99914277
+3536,2816.1177,478.20804,2862.0762,533.3421,0.9991014
+3536,2869.42,263.6464,2888.127,307.62967,0.9990816
+3536,2947.2744,508.77097,2988.058,572.9164,0.9989612
+3536,2491.2222,640.23755,2528.8516,716.1184,0.9989361
+3536,1195.2542,335.2665,1217.271,390.8337,0.998869
+3536,2980.8228,372.05463,2999.315,417.47034,0.99879396
+3536,2940.5007,360.8693,2966.779,413.07028,0.99875975
+3536,3079.6394,272.4167,3103.095,314.07947,0.9985967
+3536,1949.6545,123.647644,1966.7424,159.75983,0.99856925
+3536,1925.8383,357.25125,1956.7789,407.65262,0.99848455
+3536,2020.5906,197.89615,2038.1697,243.76498,0.99831784
+3536,3354.9326,387.58087,3376.8457,431.508,0.99760395
+3536,2671.3367,323.92593,2694.5896,367.06277,0.99722207
+3536,3829.0762,754.49915,3860.5303,812.8425,0.9970521
+3536,3521.3367,571.35004,3563.2268,622.32184,0.99626297
+3536,2283.1504,140.00237,2298.5972,177.12321,0.9961733
+3536,2016.8748,916.3358,2059.247,1013.97095,0.99616206
+3536,3699.7993,782.5888,3732.8892,846.99615,0.82957816
+3536,3887.483,768.3085,3914.2754,829.4354,0.5149628
+3537,2354.5488,610.7217,2406.7886,680.64905,0.99955493
+3537,1951.7102,320.91382,1977.3232,379.05975,0.9994754
+3537,2750.9016,654.2831,2778.6443,730.4199,0.9994691
+3537,2446.3137,266.6092,2467.5935,315.87054,0.9994037
+3537,1991.0903,405.00592,2016.7917,465.85175,0.99930537
+3537,2780.688,767.5628,2826.0596,854.97955,0.9992465
+3537,2921.6929,437.86264,2946.8828,493.0021,0.99922705
+3537,1195.0955,335.91293,1217.397,391.34238,0.99910957
+3537,2823.196,480.77417,2869.1184,535.768,0.9991014
+3537,2869.6917,263.80075,2887.9338,308.2394,0.99909604
+3537,2000.9553,215.02664,2019.0298,261.0047,0.99905306
+3537,3442.877,556.2727,3465.1582,611.01074,0.9990277
+3537,2981.7722,372.67145,3001.0208,418.20178,0.9988809
+3537,2951.15,511.19778,2995.399,575.61426,0.99884665
+3537,1926.4493,358.7859,1955.9554,408.34216,0.99878913
+3537,2942.5222,361.5347,2967.633,413.16806,0.99875724
+3537,2674.0522,324.2492,2695.834,368.27594,0.9987062
+3537,1949.6794,123.648224,1966.7368,159.7457,0.99856
+3537,3355.6028,387.0439,3378.7043,432.59207,0.9985006
+3537,3079.5044,272.194,3102.0356,315.2772,0.9983209
+3537,2019.8031,198.19633,2038.1559,243.86235,0.99776626
+3537,2497.0005,638.02344,2534.8442,715.9845,0.9977513
+3537,3829.1592,754.74225,3860.3784,812.31586,0.9969409
+3537,3522.152,572.2308,3561.9426,623.94,0.99688077
+3537,2283.1099,140.05656,2298.893,177.05714,0.99628747
+3537,2017.171,916.4988,2059.8257,1014.18494,0.99618775
+3537,3700.08,784.0466,3730.0098,846.78815,0.72617257
+3537,3886.6018,768.8011,3914.0027,830.4489,0.6973207
+3538,2369.2986,611.3458,2414.619,680.58716,0.9997142
+3538,2749.63,654.01245,2778.1821,730.3623,0.9995629
+3538,3447.7114,558.4318,3468.541,611.6375,0.9993772
+3538,1951.6536,322.12534,1977.8357,379.2286,0.9993338
+3538,1993.0576,406.06277,2017.3054,467.24057,0.99924845
+3538,2832.7407,485.09375,2873.1738,538.99646,0.9992378
+3538,1195.175,336.75116,1217.3691,391.76984,0.9991559
+3538,2956.75,513.52374,3002.0547,577.7868,0.99914813
+3538,2447.655,268.68527,2468.595,317.59494,0.99913245
+3538,2498.605,635.3524,2542.416,716.28564,0.99908817
+3538,2921.5762,438.34222,2946.396,492.7467,0.99906737
+3538,3356.9236,387.4676,3378.8542,432.8692,0.99905294
+3538,2869.7437,264.1138,2888.0386,308.8306,0.99899894
+3538,2779.1643,769.1221,2824.8904,855.3224,0.99891603
+3538,2000.2687,215.0828,2018.7826,260.67096,0.9988914
+3538,2982.175,373.6925,3002.2268,418.12183,0.99888605
+3538,1949.7108,123.56219,1966.5795,159.5892,0.9986765
+3538,2675.3083,324.49603,2697.3674,370.05457,0.9985795
+3538,2946.0867,362.04803,2968.6228,413.3225,0.99841404
+3538,1927.2396,359.73352,1956.8986,408.8294,0.99834406
+3538,3079.5981,272.34637,3100.8403,314.6371,0.9980088
+3538,2019.9099,198.10252,2037.9968,244.39674,0.99759394
+3538,3828.9304,755.0982,3859.49,812.9045,0.9966762
+3538,2282.9436,140.69786,2299.4763,177.63658,0.99662423
+3538,2017.5355,916.31696,2060.2822,1014.41376,0.9963421
+3538,3523.926,574.9649,3556.7825,622.7923,0.99573416
+3538,3699.182,783.7132,3727.5105,846.6977,0.9336815
+3538,3886.2793,768.5766,3913.5127,831.1175,0.43082726
+3539,2373.3433,611.2371,2424.1714,680.9057,0.9997472
+3539,2749.3152,653.84357,2778.052,730.2631,0.9996197
+3539,1951.6766,323.26138,1977.975,381.0842,0.9992855
+3539,1994.2439,407.50693,2017.8916,468.78024,0.99919194
+3539,2920.8516,438.89578,2945.7065,492.58377,0.9991566
+3539,1195.123,336.67688,1216.9363,391.4048,0.99914205
+3539,2869.6602,264.56885,2888.1333,309.38348,0.9990808
+3539,2778.553,769.13104,2823.7488,856.80975,0.9990478
+3539,2501.4592,633.1439,2544.9006,714.2024,0.9990252
+3539,1999.9323,215.09108,2018.5873,260.7178,0.9990019
+3539,2678.0679,325.45724,2699.5718,371.33975,0.99895525
+3539,2448.7463,270.50598,2469.7693,317.50824,0.9989365
+3539,3451.127,559.263,3472.9785,612.2177,0.99885607
+3539,2846.193,488.16534,2874.5579,540.0264,0.99877495
+3539,2982.9238,373.74527,3004.1108,418.41025,0.99875355
+3539,1949.7488,123.63377,1966.5623,159.55,0.99868804
+3539,3358.5283,388.40082,3379.566,433.4228,0.99867886
+3539,2950.43,362.43448,2969.5867,413.07303,0.99865377
+3539,2962.8462,516.09985,3008.2075,579.8685,0.9985153
+3539,3079.5266,272.39432,3099.2952,314.53122,0.99833435
+3539,1928.0006,361.35992,1957.2557,408.93976,0.9981109
+3539,2020.008,198.0112,2037.9836,244.24918,0.99799395
+3539,2018.1034,916.452,2060.7927,1014.57935,0.997244
+3539,3828.4995,755.373,3858.5415,813.19354,0.99661106
+3539,2282.8638,140.97592,2299.5283,177.93146,0.99636555
+3539,3526.9658,575.3025,3555.7861,622.9033,0.99552155
+3539,3697.9055,783.2899,3725.0994,846.3733,0.914206
+3539,3885.461,768.1105,3913.0283,834.20825,0.22024469
+3540,2747.9612,653.2065,2777.012,730.4466,0.999645
+3540,2373.9338,610.40314,2431.6335,680.39813,0.9995826
+3540,2920.2117,439.64435,2945.1516,493.82605,0.99925166
+3540,1952.1642,324.1579,1977.8341,382.04126,0.9992291
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+3543,1954.3475,325.2949,1978.3075,383.3948,0.9993777
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+3567,1974.8613,227.30891,1995.313,272.3762,0.99917245
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+3567,1942.749,122.52742,1959.7048,159.42696,0.9986486
+3567,1982.3175,389.37656,2023.3282,443.26376,0.99862766
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+3567,1972.4034,348.2917,2010.9086,398.24872,0.99795747
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+3567,2011.0472,425.85913,2052.975,485.79596,0.9975896
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+3567,2942.5854,377.40735,2962.582,430.87964,0.99647516
+3567,2028.4137,919.4789,2070.0083,1015.2258,0.99248016
+3567,2681.5403,347.6926,2701.9104,394.09323,0.9882569
+3567,3642.325,786.63983,3673.864,849.3735,0.82732207
+3567,3833.0688,777.6579,3860.6064,838.2968,0.66768634
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+3568,2479.776,579.07745,2526.6323,642.87823,0.99972373
+3568,2432.4548,303.21338,2454.9988,358.78888,0.99962044
+3568,2685.9316,648.16327,2723.9268,723.075,0.9996195
+3568,2868.5908,534.5719,2905.1206,596.10486,0.9995714
+3568,1180.3833,341.71643,1202.7888,397.0605,0.9993718
+3568,3063.2817,573.3344,3089.4595,639.1507,0.99927
+3568,2852.9885,453.45724,2884.8752,505.8738,0.9992538
+3568,2976.2378,403.55307,3004.6167,455.72604,0.99917066
+3568,1973.5764,228.94379,1993.9841,272.25455,0.99914587
+3568,2576.0383,563.0418,2612.7737,638.0683,0.9990922
+3568,2752.152,754.6227,2790.4895,844.8268,0.9990119
+3568,2263.488,141.06213,2283.2263,182.50305,0.99894077
+3568,3488.5547,569.6596,3519.269,626.0727,0.99889827
+3568,3465.089,601.9839,3495.702,660.8108,0.99889743
+3568,1985.5607,387.8842,2028.7833,441.75888,0.99865156
+3568,1942.6561,122.182846,1959.6417,159.12355,0.9985683
+3568,2006.6014,214.47278,2027.0511,260.52466,0.99837613
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+3568,2852.6882,273.38467,2872.9763,316.2285,0.9978369
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+3568,2013.1548,425.81207,2055.2266,485.56683,0.9972325
+3568,3348.429,392.60776,3366.8064,439.3298,0.9965938
+3568,2680.0679,345.92032,2701.484,394.29727,0.9951924
+3568,1973.2214,349.88235,2017.1101,397.25357,0.9951735
+3568,3785.908,767.8116,3821.7458,820.368,0.9921119
+3568,2028.4517,919.5118,2069.8828,1015.2354,0.9919344
+3568,3828.3992,776.7281,3857.821,838.12775,0.8679497
+3568,3642.4722,786.54333,3672.7964,849.7888,0.7983331
+3568,3966.0295,750.3871,3998.8079,809.9241,0.20499282
+3568,2676.8354,337.00153,2697.023,380.03046,0.19780819
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+3569,2476.038,576.22974,2525.1367,642.1266,0.999711
+3569,2685.9238,647.5036,2722.463,722.74664,0.99966264
+3569,1973.6367,350.69376,2023.6404,400.60898,0.99953485
+3569,2849.3013,453.9201,2880.8315,507.7973,0.9993864
+3569,2574.112,558.8825,2609.2234,631.55914,0.99925596
+3569,1178.9159,341.89084,1202.1342,397.27286,0.99922836
+3569,2430.9033,305.5886,2453.6118,360.83237,0.9991273
+3569,1971.7812,230.28677,1993.3616,272.82504,0.9991248
+3569,3063.4446,575.514,3089.2908,639.4355,0.9990952
+3569,2262.7695,140.8079,2282.901,182.62868,0.9990915
+3569,2749.7778,753.79065,2789.2861,845.0359,0.9990146
+3569,3486.3528,569.7382,3516.119,625.31866,0.99873
+3569,2974.6587,403.97684,3001.8584,456.57645,0.9987255
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+3569,1991.9994,388.1553,2033.2103,440.11136,0.99861765
+3569,2005.969,215.33301,2024.44,261.36713,0.9985636
+3569,1942.7715,122.14442,1959.3567,159.20584,0.9985524
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+3569,2014.3969,422.95508,2057.4817,485.3791,0.996923
+3569,2028.4695,919.5302,2069.7664,1015.2496,0.9915935
+3569,3781.793,767.82086,3818.7402,820.70294,0.99131536
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+3570,2003.7972,216.43472,2022.7697,260.9478,0.99896276
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+3570,1970.6122,231.13217,1992.4398,274.9514,0.99888486
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+3570,3458.9045,603.0751,3487.843,660.7634,0.99846977
+3570,1942.8892,122.32521,1958.9895,159.0259,0.99827385
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+3570,3346.0923,390.35184,3363.7012,439.83334,0.9973769
+3570,2014.0193,421.668,2057.95,485.17014,0.9972728
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+3571,1942.8922,122.467316,1958.9508,159.03897,0.9983639
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+3572,1967.7985,232.54237,1991.7804,275.5509,0.99931514
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+3572,2259.3198,142.42825,2279.2285,182.84778,0.99862325
+3572,2001.0964,217.9089,2019.9905,261.08295,0.9985777
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+3573,1942.8291,122.875694,1959.3022,159.01495,0.9983752
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+3574,1997.3058,218.08073,2017.7035,262.7307,0.99909985
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+3574,2003.4647,354.95105,2041.7491,408.12738,0.9990356
+3574,1942.7289,122.87877,1959.6871,158.97606,0.99850035
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+3894,3113.2295,356.52606,3136.9907,406.07007,0.99846125
+3894,2420.7126,316.2166,2438.367,367.29608,0.9984133
+3894,2403.6611,287.24673,2433.1245,336.7938,0.99822634
+3894,2506.3965,211.79395,2530.1294,256.63232,0.9981584
+3894,1915.6707,125.10942,1929.4541,161.40111,0.9980527
+3894,2438.5315,164.26459,2453.386,204.99405,0.9977254
+3894,1061.2013,324.8536,1107.1976,382.4891,0.9975407
+3894,2325.8215,347.71024,2352.8093,409.25266,0.99417263
+3894,3170.8857,867.019,3215.561,942.0861,0.9909827
+3894,3675.1475,793.21204,3716.5469,865.1854,0.99071246
+3894,3509.7754,798.8264,3548.5771,868.5016,0.98363113
+3894,2020.2537,924.1393,2063.657,1018.42615,0.57737404
+3894,1000.3963,150.46881,1014.1524,187.76938,0.11135137
+3895,2897.513,664.6816,2946.9045,738.6236,0.99963
+3895,1985.5878,585.6671,2019.2953,659.3613,0.9996252
+3895,1796.088,288.71127,1816.6586,345.36423,0.9994599
+3895,1688.3044,494.3307,1712.665,564.74695,0.9993687
+3895,2069.3027,340.5215,2096.8188,396.16934,0.9992661
+3895,2089.0625,410.69293,2107.1147,472.79974,0.99924517
+3895,2815.9382,578.96906,2853.2415,649.9576,0.9991775
+3895,2609.7146,304.97278,2633.7092,358.89056,0.99916255
+3895,1663.145,375.13593,1689.0562,434.00287,0.99915147
+3895,2631.3655,460.85834,2656.758,521.71814,0.9991172
+3895,1712.7683,226.67566,1730.8723,273.15314,0.9989644
+3895,2023.6357,310.99283,2045.3547,369.616,0.9988513
+3895,2042.3225,261.23083,2061.7737,316.57697,0.9988462
+3895,2714.303,428.75867,2736.3425,488.57874,0.9988254
+3895,1915.1819,124.914665,1928.9844,160.89691,0.9985896
+3895,2321.5657,405.92352,2347.5198,470.7403,0.9985202
+3895,2506.8105,211.66866,2529.707,256.50647,0.99847215
+3895,1829.6537,267.54672,1855.0292,318.33273,0.9982498
+3895,3114.8242,356.38907,3137.2842,406.303,0.9982121
+3895,2403.5098,287.16324,2432.6733,336.75345,0.99816185
+3895,2421.1748,316.07538,2438.5322,367.31415,0.9981312
+3895,1060.6824,324.97247,1107.1414,382.5531,0.9972819
+3895,2437.846,163.81732,2452.7478,205.13861,0.99727374
+3895,2325.0466,347.67026,2352.174,408.1578,0.9950004
+3895,3170.7922,867.14215,3215.5652,942.21063,0.9931427
+3895,3509.4614,799.0403,3546.1597,869.28674,0.98045266
+3895,3680.3052,792.04584,3718.6724,864.39386,0.95798486
+3895,2020.2428,924.1348,2063.6597,1018.418,0.5774101
+3895,2580.719,865.3401,2624.8752,948.34766,0.2975372
+3895,1000.3995,150.4653,1014.14825,187.77286,0.10936952
+3896,2899.6167,665.20917,2947.3296,738.62067,0.99970895
+3896,1985.5453,586.6785,2019.3156,661.2786,0.9996124
+3896,2068.4688,340.3637,2096.2515,396.20306,0.9996075
+3896,1664.4227,374.77975,1690.2037,434.1295,0.99951637
+3896,1796.1028,288.72012,1816.8682,345.4208,0.99940884
+3896,1688.1364,494.43155,1712.7098,564.7344,0.9993173
+3896,2088.8828,410.75827,2107.731,472.6957,0.9992931
+3896,1714.9446,226.5066,1731.2322,273.36526,0.9992249
+3896,2609.2224,305.05914,2633.55,358.8216,0.9991518
+3896,2818.6733,578.8058,2853.4634,649.9088,0.9991517
+3896,1829.9132,267.6985,1855.5753,318.42114,0.99901986
+3896,2714.383,428.62305,2735.403,488.49457,0.9988564
+3896,2023.32,311.2731,2045.1796,369.57352,0.9988386
+3896,2506.8455,211.52623,2529.3708,256.7522,0.9986657
+3896,2321.8555,405.87415,2347.4253,470.65735,0.998552
+3896,1914.9316,124.74634,1928.6653,160.53387,0.9982957
+3896,2403.2112,287.14145,2432.4001,336.78183,0.9982217
+3896,2042.268,262.47888,2061.5823,316.70752,0.998161
+3896,2423.897,316.83612,2437.853,366.88287,0.99813324
+3896,2630.2058,460.7749,2656.1013,521.80634,0.99810874
+3896,3115.1086,356.30936,3137.6936,406.56516,0.9980495
+3896,2437.5366,163.57655,2452.4463,205.25743,0.99732155
+3896,1060.1637,325.09274,1107.2074,382.62393,0.99722326
+3896,2324.8303,346.9995,2351.3474,406.09717,0.99708503
+3896,3170.789,867.30536,3215.6807,942.1414,0.9922065
+3896,3509.332,799.5598,3543.8633,868.67065,0.98560727
+3896,3686.7068,791.80414,3721.1287,862.3077,0.7111883
+3896,2020.2291,924.1275,2063.6555,1018.4001,0.5900478
+3896,2580.812,865.0128,2621.9873,948.56177,0.29192254
+3896,1000.4159,150.45816,1014.1465,187.77103,0.106256604
+3897,2068.1257,340.4878,2095.1926,396.1999,0.99963105
+3897,1985.5964,587.5009,2019.2373,662.9937,0.9996137
+3897,2904.1562,665.26166,2947.4834,738.89166,0.9996008
+3897,1666.1199,374.6964,1691.94,434.18817,0.9994923
+3897,1796.2433,288.60748,1817.1332,345.42627,0.9994406
+3897,2628.3845,460.89532,2655.345,521.9959,0.9993905
+3897,1688.1139,494.53345,1712.9318,564.66455,0.99938536
+3897,1716.6006,225.8698,1731.9417,273.4559,0.9993364
+3897,2089.1294,410.5786,2108.0396,472.6568,0.99930525
+3897,2609.1138,305.05334,2633.1187,358.8546,0.9991743
+3897,2820.8857,578.7733,2853.6665,650.9756,0.99915254
+3897,1830.7345,267.44388,1855.8358,317.52603,0.998931
+3897,2023.2268,311.4188,2044.9358,369.801,0.99890006
+3897,2506.8345,211.33499,2529.2686,256.7346,0.998776
+3897,2714.1836,428.72754,2734.605,488.73102,0.99872226
+3897,2321.35,405.73825,2347.3252,470.79727,0.9986902
+3897,2042.179,262.74155,2061.6184,316.49417,0.99849784
+3897,3116.1746,356.2352,3137.7317,406.72354,0.9984363
+3897,2403.3384,286.7615,2432.5098,336.7491,0.9981096
+3897,2421.4492,316.22488,2438.5884,367.48203,0.997938
+3897,1914.3121,124.51402,1928.7152,160.87392,0.9976083
+3897,1060.3135,325.19757,1106.9841,382.6045,0.9970028
+3897,2435.0315,163.28783,2452.1165,204.74318,0.9969831
+3897,2324.5774,346.56342,2350.4495,406.94476,0.9958662
+3897,3170.789,867.40497,3216.2417,942.1114,0.9942669
+3897,3509.2637,799.52106,3542.795,868.6429,0.97637606
+3897,2020.2183,924.1074,2063.6812,1018.4022,0.58019567
+3897,3689.2266,793.0309,3722.5728,863.0183,0.42510754
+3897,2580.6458,864.7469,2619.4148,948.4704,0.12831411
+3897,1000.4049,150.42242,1014.1698,187.7937,0.1272959
+3898,1667.6862,375.04333,1695.1512,434.99683,0.999666
+3898,1985.6737,588.28723,2019.1349,665.1173,0.99959546
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+3898,2908.9473,664.74176,2947.6802,738.731,0.9995271
+3898,1796.4795,288.6567,1817.9719,345.43124,0.9995208
+3898,1717.2424,225.83626,1732.6208,273.52515,0.99941444
+3898,2089.304,410.54425,2108.6345,472.50958,0.99928504
+3898,2506.5623,211.17769,2527.932,256.70752,0.9992384
+3898,2823.97,578.6315,2853.9084,651.0217,0.99920976
+3898,2608.9485,305.16388,2633.0686,359.0191,0.9991916
+3898,2626.6274,460.81226,2654.8052,521.9867,0.999186
+3898,2067.6243,340.49557,2094.3425,396.1564,0.9990526
+3898,1831.421,267.25824,1856.0201,316.73236,0.99890184
+3898,2023.1702,311.6119,2044.8042,369.83334,0.9987943
+3898,2713.901,428.68967,2734.0,488.8068,0.9986981
+3898,2042.8585,262.9666,2062.1497,316.6834,0.9986902
+3898,2321.134,405.28574,2347.237,470.90073,0.9986179
+3898,3116.5337,356.10062,3137.747,406.91263,0.9983646
+3898,2403.477,286.50098,2432.8394,336.72583,0.9981054
+3898,2421.5247,316.03717,2438.7097,367.3725,0.9979303
+3898,2432.9377,162.98198,2451.924,204.68541,0.99732274
+3898,1059.8282,325.32462,1107.1923,382.61832,0.99702793
+3898,1913.8336,124.783394,1928.2604,160.92429,0.99695337
+3898,2323.5718,346.28775,2350.227,405.71262,0.99590755
+3898,3170.778,867.4994,3216.471,942.12915,0.9951768
+3898,3509.3796,799.4117,3541.3171,868.7271,0.98421824
+3898,3689.5996,793.4581,3724.0796,864.01074,0.8331648
+3898,2020.2462,924.1049,2063.6917,1018.414,0.5524948
+3898,2580.7742,864.2337,2616.573,948.2802,0.14019962
+3898,1000.3998,150.42346,1014.17285,187.80435,0.12942965
+3899,1668.7277,376.30832,1698.5145,435.22153,0.9997049
+3899,1688.2831,494.82266,1713.4718,564.52844,0.99959356
+3899,1985.9944,588.7006,2019.7739,665.71967,0.9995407
+3899,2914.3328,664.86383,2947.4783,738.6558,0.9994929
+3899,1796.6486,288.56293,1818.2277,345.42743,0.99947685
+3899,2506.6045,211.02615,2527.2373,256.72806,0.99935585
+3899,1717.7452,225.21243,1733.4479,273.49716,0.9993494
+3899,2624.4612,460.99765,2654.1477,521.89624,0.9993425
+3899,2608.6567,305.03925,2632.1582,359.13208,0.99932396
+3899,2088.0308,410.5255,2109.1235,472.30597,0.99928814
+3899,2826.9624,578.8624,2854.5063,651.0978,0.99911433
+3899,2067.1821,340.096,2093.3774,396.18225,0.9990652
+3899,1832.8121,266.87677,1856.2643,316.80054,0.9990038
+3899,2321.4153,405.77728,2346.97,470.92535,0.9987644
+3899,2023.1727,311.83856,2044.5685,369.85345,0.9986206
+3899,2043.071,263.44022,2062.7395,316.27145,0.9985689
+3899,2713.1929,428.9483,2733.0205,488.72437,0.9983424
+3899,2403.485,286.18076,2432.819,336.83273,0.99825734
+3899,3116.7944,355.8918,3137.796,406.84262,0.9982326
+3899,2421.6216,315.77905,2438.7622,367.34717,0.99819225
+3899,2432.8213,162.84204,2451.6958,204.63303,0.9971426
+3899,1059.7941,325.49695,1107.1381,382.64117,0.9965918
+3899,1913.0293,124.61914,1928.7139,161.13266,0.99508685
+3899,3170.7761,867.47125,3216.5754,942.0164,0.9941188
+3899,2323.1099,345.78033,2349.4414,405.0971,0.9926474
+3899,3509.2197,798.602,3540.7915,868.5507,0.9751618
+3899,3690.0125,795.3322,3725.173,864.3476,0.6330803
+3899,2020.2457,924.10547,2063.6855,1018.41296,0.5478035
+3899,2580.7979,864.8474,2612.0586,948.90796,0.48467046
+3899,1000.4077,150.41417,1014.16125,187.80766,0.13240926
+3900,1669.3379,376.99973,1701.1936,436.29465,0.9996848
+3900,1688.249,494.81512,1714.7925,564.5313,0.9996846
+3900,1987.8977,588.68134,2020.936,665.8411,0.99952424
+3900,1796.6941,288.4581,1818.867,345.45425,0.9994518
+3900,2918.8396,664.8482,2948.6658,738.62714,0.99941814
+3900,2608.2039,304.98004,2630.9875,359.2746,0.99940956
+3900,2506.5288,210.93738,2527.0781,256.74988,0.99934214
+3900,1718.4242,225.14995,1735.6525,273.06857,0.99928194
+3900,2829.528,578.87006,2854.9202,651.50165,0.9992642
+3900,2623.4119,460.87378,2654.012,522.0731,0.99922955
+3900,2066.855,340.47882,2092.9834,396.0503,0.9991014
+3900,2084.5884,410.48166,2109.6587,472.4252,0.99888116
+3900,2044.7056,263.65738,2063.081,315.91837,0.99884546
+3900,2023.2008,311.84793,2044.3683,369.88095,0.99869066
+3900,2321.7764,405.48822,2346.725,470.922,0.9985532
+3900,1834.6396,266.1407,1856.6057,315.93677,0.9985001
+3900,2403.4622,285.92194,2432.907,336.71014,0.9983027
+3900,2712.1228,429.01157,2732.2786,488.74484,0.9982223
+3900,2421.4194,315.70084,2438.8887,367.34946,0.9980519
+3900,3116.8914,355.82047,3137.824,406.8074,0.9980349
+3900,2432.7415,162.62521,2451.386,204.69489,0.99693716
+3900,1059.2842,325.5466,1107.2009,382.62552,0.9966389
+3900,2321.7275,344.72952,2349.224,404.14853,0.9954586
+3900,3170.764,867.3534,3216.6204,942.0027,0.9943639
+3900,1912.0409,124.46092,1928.8033,161.01375,0.99394906
+3900,3509.0652,798.4902,3540.1462,868.6065,0.9694773
+3900,3689.5637,795.69525,3727.2239,864.11395,0.76116437
+3900,2580.627,864.40924,2610.4106,948.257,0.72886246
+3900,2020.2319,924.0883,2063.7058,1018.3909,0.5454787
+3900,1000.4091,150.41718,1014.1346,187.79532,0.1338281
+3901,1688.5345,495.1398,1716.098,564.6654,0.99976605
+3901,1669.8964,377.52878,1701.6014,436.92184,0.9997017
+3901,2924.1555,665.24396,2949.9202,738.54877,0.9994861
+3901,1796.764,288.49307,1819.0204,345.4152,0.99940884
+3901,1989.8235,588.8121,2022.7422,665.8774,0.9993845
+3901,2607.9849,305.23114,2630.31,359.153,0.99937266
+3901,2506.5864,210.7042,2526.8887,256.53625,0.9993604
+3901,2829.5317,578.932,2855.211,652.307,0.9993181
+3901,2622.7075,460.93393,2653.4653,522.13385,0.9991654
+3901,1718.8276,224.65286,1737.4973,273.2673,0.9991037
+3901,2066.972,340.3511,2092.9036,395.44125,0.9989587
+3901,2082.0098,410.63184,2109.98,472.07886,0.99892575
+3901,2023.1659,311.88202,2044.3563,369.87628,0.9986651
+3901,1837.5936,265.47018,1856.8646,315.71152,0.9986085
+3901,2710.3445,429.10287,2731.5593,488.74234,0.99848735
+3901,2403.792,285.69452,2433.12,336.6734,0.99830085
+3901,2045.5715,264.12347,2063.929,316.52173,0.9982358
+3901,2322.0217,405.3187,2346.5042,470.90012,0.99817145
+3901,2421.3105,315.56433,2439.105,367.377,0.99805474
+3901,3116.5737,355.7186,3137.7998,406.743,0.99793494
+3901,3170.7207,867.4587,3216.608,941.97845,0.9968688
+3901,1059.5199,325.67047,1107.1708,382.67096,0.996612
+3901,2433.054,162.48218,2451.1658,203.64212,0.9956892
+3901,2321.226,342.95047,2348.5024,404.8644,0.99513865
+3901,1911.1038,124.3272,1928.9893,161.2829,0.99031913
+3901,3509.4287,797.99304,3539.9473,868.9509,0.9554088
+3901,3689.8394,796.4093,3729.2363,864.0669,0.93080175
+3901,2579.8137,864.85,2608.6677,948.19556,0.88132036
+3901,2020.2368,924.0845,2063.6863,1018.40125,0.5445851
+3901,1000.42365,150.43738,1014.14557,187.7774,0.118394345
+3902,1670.485,377.77106,1701.9342,437.8933,0.999691
+3902,1688.6763,495.6169,1717.0569,564.98364,0.9995266
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+3902,1718.9681,225.06091,1738.1866,273.05508,0.9993055
+3902,2923.885,665.508,2952.7668,738.43585,0.99919325
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+3902,2082.1248,410.64233,2110.4456,472.41846,0.99906653
+3902,2709.2407,429.12393,2731.0518,488.32224,0.9990225
+3902,2622.336,461.00623,2652.6064,522.4291,0.9990183
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+3902,2405.1094,285.53558,2433.3193,336.70892,0.9984968
+3902,3170.9873,867.7146,3216.5898,941.8385,0.99848986
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+3902,3116.4478,355.57892,3137.896,406.63593,0.9977767
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+4186,1983.1925,215.76967,2001.8365,264.41614,0.99928325
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+4186,2008.1173,287.61694,2039.3265,342.35028,0.9979292
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+4187,2820.888,561.0328,2851.5725,630.94366,0.9994467
+4187,1983.452,215.7116,2001.9684,264.38965,0.99936
+4187,1781.2223,158.71693,1803.0831,199.92427,0.99934393
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+4187,1794.5455,273.00854,1813.9994,329.41907,0.9990866
+4187,2027.5488,322.73004,2055.939,376.88702,0.9990275
+4187,2067.2915,263.1499,2085.4287,313.9132,0.99897707
+4187,2126.2844,458.38977,2146.9397,528.9233,0.9989491
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+4187,2226.4827,372.65353,2253.181,431.15775,0.9988984
+4187,2099.3967,285.90506,2125.7395,340.78146,0.99888486
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+4187,2478.391,191.4939,2495.599,236.4595,0.9986532
+4187,2458.7327,304.51575,2476.9382,354.19067,0.9985592
+4187,1798.6266,123.91026,1818.2396,161.03072,0.9984421
+4187,1106.8239,317.4789,1126.6844,371.89474,0.9983432
+4187,1750.1273,388.31152,1773.9027,451.19037,0.9977659
+4187,3130.6628,353.9902,3153.842,402.5233,0.9977429
+4187,2008.322,288.3333,2039.9717,344.57068,0.99748105
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+4187,3852.8154,767.79407,3884.8154,827.20056,0.81835496
+4187,3766.1953,782.51697,3797.2134,845.6288,0.7300959
+4187,2021.961,924.4209,2063.5889,1018.1907,0.5818569
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+4188,1983.931,215.53775,2002.5311,264.28528,0.99939895
+4188,2819.872,561.1481,2853.1953,631.07214,0.9993772
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+4188,2518.6887,398.80508,2537.931,460.0778,0.9991658
+4188,1794.8716,273.14774,1814.9648,329.47946,0.99913245
+4188,1781.651,159.20244,1804.2482,200.17409,0.99910176
+4188,3145.5234,871.5618,3194.7666,951.7601,0.99909675
+4188,2072.9902,394.9281,2096.0483,455.97546,0.9990938
+4188,2226.4849,372.53625,2252.792,431.06946,0.99901545
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+4188,2637.086,404.29578,2656.8252,465.09668,0.99881774
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+4188,2027.4672,322.90314,2055.886,376.95935,0.9986682
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+4188,2459.4922,304.32245,2476.9434,354.27246,0.9986386
+4188,1799.0045,124.04426,1818.33,161.54295,0.9981346
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+4188,2008.4186,288.67252,2040.7511,345.4938,0.9977665
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+4188,3765.8975,782.95764,3796.5771,845.64075,0.8626844
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+4189,1983.9773,215.54645,2002.7849,264.19913,0.9992368
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+4189,3145.8042,871.4514,3195.6968,951.79614,0.9989945
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+4189,1795.6017,273.4864,1815.9535,329.75586,0.9989046
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+4189,2126.416,459.25513,2147.0142,530.9053,0.99878484
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+4189,2008.0605,288.87192,2041.0559,344.68686,0.99792755
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+4189,3765.625,782.8257,3795.7783,845.5928,0.9004255
+4189,2021.9874,924.44104,2063.5884,1018.1814,0.5685762
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+4191,1985.0392,215.31934,2003.7235,263.26395,0.9989913
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+4191,2007.5787,289.0664,2041.5756,346.2978,0.9967258
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+4337,2455.7341,468.20645,2483.7283,533.53674,0.9971625
+4337,1868.6436,125.07251,1885.96,161.39798,0.9971091
+4337,2454.5754,151.52124,2470.466,188.73419,0.9970325
+4337,3706.926,785.4569,3744.8997,849.1897,0.98739165
+4337,3446.411,819.16724,3491.6096,893.34644,0.98442554
+4337,3605.3154,810.7981,3637.2412,881.9828,0.7600805
+4337,2021.4652,924.80457,2063.7388,1018.3102,0.56125295
+4338,3068.504,760.5522,3102.3857,838.5463,0.99965477
+4338,2363.9023,748.79785,2402.5273,834.12524,0.99962175
+4338,2385.303,554.6951,2418.277,628.8814,0.9994722
+4338,2208.4807,325.41742,2234.799,381.72034,0.9994642
+4338,1757.0664,280.01382,1781.2039,336.43295,0.9994618
+4338,2668.0454,315.3718,2692.079,368.7518,0.99938905
+4338,2638.2927,426.84412,2664.3396,487.54443,0.99929655
+4338,1971.2609,164.68837,1993.8348,207.00456,0.99928766
+4338,2461.0447,468.12793,2485.3518,533.9473,0.9992735
+4338,2495.303,453.6414,2520.512,519.4932,0.9992599
+4338,3223.7212,842.1103,3273.7974,920.67535,0.9992374
+4338,1148.0697,326.6803,1168.1761,379.97308,0.9990965
+4338,2502.1538,284.99963,2525.774,334.2992,0.99902654
+4338,3150.5994,361.3562,3178.1838,411.03217,0.99901927
+4338,2175.341,333.34174,2205.325,389.62262,0.9990103
+4338,2149.4587,236.8668,2170.1404,286.29712,0.99892384
+4338,1938.401,423.96405,1962.1327,489.89288,0.9988458
+4338,2677.1,420.2092,2714.6753,473.30856,0.9988267
+4338,2187.7485,259.3971,2214.0508,315.94965,0.9988018
+4338,1998.233,350.78827,2019.8866,407.41766,0.99879634
+4338,2564.6897,208.40244,2582.6707,254.4867,0.99877936
+4338,2973.3657,605.8209,3002.1626,678.2905,0.9986816
+4338,2567.3884,329.64502,2590.1448,380.93292,0.9982601
+4338,2455.8518,151.75114,2470.7654,188.56279,0.9971027
+4338,1868.2687,124.76342,1885.516,161.91092,0.9969734
+4338,3706.9985,785.5625,3744.5488,848.89136,0.9857895
+4338,3446.3787,818.8916,3491.4583,893.3273,0.98453104
+4338,3605.3035,811.04663,3637.1135,881.9901,0.681362
+4338,2021.3021,924.9392,2063.5771,1018.23584,0.41561896
+4339,3069.9211,760.23804,3102.6262,837.03784,0.9997073
+4339,2365.2866,748.6118,2403.1084,834.12524,0.9996811
+4339,1756.9647,280.12024,1781.1742,336.45697,0.99955064
+4339,2495.3792,453.79785,2523.8547,519.3447,0.9994454
+4339,2385.4272,555.50415,2421.853,630.79675,0.99942696
+4339,3223.6445,841.4133,3274.3247,918.3533,0.9993806
+4339,2638.7434,426.59506,2664.449,487.3403,0.9993692
+4339,2465.0847,467.78665,2486.4915,534.3452,0.9993493
+4339,1972.0282,165.0741,1994.774,207.10449,0.99931216
+4339,2669.4019,315.31668,2692.3647,368.71133,0.9992557
+4339,2502.2163,284.87686,2526.2598,334.24112,0.9991549
+4339,2176.57,333.64117,2207.9304,389.87878,0.99912804
+4339,3150.5972,360.8101,3177.6875,410.83176,0.9990596
+4339,1997.7556,350.76755,2019.8333,407.4606,0.9989261
+4339,2566.4163,208.32458,2583.0369,254.16513,0.9988796
+4339,2210.539,325.47363,2236.6362,382.22736,0.99887127
+4339,2190.7234,258.39706,2215.801,315.70267,0.9988475
+4339,2677.1938,420.19397,2715.2065,473.23572,0.99882525
+4339,2973.4624,606.32263,3002.3438,678.31287,0.9987154
+4339,1938.4124,423.74973,1961.9634,489.94278,0.99868613
+4339,2149.791,237.56624,2172.1133,286.3152,0.998681
+4339,1148.6383,326.5478,1172.5363,379.9361,0.99845684
+4339,2567.4263,329.54443,2590.1548,380.95264,0.99832875
+4339,1867.9644,124.64795,1885.4712,161.97357,0.9976477
+4339,2457.0132,151.14638,2471.3833,188.65938,0.9965791
+4339,3446.4304,819.0991,3491.3196,893.3639,0.98374987
+4339,3707.042,785.69104,3744.1904,848.90796,0.9822965
+4339,3605.2903,811.3454,3637.1472,881.96185,0.634429
+4339,2021.3021,924.9392,2063.5771,1018.23584,0.41561896
+4340,3070.933,760.0614,3102.912,836.7036,0.99979097
+4340,2366.84,748.81104,2403.8835,834.14575,0.99963665
+4340,1756.6813,280.49377,1781.2545,336.61414,0.99954623
+4340,3223.9653,840.22833,3274.5098,917.1341,0.9994435
+4340,2495.872,453.8316,2526.4302,518.9825,0.9993868
+4340,2503.1099,284.49716,2526.7432,334.0178,0.99930537
+4340,2639.0198,426.49005,2664.7927,487.6394,0.9992323
+4340,2212.1887,325.72403,2238.7068,382.44052,0.9992225
+4340,2467.4253,467.75717,2488.046,533.1187,0.999199
+4340,2385.553,556.2381,2423.389,632.28094,0.9991912
+4340,2566.9883,208.49106,2583.776,254.32513,0.9991258
+4340,3150.6882,360.6031,3177.4495,410.73456,0.9991109
+4340,2669.745,314.7609,2692.8936,368.87253,0.999106
+4340,2177.4194,335.32288,2210.605,391.0849,0.9991026
+4340,1996.3452,350.66956,2019.8721,406.16632,0.9991014
+4340,2150.4358,237.29396,2174.591,285.7995,0.99886876
+4340,2973.5107,606.4753,3002.4175,678.82855,0.9988194
+4340,1938.0653,423.44318,1961.7609,489.91797,0.9988092
+4340,1974.3309,164.85678,1995.3026,206.88406,0.99880874
+4340,1149.4039,326.4481,1175.6066,380.08237,0.998728
+4340,2567.5977,329.45676,2590.1,380.93753,0.99866545
+4340,2677.952,419.51837,2716.4084,473.33594,0.9985898
+4340,2196.826,257.7592,2217.1394,315.53494,0.99819344
+4340,1867.7535,125.06628,1884.4159,161.81213,0.99664813
+4340,2458.3604,150.5817,2472.4448,188.77777,0.9965078
+4340,3446.47,819.27234,3491.261,893.16895,0.98615843
+4340,3707.0107,785.8346,3743.877,849.053,0.9802294
+4340,3605.351,811.3266,3637.2695,882.03156,0.68662846
+4340,2021.3107,924.9369,2063.5654,1018.1969,0.41810107
+4341,3071.6697,759.3042,3103.3865,836.354,0.9998155
+4341,1756.4447,280.47784,1781.3395,336.59692,0.9995821
+4341,2367.973,748.3989,2404.5793,833.98645,0.9995766
+4341,2212.8894,325.77466,2239.6116,382.98395,0.99954945
+4341,3224.172,839.3262,3275.306,916.4761,0.99947006
+4341,2503.5056,284.34604,2527.155,333.85306,0.9993461
+4341,2496.186,453.88297,2528.2305,519.13824,0.99931145
+4341,2468.171,468.109,2490.7368,532.9482,0.9993044
+4341,2640.121,426.4868,2664.7676,487.73697,0.9992519
+4341,2178.8218,337.0161,2210.9517,391.7359,0.9991948
+4341,3150.6213,360.3122,3177.1946,410.45178,0.99916506
+4341,2567.1506,208.30154,2584.738,254.84772,0.99912983
+4341,2384.6099,557.6327,2424.4985,632.1421,0.99904567
+4341,1995.5204,350.64954,2019.914,405.58533,0.9990182
+4341,2670.213,314.78656,2693.0918,368.83545,0.99900585
+4341,1977.1525,164.9766,1995.8441,206.98682,0.99895704
+4341,1938.0607,423.32812,1961.659,489.91962,0.9987702
+4341,2974.366,606.54376,3002.5564,679.5865,0.9987692
+4341,2680.1929,419.263,2717.5,473.1615,0.99872154
+4341,1149.6189,326.1055,1177.8394,380.01895,0.9986493
+4341,2150.4932,237.48582,2177.022,285.91153,0.9986344
+4341,2567.7085,329.59503,2589.9536,380.9381,0.9985057
+4341,2200.4016,257.1687,2218.9895,316.0105,0.99783283
+4341,2459.036,150.13419,2472.5193,188.74677,0.9971595
+4341,1867.341,124.979576,1883.0304,161.75717,0.9955024
+4341,3707.0234,785.95306,3743.6958,848.9338,0.9864715
+4341,3446.5005,819.56134,3491.0981,892.96893,0.98247033
+4341,3605.3079,811.22833,3637.2844,881.9841,0.6064205
+4341,2021.4506,924.7793,2063.511,1018.2583,0.38915613
+4342,3071.8938,758.71375,3104.3757,835.67725,0.9997296
+4342,2369.3657,747.5057,2405.9375,833.69977,0.99960226
+4342,1756.1108,280.53833,1781.3745,336.5583,0.99960154
+4342,2504.4607,284.23248,2527.1428,333.6424,0.99942267
+4342,3224.297,838.9725,3275.9568,914.8776,0.99941874
+4342,2496.2935,454.94,2529.4902,519.1793,0.99940455
+4342,2468.3518,468.26434,2494.2097,532.78107,0.99935883
+4342,2567.2876,208.26025,2586.3008,254.51736,0.99930227
+4342,2179.942,338.05475,2211.0488,391.7458,0.99928087
+4342,2639.861,426.07602,2664.8855,487.60947,0.9992507
+4342,2670.4333,314.53754,2693.0989,368.6548,0.9991768
+4342,3150.5342,360.15546,3177.1172,409.83923,0.9991555
+4342,2384.9492,558.2482,2425.8423,632.41656,0.999097
+4342,1994.0645,350.6568,2019.783,405.53326,0.9990196
+4342,2683.5105,418.79095,2718.677,472.7959,0.99901843
+4342,1980.0616,164.92564,1996.2184,207.01366,0.99891055
+4342,1149.8804,325.9799,1178.6394,380.0272,0.9988869
+4342,2974.933,606.7888,3002.5845,679.19543,0.99885476
+4342,2213.1277,326.78513,2240.9797,383.9877,0.9988279
+4342,1938.0055,422.9657,1961.3881,489.88898,0.99876225
+4342,2567.637,329.61032,2589.9167,380.9462,0.998722
+4342,2151.1045,237.27457,2177.1611,285.74686,0.9983558
+4342,2201.493,257.38876,2220.8108,315.678,0.9980756
+4342,2459.3708,150.33704,2472.7883,188.63055,0.9969318
+4342,1867.1185,124.66468,1881.1407,161.74402,0.99390405
+4342,3446.5156,819.6419,3491.1167,892.82745,0.98760426
+4342,3706.9453,786.03217,3742.9927,849.0871,0.97953886
+4342,3605.253,811.1732,3637.413,882.02515,0.5220455
+4342,2021.3063,924.94794,2063.5698,1018.20905,0.42748183
+4343,3071.7092,758.48444,3104.7678,834.1707,0.9996855
+4343,2370.4446,746.5685,2406.3967,833.41284,0.99964035
+4343,1755.5476,280.7959,1781.2605,336.4325,0.9995974
+4343,3225.079,838.4659,3276.5288,914.5992,0.9995647
+4343,2496.5781,455.27795,2529.9268,519.27734,0.9993993
+4343,2504.7275,283.9641,2527.253,333.52734,0.99933654
+4343,1991.4232,350.38718,2019.1115,405.1025,0.9992918
+4343,2468.3474,468.48514,2497.2815,532.48883,0.9992859
+4343,2567.3508,208.19717,2588.1096,255.20123,0.99921167
+4343,3150.56,359.92102,3177.1235,409.00317,0.9992047
+4343,2180.2651,339.19547,2211.3413,392.0803,0.9992035
+4343,2641.3884,426.00833,2664.6687,487.67618,0.9991302
+4343,2687.0945,418.13882,2719.3142,472.2366,0.9991289
+4343,1150.4496,326.14786,1178.3353,380.0684,0.9990977
+4343,2974.809,606.92285,3002.5264,679.4625,0.999008
+4343,2670.7036,314.9768,2693.186,368.71887,0.9989606
+4343,1983.1189,164.672,1997.3152,207.21832,0.9989336
+4343,2567.6843,329.63904,2589.8235,380.89453,0.9988366
+4343,1937.9805,422.7617,1961.2166,489.7513,0.9986578
+4343,2213.6157,326.8757,2242.06,384.37808,0.99865
+4343,2386.9438,558.36176,2428.4858,632.20953,0.9986156
+4343,2201.667,257.39252,2223.0469,315.30035,0.99858713
+4343,2151.6294,237.21198,2178.3472,285.7578,0.99852234
+4343,2459.6453,150.3213,2473.4958,188.38597,0.9969007
+4343,1866.8086,124.47957,1880.064,161.70445,0.9932018
+4343,3446.5554,819.6039,3491.2146,892.6754,0.9878418
+4343,3706.8315,786.1671,3742.4595,849.0331,0.97376186
+4343,3605.2563,811.1994,3637.5059,881.9366,0.6085173
+4343,2021.4473,924.7715,2063.4968,1018.24646,0.40513882
+4344,3072.0193,758.37115,3105.1775,833.6463,0.999724
+4344,1755.315,280.87784,1780.8682,336.40207,0.9996635
+4344,2372.2563,745.9539,2408.1523,833.4123,0.99964154
+4344,3226.074,838.6493,3277.3904,915.1039,0.999446
+4344,2468.3037,468.98193,2499.3384,533.55066,0.9993987
+4344,2504.6528,283.80725,2527.3843,333.83685,0.999382
+4344,2567.8386,208.67392,2589.485,255.25034,0.99935335
+4344,3150.6743,359.87494,3177.3574,408.8924,0.99926084
+4344,1151.3516,326.21942,1177.8169,380.1753,0.9992607
+4344,2641.731,425.69116,2664.7148,487.53656,0.9992268
+4344,1984.5454,164.63219,1998.7844,207.48354,0.99918884
+4344,2215.1511,327.12656,2242.8318,385.3149,0.99917597
+4344,2181.2725,339.6958,2211.0933,391.90063,0.9990969
+4344,2670.9863,314.90247,2693.3438,368.65576,0.99909616
+4344,2690.9478,417.62482,2720.1855,471.71332,0.99909174
+4344,2496.7192,455.45068,2530.1562,519.3137,0.99904245
+4344,1989.8236,350.5398,2018.7655,405.13098,0.9989819
+4344,2392.4368,558.1788,2429.8787,632.243,0.99896765
+4344,2974.767,607.2825,3002.5752,679.5604,0.9989249
+4344,1937.9497,422.66257,1961.1709,489.72433,0.99869484
+4344,2567.6138,329.63828,2589.7476,380.9587,0.9986759
+4344,2153.093,237.25238,2180.9485,285.92487,0.9986676
+4344,2201.8772,258.195,2225.7258,315.2605,0.99770385
+4344,2460.0444,150.76036,2473.8901,188.45454,0.9973571
+4344,3446.5303,819.9131,3491.7148,892.5099,0.98953587
+4344,1865.2112,123.89407,1879.7261,161.4397,0.9820967
+4344,3706.7263,786.3264,3742.3855,848.8624,0.9742691
+4344,3605.4536,811.1952,3637.6084,881.5835,0.45366374
+4344,2021.3093,924.9726,2063.5403,1018.2199,0.41824204
+4345,3071.6904,758.35846,3105.7832,833.4024,0.9997451
+4345,1754.8904,280.7836,1780.4209,336.37985,0.9997267
+4345,2373.8228,745.6888,2409.5234,832.11676,0.9996872
+4345,2468.447,469.48367,2500.8762,533.5956,0.9994985
+4345,3226.503,838.494,3278.665,914.8795,0.9994684
+4345,2504.6506,283.88538,2527.5056,334.02777,0.99945384
+4345,2183.4883,339.6971,2210.6543,393.0922,0.99941313
+4345,2567.7407,208.65273,2590.7568,255.07445,0.9994093
+4345,1151.7194,325.2583,1177.7758,380.52808,0.9993594
+4345,2671.464,314.13095,2693.7683,367.3367,0.9993186
+4345,2397.6677,557.94574,2431.013,632.19867,0.99929774
+4345,2641.9392,425.46378,2664.7385,487.37326,0.99929166
+4345,3150.589,359.8214,3177.4744,408.33868,0.99926704
+4345,2694.2437,417.0088,2721.3965,471.9909,0.99904805
+4345,1987.611,350.44012,2018.109,405.2235,0.9990164
+4345,2216.186,327.38474,2244.0586,385.7333,0.99900264
+4345,1985.2546,164.5167,2000.6938,207.24597,0.9988944
+4345,2974.7834,607.33826,3002.7068,679.6853,0.99876165
+4345,1937.8838,422.58762,1961.1528,489.73404,0.9986437
+4345,2567.536,329.52588,2589.8337,380.9773,0.99854195
+4345,2496.47,455.6252,2531.2175,519.21545,0.9984756
+4345,2155.1506,235.85445,2182.7039,284.9087,0.997969
+4345,2202.0603,258.8335,2229.9163,314.98932,0.9974547
+4345,2460.22,150.4866,2474.1736,188.53226,0.9971962
+4345,3446.5623,819.9922,3491.9685,892.41797,0.9918126
+4345,1865.4677,124.48578,1878.7238,161.51682,0.9886085
+4345,3706.6655,786.4475,3742.6182,848.9885,0.9661471
+4345,3605.54,811.1901,3637.6875,882.09625,0.46808925
+4345,2021.4501,924.7884,2063.5188,1018.25494,0.39922
+4346,1754.6387,280.75455,1779.5557,336.39133,0.9996731
+4346,3071.6506,758.3478,3106.4622,833.09595,0.9996371
+4346,2504.723,282.7987,2527.5222,333.02307,0.99962616
+4346,2376.2676,745.16724,2411.2222,831.2908,0.9996132
+4346,2403.863,558.03143,2431.4426,632.4101,0.99957293
+4346,3228.1238,838.66205,3279.2854,915.4123,0.999483
+4346,2567.6782,208.96742,2591.084,254.83665,0.9994382
+4346,1152.5323,325.7319,1178.0548,380.26505,0.99942327
+4346,2642.0483,425.19818,2664.7437,486.53192,0.9992981
+4346,2468.717,469.78366,2502.406,533.8665,0.99928916
+4346,2185.3394,339.43317,2210.562,393.29047,0.9992175
+4346,3149.9863,359.86926,3178.1,408.21216,0.99918395
+4346,2671.3118,314.6022,2693.1682,368.5607,0.999121
+4346,2695.6914,416.78482,2721.9922,470.94034,0.99909294
+4346,2974.9731,607.6821,3002.8647,679.74243,0.9990164
+4346,1985.6929,164.83334,2002.3218,206.88467,0.9989242
+4346,1985.9252,350.19604,2017.4298,405.235,0.99891424
+4346,1937.8453,422.4241,1960.8021,489.49405,0.998862
+4346,2217.6992,328.3509,2244.85,386.64514,0.9986896
+4346,2567.5305,329.5018,2589.9685,380.96524,0.9986761
+4346,2202.583,261.17374,2231.2246,315.1522,0.99861014
+4346,2157.5776,235.14253,2184.3525,284.44623,0.99834
+4346,2499.169,456.05798,2532.6084,519.195,0.9976801
+4346,2460.3516,150.24289,2474.3994,188.57144,0.99759644
+4346,3446.5278,820.11523,3492.225,892.51196,0.9931457
+4346,1863.4142,124.5889,1878.4913,161.52042,0.986587
+4346,3706.6924,786.55975,3742.2925,848.8848,0.9738468
+4346,3605.5242,811.1792,3637.8044,882.1229,0.4739274
+4346,2021.3074,924.94977,2063.6116,1018.22296,0.42601788
+4347,1754.3356,281.0678,1779.162,336.36053,0.9997296
+4347,2504.7332,282.55746,2527.6404,333.0336,0.99964035
+4347,2378.28,744.9721,2413.2664,829.98236,0.99963593
+4347,3071.645,758.2654,3107.6235,832.7451,0.99960655
+4347,2567.6458,209.66678,2591.3948,255.92279,0.9995608
+4347,3229.599,838.71515,3280.2188,915.42645,0.9995567
+4347,2642.455,425.03125,2664.7188,485.87524,0.9993894
+4347,1153.3414,325.3401,1178.5128,380.1413,0.99937314
+4347,2696.615,417.04675,2722.5593,471.98126,0.99937195
+4347,3149.3667,359.73682,3178.4336,407.71942,0.99928135
+4347,2406.416,558.63635,2433.037,632.11475,0.99908614
+4347,2671.3071,314.53156,2693.0425,368.5711,0.9990846
+4347,1985.544,350.10828,2016.7122,405.28943,0.99905854
+4347,2185.951,339.06384,2210.6472,394.1245,0.99901927
+4347,1985.6783,164.92345,2006.1256,207.12263,0.9989453
+4347,2567.5278,329.44403,2590.3018,380.91235,0.99888057
+4347,1937.8665,422.35315,1960.742,489.38318,0.99883586
+4347,2975.1243,607.77405,3003.1575,679.86816,0.99873203
+4347,2220.376,329.46292,2245.7866,387.34915,0.9987178
+4347,2468.782,470.06348,2503.1775,534.20233,0.99846977
+4347,2160.1628,234.66187,2184.7664,283.90656,0.9983675
+4347,2203.0557,258.1754,2231.2808,315.76556,0.99807656
+4347,2502.2336,455.38672,2534.0764,519.1724,0.9979412
+4347,2460.6,150.0789,2474.6748,188.62633,0.99724686
+4347,1863.0936,124.458244,1877.6708,161.01797,0.9936085
+4347,3446.4897,820.3844,3491.998,892.359,0.99337924
+4347,3706.6807,786.83813,3742.0542,848.7998,0.9734318
+4347,3605.4941,811.1904,3637.8462,882.1293,0.50716287
+4347,2021.2932,924.9374,2063.6106,1018.2283,0.42916286
+4348,1754.002,280.8443,1778.6143,336.3446,0.99970907
+4348,2504.6545,282.05966,2527.8079,332.91525,0.9996573
+4348,2379.8596,744.3102,2414.8406,829.17395,0.9996172
+4348,3071.5579,757.9126,3108.7317,832.7158,0.99957615
+4348,3231.2312,838.8337,3281.715,915.24677,0.9995461
+4348,2567.6123,209.83398,2591.7754,256.14374,0.99953437
+4348,2642.503,424.2514,2664.6343,484.81018,0.99947697
+4348,2696.5193,417.4695,2723.6155,470.61795,0.9994741
+4348,1985.9203,164.71518,2008.3739,206.8038,0.99934083
+4348,3148.1328,359.57303,3178.565,407.6738,0.9993387
+4348,2185.5042,339.09875,2210.9412,395.17206,0.99930084
+4348,1154.2504,325.04626,1178.5856,380.31213,0.9992919
+4348,2671.2583,314.3589,2692.6943,368.599,0.99910176
+4348,2974.755,608.3373,3004.0122,680.0243,0.9990482
+4348,1985.4025,350.41528,2015.7418,405.1164,0.9990369
+4348,2567.496,330.01236,2590.9297,380.64743,0.9988883
+4348,1937.9309,422.3451,1960.5483,489.33838,0.99888796
+4348,2222.2866,330.29663,2246.3774,387.08356,0.9987588
+4348,2469.6294,470.3593,2504.1777,534.2672,0.9987583
+4348,2163.2134,232.33923,2185.483,283.59058,0.99874663
+4348,2408.3887,559.3301,2434.5317,632.07983,0.99874014
+4348,2506.1816,454.84946,2534.5942,519.14124,0.9974728
+4348,2205.8284,257.90894,2232.882,314.0722,0.9973441
+4348,2460.8022,149.47696,2475.1729,188.51596,0.9972038
+4348,3446.5588,820.37714,3492.7048,892.26434,0.9943646
+4348,1861.8485,124.64949,1877.2943,160.61592,0.9939726
+4348,3706.7532,786.9737,3742.0745,848.67535,0.97600526
+4348,3605.4878,811.21356,3637.8853,882.18134,0.5898228
+4348,2021.298,924.9413,2063.6008,1018.2416,0.4298724
+4349,1753.6677,281.1009,1778.252,336.30524,0.99971646
+4349,2380.953,744.2896,2415.8674,829.1734,0.9996369
+4349,2504.5957,282.20996,2528.0337,332.75812,0.99963367
+4349,3071.571,757.81915,3109.9768,832.7342,0.9996289
+4349,3231.8638,838.9984,3282.8828,915.5294,0.99954826
+4349,2642.516,423.96558,2664.4565,482.46948,0.9995433
+4349,2568.0923,210.15335,2592.1787,256.34543,0.99945384
+4349,3147.0444,359.52435,3178.666,407.76678,0.9994198
+4349,2185.1072,339.337,2209.8762,395.00272,0.99928206
+4349,2671.2046,314.19012,2692.35,368.77966,0.9992343
+4349,1154.3997,324.9396,1178.6787,380.17117,0.99917346
+4349,1986.2606,164.62665,2010.0167,206.85483,0.9991191
+4349,1985.3805,350.51956,2015.1774,405.184,0.9990885
+4349,2696.3137,417.95126,2726.2864,470.26517,0.9990221
+4349,1937.9623,422.33185,1960.2875,489.30865,0.9989976
+4349,2165.5784,232.07631,2186.0398,283.24957,0.99898773
+4349,2974.5713,608.8752,3004.4424,680.2454,0.9989058
+4349,2510.8572,454.14545,2536.2722,518.81635,0.99858785
+4349,2567.3032,330.33582,2591.9385,380.7979,0.99857664
+4349,2409.2537,560.4102,2437.2283,632.1236,0.99852055
+4349,2222.9329,330.84375,2246.9548,387.05536,0.9982553
+4349,2471.1775,470.7692,2505.3372,534.4253,0.9980773
+4349,2460.88,149.22,2475.2979,188.59814,0.99773407
+4349,2207.5732,256.66418,2234.1094,313.62335,0.99749786
+4349,1860.6512,124.35841,1876.8632,160.79033,0.9958514
+4349,3446.703,820.6008,3492.6257,892.19836,0.99382085
+4349,3706.7695,787.0509,3742.0425,848.681,0.9762431
+4349,3605.352,811.1994,3638.0122,881.9653,0.68476564
+4349,2021.2972,924.94147,2063.5938,1018.22894,0.43023875
+4350,1753.3918,281.01822,1777.8562,336.0969,0.999752
+4350,3071.5051,757.58307,3110.986,832.9454,0.9996531
+4350,2504.6738,282.33237,2528.2573,332.11484,0.9996145
+4350,3233.28,838.7297,3284.9548,915.93024,0.9996075
+4350,2381.5842,744.1075,2416.1575,829.46124,0.9995859
+4350,2642.5527,423.49548,2664.685,482.44495,0.9994843
+4350,2568.371,210.37546,2592.8115,255.98666,0.9994777
+4350,3146.1616,359.6436,3178.8853,408.09323,0.99942017
+4350,1155.6024,324.9969,1179.7552,379.96326,0.9994068
+4350,2184.65,339.533,2209.1333,394.9133,0.99937624
+4350,1986.6487,165.00299,2010.9407,207.01807,0.99935097
+4350,2671.3098,314.0277,2692.0608,368.2575,0.99934334
+4350,2223.4414,332.80484,2247.3247,386.84286,0.9991768
+4350,2166.9463,231.83177,2186.666,283.4555,0.9991454
+4350,2515.779,454.1986,2537.95,519.2722,0.9990528
+4350,1937.9655,422.32886,1960.3695,489.32465,0.99903035
+4350,2974.4631,609.34607,3005.223,680.2484,0.9989889
+4350,1985.194,350.5973,2014.8304,405.16077,0.9988519
+4350,2696.0618,418.06073,2728.6233,470.56262,0.99875975
+4350,2567.3901,330.36752,2592.1362,380.69382,0.9987556
+4350,2409.1975,560.6464,2441.76,632.205,0.99874383
+4350,2210.8384,255.21182,2235.4429,313.19778,0.9983577
+4350,1859.9994,124.02762,1875.9633,161.0029,0.9975242
+4350,2461.101,149.9364,2475.6455,188.51636,0.996843
+4350,2473.84,471.12787,2506.614,534.30566,0.9966426
+4350,3446.6538,820.7001,3492.5781,892.22095,0.9949275
+4350,3706.8464,787.04126,3741.966,848.83484,0.96867305
+4350,3605.4468,811.1707,3638.041,882.1684,0.7232727
+4350,2021.3289,924.8992,2063.588,1018.219,0.4406427
+4351,1753.192,281.0023,1777.6456,336.061,0.9997857
+4351,2381.8997,743.503,2416.2175,828.9272,0.9996991
+4351,3072.3608,757.0884,3112.4976,832.5171,0.99966764
+4351,2184.0022,339.9055,2207.4846,394.70065,0.99956805
+4351,3235.216,838.6093,3287.1223,913.7984,0.99956125
+4351,2642.5388,422.36926,2665.0818,481.60822,0.99950826
+4351,1156.3447,325.1341,1179.468,379.43726,0.99941576
+4351,3146.276,359.64365,3179.2192,407.7861,0.99939585
+4351,2671.2224,313.78375,2691.9001,367.86115,0.9993808
+4351,1987.6089,164.73645,2011.1912,206.90875,0.99934345
+4351,2504.3838,283.16214,2528.2354,332.80698,0.99934226
+4351,2409.0366,560.8665,2447.7812,632.5114,0.9992879
+4351,2567.6104,210.50882,2593.0557,255.71164,0.9992766
+4351,2167.9336,231.99178,2188.3496,282.75385,0.9991906
+4351,1985.0898,350.76926,2014.6123,405.18008,0.999164
+4351,2223.8257,333.92566,2247.2534,386.9416,0.9990207
+4351,1937.9683,422.3237,1960.6511,489.3549,0.99899095
+4351,2975.0315,609.3749,3005.571,680.06226,0.99896383
+4351,2517.2273,454.66656,2539.4626,519.16736,0.9989556
+4351,2696.0225,418.0742,2730.4893,470.66483,0.998741
+4351,2462.5073,150.37247,2475.6357,188.41727,0.998343
+4351,2476.968,470.29938,2508.824,534.24274,0.99823016
+4351,2567.2273,330.74564,2592.909,381.0958,0.9980842
+4351,1859.5356,123.677444,1875.4458,160.76643,0.9977798
+4351,2214.7708,254.59827,2236.9622,312.71735,0.99725574
+4351,3446.8599,821.3772,3492.855,891.4481,0.99402666
+4351,3706.872,787.06396,3741.96,848.7682,0.97441953
+4351,3605.5566,811.3146,3638.4849,881.9647,0.70260495
+4351,2021.4673,924.91986,2063.5725,1018.3233,0.388723
+4352,1752.9843,280.94653,1777.4078,335.9043,0.9998035
+4352,2382.5906,743.06726,2416.0593,828.37427,0.99974924
+4352,3072.1687,756.809,3113.985,832.6984,0.99968636
+4352,3236.4294,837.74976,3289.1492,912.65234,0.99962175
+4352,2183.6338,340.08313,2206.9165,394.5324,0.99956506
+4352,2642.5947,421.92676,2665.104,481.28485,0.9995066
+4352,2408.9414,561.3702,2450.0845,633.03705,0.99939466
+4352,2671.2688,313.293,2691.7087,366.92227,0.9993741
+4352,2505.3164,282.50397,2528.5688,331.98004,0.99933976
+4352,3146.5227,359.65683,3179.277,407.8541,0.9992545
+4352,1984.835,350.73157,2014.1399,405.02057,0.9992387
+4352,2168.7224,232.63208,2189.0417,282.73663,0.99923253
+4352,1156.9232,325.28894,1179.452,379.21735,0.99920875
+4352,1989.3912,164.73941,2011.6449,206.62833,0.9991573
+4352,2224.3984,334.57755,2247.352,386.88583,0.9991394
+4352,1937.9188,422.36545,1960.805,489.40842,0.9990062
+4352,2568.8955,210.5228,2593.727,255.76285,0.99896955
+4352,2976.1865,609.60944,3006.0708,679.9079,0.9988637
+4352,2695.9758,418.28003,2730.972,470.81158,0.99877995
+4352,2517.7678,454.3448,2542.033,519.2189,0.99873406
+4352,2479.2869,470.3637,2510.4211,534.3067,0.9985007
+4352,2462.7588,150.23247,2475.9126,188.34264,0.99822646
+4352,1858.9072,123.77517,1875.6252,161.0042,0.9981731
+4352,2219.152,255.94545,2238.6643,312.1748,0.9980128
+4352,2567.1604,330.96072,2593.4666,380.8887,0.9978236
+4352,3446.8494,821.3532,3492.847,891.4616,0.9925126
+4352,3706.8691,787.07904,3741.9468,848.7686,0.9755088
+4352,3605.2026,811.4551,3638.5747,882.0515,0.65709597
+4352,2021.4672,924.9239,2063.5732,1018.3245,0.38671944
+4353,1752.9551,280.92603,1777.2202,335.81104,0.99981374
+4353,2383.0608,742.8927,2416.4294,827.81885,0.9997546
+4353,3073.1394,756.5415,3115.8083,831.12695,0.99969745
+4353,2505.3796,281.6195,2528.8572,331.8413,0.9995987
+4353,2642.5547,421.532,2665.038,481.1802,0.9995183
+4353,2183.311,340.24536,2206.7354,394.432,0.99950886
+4353,3238.4673,837.1105,3291.1597,911.527,0.99950266
+4353,2169.4004,234.25462,2190.8804,282.7837,0.9994485
+4353,2408.953,562.33545,2451.868,633.68994,0.9994351
+4353,3146.6936,359.4901,3179.4006,407.93335,0.9993382
+4353,2671.2886,312.95868,2691.8447,365.99182,0.9992348
+4353,1156.944,325.4645,1179.0265,378.96225,0.9991497
+4353,2225.1658,334.86838,2247.5042,387.13675,0.9991258
+4353,1984.073,350.17206,2012.8616,404.64612,0.9991242
+4353,2977.6555,609.63556,3006.3157,680.0225,0.99899524
+4353,2569.5693,210.17937,2594.188,255.89577,0.99897254
+4353,2482.77,470.35797,2511.794,534.2158,0.99890137
+4353,1937.9077,422.44354,1960.991,489.42322,0.9988961
+4353,1991.5793,164.77534,2012.4504,206.74858,0.99887985
+4353,2696.0361,417.3357,2731.3945,471.09564,0.9987332
+4353,2517.7866,454.41855,2545.3228,519.1803,0.9986248
+4353,1858.2789,123.79281,1875.3727,161.51767,0.998467
+4353,2462.6755,149.75348,2476.2107,188.50238,0.9984326
+4353,2567.2625,331.0348,2594.1995,381.0346,0.99805534
+4353,2222.7212,253.98795,2240.3003,311.9802,0.99792826
+4353,3446.8438,821.33673,3492.8428,891.46796,0.9918663
+4353,3706.8975,787.20526,3742.314,848.76056,0.9787759
+4353,3605.5134,811.549,3638.5193,882.0086,0.59278816
+4353,2021.4664,924.9098,2063.5693,1018.3202,0.39025941
+4354,1753.0038,281.06024,1777.0936,335.84564,0.9998385
+4354,2383.499,742.2319,2417.1929,827.88495,0.9997166
+4354,2183.3293,340.565,2206.5994,395.3587,0.9996941
+4354,3073.6682,756.0124,3117.1824,830.5184,0.99967766
+4354,3241.937,835.8344,3293.3652,909.8796,0.9995472
+4354,2642.7275,421.1881,2665.1514,480.83307,0.9995382
+4354,2505.626,281.16916,2529.6729,331.75528,0.99942833
+4354,1983.709,350.2311,2011.9736,404.63437,0.9993777
+4354,2980.3438,609.5852,3007.1636,680.14465,0.99932027
+4354,2169.7812,234.48837,2193.7388,282.79208,0.99927
+4354,2225.6777,334.7026,2247.373,388.57816,0.9992029
+4354,2485.9233,470.25598,2512.8657,534.3637,0.999201
+4354,2408.7078,562.83716,2452.277,634.1594,0.9991605
+4354,3146.7795,359.09592,3180.1907,407.99808,0.9991574
+4354,2517.5066,454.8356,2549.1487,519.21265,0.9991447
+4354,1993.9974,164.73262,2012.7521,206.6072,0.99906164
+4354,2671.4116,312.99603,2691.9126,365.60156,0.9990565
+4354,1938.0419,422.51462,1961.163,489.44485,0.99893564
+4354,2569.9072,210.01578,2595.1865,255.6843,0.9988991
+4354,1857.9994,123.8799,1874.7374,160.82631,0.99847
+4354,2224.0005,254.00813,2242.1406,310.74164,0.99840075
+4354,2567.4775,330.99396,2594.4307,380.88373,0.9983912
+4354,2696.1104,416.7411,2731.5815,471.09564,0.99838376
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+4355,1983.0983,350.22623,2010.4989,404.5436,0.9994392
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+4364,1978.5767,349.85098,2006.5508,404.5944,0.999276
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+4364,2463.793,149.88097,2480.4434,186.98302,0.99432135
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+4364,3706.886,785.2843,3744.1428,848.92993,0.98504364
+4364,3605.6726,809.91315,3642.0442,882.2266,0.87092495
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+4365,1978.6818,349.7821,2006.3759,404.4574,0.9994036
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+4365,2538.0095,453.4629,2563.18,517.95184,0.99909174
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+4365,3148.3562,356.7895,3185.1194,408.34436,0.9990754
+4365,1942.5281,424.53522,1965.9497,489.84772,0.9990079
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+4365,2007.6844,164.31224,2026.3065,206.85506,0.9972914
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+4365,3605.716,809.84375,3642.4456,882.11926,0.915533
+4365,2021.149,924.8811,2063.4993,1018.2716,0.4250901
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+4366,2179.3298,344.80142,2203.3396,402.89102,0.9996037
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+4366,1978.9188,349.69174,2006.1984,404.51767,0.999343
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+4366,1944.1272,424.75958,1967.0183,489.87152,0.9992194
+4366,2489.043,470.5067,2526.7188,534.9404,0.999193
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+4366,1846.6985,123.38469,1864.7461,160.77022,0.9977017
+4366,2010.0865,163.66519,2027.3265,207.00067,0.9967542
+4366,2464.629,149.48538,2480.7725,187.2276,0.99465054
+4366,3446.2593,819.26196,3490.2466,892.5408,0.9870861
+4366,3707.0007,785.28955,3743.8074,848.8607,0.9750745
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+4367,2226.445,336.57504,2249.111,392.34225,0.9994617
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+4367,2489.7942,470.148,2527.36,534.6487,0.9993512
+4367,1945.5916,424.69717,1968.0071,489.944,0.99931574
+4367,1979.8213,349.613,2005.9988,404.6284,0.9993143
+4367,2510.3796,279.57303,2536.4275,328.77573,0.9992428
+4367,3147.7703,356.5489,3184.7239,408.2625,0.9991677
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+4367,2437.499,566.53656,2475.0518,641.6462,0.9988789
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+4367,2713.2495,412.0362,2737.7158,470.49377,0.9984993
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+4367,1845.8666,123.34,1864.2521,160.86697,0.9982387
+4367,2013.3777,162.3008,2030.5249,207.09029,0.9963309
+4367,2465.0417,149.13098,2480.8079,187.22702,0.9947961
+4367,3446.2798,819.2938,3489.6753,892.6146,0.98819995
+4367,3706.9976,785.28864,3743.789,848.884,0.9748828
+4367,3605.713,808.5859,3643.5898,882.1562,0.9721357
+4367,2021.1572,924.8646,2063.5159,1018.2739,0.40597966
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+4368,1756.9651,283.45435,1780.0735,339.5133,0.99958676
+4368,3096.1702,749.6818,3132.078,826.53217,0.99957865
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+4368,2670.8472,308.85748,2694.6035,361.68726,0.9994086
+4368,2643.461,415.81686,2666.9985,474.16544,0.9993622
+4368,2490.6873,470.1773,2527.738,534.251,0.9993467
+4368,2226.8435,337.12015,2249.569,392.9247,0.9992841
+4368,1980.2994,349.6582,2005.0865,404.70575,0.9992762
+4368,3147.7078,356.73236,3184.616,408.15027,0.9992119
+4368,2578.1465,209.81224,2597.296,255.58925,0.99919695
+4368,1945.8451,424.84595,1968.6139,489.9436,0.99904686
+4368,2511.0815,279.0868,2536.7686,328.73523,0.9989975
+4368,2985.2427,608.46405,3020.023,678.91205,0.99899167
+4368,2538.8206,454.54807,2572.0994,518.53656,0.9989693
+4368,2437.6975,567.3545,2475.7664,641.9791,0.99895716
+4368,2248.1365,247.68356,2267.3123,308.36954,0.9987907
+4368,2583.0388,331.16376,2608.86,381.28632,0.99871075
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+4368,2713.6528,411.3521,2737.608,469.42572,0.99802727
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+4368,2015.1035,161.8947,2031.2822,207.5237,0.9969375
+4368,2465.2551,148.6539,2480.9885,187.19904,0.99515605
+4368,3446.2988,819.2698,3489.8628,892.60803,0.99019206
+4368,3706.8865,785.2833,3743.6023,848.79614,0.977135
+4368,3605.6492,807.9669,3644.214,883.4027,0.96199536
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+4369,3097.8638,749.15497,3132.8687,825.3045,0.99959725
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+4369,2670.5728,308.67175,2694.667,361.6286,0.9994417
+4369,1981.3003,349.42804,2004.5425,404.60468,0.9994037
+4369,2491.7913,470.26883,2528.2239,534.05597,0.9993753
+4369,3272.6519,835.1861,3323.7456,907.3045,0.99936575
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+4369,2538.7893,454.16876,2574.2214,518.8709,0.999283
+4369,2227.3008,337.45245,2250.0952,394.2071,0.999231
+4369,2643.0354,415.764,2666.8909,474.08353,0.99920064
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+4369,3147.6245,356.76495,3184.1724,408.07,0.99917233
+4369,2987.6357,608.4308,3020.393,679.02905,0.99916434
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+4369,2585.0344,331.11893,2609.5378,381.3121,0.9990195
+4369,2511.562,278.89517,2537.213,328.80466,0.9989002
+4369,1945.9917,424.85297,1969.1074,490.12402,0.9987827
+4369,1166.6523,326.23477,1186.5627,381.55563,0.99862325
+4369,2437.682,568.02454,2476.549,642.0071,0.998585
+4369,2713.2107,410.74777,2737.719,468.70773,0.9982681
+4369,2250.357,247.51434,2268.817,307.29993,0.99823785
+4369,1844.3586,123.54461,1863.4214,160.12296,0.9978701
+4369,2016.2332,162.82797,2031.8809,207.7215,0.99771047
+4369,2465.4377,148.44193,2481.1858,186.95657,0.9954359
+4369,3446.3867,819.3403,3489.7036,892.62994,0.9866898
+4369,3706.8984,785.3062,3743.585,848.8161,0.9748452
+4369,3605.6562,807.6391,3644.4302,883.482,0.9691177
+4369,2021.1488,924.8939,2063.5093,1018.2666,0.42412278
+4370,2386.1775,733.5725,2430.3098,820.53687,0.99969935
+4370,3098.6697,748.5396,3133.5198,825.804,0.9996069
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+4370,2670.3145,308.84842,2694.3809,361.69125,0.9994816
+4370,2492.605,470.30353,2528.483,534.1756,0.99942327
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+4370,2227.6042,337.8019,2250.5667,395.15414,0.99937963
+4370,2538.5454,455.19745,2574.6885,518.97235,0.9993641
+4370,1981.621,349.09326,2004.201,404.2193,0.9993537
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+4370,3147.4114,356.78906,3183.9011,408.13922,0.99912137
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+4370,2177.4597,348.73532,2200.7747,409.24454,0.998995
+4370,2642.5813,414.98465,2666.6575,474.39835,0.998847
+4370,2183.6292,230.39305,2206.777,279.41504,0.9988274
+4370,2586.3303,331.23795,2610.5007,381.4238,0.99863786
+4370,1167.2788,326.56494,1186.5898,381.65527,0.99860483
+4370,2438.4583,568.5473,2477.6233,642.16425,0.99859613
+4370,2252.0107,247.97537,2270.0845,306.29056,0.99857605
+4370,1946.2411,424.82712,1970.3998,489.68118,0.9985567
+4370,1843.9684,122.76094,1863.6578,160.13292,0.99814975
+4370,2713.633,409.77826,2737.548,467.3365,0.99774975
+4370,2016.9436,162.93329,2033.3408,207.09067,0.99625117
+4370,2465.7615,148.06082,2481.4583,186.84564,0.99481153
+4370,3446.3481,819.56573,3489.9058,892.6685,0.98488533
+4370,3706.963,785.313,3743.1182,848.8108,0.9729443
+4370,3605.6472,807.3959,3644.9255,883.8926,0.97074753
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+4371,3102.1873,747.7055,3133.8425,823.17084,0.9997099
+4371,2669.874,309.04068,2694.1675,361.74802,0.99949634
+4371,3280.2327,832.194,3327.9675,907.20715,0.999458
+4371,2227.567,338.22638,2251.9941,395.2328,0.99944896
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+4371,2492.409,470.25882,2528.8572,534.23883,0.9993799
+4371,2538.6472,455.9624,2574.9895,519.01416,0.99937254
+4371,1982.1367,349.27908,2003.9229,404.19168,0.99932027
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+4371,2587.5278,331.2815,2611.2168,381.61218,0.9991953
+4371,2578.2131,209.52054,2597.114,255.62695,0.9991928
+4371,3147.2573,356.7895,3183.355,408.09882,0.99912995
+4371,2515.3796,278.7101,2538.0925,328.68393,0.99907506
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+4371,2438.99,568.91077,2479.2341,642.1603,0.99897397
+4371,2992.5298,608.7565,3021.9004,678.98535,0.9989299
+4371,1167.55,326.77737,1186.7437,381.4767,0.9988961
+4371,2640.9595,414.78915,2666.5166,474.33182,0.99879897
+4371,1945.8207,424.85513,1971.3483,489.64938,0.9987231
+4371,1843.8315,122.34712,1862.9666,160.19093,0.99821496
+4371,2253.4434,248.90666,2271.9463,306.20596,0.998171
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+4414,2537.0647,283.21082,2557.7449,330.31268,0.9992435
+4414,2578.7642,209.73296,2598.754,255.28099,0.9992083
+4414,2593.6694,333.31842,2613.0288,384.79633,0.999185
+4414,2646.682,404.28296,2670.444,460.4641,0.99909854
+4414,2128.1445,362.26227,2162.6484,418.1676,0.999057
+4414,1180.7676,327.08127,1197.9551,383.55814,0.99894637
+4414,2318.9294,250.52724,2339.4187,303.72153,0.99889046
+4414,2663.8313,307.83722,2693.5183,358.0738,0.99880993
+4414,1960.3175,358.45343,1992.3353,409.57355,0.9986222
+4414,2171.8667,362.3171,2210.8574,410.80386,0.9985208
+4414,3150.12,360.12778,3182.254,407.53036,0.99850225
+4414,1993.2589,432.08853,2023.2472,490.3845,0.998323
+4414,2686.2168,400.7453,2717.024,452.78778,0.998259
+4414,3016.8389,608.6316,3045.647,680.32007,0.997451
+4414,1821.8184,124.09631,1838.324,160.9714,0.9966947
+4414,3297.6018,846.58997,3340.6282,921.3661,0.99666107
+4414,2486.5938,147.61084,2500.5674,186.6435,0.99613297
+4414,3605.2224,807.95825,3648.2668,881.5835,0.9904744
+4414,3446.4673,818.3936,3490.3716,893.6551,0.98677766
+4414,3707.0376,785.10297,3741.9517,849.368,0.90206766
+4414,2019.7628,924.6114,2063.7156,1018.14777,0.57822186
+4415,2358.7974,707.92944,2393.4502,787.41846,0.9997702
+4415,3135.32,737.7544,3177.8962,810.35315,0.9997092
+4415,2371.3164,464.78394,2411.71,531.5565,0.9996238
+4415,2450.5413,449.30914,2495.1897,512.26935,0.9994868
+4415,2127.5193,363.64896,2161.5046,421.541,0.9994173
+4415,1806.4006,285.39557,1831.0828,340.48706,0.9994081
+4415,2537.689,282.92184,2558.3906,330.67416,0.9993358
+4415,2472.0925,585.77673,2504.5422,657.20483,0.99927443
+4415,2578.976,210.01389,2598.7275,255.33615,0.9992341
+4415,2061.6868,168.0952,2079.404,211.22853,0.9992065
+4415,2164.1897,362.30124,2209.6536,411.67996,0.99918336
+4415,2206.3433,223.52835,2225.8462,270.2633,0.9991652
+4415,2646.4983,404.83035,2670.277,461.69278,0.9990464
+4415,2593.3757,333.51996,2612.5042,384.76654,0.9989367
+4415,1180.7654,327.11182,1197.9797,383.60187,0.9988758
+4415,2664.458,308.0791,2694.0376,358.10626,0.99885786
+4415,2318.7908,249.7289,2341.5925,303.96185,0.99877137
+4415,1960.8011,357.98474,1992.6195,409.4616,0.9987093
+4415,3150.1423,360.28027,3182.2566,407.45697,0.99842334
+4415,2686.1636,400.70316,2717.0132,452.7004,0.998302
+4415,1993.348,432.99954,2022.1771,490.93015,0.997985
+4415,3016.9026,608.56274,3045.4968,680.32117,0.99725854
+4415,3297.7126,846.71783,3340.2996,921.59894,0.996865
+4415,2486.969,147.63016,2500.6282,186.5759,0.9966987
+4415,1821.7831,124.11197,1838.2968,160.82086,0.9966534
+4415,3605.2515,808.0415,3650.1733,881.77844,0.99354184
+4415,3446.4753,818.3014,3490.3547,893.5797,0.9877934
+4415,3707.0774,785.0939,3741.885,849.54126,0.904988
+4415,2019.6682,924.63617,2063.7131,1018.14825,0.57294685
+4416,1806.3654,285.66754,1832.8192,340.3141,0.9997002
+4416,2356.224,708.46045,2393.213,787.2621,0.99968624
+4416,3135.2654,737.87866,3179.2747,810.3342,0.9996495
+4416,2447.601,449.73666,2488.5435,513.5958,0.99952054
+4416,2369.824,465.6567,2408.5461,533.1417,0.9994036
+4416,2469.1023,586.39844,2504.3508,657.432,0.99939096
+4416,2537.4358,282.94177,2559.1584,330.51776,0.9993895
+4416,2206.2817,223.41266,2226.0498,270.1399,0.99923396
+4416,2647.3076,404.4646,2670.372,461.03546,0.9991959
+4416,2578.8003,209.94733,2598.3257,255.18063,0.99916637
+4416,2062.512,168.31412,2080.034,212.05469,0.99898046
+4416,1962.5321,356.28052,1992.901,408.76276,0.9989741
+4416,2593.235,333.6028,2612.1462,384.6638,0.99893326
+4416,1180.8098,327.11832,1198.1028,383.57126,0.9989273
+4416,2126.8113,365.0554,2162.8215,423.04214,0.9988225
+4416,1993.378,434.22583,2020.8041,490.84766,0.9987816
+4416,2666.1462,308.09402,2694.8752,358.10794,0.99875724
+4416,2319.0796,250.00122,2342.484,304.00275,0.9986125
+4416,3150.309,360.41574,3182.0771,407.44815,0.99850345
+4416,2160.278,362.74213,2205.5022,411.17773,0.99820745
+4416,2686.2246,400.74414,2717.5112,452.75208,0.9982002
+4416,3016.855,608.58215,3045.4014,680.30664,0.9975567
+4416,3297.45,846.7317,3339.9436,921.7705,0.99711454
+4416,1821.8445,123.987366,1838.3853,160.64789,0.9967828
+4416,2487.1667,147.78262,2500.743,186.53485,0.99652916
+4416,3605.077,808.51086,3651.593,881.5209,0.9944137
+4416,3446.4626,818.30536,3490.3064,893.58746,0.9893902
+4416,3707.054,785.1118,3741.9114,849.5088,0.9202772
+4416,2019.6266,924.6448,2063.7178,1018.1437,0.5782854
+4417,3135.3691,737.6547,3179.6973,810.19635,0.9997042
+4417,2352.499,710.5569,2392.5957,787.2456,0.9996226
+4417,2369.668,466.24832,2404.7583,533.07465,0.9995709
+4417,1806.2506,286.34525,1836.6398,339.96292,0.9995573
+4417,2537.3916,283.02948,2559.544,330.98444,0.99947876
+4417,2447.5903,450.5441,2481.1128,513.65,0.99942005
+4417,1992.7788,434.77756,2020.0347,490.91336,0.9992378
+4417,2578.5361,209.6434,2598.272,254.9813,0.99922156
+4417,2668.705,308.11267,2695.879,358.19202,0.99919456
+4417,2465.6938,586.80457,2504.2563,657.5166,0.99919397
+4417,1965.8807,355.5725,1993.2787,407.9652,0.9991929
+4417,2206.206,223.45241,2225.9927,270.2929,0.99918085
+4417,2647.4377,404.4281,2670.2053,461.32855,0.9991217
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+4417,2063.4094,168.30634,2080.5305,212.4151,0.9988895
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+4417,3297.2668,846.73,3339.639,921.37305,0.99717784
+4417,2487.3235,147.67848,2500.973,186.55824,0.99689984
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+4417,3605.127,808.58606,3652.9702,881.52405,0.9937522
+4417,2154.7937,363.84665,2194.5535,413.24332,0.9912485
+4417,3446.443,818.3236,3490.3499,893.6034,0.99051595
+4417,3707.047,785.11115,3741.9363,849.51654,0.91280496
+4417,2019.6038,924.66614,2063.7368,1018.15564,0.5569499
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+4418,1991.2183,435.5862,2019.8066,490.8785,0.9994154
+4418,2578.2773,209.52086,2598.1445,254.96281,0.99928623
+4418,1969.117,354.24976,1993.1406,406.94165,0.999263
+4418,2064.1196,168.70605,2081.2114,212.60709,0.99923766
+4418,2647.9058,404.59906,2669.9355,461.6665,0.9991875
+4418,2206.0608,223.37053,2226.0852,269.8834,0.99916816
+4418,2442.8691,450.40048,2472.0557,513.74774,0.99903333
+4418,2538.5193,283.52808,2560.1155,331.37427,0.99901843
+4418,2463.6128,587.9527,2504.087,657.92816,0.9990127
+4418,1180.8011,327.02255,1198.2565,383.55618,0.9989925
+4418,2671.8843,308.46063,2696.4702,357.90662,0.9989675
+4418,2320.91,252.97057,2343.047,303.45853,0.99878544
+4418,3149.3235,360.59116,3180.6604,407.3243,0.99869114
+4418,2686.1084,400.7226,2718.4087,452.80908,0.9985056
+4418,2592.2056,334.00217,2611.6714,384.83438,0.99820626
+4418,2124.7896,370.88925,2157.2266,428.01505,0.99802196
+4418,3016.8564,608.65497,3045.186,680.28973,0.9976115
+4418,3296.1904,846.6668,3339.7134,921.5898,0.9966871
+4418,2487.3835,147.91336,2501.0862,186.55344,0.99643695
+4418,1821.8572,123.86703,1838.6277,160.51689,0.99625385
+4418,3446.4238,818.32776,3490.2368,893.5819,0.9925581
+4418,3605.228,808.45105,3654.895,881.1095,0.99225545
+4418,2150.5425,365.6823,2187.835,414.04144,0.9893476
+4418,3707.073,785.12067,3741.97,849.3643,0.92930555
+4418,2019.5983,924.5829,2063.752,1018.15063,0.5626446
+4419,3135.7957,737.234,3180.799,809.9691,0.9997092
+4419,2344.7578,712.9456,2391.8125,787.0297,0.99970907
+4419,1806.7393,286.64737,1841.3411,340.2977,0.99957055
+4419,1989.1241,435.451,2020.1908,491.6081,0.9994765
+4419,2438.3816,449.88098,2463.6072,514.1025,0.9993648
+4419,2064.6638,168.73727,2081.591,212.73163,0.99931276
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+4419,2368.7449,463.84454,2397.1116,531.0407,0.99928695
+4419,2578.0833,209.45653,2598.073,254.90263,0.9992119
+4419,2461.777,588.53,2504.098,658.2899,0.9991196
+4419,2205.753,222.97858,2226.1191,269.6226,0.99907625
+4419,2322.3071,254.16534,2343.495,303.90802,0.9990404
+4419,2539.4062,283.62064,2560.8408,331.66617,0.9990283
+4419,1180.8198,327.01227,1198.2866,383.50842,0.9990109
+4419,2647.5122,404.86462,2669.7544,462.1737,0.99900836
+4419,3148.7202,361.70065,3179.773,407.18143,0.9986345
+4419,2686.2717,401.01096,2718.6257,452.79422,0.9985512
+4419,1971.9606,353.94257,1993.5704,406.26306,0.99853873
+4419,3016.8809,608.7066,3045.1265,680.2448,0.9977119
+4419,2591.9878,334.2866,2611.0415,384.84512,0.9976326
+4419,3294.549,846.9638,3339.7039,921.5599,0.99696845
+4419,1821.8815,123.8295,1839.0543,160.51364,0.9965077
+4419,2487.544,148.23457,2501.4536,186.5356,0.9957218
+4419,3446.4648,818.2875,3490.186,893.53296,0.9934994
+4419,2123.879,373.0267,2155.2925,427.3669,0.9915632
+4419,3604.8916,808.53046,3655.9038,881.13763,0.9873769
+4419,2143.1206,366.7196,2178.5703,414.67896,0.97256607
+4419,3707.108,785.1152,3742.0107,849.3675,0.9163439
+4419,2019.4596,924.6172,2063.7576,1018.14966,0.5645635
+4420,3136.0107,737.20935,3181.4849,809.9789,0.99971336
+4420,2341.6973,713.5515,2391.3022,787.0039,0.999632
+4420,2676.114,309.73013,2697.7058,358.6218,0.9994808
+4420,2433.6157,448.6338,2461.6113,514.31445,0.9994404
+4420,2367.8381,462.34296,2392.1233,528.49805,0.999374
+4420,1807.5632,286.65323,1841.7092,340.30307,0.99936503
+4420,1986.7972,436.42905,2019.4572,494.83524,0.9993345
+4420,2460.7827,589.2698,2503.669,658.9485,0.9992411
+4420,2064.885,168.70255,2082.3762,212.2717,0.99916244
+4420,2577.8333,209.40001,2597.7156,254.55089,0.999141
+4420,2540.7434,284.02267,2561.41,332.21555,0.9990982
+4420,1180.8206,327.013,1198.3755,383.4674,0.9990013
+4420,2323.428,255.06857,2344.3137,303.7772,0.99893695
+4420,2647.7178,405.8445,2668.9985,462.77103,0.9989052
+4420,2205.7966,222.93634,2226.0315,269.62225,0.9989047
+4420,3148.0354,362.07275,3178.9714,407.22833,0.9984939
+4420,1972.7546,353.57022,1994.1799,404.8618,0.99848974
+4420,2686.2156,401.159,2719.0623,452.83997,0.9984622
+4420,3016.8892,608.6329,3045.0913,680.195,0.99795413
+4420,2591.4163,334.78284,2610.4827,385.03436,0.9978205
+4420,1821.7578,123.710014,1839.6477,160.87708,0.996849
+4420,3293.4573,847.5165,3339.5974,921.9403,0.99670583
+4420,2121.9956,374.32175,2150.5386,427.3528,0.99593663
+4420,2487.638,148.39291,2501.738,186.61203,0.9955664
+4420,3446.4868,818.30835,3490.1572,893.4867,0.993779
+4420,3604.2263,808.71844,3656.947,881.1701,0.9885387
+4420,2139.85,368.00375,2178.7563,419.05673,0.96750945
+4420,3707.095,785.05206,3741.9983,849.42584,0.92294455
+4420,2019.2606,924.55194,2063.7622,1018.1403,0.57306325
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+4421,2677.3657,310.33563,2697.9312,359.6396,0.99947995
+4421,2065.0435,168.73439,2082.977,211.91878,0.99936646
+4421,2461.3057,590.1031,2503.2954,659.3737,0.9993419
+4421,2541.5522,284.64398,2562.3887,332.87054,0.99933845
+4421,2424.784,448.6734,2459.7224,513.9607,0.99928826
+4421,2366.069,460.81055,2389.488,527.34845,0.9992112
+4421,2205.7893,222.92041,2225.7634,269.69052,0.9991091
+4421,1809.8158,286.3517,1842.1031,340.0463,0.9990771
+4421,2324.6196,255.4716,2345.38,303.38303,0.99907684
+4421,2577.4153,209.30551,2597.1477,254.87326,0.9990621
+4421,2647.271,406.45142,2668.832,463.7556,0.9990331
+4421,1180.8334,327.0382,1198.4315,383.45636,0.998901
+4421,1985.1577,437.214,2016.3315,495.29785,0.9988194
+4421,1973.6178,353.62747,1994.8336,404.46927,0.9987931
+4421,2589.93,334.62143,2610.0056,385.08365,0.9986607
+4421,2686.198,401.33304,2719.3518,452.90732,0.9984825
+4421,3147.5051,362.05646,3178.1877,407.28168,0.9982474
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+4421,3292.7751,848.37634,3339.6506,922.9249,0.99703515
+4421,1821.8391,123.80208,1840.2849,160.98622,0.996042
+4421,2487.8123,148.47302,2501.9402,186.59174,0.99544686
+4421,2119.9348,374.53842,2146.244,427.01755,0.9933796
+4421,3446.5022,818.33606,3490.0266,893.4348,0.9932939
+4421,3603.1216,808.50604,3657.5605,881.3741,0.9876143
+4421,2133.5913,370.5957,2180.3618,423.26154,0.9653156
+4421,3707.0923,785.08344,3742.0098,849.4902,0.9428207
+4421,2019.1027,924.60333,2063.7483,1018.11566,0.59790057
+4422,3136.2026,737.2258,3181.9648,810.0105,0.99974126
+4422,2339.4663,712.30786,2390.3271,787.4712,0.99973553
+4422,2677.7476,310.7928,2699.0059,360.48926,0.9994796
+4422,2364.4817,459.7258,2387.0862,526.89545,0.9994686
+4422,2541.8801,285.46848,2563.3137,333.43204,0.9993304
+4422,2460.4133,590.68616,2502.9387,659.54736,0.9993154
+4422,2325.694,256.34628,2346.4182,303.7153,0.99926037
+4422,2065.561,168.8406,2084.269,212.44171,0.99925035
+4422,2577.4219,209.61353,2596.1182,254.3222,0.99913174
+4422,2414.6716,448.79044,2457.3591,513.5869,0.9991185
+4422,2647.166,406.67773,2668.8315,464.27374,0.9990188
+4422,1180.8457,327.06247,1198.428,383.452,0.9989299
+4422,1982.7216,438.96622,2012.0184,496.6542,0.9988165
+4422,2205.8196,223.09041,2225.6404,269.58472,0.9988079
+4422,1813.2219,286.1146,1842.4031,339.4614,0.99879885
+4422,2586.9458,334.63522,2609.8657,385.03262,0.9987507
+4422,2687.9106,401.40204,2719.3037,452.87653,0.9984794
+4422,3147.7837,363.07382,3177.2798,407.38358,0.9983089
+4422,1973.9161,353.37247,1996.3563,404.76883,0.9979596
+4422,3016.9258,608.6562,3044.9185,680.1795,0.99792886
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+4456,2002.744,338.77905,2023.6764,385.86816,0.9987047
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+4457,1879.4071,470.1879,1924.349,532.96606,0.9989242
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+4457,2634.7297,418.01697,2660.736,476.41876,0.99872786
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+4457,2000.8368,339.83722,2022.5189,386.00793,0.99816114
+4457,2295.735,466.9556,2332.056,526.0442,0.9981584
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+4457,2018.9839,924.6067,2063.8323,1018.15283,0.58244205
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+4458,2543.9355,287.05286,2566.2573,335.74524,0.9993249
+4458,3127.2837,740.3521,3162.5835,814.2525,0.99930024
+4458,2187.0315,219.67844,2209.1848,267.502,0.9990097
+4458,2055.458,167.88982,2072.4824,210.81184,0.9989755
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+4458,2634.906,418.37747,2661.5496,476.3535,0.9987804
+4458,3001.6484,610.5845,3030.2246,677.55725,0.9984962
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+4458,1997.3981,341.9046,2021.0319,386.36682,0.99815434
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+4458,2295.6829,466.68658,2333.0427,525.7482,0.99811894
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+4458,2019.0135,924.59296,2063.8257,1018.1326,0.61451805
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+4459,3127.3508,740.8794,3161.5247,813.62683,0.999313
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+4459,1182.2096,330.54062,1201.0863,385.98447,0.99897194
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+4460,1990.1127,344.5548,2018.8019,386.0138,0.9956897
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+4460,2019.0316,924.5925,2063.8057,1018.155,0.60263956
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+4461,2335.439,456.2804,2367.1182,511.20673,0.9949384
+4461,1988.1382,346.20978,2018.169,386.56866,0.9939892
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+4462,3126.9553,740.5193,3157.9138,811.39233,0.99906725
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+4462,1986.8501,347.15344,2018.2441,387.44543,0.994511
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+4463,1985.5688,347.5968,2017.7585,387.52283,0.9937271
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+4463,3600.839,809.35144,3649.8733,882.4955,0.98929745
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+4463,3706.9016,785.5444,3741.6199,849.45087,0.96994835
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+4464,1985.3293,348.95813,2016.4473,387.30328,0.9965765
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diff --git a/data/second_half.txt b/data/second_half.txt
new file mode 100644
index 0000000..2e1b4a7
--- /dev/null
+++ b/data/second_half.txt
@@ -0,0 +1,136685 @@
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+267,1478.5771,237.71977,1497.9302,288.2068,0.9991322
+267,2353.1028,369.07703,2375.2434,428.07117,0.9990901
+267,2989.9905,855.5505,3033.741,933.7028,0.99903286
+267,2587.2346,496.49152,2613.4631,562.3327,0.9990326
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+267,2305.136,289.03336,2323.9192,341.0557,0.99901414
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+267,2073.5125,324.96783,2098.9744,375.97522,0.9986286
+267,2057.0881,407.70792,2089.3381,463.23947,0.9984092
+267,867.4787,329.8063,884.7508,380.97238,0.9983943
+267,1775.7327,324.94727,1793.3503,378.36267,0.99811447
+267,2032.7731,260.81314,2053.9192,303.83975,0.9980742
+267,3182.321,358.675,3206.512,407.037,0.9980428
+267,2110.397,548.4228,2145.2695,615.03326,0.99802136
+267,1990.801,187.15437,2017.3198,236.98372,0.9971841
+267,2046.5973,248.91586,2074.0522,287.32455,0.99460804
+267,1989.5752,170.72423,2014.929,211.12132,0.9941275
+267,2536.9,893.1876,2578.6,982.1974,0.99267006
+267,2097.1313,556.6972,2128.9849,626.0413,0.987688
+268,1387.8928,373.03192,1408.7051,433.35938,0.99945456
+268,1836.033,264.16833,1856.6528,316.70435,0.9993679
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+268,2473.5374,292.00327,2497.7634,343.06766,0.99915814
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+268,2353.1338,369.0728,2375.2412,428.09906,0.99907506
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+268,2587.2312,496.4726,2613.47,562.33685,0.9990245
+268,2989.7131,855.52637,3033.8333,933.69336,0.9990225
+268,1443.5394,553.93585,1467.6388,629.0098,0.99901843
+268,2305.138,288.968,2323.9172,341.05954,0.99901426
+268,2073.512,324.921,2098.9788,375.97446,0.9986498
+268,867.4745,329.7288,884.7573,380.96982,0.99841046
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+268,2032.7606,260.80994,2053.9333,303.88635,0.9980348
+268,2110.4114,548.4391,2145.2683,615.0265,0.9980348
+268,3181.6594,358.6893,3206.5002,407.01254,0.99786806
+268,1990.7876,187.15932,2017.3762,236.98347,0.9972223
+268,2046.5925,248.92636,2074.047,287.32443,0.9946319
+268,1989.5697,170.81319,2014.9574,211.11986,0.99429965
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+269,2472.9082,292.10712,2497.3955,343.07172,0.9993436
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+269,1779.9221,152.47208,1799.9812,198.12207,0.9991485
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+269,2353.0576,369.22684,2375.77,428.10562,0.99908745
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+269,1443.5067,553.9338,1467.6395,628.99005,0.999079
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+269,2587.2656,496.48434,2613.4785,562.34045,0.99900335
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+269,2108.883,547.5963,2141.365,614.3873,0.99622285
+269,1994.095,186.70653,2016.137,236.70558,0.99591136
+269,2043.9625,249.9202,2075.2112,287.83444,0.9957562
+269,1991.4465,170.51794,2015.8398,211.3458,0.99190044
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+270,1996.1249,186.23543,2016.9974,235.87978,0.99710494
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+270,2104.6433,545.76654,2137.283,613.4118,0.9916825
+270,1995.458,171.12357,2016.9155,211.09781,0.98194075
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+271,1385.1318,373.64383,1405.4282,433.8375,0.99909353
+271,2106.4102,195.78465,2125.5513,242.56456,0.9990735
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+271,2353.145,369.40836,2376.206,428.22067,0.99897194
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+271,2042.309,252.55797,2078.672,288.8893,0.9968646
+271,1998.1425,187.19904,2016.9735,236.9271,0.9958877
+271,2102.915,557.40015,2134.752,627.0127,0.99495894
+271,2000.4836,170.65088,2018.5464,212.37097,0.94891864
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+272,2076.6465,323.4296,2105.7515,375.74277,0.99814343
+272,2042.0641,253.15074,2080.7551,288.46503,0.99756914
+272,2000.5208,187.2538,2017.8281,235.84444,0.9975218
+272,1774.5955,326.7551,1792.3633,380.13467,0.9974074
+272,2104.5024,556.7349,2135.5776,627.30756,0.9969169
+272,3176.469,358.20117,3202.9016,407.10388,0.9965874
+272,2002.4335,170.56459,2020.1837,212.25749,0.92234766
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+273,2471.8806,292.28058,2496.432,343.26215,0.99965477
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+273,1778.1,152.16794,1800.2374,197.69244,0.9990964
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+273,2586.5444,496.96286,2613.3623,562.44147,0.99889934
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+273,866.20636,329.3247,883.9559,380.16174,0.9988256
+273,2001.5731,188.20119,2018.7023,235.87572,0.99881977
+273,2354.569,369.5173,2376.5022,428.2783,0.99880385
+273,2106.9553,195.53394,2125.4202,243.14194,0.99876887
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+273,3176.653,358.1845,3202.1975,407.13635,0.9982602
+273,2042.4265,253.76657,2082.6926,288.85086,0.9976878
+273,1774.3928,327.0532,1792.0376,380.46115,0.99716765
+273,2104.5867,556.9546,2136.5505,627.1229,0.99713564
+273,2004.4539,171.3437,2020.729,207.31981,0.9568132
+273,2545.1313,893.556,2585.7202,982.3617,0.9308361
+274,2471.8464,292.306,2495.869,343.243,0.99966896
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+274,2331.8303,207.70107,2358.9373,252.62282,0.99941194
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+274,2977.0808,854.9118,3035.6702,932.9086,0.9989176
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+274,865.82855,329.13983,884.27374,380.23352,0.9987766
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+274,2355.1616,369.57666,2376.7056,428.3405,0.99867505
+274,2299.8252,286.20026,2321.31,337.61774,0.99865913
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+274,1475.4998,235.82498,1492.6348,285.52524,0.99860054
+274,1612.3124,124.11154,1629.3536,161.44913,0.9983943
+274,1773.9913,327.08636,1792.5045,380.2838,0.9975139
+274,2045.0103,254.01645,2084.0107,289.21323,0.996619
+274,2545.373,893.4326,2587.224,982.0669,0.99249387
+274,2098.7495,544.7229,2132.5889,609.8773,0.9894243
+274,2005.6415,171.5148,2021.9762,211.47598,0.9649575
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+275,2087.544,320.38037,2110.1475,375.79138,0.99882466
+275,2014.189,260.83768,2041.1506,308.54538,0.9988049
+275,2355.1975,369.66074,2376.7175,428.36935,0.99870217
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+275,2107.0679,195.41747,2125.2183,242.73558,0.9986343
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+276,2001.9132,189.67078,2027.3663,235.5115,0.99961406
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+938,2026.0637,682.7554,2060.5024,759.30347,0.99880874
+938,1984.3444,435.987,2012.9349,501.0525,0.99879944
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+938,909.4484,517.3741,946.0495,579.4854,0.99763215
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+939,2222.8967,470.03146,2247.4543,536.88666,0.9994698
+939,772.11755,368.05002,791.12256,419.27182,0.9994178
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+939,2026.0522,682.83655,2060.626,759.2865,0.99886703
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+939,2021.8403,308.7572,2049.4407,365.79138,0.99880254
+939,1985.3162,435.98413,2013.2783,501.0631,0.9987834
+939,612.3195,766.2539,648.6475,824.9209,0.99873525
+939,977.0866,467.56598,1006.71014,527.65344,0.9986859
+939,3104.2668,457.27698,3129.786,515.2458,0.99839514
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+939,1084.2911,800.2166,1137.3824,877.0965,0.9968143
+939,1882.02,975.0211,1918.7686,1039.9662,0.282813
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+941,1986.7092,435.94806,2013.4937,500.99695,0.9984458
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+942,2026.0186,682.8755,2061.1775,759.28125,0.9989177
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+942,1986.7151,435.82065,2013.7095,501.06363,0.9985098
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+946,1986.7031,435.6552,2014.3052,501.0874,0.99865204
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+956,1314.0123,284.00204,1334.5221,335.31882,0.99911696
+956,2025.8721,682.648,2062.1782,759.3427,0.9990362
+956,1406.7517,500.39005,1428.2642,565.9039,0.9990115
+956,1986.6588,435.37567,2014.5089,500.85284,0.9987748
+956,928.15546,506.13312,952.28265,568.1409,0.99864644
+956,2226.7458,471.15924,2250.782,539.2039,0.9986228
+956,1136.183,322.0943,1158.753,378.97162,0.9985707
+956,982.9167,474.20447,1014.28033,533.6564,0.9980515
+956,1300.5612,134.09744,1313.1974,170.49289,0.99698645
+956,1065.8558,917.9646,1099.3292,992.0918,0.5298192
+956,1891.0624,978.08655,1926.4843,1039.9282,0.4967301
+956,3708.026,789.6154,3744.1406,858.8853,0.22192195
+956,3652.3748,779.9982,3683.221,845.09454,0.2063848
+956,3683.8735,808.93427,3716.1597,881.0433,0.19881804
+956,3724.2188,811.4657,3754.7012,877.57886,0.13030954
+957,1646.3855,685.48016,1680.8367,763.76544,0.9997739
+957,1149.5796,504.5468,1180.6606,568.0976,0.99974626
+957,2161.3342,702.68005,2188.0095,785.34265,0.9997086
+957,1566.5625,491.1225,1597.7336,558.1611,0.99968183
+957,773.4155,368.02994,791.7572,419.28018,0.99949133
+957,613.8438,756.46875,648.4639,825.20776,0.99945205
+957,1457.0333,382.14755,1485.2294,442.84518,0.9994429
+957,1053.043,795.9193,1093.0293,877.50085,0.9994318
+957,909.86035,515.64075,945.042,578.56494,0.999403
+957,1278.1222,516.6633,1303.3099,584.9458,0.9993773
+957,1536.9158,674.4372,1565.4885,749.20197,0.9993382
+957,2334.1194,857.9636,2376.9182,947.68335,0.99929845
+957,613.1551,692.4423,646.6016,763.5373,0.99925655
+957,2016.5354,313.39105,2037.4255,370.30548,0.99925596
+957,3096.3562,455.83868,3118.6492,512.1195,0.99910516
+957,1313.0635,284.28427,1334.2412,334.60617,0.9990557
+957,1407.2533,500.28418,1428.537,565.91516,0.9990446
+957,2025.8721,682.64795,2062.1782,759.3428,0.9990361
+957,1986.6587,435.37527,2014.5088,500.853,0.99877554
+957,928.30585,506.22775,952.5319,568.1152,0.99862146
+957,2226.7478,471.15417,2250.78,539.2012,0.99860364
+957,1135.4645,322.038,1157.3517,378.955,0.99844766
+957,982.7505,474.23315,1014.8673,534.1459,0.99773186
+957,1300.1912,134.12405,1312.7498,170.39359,0.99661523
+957,1891.0059,979.0879,1927.6189,1040.0,0.47125208
+957,1066.8066,915.67267,1100.8027,991.97235,0.33110657
+957,3654.0522,780.26776,3684.4648,846.1086,0.15795188
+957,3724.4832,811.2982,3754.7922,877.3872,0.1231467
+957,3708.135,789.0846,3743.756,858.57227,0.12039264
+957,3753.6672,837.9346,3778.9695,874.61487,0.10230149
+958,1646.1035,685.4897,1680.8875,763.7288,0.99973077
+958,1149.2068,504.96893,1178.8298,568.2327,0.9996691
+958,1565.7399,491.0162,1597.0726,558.06256,0.999629
+958,2159.0925,702.6386,2186.806,785.46234,0.99962294
+958,1277.0364,516.8248,1302.8291,586.39856,0.999495
+958,1455.7749,382.3539,1484.0273,443.45694,0.99946564
+958,773.50037,367.9956,791.80286,419.28137,0.9994474
+958,613.834,756.1711,648.5045,825.12665,0.999426
+958,909.6641,515.63116,944.8879,578.5151,0.99941504
+958,1537.1166,674.5679,1565.423,749.35657,0.9993761
+958,612.7143,692.65753,646.69293,763.22076,0.99928707
+958,2336.1396,857.7389,2376.8481,947.66187,0.9992446
+958,1312.6222,284.46487,1333.9999,334.61935,0.99918276
+958,2015.3953,313.73157,2037.0325,370.54602,0.99915624
+958,1051.3745,795.9438,1090.7866,877.2343,0.9991283
+958,2025.8723,682.64764,2062.1753,759.34344,0.999032
+958,3096.1711,455.58694,3118.2883,511.93716,0.9990288
+958,1407.7081,500.0833,1429.1588,565.884,0.9988631
+958,1986.6527,435.3947,2014.5197,500.85297,0.99875987
+958,928.4297,506.18735,952.38464,568.143,0.99869686
+958,2226.7144,471.18216,2250.7524,539.19147,0.9986412
+958,1133.8525,322.4378,1156.5051,378.96667,0.99820685
+958,982.9755,474.42157,1015.0922,534.3199,0.9976472
+958,1299.553,133.90341,1312.2874,170.4902,0.99555814
+958,1890.9961,979.47784,1927.9329,1040.0,0.47367537
+958,1068.4431,913.2627,1100.3972,992.08386,0.34904334
+958,3655.8108,778.65295,3684.444,844.1704,0.23553705
+958,3707.6375,789.2598,3744.4836,859.05194,0.17429501
+958,3754.7432,837.8754,3779.4937,874.6165,0.12888552
+958,3724.393,811.7826,3754.9043,876.7927,0.11641007
+959,1645.8134,685.51697,1680.6759,763.23865,0.99973553
+959,1564.5956,490.86526,1596.6039,558.1108,0.9997124
+959,1148.5245,505.10016,1176.9039,568.2321,0.99964356
+959,1454.9326,383.229,1482.9297,443.92236,0.9995777
+959,2157.1396,702.7233,2186.0298,785.3858,0.99954957
+959,1537.2247,674.7173,1565.3466,749.25195,0.9994611
+959,773.4843,367.97437,791.8427,419.29578,0.99946004
+959,1275.624,516.84,1302.7329,587.37726,0.9994394
+959,613.8488,756.0402,648.46124,825.14996,0.9993766
+959,2013.912,314.0537,2036.7467,370.40363,0.9993259
+959,2338.0227,857.53345,2376.8347,947.624,0.9992737
+959,1312.2457,284.2527,1333.8365,334.77124,0.99924845
+959,612.33167,692.9013,646.66125,763.3133,0.9992218
+959,2025.872,682.6466,2062.1711,759.3434,0.99903274
+959,909.3686,515.91907,945.01276,578.3613,0.9990208
+959,3095.4463,455.5568,3117.891,512.383,0.9989629
+959,1408.3942,499.7736,1430.0577,565.6753,0.99887234
+959,1050.349,795.2617,1084.8224,877.2754,0.99876356
+959,1986.6542,435.4136,2014.5314,500.85123,0.9987207
+959,2226.6782,471.1744,2250.7275,539.1877,0.9986443
+959,1133.4276,322.46826,1155.2635,379.0417,0.998251
+959,928.05115,506.2823,952.2931,568.1541,0.998064
+959,983.5981,474.54947,1014.6917,535.11584,0.9968273
+959,1299.2396,133.97122,1312.0441,170.2575,0.99586177
+959,1891.0839,979.94666,1928.5514,1040.0,0.56857306
+959,1069.7461,911.5167,1101.5793,992.14404,0.50351155
+959,3657.2195,778.6677,3685.0544,845.03577,0.19045778
+959,3755.8518,837.77704,3780.4055,874.84174,0.17199545
+960,1147.7772,505.39682,1175.1168,569.0197,0.99972004
+960,1563.8988,490.93866,1595.5211,558.5776,0.999706
+960,1645.4727,685.43024,1679.9314,762.87555,0.99965894
+960,2012.504,314.23602,2036.4296,370.37897,0.9994649
+960,1537.0823,674.91693,1565.3967,749.1182,0.99945754
+960,613.87,755.8429,648.474,825.20557,0.999356
+960,773.62836,367.95282,791.82355,419.2365,0.9993457
+960,2149.803,702.8808,2185.703,785.3183,0.9993363
+960,1311.7938,284.25192,1333.7513,335.50546,0.99929
+960,612.0471,693.4883,646.6266,763.14,0.9992798
+960,1454.0376,383.68915,1482.3884,444.48816,0.999276
+960,1049.722,795.5621,1083.2113,877.5695,0.999243
+960,2339.7817,857.3274,2376.6133,947.58093,0.99914277
+960,3094.352,454.9282,3116.7798,511.9844,0.99908483
+960,1274.7102,517.00726,1302.3828,588.6871,0.9990606
+960,2025.877,682.65656,2062.1733,759.3626,0.99903095
+960,1409.1782,499.7912,1431.0024,565.7398,0.99900776
+960,1986.6495,435.3593,2014.5006,500.8853,0.9987754
+960,1132.4723,322.31827,1155.0406,379.23495,0.99861395
+960,2226.643,471.11942,2250.6313,539.1968,0.9985701
+960,908.8893,516.106,945.19543,578.8033,0.99835557
+960,927.46484,506.21436,952.6233,568.3799,0.9978483
+960,983.88354,474.94775,1016.0239,535.9755,0.9969892
+960,1298.5369,134.03271,1311.158,170.37405,0.9954595
+960,1070.315,908.9503,1102.7476,991.9442,0.7172052
+960,1891.1248,980.3889,1928.1155,1040.0,0.63656205
+960,3657.1143,778.6545,3685.7021,845.0351,0.23656237
+960,3724.0444,812.0672,3755.2935,876.3492,0.17201866
+960,3707.9507,788.6057,3744.8398,857.91016,0.12227105
+960,3756.6948,837.7158,3781.0024,874.78796,0.105724975
+961,1644.9186,685.3309,1679.1498,762.5494,0.99965703
+961,1562.9941,490.85294,1594.4529,559.12494,0.9996203
+961,1536.1576,675.04156,1565.4655,749.00946,0.9994954
+961,1147.1292,505.73273,1173.6992,569.3547,0.9994282
+961,2011.5626,314.36276,2036.0985,370.6769,0.9994068
+961,1311.4025,284.47507,1333.3822,336.09848,0.99935204
+961,613.8821,755.57556,648.50305,825.2001,0.99935013
+961,2145.7866,702.7073,2185.2998,785.4404,0.99934965
+961,611.4383,694.0174,646.37775,763.03217,0.99934787
+961,1453.2235,383.79626,1481.7216,445.187,0.99932694
+961,2341.1333,857.42017,2376.52,947.80774,0.9993217
+961,773.8259,367.95517,791.8181,419.2253,0.99925596
+961,1048.4957,795.87366,1082.8602,877.3981,0.99923897
+961,2025.8796,682.6617,2062.1895,759.3466,0.9990338
+961,1410.4282,499.3643,1431.9612,565.5526,0.99899215
+961,908.3832,516.73926,945.3956,578.62537,0.9989189
+961,3090.449,454.48563,3116.2312,512.57135,0.998911
+961,1273.316,517.7317,1302.1412,589.2943,0.9988127
+961,1986.6478,435.3823,2014.5197,500.87967,0.998808
+961,1131.6604,322.46213,1154.7524,379.19852,0.9987657
+961,2226.5278,471.04037,2250.6465,539.18744,0.9986499
+961,926.78235,506.25986,952.8363,568.1873,0.99790144
+961,984.59625,475.97736,1016.3652,537.41565,0.99763846
+961,1298.2681,134.04214,1311.0725,170.3541,0.99664766
+961,1071.604,908.55817,1105.2864,991.4682,0.7201871
+961,1891.1287,980.4792,1928.1575,1040.0,0.65239334
+961,3657.2617,778.59955,3686.1553,845.47516,0.22295687
+961,3757.7756,837.8818,3781.4246,874.55133,0.15845679
+961,3724.2485,811.87213,3755.2969,876.18524,0.13779093
+962,1562.1285,490.7157,1593.121,558.5569,0.9996008
+962,1643.8666,684.8158,1677.7115,761.728,0.99958736
+962,2143.7703,703.04736,2184.7546,785.2544,0.99953973
+962,1311.2494,284.38586,1332.8724,336.18457,0.99942005
+962,2341.266,857.42255,2376.5,947.91693,0.99937016
+962,613.87103,755.58484,648.4777,825.2029,0.99935895
+962,1146.6688,506.2625,1171.8038,570.2775,0.99935335
+962,1533.2737,675.08716,1565.5552,748.9833,0.9993167
+962,610.6763,694.4125,646.2407,763.03345,0.9993129
+962,1452.4425,384.29565,1481.08,446.0968,0.99926394
+962,2010.7832,314.42365,2036.0066,373.19427,0.9992379
+962,773.87744,367.99768,791.8203,419.22333,0.99923706
+962,1272.6936,518.50867,1301.6533,590.35645,0.99911445
+962,1046.5674,796.1143,1082.5066,877.002,0.9990664
+962,2025.8833,682.6878,2062.1858,759.34467,0.9990404
+962,1411.2412,499.1706,1433.3064,565.64923,0.9988851
+962,907.76685,517.2941,945.6798,579.1795,0.9988475
+962,1986.638,435.3412,2014.5208,500.8822,0.99881506
+962,3087.8381,454.37872,3115.5295,512.4024,0.99869543
+962,2226.344,470.99985,2250.5295,539.1587,0.99858236
+962,1131.3618,322.09024,1154.6433,378.9589,0.9984085
+962,926.4498,506.2584,952.78613,568.6229,0.9981639
+962,985.8827,476.7811,1016.51196,538.92535,0.9974412
+962,1297.8246,134.10188,1310.8265,170.59871,0.9972748
+962,1072.059,906.28174,1106.256,991.0602,0.75718725
+962,1891.1016,980.7443,1928.3079,1040.0,0.5758617
+962,3657.2478,778.65076,3687.235,844.83765,0.2136315
+962,3758.8235,837.88,3781.6897,874.07605,0.13204549
+963,1642.9678,685.25525,1676.2622,761.5261,0.99968696
+963,1561.2927,490.59302,1591.124,558.5237,0.999572
+963,2143.1926,702.6392,2184.381,785.39655,0.99952567
+963,1310.9508,284.47244,1332.8234,336.1555,0.9994216
+963,907.172,517.4424,945.56287,579.2621,0.9993735
+963,613.93335,755.76587,648.4933,825.1997,0.99936
+963,2341.4768,857.4763,2376.4827,947.8927,0.99930155
+963,1043.9681,795.8977,1082.7059,877.0818,0.99926406
+963,773.81165,368.01105,791.8468,419.24225,0.99924207
+963,610.1473,694.87915,646.1038,763.0648,0.99923813
+963,2010.2174,314.68155,2036.068,374.10294,0.9992218
+963,1411.7976,498.97272,1435.1931,565.4736,0.999199
+963,1271.0055,519.2864,1301.4994,590.73267,0.9991922
+963,1146.3038,506.48822,1170.5253,571.9019,0.9991641
+963,2025.8828,682.66565,2062.1772,759.33875,0.9990294
+963,1451.7202,385.09647,1480.5635,445.9409,0.99901235
+963,1530.3491,674.9461,1565.612,749.0213,0.9989936
+963,1986.6516,435.34317,2014.5251,500.8741,0.99879503
+963,2225.811,470.9608,2250.2988,539.2444,0.99853945
+963,1130.7081,321.7958,1154.4249,378.32687,0.998125
+963,3085.1653,454.24335,3114.7478,512.84845,0.9980438
+963,925.73926,506.40726,952.953,568.23944,0.9980108
+963,987.1929,478.06348,1016.7296,540.10895,0.99792945
+963,1297.4491,133.91594,1311.2032,171.02666,0.99752563
+963,1072.7722,905.4739,1108.0154,990.6647,0.9417374
+963,1891.1649,980.92255,1928.5519,1040.0,0.5241837
+963,3657.359,778.42255,3687.7803,844.1474,0.3959205
+963,3759.249,838.2287,3782.1162,873.79144,0.18774192
+963,3972.11,725.4158,3993.3977,749.76135,0.10369266
+964,1560.4335,490.69827,1589.0929,558.51904,0.9997662
+964,1642.5175,685.44507,1674.3439,761.4144,0.99972004
+964,2143.2205,702.05334,2184.1985,785.4071,0.9994742
+964,1311.0502,284.97437,1332.5509,336.14124,0.9994293
+964,613.99066,755.73926,648.4681,825.21155,0.99936265
+964,2009.365,315.57162,2035.0293,374.13663,0.99934715
+964,2341.2542,857.5514,2376.428,947.8866,0.9992974
+964,906.351,517.6194,945.56305,579.292,0.99929595
+964,773.8435,368.03476,791.8384,419.21915,0.99926275
+964,1270.5422,519.48114,1301.0579,591.0417,0.9992367
+964,1412.5117,499.0603,1436.0879,565.53076,0.99923646
+964,609.3846,695.37396,646.07916,763.05646,0.9991928
+964,1145.5033,507.2728,1170.3671,572.3423,0.99915946
+964,1041.9376,796.9652,1082.8693,876.6416,0.99914813
+964,1528.4883,674.9354,1565.5308,748.93994,0.9990577
+964,2025.8806,682.6579,2062.201,759.34296,0.99901557
+964,1451.1141,385.36536,1479.9554,445.82404,0.9990024
+964,1986.663,435.3037,2014.4637,500.91193,0.99878854
+964,2224.9907,471.1132,2250.1597,539.06085,0.99878746
+964,3084.514,454.44168,3114.0437,512.63446,0.99851745
+964,988.25507,478.76556,1016.6512,540.21655,0.9983767
+964,1130.3274,321.90884,1154.5479,378.10867,0.9981755
+964,925.9321,506.69345,952.9547,568.55707,0.9978917
+964,1297.1678,133.6493,1310.9103,170.99278,0.9973718
+964,1073.3002,903.366,1108.6195,989.2614,0.876962
+964,1891.1501,981.31635,1928.4783,1040.0,0.5467444
+964,3657.3918,778.7415,3688.3005,843.3752,0.21175785
+964,3759.463,838.09283,3782.9185,873.8046,0.15859802
+965,1559.7517,490.8356,1586.5334,558.34045,0.9996879
+965,1642.0842,685.0835,1672.3486,761.287,0.99966776
+965,2142.4873,701.34845,2184.0503,785.40155,0.9995633
+965,1310.9601,284.8601,1332.028,336.2345,0.999458
+965,1269.9928,519.5298,1300.4174,591.3927,0.9993741
+965,1144.9967,507.4099,1170.1461,572.4474,0.9993635
+965,614.0769,755.7411,648.4645,825.1919,0.9993475
+965,2008.4222,315.99744,2034.4696,374.1715,0.9993247
+965,773.8352,368.06067,791.83887,419.21295,0.99931765
+965,608.9564,695.7876,646.0499,763.2693,0.9993124
+965,2341.1196,857.64874,2376.3975,947.9019,0.9992878
+965,1412.7214,499.09576,1437.2241,565.61115,0.9992323
+965,1040.7775,797.3509,1082.9386,876.62506,0.9992318
+965,905.38495,517.6309,945.5667,579.2806,0.99921525
+965,1526.7996,674.9942,1565.5786,748.93854,0.99903417
+965,2025.8754,682.6591,2062.1924,759.342,0.9990074
+965,1986.669,435.29242,2014.5007,500.9137,0.9988005
+965,1450.5856,385.44098,1479.2823,445.96466,0.99874324
+965,2223.649,471.13043,2250.043,539.17395,0.9985672
+965,925.77277,506.7273,952.8161,569.4354,0.9982652
+965,3084.7026,453.26868,3113.0303,512.3923,0.9981774
+965,1129.8934,322.05304,1154.2684,377.19427,0.9980757
+965,1297.0555,133.74364,1310.8314,170.99574,0.99780077
+965,987.2106,479.77225,1015.38306,541.2944,0.9976405
+965,1074.124,901.9362,1109.7119,989.0339,0.946621
+965,1891.1255,981.9945,1928.8887,1040.0,0.43107057
+965,3657.9922,778.4838,3689.0806,842.2508,0.24965443
+965,3760.009,837.8033,3783.822,873.9703,0.14842695
+965,3972.073,724.8661,3993.5906,757.02405,0.1113053
+966,1641.4886,685.08746,1668.9816,761.5893,0.9997458
+966,1559.5802,490.829,1584.6217,557.89594,0.99965084
+966,2142.4758,700.80524,2183.8577,785.40826,0.99954516
+966,1413.5709,499.06482,1438.9437,565.51746,0.99951637
+966,614.235,755.48566,648.5089,825.21686,0.9993936
+966,1144.3685,507.8968,1169.2877,572.8143,0.9993832
+966,1310.6796,284.82944,1331.2145,336.38193,0.99934715
+966,773.8293,368.0833,791.8378,419.21356,0.9993204
+966,2341.471,857.68506,2376.4695,947.97437,0.99931
+966,1269.5383,519.7288,1299.9209,591.337,0.99929893
+966,1526.7993,675.26666,1565.6003,748.9986,0.9992009
+966,2007.6925,316.36142,2033.6649,374.5419,0.99918395
+966,608.552,696.0501,645.9054,763.36383,0.99917835
+966,1040.1422,797.88855,1083.1095,876.78955,0.9991191
+966,2025.8712,682.7147,2062.216,759.33997,0.99902785
+966,1986.6722,435.31415,2014.4911,500.9195,0.9987936
+966,2223.2102,471.20334,2249.9695,539.14264,0.9987532
+966,1450.2739,385.63727,1478.2532,445.97418,0.9984756
+966,925.1804,507.09378,952.97363,570.4492,0.9984602
+966,1129.2322,322.1208,1153.4746,375.85876,0.99842167
+966,904.6536,517.6548,944.2497,579.4889,0.9983151
+966,3084.535,452.7966,3112.4968,512.03815,0.99816716
+966,988.8251,480.67188,1014.2866,542.12024,0.9981018
+966,1296.2936,133.93411,1310.6422,171.02194,0.9977295
+966,1076.0199,900.18396,1110.5426,985.7981,0.9137232
+966,1891.3824,982.97406,1928.1798,1040.0,0.3886444
+966,3760.5447,838.01154,3784.382,873.82684,0.15125613
+966,3658.0942,778.7077,3689.8164,841.55426,0.13961612
+966,3972.4272,725.32794,3993.4248,750.24603,0.12930022
+967,1640.6455,684.80005,1666.7578,761.52515,0.99973863
+967,1559.3716,490.55347,1583.1729,557.7865,0.99970514
+967,2142.4531,700.5918,2183.4502,785.37573,0.99960214
+967,1414.0979,498.95215,1440.4045,565.3075,0.99947304
+967,614.4803,755.40436,648.52606,825.1984,0.9994505
+967,1143.7406,508.13672,1168.9052,573.27563,0.9994241
+967,1269.293,519.8831,1298.5962,591.2629,0.9994074
+967,1310.3157,284.9381,1330.0166,336.4171,0.9993679
+967,773.8238,368.0861,791.8482,419.22977,0.9993192
+967,1528.374,674.9278,1565.5015,748.9539,0.99931324
+967,2341.757,857.7195,2376.4905,947.7677,0.99931014
+967,607.8808,696.3852,645.86127,763.6812,0.999308
+967,2007.377,316.6841,2032.8889,375.2872,0.99919885
+967,2025.8687,682.6996,2062.2024,759.3329,0.99902153
+967,1039.0006,798.48267,1083.0414,876.7555,0.9990188
+967,3084.715,452.4385,3110.804,511.62662,0.99899405
+967,925.2243,506.65457,953.1923,569.88184,0.99898964
+967,1450.1879,385.9569,1477.1471,445.82477,0.9988532
+967,2223.1765,470.98364,2249.91,539.1296,0.9988514
+967,1986.6733,435.3049,2014.5298,500.92404,0.99878067
+967,1128.6819,321.21667,1153.3022,376.01947,0.9987764
+967,903.6377,517.0804,941.4873,579.67084,0.9984055
+967,989.8089,481.3444,1013.12823,542.29736,0.9980082
+967,1295.9556,133.74863,1310.5115,171.04947,0.9975908
+967,1077.5818,897.39386,1111.4385,983.0518,0.97054625
+967,1891.3033,984.0252,1928.0531,1040.0,0.46950972
+967,3972.6943,725.2078,3993.3545,752.596,0.22077774
+967,3760.8694,838.3032,3784.7976,874.10986,0.1543616
+967,3658.4265,779.22,3690.6018,841.5226,0.1148673
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+968,1558.8828,490.8241,1582.772,558.0469,0.9997125
+968,2142.3608,700.0586,2183.3115,785.49634,0.9996184
+968,1639.6176,684.70984,1663.3881,762.1443,0.99957234
+968,1414.401,498.70676,1440.6893,565.12976,0.9995096
+968,614.8141,755.66656,648.55054,825.1468,0.9994553
+968,1529.7621,675.4049,1565.5436,749.01794,0.9994343
+968,1268.7361,519.9722,1297.8098,591.2602,0.9993949
+968,1309.8932,284.95697,1328.9076,336.57324,0.9993801
+968,1143.3829,509.10022,1168.622,574.3843,0.99934727
+968,2007.1342,317.09183,2031.5726,375.70328,0.99934274
+968,773.8303,367.94196,791.85547,419.23566,0.9993315
+968,2341.3955,857.75,2376.5625,948.0581,0.9993061
+968,607.6302,696.7113,645.84344,763.00745,0.9991968
+968,1450.0048,385.7906,1475.4633,446.09735,0.99917644
+968,2025.8744,682.7229,2062.1978,759.33386,0.9990226
+968,2223.1812,470.99097,2249.8022,539.10016,0.9988174
+968,1986.6674,435.28,2014.5638,500.92722,0.99880266
+968,1127.8491,321.3089,1150.3906,376.94556,0.99874747
+968,3084.4734,452.26434,3110.2234,511.22748,0.99872166
+968,991.1343,481.66223,1014.3429,542.3063,0.9987159
+968,1038.1268,798.5678,1082.1141,876.8537,0.9987097
+968,922.7438,506.42966,953.0846,571.50183,0.9985612
+968,902.8821,516.6399,938.1245,579.76685,0.9976241
+968,1295.7611,133.88297,1310.1288,171.0357,0.99750745
+968,1078.8804,895.552,1112.0559,981.5552,0.9915792
+968,1891.0869,984.5768,1927.9636,1040.0,0.46046376
+968,3973.2676,725.4545,3993.3418,752.63837,0.21838495
+968,3725.0588,810.0166,3756.844,874.0366,0.1339624
+968,3658.791,779.7241,3691.2388,841.3286,0.11484276
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+971,1986.6661,435.23593,2014.587,500.8873,0.9988207
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+972,993.8259,482.84308,1019.3623,542.49493,0.9989023
+972,1986.7284,435.24207,2014.5558,500.88275,0.9988495
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+973,1986.7894,435.22028,2014.5182,500.90106,0.99886477
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+983,1519.6755,672.3308,1549.1687,747.8668,0.9992461
+983,1986.8503,435.21954,2014.8022,500.8921,0.9992435
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+984,1998.5128,320.8681,2020.6122,378.67395,0.99944836
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+984,1986.8579,435.2728,2014.7886,500.91904,0.99927205
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+985,1998.3335,320.94736,2020.1653,378.72256,0.99936026
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+986,1986.8947,435.3035,2014.8561,500.91953,0.9991266
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+992,625.10046,756.35565,655.66345,829.2226,0.9984882
+992,1439.0884,491.73474,1465.0364,555.2628,0.9984712
+992,1011.6825,501.79712,1036.8038,557.3458,0.9984425
+992,889.41626,508.98547,923.19727,575.45374,0.99652976
+992,1082.7715,857.9433,1125.6189,938.7503,0.9948657
+992,1295.207,133.04391,1314.6406,170.31839,0.9944126
+992,914.7702,501.60846,940.0406,562.9328,0.9936733
+992,3697.878,794.05035,3747.7432,867.6728,0.15908183
+993,1601.2144,685.15295,1638.7544,764.4452,0.99966204
+993,1116.752,524.3351,1139.6416,590.25024,0.9996364
+993,1512.2742,668.6801,1543.5564,740.7868,0.9995466
+993,1251.8903,528.74646,1280.5819,597.8446,0.9995383
+993,770.99725,367.66205,791.2984,418.36853,0.99939823
+993,1990.6187,322.83887,2015.4268,381.18054,0.99938
+993,2200.722,468.77734,2235.3347,533.3769,0.9993349
+993,1028.3918,787.444,1066.8455,865.97424,0.9993166
+993,1289.7521,287.32504,1310.3651,339.70267,0.99930537
+993,2339.4634,857.9637,2375.8726,947.52704,0.9992968
+993,618.65344,699.0996,648.996,769.2451,0.9992094
+993,1438.8177,390.723,1466.1461,453.17343,0.99910456
+993,2142.048,700.8322,2169.7097,788.4333,0.999049
+993,3063.9563,442.31146,3088.8708,499.61926,0.9989887
+993,625.8119,756.417,657.44214,829.32056,0.9989661
+993,2026.9537,682.48834,2060.3225,759.33905,0.9988858
+993,1986.803,435.28778,2014.8623,500.92145,0.9987955
+993,1111.133,321.1731,1137.9055,374.05798,0.9985201
+993,1439.3004,491.33173,1465.5048,555.2607,0.99833316
+993,1013.0474,502.38486,1037.4221,557.2987,0.9983228
+993,1538.1924,490.4013,1562.5735,555.64044,0.997607
+993,889.24585,509.05212,922.9492,575.40295,0.9963258
+993,915.701,501.64117,939.73615,562.6038,0.9959864
+993,1295.2935,132.9604,1314.6455,170.27486,0.9934335
+993,1081.3982,856.55835,1124.3423,938.5032,0.98325413
+993,3682.2805,779.0773,3714.8616,836.323,0.24927327
+994,1600.1167,685.35187,1638.6079,764.4775,0.9997002
+994,1115.2,525.38245,1139.0317,592.5242,0.9996014
+994,1251.1637,528.9131,1279.9462,598.83594,0.99957806
+994,1509.8771,668.4818,1543.5272,740.3556,0.9995136
+994,770.79364,367.3247,791.2391,418.0498,0.99943084
+994,2200.66,468.1774,2234.0808,533.2256,0.9993309
+994,1989.7587,323.18448,2014.7242,381.03854,0.999302
+994,619.40393,699.00684,649.68176,769.24927,0.9992623
+994,1026.991,787.44965,1066.855,865.262,0.99924576
+994,1289.4186,287.32443,1310.0555,339.22916,0.99922895
+994,2339.5442,857.9768,2375.6638,947.5348,0.9992217
+994,1438.5505,390.5825,1465.9478,453.36148,0.9990538
+994,626.24005,757.2275,658.2861,829.481,0.99901927
+994,2142.0442,700.9866,2169.7253,788.5293,0.99900347
+994,1535.9397,489.85333,1562.6257,555.61395,0.99893564
+994,2027.0363,682.7802,2059.7249,759.31903,0.9989033
+994,3061.5657,442.3993,3087.8743,499.8495,0.9989022
+994,1440.5222,490.524,1466.513,555.30524,0.9988772
+994,1986.7758,435.2879,2015.0143,500.89557,0.99886453
+994,1110.5292,321.55423,1137.5428,374.95236,0.998548
+994,1014.1564,502.58594,1038.392,557.24304,0.9984124
+994,889.2498,508.90997,922.2962,575.3952,0.9962327
+994,914.56885,501.83405,939.7456,562.3051,0.9954716
+994,1295.017,133.24057,1314.879,170.1583,0.99208117
+994,1081.696,855.13965,1124.7563,939.45557,0.96952224
+994,3682.7305,779.13184,3715.2788,837.0049,0.19501583
+995,1599.2776,686.5536,1638.5662,764.6715,0.9997094
+995,1113.9742,527.2048,1138.955,592.2657,0.99964404
+995,1250.5226,529.33636,1279.6241,599.5682,0.9996125
+995,770.5484,366.89948,791.1833,417.7597,0.9994721
+995,1025.4336,787.09015,1066.9438,865.0562,0.9993944
+995,2200.507,467.84793,2232.8342,533.22766,0.9993793
+995,1507.6194,668.3561,1543.219,739.83057,0.999363
+995,1988.3992,323.3412,2013.4172,381.33258,0.9993292
+995,1438.4031,390.47293,1465.0908,453.55124,0.9992525
+995,2339.7566,858.00555,2375.4534,947.5119,0.9992416
+995,619.74414,699.4275,650.412,770.2864,0.9992223
+995,626.7151,757.9666,658.56274,829.6031,0.99913824
+995,3059.4368,442.27823,3087.0461,499.64194,0.99910307
+995,2142.0645,701.3209,2169.6436,788.55054,0.9990941
+995,1441.4568,490.3409,1467.9575,555.34143,0.99901855
+995,1288.8491,287.5383,1309.8577,339.5646,0.999014
+995,1014.6114,502.64893,1040.057,557.3109,0.9990125
+995,2027.146,682.9974,2058.797,759.2817,0.9989857
+995,1534.8164,489.5345,1562.4844,555.65594,0.99895906
+995,1986.788,435.29428,2015.0118,500.897,0.99886996
+995,1110.0128,321.5367,1136.341,374.22012,0.9985683
+995,914.6607,501.8964,939.6849,561.57983,0.99664515
+995,889.17615,508.78253,922.5149,575.41,0.99636406
+995,1294.5845,132.71577,1315.1514,170.23839,0.9931906
+995,1081.0923,854.61816,1124.2661,939.60706,0.9846942
+995,3974.566,756.13873,4003.038,810.98553,0.11121466
+996,1112.5939,528.47516,1138.5392,593.5584,0.99965847
+996,1598.359,686.71075,1638.5653,764.575,0.99964666
+996,1249.8977,529.9,1279.1926,600.22864,0.9995426
+996,770.5004,366.63394,791.15936,417.5726,0.9995178
+996,1987.6577,324.26715,2012.2278,381.51648,0.99942124
+996,1505.4678,667.99255,1543.042,739.51013,0.9993623
+996,2200.6526,468.07718,2230.365,532.9813,0.999345
+996,1438.274,390.5714,1464.4012,453.65668,0.9993247
+996,2339.7358,857.996,2375.3403,947.58167,0.9991867
+996,1534.0776,489.31235,1562.4988,555.7036,0.99917775
+996,627.0933,758.49207,658.7707,829.551,0.9991466
+996,2142.0947,701.7205,2169.5674,788.611,0.9990625
+996,620.2113,699.623,651.5924,769.45856,0.99900585
+996,1024.5251,787.0583,1066.8545,865.1243,0.99895954
+996,1986.7643,435.32996,2015.0172,500.90955,0.9989324
+996,1109.2899,322.635,1135.3663,374.79022,0.9988859
+996,2027.0907,683.03485,2058.2607,759.3487,0.9988538
+996,1287.722,287.7352,1309.4606,339.12753,0.9988055
+996,1441.8541,489.91876,1470.079,555.34,0.99842083
+996,1014.87036,502.80206,1041.929,560.2275,0.998379
+996,3058.2263,441.27948,3086.1218,499.46832,0.9983418
+996,915.2459,501.59433,939.50116,560.60895,0.9972978
+996,889.3516,508.47787,922.26337,575.3391,0.99535024
+996,1295.3817,133.07405,1314.9865,170.06755,0.99192363
+996,1079.4565,853.88654,1123.2434,939.88153,0.9369866
+996,3974.4482,756.18695,4002.026,810.65326,0.15727632
+997,1597.853,686.7055,1638.0107,764.82245,0.99959904
+997,2200.6543,468.41336,2228.0269,532.7702,0.9995815
+997,1249.3658,530.70294,1278.8414,600.7951,0.9995173
+997,770.47217,366.46744,791.47156,417.47012,0.99951696
+997,1987.318,324.33893,2011.3575,381.9677,0.9995009
+997,1110.9147,529.6179,1138.652,595.31714,0.9994392
+997,1503.8394,667.7799,1542.7432,739.46716,0.9994217
+997,1438.244,390.7536,1463.1217,453.65546,0.99941635
+997,627.66675,758.29626,658.82654,829.60645,0.99925846
+997,2339.859,858.0019,2375.2598,947.6141,0.99919957
+997,1023.53705,787.11646,1066.8477,865.1565,0.99915814
+997,2142.1543,701.82654,2169.5713,788.7079,0.99911326
+997,620.80237,699.72894,652.37134,769.1458,0.9989503
+997,1533.5546,489.05386,1562.1564,555.52997,0.9989286
+997,1986.763,435.33267,2015.011,500.90738,0.998928
+997,2026.9622,682.85077,2057.6785,759.2797,0.9988784
+997,1108.0619,322.71307,1134.8278,376.0354,0.99880445
+997,1287.2961,287.74667,1309.4644,339.61026,0.9987539
+997,1442.6432,489.3643,1471.6075,555.3941,0.99873406
+997,3058.4722,441.1417,3085.0044,499.4412,0.9986206
+997,1014.95984,503.2902,1043.0006,562.69196,0.9982942
+997,913.8061,501.53445,939.84955,560.53937,0.99751186
+997,889.3425,507.42532,921.55896,574.45856,0.99575835
+997,1296.5416,132.9418,1314.8951,170.18704,0.991833
+997,1079.5068,853.1662,1123.2146,938.4672,0.9784239
+997,3974.7183,756.1587,4001.7358,810.35645,0.35437128
+998,770.2154,366.60068,791.72406,417.36124,0.9994849
+998,1987.5928,324.50534,2010.6418,382.58694,0.99947125
+998,2200.417,468.4485,2224.6768,532.9679,0.99946946
+998,1248.5115,531.42505,1278.582,601.6406,0.99943656
+998,1597.4193,686.6073,1636.7631,764.8241,0.9994288
+998,1109.9478,529.6143,1138.5371,596.46796,0.999368
+998,1502.7932,667.4167,1542.6172,739.60077,0.99936146
+998,1438.1359,390.7336,1461.7736,453.7532,0.9992994
+998,628.7248,757.733,658.83746,829.67596,0.99929786
+998,1533.1483,489.01505,1562.0604,555.4479,0.9992113
+998,2339.793,857.927,2375.1646,947.7107,0.99917054
+998,2142.1987,702.2033,2169.6929,788.6882,0.99913055
+998,2027.1046,682.8067,2055.9917,759.31305,0.99899536
+998,1287.3962,287.87595,1309.1687,339.725,0.99896204
+998,3057.1191,441.33176,3084.1055,499.12357,0.9989267
+998,1986.7814,435.34137,2014.9874,500.90253,0.99891543
+998,1443.7638,489.0984,1473.2198,555.3711,0.99890804
+998,1022.6189,786.9656,1066.814,865.2737,0.9988914
+998,621.0403,700.0762,653.20667,768.8109,0.9988575
+998,1015.4379,504.23972,1043.4435,564.41656,0.9980732
+998,1106.9857,323.49997,1134.3551,376.317,0.99799216
+998,912.94653,501.45114,940.0421,560.2863,0.9979613
+998,889.6052,505.94617,918.489,573.4907,0.996042
+998,1296.9336,133.09108,1315.1255,170.26631,0.99313605
+998,1078.8939,851.655,1122.0736,936.7195,0.94492036
+998,3973.1484,756.08765,4001.4653,810.30835,0.49655694
+998,2753.3765,891.4714,2786.6519,975.4937,0.30800456
+999,1501.5851,667.6159,1542.4576,739.6164,0.99952936
+999,1532.7667,488.955,1561.9003,555.2757,0.999522
+999,1109.0441,530.07684,1136.7247,596.5805,0.99952173
+999,1597.5046,686.5293,1635.2754,764.7842,0.999521
+999,1247.7042,532.31055,1278.2726,602.46313,0.9994723
+999,770.21716,366.70004,792.0665,417.24545,0.9994555
+999,1438.0243,390.61172,1460.2391,453.58994,0.99935
+999,629.29767,758.5476,658.91205,829.5946,0.9993432
+999,1986.9167,324.7254,2009.9622,382.53137,0.9993222
+999,2142.2087,702.78174,2169.756,788.781,0.9991868
+999,2339.8103,857.9009,2375.1511,947.6555,0.9991499
+999,2199.751,467.6731,2221.8047,533.0234,0.9991373
+999,1287.4612,288.0932,1309.0491,339.85754,0.9990551
+999,2026.843,682.8976,2055.2244,759.24365,0.9989593
+999,1444.6504,488.93344,1474.4131,555.34753,0.9989511
+999,1986.7722,435.34106,2014.9985,500.90247,0.99890673
+999,621.20886,700.3009,653.4667,769.1477,0.9988649
+999,3056.7886,439.94937,3082.772,499.1146,0.9988638
+999,1016.19635,505.4147,1043.5177,566.8489,0.9988255
+999,1021.1898,786.9625,1065.6407,864.9795,0.99868554
+999,912.27924,501.1943,940.61664,560.4777,0.9980033
+999,1105.7854,323.4525,1134.458,376.80157,0.99757236
+999,889.7806,504.9754,918.22943,572.79865,0.99656063
+999,1297.1353,133.1726,1315.041,170.45197,0.99292207
+999,1078.685,849.74097,1119.7058,934.19556,0.9675001
+999,3970.4187,755.9267,4001.1467,810.31366,0.5027345
+999,2751.8726,890.8162,2786.9404,975.6243,0.3467727
+1000,770.18744,366.6855,792.0589,417.15027,0.9995223
+1000,1532.5083,488.7877,1561.685,555.065,0.99949396
+1000,1247.1438,532.50024,1277.9182,602.58765,0.9994469
+1000,1107.6127,530.35645,1134.2699,596.43506,0.9994431
+1000,629.7837,759.1216,658.9927,829.63965,0.9994361
+1000,1597.4365,686.76013,1632.8643,764.9884,0.9994118
+1000,1986.7943,324.73856,2009.2347,382.61655,0.99939764
+1000,1437.845,390.6025,1459.5063,453.5256,0.99939585
+1000,1500.5762,667.402,1542.3257,739.7367,0.9993728
+1000,2339.8103,857.9009,2375.1511,947.6555,0.9991499
+1000,2142.224,703.1158,2169.7778,788.76764,0.99914503
+1000,1287.2285,288.1906,1308.8906,339.9984,0.99912626
+1000,2198.118,467.9789,2219.9192,532.76514,0.9991078
+1000,2026.7902,682.169,2054.8909,759.2664,0.99902534
+1000,1986.7723,435.3409,2014.9984,500.90262,0.99890614
+1000,1445.1615,488.68353,1475.722,555.4814,0.99887115
+1000,621.3873,700.25354,653.6874,769.0665,0.9988381
+1000,1017.44574,507.1891,1043.2454,567.7749,0.9987651
+1000,3055.992,439.32016,3081.8918,498.7756,0.99859565
+1000,911.7109,500.95627,940.36017,560.4112,0.9985648
+1000,889.7721,504.48483,917.2621,571.1031,0.9975545
+1000,1104.0632,323.70056,1134.2139,377.20782,0.9974252
+1000,1020.0553,786.5687,1065.4681,864.7229,0.99721247
+1000,1297.1787,133.14703,1315.1333,170.45135,0.99260336
+1000,1077.0679,845.4426,1122.3994,931.08777,0.9804749
+1000,3968.8386,755.8346,4000.1877,810.3937,0.45193008
+1000,2753.4604,890.3168,2787.3901,975.6825,0.23271744
+1000,3683.8623,778.14105,3722.5195,835.14044,0.11488331
+1001,769.7864,366.775,791.6411,416.74216,0.9995871
+1001,1498.6361,666.46375,1541.7701,739.67456,0.9995129
+1001,1597.5535,687.3148,1630.3545,764.75903,0.99948704
+1001,630.3699,759.42004,659.4951,830.04834,0.99948657
+1001,1437.4746,389.92212,1459.1946,453.45624,0.99948525
+1001,1986.7122,324.94617,2008.4697,383.11694,0.99943846
+1001,1246.379,531.9632,1277.3182,603.325,0.9994252
+1001,1532.1262,488.31543,1561.4509,555.7197,0.9994067
+1001,1106.8843,530.88434,1130.956,596.61725,0.99935275
+1001,2195.8625,467.1131,2218.9446,533.3435,0.99928313
+1001,2339.5593,857.806,2374.949,947.72595,0.99925035
+1001,911.75525,500.81015,940.1483,560.6916,0.9991704
+1001,1286.9705,288.47934,1308.6602,340.6257,0.99914026
+1001,2142.2502,703.3759,2170.0354,788.8238,0.99912435
+1001,1018.8367,786.6033,1065.2886,864.5918,0.9991008
+1001,3055.644,438.09244,3080.8955,497.83463,0.9990538
+1001,2026.4034,681.8691,2054.2273,759.1883,0.9988506
+1001,1445.7373,488.21,1477.4038,554.8909,0.99881315
+1001,1986.8887,435.20697,2015.0415,500.9704,0.9987901
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+1001,2754.5042,889.9383,2788.0764,975.7249,0.13347065
+1002,769.7463,366.74847,791.3276,416.76636,0.9995396
+1002,1497.2991,665.8748,1539.2156,738.13446,0.99949014
+1002,1986.7207,325.1554,2007.6777,383.41797,0.9994785
+1002,1597.0468,687.2419,1626.4967,764.82465,0.9994223
+1002,1105.6602,531.1736,1129.4751,596.6045,0.9993901
+1002,630.7912,759.08264,659.815,830.04224,0.99935657
+1002,1437.1938,389.76385,1458.6873,453.52972,0.9993554
+1002,1246.099,533.8287,1275.8788,603.2237,0.9993451
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+1020,1986.834,434.99887,2015.0947,501.25272,0.9987086
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+1021,1986.8342,434.99893,2015.0947,501.2529,0.9987099
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+1022,1971.0497,328.07037,1993.9589,385.76465,0.9992242
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+1022,1987.1603,435.00836,2015.0868,501.2478,0.99871874
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+1022,872.52686,493.7436,906.64,557.60126,0.9981646
+1022,901.2691,492.8742,927.06305,554.6364,0.9968585
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+1023,1987.011,435.01367,2015.0027,501.29395,0.99867094
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+1025,1966.5674,328.56943,1993.8472,387.21957,0.99900025
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+1025,1994.6338,684.00476,2026.0166,757.18384,0.9989116
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+1025,1986.8518,434.98062,2014.8916,501.21066,0.99855775
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+1026,763.8454,368.2252,791.11176,415.98068,0.998833
+1026,1987.1022,435.07138,2014.8087,500.99173,0.9987728
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+1027,3028.068,428.42337,3055.0964,487.04135,0.99935645
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+1027,1464.4983,484.3741,1489.6299,547.569,0.9991942
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+1027,1987.4285,435.11002,2014.7124,500.71927,0.9990219
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+1027,1303.2584,133.41884,1319.2416,170.30223,0.99838483
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+1028,665.1225,765.31024,699.8383,839.35565,0.99954516
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+1028,1515.9299,485.26782,1545.6477,550.4496,0.99953914
+1028,2158.922,465.52405,2192.7434,528.6816,0.99951446
+1028,2111.9531,697.18774,2159.2812,778.30334,0.9994412
+1028,1213.0833,545.11707,1242.6299,615.42346,0.9994337
+1028,1471.034,657.4244,1512.1432,729.0262,0.9993948
+1028,2328.9753,860.6415,2371.1526,944.58496,0.9992623
+1028,1987.652,435.04822,2014.629,500.5918,0.99921274
+1028,1464.6967,484.18198,1489.828,547.51263,0.9992034
+1028,648.14185,703.61926,678.9823,779.80273,0.9990952
+1028,3028.3826,427.96024,3054.2336,486.39267,0.99906915
+1028,1991.2208,682.7549,2014.2079,757.0625,0.9990257
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+1028,1020.0027,544.6918,1049.3911,605.2306,0.9950954
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+1029,1987.8586,434.94327,2014.5786,500.39145,0.9992919
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+1029,1987.8212,682.4447,2013.5331,757.209,0.9990963
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+3543,1019.8116,416.1539,1048.0255,474.28738,0.99873227
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+3543,1279.2687,220.20941,1302.3268,265.51242,0.998432
+3543,1997.4137,273.85825,2017.6188,328.30344,0.9982498
+3543,790.62994,331.25995,808.67426,379.50043,0.9980558
+3543,1339.6259,132.38553,1358.3876,166.17386,0.99766695
+3543,1985.9932,247.27725,2004.9343,295.46902,0.9973744
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+3543,3626.4736,776.8152,3660.6084,838.6803,0.99693596
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+3544,1997.0562,274.01276,2017.2056,327.90125,0.9982918
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+3545,1996.5288,273.60602,2016.8787,327.97363,0.9985331
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+3545,1978.4796,247.27872,2003.8507,294.50894,0.99735165
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+3546,1995.72,273.01663,2016.6975,327.0123,0.9990983
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+3547,1994.8446,272.48172,2016.0599,326.0823,0.99906784
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+3761,1288.2521,267.0872,1306.9886,315.7496,0.9987735
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+3763,3676.4248,806.7344,3707.9023,869.8257,0.69391716
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+3765,1994.5803,239.12149,2019.0198,288.20975,0.99954826
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+4271,2004.5519,125.21146,2020.1237,163.0332,0.9909339
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+4307,3091.0696,456.1191,3118.2551,511.94046,0.9989385
+4307,3166.2947,434.999,3191.1301,490.32962,0.9988858
+4307,2179.347,230.53271,2195.9426,283.18475,0.99888486
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+4307,3055.1895,456.11215,3081.517,502.13998,0.9976851
+4307,1965.3573,890.3333,2003.8531,985.8293,0.99562836
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+4307,3160.3672,376.11786,3190.0225,421.974,0.99227506
+4307,2010.3817,124.30759,2028.618,162.95596,0.97781163
+4307,3873.1702,755.5057,3906.4255,815.91,0.37200898
+4307,3715.9666,799.4687,3750.1243,864.74664,0.17731051
+4307,3965.4075,730.72205,3992.8718,789.4448,0.1176065
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+4308,3103.8103,374.1079,3129.1672,427.12366,0.99931145
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+4308,2626.823,424.91342,2645.635,481.30728,0.9991654
+4308,2891.0994,295.15817,2914.1638,343.7009,0.99912757
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+4308,3088.4736,456.72357,3116.5093,511.99927,0.9990382
+4308,1292.58,324.25946,1311.8759,380.64868,0.9989868
+4308,2221.9172,239.04767,2238.4827,288.62054,0.998877
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+4308,2414.9788,433.092,2439.113,498.65872,0.99884605
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+4308,3135.9927,380.3856,3158.2314,427.42148,0.9976441
+4308,1965.8086,889.9953,2005.5574,985.63947,0.9972869
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+4308,2714.8555,359.5307,2737.12,415.3919,0.99600357
+4308,3750.0547,770.5981,3790.7446,830.2006,0.99013305
+4308,2010.2637,124.2228,2028.5754,163.03876,0.98042744
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+4308,3965.3142,730.47266,3992.8274,789.7831,0.21012104
+4308,3715.3022,798.9069,3751.8179,866.10443,0.16075116
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+4309,3088.8352,456.4203,3116.138,511.9309,0.9991296
+4309,3178.8918,545.3705,3202.6511,604.5857,0.9991136
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+4309,2442.7378,442.8495,2475.8481,503.1112,0.999086
+4309,3205.7947,399.74203,3224.9412,451.08047,0.9990339
+4309,3165.1738,433.552,3191.4478,486.71185,0.998965
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+4309,1966.283,889.3145,2005.207,985.59894,0.9975172
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+4309,3165.968,373.98495,3192.1423,423.59598,0.99477863
+4309,2010.2738,124.21863,2028.4904,163.03403,0.97937405
+4309,3873.3093,755.96124,3905.821,815.86005,0.5642819
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+4310,3178.2896,545.0386,3201.9937,604.6228,0.99923146
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+4310,2891.3347,295.19904,2914.3777,343.6267,0.9991391
+4310,1292.8308,323.9688,1312.0212,380.6186,0.9990877
+4310,3206.9102,399.66873,3226.5728,450.63565,0.99907756
+4310,2442.3003,443.1372,2474.9604,503.0398,0.999074
+4310,3088.8965,456.3197,3115.0762,511.87244,0.9990263
+4310,3201.2676,459.98074,3227.266,510.7225,0.9989692
+4310,2222.045,238.42203,2243.357,288.327,0.99894994
+4310,3491.235,601.35864,3522.616,661.0702,0.9989497
+4310,2180.345,230.8425,2195.2463,282.97482,0.9987703
+4310,2716.908,361.362,2739.1067,416.16913,0.9986964
+4310,3164.6003,433.37393,3190.052,485.72995,0.9984016
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+4310,1967.1521,888.9034,2005.0007,985.0607,0.9977241
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+4310,3049.8865,453.83563,3078.5378,502.3827,0.99581194
+4310,2010.3663,124.372856,2028.5033,163.02905,0.9752871
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+4311,1967.6714,888.4919,2005.082,984.7896,0.996795
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+4311,3753.1248,769.8774,3796.6072,827.0601,0.99513656
+4311,2010.5302,124.450356,2028.2863,163.0055,0.96466774
+4311,3873.442,755.94696,3905.5054,815.9394,0.69554925
+4311,3924.0715,741.8414,3949.3196,794.4777,0.3727197
+4311,3965.7935,730.57275,3992.6514,789.8319,0.33834553
+4311,3715.5305,798.8069,3753.951,864.9126,0.14220563
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+4312,3108.2708,376.0128,3135.1848,427.06277,0.99917394
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+4312,3169.1963,543.8875,3200.1543,604.7099,0.9989479
+4312,3088.889,456.34494,3113.299,511.7111,0.9989141
+4312,3200.951,461.0106,3228.9695,512.09906,0.99887425
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+4312,2405.8147,435.31256,2434.9158,502.34027,0.99859506
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+4312,3162.1968,432.0421,3186.4033,483.17712,0.998315
+4312,3139.1794,378.78568,3165.1106,425.8055,0.99809104
+4312,3754.2847,769.12195,3799.2925,827.5078,0.9979169
+4312,2702.2607,382.18234,2723.4702,438.90372,0.9975968
+4312,3049.7686,451.93933,3077.1724,502.6059,0.9964149
+4312,1968.1904,887.71313,2005.5879,983.3673,0.9948527
+4312,3293.6963,355.5554,3312.5146,403.91165,0.9944003
+4312,2010.2228,124.46467,2028.1915,162.98337,0.96762633
+4312,3873.991,756.4086,3905.2756,815.8284,0.72574914
+4312,3924.2012,743.1443,3947.9585,793.53455,0.68496245
+4312,3965.7913,730.4936,3992.7068,789.8576,0.44622928
+4312,3715.594,799.46704,3755.2703,865.0198,0.23873153
+4313,2717.8848,362.22397,2740.6216,415.32138,0.9993125
+4313,3490.9016,600.7207,3518.4856,660.2118,0.99928755
+4313,2222.5278,238.12921,2247.1743,287.8534,0.99922514
+4313,3166.2024,543.45807,3199.0085,604.4355,0.9992156
+4313,3088.808,455.8056,3112.478,511.95068,0.9991466
+4313,2947.8457,427.28726,2973.7568,483.34402,0.999108
+4313,1292.857,323.8247,1311.976,380.63513,0.99910444
+4313,3210.2942,399.46255,3230.993,449.82913,0.9990134
+4313,2440.8904,443.4141,2471.453,503.16586,0.9989557
+4313,2627.1553,424.2181,2648.3843,480.91287,0.99893266
+4313,2891.7495,294.8209,2914.13,343.59152,0.99892956
+4313,2186.7131,306.82608,2209.1912,363.10568,0.9989046
+4313,3160.8872,430.7859,3184.6914,481.33466,0.9988833
+4313,3109.7905,376.70978,3136.3208,427.1618,0.99887747
+4313,2180.488,230.83083,2195.4363,282.97342,0.99877423
+4313,3200.8633,461.92633,3229.8315,511.83765,0.9987483
+4313,3139.2778,378.1443,3166.8008,424.9859,0.9985998
+4313,2404.6355,435.7777,2433.4924,502.20752,0.9985996
+4313,3084.5557,311.26038,3110.086,355.65186,0.9984888
+4313,2701.2278,380.7251,2723.1619,438.36688,0.99821967
+4313,3754.7937,768.0026,3801.118,826.42706,0.99793017
+4313,3049.6606,451.52002,3076.4731,502.25226,0.99753785
+4313,3167.5574,371.21808,3197.57,418.64716,0.99625707
+4313,3292.4216,355.03717,3311.765,404.28424,0.99504256
+4313,1968.8387,887.28546,2006.1302,982.79376,0.9924791
+4313,2009.9623,124.5917,2027.9613,162.97562,0.96715343
+4313,3874.5989,756.94556,3905.0334,815.6621,0.5261871
+4313,3966.043,730.51227,3992.8208,789.89056,0.48724294
+4313,3922.8328,744.1665,3945.2678,793.1283,0.21843812
+4313,3715.6797,798.7461,3756.5576,864.82434,0.21449657
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+4314,2403.2896,436.0094,2432.3447,502.755,0.9988657
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+4314,3111.7612,376.70175,3137.5566,427.60934,0.99885833
+4314,3088.9238,455.96268,3111.831,511.89035,0.9988432
+4314,2701.2788,380.4741,2723.3848,438.0848,0.99877626
+4314,2947.9119,426.78946,2973.5715,484.21536,0.9987508
+4314,3200.8896,462.0624,3230.567,511.40445,0.9987363
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+4314,3049.5103,450.34198,3076.623,502.27502,0.99754995
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+4314,1970.0035,886.3765,2007.5563,981.43536,0.99318063
+4314,2008.6726,124.99733,2026.5327,163.03972,0.95708764
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+4316,2700.88,380.07904,2724.7925,438.24164,0.9989668
+4316,2402.8994,436.03513,2429.796,503.309,0.998949
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+4316,3173.69,370.86554,3198.4556,418.36884,0.9986273
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+4316,3088.463,307.92816,3114.4111,349.52502,0.9984547
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+4316,3049.3088,449.13138,3075.172,502.27792,0.9983797
+4316,3146.7751,378.37875,3171.6106,424.70584,0.9983718
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+4316,1971.6981,884.8089,2009.7059,981.8814,0.99648225
+4316,2007.8121,124.86746,2024.8265,162.87921,0.9662026
+4316,2012.3864,124.70857,2028.4608,161.75725,0.8784014
+4316,3875.156,758.1066,3904.5408,815.5754,0.6240272
+4316,3965.975,730.1734,3992.7207,789.9533,0.44587845
+4316,3716.3108,798.8883,3761.448,865.30786,0.32340112
+4316,2935.0417,831.8852,2965.986,913.78204,0.18383795
+4317,3767.4082,764.51654,3810.4912,823.523,0.99941385
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+4317,2722.1533,361.6285,2743.8906,415.40732,0.9993187
+4317,3485.7793,600.264,3518.3486,662.06305,0.9991762
+4317,2951.0293,423.4542,2974.2935,478.5757,0.99917334
+4317,3086.6277,455.64703,3109.1335,512.0161,0.99910516
+4317,2439.0532,443.6911,2465.6836,503.74335,0.9990922
+4317,2700.3838,379.83475,2723.6982,437.91434,0.9990508
+4317,1292.976,323.76804,1311.3802,380.48672,0.99900985
+4317,2890.832,293.90112,2912.224,343.41608,0.99899286
+4317,2402.5632,435.67432,2429.0037,503.56543,0.9989926
+4317,3164.3665,541.7473,3193.591,601.20374,0.99884254
+4317,2627.0442,423.74017,2651.0627,480.70508,0.9988355
+4317,3150.3254,377.56317,3173.4417,424.34167,0.9987256
+4317,3119.7407,374.4251,3139.874,426.48523,0.99869615
+4317,3089.944,307.59793,3115.721,349.39694,0.9986083
+4317,3173.8923,369.70685,3198.716,418.18573,0.99858546
+4317,2180.0632,231.04683,2196.7712,282.84326,0.99844664
+4317,3049.2456,448.76852,3074.8555,502.3242,0.99829215
+4317,3204.9915,460.62766,3234.2761,509.18326,0.99827373
+4317,3153.6787,425.12128,3176.3193,478.03357,0.9982285
+4317,3286.711,354.09393,3307.9673,400.77515,0.99797636
+4317,1972.5795,884.5273,2010.7738,979.8189,0.9977842
+4317,2187.3103,306.74243,2205.3044,363.5249,0.99728525
+4317,2007.8756,124.78851,2025.15,162.8115,0.96557325
+4317,3876.3552,758.8164,3904.3074,816.17505,0.8478553
+4317,2013.4951,124.08595,2028.8306,161.34172,0.7439139
+4317,3965.9812,730.4586,3992.6536,789.906,0.3608446
+4317,3719.897,799.05054,3759.9604,864.23315,0.32739154
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+4318,2699.3906,379.35245,2723.6455,437.9337,0.99929535
+4318,2952.2654,422.68954,2974.6062,477.79288,0.99927205
+4318,3215.1125,396.86124,3236.4456,448.1265,0.9992285
+4318,2402.4177,435.64563,2427.4094,503.6759,0.9992231
+4318,3768.8523,764.7899,3812.2712,822.1029,0.9992099
+4318,3084.4915,455.5742,3108.5896,511.66263,0.99919015
+4318,2890.017,294.0985,2911.5317,343.42462,0.9991756
+4318,3175.0059,367.18082,3198.4082,417.5521,0.99917114
+4318,3484.1985,600.7745,3517.4255,662.13336,0.99911493
+4318,2438.9497,443.35117,2465.6533,503.69998,0.9990539
+4318,1293.007,323.78473,1311.3888,380.48938,0.9990126
+4318,2627.195,423.70047,2652.0872,480.75253,0.99893147
+4318,3150.007,423.9406,3175.9832,477.5326,0.9989104
+4318,3164.3638,541.4597,3191.8296,600.45593,0.99888116
+4318,3152.2332,376.30338,3173.8542,423.22525,0.9988803
+4318,2722.9417,361.84344,2745.5076,415.1811,0.9988374
+4318,3122.6094,374.1148,3140.8618,425.63153,0.9988192
+4318,2179.8804,231.08858,2196.814,283.01096,0.9985417
+4318,3286.9597,353.20084,3307.7556,399.43106,0.9983139
+4318,3049.2654,448.7252,3074.2747,502.4216,0.9982424
+4318,3207.065,459.82956,3235.6836,509.19217,0.9980622
+4318,3091.1545,307.35156,3116.8918,349.3606,0.99796754
+4318,1973.5621,884.2385,2011.7733,978.4614,0.99796355
+4318,2187.8584,306.69815,2205.4434,363.62112,0.997336
+4318,2007.8792,124.80942,2025.0771,162.81155,0.96425235
+4318,3876.765,758.7379,3904.2937,816.00244,0.9424359
+4318,2013.5776,124.09758,2028.8643,161.33604,0.7535009
+4318,3965.9768,730.5674,3992.845,789.8867,0.49831524
+4318,3722.1892,798.4033,3762.8923,865.08813,0.4869352
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+4319,3149.3992,423.75845,3175.3909,475.96457,0.99927443
+4319,3175.0107,366.2931,3198.204,417.02063,0.99924386
+4319,2699.3623,378.6842,2722.9116,437.18707,0.99924326
+4319,2889.5254,294.06564,2910.752,343.40134,0.9992053
+4319,3773.0073,765.0779,3813.734,822.33997,0.99913895
+4319,2401.6997,435.8462,2426.3115,503.83234,0.99912256
+4319,3481.1462,600.6735,3516.6394,661.6171,0.9990742
+4319,1293.0114,323.74286,1311.4227,380.49908,0.9989986
+4319,2438.8923,443.33356,2465.3333,503.61182,0.99897707
+4319,3215.7383,397.07254,3237.2373,447.8571,0.9989579
+4319,3123.9456,373.9692,3142.2068,425.89902,0.99893624
+4319,3286.5378,352.02667,3307.4548,399.1767,0.9988772
+4319,2627.2366,423.51047,2653.2722,480.85355,0.99885845
+4319,2723.1304,361.77325,2747.1763,415.1192,0.9988405
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+4319,3081.6738,455.76053,3107.9888,511.58304,0.9986633
+4319,1974.4623,883.9675,2012.9413,977.6124,0.99845815
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+4319,3153.985,375.02695,3174.6946,422.9493,0.9947324
+4319,2007.8348,124.86783,2024.8617,162.87308,0.9650415
+4319,3877.0366,758.6967,3903.982,815.4157,0.9554168
+4319,2012.3356,124.73639,2028.4886,161.93893,0.8809581
+4319,3725.6682,797.17053,3764.9812,862.81116,0.5257254
+4319,3965.9863,730.6986,3993.0898,789.89514,0.51586115
+4319,3907.5964,742.8218,3934.8186,792.7728,0.169853
+4320,2889.4236,293.81305,2910.1292,343.49927,0.99942696
+4320,2953.1238,422.58917,2974.8782,476.58167,0.99941325
+4320,2400.6223,435.92993,2425.177,503.44812,0.99918514
+4320,3147.8032,424.6994,3174.5957,475.5052,0.9991667
+4320,2229.4167,236.37187,2248.7239,286.61664,0.99916506
+4320,3125.6892,373.65573,3143.408,426.01077,0.9991381
+4320,3287.1016,351.38818,3306.8398,398.9699,0.9990682
+4320,3215.9492,397.67908,3238.0254,447.7351,0.99906427
+4320,3164.3645,540.2299,3188.8987,598.2316,0.99905425
+4320,1293.0646,323.7527,1311.4266,380.46606,0.99903286
+4320,2699.5273,378.51053,2722.4053,436.51163,0.99902105
+4320,3776.493,765.56244,3815.7625,822.15204,0.998961
+4320,3477.3862,601.04266,3515.5454,660.8842,0.9989442
+4320,3212.4568,459.0581,3237.7478,508.63416,0.99892586
+4320,2438.7983,443.5677,2465.165,503.6374,0.9988741
+4320,2627.2898,423.06384,2653.7659,480.7469,0.9987337
+4320,2179.603,231.0364,2196.817,283.15652,0.99870086
+4320,3174.6917,365.68823,3197.678,416.43408,0.998635
+4320,2723.2334,361.7639,2748.8716,415.20236,0.9985695
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+4320,3049.5403,447.5537,3073.4573,502.52142,0.9981085
+4320,1975.2142,883.8051,2013.5682,976.0599,0.9977876
+4320,3093.595,305.5292,3118.073,349.56497,0.9971865
+4320,2188.1802,306.63214,2205.5322,363.85706,0.99706846
+4320,3154.6829,373.92245,3174.6946,423.86044,0.99471754
+4320,2007.8846,124.81857,2025.0887,162.83762,0.9634225
+4320,3876.284,758.8883,3903.3845,815.4386,0.9215299
+4320,2012.7484,124.31145,2028.7684,161.71123,0.84329414
+4320,3731.6145,796.2808,3766.174,861.77606,0.7787697
+4320,3965.9495,731.0903,3993.1365,789.71246,0.53878635
+4320,3904.3794,742.93,3933.7417,793.0129,0.16640453
+4320,2933.4314,831.88385,2964.9866,913.878,0.14463395
+4321,2953.2554,422.3973,2975.0415,476.19138,0.99937975
+4321,2889.2136,293.5074,2909.4207,343.49817,0.99935156
+4321,3163.7737,539.7861,3187.681,597.648,0.9992562
+4321,3216.7148,398.03125,3238.8066,447.06097,0.99921775
+4321,2229.4697,236.10118,2249.9438,286.13126,0.9991848
+4321,3475.5183,601.5774,3513.7605,660.8762,0.9991679
+4321,3214.437,458.7232,3238.8188,508.33557,0.9991567
+4321,3126.9385,374.8945,3145.1548,426.0651,0.99911183
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+4321,2399.2896,435.95874,2424.4707,503.45337,0.9989273
+4321,3148.1565,423.69714,3173.0774,473.48407,0.9989208
+4321,2627.7651,422.73834,2654.3408,480.45544,0.99888176
+4321,2438.6572,443.81738,2464.913,503.61792,0.99887687
+4321,2699.2043,377.29156,2722.432,435.56073,0.9988594
+4321,2179.5908,230.73181,2196.7876,283.2251,0.99873394
+4321,3076.4495,455.56345,3106.573,512.0163,0.99865365
+4321,3286.3706,350.6756,3307.12,398.8031,0.998632
+4321,2723.4453,361.6924,2749.4717,415.13437,0.998628
+4321,3049.5642,447.33203,3073.3289,502.32697,0.9983394
+4321,3174.4553,364.97507,3197.3738,415.4083,0.99818283
+4321,1976.7292,883.333,2014.1213,975.2948,0.9977627
+4321,2187.9983,306.60294,2205.5813,364.00937,0.9970636
+4321,3094.3577,303.58093,3117.7185,348.63672,0.9969586
+4321,3157.3452,373.11304,3175.4365,421.53583,0.99331665
+4321,3773.793,764.628,3817.689,822.2619,0.98162854
+4321,3875.9963,759.0543,3903.221,815.4403,0.9790989
+4321,2007.8617,124.86992,2024.9926,162.93217,0.9639254
+4321,2012.6835,124.32917,2028.8029,161.7009,0.8458938
+4321,3735.4263,793.4468,3784.997,860.964,0.7873649
+4321,3965.959,731.1588,3993.1167,789.7339,0.55796
+4321,2932.787,832.00836,2964.7324,915.0993,0.2797021
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+4322,3127.9573,375.98663,3145.6692,425.98206,0.99935836
+4322,2953.3076,422.015,2975.2686,476.4276,0.9993363
+4322,2397.5286,436.44952,2422.117,503.52405,0.9992113
+4322,2229.5361,235.90332,2251.6348,286.375,0.9991874
+4322,2629.6848,422.25836,2654.782,480.40985,0.9991849
+4322,2889.4995,293.09576,2908.7607,343.01953,0.999108
+4322,3214.7148,458.98407,3240.3462,507.9497,0.999057
+4322,2438.532,443.81323,2464.7605,503.63422,0.99901485
+4322,3284.618,350.44775,3306.8665,397.15607,0.99901056
+4322,3075.2996,455.60117,3105.677,511.89798,0.9990075
+4322,1293.0923,323.71542,1311.407,380.50467,0.9990031
+4322,3161.7856,539.71344,3185.6982,598.6462,0.9989875
+4322,3217.8557,397.77072,3239.7917,446.5142,0.99897766
+4322,2723.4124,361.6937,2749.4446,415.09085,0.9987716
+4322,2179.4988,230.626,2196.78,283.28024,0.99871325
+4322,3147.9736,421.92557,3169.253,470.89548,0.9985586
+4322,3049.745,447.19894,3073.2012,502.31747,0.99832004
+4322,1977.4489,883.38806,2014.5939,974.61707,0.9982869
+4322,2696.9688,375.8966,2721.8315,436.14417,0.9982332
+4322,3095.1465,301.83142,3117.3618,349.37885,0.9982139
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+4322,2187.858,306.5673,2205.629,364.10153,0.99758434
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+4322,3778.0862,762.8324,3820.8962,822.41016,0.9939995
+4322,3874.9214,759.3883,3903.662,815.41614,0.9861859
+4322,2007.8931,125.06673,2025.1062,162.93312,0.9532949
+4322,2012.6577,124.33047,2028.8301,161.69778,0.8458379
+4322,3738.851,793.93427,3770.213,860.35675,0.8275479
+4322,3965.9875,731.1093,3993.097,789.89215,0.58364314
+4322,2932.7178,832.6188,2964.2842,915.9702,0.30451602
+4322,3738.4375,792.21704,3789.4458,860.50244,0.17032331
+4322,3652.457,822.7214,3683.1294,887.5738,0.1007269
+4323,3075.086,455.3793,3104.7231,511.5129,0.9993431
+4323,3474.5017,601.3655,3508.9377,661.97815,0.9993364
+4323,2953.2566,421.73996,2975.576,476.5402,0.9992586
+4323,2438.4153,443.74374,2464.424,503.61673,0.9992194
+4323,2630.3816,422.00766,2655.5251,479.98898,0.99916804
+4323,3128.2646,376.45084,3146.0542,425.7668,0.9991623
+4323,2394.6968,436.3868,2421.2783,503.692,0.99910635
+4323,2229.7227,235.75656,2252.2476,286.24545,0.99910295
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+4323,3159.7075,539.1864,3184.7642,598.3629,0.9989312
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+4323,1977.95,882.94727,2015.2456,974.4027,0.9983215
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+4323,3740.3057,793.7803,3771.1587,859.97314,0.68276936
+4323,3966.0317,731.43036,3993.0884,789.82184,0.63581645
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+4324,3474.2766,601.2054,3506.346,661.4893,0.9992544
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+4324,2229.6387,235.63249,2253.7798,286.34802,0.9990428
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+4324,1978.2906,882.71454,2016.1017,974.03436,0.9976857
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+4324,2008.314,124.83502,2025.7046,162.9556,0.958076
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+4325,2722.4187,361.6977,2746.6106,414.63635,0.99929976
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+4325,3149.8835,539.46045,3181.7317,597.4581,0.9992575
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+4325,3220.294,396.3508,3240.1753,445.09988,0.999054
+4325,2229.7427,235.65549,2253.2349,286.38132,0.9989544
+4325,1293.2609,323.7403,1311.399,380.53235,0.99895227
+4325,2387.5532,436.64078,2419.4268,504.16843,0.99886835
+4325,2697.699,374.40936,2718.8962,430.81305,0.9988564
+4325,2888.7046,292.7075,2907.5728,341.8428,0.9988317
+4325,3214.7456,459.3197,3243.9043,509.19086,0.9988254
+4325,2179.2397,230.36314,2195.9326,283.20978,0.99876416
+4325,3129.1821,376.3709,3149.7148,425.94318,0.9987625
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+4325,3145.8147,419.33054,3164.6272,469.09616,0.99860436
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+4325,3159.806,374.02887,3176.6404,420.1728,0.9975132
+4325,1978.4751,882.7061,2016.509,973.941,0.9971275
+4325,2008.5653,124.71134,2026.0953,162.96887,0.9589493
+4325,3873.9094,759.948,3903.1829,814.9198,0.95678264
+4325,3740.507,795.4431,3772.5032,861.4487,0.7512992
+4325,3966.0286,731.10455,3993.242,789.86786,0.5572331
+4325,3893.87,742.2477,3919.1104,793.5209,0.55522203
+4325,3875.4758,742.77313,3919.6667,793.24493,0.33833072
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+4326,1978.5298,882.70544,2016.6418,973.86523,0.9976441
+4326,2010.144,124.44537,2027.8455,162.88577,0.94840103
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+4327,2720.936,361.43207,2745.291,414.82678,0.9994721
+4327,2953.3503,420.55194,2977.2288,475.66925,0.9993098
+4327,3474.3958,600.14984,3499.4065,661.91876,0.9991936
+4327,2386.3804,437.15424,2418.3857,504.7818,0.999132
+4327,3143.577,539.36865,3178.5037,597.2876,0.99911886
+4327,2438.2354,443.80957,2465.191,503.85565,0.99910575
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+4327,3098.0105,298.3143,3118.7224,342.2772,0.9989888
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+4327,2696.7424,373.81735,2717.2786,430.0875,0.9989496
+4327,1293.3422,323.72824,1311.369,380.48648,0.99894696
+4327,3221.3381,394.52884,3240.178,444.88474,0.99894184
+4327,3074.5027,454.92093,3101.1223,511.12686,0.9988883
+4327,3215.874,459.40704,3247.4795,510.67462,0.9987723
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+4327,2888.5269,292.71976,2906.3198,340.64688,0.99856865
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+4327,3794.0398,758.7953,3832.1243,815.01624,0.99835336
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+4327,3137.7131,417.49167,3164.9177,468.55014,0.997726
+4327,1978.4838,882.73773,2016.8546,973.95465,0.99729913
+4327,2010.0854,124.43708,2027.7,162.92905,0.9517597
+4327,3873.9062,760.63135,3901.9497,815.6918,0.81293726
+4327,3883.7046,740.4878,3914.4443,793.31335,0.5418495
+4327,3965.7808,731.13464,3993.145,789.3303,0.31764016
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+4328,2953.7104,419.52884,2977.704,475.10214,0.99925774
+4328,2386.0688,436.9198,2416.6875,504.43176,0.9992355
+4328,2227.9668,235.61911,2253.5176,286.28482,0.9991054
+4328,3098.428,296.66275,3118.6497,341.5693,0.99902916
+4328,3221.7285,394.45193,3240.3364,444.78757,0.99899024
+4328,2438.1301,443.70612,2465.619,503.97998,0.99896455
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+4328,3142.1824,539.5697,3177.8362,597.3347,0.998955
+4328,2187.5144,307.35815,2205.323,364.365,0.9989329
+4328,3473.9373,599.4668,3498.0002,661.5532,0.9989126
+4328,2695.8708,373.2791,2716.2117,429.9604,0.9988998
+4328,3219.4292,459.29492,3247.6797,508.65112,0.998703
+4328,2178.7063,230.1446,2194.6677,282.6268,0.99870086
+4328,3174.963,359.8616,3195.8428,409.80368,0.99863917
+4328,3074.1758,454.71936,3100.317,511.2107,0.9986077
+4328,3281.5127,348.13763,3302.3906,392.6405,0.99847645
+4328,2888.2678,292.7656,2905.7317,340.22983,0.99844426
+4328,3132.9487,417.08618,3164.168,468.66827,0.99838483
+4328,3160.3447,371.99756,3177.77,418.19293,0.9982322
+4328,3047.8857,446.5544,3072.5469,503.05093,0.99803036
+4328,3128.664,374.70303,3153.4697,426.12064,0.99800366
+4328,1978.5759,882.77185,2016.94,973.9823,0.9975343
+4328,3794.34,758.9249,3834.0579,814.6422,0.99674135
+4328,2010.0354,124.34769,2027.9888,162.87991,0.9693382
+4328,3865.082,760.6404,3901.338,815.4192,0.90678
+4328,3889.2825,739.41077,3912.744,791.87634,0.6723126
+4328,3965.753,730.5374,3993.1338,789.3232,0.5901195
+4328,3740.7554,797.7637,3774.9595,863.5343,0.25241274
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+4329,2719.1418,361.0889,2744.781,414.74118,0.9993106
+4329,3098.7998,295.53455,3117.836,340.69324,0.99918634
+4329,2228.0044,235.64691,2253.7861,286.1507,0.99912006
+4329,2386.1704,436.7628,2414.0762,504.08505,0.99911493
+4329,3472.1345,599.5165,3496.7854,661.64136,0.9989988
+4329,1293.4073,323.7381,1311.43,380.46478,0.9989963
+4329,3175.2761,358.9923,3195.827,409.67987,0.9989544
+4329,3221.8784,394.44754,3240.6528,444.7465,0.9989532
+4329,2187.5134,307.43155,2205.1863,364.3038,0.9989091
+4329,2178.4128,230.04509,2194.661,282.51453,0.99888843
+4329,2438.1711,443.7009,2465.6497,503.9999,0.99886775
+4329,2694.5398,372.4262,2715.0398,430.21692,0.99880695
+4329,3141.7642,538.9064,3176.665,597.29407,0.9987544
+4329,3279.537,346.84222,3301.9746,392.30365,0.998681
+4329,2953.899,418.95685,2978.0488,473.5091,0.99867636
+4329,3131.3862,415.81662,3163.5703,468.30515,0.998589
+4329,2887.772,292.64792,2905.3955,339.79184,0.9985794
+4329,3047.8044,446.58365,3072.3323,503.12643,0.9980782
+4329,3223.0552,459.1588,3248.6606,508.82275,0.9980165
+4329,3161.0703,371.00235,3177.3364,417.9844,0.99794644
+4329,3074.1357,454.8638,3099.123,511.57425,0.9978836
+4329,1978.6373,882.7613,2017.1766,973.9454,0.99766195
+4329,3128.384,374.7343,3154.138,425.90228,0.9972583
+4329,3796.0928,758.88055,3835.6367,815.4941,0.9966864
+4329,2010.0302,124.340935,2028.0438,162.86597,0.97314477
+4329,3862.3752,761.4087,3901.007,814.6293,0.92028767
+4329,3887.964,739.3909,3911.1565,788.84064,0.70605487
+4329,3965.9878,730.758,3993.0889,788.9521,0.56315887
+4329,3740.6108,797.73737,3776.4277,862.737,0.5059605
+4330,3099.764,293.07797,3117.8162,339.14035,0.9994066
+4330,2634.651,421.38803,2656.8828,478.34305,0.9993781
+4330,2718.5833,360.8071,2744.3962,414.75443,0.9992436
+4330,2386.1562,436.72696,2410.808,503.9465,0.99923587
+4330,2227.6746,235.65248,2252.4934,286.33612,0.9991499
+4330,2954.3337,417.98975,2978.3977,472.5064,0.9990231
+4330,1293.4536,323.70605,1311.4814,380.45752,0.9989998
+4330,3174.4194,358.10315,3195.689,408.41162,0.9989792
+4330,2187.5264,307.511,2205.2563,364.28156,0.99892175
+4330,3470.1943,598.9497,3495.2397,661.95154,0.99891794
+4330,3221.9465,394.38177,3240.881,444.41742,0.9988889
+4330,2438.1785,443.66656,2465.8723,503.88965,0.99887174
+4330,2692.8926,372.2396,2714.6763,430.24063,0.9988317
+4330,3141.8928,538.557,3176.1062,596.9315,0.9988034
+4330,3074.117,454.24097,3098.8215,510.78333,0.9987582
+4330,2887.6404,292.35022,2905.1287,339.57867,0.9987405
+4330,2177.9126,229.69028,2194.565,281.8405,0.99872357
+4330,3278.3987,345.8527,3301.577,392.15848,0.9983145
+4330,1978.7076,882.80554,2017.2836,974.0049,0.9980975
+4330,3047.811,446.5923,3072.3384,503.11017,0.99808705
+4330,3797.297,758.46277,3837.758,814.8375,0.9978921
+4330,3129.881,415.5006,3161.9514,467.12744,0.9978491
+4330,3223.8674,458.6855,3248.5999,509.38702,0.9977451
+4330,3161.5874,370.14978,3178.229,417.9803,0.9976514
+4330,3127.77,375.06848,3154.1836,425.47675,0.9976513
+4330,2010.0227,124.40996,2028.1306,162.90434,0.9797972
+4330,3861.0981,758.71765,3902.5518,817.1277,0.84549594
+4330,3885.813,739.61804,3908.502,786.51636,0.59115887
+4330,3740.1272,797.5477,3778.4167,862.02075,0.5343357
+4330,3965.71,731.5868,3993.1416,788.1566,0.27795124
+4330,3874.15,742.995,3908.3633,808.3002,0.10081198
+4331,2953.928,417.06226,2978.6526,471.98816,0.9994055
+4331,3100.345,291.73853,3118.3943,337.85858,0.9993843
+4331,2635.502,421.01035,2657.475,478.20505,0.9993223
+4331,2717.8755,360.6206,2744.0757,414.7398,0.9992506
+4331,2385.3247,436.35495,2407.2017,503.97928,0.99923277
+4331,2227.4229,235.71706,2251.9019,286.03964,0.9991417
+4331,3141.8682,538.31604,3175.459,596.49036,0.99909127
+4331,2187.3254,307.81494,2205.0051,364.2196,0.9990276
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+4331,3221.7734,394.37863,3241.0322,444.20615,0.9989323
+4331,2177.5718,229.5609,2194.6074,281.0232,0.9989146
+4331,2438.1191,443.4451,2466.109,503.9169,0.99888223
+4331,2887.382,291.97092,2904.992,339.25107,0.9988336
+4331,3073.5303,454.40237,3098.226,510.82834,0.9986111
+4331,3466.007,598.9702,3493.922,661.1968,0.99855477
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+4331,3174.1348,358.11832,3195.5132,405.65933,0.99849164
+4331,3278.135,345.11252,3301.2551,391.80972,0.99844295
+4331,2691.1545,371.8474,2714.0256,430.1244,0.9982917
+4331,1978.6172,882.8761,2017.3574,974.0316,0.9982161
+4331,3047.8093,446.6021,3072.2854,503.19208,0.99810874
+4331,3126.5818,375.35086,3154.34,425.41904,0.99728346
+4331,3161.593,369.84613,3177.8152,417.92023,0.99707115
+4331,3130.153,414.74213,3160.4124,465.27454,0.9968579
+4331,3226.011,458.6745,3249.8196,510.4975,0.99685246
+4331,2010.0165,124.433304,2028.1288,162.98915,0.9825881
+4331,3862.4185,758.6332,3904.666,817.6107,0.9046425
+4331,3880.3462,740.386,3905.6504,785.6653,0.4821884
+4331,3740.7932,797.3998,3779.7659,861.38855,0.463757
+4331,3965.285,731.27405,3993.157,788.53613,0.21887663
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+4332,2636.2634,420.77582,2657.467,477.90308,0.9993228
+4332,2954.8135,416.04373,2979.4565,469.09976,0.9992836
+4332,2717.7532,360.4974,2743.3425,414.91348,0.99927217
+4332,3100.4546,290.66833,3118.4282,335.96167,0.99917644
+4332,2383.163,436.10748,2405.083,504.0976,0.9990464
+4332,1293.4901,323.68744,1311.4457,380.47107,0.99900985
+4332,2187.0457,308.01062,2204.9836,364.19434,0.99899274
+4332,3141.823,538.04114,3173.8816,596.27625,0.9989749
+4332,2177.3232,229.39206,2194.5566,280.85587,0.9989298
+4332,2438.2717,443.41675,2466.1736,503.62433,0.9989255
+4332,3460.2854,598.94354,3492.076,661.37103,0.99892104
+4332,3221.695,394.35123,3240.9846,444.20517,0.9988061
+4332,3073.3772,454.3311,3097.3445,510.62387,0.9988041
+4332,2886.9495,291.90842,2905.055,339.4418,0.99862134
+4332,2689.3318,371.44208,2713.2268,429.74738,0.9985845
+4332,1978.6104,882.9015,2017.0747,974.2533,0.99840456
+4332,3173.0679,358.37097,3195.3657,404.43842,0.99834144
+4332,3048.0322,446.55182,3072.2212,503.43384,0.9980882
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+4332,3801.9133,755.72095,3843.0803,815.56335,0.9978916
+4332,3279.2434,344.84985,3300.866,391.45728,0.99761766
+4332,3226.9875,458.6193,3250.825,510.33737,0.99724233
+4332,3130.2446,413.6869,3157.6846,464.23273,0.99710745
+4332,3161.624,369.74597,3177.7354,417.32983,0.996824
+4332,2010.0763,124.367836,2028.284,163.01004,0.98588073
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+4332,3741.9473,796.403,3782.3481,860.6154,0.37685034
+4332,3876.8752,741.22766,3901.6438,788.4623,0.3476071
+4332,3680.865,801.92847,3716.3572,870.51184,0.15689343
+4332,3964.4338,730.86707,3993.4973,787.6859,0.13589472
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+4333,3101.195,289.4477,3118.3396,331.5841,0.9992162
+4333,2636.3335,420.59232,2657.691,477.86752,0.9992035
+4333,3454.5835,598.71704,3490.7622,661.25867,0.99917173
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+4333,2177.0486,229.16129,2194.552,280.76807,0.99892765
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+4333,3141.9365,538.0057,3171.9956,595.9194,0.9983494
+4333,1978.5287,882.83,2017.2201,974.5087,0.99826616
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+4333,3808.8926,754.42523,3844.7422,813.6159,0.9969447
+4333,3226.9788,458.90033,3252.9226,510.38928,0.99633235
+4333,3161.8147,369.3422,3177.3464,417.41135,0.9958021
+4333,2010.1904,124.33692,2028.3481,163.01016,0.9881742
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+4333,3863.4585,762.10016,3901.0132,818.0296,0.2542738
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+4334,3451.5105,598.38904,3488.72,661.37573,0.9992124
+4334,2636.2153,420.13358,2657.7046,477.62302,0.9991836
+4334,2229.0671,234.12854,2249.53,282.3415,0.99912554
+4334,2438.638,443.29233,2466.3064,502.76828,0.9991025
+4334,2955.0293,415.3661,2980.6274,469.65265,0.9990257
+4334,1293.4928,323.66647,1311.4664,380.4564,0.9990244
+4334,3101.332,289.13785,3118.2642,329.71786,0.99899536
+4334,2186.6707,308.1206,2204.869,364.19556,0.9989643
+4334,2176.799,229.0464,2194.368,280.62762,0.9989538
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+4334,3072.5015,454.34778,3095.9224,510.5611,0.9987447
+4334,2376.852,435.87122,2401.8955,503.7804,0.99866414
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+4334,3124.7534,375.69778,3150.3345,425.75754,0.9986305
+4334,1978.5629,882.8211,2017.13,974.51544,0.9984487
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+4334,3279.1072,345.0845,3299.777,391.51187,0.9957386
+4334,2010.2911,124.42664,2028.318,163.00986,0.9891571
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+4334,3681.7961,801.96686,3716.5027,870.59955,0.11880416
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+4335,2229.6106,234.02924,2248.9592,282.06207,0.99921966
+4335,2635.953,419.63065,2657.907,477.29153,0.9991842
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+4335,3450.227,598.2084,3487.605,660.7577,0.99913365
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+4335,3220.424,394.10382,3240.882,442.67694,0.9991085
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+4335,3128.899,411.76813,3147.0308,462.7433,0.9990293
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+4335,2176.599,228.96893,2194.5747,280.6829,0.9989183
+4335,3141.909,537.5105,3167.7537,595.76013,0.9988926
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+4335,3124.129,375.5671,3149.8599,425.74454,0.99883324
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+4335,3227.1755,459.6634,3255.907,510.73706,0.9987251
+4335,2684.9487,369.87888,2710.8062,428.10318,0.99853015
+4335,1978.5525,882.79816,2017.1147,974.477,0.99852043
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+4335,3171.96,358.05466,3195.2686,403.34537,0.9975158
+4335,3812.42,752.91895,3849.103,810.89746,0.9963033
+4335,3160.969,368.70068,3177.3557,415.5241,0.99475217
+4335,2010.3654,124.466034,2028.3602,163.00793,0.9899884
+4335,3862.9578,759.5846,3899.1907,818.4668,0.16425252
+4335,3753.189,792.2042,3788.1704,857.8579,0.124519594
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+4336,2636.221,419.24878,2658.056,476.93628,0.9992204
+4336,2229.5955,233.57516,2248.5725,281.50668,0.9992199
+4336,2717.1487,359.6168,2740.5671,414.80215,0.9991941
+4336,2368.4287,435.763,2399.9868,503.3938,0.99917823
+4336,3141.7478,537.6632,3166.5457,595.64294,0.9990558
+4336,3124.2534,375.5525,3148.88,425.71606,0.99903935
+4336,3125.4377,413.03192,3145.6467,461.96942,0.9990151
+4336,1293.4934,323.6914,1311.4807,380.39294,0.99900997
+4336,2955.1587,415.08667,2981.254,469.9049,0.99900097
+4336,3220.6658,393.37183,3240.9368,443.45496,0.99900013
+4336,3449.5818,597.997,3485.6262,661.4948,0.99895346
+4336,2186.6775,308.15182,2204.6575,364.30527,0.99889815
+4336,2176.267,228.42827,2194.293,280.7021,0.998801
+4336,3227.4268,460.01007,3257.163,511.24646,0.9986791
+4336,3101.4229,289.3671,3117.437,330.2684,0.99866414
+4336,1978.5002,882.7725,2016.9272,974.5633,0.9984743
+4336,3278.0945,345.1277,3298.762,390.20026,0.9982486
+4336,3812.0544,753.5415,3850.911,809.5569,0.99810505
+4336,2683.5674,369.02573,2709.3008,426.24814,0.99794644
+4336,2883.4636,291.417,2904.0085,339.3758,0.99774987
+4336,3172.2466,356.87613,3194.9883,403.53604,0.99770004
+4336,3048.37,446.42285,3072.5454,502.8075,0.99757653
+4336,3161.2532,368.0841,3177.035,414.28546,0.995359
+4336,3067.982,456.76675,3093.4102,511.306,0.9932033
+4336,2010.3345,123.88023,2028.6831,162.9511,0.9918719
+4336,3862.6018,758.66425,3897.861,818.8359,0.2683413
+4336,3756.9119,789.55396,3789.8997,855.1057,0.22586477
+4336,3868.1892,739.807,3892.3518,786.5383,0.1815949
+4336,3963.9092,732.2851,3992.9214,789.07025,0.14450993
+4337,2716.846,359.3259,2739.8816,414.59842,0.99951315
+4337,3140.8794,537.7767,3165.6401,595.92267,0.9993554
+4337,2636.723,418.62677,2658.777,476.79828,0.9993482
+4337,2365.6094,435.72855,2399.667,503.17123,0.9993198
+4337,2438.7864,443.06006,2466.322,502.30176,0.9992125
+4337,2229.4773,233.164,2248.3645,281.59344,0.99918216
+4337,3220.7412,392.66342,3240.42,442.80466,0.9991404
+4337,2955.1716,415.0605,2981.2576,469.58777,0.99906605
+4337,1293.4492,323.68375,1311.4939,380.38736,0.99898785
+4337,2176.0708,228.30147,2193.8687,280.57108,0.9989635
+4337,3124.1472,375.41223,3148.0925,425.70026,0.9989459
+4337,3227.5537,460.72726,3258.4224,511.9545,0.9989273
+4337,3449.6755,597.6614,3485.2458,661.6771,0.9989114
+4337,2186.6895,308.15497,2204.56,364.24707,0.9988024
+4337,3101.5754,288.88498,3117.0051,330.6722,0.9986778
+4337,3277.9612,344.8946,3298.4255,389.4281,0.99861157
+4337,1978.6266,882.77905,2016.8829,974.7809,0.9985051
+4337,2883.1438,291.29425,2903.5588,338.93335,0.99847585
+4337,2683.0286,367.8265,2708.0466,425.26376,0.99842834
+4337,3814.1968,753.6616,3852.6372,809.9645,0.9978573
+4337,3172.5762,355.10944,3194.255,403.37793,0.99781716
+4337,3119.707,412.93164,3145.0415,460.52075,0.9976306
+4337,3048.427,446.49017,3072.3767,502.49548,0.9974369
+4337,3160.5576,367.41904,3177.1802,413.6484,0.994044
+4337,3065.5103,455.61304,3093.1807,510.73016,0.9938818
+4337,2010.2649,123.8616,2028.792,162.94751,0.99229175
+4337,3866.6387,739.748,3890.9028,788.5159,0.4014996
+4337,3863.0889,758.65674,3896.183,819.19495,0.3108861
+4337,3760.6648,789.1745,3791.1345,854.2304,0.15613914
+4337,3963.9316,732.86993,3992.7969,789.58527,0.14720099
+4337,3652.2773,822.69037,3682.8325,887.75226,0.1120403
+4337,3682.9595,802.4481,3716.1313,871.32715,0.10155083
+4338,2716.9683,358.86752,2739.1855,413.61325,0.9994703
+4338,2229.3484,232.73589,2247.9548,281.58923,0.99941814
+4338,2955.5813,414.76102,2981.4695,468.7214,0.9992698
+4338,2637.3914,418.08246,2658.967,476.46997,0.9992362
+4338,3116.0513,410.79892,3143.4727,461.32516,0.99921894
+4338,3449.6323,597.3324,3484.1816,661.8567,0.99915683
+4338,2438.4011,442.77917,2466.3484,502.43915,0.9991529
+4338,2362.9746,435.61063,2399.3677,502.76706,0.9991297
+4338,3220.4968,392.42276,3240.5793,441.26987,0.9990326
+4338,3101.3452,287.78906,3117.0928,330.0114,0.99898523
+4338,3228.9075,461.08636,3259.9343,512.17786,0.9989826
+4338,1293.438,323.67303,1311.5027,380.4151,0.9989802
+4338,3138.176,537.5668,3165.0002,596.29724,0.99897206
+4338,2175.8083,228.55618,2193.8118,280.67804,0.9989691
+4338,3064.9067,454.65222,3092.7866,510.6396,0.998889
+4338,2186.7278,308.27374,2204.5144,364.2845,0.99874926
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+4338,2882.7532,290.91388,2903.0193,338.6631,0.9985954
+4338,3124.7273,375.24255,3147.0671,424.84576,0.9985128
+4338,1978.6199,882.7455,2016.7969,974.9331,0.9983334
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+4338,3159.5874,367.34332,3176.9844,412.6444,0.9939388
+4338,2010.2078,123.77259,2028.8718,163.06326,0.99336916
+4338,3861.3916,740.01154,3887.707,791.3471,0.7274598
+4338,3863.5566,758.56195,3896.045,818.9843,0.56087965
+4338,3683.4802,802.63074,3715.5085,871.2145,0.2858874
+4338,3764.173,789.44904,3793.2444,854.4328,0.20076393
+4338,3963.732,733.0619,3992.5771,790.09607,0.1945928
+4338,3652.2263,822.5895,3683.281,887.75854,0.12015736
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+4339,2229.2803,232.39761,2247.8691,281.99014,0.99947923
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+4339,2363.2031,435.57257,2398.462,502.64703,0.99927
+4339,2637.2456,417.50012,2658.8638,475.2787,0.99923337
+4339,3449.516,596.5048,3483.1055,661.1274,0.9991967
+4339,2438.3596,442.63702,2466.2654,502.39618,0.9991615
+4339,3219.9082,391.99176,3240.6577,441.18964,0.99899906
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+4339,2175.4292,228.35211,2193.6206,280.70682,0.99884254
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+4339,3278.048,343.51425,3298.0168,388.5894,0.9986156
+4339,3229.7197,461.12292,3260.3193,511.8698,0.99856406
+4339,3112.0105,407.22354,3142.827,461.2457,0.9984194
+4339,1978.5989,882.7582,2016.8098,975.0891,0.998206
+4339,2683.3342,366.3859,2705.2498,422.63828,0.9979626
+4339,3064.9888,454.66656,3092.1006,511.1693,0.99794847
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+4339,3159.4502,367.1562,3177.4067,412.51923,0.9945486
+4339,2010.161,123.768364,2028.8663,163.09225,0.9935874
+4339,3864.2112,757.29333,3896.1125,819.24976,0.89667636
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+4339,3765.7556,789.2162,3794.466,854.2112,0.28537226
+4339,3683.6416,802.5478,3715.978,870.85516,0.27123898
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+4340,2716.7495,358.1812,2737.6118,412.18793,0.9994411
+4340,2956.3628,414.2953,2982.3403,466.93445,0.9992613
+4340,2637.4177,417.10864,2658.8738,474.68988,0.9992424
+4340,3449.5068,595.7453,3480.6353,659.2601,0.9992269
+4340,3100.9626,284.37302,3116.6023,329.34225,0.99921167
+4340,3130.6008,537.79913,3162.61,595.523,0.999188
+4340,2438.3203,442.38364,2466.254,502.38797,0.9991425
+4340,2362.981,435.046,2396.0942,502.47086,0.9990595
+4340,3064.699,454.46332,3091.6755,511.3756,0.999009
+4340,3219.9395,391.41913,3240.6562,440.5191,0.9989988
+4340,2186.5315,308.55252,2204.3606,364.43777,0.9989982
+4340,1293.4125,323.6371,1311.4486,380.44867,0.9989737
+4340,3277.9749,342.34085,3297.7253,388.11667,0.9988539
+4340,2175.205,228.42838,2193.504,280.82867,0.9987739
+4340,2682.531,365.07733,2704.5198,421.37158,0.99874204
+4340,2882.3325,290.77148,2901.9897,337.63885,0.99868506
+4340,3234.3416,461.19196,3260.5735,511.85187,0.99863064
+4340,1978.6027,882.5707,2016.8326,975.1006,0.998442
+4340,3110.6707,405.917,3142.1501,461.61652,0.9982205
+4340,3171.188,352.34024,3191.2231,402.57535,0.9980907
+4340,3047.9348,446.49518,3072.2908,502.72852,0.99712616
+4340,3824.912,753.0071,3859.3984,809.55286,0.9960439
+4340,3124.9648,374.62775,3145.9116,423.8095,0.9959253
+4340,2010.2191,124.03209,2028.8893,163.1574,0.99391043
+4340,3159.2031,366.24323,3176.2651,411.70605,0.99260265
+4340,3863.66,757.57227,3896.1106,818.9695,0.95697135
+4340,3855.8477,741.92975,3881.5347,792.116,0.6337453
+4340,3683.282,801.3459,3716.949,870.6496,0.46792126
+4340,3767.2483,789.23425,3795.113,854.5598,0.29328102
+4340,3958.46,734.12067,3992.7417,790.26044,0.16240188
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+4341,2715.9827,358.02527,2736.9534,411.81885,0.99947804
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+4341,3449.732,595.43604,3477.893,657.76733,0.999218
+4341,2637.239,416.84845,2658.8777,473.53314,0.99921286
+4341,3126.939,537.81885,3161.8135,595.57007,0.9991917
+4341,2438.6643,442.14862,2466.2717,502.26154,0.999169
+4341,3219.8433,390.91553,3240.796,439.9314,0.99914277
+4341,2957.3257,413.78024,2982.6982,467.5458,0.9990012
+4341,1293.4019,323.62747,1311.4456,380.44827,0.99895895
+4341,3064.632,454.385,3090.7615,511.34033,0.998958
+4341,2186.562,308.77716,2204.2832,364.4355,0.9989318
+4341,3277.8232,342.21768,3297.691,388.79056,0.99876046
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+4341,2682.4688,365.01807,2703.0635,421.53082,0.9985648
+4341,1978.6134,882.5475,2016.8375,975.1061,0.99852836
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+4341,3237.1833,461.45667,3260.8699,512.8947,0.9983308
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+4341,3170.4292,353.0871,3190.7075,402.6474,0.9981351
+4341,3047.8706,446.47208,3072.4639,502.8301,0.99665123
+4341,2010.233,123.813324,2028.9725,163.11972,0.9936528
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+4341,3124.737,374.8874,3145.297,422.13214,0.99287224
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+4341,3853.4377,743.33826,3878.8752,792.14526,0.88489115
+4341,3683.694,800.5155,3717.9675,869.9485,0.5375667
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+4341,3951.0613,734.5341,3992.5178,790.86444,0.18957487
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+4342,3123.6387,537.66797,3160.7607,595.4082,0.9993759
+4342,2439.0723,441.98553,2466.3843,502.29993,0.99924445
+4342,2637.1978,416.6547,2658.9116,473.41653,0.99921083
+4342,3449.6018,594.8208,3476.6047,656.4779,0.9991726
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+4342,1293.3983,323.6291,1311.4337,380.45612,0.9989818
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+4342,2881.6501,290.4786,2900.533,336.55118,0.99857557
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+4342,1978.6155,882.5642,2016.832,975.011,0.99846834
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+4342,3124.0796,375.13083,3145.317,422.27005,0.99386215
+4342,2010.2275,123.83572,2028.9773,163.07831,0.99331677
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+4342,3152.2175,366.20648,3175.196,412.84943,0.9848104
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+4342,3683.7,800.0383,3717.9373,869.5293,0.3442039
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+4343,2439.4126,441.826,2466.4487,502.30646,0.9993343
+4343,3122.242,538.08453,3159.8518,595.36993,0.9993001
+4343,2362.813,432.87433,2387.9604,499.12115,0.99919385
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+4343,3099.7832,283.01843,3117.3115,324.91315,0.99868995
+4343,1978.6191,882.53876,2016.862,975.0103,0.99849653
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+4343,3169.5017,353.49185,3188.348,401.25278,0.9964736
+4343,2010.2451,123.85672,2029.0657,162.98941,0.99317294
+4343,3123.2122,375.0643,3144.571,423.2899,0.99297357
+4343,3152.0151,366.22137,3174.4087,412.51025,0.9877636
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+4343,3767.1934,792.88495,3798.6807,855.7299,0.34818852
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+4344,3121.955,537.97363,3159.5874,595.6444,0.99935967
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+4344,3449.4436,594.4822,3473.7146,654.8992,0.99916875
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+4344,2172.9731,227.74432,2193.8877,280.24078,0.9989483
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+4344,3219.796,390.1896,3241.0117,439.55252,0.9988299
+4344,3277.4294,341.72574,3297.072,388.35275,0.9987679
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+4344,3099.338,282.5639,3118.1045,323.84863,0.9985039
+4344,1978.6212,882.5259,2016.8217,975.06213,0.9984487
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+4344,2671.8628,364.19324,2699.1343,421.44537,0.9978543
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+4344,3151.2783,364.97922,3173.9224,411.49442,0.9926509
+4344,2010.3701,123.94561,2029.0789,162.98657,0.9925754
+4344,3854.6348,759.8547,3895.0947,821.7022,0.97318745
+4344,3767.3716,792.64166,3800.69,855.0778,0.7681502
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+4345,3121.6816,537.80176,3158.7607,595.5243,0.9993311
+4345,2439.9312,441.51483,2466.7173,502.43,0.9993131
+4345,2961.226,412.03677,2984.2998,462.38992,0.999288
+4345,3446.091,594.44,3471.9812,654.83563,0.99926
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+4345,3220.141,389.82404,3241.2769,439.5359,0.99899024
+4345,2638.1023,415.84442,2659.361,473.0661,0.9989673
+4345,3064.3545,454.82843,3087.9585,511.39716,0.99895334
+4345,2186.3281,309.49225,2204.2266,364.4124,0.998781
+4345,3276.813,341.5061,3297.0713,388.67615,0.998703
+4345,2358.769,432.25003,2382.189,498.4208,0.99854904
+4345,1978.6311,882.54974,2016.8469,975.0186,0.99844766
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+4345,3111.6868,407.91397,3132.8606,458.1205,0.9957131
+4345,3835.735,745.6157,3866.3171,810.23926,0.9948931
+4345,2010.3378,123.82317,2029.174,162.9834,0.9921123
+4345,3152.937,364.19128,3173.353,411.60547,0.98277456
+4345,3853.8818,761.43823,3893.7017,821.9625,0.90160996
+4345,3767.0183,791.6145,3802.0647,854.703,0.5361057
+4345,3681.0999,800.12427,3718.1682,869.6316,0.17441891
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+4346,3121.747,537.74207,3157.3306,595.65125,0.9993278
+4346,2439.8323,441.48282,2466.72,502.4368,0.99927574
+4346,2227.1458,231.7273,2245.6702,280.73114,0.99923146
+4346,2961.3416,412.1142,2984.6843,462.30524,0.9992157
+4346,3064.4219,454.52628,3087.311,511.34915,0.9990852
+4346,2356.7795,431.38528,2380.6003,498.29648,0.9990752
+4346,1293.3633,323.64084,1311.4878,380.44846,0.9990181
+4346,3220.0308,389.58954,3241.1182,439.377,0.9990101
+4346,2638.2478,415.5395,2659.3298,473.0633,0.99895203
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+4346,3440.4248,594.5835,3470.477,654.552,0.998949
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+4346,3242.1865,460.46872,3262.7446,512.2324,0.99844044
+4346,1978.6426,882.535,2016.916,975.0302,0.9982686
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+4346,3835.627,748.5681,3867.6328,806.0602,0.9934457
+4346,2010.3619,123.726425,2029.164,163.0165,0.9913924
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+4346,3852.789,762.3068,3892.6284,822.31195,0.8929479
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+4347,2439.7292,441.33682,2466.9387,502.4346,0.9992588
+4347,3064.4927,454.47882,3086.7476,511.55035,0.9992322
+4347,3097.5442,280.61298,3116.3503,321.4004,0.9991899
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+4347,3121.7666,537.5275,3154.9863,595.7874,0.99907136
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+4347,2876.7043,288.07446,2893.7346,334.79218,0.9983157
+4347,3119.874,376.16904,3142.8154,424.34854,0.9982664
+4347,1978.6299,882.55536,2016.9424,975.03107,0.99819154
+4347,3048.0393,446.23926,3071.785,502.50745,0.99771726
+4347,2667.1235,362.2681,2697.0552,419.21982,0.9975955
+4347,3836.4158,750.6072,3869.3845,805.48096,0.9973049
+4347,3168.0713,351.83954,3186.2598,395.40802,0.9966222
+4347,3110.2322,405.92657,3129.3767,458.0838,0.99627954
+4347,2010.32,123.55518,2029.2201,163.04724,0.9909032
+4347,3832.8738,742.9231,3863.4646,792.8308,0.99037814
+4347,3152.0774,364.1214,3172.3489,409.00952,0.98509884
+4347,3767.6401,790.2402,3804.8818,853.3193,0.8574387
+4347,3852.9622,762.72546,3891.5662,822.8657,0.6745494
+4347,3960.5432,735.51654,3989.657,790.42596,0.29750276
+4348,3431.8418,595.06934,3468.5757,655.6511,0.99945265
+4348,2709.9578,356.81775,2731.9866,410.95996,0.9994261
+4348,3096.7766,279.16623,3114.0935,320.6241,0.99926144
+4348,2439.7314,441.15802,2466.75,502.406,0.99922025
+4348,3064.363,454.50827,3085.5168,511.46976,0.9991731
+4348,2346.8682,431.33792,2377.5679,497.71353,0.99913174
+4348,3121.7124,537.30914,3153.309,596.22894,0.99904877
+4348,1293.1951,323.52893,1311.457,380.27014,0.9989993
+4348,2225.4858,230.55031,2244.3872,278.2731,0.9989518
+4348,2638.5542,415.0616,2659.4849,472.92386,0.9989201
+4348,2186.5088,309.7791,2204.2158,364.61322,0.99891126
+4348,2961.622,411.4451,2985.5688,462.00778,0.998855
+4348,3119.5166,376.79202,3142.9043,425.7432,0.9988281
+4348,2170.2651,227.80069,2193.6753,278.29782,0.99865663
+4348,2874.3882,288.76147,2892.479,335.29004,0.99862516
+4348,3219.182,388.817,3240.6868,439.0986,0.99851316
+4348,3242.2947,460.64386,3264.7156,512.4301,0.99847895
+4348,2666.6362,361.56644,2696.3296,418.9434,0.9980738
+4348,3276.4124,341.15717,3296.679,388.04285,0.9980123
+4348,1978.4541,882.3672,2017.154,975.39404,0.99799585
+4348,3166.7888,350.38684,3185.9841,395.54834,0.9975266
+4348,3048.035,446.3335,3071.7751,502.48944,0.9974433
+4348,3151.1948,362.81982,3170.79,407.07965,0.9972932
+4348,3108.2324,405.75397,3127.0967,457.4303,0.9968928
+4348,3838.4705,750.05426,3870.2366,805.81647,0.99634224
+4348,2010.347,123.696106,2029.6674,163.01697,0.9912429
+4348,3825.1814,742.6031,3857.9778,792.7149,0.97364104
+4348,3769.3762,790.35913,3806.758,852.666,0.93968266
+4348,3853.0417,763.11115,3890.427,823.3979,0.7104589
+4348,3961.257,735.9076,3988.5935,790.43726,0.2594098
+4349,3429.0444,595.394,3467.2612,654.9819,0.9993641
+4349,2707.2947,356.71188,2731.0745,411.07974,0.99931145
+4349,3096.4763,277.86847,3113.221,319.96326,0.99927133
+4349,3063.991,454.42493,3085.0837,511.57098,0.99917394
+4349,2439.6655,440.94357,2467.1064,502.39413,0.99915695
+4349,2225.0996,230.35904,2243.8276,276.87958,0.9990644
+4349,1293.1941,323.5285,1311.4578,380.27032,0.9990011
+4349,2186.5078,309.81314,2204.1572,364.61142,0.9989048
+4349,2961.4104,410.9607,2986.2976,462.07452,0.9989028
+4349,2638.7195,414.7942,2659.6545,472.59473,0.9988784
+4349,3218.4082,388.56018,3239.8506,438.9679,0.99880385
+4349,2169.4497,227.32602,2192.3472,277.88815,0.99873513
+4349,3118.8232,377.4871,3142.6978,425.97183,0.9986765
+4349,3121.9143,536.9884,3150.7263,596.03687,0.9986135
+4349,2870.4792,287.78516,2891.867,335.36224,0.9984659
+4349,2343.0156,431.46982,2376.9067,497.6602,0.9983229
+4349,3242.219,460.45248,3265.7664,512.84326,0.9982716
+4349,3276.0757,340.6554,3296.7793,388.43103,0.99820864
+4349,2666.4321,361.53574,2695.1982,418.8018,0.9980659
+4349,1978.4541,882.3672,2017.154,975.39404,0.99799585
+4349,3048.1104,446.445,3071.3716,502.32794,0.9978423
+4349,3102.1572,405.6024,3125.601,457.0587,0.99754095
+4349,3166.0754,349.68094,3185.5168,396.62247,0.99727434
+4349,3151.364,362.3203,3169.9905,406.7149,0.997063
+4349,2010.3462,123.69158,2029.6685,163.01741,0.991264
+4349,3842.687,749.9792,3872.0806,804.1337,0.9901354
+4349,3771.3171,790.0243,3808.7312,853.2406,0.9155618
+4349,3820.9182,743.49164,3855.7947,792.6508,0.8943342
+4349,3852.9126,763.6326,3889.5176,824.0363,0.4028369
+4349,3958.734,735.95184,3988.1934,790.1268,0.2598313
+4350,3428.4277,595.7001,3465.4253,655.58154,0.99946684
+4350,2705.2195,356.7117,2730.1873,411.1873,0.99937207
+4350,2439.643,440.96265,2466.662,502.3781,0.9991436
+4350,2224.8486,229.77994,2243.3022,276.94995,0.99902606
+4350,1293.2028,323.5267,1311.4569,380.27084,0.99900407
+4350,3121.9722,536.65356,3148.8828,596.5303,0.9989772
+4350,2961.4553,411.5599,2987.0593,461.9067,0.99896634
+4350,3062.925,454.40796,3084.6008,511.71918,0.99894863
+4350,2186.5054,309.79752,2204.2788,364.60745,0.99894756
+4350,2638.8342,414.53378,2660.2253,471.91708,0.9988901
+4350,2339.883,431.3467,2376.3386,497.3779,0.9987
+4350,3218.3347,388.41904,3239.431,438.73343,0.9986513
+4350,3094.9585,276.99023,3112.6504,319.74176,0.9985784
+4350,3242.3633,460.807,3265.7583,512.7307,0.99855584
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+4350,3275.8362,340.05872,3296.176,388.53748,0.99810946
+4350,3118.365,377.6948,3142.3127,426.32535,0.998094
+4350,2666.3687,361.15082,2694.018,417.4806,0.998063
+4350,1978.4541,882.3672,2017.154,975.39404,0.99799585
+4350,2168.6086,227.01683,2192.2512,276.99872,0.99799466
+4350,3097.0996,405.4245,3123.8262,457.6139,0.99778134
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+4350,3151.3896,360.92892,3168.8438,406.1205,0.9963505
+4350,3844.8933,747.99243,3873.6082,804.1688,0.9948409
+4350,2010.3462,123.69158,2029.6685,163.01741,0.991264
+4350,3773.5637,788.91614,3810.4421,851.42957,0.9839903
+4350,3852.8604,763.64075,3888.8374,823.8353,0.8319349
+4350,3818.147,744.29974,3853.5396,792.0987,0.7624177
+4350,3960.4153,736.3867,3987.1096,790.3164,0.19707099
+4350,3681.6724,800.9623,3719.3457,869.40894,0.18178959
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+4351,2439.6692,440.9908,2466.1365,502.32767,0.99922717
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+4351,2339.582,431.06644,2375.9707,497.165,0.9990803
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+4351,2186.4736,310.0051,2204.1934,364.60336,0.998992
+4351,2223.9712,229.35724,2242.9468,276.79785,0.99894375
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+4351,3061.4556,454.64627,3084.0967,511.59744,0.99885476
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+4351,2961.5967,410.98483,2988.162,461.88718,0.99884355
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+4351,3217.8682,388.292,3238.2695,438.06915,0.99871516
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+4351,2167.6265,226.7159,2190.9922,276.75815,0.99822253
+4351,1978.4541,882.3672,2017.154,975.39404,0.99799585
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+4351,3150.4336,360.6602,3167.71,405.80685,0.9945669
+4351,2010.3462,123.69158,2029.6685,163.01741,0.991264
+4351,3845.4678,746.5053,3874.9424,802.8201,0.9653324
+4351,3778.2537,787.2882,3812.8157,850.48706,0.9404768
+4351,3852.4863,763.1178,3887.9834,823.6057,0.5977148
+4351,3959.5237,735.6518,3987.4475,790.283,0.4782563
+4351,3818.5764,746.03284,3847.6946,791.4823,0.39805067
+4351,3681.1812,800.19666,3720.9917,869.6598,0.17028037
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+4352,2639.4656,413.08737,2661.674,469.95963,0.9992951
+4352,3120.9348,535.93,3144.9426,596.9798,0.9992756
+4352,2439.7336,440.9684,2465.8025,502.36716,0.99927145
+4352,2339.6223,431.0917,2375.5437,496.66806,0.9992567
+4352,2703.568,356.1432,2727.9739,410.87213,0.9992015
+4352,3092.633,275.0187,3111.9905,318.95126,0.9990435
+4352,1293.1886,323.50818,1311.4327,380.27856,0.9990276
+4352,2186.4316,310.02957,2204.0479,364.61777,0.99900573
+4352,2223.1663,228.77838,2242.5037,277.05466,0.99880743
+4352,3275.2427,339.17172,3295.2354,384.53238,0.9987128
+4352,2961.6152,410.74036,2989.2563,461.9549,0.99864453
+4352,3217.7295,387.60526,3237.5854,437.3019,0.998566
+4352,2666.1475,359.67868,2688.481,415.15042,0.9985531
+4352,3242.8962,460.46384,3268.7986,512.7752,0.9985511
+4352,3117.166,377.93677,3139.962,427.6355,0.9984357
+4352,3058.864,454.6009,3083.6057,511.40906,0.9982765
+4352,3091.4905,404.43808,3121.869,456.74588,0.9981989
+4352,3163.4724,348.31848,3183.3381,396.78186,0.99812037
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+4352,2866.2402,287.02588,2889.9355,333.96167,0.99805593
+4352,1978.4547,882.3894,2017.1527,975.40674,0.9980184
+4352,3048.1,446.61545,3071.4893,502.61288,0.9972976
+4352,3151.0312,359.9323,3167.1719,405.45294,0.99592054
+4352,2010.4467,123.6672,2029.7411,163.00928,0.991327
+4352,3845.1782,744.83307,3877.4946,802.1436,0.87273276
+4352,3782.7825,785.51666,3814.3235,849.49005,0.8551823
+4352,3947.238,735.2402,3987.3542,790.3691,0.60822403
+4352,3851.308,762.74475,3886.435,824.34375,0.5473099
+4352,3055.9272,451.27008,3075.2163,506.12518,0.5309268
+4352,3817.121,746.70056,3845.8613,792.3638,0.36598903
+4352,3682.4265,800.23596,3721.8284,868.937,0.3410317
+4353,2439.8145,440.96304,2465.441,502.36923,0.99940944
+4353,2702.6821,355.79633,2727.065,410.7965,0.99934393
+4353,2639.642,412.93872,2661.8794,469.3412,0.99930227
+4353,3427.4253,594.9512,3460.3208,654.6007,0.99929214
+4353,3118.547,535.81177,3143.7366,597.4042,0.99912614
+4353,2222.1367,228.4528,2241.4731,276.77957,0.99908257
+4353,1293.1968,323.51352,1311.4619,380.27487,0.9990109
+4353,2186.407,309.99387,2204.0364,364.64896,0.99898154
+4353,2962.9497,410.855,2990.1758,460.88467,0.9989649
+4353,2339.0564,430.44128,2374.323,496.1231,0.9989478
+4353,2166.636,226.8304,2187.0618,277.27435,0.99880064
+4353,3217.405,386.86234,3236.6716,436.58994,0.99873
+4353,3090.181,274.44492,3111.6013,318.01663,0.998705
+4353,3273.8018,338.41992,3295.3965,384.93854,0.9987029
+4353,2666.2136,359.31183,2685.5134,414.1411,0.9986584
+4353,3055.5806,454.373,3082.9717,511.7812,0.99846965
+4353,2864.6733,286.56653,2889.4688,333.65863,0.998401
+4353,3116.2131,377.88245,3138.9382,427.72168,0.9982944
+4353,3243.08,460.53793,3269.7285,512.78864,0.99807054
+4353,1978.4574,882.3884,2017.1498,975.39136,0.9980149
+4353,3089.2974,404.14908,3121.3618,456.48813,0.9978249
+4353,3162.601,347.89374,3182.9658,396.69666,0.99730706
+4353,3047.8584,446.4965,3071.2979,503.90988,0.9968315
+4353,3150.2566,359.78867,3166.8308,405.53763,0.9948691
+4353,2010.5623,123.49965,2029.8887,163.04597,0.9914014
+4353,3786.054,784.59314,3816.7766,847.3408,0.9498253
+4353,3845.2065,743.7984,3879.4438,801.49005,0.88072926
+4353,3851.9634,762.3335,3887.4243,824.20715,0.6915881
+4353,3817.059,746.2211,3844.6123,792.2802,0.5537281
+4353,3946.9846,735.05133,3987.1482,790.3487,0.33917195
+4353,3682.7043,800.3855,3722.7961,868.5841,0.10126383
+4354,2439.8398,440.95392,2465.2437,502.3393,0.9994832
+4354,2639.8606,413.14136,2661.9382,468.6584,0.99941874
+4354,2702.555,355.47903,2725.7366,409.7313,0.9992686
+4354,2963.429,410.77832,2991.1135,460.53247,0.9991503
+4354,2186.4204,310.20172,2204.0054,364.6369,0.99908054
+4354,2165.572,226.92723,2185.6301,276.97354,0.9990608
+4354,3427.3474,594.2018,3457.7742,653.3229,0.9990392
+4354,1293.1967,323.53696,1311.4603,380.2784,0.9990054
+4354,3090.4744,274.64325,3110.7097,315.93585,0.9990023
+4354,2219.5977,228.51315,2240.623,276.52747,0.9989759
+4354,3216.6174,386.72528,3235.8225,435.82,0.9989693
+4354,2339.012,429.9318,2372.8088,496.71994,0.9988739
+4354,3274.0752,338.15546,3294.9087,384.74298,0.99873286
+4354,2664.7815,358.89096,2684.5808,414.262,0.9986841
+4354,3113.0251,535.4178,3142.2048,597.90094,0.9985581
+4354,3242.9497,460.04184,3269.917,513.02985,0.9984754
+4354,3050.4043,454.2901,3082.4204,511.79675,0.99841547
+4354,2864.5354,286.53372,2888.5994,332.81137,0.9983911
+4354,1978.4531,882.3808,2017.1785,975.38873,0.99805033
+4354,3089.7134,403.68564,3120.5103,454.95602,0.99792033
+4354,3115.7437,377.92862,3137.5107,428.30612,0.9978824
+4354,3160.6968,348.442,3181.8906,396.5673,0.99666303
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+4354,3149.0823,359.21838,3166.3767,405.2329,0.9932892
+4354,2010.5414,123.46529,2029.9594,163.10213,0.9918934
+4354,3789.4194,784.6447,3817.73,845.8283,0.9367337
+4354,3845.4077,743.8802,3880.833,801.8149,0.7542938
+4354,3850.6343,761.9964,3886.939,823.7131,0.5259099
+4354,3816.692,746.25995,3843.811,793.0472,0.5209267
+4354,3685.0022,800.1651,3723.756,868.40875,0.12546544
+4354,3949.4595,734.6118,3986.231,790.48645,0.116029285
+4355,2439.949,440.9524,2465.074,502.42725,0.9995442
+4355,2639.6914,412.75665,2662.0186,468.22034,0.99952173
+4355,3108.9185,536.0157,3141.3398,596.877,0.9993297
+4355,2339.108,429.9345,2370.0054,495.5697,0.9992236
+4355,2186.3887,310.27704,2203.901,364.6626,0.99916613
+4355,3089.4639,274.40985,3109.3735,314.4638,0.99911827
+4355,2964.5945,410.41537,2992.532,461.39987,0.9991026
+4355,3215.9126,386.477,3235.2046,434.3485,0.9990706
+4355,2702.8848,355.44754,2724.9775,409.4443,0.99900025
+4355,1293.1874,323.56296,1311.465,380.28848,0.9989943
+4355,2163.3374,226.48187,2185.4849,276.61627,0.99895823
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+4355,3089.26,403.362,3119.0706,453.77112,0.99841475
+4355,3242.9368,460.19568,3270.07,513.14215,0.99825567
+4355,3271.8904,338.38684,3294.365,385.04205,0.9982135
+4355,1978.4476,882.3861,2017.1581,975.4165,0.99790597
+4355,3114.6328,377.98956,3136.4697,429.0451,0.9978998
+4355,3159.9863,347.88904,3181.4165,396.09235,0.99652135
+4355,3146.7507,359.0115,3165.9822,404.85287,0.9943019
+4355,3048.9336,446.37527,3071.295,502.29507,0.9938054
+4355,2010.5458,123.44861,2030.0499,163.1312,0.9918893
+4355,3792.939,783.9296,3818.3345,844.88007,0.97078115
+4355,3845.2397,743.9104,3882.0542,800.3053,0.8402574
+4355,3849.853,762.3201,3887.2563,823.88727,0.70176464
+4355,3815.8655,745.92596,3841.7517,792.03925,0.6278605
+4355,3951.2576,734.3963,3986.2078,790.15533,0.23753564
+4356,2440.1484,440.9681,2464.8105,502.37955,0.999553
+4356,2639.8516,412.51633,2662.1855,467.81827,0.9994943
+4356,3104.0742,536.2379,3139.9668,596.99243,0.99934214
+4356,2702.5427,354.30524,2723.054,408.41357,0.99930465
+4356,3088.7988,273.66882,3107.6626,314.25446,0.9992299
+4356,2160.0693,226.3398,2184.102,276.4665,0.9991191
+4356,2186.3416,310.2693,2203.6438,364.67438,0.9990865
+4356,3214.8225,385.63855,3234.1873,434.0531,0.9990308
+4356,2214.9023,228.34155,2239.4731,276.13797,0.99902165
+4356,1293.1564,323.6052,1311.4442,380.29752,0.9989587
+4356,3048.1387,454.15308,3080.9644,511.54694,0.99878234
+4356,3243.1611,460.3244,3270.3818,513.02936,0.99870646
+4356,2965.2693,409.1166,2993.6443,461.34152,0.9984829
+4356,3427.475,592.3669,3453.6045,651.31256,0.9984586
+4356,2658.9358,358.52325,2681.6409,414.00104,0.99841845
+4356,3089.329,402.16522,3117.4146,452.72137,0.9983211
+4356,3113.8596,378.29446,3136.0027,430.07596,0.99792033
+4356,1978.449,882.3552,2017.1272,975.3932,0.9978613
+4356,2864.413,286.73822,2885.3203,331.5468,0.9977112
+4356,2339.3015,428.67746,2365.9226,493.25558,0.99762934
+4356,3270.667,337.889,3294.1045,385.02493,0.99712545
+4356,3159.565,347.534,3181.143,395.77032,0.99677175
+4356,3145.1755,358.92825,3165.2341,405.00803,0.99607486
+4356,3048.5715,446.30792,3071.0828,502.31067,0.9932761
+4356,2010.5372,123.55975,2029.8864,163.08945,0.9916577
+4356,3794.0679,783.6369,3818.9614,844.4233,0.95827687
+4356,3844.6704,744.0748,3883.2246,799.71765,0.8072428
+4356,3850.829,761.30176,3886.94,823.63354,0.52365506
+4356,3954.9236,732.98315,3988.1628,790.1094,0.20008402
+4356,3812.9182,746.333,3840.1736,791.7483,0.18913919
+4357,2440.1147,440.90778,2464.732,502.33618,0.99953437
+4357,2639.9172,412.3166,2662.3152,467.5003,0.9993936
+4357,3047.6514,454.52713,3079.769,511.42606,0.99915385
+4357,2214.6533,228.28955,2239.371,275.69928,0.9991233
+4357,3101.5322,536.7593,3139.275,597.24414,0.9990972
+4357,3087.6523,272.74728,3105.2178,314.21323,0.99908733
+4357,2186.3213,310.25864,2203.6123,364.67404,0.9990601
+4357,3213.8271,385.47382,3233.545,433.5562,0.9989736
+4357,2702.184,354.31784,2721.6055,406.72482,0.9989524
+4357,1293.1543,323.6626,1311.4497,380.3006,0.99891853
+4357,2967.9358,409.00266,2994.6042,461.3008,0.99877495
+4357,3243.241,460.523,3270.0203,512.9687,0.998665
+4357,3427.148,592.44403,3452.4038,651.7757,0.9986243
+4357,2158.5317,226.76445,2184.0034,276.579,0.9985721
+4357,3113.68,377.9937,3135.7073,429.25793,0.9985557
+4357,2339.1611,428.47864,2363.269,493.05762,0.998522
+4357,3158.8713,347.56845,3180.4802,391.94943,0.9984017
+4357,2654.0977,357.32895,2679.707,413.97073,0.9982443
+4357,1978.4386,882.33484,2017.0394,975.4486,0.99791986
+4357,2864.6338,286.3768,2883.504,331.25577,0.9972429
+4357,3143.6638,358.35538,3164.7488,405.9,0.9963703
+4357,3089.6294,401.44995,3113.8916,451.06866,0.9961063
+4357,3270.1943,337.42062,3293.7988,384.6483,0.996027
+4357,3048.3506,446.34717,3071.2935,501.96118,0.99258435
+4357,2010.5518,123.67063,2029.9114,163.06828,0.99151295
+4357,3795.455,785.1094,3820.1592,844.984,0.98232794
+4357,3845.1792,744.8714,3884.5063,799.83124,0.75416505
+4357,3850.466,762.436,3887.2517,823.7177,0.6177777
+4357,3811.4587,746.65265,3837.5964,787.57416,0.27619198
+4357,3955.0105,733.697,3987.8503,790.3209,0.16722707
+4357,3693.5002,796.4191,3727.029,866.0452,0.15064579
+4358,2440.1003,440.9103,2464.9294,502.33725,0.9994881
+4358,2214.7288,228.38567,2238.714,276.43872,0.9993794
+4358,2639.7356,411.8777,2662.2942,467.57684,0.99932396
+4358,3086.1812,271.23557,3102.5747,313.12778,0.9992799
+4358,2186.3313,310.3601,2203.5544,364.68237,0.999119
+4358,3212.324,384.84732,3233.048,433.5317,0.9990753
+4358,2701.498,353.2746,2719.8618,406.31976,0.9990277
+4358,3158.947,346.6693,3179.305,392.37927,0.99902344
+4358,3101.552,537.03595,3137.327,597.4449,0.99900633
+4358,1293.1648,323.63315,1311.4558,380.30386,0.9989274
+4358,3048.0588,454.46637,3078.735,511.49176,0.9988953
+4358,2158.0615,226.97176,2183.894,276.78104,0.9988104
+4358,3426.0703,592.1336,3450.4014,651.08905,0.9988035
+4358,2970.5317,408.45755,2996.1177,460.5059,0.9987521
+4358,3243.5718,460.66803,3270.002,512.7873,0.99870145
+4358,3111.7087,377.87997,3134.2605,429.00272,0.99847704
+4358,2337.7515,427.9718,2360.0835,493.0935,0.99822193
+4358,2648.7937,357.453,2678.8274,413.84625,0.9981279
+4358,1978.4547,882.34125,2017.0714,975.43585,0.99800414
+4358,2865.2559,286.61017,2881.9937,330.89703,0.99703884
+4358,3089.9268,400.35785,3110.873,450.2254,0.99683607
+4358,3143.6277,358.49588,3163.5198,404.82138,0.9958021
+4358,3270.389,337.54874,3293.2917,384.405,0.9929148
+4358,2010.5883,123.80716,2030.0255,163.04895,0.99180686
+4358,3048.6382,446.37344,3071.3325,501.84024,0.9911288
+4358,3796.5837,786.9762,3820.4285,844.9077,0.9746099
+4358,3845.478,745.2496,3885.4595,798.58563,0.7306217
+4358,3850.6216,762.81793,3887.877,824.0534,0.62549996
+4358,3806.1765,747.55536,3835.159,796.0942,0.38628685
+4358,3694.8877,795.9983,3727.4438,865.2318,0.27682602
+4358,3643.5522,819.9142,3684.499,887.8556,0.17640801
+4358,3955.7336,735.1837,3985.4294,791.03784,0.14208402
+4359,2440.0176,441.11703,2464.9941,502.33383,0.9994836
+4359,3101.1738,537.3597,3135.4453,597.4462,0.9994172
+4359,3084.8953,269.48947,3101.9065,312.95932,0.99936444
+4359,2639.4644,410.95993,2662.4219,467.49936,0.9993032
+4359,2213.7373,228.25688,2238.294,276.055,0.99927825
+4359,2700.6477,352.8439,2719.096,406.2482,0.99912053
+4359,2186.349,310.34018,2203.5908,364.69382,0.99909806
+4359,3422.2239,592.317,3449.0588,651.29944,0.9989619
+4359,1293.1575,323.67166,1311.4639,380.3024,0.998904
+4359,3047.6555,454.0586,3077.4553,511.47656,0.99889755
+4359,2157.6768,227.04756,2184.005,276.78745,0.99886656
+4359,3210.234,384.13138,3232.6455,433.8024,0.9988386
+4359,2334.8528,427.48175,2359.5154,493.13977,0.9988128
+4359,3244.2432,460.692,3270.0845,512.9131,0.99879766
+4359,2974.519,408.08322,2997.442,460.6011,0.99866533
+4359,3109.6316,377.87717,3133.3274,428.51627,0.9986235
+4359,2646.7446,357.02475,2678.412,413.9009,0.99856514
+4359,3159.0354,345.6944,3178.3655,390.77454,0.9982547
+4359,2864.8064,286.02908,2880.6633,331.00705,0.9981192
+4359,1978.4476,882.37054,2017.0316,975.45355,0.9980884
+4359,3089.4023,400.2281,3108.4531,449.93347,0.9979115
+4359,3269.4783,336.86688,3292.7683,384.70837,0.996887
+4359,3143.4492,358.5356,3162.3882,404.53458,0.994852
+4359,2010.5796,123.7266,2030.0825,163.08142,0.9915159
+4359,3048.561,446.245,3070.6611,501.4308,0.99113184
+4359,3846.2139,745.8479,3887.4243,796.652,0.9169651
+4359,3796.6646,789.45074,3820.3623,845.71857,0.8842748
+4359,3851.1199,763.52264,3888.8103,823.91473,0.6740787
+4359,3643.7136,819.95374,3683.9841,887.5471,0.29152283
+4359,3696.5637,796.4496,3729.6824,866.02405,0.27005348
+4359,3804.9668,748.86755,3831.6157,798.30884,0.2219928
+4359,3955.78,734.7652,3985.1448,789.72174,0.19922024
+4360,2440.005,441.51498,2464.7002,502.39975,0.99948907
+4360,2213.858,228.33578,2237.7236,275.29764,0.9994106
+4360,3101.413,537.83624,3134.6172,597.9296,0.9993389
+4360,2697.394,352.68042,2717.688,405.65717,0.9993075
+4360,2639.2468,410.8599,2662.6897,467.2715,0.99925286
+4360,2976.8728,408.16943,2998.9802,459.65863,0.9991295
+4360,2186.3826,310.32236,2203.614,364.66696,0.99908257
+4360,3082.4106,268.86914,3099.8804,311.4226,0.99899465
+4360,2157.92,226.83911,2183.5488,276.70215,0.9989675
+4360,3206.805,384.25467,3231.325,433.70547,0.99896526
+4360,1293.1663,323.6337,1311.4329,380.30136,0.99893266
+4360,3414.872,592.20496,3447.17,652.213,0.99880874
+4360,3047.816,453.68033,3076.1543,511.07693,0.9987889
+4360,2863.9243,286.00403,2879.6855,331.2525,0.9986833
+4360,2645.2915,356.7631,2677.775,413.36478,0.99856204
+4360,2332.1824,427.57443,2359.3562,493.11783,0.9985103
+4360,3244.6804,460.50543,3270.0955,513.1605,0.99849045
+4360,3088.7095,399.70932,3107.0728,450.12723,0.9984785
+4360,3105.8003,377.8441,3132.4243,429.0764,0.9983039
+4360,1978.4407,882.3784,2017.1003,975.438,0.99800724
+4360,3269.9133,337.0673,3292.328,383.14285,0.9975032
+4360,3157.3792,344.3952,3176.6555,390.96613,0.99584794
+4360,3142.685,356.87665,3161.8296,404.2981,0.99490535
+4360,3048.5962,446.02527,3070.6675,501.39307,0.9934316
+4360,2010.696,124.83185,2030.2932,163.06796,0.9911588
+4360,3796.274,788.663,3821.289,845.10297,0.94352275
+4360,3851.525,763.534,3889.4258,824.0278,0.495797
+4360,3850.6445,744.67975,3888.809,793.6662,0.46391934
+4360,3640.6594,819.57996,3683.6077,888.2971,0.25589946
+4360,3696.9497,796.733,3730.101,866.6108,0.23636356
+4361,2440.279,441.921,2464.57,502.4316,0.9994809
+4361,2213.6646,227.91762,2236.8042,274.82172,0.9993038
+4361,3204.1233,383.8913,3230.198,433.73798,0.9993031
+4361,3101.4612,538.18195,3133.1028,597.7264,0.9992071
+4361,2693.3372,352.70834,2716.7883,405.23502,0.9991979
+4361,3409.3062,592.17523,3445.4292,652.32086,0.99911815
+4361,2638.918,410.93924,2662.2754,467.24942,0.9991093
+4361,2186.4312,310.32443,2203.6255,364.64505,0.999094
+4361,3080.298,268.1288,3098.5764,311.08347,0.9990324
+4361,3103.0347,377.61975,3131.067,429.43384,0.99902916
+4361,2157.9797,226.22264,2182.657,275.93823,0.998992
+4361,2977.5552,408.18903,2999.4053,459.73322,0.9989784
+4361,2644.4592,356.35785,2676.8157,413.14575,0.9989386
+4361,1293.169,323.64465,1311.4436,380.302,0.9989214
+4361,2329.6199,427.41235,2358.9255,492.27338,0.9986394
+4361,3244.619,460.4041,3270.118,513.3629,0.99854636
+4361,2861.8386,285.06476,2879.883,331.11542,0.9985453
+4361,3047.851,452.3664,3074.9653,510.67926,0.9984653
+4361,1978.4834,882.37427,2017.0991,975.3611,0.9981401
+4361,3269.7068,336.9254,3292.1418,382.3374,0.99806803
+4361,3086.2234,399.15097,3105.7102,449.80173,0.99784636
+4361,3155.6313,343.77924,3176.9067,391.0567,0.99558854
+4361,3142.7927,357.36557,3160.6677,404.01773,0.9928254
+4361,2010.7495,124.86106,2030.5645,163.02698,0.99229664
+4361,3795.2227,789.3865,3821.478,844.5575,0.914281
+4361,3852.7869,764.00244,3890.3157,824.0762,0.7558074
+4361,3856.2766,743.9153,3890.4407,798.0563,0.599592
+4361,3798.7234,753.06665,3824.5818,801.01746,0.39436552
+4361,3641.358,819.11285,3682.565,887.99786,0.33753955
+4361,3697.2446,797.31177,3730.439,866.7081,0.1675848
+4362,2440.4956,441.95837,2465.0571,502.4768,0.9994649
+4362,2213.5837,227.85753,2235.3538,274.06845,0.9994192
+4362,2643.7087,355.67215,2675.8167,412.40018,0.9993206
+4362,2689.083,352.21277,2715.0747,405.48578,0.9992386
+4362,3200.832,383.89517,3229.414,433.91727,0.9991381
+4362,2186.4185,310.34534,2203.6309,364.63763,0.9990753
+4362,2157.3628,225.94893,2181.8242,276.11435,0.99907494
+4362,3406.2559,591.24744,3443.4546,651.402,0.99906677
+4362,2327.116,427.33673,2357.7502,492.05652,0.99903417
+4362,2638.6743,410.66437,2661.919,467.36633,0.99899966
+4362,2977.8762,408.13385,3000.7864,459.69287,0.99899846
+4362,3101.467,377.91776,3130.3113,429.76437,0.9989191
+4362,1293.1763,323.7024,1311.4148,380.31757,0.9988991
+4362,3101.3538,538.1624,3131.0647,598.29755,0.99884254
+4362,3078.304,267.72723,3096.9958,310.62726,0.9984976
+4362,2859.2969,285.55658,2878.5356,330.97687,0.99815375
+4362,1978.4811,882.37463,2017.1781,975.3994,0.99809986
+4362,3047.611,453.62283,3073.6936,510.2997,0.997766
+4362,3080.5981,398.91254,3104.0962,449.78107,0.99743795
+4362,3269.565,336.8494,3291.6782,382.3677,0.99742067
+4362,3245.2478,460.44443,3270.135,513.3893,0.99577206
+4362,2010.212,122.753136,2030.7142,163.2285,0.99471116
+4362,3141.83,356.06818,3160.0298,404.2063,0.9946296
+4362,3155.0364,343.44736,3175.194,390.2805,0.99435
+4362,3793.2869,789.32416,3821.9578,844.97784,0.8905794
+4362,3852.8113,764.7356,3890.5828,823.94653,0.6366677
+4362,3697.1619,798.0787,3731.5925,866.64703,0.41842422
+4362,3640.7017,819.0507,3681.6372,888.14557,0.32965264
+4362,3858.379,742.3363,3891.6187,798.33984,0.32826382
+4362,3796.2039,753.518,3821.6643,801.23285,0.17585824
+4363,2440.8345,442.22357,2465.1982,502.49384,0.99949205
+4363,2213.268,227.48938,2233.6104,273.93423,0.9993299
+4363,3404.1829,591.73975,3441.716,651.3955,0.99917066
+4363,2157.063,225.36873,2181.1743,275.52847,0.99912053
+4363,3100.6702,378.3453,3129.1511,429.85403,0.99911493
+4363,3198.8474,383.5516,3227.9646,433.53946,0.9990262
+4363,2638.2615,410.30368,2661.866,467.31827,0.999022
+4363,2643.0686,354.21487,2673.8967,412.84256,0.99899036
+4363,2186.3984,310.35852,2203.6255,364.7171,0.99898976
+4363,2977.9653,408.25998,3001.2559,459.69156,0.9989832
+4363,2325.2668,426.96753,2357.5623,491.79987,0.99887866
+4363,1293.1737,323.7273,1311.4154,380.33142,0.99885607
+4363,3101.3643,537.79675,3128.4165,598.18445,0.9986929
+4363,3078.202,268.01843,3096.0125,309.3637,0.9986168
+4363,1978.4805,882.37866,2017.1304,975.40137,0.9981639
+4363,3074.5598,398.8325,3102.5725,450.0823,0.99798906
+4363,2854.8877,285.17325,2877.9966,331.00034,0.9978885
+4363,2684.1199,352.0457,2714.367,405.56778,0.99782026
+4363,3269.361,336.90866,3291.2527,381.97696,0.99766445
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+4363,3245.523,460.48184,3270.2212,513.36896,0.99578047
+4363,3153.73,343.37476,3173.401,390.11304,0.9957782
+4363,2010.208,122.73929,2030.7214,163.2239,0.9947148
+4363,3141.8645,356.36667,3159.2913,403.3762,0.9929236
+4363,3792.7385,790.15027,3822.3987,844.54016,0.9593999
+4363,3852.4705,765.0091,3890.615,823.88654,0.46484202
+4363,3794.3706,752.8107,3819.376,802.0656,0.3996028
+4363,3697.083,797.7545,3733.0322,866.666,0.3921223
+4363,3640.1785,818.5771,3680.84,887.82684,0.31205067
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+4364,3100.4705,378.83334,3127.8752,429.84305,0.9993019
+4364,2977.6423,407.91013,3001.53,459.74057,0.99921024
+4364,3403.8093,591.65784,3439.9426,651.66223,0.9991786
+4364,3198.926,383.60315,3226.68,433.53577,0.9991122
+4364,2643.145,353.6697,2670.0566,410.66873,0.99900925
+4364,2638.391,410.06876,2661.599,467.09622,0.99900204
+4364,2186.3777,310.35953,2203.6443,364.71713,0.9990007
+4364,1293.1733,323.72736,1311.416,380.3313,0.9988562
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+4364,3101.3618,537.8513,3126.1748,597.76587,0.9983146
+4364,1978.4033,882.36584,2017.0518,975.44946,0.99828917
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+4364,2681.8862,352.46533,2713.9814,405.64093,0.9974076
+4364,2010.4784,123.57352,2030.8761,163.18771,0.9950529
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+4364,3140.4404,355.47733,3158.3813,402.72733,0.98951375
+4364,3792.3555,789.7559,3822.142,844.6807,0.93558955
+4364,3696.8313,797.65515,3734.1746,866.0205,0.6651138
+4364,3852.5757,764.5521,3889.9058,824.1373,0.63745517
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+4364,3640.5806,818.29315,3679.0977,887.35675,0.34241927
+4364,3050.9976,446.84137,3072.9307,496.58514,0.3060936
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+4365,2156.8157,224.87056,2176.3586,274.3756,0.9992231
+4365,2977.6633,408.48737,3002.0095,459.6859,0.9992118
+4365,2212.8987,226.32413,2230.3337,273.54608,0.99917907
+4365,3100.597,378.5987,3127.094,430.15082,0.9991731
+4365,3403.6587,591.0543,3438.7515,651.2925,0.99905354
+4365,2638.6226,409.9254,2661.206,466.96436,0.9990237
+4365,2186.3948,310.35837,2203.6765,364.7045,0.99900985
+4365,2642.9172,352.99817,2667.3557,409.25507,0.99899715
+4365,1293.1735,323.7274,1311.4159,380.33133,0.9988562
+4365,2323.0571,426.65976,2353.4863,490.38852,0.99880695
+4365,3101.1482,537.6424,3125.173,597.92755,0.9987581
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+4365,2681.7273,352.35583,2713.6404,405.26483,0.9983304
+4365,3246.0662,460.9053,3270.2522,513.451,0.9983014
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+4365,3270.0037,336.1089,3290.5007,379.85645,0.9982298
+4365,1978.394,882.35455,2017.03,975.45355,0.9982237
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+4365,3069.4724,398.8988,3101.2263,449.8429,0.9977429
+4365,2010.8073,123.444084,2030.7821,163.12689,0.99626833
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+4365,3046.9204,456.2309,3069.7476,509.4151,0.9848583
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+4365,3858.8203,741.8125,3894.4185,783.06604,0.78251135
+4365,3852.3174,765.1849,3888.7534,824.1348,0.5816929
+4365,3696.4907,797.4217,3736.287,865.4262,0.5330359
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+4366,2978.3071,408.40393,3004.585,459.04034,0.9993899
+4366,3099.8823,378.55798,3126.357,429.8017,0.99931645
+4366,3100.3843,537.42505,3123.664,597.8646,0.99920756
+4366,2441.355,442.97464,2466.9448,502.50888,0.9990596
+4366,2186.2847,310.3594,2203.686,364.71585,0.9990397
+4366,3403.399,590.69495,3437.8926,652.3633,0.99900144
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+4366,2156.8042,225.26952,2174.1973,274.63306,0.9988643
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+4366,2643.3018,352.11942,2664.1113,407.30972,0.99831617
+4366,1978.3945,882.3661,2016.9978,975.51917,0.99824023
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+4366,2851.1162,283.46793,2874.753,330.14035,0.99800855
+4366,2010.95,123.97145,2030.812,163.09456,0.9943929
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+4366,3852.7986,764.9571,3888.9915,824.6253,0.65733296
+4366,3639.8228,817.2678,3677.0146,886.7571,0.20012414
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+4367,3097.725,537.2145,3122.541,597.2378,0.9991234
+4367,2638.377,408.60336,2660.231,466.038,0.999038
+4367,2186.212,310.33752,2203.6826,364.72345,0.9990256
+4367,2441.919,443.2442,2468.3477,502.63843,0.99901676
+4367,3099.0588,378.08887,3125.695,430.47284,0.99898344
+4367,3403.354,589.81616,3436.2778,651.4398,0.99897087
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+4367,2681.68,351.0605,2711.677,403.92206,0.998835
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+4367,1978.3945,882.38165,2016.9595,975.4875,0.9983193
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+4367,3150.544,342.88632,3170.1846,390.70364,0.99498713
+4367,2010.8002,123.49953,2031.1366,163.13298,0.99298507
+4367,3045.8413,454.5556,3065.526,508.2566,0.9827429
+4367,3052.6868,446.68396,3073.661,502.72778,0.9655717
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+4367,3853.1138,765.01245,3889.456,824.9374,0.77436537
+4367,3697.2495,797.06177,3739.3013,865.17236,0.5709376
+4367,3778.7827,752.8105,3812.5845,808.24664,0.55612415
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+4368,2209.878,224.27573,2229.4033,272.72675,0.9992712
+4368,2638.202,407.41846,2659.8694,465.46094,0.999268
+4368,3403.1816,589.58246,3433.8481,650.4711,0.9991617
+4368,3091.2446,537.28516,3121.2314,596.8235,0.9991523
+4368,2186.2131,310.35675,2203.764,364.73254,0.99905306
+4368,2154.702,224.76631,2170.6199,274.36118,0.998946
+4368,2442.7346,443.34747,2469.1423,502.8634,0.99893624
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+4368,3098.8994,377.7613,3124.561,430.26312,0.99853027
+4368,1978.4199,882.40466,2016.9292,975.4984,0.99834704
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+4368,2850.2969,282.62488,2872.0103,328.92743,0.9971252
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+4368,3246.3716,461.40955,3270.0938,513.43823,0.99666816
+4368,3043.9468,453.10065,3064.4482,508.34332,0.9937056
+4368,2010.7872,123.542404,2031.0726,163.09654,0.99314183
+4368,3792.4167,786.64417,3819.7097,843.8202,0.98038346
+4368,3053.708,446.49142,3073.4521,503.34268,0.9750753
+4368,3852.9243,766.6929,3889.9824,825.7632,0.88414586
+4368,3698.423,796.80945,3740.4778,864.55566,0.769506
+4368,3777.1003,754.3213,3808.3103,809.2035,0.42505938
+4368,3865.772,743.1264,3897.4258,783.2402,0.38717508
+4368,3640.0845,816.3734,3675.229,885.2174,0.16629086
+4368,3944.418,738.49994,3979.67,792.31866,0.15269963
+4368,3874.3586,743.0172,3897.527,796.4336,0.10554825
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+4369,2681.782,350.1508,2707.6458,401.05734,0.9991953
+4369,2208.3132,225.03323,2228.2341,273.30798,0.9991621
+4369,2637.8362,406.8744,2659.3577,464.93317,0.99913555
+4369,2186.2612,310.3716,2203.711,364.709,0.9991032
+4369,2980.9368,408.02567,3006.719,457.83182,0.9989316
+4369,3066.2112,263.8687,3084.8586,305.83362,0.99890506
+4369,3403.0383,588.8118,3430.9792,649.9587,0.9988939
+4369,1293.1796,323.72678,1311.4169,380.33463,0.99886596
+4369,2319.2517,424.79117,2345.0295,488.33713,0.99880874
+4369,2442.755,443.4706,2470.3457,502.69583,0.99873894
+4369,2153.6775,224.62245,2169.7214,274.62894,0.9987099
+4369,3269.4697,335.91394,3289.9868,379.1834,0.99859256
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+4369,3098.7239,377.71695,3124.1326,429.83615,0.99843615
+4369,1978.4355,882.422,2016.9883,975.4645,0.9982672
+4369,3039.8604,452.1907,3064.3164,507.44907,0.99802554
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+4369,3146.703,341.88818,3166.8196,389.5511,0.99763286
+4369,3127.6294,353.7979,3153.956,399.9356,0.9972364
+4369,3246.3228,461.68326,3269.9814,513.4937,0.9968616
+4369,3793.4294,785.31744,3819.2747,842.5716,0.9931189
+4369,2010.74,124.25944,2031.103,163.13708,0.9921228
+4369,3052.6038,446.142,3073.3298,503.8514,0.97913367
+4369,3700.0625,796.5324,3741.5654,863.98224,0.84234416
+4369,3852.7454,766.8919,3891.264,826.6939,0.7917595
+4369,3943.6956,738.508,3978.2048,792.7321,0.6595544
+4369,3775.6516,756.6322,3805.8152,811.2759,0.56848615
+4369,3864.2646,742.6265,3898.0918,777.9244,0.5267489
+4369,3868.463,742.5553,3898.504,799.18396,0.18405361
+4369,3639.9822,815.65533,3674.8699,883.7032,0.14083114
+4370,3082.059,538.62195,3118.004,598.1836,0.99960893
+4370,2206.6082,224.45648,2227.492,273.2232,0.9991805
+4370,2637.8286,406.37866,2659.2158,464.73273,0.99917185
+4370,2186.3115,310.37274,2203.6162,364.71588,0.9991129
+4370,2681.6582,348.40323,2704.8486,400.4896,0.9988977
+4370,1293.1831,323.72592,1311.4243,380.31882,0.9988716
+4370,2151.8232,224.06023,2169.519,274.67795,0.9987992
+4370,2318.1316,424.62192,2343.0496,488.4199,0.99874675
+4370,3403.5515,588.6698,3429.216,647.6383,0.9987197
+4370,2629.0125,351.14478,2657.117,407.2024,0.9984301
+4370,3098.787,377.88077,3122.4326,429.91238,0.9983966
+4370,3062.3503,262.6544,3084.573,305.70914,0.99838173
+4370,3068.4104,397.61374,3093.5637,447.48874,0.99837047
+4370,3269.4792,335.74573,3289.9216,379.10864,0.9983558
+4370,1978.4458,882.4577,2017.0264,975.5052,0.99829775
+4370,2982.7192,407.533,3008.0894,457.7338,0.99826604
+4370,2442.7246,444.6202,2472.5557,502.31815,0.998214
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+4370,3127.0493,353.98282,3153.228,399.51175,0.9972675
+4370,2010.2695,123.868256,2030.4517,163.2345,0.9935679
+4370,3793.5947,783.92065,3818.871,842.92786,0.99034226
+4370,3245.9072,461.821,3269.9517,514.2352,0.989139
+4370,3051.2922,446.02737,3073.3557,505.26144,0.98853487
+4370,3852.7288,767.6351,3892.926,826.7227,0.9265359
+4370,3703.7488,795.47015,3743.4348,863.14777,0.75339586
+4370,3866.807,742.6792,3899.9631,784.692,0.70240104
+4370,3774.5493,758.3682,3803.0576,812.85785,0.69856703
+4370,3942.8848,739.0247,3978.0522,792.61945,0.5449525
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+4371,2204.5212,224.46461,2227.2375,273.14648,0.9992366
+4371,2637.6523,405.60095,2658.9473,463.92255,0.9991322
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+4371,2443.028,444.66022,2473.3455,502.46527,0.9983442
+4371,1978.4417,882.46277,2016.9575,975.5133,0.9982924
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+4394,3029.56,389.2319,3055.3794,440.1512,0.99631953
+4394,3094.1116,343.027,3121.344,388.2794,0.9961696
+4394,2010.8391,124.32644,2029.093,163.20007,0.99189353
+4394,3010.2556,406.39758,3040.1921,458.19336,0.9915216
+4394,3868.827,765.33875,3905.3845,825.96497,0.91645455
+4394,3877.378,728.9284,3918.767,811.4483,0.86391
+4394,3738.6768,787.46466,3781.059,860.7078,0.4812657
+4394,3632.3982,820.48987,3673.0188,880.50964,0.3319212
+4394,3941.7034,741.72736,3972.8362,798.03375,0.17665097
+4395,3013.491,444.1548,3036.7068,501.10797,0.9994062
+4395,3237.831,467.84116,3259.4854,519.89575,0.9993086
+4395,2125.1328,220.4629,2145.0635,270.1757,0.9992901
+4395,2185.282,219.08957,2201.964,267.06766,0.9992754
+4395,3069.8699,378.8573,3097.9802,431.62543,0.99910235
+4395,1291.5204,324.46564,1312.5724,380.1869,0.999059
+4395,2281.4878,424.0205,2302.2788,488.8075,0.99893683
+4395,2590.3396,341.74353,2613.1267,397.2881,0.9988702
+4395,2459.4902,448.98773,2493.1494,512.09265,0.9987956
+4395,3048.6687,541.14105,3080.0374,599.9875,0.99871933
+4395,3048.7556,445.81558,3076.619,502.51584,0.9986771
+4395,3356.5383,586.0679,3381.9631,645.8634,0.9986745
+4395,1978.2605,882.5115,2016.7908,975.3623,0.9985293
+4395,3165.9202,372.2881,3194.1785,421.5904,0.99845564
+4395,3273.041,333.8797,3296.183,379.6371,0.9984108
+4395,2640.6572,339.3343,2660.3105,390.80646,0.9983139
+4395,2630.0132,400.15112,2659.332,454.9936,0.99829787
+4395,2820.2273,271.52942,2839.4333,317.81873,0.99811953
+4395,2174.6394,310.33533,2200.2292,364.66876,0.9978846
+4395,3112.6963,332.4741,3135.6152,377.85587,0.9977488
+4395,3021.604,252.89612,3036.4907,293.57043,0.997612
+4395,3028.01,388.95135,3052.9856,438.81238,0.9965522
+4395,3092.651,343.4148,3120.9902,388.0415,0.99618125
+4395,2010.8652,124.31087,2029.0422,163.12804,0.99161303
+4395,3010.3223,406.68405,3037.7583,458.70297,0.9910142
+4395,3871.6619,763.9622,3905.818,825.61664,0.9338464
+4395,3878.0408,729.3109,3919.0823,810.88135,0.87277937
+4395,3739.2546,786.5369,3779.7732,860.4222,0.6934801
+4395,3632.411,821.6265,3673.054,880.5098,0.3323451
+4395,3941.8147,741.7722,3972.7527,797.98267,0.124284625
+4396,3237.282,468.03198,3258.8079,520.1283,0.99931216
+4396,3043.7432,541.268,3078.7544,600.4067,0.99928015
+4396,3069.8433,379.29398,3096.7236,431.5787,0.9992557
+4396,3356.613,585.941,3380.1487,645.64545,0.999215
+4396,1291.6431,324.65796,1312.6306,380.1548,0.9991731
+4396,2184.1448,219.15356,2201.5544,266.6248,0.999169
+4396,3013.291,444.574,3035.6812,500.55527,0.999009
+4396,2640.022,339.32068,2657.8008,391.24292,0.99900144
+4396,2123.9558,220.21417,2143.7493,270.0011,0.99896944
+4396,2175.0686,309.79236,2200.051,363.48334,0.9989027
+4396,3048.6316,445.98627,3076.3591,502.40466,0.99880004
+4396,2459.7498,449.23175,2491.3987,513.53186,0.99872106
+4396,2583.8108,341.68274,2611.323,397.20868,0.9985916
+4396,1978.2972,882.50287,2016.9003,975.3455,0.9984346
+4396,2629.3472,399.60657,2657.9917,455.0492,0.99840945
+4396,3272.8247,333.8497,3296.418,379.74228,0.9984079
+4396,2277.4773,423.63367,2300.325,488.5761,0.9983198
+4396,3163.665,372.1612,3193.5205,421.6306,0.99824846
+4396,3020.0771,252.07831,3035.605,293.6012,0.9979802
+4396,3110.4175,332.19174,3134.3838,376.69357,0.99770445
+4396,3092.146,343.3401,3119.871,387.94098,0.9976453
+4396,2820.165,271.09247,2837.4526,316.97357,0.99758613
+4396,3010.4954,406.63583,3036.8196,458.40933,0.99492407
+4396,2010.8551,124.30954,2028.9689,163.13358,0.99178505
+4396,3029.0725,388.29077,3049.7068,438.6391,0.9896716
+4396,3739.3147,786.543,3779.6028,860.373,0.88736326
+4396,3879.0798,729.9374,3919.1687,811.1133,0.8345745
+4396,3873.0627,763.44714,3906.9875,824.926,0.81349087
+4396,3632.465,821.77167,3673.156,880.4067,0.29155266
+4397,3040.7126,542.14954,3077.131,600.7655,0.9992786
+4397,1291.7229,324.9254,1312.7976,380.1281,0.9992299
+4397,3069.7905,379.301,3095.4849,431.2216,0.9992003
+4397,3048.7158,445.92114,3075.7437,502.48816,0.99913883
+4397,3236.5754,468.009,3258.2415,520.6122,0.99909997
+4397,3012.588,443.3078,3034.4893,500.54285,0.99902606
+4397,2578.857,342.29007,2610.9521,398.46188,0.99895155
+4397,2182.2512,218.96819,2201.0564,266.15143,0.9988819
+4397,3354.295,585.9105,3379.08,645.46765,0.9988122
+4397,2123.0469,219.69872,2141.4292,270.26306,0.998648
+4397,3163.5916,372.0334,3192.9324,421.6543,0.9985032
+4397,2626.8438,399.21515,2656.63,455.1671,0.9984798
+4397,1978.3347,882.4714,2016.9736,975.3184,0.99830854
+4397,2460.3076,449.84518,2489.7295,513.56824,0.9982724
+4397,2273.3572,423.76907,2300.179,488.64554,0.9981536
+4397,3272.3708,333.78424,3296.4695,379.94342,0.99814546
+4397,2818.371,271.01807,2835.999,316.31915,0.9980039
+4397,2638.8,338.69028,2656.244,391.6005,0.9979055
+4397,2174.029,310.15088,2200.1692,364.43457,0.9976815
+4397,3109.2495,332.16187,3133.1274,376.24274,0.9975432
+4397,3015.6824,251.69186,3034.2976,293.397,0.9974406
+4397,3091.6165,343.00452,3118.703,387.39172,0.99454474
+4397,3012.8333,406.81018,3036.136,457.44537,0.9930902
+4397,2010.8448,124.31598,2028.8959,163.16147,0.99187225
+4397,3028.5037,387.71143,3048.6165,439.75916,0.9803771
+4397,3874.6604,762.3009,3909.3079,824.9745,0.8800042
+4397,3879.9229,730.1333,3919.3516,810.2947,0.8724504
+4397,3739.0515,785.4843,3779.4329,862.0689,0.64665693
+4397,3632.4717,822.3806,3673.2573,880.1179,0.40125564
+4398,3039.6667,542.5978,3075.6042,601.2914,0.9992161
+4398,1291.9459,324.9431,1312.7648,380.14014,0.9991897
+4398,3048.976,445.98566,3075.457,502.42578,0.9991093
+4398,3069.5884,379.08716,3094.4478,431.23138,0.9990876
+4398,3350.4282,585.7918,3377.8325,646.1445,0.99908066
+4398,2576.2024,342.2763,2609.4368,399.1032,0.9990569
+4398,2180.144,218.72629,2201.288,266.1407,0.9989832
+4398,3235.5508,468.08484,3257.4746,520.70123,0.9989064
+4398,3163.7148,371.48395,3192.1733,420.5531,0.9988518
+4398,3010.3594,442.47095,3033.145,500.53284,0.9988192
+4398,2270.1877,423.51675,2300.1272,488.59012,0.99869746
+4398,2626.1104,398.99585,2655.938,455.37317,0.9986702
+4398,2120.938,219.28282,2141.19,270.38342,0.9986376
+4398,2817.489,270.55637,2834.239,316.67062,0.99856746
+4398,1978.3187,882.52673,2017.0768,975.36206,0.9982508
+4398,3272.2358,333.75977,3296.772,380.3094,0.99791235
+4398,3109.5298,331.6349,3131.0723,376.7563,0.99725467
+4398,2173.1763,310.00378,2200.5723,364.45917,0.99721366
+4398,3010.8186,250.71211,3033.493,292.8333,0.9971666
+4398,2460.3408,450.73752,2489.2358,513.46857,0.9968983
+4398,2633.373,338.48035,2653.625,390.83478,0.99590695
+4398,3091.2407,342.1641,3117.162,387.27518,0.9957782
+4398,2010.835,124.272156,2028.8491,163.17789,0.992067
+4398,3013.3274,406.81485,3037.7463,456.02573,0.9885028
+4398,3026.8418,387.42523,3049.0996,437.7196,0.9570574
+4398,3877.7158,762.268,3911.3965,824.1341,0.88014764
+4398,3879.8813,730.5561,3919.3433,810.2369,0.87893146
+4398,3740.2993,785.1371,3782.1807,860.5453,0.7241955
+4398,3632.4531,822.32556,3673.2227,880.09985,0.5011082
+4398,3686.8438,783.481,3712.8066,842.6053,0.14463341
+4399,3069.3853,379.5898,3093.021,431.72916,0.9995952
+4399,1292.0559,324.9166,1312.6743,380.14444,0.9991534
+4399,3163.564,371.29938,3191.7334,419.92078,0.9990382
+4399,3039.9414,543.0801,3074.3423,601.8423,0.99900466
+4399,2574.6729,341.85175,2608.6333,398.5437,0.99897504
+4399,3345.663,586.1814,3376.2783,646.2263,0.9989537
+4399,2626.2463,399.02878,2655.2874,455.36008,0.99885607
+4399,2266.8738,424.1875,2300.0408,488.84912,0.9988121
+4399,3049.0405,445.91553,3075.3354,502.46515,0.9987675
+4399,2120.0486,218.44087,2142.2708,269.85718,0.9987669
+4399,3234.0046,467.6408,3256.99,521.05786,0.998719
+4399,2461.0144,450.72952,2489.3235,513.41187,0.9984725
+4399,2815.0374,270.42627,2833.237,316.25104,0.9983718
+4399,2178.2222,218.5301,2200.956,267.03125,0.99835205
+4399,2627.0356,338.29477,2653.353,391.26382,0.9983176
+4399,1978.3197,882.6156,2017.1415,975.4044,0.9982437
+4399,2172.6301,309.9651,2200.7942,364.2992,0.9977258
+4399,3273.952,333.70047,3297.0662,380.0571,0.997489
+4399,3090.8582,341.166,3115.3157,385.83517,0.9971058
+4399,3007.8726,250.42467,3033.6914,292.85077,0.9970567
+4399,3005.505,443.5744,3030.1265,500.03412,0.9963097
+4399,2010.8253,124.26478,2028.8431,163.19519,0.9921435
+4399,3109.1497,330.77786,3128.945,375.8749,0.99213046
+4399,3016.3354,407.17044,3037.7754,452.58115,0.9717453
+4399,3026.9614,386.9861,3050.5444,435.92856,0.9404541
+4399,3879.196,730.8465,3919.4026,811.1014,0.8594998
+4399,3878.8665,763.09924,3911.7122,823.6721,0.81517506
+4399,3741.9595,789.3042,3780.1787,857.21375,0.7663434
+4399,3632.545,821.98346,3673.246,880.0743,0.4666142
+4399,3683.2764,784.6358,3708.7607,842.46173,0.17668322
+4399,3018.5215,388.66168,3044.9385,440.781,0.1103069
+4400,3340.7986,587.3404,3374.1594,647.1905,0.9996569
+4400,3069.386,379.3095,3092.867,431.80005,0.9993793
+4400,1292.1619,324.87296,1312.8005,380.0757,0.99910444
+4400,2574.62,341.64478,2607.787,398.0592,0.9990644
+4400,3163.0337,370.7898,3190.4492,418.96564,0.99904996
+4400,3233.3906,467.4923,3256.3848,520.26404,0.9988432
+4400,3039.2322,543.30304,3072.3376,602.5076,0.9988055
+4400,3048.9639,445.88083,3075.1865,502.38028,0.99868196
+4400,2626.3057,398.61154,2655.773,455.41342,0.99864596
+4400,2462.0825,451.46222,2489.834,513.3723,0.9986285
+4400,2264.3745,424.09552,2300.067,488.8697,0.99832493
+4400,3000.8293,443.03363,3029.9148,499.62628,0.99830544
+4400,2119.0388,219.07062,2141.777,270.0065,0.9982936
+4400,2621.8765,338.40823,2652.81,390.803,0.9982766
+4400,1978.3026,882.5902,2017.0931,975.3751,0.99827325
+4400,2172.289,309.62808,2200.5063,364.3466,0.99809724
+4400,2810.5515,270.49658,2831.8328,316.12775,0.9979285
+4400,2178.1387,218.65424,2200.978,267.04236,0.99791366
+4400,3274.189,333.8925,3297.2095,379.79507,0.99767834
+4400,3005.975,250.47786,3033.9702,292.8417,0.9964645
+4400,3090.7322,341.22525,3112.6262,385.47177,0.9959676
+4400,2010.7863,124.51225,2028.9235,163.29897,0.9922007
+4400,3109.3232,329.9154,3126.8135,373.4798,0.97335523
+4400,3018.028,406.1916,3039.821,453.6878,0.934045
+4400,3020.8745,387.3817,3050.651,435.88696,0.8413719
+4400,3878.8264,730.52997,3919.4587,811.3006,0.7795516
+4400,3878.746,764.8967,3911.4932,824.40967,0.77021134
+4400,3741.1091,782.04,3771.777,849.6774,0.7668103
+4400,3746.3032,791.35876,3785.9585,860.1117,0.7214373
+4400,3632.5757,821.83496,3673.288,880.12476,0.406863
+4400,3678.5312,783.5128,3705.2046,842.5386,0.25796163
+4400,3105.635,335.0864,3125.3474,382.75467,0.19380811
+4401,3336.6516,586.6911,3371.8206,645.852,0.9994375
+4401,1292.2942,324.3605,1312.7659,380.05777,0.99922955
+4401,3069.664,379.1063,3092.4058,430.64047,0.9991301
+4401,2176.7424,218.22919,2199.493,266.5828,0.99894434
+4401,3162.8174,370.2092,3189.5645,418.9363,0.99891424
+4401,3038.7043,542.96216,3070.678,602.67957,0.9989073
+4401,2573.955,340.24805,2607.5005,395.8011,0.9988722
+4401,3231.3352,467.61176,3255.616,520.3563,0.9987496
+4401,2462.5034,452.1581,2490.5137,513.4091,0.99871707
+4401,2118.4949,218.95044,2141.8254,269.98868,0.9986808
+4401,2997.3027,442.8401,3029.5479,499.48645,0.99859506
+4401,2262.2554,424.03812,2299.3662,488.83987,0.99857926
+4401,2171.9644,309.48157,2200.7056,364.35608,0.99850863
+4401,2620.2363,338.34515,2652.3042,390.97302,0.9984085
+4401,2625.862,398.56815,2655.9885,454.99393,0.9983877
+4401,1978.3055,882.72656,2017.1444,975.3479,0.9983216
+4401,2806.2632,270.1915,2830.459,316.4786,0.99811184
+4401,3049.2358,445.9954,3074.833,502.31088,0.9979443
+4401,3274.1484,333.68063,3297.2124,379.62704,0.9970202
+4401,3090.9731,341.24866,3110.2422,385.00928,0.996525
+4401,3005.782,250.42006,3032.7385,292.5873,0.99651045
+4401,2010.696,124.55263,2029.052,163.3936,0.9920704
+4401,3107.0942,329.51962,3124.917,375.3746,0.98575956
+4401,3013.7415,387.0637,3051.8254,435.98233,0.97815436
+4401,3018.9128,405.10034,3043.3787,454.40216,0.8455236
+4401,3741.4854,780.5274,3767.5938,844.1252,0.7980697
+4401,3878.5408,730.5811,3919.4363,812.8232,0.72499406
+4401,3751.2695,790.41925,3786.2344,858.9903,0.69852227
+4401,3878.455,765.54736,3911.7588,824.25037,0.56991476
+4401,3632.628,821.34216,3673.5552,879.8926,0.4437453
+4401,3675.0063,783.23346,3701.9194,841.9527,0.3311378
+4402,3335.7275,587.48004,3369.099,645.9693,0.99934477
+4402,1292.3344,324.36926,1312.6942,380.0597,0.99920505
+4402,3069.301,379.33713,3091.034,431.15634,0.9991672
+4402,3163.042,369.9315,3187.0723,417.68143,0.9991412
+4402,2995.1018,443.1734,3028.514,499.39606,0.9990588
+4402,2260.4316,424.30853,2298.7393,489.10712,0.9990289
+4402,2115.8584,219.0131,2141.731,270.21518,0.9989881
+4402,3229.436,467.7883,3254.6348,520.52594,0.9987953
+4402,2620.192,338.02567,2651.7827,390.81992,0.9987237
+4402,2171.8281,309.63638,2200.396,364.24203,0.99871325
+4402,2176.0652,217.67896,2198.3987,265.81668,0.99870896
+4402,3038.6648,543.0828,3068.7522,602.6514,0.9986785
+4402,2574.1628,339.8023,2605.9387,394.78217,0.9985904
+4402,3005.3525,250.25046,3031.0054,291.95535,0.99851865
+4402,2463.602,452.61093,2493.662,513.4972,0.9985067
+4402,2625.9358,398.38016,2655.1829,454.80112,0.99838746
+4402,1978.3055,882.72656,2017.1444,975.3479,0.9983216
+4402,3049.2654,446.0321,3074.818,502.3321,0.9981647
+4402,2803.788,270.24866,2829.9834,316.53204,0.99756795
+4402,3104.9983,329.81998,3123.8083,375.3419,0.9972452
+4402,3274.4097,333.64478,3297.251,379.99573,0.99481153
+4402,3090.9824,341.88754,3109.2065,384.96054,0.9945662
+4402,2010.6956,124.55104,2029.0532,163.39595,0.9920644
+4402,3012.8318,387.17526,3048.2131,434.69034,0.98369366
+4402,3017.8213,406.1961,3052.7437,454.76654,0.97246855
+4402,3878.068,730.7121,3919.1672,811.3066,0.7779845
+4402,3878.3787,767.387,3911.8167,824.476,0.7493535
+4402,3739.024,779.806,3765.5044,844.6593,0.53677005
+4402,3754.9888,789.523,3787.6655,858.538,0.3896435
+4402,3632.723,820.52875,3673.2776,879.8228,0.34029016
+4402,3670.3264,782.77264,3697.9846,840.92413,0.14455582
+4403,3335.0637,588.35974,3367.072,646.02515,0.99932444
+4403,2994.6782,442.67276,3026.9004,499.24368,0.9991911
+4403,1292.3749,324.4332,1312.6659,380.06497,0.9991898
+4403,3067.425,379.3955,3090.4407,431.5937,0.9990395
+4403,2574.2178,339.3169,2603.3599,394.758,0.99883395
+4403,3104.3188,329.64728,3122.5781,375.11688,0.9987937
+4403,3038.7085,543.0951,3067.4146,602.4385,0.9987752
+4403,2619.9639,337.39636,2650.913,389.80603,0.9987413
+4403,2626.0564,397.69962,2652.8567,453.93057,0.9986694
+4403,2260.3296,424.16235,2297.0737,488.91125,0.9986633
+4403,2171.656,309.42953,2200.3113,364.15262,0.99863464
+4403,2176.086,217.12262,2196.939,264.90677,0.9984983
+4403,3163.2253,369.6826,3184.8337,417.7939,0.9984749
+4403,2114.6953,218.91475,2141.1646,270.24744,0.998381
+4403,2464.354,451.28223,2499.437,513.11505,0.99832886
+4403,1978.3055,882.72656,2017.1444,975.3479,0.9983216
+4403,3226.1035,467.87378,3253.787,521.1006,0.9981887
+4403,3049.255,445.95346,3074.7861,502.3469,0.9981627
+4403,3005.2327,250.16167,3029.157,291.5625,0.9969472
+4403,2803.1194,270.42792,2829.6472,316.44092,0.9967564
+4403,2010.6962,124.54819,2029.0541,163.39493,0.99207085
+4403,3276.0984,333.72003,3297.2708,379.25793,0.9910795
+4403,3090.5024,340.85425,3107.8599,384.90826,0.99010146
+4403,3011.1948,386.91882,3039.7676,435.58588,0.98904115
+4403,3018.6826,406.48535,3053.9058,454.92194,0.9852041
+4403,3878.1167,766.4881,3912.7578,823.9799,0.8204991
+4403,3877.8691,731.11865,3918.979,811.21423,0.74722314
+4403,3733.2993,780.0071,3763.3984,843.7688,0.32814887
+4403,3758.7888,789.3839,3788.5432,857.6293,0.2818908
+4403,3632.7947,819.2656,3673.235,879.7256,0.16605225
+4404,3335.0227,588.39465,3365.6296,646.24927,0.9992593
+4404,2619.8748,336.8456,2648.55,389.72897,0.99922705
+4404,2175.8372,216.4921,2194.2664,264.0345,0.99909425
+4404,2993.6768,442.04016,3025.8823,499.33264,0.9990594
+4404,2171.5254,308.80447,2199.4595,363.58548,0.9989968
+4404,3064.6094,379.32227,3090.1104,431.3626,0.99890757
+4404,3163.3892,368.89697,3182.5024,417.1632,0.99890184
+4404,1292.4562,324.2816,1312.7279,380.02658,0.9987974
+4404,3038.7747,542.9535,3065.7903,602.65674,0.99878496
+4404,2260.0889,424.01422,2296.4863,488.87427,0.9987696
+4404,2804.002,270.1593,2829.2437,315.94305,0.9986966
+4404,2465.5918,452.484,2501.831,513.15985,0.99859256
+4404,2625.9915,396.97202,2652.5735,453.45627,0.99838245
+4404,1978.3501,882.7547,2017.1536,975.29266,0.9983491
+4404,3224.098,468.29214,3252.964,520.2513,0.9983271
+4404,2114.3958,219.45251,2141.2146,270.11218,0.99829036
+4404,2574.3262,338.70563,2600.671,393.37213,0.99822634
+4404,3049.231,445.85535,3074.7886,502.37012,0.9982248
+4404,3005.2102,249.91629,3026.3523,289.83463,0.9974022
+4404,3103.4229,330.01474,3121.003,375.19968,0.9958259
+4404,3088.5156,339.99612,3106.643,384.89066,0.99490523
+4404,3277.1768,333.622,3297.252,379.6067,0.99332076
+4404,2010.6759,124.47429,2029.1932,163.37279,0.9914984
+4404,3020.3918,407.4337,3054.9998,455.91202,0.98922837
+4404,3009.7913,387.57227,3038.4563,435.79852,0.98672265
+4404,3727.3528,780.484,3761.702,844.05505,0.9478688
+4404,3878.8599,766.9771,3913.7231,823.7297,0.7645196
+4404,3876.721,731.43536,3918.5574,807.6341,0.6185141
+4404,3761.4211,789.4724,3789.5662,858.29956,0.47576234
+4404,3011.8928,391.90417,3053.153,442.05695,0.11893533
+4405,3335.0212,588.60504,3362.489,645.68475,0.9992957
+4405,2993.2622,442.0102,3024.876,498.55756,0.99914396
+4405,3038.96,543.11475,3064.92,602.886,0.9991117
+4405,2259.9749,424.16705,2294.3708,488.67676,0.9990571
+4405,1292.6,324.14127,1312.8514,380.00784,0.9990376
+4405,2174.8835,216.2509,2192.781,263.4163,0.9989667
+4405,3060.6465,379.47723,3089.8003,431.36456,0.9989273
+4405,2803.1895,270.0432,2828.501,315.05682,0.99892056
+4405,2171.6765,309.09012,2197.784,363.6793,0.9988686
+4405,3223.715,468.56564,3251.7253,521.09064,0.99876726
+4405,3163.383,368.84366,3181.1184,417.04288,0.9987184
+4405,2114.1519,219.0768,2139.586,269.75098,0.99865377
+4405,3098.6838,328.89017,3119.944,375.40042,0.9983797
+4405,2625.8757,396.34918,2653.3088,452.86957,0.99830997
+4405,1978.336,882.7852,2017.2172,975.2786,0.99825794
+4405,3049.2163,445.93314,3074.728,502.3608,0.99814713
+4405,2619.5293,336.6739,2645.7173,388.53723,0.9981025
+4405,3005.4873,249.00665,3022.3745,289.58856,0.9974534
+4405,2574.1816,337.65268,2597.478,393.10764,0.99733186
+4405,3277.1853,333.49982,3297.3684,379.87225,0.99628216
+4405,3084.779,339.57382,3105.9763,384.54044,0.9961564
+4405,2466.691,452.47073,2502.7275,513.24316,0.99502075
+4405,3008.2388,386.63535,3036.2148,436.06094,0.99501437
+4405,3021.766,406.12085,3055.3242,457.55414,0.99321413
+4405,2010.6584,124.452286,2029.1885,163.37248,0.99067116
+4405,3722.4644,781.5548,3759.484,844.54956,0.96250606
+4405,3876.1514,731.4875,3917.6519,802.22174,0.94778466
+4405,3878.662,767.0279,3914.482,824.1507,0.7502761
+4405,3632.2324,787.39923,3690.168,880.26117,0.60770905
+4405,3762.5674,790.2614,3789.7036,857.6917,0.4571763
+4406,3038.6301,543.21,3064.491,604.13556,0.9992545
+4406,2171.627,308.58398,2196.8345,363.76953,0.9992155
+4406,2260.164,424.02396,2292.1284,488.5601,0.9991736
+4406,3057.6487,379.9751,3087.5833,431.60297,0.9991436
+4406,2174.1294,216.02095,2191.5205,263.12732,0.99905115
+4406,1292.856,324.31055,1313.2134,379.92474,0.99901223
+4406,2993.439,442.02414,3023.5278,497.91086,0.9989839
+4406,2803.3342,269.4532,2826.9543,313.7611,0.9989785
+4406,3162.443,368.40826,3180.1985,417.7068,0.9989446
+4406,2619.3713,335.81015,2641.1033,387.83054,0.9988299
+4406,3334.7437,588.3652,3359.8535,645.9692,0.9988242
+4406,3223.313,469.582,3250.7178,521.53656,0.99873644
+4406,3006.3564,250.21461,3019.338,288.78998,0.99871624
+4406,2625.8215,395.7702,2652.4963,453.06122,0.99864835
+4406,2114.0332,218.82304,2138.1816,269.4568,0.9985967
+4406,3049.1743,445.90802,3074.7007,502.3412,0.99832755
+4406,2467.9167,453.06708,2504.3381,513.21027,0.9983021
+4406,1978.3586,882.74774,2017.2197,975.2663,0.99829143
+4406,2573.719,337.87067,2595.2205,392.85107,0.9974794
+4406,3079.2178,340.3526,3104.9258,384.29498,0.99691296
+4406,3277.2346,333.5121,3297.472,380.19067,0.9965437
+4406,3023.3386,406.2433,3054.1624,456.87616,0.9960097
+4406,3097.3838,329.52325,3119.0718,375.19952,0.9960014
+4406,3007.6858,385.37567,3034.2742,435.62665,0.99393
+4406,2010.6505,124.43978,2029.3309,163.35066,0.9902536
+4406,3875.211,729.9246,3917.415,800.4748,0.93804216
+4406,3632.7034,789.47894,3686.3386,879.5059,0.76718426
+4406,3716.7393,783.1243,3756.7695,845.1661,0.7406915
+4406,3762.49,792.02124,3789.948,858.56177,0.7175964
+4406,3879.0325,766.21674,3916.4934,823.81793,0.5370737
+4407,3037.5837,543.7839,3062.8904,604.5727,0.999485
+4407,2171.5852,308.57535,2195.3655,363.55966,0.9993449
+4407,2172.8687,215.63567,2190.4355,263.2172,0.9992225
+4407,3333.3115,587.75073,3354.2183,646.1919,0.99919313
+4407,2619.2207,335.9052,2637.4312,387.23474,0.99914634
+4407,2260.3643,423.12946,2286.873,488.34247,0.9991441
+4407,3055.2246,379.45523,3086.5942,431.43893,0.999124
+4407,2625.9048,395.4968,2651.232,451.5397,0.9990578
+4407,1293.0408,324.22495,1313.3489,379.9128,0.99903214
+4407,2572.9863,338.0096,2594.0625,392.7054,0.9988669
+4407,3003.8567,248.97258,3018.7014,289.87054,0.9986203
+4407,2993.548,441.79907,3022.2932,497.65485,0.99860555
+4407,3159.6418,368.34503,3178.7063,417.11194,0.9985152
+4407,3049.1829,445.97916,3074.7073,502.3137,0.99838495
+4407,2803.0,268.42377,2825.101,313.89087,0.9983474
+4407,1978.3496,882.7097,2017.2168,975.2727,0.9982658
+4407,2469.207,452.7218,2505.1475,513.33746,0.99825436
+4407,3223.1643,469.4445,3249.6482,521.9167,0.9981652
+4407,2113.373,218.60574,2137.5854,268.8073,0.9981402
+4407,3077.2246,339.55823,3103.5737,384.24536,0.9979715
+4407,3008.3364,384.8362,3032.0225,435.0405,0.9973362
+4407,3023.449,405.8475,3053.575,456.76468,0.9972662
+4407,3095.2583,329.28806,3117.983,374.8765,0.9968484
+4407,3277.8796,333.031,3297.5857,380.16705,0.99633694
+4407,2010.6626,124.46465,2029.5186,163.3257,0.9902857
+4407,3874.203,729.1944,3917.8406,800.2509,0.9593412
+4407,3712.178,784.1587,3753.68,845.9468,0.6578669
+4407,3878.9846,765.1042,3920.3792,823.2963,0.6503428
+4407,3633.2144,791.9535,3680.9849,877.7549,0.4943204
+4407,3762.9368,792.62805,3790.703,858.87195,0.48732236
+4408,2171.553,308.91473,2192.507,363.45483,0.9993767
+4408,3033.264,544.14166,3062.5261,604.6794,0.9992505
+4408,1293.4089,324.0861,1313.4368,379.91586,0.99910873
+4408,2617.6675,335.66833,2634.9731,387.5838,0.99910563
+4408,3330.6746,586.4969,3351.944,646.1224,0.9990308
+4408,2172.3613,215.36221,2189.2354,263.10742,0.9990289
+4408,3054.4517,379.6997,3084.5962,431.21686,0.9990262
+4408,2260.5215,423.02292,2282.7075,488.56754,0.99899954
+4408,2112.7644,218.39418,2135.4744,268.4648,0.9987023
+4408,3223.1602,469.4772,3248.6338,522.49023,0.9984724
+4408,2625.5522,394.79617,2651.6304,450.7183,0.998428
+4408,3049.2036,445.9798,3074.6992,502.28693,0.9983442
+4408,2993.4216,441.40985,3020.884,497.70776,0.9982687
+4408,1978.3538,882.71674,2017.2163,975.2763,0.9982326
+4408,2570.6377,336.8317,2592.7295,393.2333,0.99816895
+4408,2803.182,268.2504,2822.1829,313.46066,0.9981686
+4408,2469.5251,452.64758,2503.7273,513.45917,0.9980106
+4408,2999.73,248.18028,3017.2964,289.04114,0.99792373
+4408,3023.802,405.6773,3053.7278,456.4436,0.9977544
+4408,3094.0486,328.84952,3117.2996,374.80325,0.9968285
+4408,3075.8013,339.8961,3102.4482,384.40085,0.99584466
+4408,3278.349,333.00092,3297.395,380.1145,0.99568486
+4408,3153.8389,368.08548,3177.4033,417.10855,0.9949569
+4408,2010.7172,124.39868,2029.5636,163.21829,0.9900187
+4408,3007.63,385.1261,3031.3638,435.37305,0.983313
+4408,3873.3882,731.5327,3916.917,799.37537,0.9531045
+4408,3708.8723,785.7215,3750.3196,846.15424,0.86035055
+4408,3879.447,763.22986,3922.9934,823.2051,0.70486397
+4408,3762.7625,792.5645,3790.9963,858.9019,0.51623505
+4408,3633.2515,791.9557,3677.7007,878.0216,0.17900056
+4409,2171.4387,308.0963,2189.598,363.67572,0.99939454
+4409,3054.227,379.65564,3082.8862,431.47015,0.9992704
+4409,3027.9426,544.79865,3061.677,605.47516,0.9992126
+4409,1293.413,323.9179,1313.5839,379.9275,0.9991316
+4409,3327.7324,585.6757,3349.9863,645.7463,0.99906546
+4409,2259.9417,423.04468,2281.4983,488.8371,0.9989334
+4409,3150.296,367.93994,3176.188,416.88904,0.9988249
+4409,2170.0623,215.36205,2188.4504,262.92218,0.9987883
+4409,3223.1658,469.21298,3247.5496,522.3153,0.99873096
+4409,2613.816,334.9774,2633.579,387.18723,0.9987098
+4409,2111.397,217.62222,2133.579,267.97583,0.9984365
+4409,2993.7747,441.20038,3019.4924,497.00037,0.9983436
+4409,1978.3595,882.7301,2017.2167,975.2733,0.9982584
+4409,3048.4675,446.27283,3074.4636,502.77374,0.9981858
+4409,2469.6777,452.36716,2501.0103,513.66754,0.9980882
+4409,2995.2473,248.4171,3016.2747,289.39697,0.9980756
+4409,2625.385,394.4285,2651.9875,449.65073,0.997962
+4409,2567.366,336.4928,2591.3894,392.94824,0.9979242
+4409,2803.0134,268.48215,2819.5754,313.31082,0.99774194
+4409,3092.595,328.10175,3116.864,375.1012,0.9974101
+4409,3023.7468,405.6007,3054.8376,456.21832,0.9967212
+4409,3278.8513,333.1124,3296.9753,380.2617,0.9932683
+4409,3074.4795,340.00867,3101.7554,383.97534,0.99198747
+4409,2010.7126,124.31603,2029.5588,163.21912,0.98999375
+4409,3008.7476,383.10458,3028.5947,433.1437,0.94502276
+4409,3871.9216,731.7419,3914.7317,799.6728,0.92156154
+4409,3707.7678,785.56256,3748.0037,846.4841,0.8726968
+4409,3005.5,394.96985,3030.1602,437.63306,0.40826526
+4409,3762.5154,793.121,3792.429,858.614,0.38513803
+4409,3882.1592,762.96594,3928.3428,822.42334,0.29351708
+4410,3322.4868,585.09216,3348.2437,645.46094,0.9992269
+4410,2170.973,308.53638,2188.557,364.00232,0.99918276
+4410,3023.96,545.781,3059.7183,605.3231,0.9990946
+4410,3054.4077,379.90253,3081.5537,431.60144,0.99906105
+4410,2607.7615,335.0671,2632.095,387.05252,0.9990163
+4410,1293.3783,323.85437,1313.7765,379.9353,0.9989843
+4410,3146.8,367.98843,3174.7458,416.59372,0.99894506
+4410,2166.4553,215.43796,2187.4373,262.55533,0.9988882
+4410,2564.711,336.291,2590.2886,392.8784,0.9987814
+4410,2109.7874,217.16533,2132.301,267.86105,0.9987388
+4410,3223.0425,469.16953,3247.121,522.76874,0.998708
+4410,2990.8237,248.37276,3015.6045,289.27188,0.9986071
+4410,2257.9053,423.36804,2280.5234,488.73584,0.9984931
+4410,1978.363,882.73083,2017.221,975.27356,0.99824715
+4410,3048.4348,446.2966,3074.4602,502.76517,0.99823296
+4410,2993.733,440.7634,3018.1177,496.81808,0.99812573
+4410,2469.7036,452.46075,2498.5352,513.6187,0.99800354
+4410,2625.2117,394.33133,2652.0808,449.39883,0.99786276
+4410,2802.6228,267.8079,2817.7966,313.40073,0.99768865
+4410,3091.0227,327.84918,3116.219,374.94067,0.997387
+4410,3023.818,406.05865,3054.6199,456.22583,0.9969699
+4410,3073.6248,339.64267,3101.0032,383.9468,0.99481994
+4410,3007.0703,383.17038,3026.7783,433.72476,0.9945475
+4410,2010.7754,124.24954,2029.6636,163.09805,0.9903144
+4410,3278.6409,333.20874,3296.7034,380.9463,0.9886961
+4410,3707.993,785.59845,3745.7302,846.26044,0.93785787
+4410,3870.6482,734.42816,3913.1277,799.33844,0.93638134
+4410,3762.3262,792.9129,3793.7627,858.59454,0.31381997
+4410,3882.658,763.9914,3928.701,822.28143,0.17723836
+4410,3632.774,791.4544,3673.7754,876.9118,0.13125284
+4411,2164.162,215.63623,2187.0103,262.69693,0.9993518
+4411,3316.8855,584.94995,3346.7449,646.3993,0.9993407
+4411,3022.2832,546.58765,3057.5854,605.57654,0.99918586
+4411,2170.5251,308.1559,2188.2317,364.0776,0.99908423
+4411,2109.5444,217.10822,2130.4443,267.63492,0.9990125
+4411,1293.8668,323.8902,1313.8663,379.88934,0.999008
+4411,2560.3772,336.75275,2589.4656,392.39844,0.9988869
+4411,3054.7026,379.419,3079.6157,431.22437,0.9988306
+4411,2989.231,248.18791,3014.8555,289.12347,0.9985612
+4411,2602.0007,335.23407,2631.2131,386.85016,0.9985506
+4411,2800.621,267.3708,2816.9102,313.59808,0.9983382
+4411,2993.527,440.68027,3017.3206,496.89615,0.99831617
+4411,2469.774,452.45532,2497.565,513.64166,0.99830496
+4411,1978.3658,882.7358,2017.1989,975.2653,0.99829286
+4411,3048.464,446.28568,3074.4607,502.7676,0.9982722
+4411,3024.2847,405.56226,3054.9907,456.4342,0.9982256
+4411,3144.2751,367.84525,3174.0974,416.702,0.9982139
+4411,3222.9648,469.2277,3246.019,523.3709,0.99816614
+4411,2254.7385,423.56293,2278.5994,488.03223,0.9981318
+4411,2624.6968,394.3322,2653.9194,449.17084,0.99697924
+4411,3005.659,382.96188,3025.3254,433.89285,0.9969676
+4411,3089.8215,327.42523,3114.2922,373.9084,0.9953551
+4411,3073.244,338.85028,3099.2449,383.73242,0.9937755
+4411,3869.8523,738.6648,3912.155,798.5023,0.991013
+4411,2010.7535,124.13567,2029.7882,163.10733,0.9905584
+4411,3278.4802,333.0966,3296.678,381.31937,0.98546576
+4411,3707.8193,785.5596,3742.8296,846.0408,0.9580759
+4411,3884.4585,764.7544,3929.711,822.2699,0.3820834
+4411,3762.4692,792.87103,3794.4902,858.4753,0.14350747
+4411,3082.8042,333.17606,3111.0146,381.152,0.10731261
+4412,3021.766,547.0412,3055.581,605.81024,0.9992937
+4412,3314.4011,584.7943,3345.073,645.30005,0.9992374
+4412,2993.309,440.49048,3016.2222,496.72852,0.9991271
+4412,2558.458,336.56937,2588.5361,392.27335,0.99912685
+4412,2162.1343,215.70027,2186.378,262.63177,0.99906594
+4412,3223.0745,468.93112,3245.284,522.8886,0.99902606
+4412,2988.1067,248.16637,3014.367,288.9172,0.99895775
+4412,2167.8835,307.94504,2187.6077,363.36545,0.9988852
+4412,1294.1196,323.65765,1313.6028,380.1433,0.99883896
+4412,2109.136,217.2749,2128.8035,267.53888,0.99876225
+4412,3054.694,379.38242,3078.237,431.21396,0.9987178
+4412,3024.7817,405.4872,3054.895,456.31595,0.9984465
+4412,2799.171,267.52368,2814.9634,313.5318,0.99837273
+4412,2599.348,335.20187,2631.1052,386.6967,0.9983094
+4412,1978.337,882.7627,2017.2506,975.19507,0.99829227
+4412,3048.4417,446.2377,3074.5574,502.77908,0.9982633
+4412,3003.4946,382.4365,3023.6328,434.13742,0.99817777
+4412,3142.8176,367.9004,3173.8723,416.60645,0.99807346
+4412,2469.678,452.52032,2497.7366,513.6095,0.9978492
+4412,3073.123,339.16843,3097.6323,383.31686,0.9966271
+4412,2250.7336,423.52557,2277.9788,488.6958,0.9963082
+4412,3089.703,326.7423,3111.965,372.8181,0.9953532
+4412,2624.1313,394.1605,2658.6885,449.31973,0.99441165
+4412,3870.158,741.65186,3912.3777,798.8801,0.9942517
+4412,2011.2052,124.01788,2030.0502,162.94904,0.9925626
+4412,3707.9866,785.13135,3740.5066,845.6304,0.98978615
+4412,3278.0503,332.98828,3296.8208,381.25488,0.98702985
+4412,3888.2327,764.9283,3929.8977,820.6828,0.18351384
+4412,3762.6208,792.3431,3795.9133,858.0788,0.17452756
+4412,3632.3372,815.18146,3673.2322,880.85236,0.14455482
+4412,3932.9834,749.0188,3974.4912,806.14905,0.14014581
+4412,579.9803,363.06973,611.93427,418.70828,0.10161583
+4413,2992.469,439.88342,3015.3767,496.60425,0.999331
+4413,2108.6694,216.98434,2127.7173,267.4306,0.99925977
+4413,3313.536,585.1421,3343.696,645.0873,0.9991697
+4413,2161.6592,215.52342,2185.732,262.48807,0.9991418
+4413,3223.2603,468.6518,3244.5078,522.86316,0.99913776
+4413,3021.395,546.86487,3053.6733,606.6388,0.99903226
+4413,2557.364,336.69354,2587.6936,392.08832,0.9989766
+4413,1294.3145,323.3529,1313.6553,380.2542,0.99871814
+4413,2987.1003,247.89575,3013.4788,288.97705,0.9985587
+4413,2469.7034,452.60168,2497.0989,513.6099,0.9984428
+4413,2597.4836,335.35336,2630.885,386.56992,0.9983925
+4413,2164.6787,307.8142,2186.5483,362.81592,0.9982987
+4413,1978.337,882.7627,2017.2506,975.19507,0.99829227
+4413,3024.894,405.6972,3055.7559,456.42188,0.9982673
+4413,3048.4473,446.2614,3074.706,502.8109,0.9982191
+4413,3143.1538,367.63214,3173.4888,416.68866,0.99820054
+4413,2794.2695,267.38104,2814.2612,313.28363,0.9980908
+4413,3054.5537,379.09326,3076.8765,431.42914,0.9980216
+4413,2998.4602,382.20825,3022.0774,433.61353,0.997595
+4413,2247.4856,423.22644,2276.5662,488.6203,0.9971988
+4413,3072.6445,338.32913,3095.164,383.3057,0.99477273
+4413,3089.7612,326.27036,3108.9312,372.29538,0.99428844
+4413,2623.688,393.93478,2660.878,449.3674,0.99418676
+4413,2011.2052,124.018036,2030.0502,162.94916,0.9925634
+4413,3277.921,333.08438,3296.94,380.91318,0.9905515
+4413,3869.0903,743.5803,3912.7202,798.4092,0.98389995
+4413,3707.7878,783.75793,3738.3625,845.5886,0.8633298
+4413,3889.8652,765.25415,3930.8154,821.23804,0.12920336
+4413,579.97144,362.95694,612.0748,418.70065,0.11675398
+4414,2160.9292,215.60365,2185.1196,262.037,0.9994473
+4414,3222.7314,469.25452,3243.5718,522.7705,0.9993186
+4414,3313.5952,585.6598,3341.4175,645.136,0.99914443
+4414,2107.174,216.65709,2126.9524,267.13458,0.99908817
+4414,3021.308,547.5214,3051.2852,606.36163,0.9990361
+4414,2596.8047,335.49615,2630.5166,386.48096,0.998941
+4414,2243.5735,423.90375,2275.8572,489.0078,0.99892884
+4414,1294.4636,323.18042,1314.054,380.0633,0.99876416
+4414,2988.241,439.83008,3014.9167,496.92328,0.998694
+4414,3143.0115,367.06866,3172.508,416.0987,0.99867237
+4414,3053.8372,379.40143,3075.9456,431.6985,0.9986412
+4414,2790.2666,267.01685,2813.1362,313.23242,0.9985663
+4414,2986.55,247.49316,3012.5725,287.59045,0.9983134
+4414,1978.3369,882.7627,2017.2507,975.19507,0.998292
+4414,3048.3882,446.25113,3074.8809,502.80374,0.99823904
+4414,2469.8384,452.4969,2496.5566,513.6023,0.99823207
+4414,2992.7517,382.92618,3020.7063,433.64438,0.9980184
+4414,2556.3218,336.2852,2586.6821,392.00143,0.9978928
+4414,3025.3403,405.7085,3055.8706,456.18127,0.99785525
+4414,2162.4167,307.52798,2184.47,362.4744,0.9974482
+4414,3088.526,325.97693,3107.504,371.70856,0.9958365
+4414,3869.385,743.93085,3913.089,798.01324,0.99493587
+4414,2623.551,393.9182,2661.7737,449.32935,0.9945644
+4414,3278.0737,332.88934,3296.852,380.15637,0.9939075
+4414,2011.1902,123.9996,2030.0554,162.95361,0.99260604
+4414,3073.2666,337.9196,3092.683,382.62027,0.9918791
+4414,3707.6357,782.5633,3735.6719,845.58453,0.93454283
+4414,3889.9397,765.7341,3930.4006,822.3735,0.27370566
+4414,579.9275,362.91705,612.2761,418.65747,0.14866787
+4414,3762.9727,791.5294,3797.0332,857.56055,0.1431396
+4415,2160.1946,215.62672,2185.013,261.9257,0.9994715
+4415,2596.0078,335.11447,2628.269,386.32236,0.99921155
+4415,2104.8499,216.32411,2126.0916,267.15543,0.99914
+4415,3220.5017,470.13968,3242.696,523.4145,0.9989849
+4415,3142.5986,366.71503,3171.4395,416.299,0.9988803
+4415,3021.3633,547.3783,3049.6055,606.67395,0.9988053
+4415,2983.852,439.44733,3013.876,496.95172,0.9987563
+4415,1294.5204,322.9698,1313.5961,379.94104,0.99868053
+4415,3312.1277,586.05273,3338.3708,644.39307,0.9986558
+4415,2241.3362,424.0756,2274.6687,489.68124,0.9986389
+4415,2469.9565,452.34265,2494.9458,513.5408,0.99859816
+4415,3052.917,379.1815,3074.873,431.72934,0.9985245
+4415,2787.007,266.58362,2812.514,313.19385,0.99838114
+4415,3087.8284,326.18222,3105.8748,371.34055,0.99832886
+4415,1978.3373,882.7628,2017.2504,975.19495,0.998298
+4415,3048.3152,446.2827,3075.074,502.7688,0.9981396
+4415,2989.3904,382.46927,3019.8972,433.90872,0.9980236
+4415,2986.4624,246.7067,3009.5059,287.47787,0.9980124
+4415,2556.267,335.3766,2585.3037,391.67804,0.99785477
+4415,2160.592,307.47992,2184.2107,362.5476,0.99721235
+4415,3026.0723,405.65613,3055.7827,456.01367,0.99688464
+4415,2622.9685,393.8156,2661.8523,449.687,0.99505025
+4415,3074.1956,337.609,3091.7297,382.48773,0.99398065
+4415,3279.0679,332.87228,3296.8955,379.38107,0.99268496
+4415,2011.1943,123.996346,2030.0581,162.9529,0.9926099
+4415,3880.8918,744.4033,3912.0935,797.7683,0.9894656
+4415,3708.0977,781.8604,3733.6401,844.8411,0.9016643
+4415,3889.731,765.55505,3933.9258,822.90186,0.694424
+4415,3928.7449,749.1909,3973.4246,806.28784,0.15384345
+4415,579.9571,362.8322,612.1053,418.69763,0.12421555
+4415,3763.317,790.8635,3797.329,857.3646,0.110132284
+4416,2160.0771,215.36267,2184.0464,261.37524,0.999466
+4416,2786.3914,266.95197,2811.8132,312.93512,0.999156
+4416,2595.9314,334.6732,2625.5266,385.09262,0.99915326
+4416,3021.0845,547.62054,3047.8818,606.5262,0.9991447
+4416,3218.6985,470.0368,3241.8015,522.74286,0.9989755
+4416,2980.7722,439.37567,3012.9255,496.79474,0.9989736
+4416,2240.094,424.4293,2274.0974,489.64188,0.99893063
+4416,3086.3918,326.0822,3104.2297,372.1541,0.9988337
+4416,2102.4624,216.26245,2125.143,266.7751,0.9988071
+4416,2470.1387,451.86316,2492.6743,513.57074,0.998696
+4416,1294.6779,322.52948,1313.8214,379.9151,0.9985421
+4416,3048.2078,446.22192,3075.2024,502.81274,0.9983565
+4416,3142.407,366.30832,3169.5696,415.61343,0.9983339
+4416,1978.3855,882.683,2017.2046,975.1382,0.9982894
+4416,2986.413,246.15082,3006.7153,287.1347,0.9981968
+4416,3051.7144,379.1058,3073.499,431.62454,0.9980872
+4416,3309.8684,586.01605,3336.423,644.12067,0.99788195
+4416,2988.2134,382.4697,3018.746,433.58676,0.9978709
+4416,2159.1724,307.60516,2183.9937,362.72876,0.997203
+4416,2555.4727,334.17273,2584.1729,390.98456,0.9967529
+4416,3026.2988,405.35706,3055.079,456.45624,0.9965011
+4416,2622.4248,393.80695,2661.503,449.5998,0.9950971
+4416,3277.824,332.4933,3296.7322,378.3957,0.9948096
+4416,3074.0383,338.2432,3091.027,382.8052,0.99361044
+4416,2011.1588,123.92359,2029.8224,162.9693,0.99243903
+4416,3706.693,781.7692,3731.1394,844.6784,0.95288
+4416,3883.5305,744.3715,3911.0916,798.01044,0.92778355
+4416,3889.4668,764.98846,3934.0513,823.4721,0.3464673
+4416,579.95764,362.99872,612.12085,418.6997,0.12368518
+4417,2979.4878,439.3206,3011.478,496.5533,0.99931633
+4417,2554.9949,333.1859,2579.6873,388.40015,0.9992524
+4417,2099.1926,216.7719,2123.5764,267.15924,0.99919885
+4417,2239.4548,424.895,2272.8982,489.76047,0.99912196
+4417,2595.9458,332.95425,2618.6157,384.5988,0.99906963
+4417,2159.9058,213.97018,2180.6875,260.6919,0.99904054
+4417,2785.4124,265.64664,2810.5852,312.41537,0.99898773
+4417,3019.0469,547.28735,3044.8022,606.84875,0.99891245
+4417,2470.145,451.45337,2491.7866,513.4771,0.99886787
+4417,1295.2118,321.9595,1314.1317,378.99985,0.9986927
+4417,2157.2346,307.4721,2183.5554,362.89673,0.9986052
+4417,3048.3242,446.3041,3075.746,502.79276,0.99851733
+4417,3215.3489,471.28696,3240.2253,523.8639,0.9984511
+4417,2986.1956,244.9018,3002.4338,286.91815,0.9983271
+4417,1978.3671,882.6732,2017.2328,975.15454,0.99828535
+4417,3082.504,325.746,3102.1484,372.058,0.99799526
+4417,3142.1758,365.3083,3163.524,413.6344,0.99772674
+4417,3304.8506,585.58575,3333.809,643.30914,0.9970898
+4417,3066.7192,337.76532,3087.984,381.91187,0.99617153
+4417,3026.532,404.3876,3050.9968,456.73196,0.9961499
+4417,3047.8647,378.97852,3071.7637,431.79974,0.9961249
+4417,3277.2605,332.19568,3296.826,378.00928,0.9955655
+4417,2621.771,393.6248,2660.9438,449.1566,0.99554944
+4417,2985.2283,381.69345,3017.6677,432.60263,0.99541783
+4417,2011.162,123.92899,2029.9266,162.95407,0.9926851
+4417,3698.1672,782.7243,3726.4119,846.0224,0.9755788
+4417,3881.7688,745.08344,3910.6667,798.7569,0.96679896
+4417,3888.824,765.45215,3936.2507,822.52014,0.58611035
+4417,579.96484,362.72128,612.2533,418.725,0.13521789
+4418,2100.305,216.2928,2123.136,266.55096,0.99936897
+4418,2156.1392,307.77612,2183.395,363.06732,0.9992895
+4418,2979.072,438.52383,3009.7942,496.7239,0.9991788
+4418,2239.2576,425.0702,2271.1394,489.83313,0.9991417
+4418,3300.7341,584.96344,3329.0447,643.7714,0.99904484
+4418,3014.8992,547.7034,3043.157,606.83966,0.99895656
+4418,2159.7012,213.29948,2178.0542,260.50754,0.99892104
+4418,2554.967,332.94412,2577.201,387.4075,0.998899
+4418,2470.2349,451.36215,2491.7007,513.47864,0.9988967
+4418,1295.6163,322.0274,1314.8605,378.3294,0.99873334
+4418,2596.0076,332.71387,2615.0251,384.5246,0.9986241
+4418,3048.23,446.26968,3075.8716,502.7914,0.99860686
+4418,3214.3901,471.56137,3239.17,524.624,0.9985057
+4418,2785.3284,264.966,2808.533,311.07465,0.99838996
+4418,3142.7373,364.6614,3161.901,413.47354,0.9982907
+4418,1978.3711,882.69574,2017.209,975.1645,0.9982742
+4418,2984.8208,245.09317,3001.0645,286.9996,0.99812144
+4418,3047.746,379.61713,3070.1133,431.99744,0.99739766
+4418,3027.6785,405.42862,3052.967,456.5721,0.9970866
+4418,2985.2021,381.9348,3014.4941,432.2273,0.9968923
+4418,3079.781,325.87704,3100.991,371.6644,0.9965539
+4418,2621.7244,392.8107,2659.117,448.69998,0.9962624
+4418,3277.8125,331.87622,3297.082,377.2564,0.9950983
+4418,3063.874,338.52457,3087.7124,382.3046,0.9946978
+4418,2011.1632,123.89041,2029.9332,162.95963,0.9926604
+4418,3877.907,744.95605,3909.95,798.66516,0.9756749
+4418,3693.026,783.6642,3724.314,845.7085,0.96755564
+4418,3889.7388,764.8434,3931.296,821.7639,0.1871618
+4418,3633.195,816.5379,3673.3489,880.59503,0.16689177
+4418,3763.7756,790.241,3798.4792,857.43976,0.15268151
+4418,580.0078,362.7412,612.214,418.7406,0.12696418
+4419,2100.162,216.28104,2122.3252,266.315,0.9993704
+4419,2155.772,308.12973,2182.8657,363.14017,0.9993457
+4419,2595.1875,332.39633,2613.2222,384.55716,0.99922585
+4419,2553.9766,332.19638,2575.875,386.73135,0.999134
+4419,3010.5964,547.5978,3041.9783,606.90027,0.9990772
+4419,2978.8818,438.2403,3008.6143,496.38177,0.9990489
+4419,3293.9102,584.4641,3325.768,643.8054,0.9990375
+4419,2470.2805,451.33868,2491.654,513.4872,0.9989122
+4419,2239.2603,424.82233,2268.2202,489.8191,0.99885356
+4419,1296.073,322.11337,1315.013,377.616,0.99883276
+4419,2158.975,212.75522,2176.7969,260.36328,0.99878854
+4419,3048.3032,446.27582,3076.067,502.7343,0.9986119
+4419,2785.0012,264.47314,2806.4167,310.21875,0.99861133
+4419,3213.8835,471.82068,3238.5671,525.05994,0.99851054
+4419,2981.9226,244.81902,3000.741,287.00217,0.99842703
+4419,1978.3353,882.6549,2017.2281,975.1925,0.9982395
+4419,2621.6252,392.8046,2658.3328,447.76028,0.99818677
+4419,3142.9895,364.69202,3161.279,411.99713,0.99776447
+4419,2985.1968,381.4458,3013.209,431.20813,0.99772817
+4419,3047.1099,379.45926,3068.852,432.1647,0.99708706
+4419,3027.5916,404.6214,3052.181,456.79175,0.9965406
+4419,3079.1648,326.15286,3100.2961,371.48178,0.9960531
+4419,3274.7896,331.41962,3297.3823,381.34235,0.9952873
+4419,3062.7717,338.293,3086.7654,382.08926,0.992867
+4419,2011.1815,123.9522,2029.9298,162.9675,0.9926755
+4419,3876.6924,744.4597,3909.3135,798.4946,0.9911999
+4419,3688.8228,785.58655,3721.2607,847.5873,0.81427133
+4419,3888.2131,764.48126,3934.8904,821.7884,0.20819196
+4419,580.02545,362.57703,612.25824,418.75208,0.11619948
+4420,2100.1616,216.28584,2122.332,266.3156,0.9993686
+4420,2155.771,308.14243,2182.8623,363.1393,0.99934644
+4420,2595.2122,332.39688,2613.203,384.55453,0.9992248
+4420,2553.974,332.19443,2575.8809,386.74136,0.99913234
+4420,3010.5908,547.59875,3041.98,606.88306,0.9990784
+4420,2978.8704,438.22992,3008.6375,496.39087,0.99905306
+4420,3293.9275,584.4653,3325.8,643.80597,0.99904925
+4420,2470.2788,451.16458,2491.6357,513.51135,0.9989274
+4420,2239.2686,424.8216,2268.2285,489.82336,0.99885166
+4420,1296.1364,322.0533,1315.2516,377.68094,0.99885106
+4420,2158.9753,212.75462,2176.7986,260.3623,0.9987897
+4420,2784.9766,264.46603,2806.419,310.24252,0.99859816
+4420,3213.8054,471.81177,3238.5647,525.1745,0.99858665
+4420,3048.3743,446.29587,3076.0823,502.73044,0.9985688
+4420,2981.8809,244.82985,3000.7407,286.99567,0.9983687
+4420,1978.3627,882.56555,2017.222,975.18005,0.99822813
+4420,2621.622,392.80768,2658.3262,447.71228,0.99818027
+4420,3142.9365,364.68384,3161.2822,412.00208,0.99777347
+4420,2985.2212,381.4454,3013.2124,431.20175,0.99771154
+4420,3047.114,379.45743,3068.8499,432.16025,0.9970541
+4420,3027.5947,404.62924,3052.1753,456.78494,0.99654925
+4420,3079.1711,326.1134,3100.3157,371.49005,0.9960257
+4420,3274.7847,331.4047,3297.395,381.30515,0.995389
+4420,2011.1688,123.922264,2029.9481,162.96164,0.9927089
+4420,3062.814,338.29233,3086.7686,382.07114,0.99265856
+4420,3876.6367,744.4741,3909.2935,798.46387,0.9916215
+4420,3688.8025,785.5656,3721.264,847.6003,0.8074575
+4420,3888.1975,764.4998,3934.906,821.80817,0.22041574
+4420,580.0054,362.63385,612.2235,418.75592,0.114138454
+4421,2099.9795,216.06406,2121.83,266.0817,0.99940395
+4421,3005.2537,548.8183,3039.6746,608.4495,0.99934894
+4421,2155.5405,308.0965,2182.4897,363.3329,0.9992048
+4421,2552.3677,332.16083,2574.851,386.98804,0.9991666
+4421,2158.7388,212.583,2175.5713,260.63416,0.9991066
+4421,2470.2695,450.97968,2491.5898,513.50543,0.99894017
+4421,2978.5344,437.4369,3006.3083,495.80316,0.9988986
+4421,3290.1492,584.67426,3323.1257,643.2414,0.998889
+4421,1296.4186,321.90353,1315.5143,377.6154,0.99886465
+4421,2592.454,332.2306,2611.9087,384.53705,0.9988587
+4421,3213.4634,471.78848,3237.3257,525.39777,0.9988426
+4421,3048.3015,446.32016,3076.0955,502.7656,0.9986212
+4421,2621.7341,392.50726,2655.9812,447.30267,0.9984769
+4421,2785.0774,264.13058,2803.8752,310.6777,0.9984754
+4421,1978.3494,882.48926,2017.2307,975.1813,0.9982052
+4421,2975.3562,244.7119,3001.0447,286.72406,0.99810815
+4421,3141.1504,364.61987,3160.1777,412.61462,0.9977315
+4421,3077.7954,325.54776,3099.1924,371.54398,0.9977049
+4421,3027.6592,405.00177,3050.6953,456.78705,0.99754626
+4421,2239.2825,424.7672,2264.6853,489.9964,0.99734527
+4421,3061.5452,338.7633,3085.719,381.81354,0.9969375
+4421,3274.5557,331.36496,3297.3008,380.431,0.9963856
+4421,3045.3528,379.89053,3067.4294,432.01352,0.9959556
+4421,2985.8271,380.6375,3009.6045,430.26147,0.99277014
+4421,3875.3105,744.62286,3907.7246,798.5428,0.992735
+4421,2011.1669,123.913635,2029.9569,162.96997,0.9927199
+4421,3683.4285,788.0554,3718.9973,849.0874,0.9384547
+4421,3889.4893,765.3264,3936.3433,822.72437,0.37070864
+4421,579.98016,362.49692,612.3444,418.7394,0.13219847
+4421,3632.4333,816.7786,3673.3552,879.7769,0.12589453
+4422,3002.0015,549.57104,3038.1006,609.452,0.9993728
+4422,2157.7595,212.61319,2174.9094,260.33948,0.9993026
+4422,2978.6682,437.05798,3004.575,495.41357,0.9991542
+4422,2155.6814,308.1476,2181.6282,362.94302,0.99914587
+4422,2099.3247,215.54126,2121.1694,264.59744,0.9990349
+4422,2550.4402,331.77383,2573.2175,386.74454,0.998987
+4422,2470.2607,450.7463,2491.5723,513.48944,0.99897504
+4422,3076.0466,324.6361,3099.3416,371.17126,0.9988456
+4422,3213.0942,471.27014,3236.9917,525.8618,0.99884534
+4422,1296.7216,321.9123,1316.3011,377.31915,0.9987367
+4422,2973.1804,244.80136,3000.175,286.5663,0.99866927
+4422,3288.915,585.2292,3320.3882,645.62396,0.998547
+4422,2622.0452,392.02417,2651.1833,446.72937,0.99852055
+4422,3048.2424,446.31625,3076.0515,502.82584,0.9984302
+4422,3137.5835,364.63275,3158.9707,412.45135,0.998412
+4422,2238.909,424.53107,2260.4763,490.2671,0.99835557
+4422,1978.3489,882.46246,2017.2327,975.18085,0.99824667
+4422,2586.0867,331.8702,2609.7024,384.48264,0.9978806
+4422,3027.3442,404.90518,3050.0542,456.82834,0.99726176
+4422,2784.8542,263.98795,2801.1228,309.98465,0.99724233
+4422,3873.4126,744.3361,3906.6802,798.8658,0.9963786
+4422,3273.0476,331.2358,3297.258,382.35477,0.9955812
+4422,3061.1174,338.65778,3084.695,381.979,0.99506974
+4422,2986.1511,379.7486,3007.468,429.9173,0.99505216
+4422,3043.9214,378.97134,3066.2515,432.07608,0.9949877
+4422,2011.1702,123.91525,2029.9631,162.97424,0.9927038
+4422,3681.901,789.69635,3715.8022,848.9073,0.86390597
+4422,3889.914,765.4185,3936.4224,823.32904,0.5811905
+4422,580.02594,362.38132,612.12067,418.67587,0.13566123
+4422,3632.4814,817.37616,3673.001,880.4432,0.12511292
+4423,2155.538,308.2418,2180.2725,363.2878,0.9994203
+4423,2547.2788,331.61917,2571.7202,386.4825,0.999332
+4423,2978.592,436.26813,3003.4363,494.2614,0.9992204
+4423,2622.2246,391.68472,2646.9946,445.90494,0.99921525
+4423,2098.5159,215.156,2119.6985,264.73364,0.9991923
+4423,2156.5483,212.36061,2174.5752,260.5204,0.9991696
+4423,3212.684,471.52264,3236.2036,525.78815,0.9991109
+4423,1296.6775,321.401,1316.4539,377.05286,0.9990778
+4423,2470.2488,450.47522,2491.5818,513.48444,0.9990102
+4423,2581.0962,332.08954,2608.7837,384.56403,0.9988871
+4423,3000.7896,550.16345,3036.6006,609.0281,0.9988856
+4423,2238.562,424.343,2258.1084,490.24863,0.998643
+4423,2972.3403,244.92802,2998.771,286.78033,0.99849117
+4423,3048.2075,446.2584,3076.045,502.87027,0.9984151
+4423,1978.3468,882.46375,2017.2645,975.1658,0.9982615
+4423,3288.5188,586.0708,3317.1135,645.6024,0.99823654
+4423,2783.4119,263.40176,2800.0896,310.38467,0.99797195
+4423,3132.7117,364.6506,3157.7058,412.00638,0.9974583
+4423,3027.1375,405.29453,3050.2083,456.8975,0.99741566
+4423,2986.1985,379.69687,3005.9377,429.74997,0.99731004
+4423,3074.9204,324.2264,3097.4,370.29782,0.99671376
+4423,3059.4934,338.6391,3083.1052,382.54187,0.9966209
+4423,3872.4446,744.8452,3905.1257,798.15295,0.99610174
+4423,3043.0244,379.01587,3065.4229,431.812,0.9959751
+4423,3273.1245,330.8317,3297.4912,381.44632,0.99277675
+4423,2011.1691,123.93666,2029.9718,162.98561,0.99268085
+4423,3890.0916,765.8376,3935.8455,825.0047,0.7492041
+4423,3683.1528,791.28503,3714.3164,848.70483,0.7227524
+4423,3632.326,817.90015,3673.195,880.31116,0.17702495
+4423,579.9772,362.4699,612.301,418.7511,0.1239007
+4423,3763.123,789.6719,3798.1772,857.1504,0.10785903
+4424,2098.3044,214.79424,2118.3787,264.57547,0.99926156
+4424,2622.278,391.23682,2645.6501,445.48962,0.99909484
+4424,3212.3098,471.12146,3235.256,525.86115,0.99902797
+4424,2470.2417,450.2982,2491.5117,513.48285,0.99900204
+4424,2154.2795,212.25912,2174.617,259.9918,0.9989103
+4424,2978.7236,436.04727,3001.8506,493.69968,0.99890554
+4424,1297.2225,321.53717,1317.202,376.95,0.9987903
+4424,2155.538,308.1263,2177.688,363.16013,0.99877745
+4424,3288.5774,586.5455,3312.6433,645.31915,0.9987362
+4424,3000.6128,550.65106,3034.9058,609.8049,0.99869883
+4424,2542.8147,331.66855,2570.1838,386.5087,0.99854493
+4424,2985.5735,379.83636,3004.529,429.83923,0.99847704
+4424,2235.2922,424.17313,2256.5945,490.2327,0.9984621
+4424,2576.9194,331.99005,2608.211,384.82495,0.9984193
+4424,3048.3176,446.37762,3075.9895,502.94464,0.9983752
+4424,1978.3324,882.3898,2017.1696,975.1787,0.9983644
+4424,2781.5972,263.72403,2798.5557,309.5356,0.9981078
+4424,2972.2703,244.80202,2997.8235,286.59467,0.99810696
+4424,3027.273,405.1728,3050.4438,456.94666,0.997209
+4424,3129.096,364.58112,3157.2048,412.16608,0.9968208
+4424,3059.0454,339.34494,3081.88,382.6827,0.99670523
+4424,3869.7039,744.92413,3903.9343,799.2155,0.9958145
+4424,3043.3965,379.59427,3064.6382,431.184,0.99548537
+4424,3074.0981,324.3425,3095.6416,370.1746,0.99539196
+4424,3272.919,330.84055,3297.4644,381.80923,0.9930661
+4424,2011.1713,123.954445,2029.8004,163.01306,0.9923304
+4424,3679.2812,792.1784,3712.3047,850.5904,0.92920005
+4424,3581.3767,803.72314,3622.0315,866.97046,0.6932309
+4424,3891.15,766.93494,3934.4868,827.7073,0.5155555
+4424,3631.8567,818.6289,3673.3225,879.6306,0.41837585
+4424,3762.9055,789.43604,3797.9924,856.76465,0.10843584
+4425,2978.417,435.48947,3000.9639,491.86777,0.9992447
+4425,2155.6301,307.86917,2175.839,363.35922,0.999127
+4425,2470.2043,450.11157,2491.5042,513.49365,0.999014
+4425,2622.3442,391.02023,2645.2393,445.2554,0.99900585
+4425,2097.1914,214.56186,2116.8643,263.94464,0.99898237
+4425,2575.736,331.9858,2607.922,384.70206,0.99893516
+4425,3210.907,471.08646,3234.3655,526.3885,0.99879944
+4425,1297.338,321.12344,1318.1097,377.04227,0.9987941
+4425,2151.7966,212.21777,2175.0242,260.20483,0.9987425
+4425,3286.8699,586.38226,3308.6497,645.8547,0.99856097
+4425,2970.8838,244.31316,2996.2793,286.48743,0.9985195
+4425,3000.076,551.1124,3033.0896,609.7052,0.99847394
+4425,3048.1775,446.53558,3075.735,502.96997,0.9984371
+4425,1978.2496,882.3086,2017.1757,975.2992,0.9983961
+4425,2778.59,264.0855,2797.3743,309.40887,0.99835193
+4425,2538.2974,330.9816,2569.0737,386.75046,0.99808395
+4425,3027.3955,405.4389,3050.3926,456.85828,0.9979417
+4425,2231.5825,424.30225,2256.0327,490.20007,0.9978721
+4425,3869.3464,745.084,3902.8694,799.32825,0.9977309
+4425,2981.7673,378.25592,3002.9797,428.70636,0.9976164
+4425,3127.0642,364.75058,3156.782,412.16806,0.99749964
+4425,3043.219,379.608,3063.297,431.527,0.9972379
+4425,3272.3916,330.7004,3297.3257,381.87466,0.99549246
+4425,3058.394,339.3407,3080.5825,382.36517,0.9948702
+4425,3072.956,323.9085,3093.2368,368.7174,0.9939015
+4425,2011.1716,123.96857,2029.6882,163.03238,0.9921157
+4425,3678.8486,793.296,3710.872,851.14124,0.97328556
+4425,3631.616,818.6839,3673.2063,879.33356,0.40430635
+4425,3581.5469,805.79474,3615.958,867.2132,0.325638
+4425,3891.9211,768.3236,3934.5881,828.4656,0.3167394
+4425,3762.8394,789.4004,3797.9346,856.57715,0.1392589
+4426,2977.368,435.82925,2999.8918,491.7596,0.999343
+4426,2155.5212,308.08566,2173.4797,363.28287,0.99909747
+4426,2574.2207,331.3703,2607.165,384.57428,0.9990433
+4426,2150.982,212.16772,2174.7065,260.10358,0.9990243
+4426,1297.8445,320.90775,1319.802,376.7015,0.9990073
+4426,2470.1582,449.90594,2491.4785,513.4881,0.99900454
+4426,2971.5479,243.8772,2994.5781,284.93597,0.9989774
+4426,2622.345,390.83795,2645.008,445.29877,0.9989693
+4426,2097.3406,214.47002,2115.489,263.7676,0.9987921
+4426,2226.9006,424.38025,2256.426,490.11432,0.9987373
+4426,3283.451,585.6285,3305.2976,645.1724,0.9985576
+4426,2999.9497,551.3583,3031.3555,610.0116,0.99849534
+4426,3209.7163,471.05228,3233.6348,526.9189,0.9984939
+4426,3047.9316,446.78058,3075.3525,502.96735,0.998406
+4426,1978.2314,882.30963,2017.1343,975.3088,0.99833137
+4426,3869.6182,745.3018,3901.6104,799.09576,0.9982091
+4426,2773.447,264.1549,2796.5237,309.3272,0.99791235
+4426,3027.4773,405.73373,3049.8318,456.7902,0.99789965
+4426,3126.414,364.84802,3156.4473,412.2547,0.9975407
+4426,2536.7366,331.01547,2567.7405,386.44717,0.9971058
+4426,3073.1892,324.02307,3090.6555,368.14685,0.9963343
+4426,3272.5103,330.66333,3297.1094,381.84875,0.99629956
+4426,3043.0083,379.07135,3062.233,430.86328,0.9958728
+4426,3058.8906,340.08942,3078.8257,382.84595,0.99480176
+4426,2011.175,123.97505,2029.5923,163.06337,0.99191284
+4426,3678.5188,794.0925,3708.175,851.7023,0.98800397
+4426,2976.098,378.86237,3001.4563,428.39746,0.98614454
+4426,3891.6094,768.8183,3937.6812,828.9202,0.4904579
+4426,3631.6465,818.77905,3673.3013,879.5382,0.313218
+4426,2772.2957,877.1182,2844.8196,959.8726,0.22611657
+4426,3761.9104,789.34393,3796.6077,856.566,0.18917723
+4427,2975.178,435.55267,2998.679,491.81738,0.99928373
+4427,1298.1865,320.62683,1321.2917,376.39203,0.9992329
+4427,2470.093,449.8649,2491.5017,513.4969,0.999057
+4427,2621.83,390.67517,2644.851,445.18207,0.9990295
+4427,2155.3103,307.78815,2172.1907,363.55597,0.9989373
+4427,2574.953,331.38992,2605.7996,384.43033,0.9988953
+4427,2096.5388,214.15889,2114.807,263.86246,0.998892
+4427,3208.009,471.53415,3232.8171,524.94055,0.99884534
+4427,3279.628,584.66815,3302.1929,644.0418,0.9987256
+4427,2150.226,212.01419,2173.9102,260.04578,0.9987128
+4427,2223.554,424.06204,2256.006,490.16446,0.99867934
+4427,2999.784,551.70294,3029.9397,610.08856,0.99848914
+4427,2971.2344,243.26137,2991.934,284.75217,0.9984511
+4427,2769.268,264.04767,2796.349,309.29407,0.99837863
+4427,1978.2224,882.29944,2017.103,975.31506,0.9983595
+4427,3867.9907,745.265,3900.5376,799.9144,0.9983334
+4427,3047.5903,446.8026,3075.059,502.73877,0.9982461
+4427,3027.46,405.39905,3050.6162,456.69897,0.9976418
+4427,2970.2668,377.98862,3000.2512,429.6677,0.99732864
+4427,3125.94,364.75214,3156.3809,412.46375,0.9972989
+4427,2535.975,330.7886,2566.829,386.17062,0.9972308
+4427,3272.2996,330.54083,3297.0178,381.91998,0.99699223
+4427,3058.464,338.33612,3078.906,382.6449,0.99565876
+4427,3042.0928,378.91473,3061.6426,430.06604,0.99529594
+4427,2011.18,123.96357,2029.5912,163.08263,0.9919436
+4427,3071.748,324.23074,3088.8018,368.31992,0.9917625
+4427,3678.5156,794.1359,3703.7783,851.4108,0.99134797
+4427,3891.1794,770.3084,3938.4553,829.5185,0.5941621
+4427,2770.798,877.0185,2843.9905,960.0208,0.58395207
+4427,3631.8228,818.62787,3673.4,879.7193,0.24515812
+4427,3578.343,805.3726,3608.0994,866.9299,0.12128847
+4428,2155.0056,307.76886,2171.515,363.63208,0.9994092
+4428,3275.8843,583.993,3300.4312,644.1419,0.99937314
+4428,2971.6064,435.70648,2997.353,491.50165,0.9992642
+4428,1298.3367,320.84894,1322.7463,376.33026,0.9991617
+4428,2470.0947,449.59055,2491.4175,513.50867,0.9990729
+4428,2620.5657,390.24115,2644.7595,444.9073,0.9990503
+4428,3205.6523,471.68103,3231.5269,525.2087,0.99895704
+4428,2095.9697,213.88849,2115.245,263.74103,0.99891007
+4428,2999.6436,551.8723,3028.871,610.1195,0.9988832
+4428,2974.1091,244.65851,2987.703,284.05548,0.99887866
+4428,2574.5886,331.39417,2603.2302,383.70483,0.99880695
+4428,2768.3782,264.2449,2795.515,309.00003,0.9987324
+4428,3867.8445,744.7823,3899.6082,799.8851,0.99866426
+4428,2219.9553,424.0121,2255.777,490.25897,0.99857116
+4428,2149.0764,211.90471,2173.2942,259.09637,0.99854636
+4428,1978.2324,882.2361,2017.2273,975.31775,0.9983335
+4428,3046.6548,447.1002,3074.4058,502.65323,0.99767524
+4428,3272.664,331.0095,3296.6094,379.32468,0.9966079
+4428,2535.7104,330.51236,2566.687,385.43845,0.99658465
+4428,3125.4668,364.7435,3155.5933,412.33145,0.9965442
+4428,3027.6743,405.95505,3052.125,456.68808,0.9959823
+4428,2968.0889,377.87686,2999.254,429.49295,0.995806
+4428,3069.9956,323.87033,3087.1714,368.2808,0.99453133
+4428,2011.1794,123.91365,2029.5525,163.07832,0.99199957
+4428,3058.5962,337.70865,3078.5435,382.6188,0.9896611
+4428,3039.9922,378.53253,3061.4453,429.00232,0.9879066
+4428,3675.4014,793.6164,3699.5435,851.38043,0.9561008
+4428,3892.32,770.3601,3940.0808,829.4772,0.61087257
+4428,2772.0303,877.4681,2841.9155,959.111,0.35090986
+4428,3577.5269,804.8658,3604.7104,866.9671,0.23068157
+4428,3631.684,818.0084,3673.667,879.9553,0.17113602
+4429,2153.128,307.7628,2170.8384,363.57053,0.9993637
+4429,1298.4368,321.03195,1324.273,376.1926,0.9993093
+4429,2575.1433,330.14594,2599.8015,381.94287,0.9992654
+4429,3201.9792,472.13516,3230.6067,525.9946,0.999204
+4429,2095.2935,213.76082,2114.747,263.74084,0.99917245
+4429,2470.0894,449.23126,2491.3906,513.52295,0.99910223
+4429,3269.0125,582.93835,3298.902,643.7805,0.9990914
+4429,2967.333,435.79224,2996.2227,491.24542,0.99903405
+4429,2217.9006,424.20624,2254.8796,490.22174,0.9989802
+4429,2767.7158,263.87897,2794.9224,308.7777,0.99888986
+4429,2999.3137,551.9513,3027.4768,609.9263,0.9988876
+4429,2149.0125,212.05676,2171.757,259.12555,0.99887496
+4429,2619.856,389.94046,2644.8179,445.0311,0.9988029
+4429,1978.2369,882.2053,2017.2194,975.29297,0.9983693
+4429,3867.6143,744.4513,3898.7822,800.27234,0.9982468
+4429,3046.126,447.4461,3073.5215,502.48267,0.9982437
+4429,2971.671,242.86292,2986.8535,283.726,0.9973859
+4429,2966.4714,377.56674,2998.5608,429.44333,0.997357
+4429,2535.8455,330.3584,2566.036,384.95093,0.9972132
+4429,3272.571,330.9303,3296.2507,378.75763,0.9964079
+4429,3125.3955,364.5137,3154.2793,412.1744,0.9954457
+4429,3068.2937,323.2198,3085.6213,369.4262,0.9937215
+4429,3027.633,405.9633,3054.405,456.53458,0.9924541
+4429,2011.1741,123.901245,2029.5105,163.08139,0.99204856
+4429,3038.684,378.12637,3060.9644,427.27072,0.98765314
+4429,3057.3357,338.63684,3077.1306,382.6447,0.9727465
+4429,3673.1533,793.243,3695.416,851.3565,0.9352304
+4429,2770.9502,877.2817,2840.9966,958.73517,0.56130826
+4429,3893.8362,768.03204,3941.988,829.0336,0.4351262
+4429,3631.7473,817.0197,3674.0315,880.15717,0.1806844
+4430,1298.6659,321.21826,1325.0704,375.95258,0.9993524
+4430,2535.7578,329.83838,2563.996,384.14587,0.9992525
+4430,2216.7542,424.4301,2253.9841,490.1654,0.9992405
+4430,2963.9177,435.3492,2994.8533,490.99643,0.9992192
+4430,2470.0894,449.237,2491.3525,513.5065,0.99909115
+4430,2997.5637,551.8542,3025.9246,610.33246,0.9990908
+4430,3265.1768,582.4556,3298.3364,643.3072,0.99905866
+4430,2094.725,213.32565,2114.7446,263.66666,0.9990414
+4430,3199.146,472.54944,3229.712,525.76434,0.9990295
+4430,2148.789,211.95523,2170.5288,257.57733,0.9990101
+4430,2150.6355,307.45175,2169.9832,363.2863,0.99884737
+4430,2574.3525,330.2236,2596.7554,382.0917,0.998845
+4430,2768.3464,263.72717,2794.2283,308.2221,0.9988412
+4430,3045.3508,447.43665,3073.0051,502.549,0.99852437
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+4430,2966.2605,376.31693,2997.778,428.93552,0.9983891
+4430,1978.2552,882.1628,2017.2196,975.28864,0.99830174
+4430,3867.5593,744.0081,3897.927,799.9699,0.99770916
+4430,3125.2905,364.49036,3149.1958,411.94067,0.99767846
+4430,2970.5344,242.46541,2985.215,283.96857,0.9976343
+4430,3273.4106,330.16794,3295.727,377.87546,0.99588364
+4430,3067.5852,323.1053,3084.5134,369.6062,0.99463475
+4430,3054.0535,338.74756,3075.904,383.23004,0.99402726
+4430,2011.1605,123.88541,2029.3666,163.09247,0.9916745
+4430,3027.5312,405.08023,3057.3135,456.5933,0.99043673
+4430,3038.2336,378.42792,3060.3616,427.09937,0.9772953
+4430,3670.6697,792.2019,3692.7468,851.77295,0.5579511
+4430,3631.6218,801.13086,3690.736,880.1129,0.28671595
+4430,2767.488,877.54285,2833.0715,960.12744,0.27025276
+4430,3759.55,790.48914,3794.3816,857.3175,0.16133596
+4430,3650.0012,791.73016,3692.447,873.5125,0.1420498
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+4431,2147.8687,210.42107,2166.2773,256.58243,0.99912304
+4431,2470.077,449.2239,2491.321,513.51624,0.9990957
+4431,2572.984,329.61124,2591.4346,381.5942,0.99904066
+4431,2960.416,434.75818,2993.0513,491.13525,0.999035
+4431,3044.018,447.18988,3071.456,502.66626,0.9990025
+4431,2090.3655,213.10437,2113.4338,263.6332,0.99889946
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+4431,2617.8877,389.55557,2643.3828,444.8458,0.99864846
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+4431,2215.7327,423.63834,2252.5076,489.63223,0.99852186
+4431,1978.2551,882.1367,2017.2236,975.28955,0.99827254
+4431,2768.1863,262.93878,2790.0715,307.8233,0.9982644
+4431,2535.3933,328.38766,2560.4812,383.15225,0.9982638
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+4431,2966.3213,375.18927,2994.2446,428.12665,0.99795073
+4431,2145.6838,306.83398,2168.7458,363.10236,0.9976642
+4431,3275.0005,329.3209,3295.1191,375.94937,0.9955539
+4431,3027.5305,406.07996,3059.091,456.5476,0.9949921
+4431,3048.9893,336.67303,3074.6138,382.95264,0.9948873
+4431,3067.2231,323.23276,3082.2876,369.33145,0.99405694
+4431,3867.302,743.63245,3895.214,799.6819,0.9931176
+4431,2011.1531,123.87837,2029.312,163.10164,0.9915486
+4431,3032.2493,378.8422,3059.5735,429.53055,0.98126155
+4431,3663.1057,789.73145,3689.8591,849.1041,0.68580717
+4431,3760.2375,790.9811,3793.0383,857.6393,0.26990864
+4431,3631.4954,809.69745,3679.28,878.4456,0.25771677
+4431,2766.74,878.136,2830.345,960.6073,0.179202
+4432,1299.5386,321.40485,1327.2617,375.7356,0.99939716
+4432,2088.0854,213.25156,2111.83,263.58426,0.9991936
+4432,3123.5442,363.1844,3143.3264,411.50757,0.99916303
+4432,3198.8052,472.92392,3225.087,526.7862,0.99916077
+4432,2470.0886,449.11792,2491.2317,513.504,0.999134
+4432,2988.9146,556.6055,3021.272,614.2293,0.9991264
+4432,2146.1768,209.51654,2163.1797,256.32834,0.999014
+4432,2960.3733,434.1438,2990.4578,490.0664,0.9989428
+4432,3043.269,447.78055,3069.4238,502.55527,0.99884254
+4432,2143.023,307.21826,2167.8154,363.36798,0.9987093
+4432,3259.1875,584.3515,3285.0366,643.1358,0.9987
+4432,2564.201,329.42215,2588.057,381.186,0.9986468
+4432,2618.0808,389.32385,2642.2278,444.71667,0.99860966
+4432,2216.0093,422.55344,2246.8755,489.19424,0.9985166
+4432,1978.2477,882.1434,2017.211,975.3049,0.99835914
+4432,2535.0667,327.9372,2556.6716,382.24225,0.9983241
+4432,2954.78,241.81648,2981.182,283.47717,0.9981812
+4432,2767.6047,261.38095,2784.4402,307.55276,0.9977399
+4432,3026.813,405.88165,3059.7007,456.68353,0.99736917
+4432,3275.48,328.87424,3294.6338,373.8422,0.99708647
+4432,2966.8853,373.83447,2989.4634,425.3183,0.9967913
+4432,3046.2827,337.77036,3071.4546,383.23752,0.9963007
+4432,3863.3306,743.9709,3892.916,798.33246,0.9956995
+4432,3024.611,379.3635,3057.7893,430.74338,0.993119
+4432,2011.155,123.88023,2029.1985,163.08336,0.99109495
+4432,3063.256,322.88092,3080.1487,369.08185,0.98895556
+4432,2765.9307,879.04626,2822.6064,959.5563,0.9561345
+4432,3631.3086,794.5533,3686.8018,880.0372,0.9039919
+4432,3760.094,792.22327,3790.6458,857.7324,0.30005294
+4432,3548.8435,813.50867,3581.3435,877.45044,0.14474438
+4432,875.0312,174.23674,925.9805,208.12267,0.11815812
+4433,1299.6399,321.41028,1327.5637,375.65533,0.99945694
+4433,2087.475,212.8954,2110.3662,263.2347,0.99920976
+4433,2216.2283,422.19855,2243.0066,488.96362,0.99918467
+4433,2559.7046,329.68527,2586.7236,381.1102,0.99917966
+4433,2470.0757,449.0423,2491.1924,513.49243,0.99915326
+4433,3256.783,584.07385,3283.194,643.5349,0.9991304
+4433,3198.8186,472.942,3224.806,526.769,0.9991066
+4433,2766.2742,260.84628,2782.9832,307.93784,0.99909425
+4433,2534.7405,328.238,2555.78,382.15543,0.9990182
+4433,2986.091,558.1306,3019.2102,614.708,0.9989512
+4433,2145.0706,209.11223,2161.7644,256.42352,0.99895096
+4433,2960.4382,433.4767,2989.203,489.68753,0.9988681
+4433,2618.089,389.04678,2641.8074,444.65396,0.9988366
+4433,3120.423,363.11252,3142.176,411.57486,0.9986564
+4433,3043.2036,448.1355,3068.7832,503.002,0.9986071
+4433,2142.6135,307.13483,2167.2644,363.37195,0.9985796
+4433,1978.253,882.1591,2017.2054,975.28143,0.99838126
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+4433,3859.2266,744.0166,3891.0938,798.53467,0.9981901
+4433,2966.5361,373.47406,2987.3706,425.2002,0.9978528
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+4433,3275.7498,328.2811,3294.2776,373.60367,0.9972867
+4433,3046.0122,337.6735,3071.2007,383.91727,0.9971944
+4433,3022.9348,379.77786,3056.5334,430.61777,0.9953329
+4433,3061.4243,321.94568,3079.8516,368.73987,0.9913094
+4433,2011.1139,123.89498,2029.2172,163.08832,0.99109846
+4433,2765.9514,879.5537,2817.8743,959.8806,0.98977613
+4433,3640.2793,790.34155,3685.3208,850.22876,0.760423
+4433,3548.8433,813.4945,3575.3198,877.83167,0.2867629
+4433,3631.4482,809.0535,3677.6406,881.7946,0.27907604
+4433,3760.099,792.2376,3790.3862,857.925,0.20452666
+4433,580.0176,362.4853,612.28577,418.7458,0.113765985
+4434,3198.5493,472.91626,3223.8345,527.2513,0.99940455
+4434,1299.8224,321.3504,1327.6593,375.1902,0.9993781
+4434,2470.0815,448.96066,2491.1875,513.48315,0.99919504
+4434,2533.3362,328.25394,2554.3333,382.0814,0.9991786
+4434,2960.6003,432.88007,2987.8484,489.0821,0.9991565
+4434,3115.1123,363.54654,3140.5107,411.87436,0.99906963
+4434,3254.8625,583.18286,3280.5872,642.9469,0.9990416
+4434,2087.2048,212.89803,2109.2273,262.8532,0.9989894
+4434,2617.995,388.45398,2641.4297,444.54126,0.99896634
+4434,2215.9136,421.98883,2239.0488,488.9126,0.9989374
+4434,2143.3037,208.73299,2161.018,255.77791,0.9988856
+4434,2764.742,260.5064,2781.3655,307.78967,0.99883765
+4434,2953.9612,242.19803,2979.9246,283.62503,0.99879324
+4434,2142.1853,307.16458,2166.8103,363.3804,0.99878234
+4434,2984.9473,559.8863,3016.8882,615.8112,0.9987404
+4434,3855.3545,744.2664,3890.2783,798.74835,0.998681
+4434,2555.8467,329.8031,2586.1304,381.13495,0.9986595
+4434,1978.2642,882.0204,2017.1995,975.30396,0.9984131
+4434,3043.1184,448.6172,3067.5964,502.93018,0.9983487
+4434,2966.1829,373.30093,2986.05,424.97202,0.9982582
+4434,3026.7769,405.9266,3059.4165,456.60178,0.99759066
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+4434,3021.7751,379.8483,3055.721,430.9985,0.99561393
+4434,3046.1494,336.55383,3069.708,384.2022,0.99543864
+4434,2011.12,123.89643,2029.2155,163.0814,0.99110246
+4434,3059.7085,321.85657,3078.7104,368.56604,0.9884614
+4434,2766.9695,879.4339,2812.8157,959.9117,0.96117336
+4434,3639.9255,791.01105,3675.3484,851.0213,0.55883956
+4434,3760.1943,791.71075,3790.3076,857.9399,0.27021584
+4434,3632.6758,796.7742,3678.038,878.31995,0.253564
+4434,3544.7737,813.76245,3571.508,878.0415,0.21180548
+4434,579.99316,362.54083,612.32166,418.71527,0.11740363
+4435,1300.0898,321.04388,1327.7136,375.10382,0.99942446
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+4435,2470.0635,448.94714,2491.1543,513.45935,0.999203
+4435,2142.0156,307.3258,2166.5342,363.46326,0.9991381
+4435,3249.3633,582.255,3278.5493,643.50916,0.9991375
+4435,2960.9153,432.35464,2985.9988,488.49643,0.9989843
+4435,3111.4124,363.0536,3139.3625,411.79755,0.9989802
+4435,2087.065,213.84955,2108.772,262.00983,0.9989784
+4435,2140.0945,208.58789,2160.3074,255.40366,0.9989448
+4435,3852.015,744.2261,3889.4753,799.0829,0.9989396
+4435,2215.442,421.94104,2236.4844,488.48413,0.9989214
+4435,2618.0867,388.253,2640.8367,444.35815,0.9987676
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+4435,2984.3857,561.7559,3014.5166,616.92096,0.9986432
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+4435,2762.247,260.39,2780.0083,307.7625,0.99860364
+4435,2553.3164,329.63733,2586.08,381.33044,0.99845433
+4435,1978.217,881.9604,2017.228,975.3216,0.99841094
+4435,3043.1748,449.5397,3066.7407,502.92532,0.9983713
+4435,2965.0608,373.27078,2984.5774,425.14243,0.9979843
+4435,3026.9912,405.5523,3059.3467,456.601,0.99778676
+4435,3275.7185,327.30844,3294.2502,372.22098,0.99756444
+4435,3046.27,337.53384,3068.3286,384.04752,0.9958753
+4435,3021.6387,379.37384,3051.7437,431.75555,0.99523383
+4435,2011.0886,123.89551,2029.2471,163.08301,0.99117494
+4435,3058.7139,320.9716,3077.4414,368.21768,0.98852134
+4435,3639.4429,793.7503,3673.4482,853.9235,0.44079018
+4435,2767.343,879.13086,2806.8845,959.0039,0.40125343
+4435,3760.1362,791.4628,3790.0132,857.965,0.38543108
+4435,3539.2637,812.9673,3568.418,877.9547,0.32921875
+4435,3632.496,797.9177,3674.4556,878.3506,0.2579231
+4435,580.0002,362.50986,612.35406,418.718,0.12096142
+4436,3241.5068,582.1506,3275.6167,644.2159,0.99928766
+4436,2470.0618,448.98438,2491.1765,513.45575,0.99923265
+4436,2756.4868,260.00772,2778.7974,307.47543,0.99920434
+4436,2960.6873,432.2146,2984.76,487.86145,0.9992041
+4436,1300.7416,321.1624,1327.9735,374.6449,0.99909353
+4436,2136.2349,208.56316,2159.6226,254.96605,0.99909043
+4436,3850.9495,744.002,3888.0876,798.9117,0.9990728
+4436,2141.7446,307.47772,2166.1216,363.38654,0.99903476
+4436,2617.9656,388.05118,2640.235,444.18533,0.99897456
+4436,2212.8625,421.9871,2234.7805,488.11752,0.9988286
+4436,3197.006,473.11465,3222.132,526.6758,0.99865156
+4436,2553.16,329.60928,2585.4568,381.01016,0.99853444
+4436,2529.5076,326.91766,2552.7708,381.9773,0.9985013
+4436,1978.2452,881.958,2017.2118,975.29834,0.998431
+4436,2086.2566,213.12708,2106.2654,262.29282,0.99823254
+4436,2954.0242,240.8282,2976.7346,282.0302,0.9981692
+4436,2983.5947,562.91254,3012.4668,617.2067,0.99816006
+4436,3108.926,363.1518,3138.2185,411.8147,0.9981505
+4436,3026.9314,405.58463,3059.7078,456.71707,0.99800056
+4436,3275.7063,327.30853,3294.341,371.94464,0.9975903
+4436,3043.2961,450.05594,3065.6658,502.53116,0.99750346
+4436,3046.5935,337.58765,3067.6555,383.6084,0.9968395
+4436,2961.6897,372.7924,2982.949,424.3408,0.99667394
+4436,3021.3665,379.73547,3048.4646,431.79266,0.99644804
+4436,2011.1038,123.89909,2029.2439,163.02693,0.990549
+4436,3057.5461,320.3312,3075.9026,367.72006,0.98190707
+4436,3535.6946,812.0355,3564.654,877.84766,0.53215027
+4436,3632.7307,796.9949,3671.6833,876.48596,0.47942477
+4436,3639.2764,794.5434,3670.647,854.27814,0.2376107
+4436,3760.1973,790.6656,3789.608,857.6806,0.19209926
+4436,2767.8728,879.30304,2805.5803,958.5734,0.17899446
+4436,579.9834,362.44757,612.3352,418.74573,0.10218817
+4436,3025.221,380.20276,3057.2205,442.58734,0.101825036
+4437,2470.0527,448.99277,2491.2183,513.4439,0.99923444
+4437,2752.555,259.92987,2778.0237,307.37122,0.99920505
+4437,2617.8196,387.7365,2639.2297,443.81677,0.99917346
+4437,2960.8257,431.5904,2983.7349,487.20935,0.9991542
+4437,3850.1956,744.4358,3887.0042,799.66797,0.99904674
+4437,3237.2852,582.41943,3272.2168,644.3175,0.9989722
+4437,1301.5814,321.02676,1328.2035,374.73392,0.99896336
+4437,2134.4543,208.72003,2159.1384,254.92474,0.998938
+4437,2552.362,329.69025,2584.636,381.28027,0.99888974
+4437,2141.5728,307.0553,2165.9258,363.351,0.99860114
+4437,2525.6926,326.5408,2551.6213,382.07968,0.9985513
+4437,3195.144,473.1208,3221.1475,527.2842,0.9985374
+4437,1978.2482,881.9628,2017.1793,975.3193,0.99844664
+4437,2085.8025,212.76079,2103.9163,261.59604,0.99839693
+4437,2954.406,240.37483,2974.0764,281.91312,0.99818194
+4437,3107.6484,363.40518,3137.209,411.87338,0.9981772
+4437,2982.539,563.8637,3009.9683,617.8874,0.9981061
+4437,2956.4177,372.48007,2981.6204,423.8173,0.99777067
+4437,3026.8167,405.5917,3059.6907,456.5513,0.9977391
+4437,2209.3145,422.10565,2233.567,487.79907,0.9973949
+4437,3021.3335,379.56088,3046.3062,431.79453,0.9973381
+4437,3275.8228,327.28653,3294.4365,372.47275,0.99733436
+4437,3043.359,450.09027,3065.147,502.2964,0.99682486
+4437,3046.6394,336.1972,3067.0803,383.32782,0.994849
+4437,2011.1202,123.977005,2029.2003,162.96101,0.9900053
+4437,3056.0415,319.7521,3073.6387,367.14908,0.9722374
+4437,3639.13,794.6516,3667.6523,852.49927,0.25230688
+4437,3760.0457,789.8285,3788.9778,856.90906,0.17995168
+4437,3631.7527,815.1812,3672.7102,878.6685,0.17335668
+4437,3531.0159,811.82324,3561.8376,876.5344,0.10795606
+4437,579.9923,362.41336,612.31836,418.7513,0.10334308
+4438,2617.8154,387.37677,2638.7217,443.34338,0.9993685
+4438,2470.021,448.97342,2491.2124,513.44714,0.99922335
+4438,3235.7278,583.3146,3269.1658,645.0205,0.9991116
+4438,2960.6995,431.47293,2982.4973,487.3174,0.99906653
+4438,2133.959,208.78516,2158.9844,255.13916,0.99902797
+4438,2751.0366,259.7073,2777.1401,307.11728,0.99897337
+4438,2552.527,329.42215,2582.6863,380.36008,0.9989543
+4438,2982.2925,565.2239,3007.6265,617.6376,0.99891245
+4438,3194.7795,473.68073,3220.1262,527.985,0.9989059
+4438,2522.9524,326.364,2550.7146,382.30774,0.9988212
+4438,2141.2683,306.91916,2165.632,363.4011,0.99869674
+4438,1302.402,320.75732,1328.4772,374.28217,0.99864024
+4438,2085.3818,212.70976,2102.6187,260.8023,0.99863285
+4438,3107.2688,363.52863,3136.3494,411.37732,0.99852836
+4438,1978.2495,881.92365,2017.1853,975.2791,0.99846303
+4438,3850.4263,744.645,3885.3506,800.1261,0.9983689
+4438,3026.8857,405.25906,3059.856,456.6796,0.998324
+4438,2953.253,239.9463,2971.6533,281.992,0.998243
+4438,2951.3655,372.57996,2980.2996,423.76428,0.9981976
+4438,2205.463,421.9244,2233.1929,488.17746,0.99797326
+4438,3021.3633,379.1723,3045.355,431.90143,0.99779415
+4438,3275.5564,327.01724,3294.2615,372.56314,0.99703276
+4438,3043.3887,450.12848,3064.8027,502.11084,0.9966664
+4438,3046.4438,336.68283,3065.7222,383.09183,0.99343187
+4438,2011.0787,124.03151,2029.2555,162.97429,0.99026954
+4438,3055.0762,319.36456,3071.169,366.39307,0.95886606
+4438,3632.8203,793.9203,3666.4448,852.90857,0.38252595
+4438,3631.7334,815.0494,3672.459,877.6889,0.2807495
+4438,896.6702,171.87003,927.2697,202.19553,0.18952726
+4438,3520.8293,812.78455,3560.7273,879.25574,0.14486739
+4438,3759.5032,789.8677,3788.7063,856.65674,0.10081355
+4439,2617.8157,387.36804,2638.722,443.32654,0.9993705
+4439,2470.0413,448.9911,2491.2131,513.45404,0.99922216
+4439,3235.7283,583.3429,3269.154,644.99963,0.9991073
+4439,2960.7131,431.4726,2982.4973,487.31726,0.99906653
+4439,2133.9597,208.7826,2158.989,255.13892,0.9990307
+4439,2751.04,259.73703,2777.1445,307.11465,0.99896157
+4439,2552.5264,329.41837,2582.69,380.363,0.9989542
+4439,2982.2964,565.22687,3007.626,617.6385,0.998909
+4439,3194.7788,473.6926,3220.1191,527.9798,0.9989034
+4439,2522.9565,326.40738,2550.7153,382.30673,0.9988141
+4439,2141.2595,306.9235,2165.6204,363.38937,0.99870634
+4439,2085.3757,212.71022,2102.6262,260.78998,0.99864286
+4439,1302.408,320.76877,1328.4915,374.2902,0.9986388
+4439,3107.2668,363.47394,3136.347,411.38116,0.9985336
+4439,3850.3438,744.6503,3885.3955,800.15497,0.99846196
+4439,1978.2423,881.95166,2017.1876,975.3202,0.9984487
+4439,3026.8857,405.25888,3059.8564,456.67978,0.998324
+4439,2953.3242,239.94418,2971.644,281.96298,0.99830174
+4439,2951.3718,372.5897,2980.303,423.76688,0.9981876
+4439,2205.4639,421.9203,2233.171,488.17743,0.9979678
+4439,3021.3633,379.17148,3045.3555,431.90158,0.9977944
+4439,3275.5486,327.02527,3294.2615,372.5064,0.9971003
+4439,3043.392,450.12726,3064.8022,502.10797,0.9966744
+4439,3046.4434,336.66147,3065.7207,383.09262,0.9933976
+4439,2011.0786,124.02686,2029.2747,162.97144,0.99033993
+4439,3055.0327,319.35883,3071.164,366.41144,0.95861447
+4439,3632.8274,793.9106,3666.4685,852.93964,0.38694316
+4439,3631.7727,815.0375,3672.4583,877.68494,0.28728786
+4439,896.616,171.85062,927.2779,202.20197,0.19030145
+4439,3520.849,812.7719,3560.7295,879.2618,0.14582752
+4439,3759.5447,789.8626,3788.7097,856.64606,0.104562946
+4440,2617.816,387.36728,2638.7217,443.33426,0.9993693
+4440,2470.0413,448.99112,2491.2131,513.454,0.99922216
+4440,3235.713,583.3425,3269.1533,645.0331,0.99910706
+4440,2960.7156,431.47104,2982.4988,487.31766,0.99906665
+4440,2133.959,208.77164,2158.9888,255.14008,0.9990312
+4440,2751.04,259.73724,2777.1445,307.1142,0.9989618
+4440,2552.529,329.4194,2582.6902,380.36343,0.9989549
+4440,2982.298,565.2277,3007.6218,617.6025,0.99890983
+4440,3194.7703,473.693,3220.1204,527.98315,0.99890053
+4440,2522.9475,326.414,2550.717,382.3056,0.9988135
+4440,2141.259,306.9395,2165.6194,363.38654,0.9987134
+4440,2085.3765,212.70969,2102.6255,260.78976,0.9986395
+4440,1302.4038,320.7596,1328.4807,374.29135,0.9986118
+4440,3107.2666,363.47385,3136.3472,411.38132,0.9985328
+4440,3850.3381,744.65,3885.3982,800.15454,0.99844885
+4440,1978.2534,881.9295,2017.177,975.2735,0.9984433
+4440,3026.8782,405.2591,3059.8552,456.68665,0.99832875
+4440,2953.324,239.94383,2971.6438,281.96304,0.99830675
+4440,2951.377,372.5897,2980.3047,423.76822,0.9981981
+4440,2205.463,421.91998,2233.1694,488.1784,0.997969
+4440,3021.367,379.1704,3045.3562,431.896,0.9977858
+4440,3275.5398,327.01584,3294.2654,372.56082,0.99708885
+4440,3043.3218,450.13412,3064.8047,502.1075,0.9966703
+4440,3046.439,336.66153,3065.7056,383.0919,0.9933236
+4440,2011.0778,124.01745,2029.276,162.97194,0.99033874
+4440,3055.003,319.3544,3071.1753,366.44467,0.9582366
+4440,3632.9355,793.9334,3666.5513,852.7858,0.40277913
+4440,3631.8079,815.7469,3672.4734,877.79364,0.2718889
+4440,896.654,171.86967,927.27545,202.18468,0.19074254
+4440,3520.8452,812.77594,3560.7334,879.26166,0.14271884
+4441,2552.6885,328.71844,2579.1216,379.6698,0.9994809
+4441,2134.0483,208.82893,2158.5093,254.8788,0.99931896
+4441,2617.586,387.38562,2638.4575,443.16144,0.999278
+4441,2470.0894,448.6315,2491.4731,513.70087,0.99924386
+4441,2959.9915,431.424,2981.611,487.75604,0.9991831
+4441,3193.8315,474.34436,3218.9849,527.8536,0.99902403
+4441,3235.542,584.83014,3265.0571,645.09674,0.998993
+4441,2981.948,564.6087,3005.2258,617.6888,0.99897003
+4441,3107.2058,363.44955,3135.4934,411.4808,0.998874
+4441,2140.7537,306.87747,2165.7961,363.2206,0.9988707
+4441,2750.0974,259.76352,2776.9363,307.08383,0.9988373
+4441,2521.374,325.733,2550.226,381.75577,0.9987563
+4441,1303.498,320.57355,1328.6802,374.1228,0.9986179
+4441,2948.514,372.4376,2978.8938,423.81967,0.99858737
+4441,1978.2396,881.9313,2017.1925,975.30115,0.99845374
+4441,2201.0618,422.2291,2232.5554,488.09158,0.99842995
+4441,3027.1763,404.97656,3059.9321,456.41156,0.99841225
+4441,3851.13,744.96246,3884.2554,800.3768,0.99840766
+4441,2953.3325,240.08212,2969.3188,281.98514,0.9983419
+4441,2083.5835,211.64334,2101.058,260.39548,0.99831176
+4441,3021.4429,379.17548,3043.5361,431.7331,0.9980957
+4441,3043.3105,449.885,3063.956,502.15466,0.9971047
+4441,3275.1921,326.65607,3294.198,372.86108,0.99667835
+4441,3046.3691,336.67548,3065.7227,383.4685,0.99615794
+4441,2011.0566,124.048744,2029.2893,162.97023,0.9907415
+4441,3053.9937,319.38937,3068.9922,366.76224,0.95149887
+4441,3631.997,793.7543,3664.5742,851.7793,0.6079148
+4441,3631.5615,816.1307,3671.9448,878.4007,0.58126915
+4441,3514.8337,814.07806,3559.968,879.7791,0.28784814
+4441,3758.3389,789.9998,3787.1318,857.1933,0.15382051
+4442,2133.312,208.55348,2157.5786,254.16618,0.9993919
+4442,2470.0825,448.62802,2491.585,513.7007,0.9992386
+4442,2617.5154,387.2227,2638.324,443.12448,0.9991755
+4442,2981.316,564.9191,3002.8447,617.5678,0.9991461
+4442,2552.8147,328.01172,2576.0217,379.51978,0.9991399
+4442,2958.111,430.58868,2981.2468,487.70874,0.99913293
+4442,2140.218,306.5078,2165.5144,363.1336,0.9990532
+4442,3235.3892,585.6022,3259.559,645.109,0.9990472
+4442,2520.8098,325.28665,2549.1667,381.50772,0.9989084
+4442,1304.6301,320.62613,1328.6272,373.8599,0.99888724
+4442,3107.1785,363.36887,3134.4465,411.486,0.9988526
+4442,3191.2542,474.96973,3218.572,528.24365,0.99877316
+4442,2947.351,372.5834,2977.8696,423.6169,0.9985183
+4442,2749.5203,259.80515,2776.492,306.9861,0.9985166
+4442,1978.2351,881.9444,2017.1882,975.33905,0.9984484
+4442,3020.683,379.04343,3042.4492,431.2715,0.9984143
+4442,3027.265,405.07065,3060.0315,456.3942,0.99829584
+4442,3851.1604,745.00977,3883.0183,799.95337,0.99823135
+4442,2952.237,240.07309,2968.2288,282.21677,0.99819785
+4442,2083.281,211.46684,2100.3713,260.54648,0.9981133
+4442,2198.0425,422.29794,2231.084,488.42294,0.998092
+4442,3043.3118,449.80524,3063.2805,502.25446,0.99733377
+4442,3274.9204,326.58167,3294.0645,373.33325,0.99625576
+4442,3046.4246,337.01425,3064.8142,385.17203,0.99564195
+4442,2010.99,123.47089,2029.3591,162.95001,0.99078304
+4442,3053.2866,319.3323,3067.955,368.52957,0.9595576
+4442,3632.0295,816.1203,3672.007,878.6484,0.66824347
+4442,3627.1008,794.06335,3661.8792,856.2567,0.3762526
+4442,3755.325,790.9654,3786.575,857.47455,0.2988113
+4442,3510.785,815.35443,3557.2346,882.96136,0.18962294
+4442,892.8804,171.25429,927.2672,202.99785,0.11366043
+4443,2133.3848,208.30176,2156.7593,254.6702,0.99948573
+4443,2470.0747,448.62125,2491.729,513.69775,0.99922884
+4443,2139.759,306.2306,2164.3796,362.7771,0.9992132
+4443,2617.464,387.16174,2638.1013,443.12964,0.9991585
+4443,2520.2063,325.0296,2548.3333,381.13348,0.9990087
+4443,2954.3635,430.42728,2981.1301,487.69485,0.9988599
+4443,2552.388,327.9814,2572.3054,379.65646,0.9987727
+4443,2978.8464,563.8699,3000.9026,617.5835,0.99873835
+4443,3233.2334,585.40784,3254.647,645.3854,0.9987245
+4443,2946.633,372.24655,2977.5222,423.01108,0.998632
+4443,1306.5914,320.93484,1328.6747,373.80734,0.99862385
+4443,2081.9336,211.61612,2099.5107,260.47498,0.9985567
+4443,3107.0693,362.77203,3133.3047,411.69232,0.9985145
+4443,1978.204,881.8563,2017.2042,975.37427,0.9985019
+4443,3850.0288,744.59247,3882.3408,800.01556,0.9984995
+4443,2749.9358,259.67883,2775.1633,306.26465,0.99849784
+4443,3027.4114,405.21402,3060.0442,456.3934,0.9984003
+4443,2950.0486,240.09665,2966.9714,281.38846,0.99828833
+4443,3190.501,475.15674,3217.4922,528.8239,0.99802446
+4443,3019.3225,378.91333,3041.2175,432.07086,0.9978752
+4443,3043.248,449.99216,3062.6855,502.4498,0.9978635
+4443,2197.2397,422.4176,2230.3008,488.2821,0.99783367
+4443,3274.4675,326.50168,3294.034,373.66367,0.99625635
+4443,3046.182,338.10873,3065.0989,384.51126,0.9934256
+4443,2010.9966,123.422134,2029.3657,162.9492,0.9907747
+4443,3050.0947,319.29257,3067.317,368.13992,0.9728598
+4443,3619.6467,794.13257,3658.2087,858.2301,0.583668
+4443,3632.072,817.433,3672.3757,878.3948,0.56417036
+4443,3510.9011,816.8406,3552.923,882.9441,0.22912626
+4443,580.0418,362.23688,612.22406,418.72485,0.12916553
+4443,3753.4797,792.7264,3785.821,858.5329,0.110739745
+4444,2520.09,324.72836,2546.9656,380.48984,0.9995658
+4444,2133.5947,208.34149,2154.7876,253.73999,0.9993907
+4444,2976.4333,562.6674,2998.9426,617.9134,0.99923456
+4444,2951.6692,430.5386,2980.137,487.4227,0.99920374
+4444,2139.315,305.51053,2162.4336,362.0792,0.9991736
+4444,2617.3884,387.1242,2637.8508,443.14954,0.9991123
+4444,2470.064,448.56207,2492.7505,513.71027,0.99902666
+4444,2945.8167,372.0613,2976.8357,422.88962,0.99896
+4444,3228.8872,584.71735,3251.208,644.9693,0.99895954
+4444,2194.599,422.89478,2229.896,488.15338,0.99891317
+4444,3107.1304,362.40012,3130.983,410.55612,0.99886394
+4444,3850.0537,744.46246,3881.5347,800.305,0.9987301
+4444,1307.8035,320.7818,1328.9514,373.83832,0.99872595
+4444,3189.958,475.51242,3216.7754,529.221,0.9986665
+4444,2079.403,211.64458,2098.4343,260.48196,0.99844885
+4444,1978.3837,881.97064,2017.049,975.19464,0.99844813
+4444,3043.1106,450.45367,3062.3538,502.43744,0.9984289
+4444,2551.6904,327.23175,2570.274,379.516,0.99833447
+4444,2748.9954,259.14917,2773.2576,305.84198,0.998009
+4444,3027.445,405.27567,3059.9827,456.4166,0.9977603
+4444,2945.0115,239.87097,2965.7712,281.1305,0.99762064
+4444,3274.5132,326.47998,3293.915,373.69635,0.99709404
+4444,3044.5579,338.3012,3065.7585,384.6859,0.99680436
+4444,3016.8386,379.08026,3039.943,431.63147,0.99599886
+4444,2011.2784,123.81269,2029.7494,162.85345,0.9904542
+4444,3046.4392,318.87155,3066.531,366.97073,0.9423269
+4444,3749.4614,794.4868,3784.0923,859.631,0.73570514
+4444,3632.7012,816.7093,3672.4238,878.5806,0.53618014
+4444,3615.4856,794.5843,3655.2185,858.28973,0.42747042
+4444,3316.949,322.02145,3338.6077,355.55246,0.22635493
+4444,3510.6384,817.4708,3547.1345,883.06006,0.22259335
+4444,580.0084,362.3318,612.16125,418.72906,0.12664011
+4445,2520.0688,324.07727,2545.7646,379.62823,0.99956316
+4445,2948.9712,430.09302,2978.9263,487.536,0.9993901
+4445,2972.678,561.9236,2997.2253,618.6105,0.99931383
+4445,2132.9824,207.7766,2152.644,253.28828,0.9991505
+4445,2470.0596,448.5222,2493.454,513.71094,0.9990833
+4445,2617.3157,387.09836,2637.6497,442.66605,0.9990633
+4445,2194.1482,423.2518,2229.2375,487.84958,0.9990295
+4445,3189.1245,475.62018,3215.9614,529.4279,0.9990264
+4445,3012.5078,379.35138,3038.3662,431.84943,0.9989932
+4445,2748.8757,259.136,2771.1863,305.4187,0.99894875
+4445,2945.4592,371.76016,2975.557,422.4155,0.99888784
+4445,1308.6978,320.68713,1329.2717,373.83356,0.9988858
+4445,2076.2905,211.34798,2097.4258,260.43286,0.9987206
+4445,3042.8486,450.6794,3061.9849,502.42673,0.99866176
+4445,3224.6765,583.13715,3248.3489,644.61993,0.99862814
+4445,2138.754,305.2034,2160.666,361.55954,0.99858224
+4445,1978.3837,881.97064,2017.049,975.19464,0.99844813
+4445,3850.0522,743.9735,3880.901,800.0149,0.99835545
+4445,2549.6384,327.11707,2568.8748,379.8399,0.9982115
+4445,3027.5095,405.39276,3059.72,456.3958,0.9979771
+4445,3106.8103,361.74805,3129.7966,410.5066,0.99796605
+4445,2940.5747,239.95627,2965.3872,281.67978,0.9974535
+4445,3274.2827,326.07538,3293.8687,373.05627,0.9956464
+4445,3042.418,339.2445,3066.1904,384.9085,0.9952425
+4445,2011.2819,123.1878,2029.8422,162.80559,0.99200296
+4445,3038.6948,319.0075,3065.7422,366.0003,0.98307496
+4445,3746.7676,795.0188,3782.4092,859.6465,0.76334715
+4445,3319.398,322.5331,3340.2515,356.7966,0.3517309
+4445,3633.435,816.778,3672.5962,879.8952,0.26458278
+4445,580.00696,362.23132,612.1172,418.73987,0.13831615
+4445,3507.6196,817.3503,3541.2104,883.0778,0.11431344
+4445,3611.863,796.4074,3653.0784,858.93195,0.11385731
+4446,3220.3062,581.8128,3246.0493,644.9588,0.99940634
+4446,2947.2244,429.84497,2978.1526,487.44354,0.99937004
+4446,2132.9375,207.46666,2150.813,253.2704,0.99927264
+4446,3188.91,475.94376,3214.82,529.60004,0.99920017
+4446,2519.9023,324.2655,2544.2388,378.80487,0.99918216
+4446,2193.6763,423.34705,2228.9702,487.95654,0.9991092
+4446,2945.5618,371.50836,2973.1697,421.59186,0.9990742
+4446,2968.3284,561.76965,2995.9045,619.72314,0.9990741
+4446,2470.0723,448.52026,2492.9023,513.7202,0.9990701
+4446,3007.6592,379.40128,3037.8652,432.12143,0.99881005
+4446,2617.132,387.0201,2637.3132,442.2888,0.998678
+4446,2073.8313,211.79984,2096.8591,260.37997,0.9986517
+4446,1309.371,320.67496,1329.8043,373.6583,0.9985967
+4446,2138.2163,304.9769,2159.6377,361.29733,0.99857056
+4446,3041.56,450.74838,3061.7163,502.58365,0.9985285
+4446,2748.9321,258.74713,2767.958,304.75067,0.9984754
+4446,1978.3774,881.9705,2016.9595,975.1983,0.9984675
+4446,3107.2192,361.91757,3127.586,410.30338,0.9984504
+4446,2938.2805,240.26996,2964.2073,282.6574,0.9981536
+4446,3027.5327,405.45297,3059.4072,456.4991,0.9981527
+4446,2546.0586,326.86948,2566.9731,379.7192,0.99807113
+4446,3848.705,743.9794,3879.3398,799.16406,0.9977822
+4446,3273.777,325.775,3293.7737,370.75894,0.99446267
+4446,3041.5476,338.82333,3065.7424,384.74777,0.99182206
+4446,2011.2452,123.08839,2029.8973,162.81274,0.9915951
+4446,3035.1458,318.63663,3065.3772,364.85556,0.98073936
+4446,3610.9507,797.7957,3645.8872,859.0285,0.43074024
+4446,3745.1401,794.9796,3781.1055,859.4308,0.33582035
+4446,3320.0293,322.93414,3337.771,356.06262,0.29624125
+4446,3633.4248,816.72327,3672.691,879.42883,0.2023887
+4446,580.0111,362.2382,612.0265,418.76797,0.13129604
+4447,2193.79,423.93402,2227.524,487.8567,0.9993462
+4447,2946.1846,429.52164,2976.9321,487.40555,0.99933916
+4447,3212.972,581.6168,3245.6477,644.8541,0.9992138
+4447,3187.9556,475.9418,3214.2144,529.971,0.9991722
+4447,2470.0876,448.54736,2492.3235,513.68744,0.9990736
+4447,2132.5247,207.09474,2149.4324,253.55367,0.9990619
+4447,2964.371,562.43005,2994.607,621.4308,0.9988213
+4447,2519.772,323.68054,2542.8481,377.40466,0.99880517
+4447,1309.631,320.43808,1330.38,373.84323,0.9987746
+4447,3004.798,379.24622,3036.1716,432.27893,0.9987348
+4447,2072.3796,211.50479,2096.4617,260.45013,0.9987344
+4447,2937.3381,239.94669,2963.828,281.9866,0.9986525
+4447,2616.76,386.9903,2637.1653,442.21252,0.9986279
+4447,2137.392,304.70767,2158.6929,361.54202,0.9986028
+4447,3040.6758,450.9361,3061.1665,502.5224,0.9985897
+4447,3107.6963,361.55917,3126.5645,410.28738,0.99853516
+4447,1978.3779,881.9719,2016.9653,975.19836,0.99846846
+4447,3027.484,405.6648,3059.2163,456.48663,0.99825186
+4447,2541.0984,326.95377,2565.5789,379.2593,0.9981792
+4447,2749.2395,258.12878,2765.86,304.41046,0.99817777
+4447,2946.1438,370.87933,2969.6282,420.2926,0.9978235
+4447,3846.9324,743.73975,3878.7004,799.156,0.99690753
+4447,3273.383,325.42032,3293.74,370.35153,0.99352396
+4447,2010.8943,123.51318,2029.6389,162.83627,0.99030167
+4447,3033.637,318.92795,3064.369,365.2485,0.98898166
+4447,3040.4353,339.2472,3064.9153,385.55096,0.98200154
+4447,3744.3796,795.0434,3780.0017,859.72626,0.3258988
+4447,3321.4326,324.318,3336.8647,351.42383,0.32190204
+4447,3610.613,799.32196,3641.4329,858.9097,0.25770754
+4447,3501.7507,816.72345,3533.202,884.0678,0.18344912
+4447,580.03217,362.1441,611.95306,418.78375,0.13571563
+4448,3188.644,477.06964,3213.5186,530.899,0.99940836
+4448,3208.8435,581.792,3243.1335,644.62634,0.9992544
+4448,2470.0886,448.45267,2491.4773,513.6821,0.99920744
+4448,3106.1853,361.454,3125.785,410.4042,0.99915886
+4448,2072.4172,211.65668,2096.179,260.6821,0.9990633
+4448,2962.1406,563.56824,2994.0186,623.375,0.99897987
+4448,2944.2893,428.65622,2975.3894,487.1771,0.9989367
+4448,2131.9233,207.07932,2148.565,253.33829,0.99886596
+4448,2536.132,327.15845,2565.2546,379.30145,0.99875927
+4448,3038.1199,451.0905,3060.4978,501.8772,0.99872166
+4448,2136.7295,304.74512,2158.13,361.36334,0.9986571
+4448,1310.2622,319.9781,1331.1934,374.13037,0.9986532
+4448,1978.4319,881.9033,2016.8823,975.10864,0.9984812
+4448,2193.5652,422.84198,2223.4646,487.31372,0.9983903
+4448,2615.5835,387.14603,2637.3525,441.6874,0.9983152
+4448,2747.967,258.25403,2764.2976,303.9453,0.9983053
+4448,3027.3513,405.3419,3059.3015,456.61624,0.99818903
+4448,2936.0583,239.63388,2962.843,281.24265,0.998176
+4448,3003.5374,379.65625,3035.7727,431.98724,0.9977568
+4448,3844.2756,743.7777,3878.093,800.17664,0.9975206
+4448,2945.669,370.7427,2967.1162,420.9739,0.99724245
+4448,2519.1316,324.85672,2541.097,376.72018,0.9954856
+4448,3273.6511,324.73477,3293.1135,369.96866,0.9947967
+4448,2010.9019,123.55455,2029.645,162.83052,0.990413
+4448,3039.9844,340.3176,3064.2822,386.35214,0.98668706
+4448,3032.722,318.8831,3063.972,365.38235,0.9864074
+4448,3744.5981,795.95984,3779.079,860.3894,0.24933307
+4448,3323.225,325.1836,3338.0923,350.1499,0.24458118
+4448,3609.1929,799.379,3636.2305,858.3666,0.24409698
+4448,3320.6274,325.13184,3335.603,356.66687,0.22565208
+4448,3633.069,816.3496,3672.6633,880.13477,0.15175317
+4448,580.03546,362.13574,612.0914,418.7818,0.13817316
+4449,2072.087,211.08978,2095.8745,259.96466,0.9993735
+4449,3206.7522,582.3215,3241.284,645.15607,0.9993098
+4449,2470.0825,448.44452,2491.4902,513.6772,0.9992083
+4449,2962.3052,564.87115,2992.5928,623.6611,0.9989304
+4449,2944.5107,427.99655,2973.958,486.5555,0.9989197
+4449,2534.9358,327.305,2564.301,379.34412,0.9989157
+4449,3188.2664,476.2581,3213.072,530.8952,0.9988832
+4449,3103.1602,361.40082,3124.747,410.94568,0.9988588
+4449,2130.927,206.20392,2148.698,253.21814,0.9988341
+4449,2193.743,422.23767,2220.5393,487.49292,0.99870384
+4449,2745.116,257.98105,2762.8064,303.5892,0.9986665
+4449,3004.1697,379.43774,3034.1965,432.27368,0.998611
+4449,1310.6359,319.75943,1331.5448,374.05154,0.998594
+4449,1978.4227,881.8822,2016.8492,975.11096,0.998459
+4449,3034.8584,451.47946,3059.9106,501.8306,0.9984276
+4449,2614.3347,386.9429,2636.9895,442.16525,0.99825233
+4449,2136.2043,304.54428,2157.3157,361.70236,0.9982016
+4449,3027.091,405.28656,3059.382,456.7044,0.99819213
+4449,3841.7727,743.7462,3877.5051,800.1643,0.9980653
+4449,2518.669,322.93518,2540.0225,376.02222,0.99785894
+4449,2935.7073,239.19727,2961.6565,280.90778,0.9974381
+4449,2945.6719,370.6029,2965.6455,420.96045,0.9973948
+4449,3273.3708,324.12524,3292.6936,369.80737,0.9944246
+4449,2011.03,123.23135,2029.9329,162.89795,0.99188554
+4449,3032.0159,318.6036,3062.4583,366.03116,0.99021673
+4449,3039.157,338.4673,3063.3171,387.65216,0.9899177
+4449,3321.9905,325.0352,3340.1643,356.19485,0.53769875
+4449,3744.93,796.005,3777.7454,860.80457,0.37106234
+4449,3606.1523,798.92645,3633.4326,859.028,0.34574926
+4449,3633.1716,816.3143,3672.7034,880.1369,0.18050084
+4449,580.0457,362.00677,612.21625,418.79596,0.124733575
+4450,2072.0823,211.19653,2095.1238,259.65442,0.99935454
+4450,2533.647,327.3865,2563.5264,379.0319,0.99933004
+4450,3206.278,584.21875,3237.9373,644.8801,0.99923015
+4450,3100.9497,361.31937,3123.0708,410.58963,0.9992003
+4450,2470.0747,448.39093,2491.7256,513.69257,0.9991314
+4450,3188.8452,476.0408,3211.936,530.0466,0.99910825
+4450,2741.1384,258.14297,2762.0588,303.38547,0.99909437
+4450,2944.3774,427.73148,2972.7588,485.49936,0.99902964
+4450,3031.6733,451.69452,3058.912,502.06213,0.9989221
+4450,2129.0195,205.59691,2148.5308,253.81163,0.99871385
+4450,3839.8,743.9351,3876.2766,799.7636,0.99869305
+4450,2193.9873,421.83014,2215.9644,487.37195,0.9986381
+4450,2517.637,321.4231,2538.905,375.84552,0.99862444
+4450,2936.4033,238.85974,2959.752,279.96313,0.9985844
+4450,3002.2937,379.36618,3033.1018,432.33353,0.99852943
+4450,1978.4142,881.9183,2016.7789,975.16986,0.99850404
+4450,2961.3164,566.7279,2989.433,624.0231,0.9984824
+4450,2135.6382,304.5094,2157.8008,361.53986,0.99842983
+4450,2613.784,386.8258,2636.7507,441.6115,0.9983347
+4450,3027.0576,405.32083,3059.3076,456.60062,0.99832004
+4450,1311.1083,319.56906,1331.9271,374.13736,0.9981173
+4450,2945.5637,370.68677,2963.6394,420.09473,0.99609214
+4450,3272.6758,324.259,3291.9404,369.71756,0.99545103
+4450,3038.8823,340.4281,3063.3345,387.8573,0.9945103
+4450,3032.1755,318.30435,3061.4753,365.486,0.9910734
+4450,2010.9801,122.99726,2030.0638,162.92625,0.98762965
+4450,3602.0247,797.7779,3630.6614,859.3891,0.5119725
+4450,3744.6143,795.31915,3776.2974,861.27814,0.46864173
+4450,3322.36,325.58456,3340.513,357.6702,0.37700704
+4450,3632.4492,817.46173,3672.7295,880.32904,0.22435325
+4450,580.0725,361.9449,612.2742,418.8009,0.13324362
+4451,2533.7131,326.9625,2562.7346,379.05093,0.9994634
+4451,2944.301,427.25125,2971.427,484.44687,0.9993193
+4451,2736.886,258.50833,2761.5554,303.343,0.99921703
+4451,2072.0,211.27495,2093.5942,259.37073,0.9991486
+4451,3187.1384,476.0875,3211.112,530.1649,0.99914587
+4451,2470.0564,448.34882,2492.3215,513.6986,0.9990687
+4451,2516.6772,321.18787,2538.0732,375.78235,0.99897647
+4451,3837.7542,744.1216,3875.4763,800.4883,0.99896
+4451,3204.5005,585.3118,3233.0444,645.4451,0.9989304
+4451,2959.3467,568.3043,2985.9243,624.57874,0.99869436
+4451,2193.3137,421.99088,2213.2576,487.43704,0.99860376
+4451,2127.2783,205.67996,2149.287,253.06094,0.9985153
+4451,1978.3965,881.9475,2016.687,975.1959,0.9985033
+4451,2134.9197,304.4285,2157.033,360.8903,0.99847287
+4451,3097.4106,361.87018,3121.9834,410.1631,0.9984074
+4451,3029.3518,451.67703,3058.1252,502.0435,0.9983998
+4451,2612.9138,386.6148,2636.4617,441.63336,0.9983828
+4451,2936.031,238.86835,2956.888,279.25977,0.99836844
+4451,3027.1262,405.14435,3059.1472,456.76056,0.998171
+4451,3001.9192,379.4037,3032.3782,432.4001,0.998135
+4451,3038.5688,340.6392,3061.9043,388.21854,0.99803144
+4451,1311.6339,319.36148,1332.7277,374.02518,0.9977849
+4451,3031.8552,317.66873,3056.8684,364.54733,0.9943164
+4451,3270.9585,323.34012,3290.9487,369.7302,0.99427277
+4451,2011.004,122.99099,2030.0526,162.91507,0.98935026
+4451,2945.9148,369.92603,2961.9363,418.65643,0.9810439
+4451,3745.018,795.2136,3775.3037,860.1948,0.54361975
+4451,3598.5603,797.1049,3626.74,859.0124,0.38550803
+4451,3323.4841,325.76242,3341.951,355.41183,0.30000788
+4451,3632.56,817.92084,3672.601,880.18207,0.19533326
+4451,580.10223,361.80188,612.3204,418.7975,0.13316967
+4451,3479.843,820.2929,3519.5603,891.6694,0.1180373
+4452,3837.0947,744.9847,3874.7446,800.50446,0.9992368
+4452,2944.248,426.7186,2970.4639,483.5887,0.99923027
+4452,2533.44,326.739,2561.4106,378.57764,0.9992244
+4452,2071.837,211.0266,2092.7012,259.46027,0.99921095
+4452,2470.0208,448.33514,2492.4016,513.7025,0.99901676
+4452,3185.568,476.1162,3209.9705,530.2888,0.99896204
+4452,2957.7253,568.8807,2984.4333,624.1214,0.99887675
+4452,3028.0833,452.49344,3057.182,502.103,0.99872285
+4452,2124.8596,205.26477,2148.8713,253.31357,0.9986744
+4452,2612.715,386.6105,2636.2537,441.4207,0.99847096
+4452,2733.6921,258.34042,2761.6736,303.38388,0.99844426
+4452,1978.403,881.94165,2016.6932,975.19434,0.9984112
+4452,2513.1338,321.06186,2537.128,376.01895,0.9983714
+4452,2191.3496,421.8889,2211.4282,487.6096,0.99833906
+4452,3094.7515,362.1111,3121.0757,410.5001,0.9982653
+4452,3027.1226,405.1391,3059.1401,456.50098,0.99821246
+4452,3001.7417,379.1697,3030.6099,432.00735,0.9981483
+4452,1312.4858,319.36514,1333.4565,373.82523,0.9981207
+4452,3199.5574,585.15485,3228.9387,645.3222,0.9980786
+4452,2133.8728,304.26147,2157.096,360.71082,0.99785644
+4452,2936.273,238.51361,2954.308,279.12726,0.9977469
+4452,3038.0178,339.7615,3060.3455,388.27005,0.9974445
+4452,3269.5776,323.38358,3290.649,369.27414,0.9954531
+4452,2936.0505,369.05362,2961.0168,419.89963,0.9953434
+4452,3031.4907,316.96277,3054.7935,364.44342,0.9948231
+4452,2010.5088,122.59195,2029.7722,162.87271,0.9898476
+4452,3591.6335,796.66846,3624.5227,858.98865,0.9175633
+4452,3745.2007,794.6202,3773.8804,860.15155,0.8589313
+4452,3632.4702,818.802,3672.5,879.98694,0.2808624
+4452,3323.5554,326.51273,3340.638,357.12296,0.198884
+4452,580.10803,361.8755,612.298,418.79663,0.13944033
+4453,3835.5276,745.1917,3873.1628,800.1866,0.9994229
+4453,2071.7017,210.8605,2091.3745,258.494,0.9992471
+4453,2533.3137,326.96124,2558.7532,378.34894,0.9992275
+4453,3093.6704,362.35214,3119.355,410.35403,0.9992015
+4453,2944.517,426.51495,2969.6567,482.1067,0.999185
+4453,2956.1377,568.1847,2982.8374,624.2527,0.9991623
+4453,2931.4883,368.68918,2959.413,420.2491,0.99901056
+4453,2469.9944,448.26868,2492.4192,513.6816,0.9989685
+4453,1313.12,319.58505,1335.0411,373.56656,0.9988538
+4453,3027.79,452.66922,3056.0718,502.67233,0.9988463
+4453,3197.493,584.7322,3227.0105,645.4221,0.99869746
+4453,3181.6943,476.9325,3209.2349,530.16266,0.99868494
+4453,2612.593,386.60648,2636.1223,441.40775,0.99854845
+4453,2123.9285,205.28915,2148.7087,253.0347,0.99851173
+4453,1978.4077,881.9463,2016.6885,975.18665,0.99845827
+4453,3027.2236,405.19598,3059.1094,456.48022,0.9981766
+4453,3037.9067,340.4973,3058.287,388.25,0.9980787
+4453,3001.6501,379.31754,3029.8035,432.16763,0.99800736
+4453,2186.6985,421.38712,2211.596,487.61166,0.9979728
+4453,2732.4146,258.2063,2761.063,303.4118,0.9979073
+4453,2131.945,304.47815,2156.9749,360.6902,0.9977748
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+4453,2935.1726,237.62057,2952.824,279.1014,0.9973568
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+4453,3031.3147,316.30435,3053.2214,363.63388,0.9957075
+4453,2010.0713,122.20982,2029.7676,162.86879,0.9892581
+4453,3583.124,797.0247,3622.1494,860.0411,0.80835956
+4453,3743.9666,793.49744,3773.116,859.6847,0.7477868
+4453,3632.205,819.25323,3672.5737,879.54254,0.4402734
+4453,580.09875,361.83966,612.3262,418.8031,0.13260792
+4454,3833.8975,745.91113,3871.745,800.1859,0.99941325
+4454,3179.9385,477.9482,3208.0952,530.70154,0.9994041
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+4454,2944.7173,425.8216,2968.4365,482.37372,0.9991866
+4454,2070.783,209.37302,2089.3923,258.36807,0.9991441
+4454,3195.3994,582.98444,3223.4927,645.6868,0.99909353
+4454,3093.2488,362.43185,3118.3489,410.87344,0.99900144
+4454,2955.3845,568.3767,2981.0369,625.162,0.99895895
+4454,3027.596,452.3768,3055.5164,503.02524,0.99891686
+4454,1313.2245,318.56744,1335.0199,372.63068,0.99878854
+4454,2731.8335,257.85648,2759.5889,303.1141,0.9987092
+4454,2469.9702,448.24536,2494.246,513.72797,0.9986426
+4454,2123.1687,205.30032,2147.347,252.90103,0.99860686
+4454,2188.9746,421.2011,2211.4536,487.25885,0.99854124
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+4454,2612.2634,386.72766,2635.702,441.40143,0.9984975
+4454,1978.3885,881.9855,2016.6813,975.1919,0.9983814
+4454,2510.2766,321.10217,2535.882,375.8954,0.9982962
+4454,3001.725,379.1426,3028.1748,432.32852,0.9982864
+4454,3027.208,405.17654,3059.0474,456.19803,0.99813193
+4454,2131.1094,304.2916,2155.9023,360.26688,0.9977592
+4454,2935.151,237.93106,2951.3486,279.01956,0.9972241
+4454,3036.6462,341.65674,3056.965,388.11798,0.99636465
+4454,3031.4165,316.41666,3051.8438,363.37503,0.99613166
+4454,3268.8188,322.54187,3289.4956,368.9875,0.9918636
+4454,2009.8903,121.83042,2029.8431,162.87167,0.98904383
+4454,3578.454,797.9953,3620.4644,861.0567,0.92699957
+4454,3744.755,793.4901,3772.5454,859.30945,0.85874313
+4454,3631.748,819.4076,3672.567,879.73596,0.34114712
+4454,580.08966,361.80072,612.3366,418.8178,0.11458227
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+4455,3832.786,745.8636,3870.5256,799.9222,0.99912924
+4455,2943.1172,425.51443,2967.9321,482.3516,0.99910825
+4455,2953.133,568.1069,2977.9402,628.46295,0.9990584
+4455,1313.2534,318.12292,1335.0249,372.3786,0.9989837
+4455,2179.8376,421.05273,2211.6575,487.11304,0.9989538
+4455,3177.1106,478.24722,3206.8035,530.66156,0.9987949
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+4455,3027.2446,452.0205,3054.4995,502.84564,0.9986743
+4455,3035.1067,341.78113,3056.4236,387.46625,0.9985826
+4455,2927.2505,368.56525,2957.8086,420.1529,0.99845994
+4455,2732.202,257.68668,2758.3958,302.93674,0.99842644
+4455,1978.3876,881.95715,2016.6732,975.1272,0.99840313
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+4455,2611.789,386.80847,2635.3418,441.34198,0.99833786
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+4455,2532.9082,325.62796,2551.9546,377.44827,0.9981673
+4455,2130.621,304.13428,2154.6987,359.91797,0.99816704
+4455,2469.9138,448.1441,2496.8948,513.6525,0.9980806
+4455,3027.3892,404.97742,3059.0112,455.71008,0.997844
+4455,3031.005,316.31677,3050.001,363.12628,0.9975358
+4455,2932.9,237.48325,2950.2173,279.19388,0.9975337
+4455,3001.7458,378.8316,3026.8035,431.89233,0.99745244
+4455,3265.7197,322.44305,3289.0215,369.19153,0.99530727
+4455,2009.1672,122.76297,2029.498,162.85623,0.9910691
+4455,3575.9436,798.88904,3618.0808,862.9082,0.93402725
+4455,3743.5217,793.5009,3771.4333,859.2143,0.6718547
+4455,3631.8137,819.38477,3672.5515,879.6489,0.26330128
+4455,580.0877,361.895,612.31366,418.79953,0.13958669
+4455,3324.7444,329.1317,3339.6687,354.54297,0.11346145
+4456,3183.4124,582.121,3217.674,646.088,0.9992919
+4456,3092.4885,362.04108,3116.3489,410.55865,0.99921954
+4456,2947.9016,568.53625,2975.9324,630.1266,0.9990694
+4456,2069.7234,208.4981,2088.8513,258.4857,0.99904865
+4456,1313.6945,317.8362,1335.2164,372.62515,0.9989416
+4456,3027.3542,452.18286,3054.1653,502.4668,0.9988933
+4456,3033.603,342.20407,3055.433,387.88248,0.99885297
+4456,2177.5532,421.015,2211.038,487.0609,0.99882287
+4456,2926.8987,368.27264,2957.2385,419.9886,0.9987814
+4456,3831.592,746.0542,3869.307,799.39514,0.998679
+4456,3177.026,478.5031,3205.7144,530.9543,0.99864346
+4456,2940.6892,425.2273,2966.9485,482.1659,0.99859303
+4456,3001.577,378.829,3025.4578,432.10745,0.9985489
+4456,2731.5796,257.43164,2756.3774,302.5512,0.9984999
+4456,2123.3135,204.57475,2144.996,252.27803,0.9984754
+4456,2129.9165,303.7255,2153.0024,359.70853,0.99846065
+4456,1978.3606,882.0026,2016.5867,975.1684,0.99843544
+4456,2510.2068,321.28406,2533.888,375.69293,0.9983329
+4456,2928.728,237.10536,2948.5278,278.65955,0.9981437
+4456,2531.5908,325.71423,2549.2046,377.52942,0.9980775
+4456,2611.5596,386.81348,2634.9263,441.49683,0.99802494
+4456,3265.2664,321.62924,3287.953,369.96194,0.9979054
+4456,3027.4346,405.4177,3058.8604,455.60373,0.9978694
+4456,2469.882,448.3478,2498.8342,513.55835,0.99784636
+4456,3030.012,316.12817,3048.3225,363.39288,0.9969789
+4456,2009.431,123.033615,2029.3646,162.84196,0.99060494
+4456,3575.1897,800.8887,3613.7922,862.93164,0.90985715
+4456,3739.7068,793.753,3770.2961,860.3416,0.72837055
+4456,2724.3506,884.1737,2763.1855,966.05566,0.45623392
+4456,3631.6606,819.26105,3672.5347,879.87994,0.16914986
+4456,580.0981,361.86078,612.3156,418.79602,0.12744914
+4457,3177.767,582.12427,3214.6592,646.2435,0.999286
+4457,3001.566,378.74146,3025.2578,431.51813,0.9992374
+4457,1313.8965,317.03696,1335.626,372.64938,0.99918
+4457,2122.8547,204.55452,2143.305,251.75444,0.99917334
+4457,2529.4377,325.82407,2547.6897,377.82956,0.9989913
+4457,2068.474,208.35759,2089.3242,258.7465,0.998988
+4457,3091.649,362.04846,3115.2627,410.78302,0.998874
+4457,3031.149,342.47675,3054.2207,388.2943,0.9988524
+4457,3027.3667,452.53162,3053.561,502.5553,0.9988248
+4457,3176.4866,478.52527,3204.6658,532.16895,0.9987888
+4457,3830.8865,746.10376,3868.202,800.04016,0.9987185
+4457,2925.9045,367.8629,2956.864,419.2589,0.9985751
+4457,2129.2356,303.66766,2151.9207,359.80994,0.998574
+4457,1978.3721,882.0219,2016.4819,975.1502,0.9985033
+4457,2943.4702,568.674,2973.3906,630.8851,0.9984756
+4457,2936.4114,424.93484,2965.4348,482.0237,0.99846065
+4457,2175.1196,421.00012,2210.3462,487.1947,0.9984133
+4457,2611.8643,386.8988,2634.9521,441.57617,0.99841154
+4457,2510.088,320.60452,2532.5977,374.75693,0.99840087
+4457,2469.88,448.51392,2499.7373,513.5184,0.9982919
+4457,2923.7786,237.13405,2947.9304,278.4986,0.9982424
+4457,2731.2246,256.78366,2753.6855,302.44138,0.9981317
+4457,3264.2612,321.85626,3287.3354,368.6847,0.99793243
+4457,3027.5288,405.40875,3058.6914,455.716,0.9977337
+4457,3027.7346,316.20993,3046.9753,362.103,0.9975134
+4457,2009.5387,122.48802,2029.5753,163.00322,0.99458116
+4457,3575.2031,801.4608,3609.9746,863.17676,0.89715266
+4457,3736.519,794.4786,3768.336,861.1235,0.50584286
+4457,3631.3445,819.07574,3672.6404,880.0462,0.16978812
+4457,580.12006,361.88495,612.30927,418.77997,0.1544731
+4457,2722.6267,883.9531,2761.2898,965.97644,0.13771898
+4458,3000.4558,378.8953,3024.465,431.79135,0.9995192
+4458,3176.6072,478.5891,3202.9675,532.1569,0.99926955
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+4458,2122.341,204.0265,2141.098,251.76036,0.9989373
+4458,2068.15,208.18419,2089.4043,258.80768,0.9989256
+4458,3175.2852,583.5228,3211.005,647.01404,0.9988581
+4458,3830.28,745.87854,3867.1213,800.10486,0.9988261
+4458,3029.7578,343.0337,3053.7485,388.34344,0.9987691
+4458,3027.0967,452.28735,3053.9321,502.596,0.9986325
+4458,2931.8665,424.787,2964.5334,482.29822,0.9986082
+4458,1978.3628,881.9949,2016.4253,975.16895,0.9985545
+4458,2920.9739,237.15712,2947.0188,278.66156,0.9985393
+4458,2173.248,420.66504,2208.728,487.1405,0.9985367
+4458,2940.9016,570.0232,2970.036,631.33264,0.9983954
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+4458,2925.7368,367.2671,2954.9902,419.32324,0.99818677
+4458,2611.5854,386.8536,2634.524,441.55997,0.99814713
+4458,2469.8171,448.54675,2500.2883,513.5094,0.99807394
+4458,2731.462,256.33652,2750.5122,302.37234,0.99801207
+4458,2509.9485,319.64435,2531.8127,373.86078,0.9976846
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+4458,3027.6255,405.11218,3058.542,455.3145,0.99762267
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+4458,3091.1406,362.02945,3113.8613,410.8563,0.99752873
+4458,2009.5494,122.487854,2029.5985,163.00443,0.9946913
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+4459,2999.361,378.4892,3023.7517,432.52722,0.9993606
+4459,2171.2808,420.64545,2207.832,487.05383,0.9992824
+4459,3176.7395,478.39038,3202.4172,531.7719,0.99919194
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+4459,2929.8293,424.42413,2963.7585,482.35895,0.99886656
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+4459,2122.1057,203.6116,2140.3796,250.01572,0.9986216
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+4459,2919.9653,237.1633,2946.4165,278.8613,0.9985108
+4459,1978.358,882.0068,2016.4552,975.1734,0.9984085
+4459,2731.2095,256.3484,2747.7837,302.55603,0.99834466
+4459,2926.3499,366.52225,2952.6418,417.39743,0.99831235
+4459,3090.9424,361.1819,3113.267,411.1728,0.99825996
+4459,3830.3838,745.81946,3866.1035,800.4481,0.997982
+4459,2469.7563,448.46982,2500.9395,513.4897,0.99785185
+4459,2611.4565,386.80835,2634.1636,441.53662,0.9978278
+4459,3263.601,320.71088,3286.3628,367.58484,0.9977877
+4459,3018.444,315.89975,3045.2034,361.48032,0.99757415
+4459,3027.6353,404.69232,3058.4194,455.10278,0.9974523
+4459,2509.8037,319.8293,2531.8286,373.6481,0.99706465
+4459,2010.1406,123.34549,2029.5842,162.90393,0.99068904
+4459,3574.8389,801.36884,3603.6118,862.6451,0.7487028
+4459,3729.8303,796.68646,3765.9265,863.816,0.24324887
+4459,3631.391,818.20264,3672.71,879.85376,0.1883761
+4459,580.12885,361.926,612.2839,418.77463,0.13735251
+4459,2707.5505,885.0747,2757.4836,967.2794,0.11829121
+4460,2517.33,326.57526,2543.5127,377.27234,0.99936277
+4460,2171.1006,420.4067,2206.3071,487.03885,0.9993374
+4460,3029.5188,343.7064,3052.5496,388.64273,0.99903524
+4460,2998.3425,378.4468,3022.3938,432.83658,0.9990314
+4460,3174.833,586.20435,3201.541,647.3865,0.99894685
+4460,2730.6301,255.95325,2746.3645,302.44653,0.9989059
+4460,1315.0515,317.0765,1336.112,373.03616,0.99889296
+4460,3091.4065,361.42255,3111.534,410.9159,0.9988527
+4460,2929.5132,424.52847,2962.5552,481.8517,0.99881506
+4460,3015.909,315.84964,3043.5544,361.57004,0.9987508
+4460,2121.239,203.16524,2138.4836,249.2596,0.9986929
+4460,2126.572,302.61847,2147.1843,357.24866,0.99861777
+4460,2938.5117,574.57623,2964.6436,634.0192,0.99861264
+4460,3025.7148,451.53986,3053.963,503.04456,0.9985991
+4460,3176.459,478.2967,3201.6206,532.37537,0.99858946
+4460,3829.5388,745.86145,3865.054,800.6306,0.9984176
+4460,1978.328,882.0547,2016.1359,975.17554,0.998408
+4460,2919.3174,237.21558,2945.8125,278.82465,0.998175
+4460,2469.6763,448.28745,2500.5503,513.49976,0.9979546
+4460,2611.207,386.93896,2633.4878,441.55597,0.9975606
+4460,3027.684,404.70486,3058.288,454.89395,0.9974624
+4460,2926.2432,366.3374,2950.3682,417.03888,0.997375
+4460,2067.3982,207.66946,2090.089,258.65048,0.9971641
+4460,3263.5764,320.7899,3285.158,366.7507,0.9968233
+4460,2509.9065,319.7,2530.869,374.47632,0.99622524
+4460,2010.8759,123.144615,2029.5883,162.95396,0.9919046
+4460,3573.3303,800.7456,3602.1995,862.07153,0.71072334
+4460,3631.439,818.07996,3672.7192,879.8994,0.24763106
+4460,3727.9346,796.67847,3763.997,863.5194,0.17854255
+4460,580.12146,361.8903,612.3159,418.77505,0.12676187
+4461,2514.612,327.2713,2543.1726,377.2928,0.99945253
+4461,2937.1255,575.869,2960.645,633.99414,0.9992829
+4461,2994.925,379.35587,3021.658,432.7673,0.9992136
+4461,3173.2893,586.4696,3196.0955,647.1863,0.9992035
+4461,3029.6191,343.72757,3051.3652,388.9299,0.9992015
+4461,2929.8032,424.40787,2961.1104,481.4952,0.99897146
+4461,3090.2842,361.15585,3110.2954,410.98392,0.9989532
+4461,2170.9182,420.1367,2204.5574,486.4059,0.9989524
+4461,1315.2117,317.00793,1336.312,373.25836,0.99885154
+4461,2728.9568,255.51471,2745.1165,302.66034,0.998723
+4461,2126.203,302.73468,2145.7092,357.6499,0.9986765
+4461,3024.9585,451.3469,3053.8838,504.66318,0.99866664
+4461,3176.3245,478.30273,3200.9558,532.54224,0.99856925
+4461,2120.3428,202.55037,2137.2114,248.99254,0.9985513
+4461,1978.3127,882.05743,2016.1572,975.1838,0.99841785
+4461,3827.6294,745.9404,3863.7725,800.8722,0.9984016
+4461,2918.7134,236.86885,2944.8618,277.75183,0.9979652
+4461,3014.81,315.10556,3042.4849,361.35672,0.99770373
+4461,2611.052,386.9615,2633.2957,441.52472,0.9974975
+4461,2469.634,448.117,2499.2874,513.5228,0.99745375
+4461,3027.5813,404.65515,3058.153,454.7162,0.997324
+4461,2066.2954,207.59769,2089.1943,258.52234,0.9971438
+4461,2925.77,366.01382,2948.3525,416.99606,0.99654216
+4461,2509.679,319.3649,2530.8962,373.54324,0.9965076
+4461,3263.3398,320.87024,3284.8623,366.0111,0.99570066
+4461,2010.8103,123.11078,2029.6079,162.96817,0.9918709
+4461,3568.94,800.3012,3600.309,866.1055,0.8981411
+4461,3631.5105,816.9936,3672.6511,879.99176,0.27279124
+4461,2702.5864,886.689,2757.88,967.1174,0.12034352
+4461,580.1294,361.8133,612.3042,418.76794,0.105036326
+4462,2513.254,327.03503,2542.8535,377.47687,0.9994174
+4462,3028.4995,343.7867,3050.3398,389.24582,0.99933004
+4462,2992.195,379.26978,3021.1956,432.0028,0.99929726
+4462,2929.1887,423.33606,2959.9006,480.6372,0.9991233
+4462,2124.78,302.6484,2145.1697,357.0917,0.99905056
+4462,3024.4734,451.21225,3053.443,505.395,0.9989961
+4462,2933.9565,576.0257,2957.7563,633.9599,0.9989814
+4462,3176.3362,478.38416,3200.4265,531.7827,0.99896765
+4462,2725.6829,255.1958,2744.47,301.886,0.99884593
+4462,2918.6848,237.30078,2941.862,277.851,0.998782
+4462,1315.5967,316.9768,1336.4814,373.2384,0.9987501
+4462,3088.3608,361.24533,3109.3135,410.7662,0.9987324
+4462,2119.324,202.40134,2136.549,248.57141,0.99855334
+4462,1978.3007,882.0561,2016.0155,975.1883,0.9984927
+4462,2170.9219,419.4505,2202.312,485.82275,0.99847025
+4462,3168.019,585.2875,3191.6416,646.8059,0.99840266
+4462,3827.3425,745.974,3862.5183,800.51196,0.998398
+4462,2610.9617,387.23898,2633.4543,441.56375,0.99810886
+4462,3014.7068,315.45157,3040.8225,360.87186,0.99771297
+4462,2925.6929,365.97757,2946.5933,417.2487,0.9976822
+4462,2066.02,207.43849,2088.4233,257.7127,0.9976586
+4462,3027.803,404.65643,3058.056,454.42114,0.9973242
+4462,2469.5967,448.38165,2497.9966,513.56555,0.99700457
+4462,3262.5522,320.47113,3283.5356,365.6748,0.9961903
+4462,2010.7916,122.670555,2029.6129,163.00464,0.99315006
+4462,2509.4224,319.02417,2531.0479,374.1881,0.9921536
+4462,3560.5037,800.7709,3597.5671,869.10547,0.9917243
+4462,3631.6318,816.5049,3672.7344,879.61804,0.48997352
+4462,2697.7363,885.6651,2758.4229,967.3239,0.23491256
+4462,3441.6194,824.0797,3484.3577,893.1024,0.15822436
+4462,3723.9587,798.41644,3762.5203,863.60114,0.15269181
+4462,580.10254,361.83664,612.31555,418.7593,0.10039675
+4463,2511.6982,326.7145,2542.3125,377.81955,0.999445
+4463,3176.2322,478.37347,3199.5115,532.36633,0.99910444
+4463,3028.5588,343.82257,3049.7073,389.3037,0.9990701
+4463,2720.5024,255.00385,2742.9316,301.94238,0.99906915
+4463,2929.179,422.81665,2958.673,480.07275,0.99903166
+4463,3083.9443,361.0906,3107.6997,411.05295,0.9989843
+4463,2990.2463,379.1244,3020.2444,432.12738,0.9989698
+4463,2122.2822,302.1956,2144.417,357.06497,0.9988739
+4463,2170.8054,418.99246,2198.6062,485.19315,0.9988218
+4463,1315.8882,317.08203,1336.6396,373.0622,0.9987716
+4463,3023.9045,451.38358,3052.9802,506.0057,0.99868053
+4463,2117.8115,202.08252,2135.6846,248.54654,0.9986719
+4463,3165.0244,584.15247,3188.5938,646.97766,0.99859446
+4463,2610.5854,387.50256,2637.0918,441.65234,0.99858046
+4463,1978.2839,882.03937,2015.9509,975.2165,0.99851614
+4463,2931.385,574.81915,2955.2605,633.8569,0.99848324
+4463,3826.9724,745.54016,3861.8289,800.29126,0.9983909
+4463,2919.4941,236.1033,2939.5273,277.07724,0.99823654
+4463,2925.4248,365.02158,2945.5557,417.65335,0.9981865
+4463,2064.47,207.24213,2088.111,257.6573,0.998063
+4463,2469.5903,448.63016,2496.838,513.62756,0.9979001
+4463,3014.2764,314.54422,3039.516,360.50858,0.99750334
+4463,3027.5247,404.64038,3057.9485,454.54993,0.9974383
+4463,3264.348,320.01636,3283.1448,364.34387,0.9935488
+4463,2010.8248,123.076614,2029.5775,163.01646,0.99282175
+4463,2509.0603,318.55182,2531.2576,372.99677,0.9851955
+4463,3555.174,801.6614,3595.028,870.4518,0.97799313
+4463,3631.5796,816.494,3672.7368,880.0066,0.25833645
+4463,3723.9133,798.0356,3760.4763,864.0656,0.14807211
+4463,580.1273,361.9537,612.24756,418.71793,0.116755985
+4464,2511.2588,326.74054,2540.3643,377.67987,0.9994342
+4464,3176.085,478.68137,3198.6733,533.22284,0.9992311
+4464,3158.7136,583.0623,3186.402,646.8028,0.999213
+4464,2717.6646,255.46576,2742.44,301.87225,0.9991042
+4464,2929.267,422.22873,2957.8193,479.46524,0.99903995
+4464,2928.8418,573.2496,2952.6255,633.67865,0.9990144
+4464,3028.495,343.46417,3049.3052,389.42163,0.9989453
+4464,1316.1812,316.9516,1336.9319,372.74567,0.99888855
+4464,2989.7517,379.61914,3018.7458,432.00494,0.9988213
+4464,2118.723,302.05484,2143.3972,357.0673,0.9987128
+4464,2171.0574,418.66953,2194.702,484.74008,0.9986958
+4464,2923.4148,364.31592,2944.176,417.43488,0.99859554
+4464,3080.6716,361.35605,3106.052,410.66183,0.9985903
+4464,3023.2095,452.5062,3052.1172,506.5128,0.9985654
+4464,1978.218,881.9647,2015.9243,975.16724,0.99851626
+4464,2114.6052,201.8749,2135.0427,248.23048,0.9984668
+4464,2063.2886,207.25836,2087.1167,257.09576,0.99836224
+4464,3827.7627,745.8469,3860.8403,799.5419,0.9982607
+4464,2918.6716,235.77045,2937.4128,277.25018,0.9981193
+4464,2610.509,387.59375,2638.8303,441.68268,0.9980767
+4464,3262.8103,320.2481,3282.1052,365.19733,0.99796295
+4464,2469.5845,448.6914,2495.5352,513.6787,0.9977471
+4464,3027.5657,404.584,3057.8816,454.83243,0.9976399
+4464,3013.875,312.9517,3038.8037,360.66873,0.9973568
+4464,2010.1766,122.82156,2029.6183,162.95532,0.986477
+4464,2508.5952,317.96225,2531.1128,372.5634,0.98371387
+4464,3551.2043,802.99304,3591.3992,870.7638,0.9598479
+4464,3631.4377,816.35724,3672.8318,880.04803,0.28666854
+4464,2694.0798,877.0024,2759.532,965.55695,0.1923383
+4464,580.2409,361.76575,612.1047,418.6609,0.14511813
+4464,3441.3982,823.13794,3475.8381,894.7965,0.13627283
+4464,3724.0095,798.33435,3758.61,864.1162,0.11572703
+4465,2923.8508,572.91266,2950.1726,635.5107,0.99942774
+4465,3152.305,582.4992,3184.7249,646.8979,0.99911433
+4465,2929.3564,421.95444,2956.6206,478.93625,0.999081
+4465,1316.3691,316.7776,1336.9238,372.31622,0.99905866
+4465,3175.0322,479.045,3198.08,534.1752,0.9990157
+4465,3076.8125,361.78708,3105.1646,410.67593,0.99901295
+4465,2170.4546,418.5911,2191.477,484.77432,0.99895847
+4465,2510.9673,326.21936,2537.4434,377.8006,0.99895287
+4465,2112.7803,201.74318,2134.7446,248.2972,0.99893254
+4465,2715.2988,255.2902,2742.046,302.07388,0.99893254
+4465,3027.9802,343.64682,3048.9817,389.33548,0.9988832
+4465,2919.5935,364.3801,2943.0632,416.65323,0.99876994
+4465,2989.3496,380.33893,3018.0815,432.122,0.9987581
+4465,3825.8442,745.71356,3860.0493,800.1252,0.9987452
+4465,3022.2505,453.1555,3051.0728,507.05685,0.998679
+4465,1978.171,881.8919,2015.9149,975.16156,0.9985461
+4465,2918.5542,235.64978,2935.9268,277.28253,0.9984432
+4465,2469.56,448.64587,2496.376,513.64514,0.99822456
+4465,2610.5327,387.6841,2639.371,441.63492,0.99815184
+4465,2063.0698,206.80621,2086.4004,255.85764,0.99813074
+4465,3027.6545,404.56494,3057.839,455.10077,0.9977658
+4465,2115.3901,301.8098,2142.8013,356.90686,0.9977648
+4465,3263.2239,320.17145,3282.1516,364.253,0.99704957
+4465,3013.6382,312.88904,3037.7651,360.62885,0.9970163
+4465,2010.1954,122.826775,2029.6422,162.9722,0.9869153
+4465,2508.3506,317.6143,2531.3428,372.76016,0.9866789
+4465,3549.179,805.6222,3587.4177,872.7025,0.9794256
+4465,3631.532,816.21027,3672.9456,879.8587,0.35028562
+4465,3724.0254,799.21735,3756.9053,864.7177,0.1716083
+4465,2692.0847,873.6914,2760.1995,964.79456,0.15463221
+4465,580.2493,361.7491,612.0811,418.6508,0.14534907
+4465,3441.6165,822.37256,3474.6116,895.1715,0.1275934
+4465,3930.856,763.859,3958.9849,825.4592,0.1138465
+4466,3147.6777,583.1103,3183.2773,646.6661,0.99939156
+4466,2919.4985,573.1161,2948.8335,637.9066,0.9992281
+4466,2989.3552,380.50348,3016.4163,432.25323,0.99922705
+4466,3027.877,343.67947,3048.357,389.77457,0.9991867
+4466,3171.5334,479.26962,3196.928,534.6325,0.9991165
+4466,1316.3195,316.7055,1336.9879,372.43243,0.9990054
+4466,2169.9504,418.613,2189.5095,483.67563,0.998899
+4466,2929.397,421.90347,2955.6123,477.6649,0.9988772
+4466,3822.9717,746.1802,3858.606,800.0045,0.998857
+4466,2111.6753,201.72693,2134.211,248.55331,0.9988141
+4466,3074.8274,361.64807,3104.0378,410.8263,0.9987105
+4466,1978.1259,881.878,2015.9535,975.1497,0.9985973
+4466,2714.384,254.97443,2741.3987,301.71136,0.99854076
+4466,3021.537,453.61356,3050.2603,507.37332,0.9985399
+4466,2914.5469,364.2435,2942.101,416.43674,0.9984308
+4466,2918.244,234.40666,2933.947,277.29886,0.9983182
+4466,2469.5488,448.49298,2496.1772,513.6736,0.9980427
+4466,3027.6648,404.6268,3057.7947,455.27713,0.9979517
+4466,3013.8538,312.51532,3036.3962,359.65198,0.9978618
+4466,2062.7043,206.32484,2085.6833,256.6969,0.9976495
+4466,2511.0405,325.63898,2534.497,377.7776,0.9975811
+4466,2610.5042,387.61334,2639.866,441.65497,0.99757344
+4466,2114.2844,302.17468,2142.5618,356.64764,0.9974794
+4466,3262.9539,319.6926,3281.972,365.03268,0.9966922
+4466,2010.9956,122.83769,2029.6707,163.01534,0.99303377
+4466,3549.424,807.61743,3583.9348,872.7019,0.9856877
+4466,2508.5615,317.48035,2529.1255,373.75446,0.9785946
+4466,3631.6199,815.7722,3673.1194,880.06433,0.35202262
+4466,3884.7573,757.5818,3929.6958,813.76807,0.15263718
+4466,580.2697,361.61975,612.0667,418.6828,0.123943664
+4467,2915.3408,574.23486,2947.7798,639.42285,0.9993411
+4467,3144.9272,583.8298,3181.933,648.08,0.99928385
+4467,3169.7166,479.6688,3196.5266,534.0953,0.9992205
+4467,3819.5566,746.13116,3857.772,800.5411,0.9992078
+4467,2929.4326,421.5076,2954.9155,476.93417,0.9990133
+4467,2111.7803,202.2735,2133.8413,248.4353,0.99897134
+4467,2989.0928,379.84525,3015.2397,432.60446,0.9989134
+4467,1316.138,316.57376,1337.0979,372.56937,0.9989008
+4467,3021.4478,453.89062,3048.6245,508.4693,0.9987429
+4467,1978.104,881.7909,2015.9226,975.12524,0.9987245
+4467,2713.9797,254.12329,2740.7224,301.19672,0.99867743
+4467,3027.6768,343.38254,3048.163,390.61575,0.99859494
+4467,3073.6755,361.79126,3103.2327,411.07025,0.9983877
+4467,2915.44,234.40436,2932.497,277.01715,0.9980805
+4467,2910.2273,364.3694,2940.8264,416.75818,0.99808
+4467,3027.7551,404.65463,3057.7546,455.60117,0.9980798
+4467,2114.019,301.98608,2142.3164,356.7387,0.99807537
+4467,2469.5273,448.25098,2496.0723,513.6736,0.99787366
+4467,2062.1155,206.27084,2084.5173,256.2969,0.9978399
+4467,3014.1372,312.03195,3034.6045,359.27426,0.99768555
+4467,2166.319,418.40942,2188.4475,483.70905,0.9973526
+4467,3262.3196,319.61264,3281.6013,365.24417,0.9972268
+4467,2610.4856,387.55612,2640.5164,441.61703,0.9971438
+4467,2511.4246,326.01004,2530.945,377.4769,0.99496835
+4467,2011.0825,123.114876,2029.6768,162.9927,0.9932848
+4467,3548.8079,808.90894,3580.0408,872.8546,0.9534729
+4467,3883.1338,757.1669,3928.957,814.1797,0.45735922
+4467,3631.0237,815.2781,3673.1995,880.4092,0.20158772
+4467,3932.275,763.4013,3959.1416,825.9091,0.16180879
+4467,3723.667,797.7527,3755.581,865.8342,0.120260105
+4468,3168.2812,480.2166,3195.748,534.90576,0.9993801
+4468,2929.5942,420.8441,2953.7153,476.65317,0.99926096
+4468,3817.3887,746.98553,3856.662,800.8322,0.9992575
+4468,2913.2588,576.4574,2946.1543,641.0592,0.9991541
+4468,2110.899,202.15938,2133.2368,248.3986,0.9990434
+4468,3020.8237,454.23422,3046.6528,508.6307,0.99900407
+4468,3026.4746,343.5412,3048.2808,391.20386,0.99898094
+4468,3144.5037,585.18665,3178.242,647.8253,0.9989293
+4468,1316.0305,316.317,1337.1614,372.63284,0.9989027
+4468,2713.5652,253.94324,2738.9778,300.0932,0.9988035
+4468,1977.8221,881.71967,2015.8778,974.97296,0.99875975
+4468,2989.02,380.08185,3014.1963,432.5498,0.9986047
+4468,2113.4734,301.47446,2142.2913,356.50162,0.99846894
+4468,3072.9138,361.64993,3102.507,411.17136,0.9983966
+4468,3028.0063,404.64636,3057.734,456.00183,0.9983657
+4468,2162.348,418.80106,2187.1794,483.47818,0.9982743
+4468,2469.5032,448.00232,2495.8704,513.71094,0.9981913
+4468,2061.5603,206.29108,2082.9753,255.58997,0.9981737
+4468,2510.2996,325.45047,2530.1677,377.61203,0.99810094
+4468,3014.4932,311.89767,3032.9746,359.11374,0.99762124
+4468,2610.3936,387.60852,2640.1162,441.44836,0.9973304
+4468,2907.8894,365.1491,2938.5315,416.0946,0.99697924
+4468,3261.6306,319.22018,3280.9397,365.46036,0.99628973
+4468,2011.0898,123.16207,2029.6565,162.98218,0.9934117
+4468,2908.6545,233.93025,2931.1296,276.83438,0.99150336
+4468,3546.92,808.91046,3577.0586,872.82,0.9680512
+4468,3882.3772,757.15186,3928.111,813.7742,0.942224
+4468,3631.1287,814.559,3673.4817,880.1833,0.22074571
+4468,3930.8608,762.6426,3960.4517,825.72876,0.18099995
+4468,2509.7527,316.98947,2528.801,358.95193,0.14424981
+4468,3723.0251,797.4371,3754.286,864.8868,0.1124357
+4469,2929.119,420.51004,2953.0593,476.72018,0.999503
+4469,3168.0195,481.51648,3194.7173,535.2294,0.99927086
+4469,3814.9954,747.0202,3855.0662,800.7928,0.9991774
+4469,2912.49,578.57196,2943.0168,642.02216,0.99891984
+4469,3142.5625,586.47614,3172.266,648.5601,0.998904
+4469,1315.6602,316.27423,1337.1665,372.64313,0.9988808
+4469,3025.4553,343.97787,3048.3416,391.98123,0.9988656
+4469,1977.664,881.65485,2015.7094,974.80536,0.9988312
+4469,2110.8088,201.82773,2132.7175,247.13043,0.99874806
+4469,2988.8787,380.5095,3013.1687,432.4515,0.99872535
+4469,3020.1467,454.01572,3045.6267,508.94455,0.99866307
+4469,2113.3542,301.2457,2141.8088,356.3298,0.99861395
+4469,2713.4314,253.97008,2736.992,299.30676,0.9985832
+4469,3028.1033,404.6128,3057.7327,456.23193,0.9984434
+4469,2469.4902,447.77158,2495.483,513.7204,0.9983394
+4469,2905.5356,234.12648,2930.2544,276.83966,0.9982772
+4469,2059.8296,206.02463,2082.4897,255.42377,0.99823666
+4469,3013.8997,312.04535,3032.2302,358.8457,0.9981719
+4469,3072.5269,361.6932,3102.0122,411.42923,0.99808073
+4469,2158.089,418.44775,2186.4333,483.20477,0.998013
+4469,2610.4492,387.61237,2639.438,441.29523,0.9979734
+4469,3260.4907,319.1719,3280.8618,365.01303,0.9971233
+4469,2907.7441,365.70856,2937.8105,416.26056,0.99683005
+4469,2509.3267,327.87967,2529.546,377.4133,0.9953257
+4469,2011.0894,123.157974,2029.6482,162.97713,0.9934754
+4469,3542.733,808.7875,3574.2178,872.74146,0.9425976
+4469,3882.454,756.9809,3927.4058,813.9024,0.82630694
+4469,2509.782,316.89618,2529.573,364.081,0.547691
+4469,3631.2585,813.9045,3673.5857,879.82574,0.18723635
+4469,3720.304,796.9753,3753.2903,864.7376,0.11780699
+4469,3931.983,762.0528,3961.3994,826.1407,0.104774036
+4470,2928.2292,420.8153,2951.8206,477.10895,0.9997229
+4470,2911.5237,580.76935,2940.0164,642.0009,0.99916863
+4470,2110.6475,201.88638,2131.5996,247.29568,0.9991333
+4470,2113.2046,300.50537,2140.6323,355.68848,0.99910647
+4470,3024.5881,344.1579,3048.6414,392.2633,0.99910045
+4470,3166.8225,481.7254,3193.6897,535.5648,0.99907887
+4470,3139.5337,586.45056,3167.5435,649.22266,0.9990206
+4470,2988.9324,380.69342,3012.4973,432.32556,0.9989623
+4470,1315.548,316.25012,1337.1989,372.5902,0.9988972
+4470,3817.5369,747.0746,3853.4983,801.7103,0.99884474
+4470,1977.7034,881.5559,2015.6123,973.7953,0.99882835
+4470,2904.4443,234.12099,2929.849,276.91998,0.9987338
+4470,3072.418,361.60394,3100.9707,411.2373,0.99868804
+4470,3011.7952,311.9039,3030.7542,358.37442,0.99851817
+4470,3028.205,404.60507,3057.7402,456.309,0.99848527
+4470,2610.2566,387.57904,2639.3313,441.36804,0.9984479
+4470,2469.4663,447.72714,2494.2534,512.90717,0.99841094
+4470,3019.2698,453.49506,3044.4763,509.0719,0.9983917
+4470,2059.462,206.01704,2081.7124,255.5113,0.9983767
+4470,2154.3784,418.85675,2185.9463,483.41522,0.9982778
+4470,2713.5007,253.12254,2734.4182,298.28622,0.9976731
+4470,2907.5571,365.28894,2936.9004,415.7519,0.9973986
+4470,3260.0059,319.0896,3280.4102,365.1991,0.9961527
+4470,2011.0929,123.14595,2029.6495,162.97783,0.99353456
+4470,2505.2666,326.28763,2527.3013,377.7363,0.98575103
+4470,3540.9512,808.25085,3570.8398,872.7019,0.96499825
+4470,3885.4119,756.75916,3926.7273,813.71375,0.72714335
+4470,2509.07,317.18497,2529.3982,369.56216,0.54335845
+4470,3714.5498,797.26843,3752.6323,863.9149,0.26411325
+4470,3633.5132,814.1239,3673.4858,879.5392,0.22109011
+4470,3932.3174,764.5253,3961.6606,825.9986,0.16793421
+4470,3420.1724,824.31836,3462.232,896.0377,0.11287959
+4471,2925.0515,419.87613,2952.1067,476.86002,0.9995504
+4471,2113.1216,300.6474,2139.337,356.26276,0.99939466
+4471,2110.4128,201.45285,2130.0544,246.98645,0.9992561
+4471,3072.2131,361.58606,3099.1443,411.24127,0.9991992
+4471,3007.9739,311.37485,3029.4895,358.65585,0.9990314
+4471,2988.5164,379.6811,3012.2087,432.6582,0.9989761
+4471,3136.9893,586.12427,3165.1724,648.73706,0.9989618
+4471,1315.3013,316.24585,1337.3096,372.5113,0.998911
+4471,3019.0225,453.2685,3042.188,508.96423,0.99890554
+4471,3166.5535,481.86307,3193.1262,535.7602,0.9988393
+4471,2469.4585,447.74866,2493.772,512.43896,0.99874496
+4471,3023.5935,345.0814,3048.6077,392.13455,0.99873394
+4471,3815.5657,746.8582,3852.3357,802.2153,0.99871373
+4471,2713.7302,252.35739,2731.2732,297.7073,0.9986721
+4471,2058.9795,205.94867,2081.187,255.49191,0.9986499
+4471,2909.8481,581.8245,2936.4233,642.05743,0.99863905
+4471,2610.2554,387.58722,2639.5,441.49683,0.99863064
+4471,2902.9807,234.05145,2929.5955,276.98276,0.9985745
+4471,2907.0051,365.47983,2936.237,415.67374,0.9984981
+4471,1977.5587,881.54694,2015.4132,973.5069,0.99848986
+4471,3028.0159,404.50916,3057.7239,456.3647,0.9984291
+4471,2152.1685,418.8617,2185.4927,483.5103,0.9980527
+4471,3259.2249,318.8587,3279.965,365.34393,0.9974141
+4471,2011.089,123.124214,2029.6444,162.98827,0.99354726
+4471,2498.3127,323.63034,2524.0886,377.46567,0.9878675
+4471,3537.014,806.9724,3569.4792,872.7014,0.9497024
+4471,2509.2832,322.2909,2529.8086,376.313,0.919586
+4471,3708.8884,797.3024,3751.4446,864.30304,0.800121
+4471,3883.4683,756.09674,3926.0947,814.01227,0.6578144
+4471,3635.021,814.03723,3673.3384,879.07117,0.28574833
+4471,3420.4685,825.53076,3459.8708,896.8607,0.22822782
+4471,3932.197,763.53253,3961.2722,825.94244,0.2001352
+4472,2903.9595,234.09889,2928.871,276.27942,0.99932885
+4472,2109.9614,200.28299,2129.0034,246.77606,0.99921095
+4472,3018.6445,453.40683,3040.7446,508.94913,0.999132
+4472,2988.1892,379.7317,3010.969,432.64227,0.999046
+4472,3166.316,481.88226,3191.7812,536.0516,0.9990337
+4472,3134.2275,585.6189,3163.5635,648.4612,0.99902403
+4472,2113.2012,300.08557,2138.2642,355.3556,0.99901175
+4472,2058.1323,204.90434,2080.5366,254.89275,0.99896646
+4472,3002.7676,311.16492,3028.5781,358.03412,0.9989543
+4472,1315.2896,316.29404,1337.3296,372.5723,0.99890864
+4472,2469.3625,448.23492,2492.8728,511.81036,0.9988562
+4472,2609.8342,387.65656,2637.3948,441.49286,0.9988067
+4472,2922.2515,419.94904,2951.4224,476.74835,0.99879897
+4472,2907.3254,582.4332,2934.3298,641.90173,0.99879044
+4472,2713.455,252.07082,2729.7402,297.30658,0.9986003
+4472,3071.8022,361.25656,3097.8237,411.181,0.9985399
+4472,3817.2063,747.23865,3850.91,801.78064,0.998401
+4472,3027.641,404.46396,3057.5757,456.38022,0.9982755
+4472,1977.6744,881.4747,2015.3939,973.3361,0.99821055
+4472,3022.819,346.20264,3048.5168,390.80585,0.99796534
+4472,2150.602,419.19586,2184.9321,483.35303,0.99785256
+4472,2906.4167,365.2595,2934.9026,415.78946,0.9974468
+4472,3258.7966,318.5671,3279.6892,365.11807,0.9973346
+4472,2494.1494,323.14984,2522.7441,377.35236,0.99690104
+4472,2011.098,123.13864,2029.6376,162.97327,0.9933895
+4472,2509.7588,317.06555,2529.7798,371.86658,0.89307165
+4472,3704.4612,798.7566,3749.8728,864.8451,0.820456
+4472,3528.5984,807.1688,3566.5671,873.6483,0.77173513
+4472,3887.267,755.5608,3924.9023,813.6426,0.5850261
+4472,3637.2595,814.1184,3673.4036,880.1848,0.38621902
+4472,3420.7632,826.1749,3458.4429,897.20496,0.33260617
+4472,3931.7454,760.67236,3959.6433,825.57776,0.17315228
+4473,2112.1465,299.7215,2135.7764,354.9909,0.9994289
+4473,2056.4893,204.87877,2079.2993,254.89003,0.99939966
+4473,2109.3667,200.1177,2127.2583,246.38904,0.99925655
+4473,3000.6672,311.28024,3027.7708,358.27274,0.9992434
+4473,3128.3386,584.6113,3159.6277,649.29236,0.99922776
+4473,2468.8833,448.17233,2492.374,511.18076,0.9992085
+4473,2903.2087,233.60808,2927.8557,275.5247,0.99913627
+4473,2608.8115,387.7296,2634.0068,441.38098,0.999132
+4473,2920.5535,419.5557,2950.8625,476.66928,0.9990963
+4473,1315.2878,316.32483,1337.3247,372.57104,0.9989054
+4473,3022.0933,346.659,3048.0854,392.2528,0.9989034
+4473,2988.0513,379.1814,3010.667,432.37433,0.9988966
+4473,2906.7273,582.14465,2932.344,641.817,0.9988914
+4473,3165.4456,481.98422,3190.6394,536.23157,0.998838
+4473,2149.655,418.97162,2184.8352,483.2279,0.9987135
+4473,3017.0554,453.61838,3039.5413,508.98697,0.99861026
+4473,2711.3184,250.87683,2728.5508,297.86218,0.9982486
+4473,3027.6921,404.37778,3057.3826,456.6086,0.99820375
+4473,2906.5425,364.32648,2932.7632,413.94672,0.9981421
+4473,3072.084,360.66006,3095.8765,410.00247,0.99800926
+4473,1977.5718,881.5151,2015.1277,973.18353,0.99792117
+4473,3816.763,746.19586,3850.2063,801.5269,0.9975177
+4473,2489.2495,323.64902,2522.9497,377.68533,0.99702305
+4473,3257.7317,318.87292,3278.964,363.4945,0.99651164
+4473,2011.0894,123.07916,2029.5898,162.97583,0.99322444
+4473,3700.0808,799.5241,3748.2703,864.81915,0.96089417
+4473,3523.0784,808.2052,3563.5095,874.70325,0.9466993
+4473,2508.9253,316.21817,2529.69,368.45297,0.8686361
+4473,3886.0344,755.318,3923.762,813.8219,0.6664619
+4473,3639.4387,812.9738,3673.464,880.61896,0.24506378
+4473,3931.8064,761.5315,3959.927,825.4302,0.15161148
+4473,3420.6921,826.03064,3457.5989,897.35315,0.12604544
+4474,3120.3323,584.87006,3155.7522,649.7601,0.9993661
+4474,2055.336,204.94113,2078.5146,254.89798,0.99929976
+4474,2467.5845,448.30524,2491.526,510.49982,0.99928916
+4474,2607.6035,387.563,2631.3877,441.43744,0.99921787
+4474,2108.9077,199.89093,2125.5244,246.73221,0.999186
+4474,2919.6724,419.46887,2950.354,476.5302,0.9991406
+4474,2903.7627,582.05554,2929.4521,644.65466,0.99912626
+4474,3021.9478,346.8875,3047.5938,394.14215,0.9991122
+4474,2149.3433,419.06525,2184.141,482.9818,0.99905413
+4474,2903.4294,233.32701,2924.867,275.55307,0.99899095
+4474,1315.3489,316.30734,1337.2263,372.57217,0.9989458
+4474,2708.9822,251.58372,2726.8264,297.2017,0.99880683
+4474,2111.5378,299.48468,2133.7478,354.7893,0.9987685
+4474,3165.221,481.77823,3190.411,536.15936,0.99876535
+4474,3015.8994,453.26434,3038.1177,508.52502,0.9987117
+4474,2999.5703,311.25885,3026.5825,358.01752,0.99868923
+4474,2489.8757,324.32794,2521.6287,377.4319,0.99865806
+4474,2984.4321,378.92163,3010.226,432.04224,0.9984888
+4474,3027.8477,404.5118,3057.374,456.5328,0.99824536
+4474,1977.3971,881.5395,2014.9056,973.09955,0.99797136
+4474,3816.7468,746.5822,3849.6428,801.81085,0.9973894
+4474,3072.6516,360.29953,3093.931,410.1105,0.9972536
+4474,3258.0042,318.70084,3278.5017,362.59964,0.9969789
+4474,2906.488,363.7013,2929.7615,413.2754,0.99598396
+4474,2011.0802,123.06458,2029.5507,162.98366,0.9931787
+4474,3519.8994,809.68036,3561.1782,876.59174,0.9836078
+4474,2509.853,316.26053,2529.6265,370.88968,0.9736347
+4474,3696.6357,800.75256,3746.9673,865.6024,0.96350324
+4474,3883.0923,755.97943,3923.4038,813.51776,0.9563387
+4474,3640.093,812.36707,3673.1833,879.9082,0.1915599
+4474,3933.0754,759.8977,3960.2869,823.31213,0.1820653
+4475,2055.0154,205.04262,2077.8572,255.43724,0.9992574
+4475,3021.9238,347.03033,3046.7124,394.16028,0.99923813
+4475,2108.049,199.8819,2124.82,246.83557,0.9992198
+4475,2918.7996,419.53668,2949.3977,476.46655,0.9991491
+4475,3114.8545,584.7796,3152.583,649.08844,0.999126
+4475,2466.2256,448.29138,2490.4585,509.76917,0.9991059
+4475,2903.8406,233.32372,2921.7278,274.28183,0.9990804
+4475,2148.8223,419.01794,2182.999,482.80048,0.9989945
+4475,1315.352,316.29416,1337.1204,372.66086,0.9989649
+4475,2898.6792,582.36005,2927.0112,648.4853,0.99883646
+4475,2110.252,298.9581,2132.6812,354.61765,0.99866784
+4475,2489.474,324.0915,2521.1528,377.62933,0.99846053
+4475,3013.9673,453.382,3036.8853,508.71426,0.99831057
+4475,2981.8223,378.8799,3009.3726,432.22055,0.9982414
+4475,3027.7964,404.4386,3057.352,456.8047,0.9981762
+4475,2703.461,250.72992,2725.8057,297.5244,0.99814856
+4475,1977.3044,881.5266,2014.5557,972.9564,0.9981317
+4475,2606.816,387.3336,2630.5444,441.4644,0.99811435
+4475,2999.4395,311.0235,3025.7148,357.5022,0.9979923
+4475,3163.496,481.52393,3189.7754,536.1539,0.997974
+4475,3257.717,318.4122,3278.4382,363.08383,0.9978125
+4475,3072.4958,360.26138,3092.9792,410.2407,0.99747235
+4475,3815.7998,746.5614,3848.226,801.79956,0.99738365
+4475,2906.2285,362.96344,2928.291,413.66052,0.99642915
+4475,2011.0748,123.06266,2029.5402,162.98697,0.99289715
+4475,2509.8406,316.04587,2529.7283,370.39755,0.98184514
+4475,3694.9111,801.34656,3744.8604,865.75525,0.9623072
+4475,3519.1604,811.52264,3558.7297,878.1386,0.87336075
+4475,3882.79,755.81714,3923.3096,813.4243,0.799674
+4475,3934.0288,759.1487,3960.7368,822.41565,0.25680098
+4475,3640.1965,811.8706,3672.973,880.734,0.16880581
+4476,2149.0374,418.91995,2180.6282,482.4869,0.9992461
+4476,3021.7075,346.926,3046.4072,394.4105,0.99923384
+4476,2054.8804,204.67061,2077.2124,255.27113,0.99918944
+4476,2918.5144,419.5023,2948.7883,475.66257,0.9991534
+4476,2106.5522,199.36801,2124.9434,246.65103,0.9989794
+4476,2464.2334,448.4324,2489.6343,509.35727,0.9989748
+4476,2892.9883,583.19434,2924.8032,649.6669,0.9989133
+4476,2489.2695,323.96207,2519.037,377.34042,0.9988024
+4476,1315.6061,315.96603,1336.8666,372.5543,0.9987859
+4476,3000.3738,310.36136,3024.8508,357.87546,0.99876297
+4476,2108.6143,298.72058,2132.8867,354.62158,0.99875224
+4476,2980.1328,378.78967,3008.6943,432.69946,0.9987109
+4476,3164.7473,482.10672,3188.6814,535.90576,0.99870837
+4476,3111.925,585.8795,3148.7761,650.5293,0.99867755
+4476,1977.5632,881.5895,2014.4897,973.33813,0.9985739
+4476,2699.7024,251.19197,2724.7673,297.43024,0.9984865
+4476,3027.808,404.59384,3057.3574,457.08743,0.99837506
+4476,2903.0193,233.01244,2919.5784,274.35092,0.99828786
+4476,3071.118,360.1334,3091.9211,411.40915,0.9981957
+4476,3010.3625,453.75302,3035.8567,509.08676,0.9981236
+4476,2606.5757,387.1986,2630.085,441.5791,0.9980685
+4476,2906.3538,362.52066,2926.617,413.65067,0.997783
+4476,2509.211,315.87067,2529.8208,368.59357,0.9971424
+4476,3256.9626,318.31537,3277.6775,362.72662,0.99672
+4476,3816.4607,746.50574,3846.988,801.7705,0.9965669
+4476,2010.9838,123.37421,2029.4833,162.93423,0.9935621
+4476,3878.3975,755.6125,3923.1807,813.41656,0.94796276
+4476,3694.0112,801.43365,3741.8706,865.1099,0.93863904
+4476,3518.9033,813.7366,3555.9067,878.3281,0.4541265
+4476,3933.9883,758.152,3961.4653,822.1161,0.41591847
+4476,3640.1597,811.62213,3672.2803,883.3774,0.23379472
+4477,2489.496,324.22885,2516.3975,377.37314,0.99937445
+4477,2054.7695,204.87332,2075.647,255.1705,0.99937195
+4477,3021.591,347.17365,3044.8982,395.06567,0.9991579
+4477,2105.5674,198.93863,2125.061,246.1723,0.99913967
+4477,2149.1816,418.48257,2176.9512,482.173,0.9990761
+4477,2918.1187,418.97955,2948.2979,475.50226,0.9990453
+4477,3069.4678,360.25397,3090.728,411.12476,0.99898356
+4477,3111.3835,586.572,3144.2644,650.65283,0.9988494
+4477,2107.2175,298.56506,2132.2395,354.82544,0.9987791
+4477,1315.8173,315.95944,1336.7975,372.57614,0.9987513
+4477,2462.6526,448.31277,2488.3455,508.91898,0.99871314
+4477,2697.6584,250.99382,2724.3752,297.5329,0.99869484
+4477,2979.3862,378.82623,3008.5474,432.70233,0.998664
+4477,3162.4417,482.22018,3188.035,536.1677,0.9985801
+4477,1977.5707,881.56586,2014.276,973.33624,0.99844885
+4477,3027.9722,404.58844,3057.3281,457.35852,0.99841416
+4477,2888.5486,584.8764,2921.7468,650.1014,0.9983808
+4477,2999.952,309.88458,3023.7307,357.16956,0.9983473
+4477,2605.871,386.9588,2629.9053,441.46436,0.9981975
+4477,3008.7556,454.1263,3034.5183,509.5038,0.9981786
+4477,2901.9575,232.7325,2918.253,273.94226,0.99814594
+4477,2509.21,315.69897,2529.915,368.44336,0.9981336
+4477,2905.5286,361.92047,2925.429,414.08398,0.99801755
+4477,3257.7095,318.03806,3276.9062,361.11472,0.99684036
+4477,3815.851,746.3485,3846.545,801.42065,0.9958675
+4477,2010.9843,123.373886,2029.4823,162.934,0.9935748
+4477,3693.8123,800.9253,3739.737,865.20715,0.82377434
+4477,3876.9622,755.71765,3922.3313,813.56445,0.77758104
+4477,3518.8098,814.17505,3553.6584,878.14075,0.6269016
+4477,3934.0037,757.99243,3961.6863,820.124,0.28432986
+4477,3639.9265,811.5088,3671.6804,885.18994,0.16186956
+4478,2489.057,323.9055,2513.4148,376.1512,0.99941146
+4478,2054.6147,204.14484,2075.771,255.20605,0.9992397
+4478,2885.5146,587.086,2917.9004,652.0882,0.9990693
+4478,2149.3086,417.81357,2172.8584,482.37332,0.99905044
+4478,2917.9797,418.5964,2946.533,474.97366,0.99898857
+4478,2104.0984,198.93736,2125.0383,246.5966,0.99891555
+4478,2696.7017,251.36191,2723.707,296.9017,0.998762
+4478,1316.0006,315.9493,1336.7992,372.50894,0.9987619
+4478,2899.8464,232.70084,2916.4978,274.35828,0.9987097
+4478,2979.353,378.59198,3007.9995,432.74475,0.99866605
+4478,3000.3826,309.24753,3022.8303,356.90964,0.99860424
+4478,3027.9878,404.83884,3057.2817,457.12314,0.9985782
+4478,3111.5935,587.9333,3138.1165,650.50275,0.99857414
+4478,3022.4443,347.2536,3044.2598,394.8152,0.9985677
+4478,1977.54,881.58984,2013.981,973.50195,0.9984877
+4478,2461.824,448.36487,2487.1008,508.6043,0.99846977
+4478,2510.0261,315.67294,2530.028,369.04025,0.99838054
+4478,3007.649,454.78693,3033.6396,509.89667,0.998292
+4478,2605.8267,386.93753,2629.6606,441.45908,0.9982597
+4478,3159.2268,482.51657,3186.9836,536.7527,0.99816906
+4478,2106.2004,298.586,2130.757,354.85114,0.998104
+4478,3066.2366,360.38968,3089.9622,411.20724,0.99802977
+4478,3814.0505,746.68475,3845.9348,802.1816,0.9973916
+4478,2903.655,361.9207,2924.3938,413.2783,0.99492294
+4478,2010.9841,123.37388,2029.4824,162.93387,0.9935768
+4478,3255.4575,317.6203,3276.7432,362.52277,0.99336505
+4478,3518.4138,814.3418,3551.639,877.9243,0.9586011
+4478,3876.677,755.4175,3921.402,813.603,0.53389776
+4478,3693.6875,801.014,3736.7402,865.242,0.4055723
+4478,3639.8655,812.09326,3671.7742,886.56616,0.2527513
+4478,3933.3296,757.4522,3961.9272,818.78815,0.20132813
+4478,3414.5955,820.3144,3453.5974,892.0882,0.102885485
+4479,2979.1968,378.2666,3007.1748,432.73138,0.9993236
+4479,2885.067,589.35913,2914.148,653.40625,0.99929154
+4479,2054.493,203.94699,2074.6208,254.93121,0.9991497
+4479,2918.2607,417.90216,2945.752,475.03864,0.9988838
+4479,2896.371,232.33902,2915.4033,273.75516,0.99880886
+4479,1316.0425,315.9585,1336.7996,372.49896,0.9987581
+4479,2149.3027,417.4742,2169.9775,482.06638,0.9987425
+4479,2697.3562,251.22919,2723.3528,296.41907,0.9986999
+4479,3007.6067,455.5646,3032.5183,510.40256,0.99867696
+4479,2102.0686,198.73965,2124.6624,246.89014,0.9986553
+4479,3000.4722,309.1215,3021.705,356.54666,0.998654
+4479,2510.0945,315.91577,2530.1555,368.82642,0.9986431
+4479,3064.547,360.57367,3088.8064,410.42194,0.9986375
+4479,2461.1814,448.12103,2486.7761,508.989,0.9986369
+4479,2104.5056,298.6304,2129.4006,354.61948,0.9985727
+4479,2488.935,323.6485,2508.795,375.85626,0.9984938
+4479,3108.937,588.0515,3132.7358,650.33716,0.9984432
+4479,3028.0342,404.903,3057.3276,457.0166,0.99841547
+4479,1977.4307,881.56604,2013.5854,973.6476,0.9983656
+4479,2605.8477,386.72668,2629.2407,441.31738,0.9982027
+4479,3156.8525,483.1646,3185.7866,537.0846,0.99813485
+4479,3022.91,347.33118,3043.259,394.8283,0.99771285
+4479,3810.3728,746.55084,3844.8962,802.2148,0.9976972
+4479,3255.0212,317.21793,3276.7195,362.5964,0.9950203
+4479,2899.7434,361.52905,2922.5881,412.14038,0.9940474
+4479,2010.984,123.37385,2029.4825,162.93427,0.9935801
+4479,3518.2441,814.0056,3550.1772,878.0443,0.8922981
+4479,3693.3872,800.8237,3735.3374,865.4758,0.26526093
+4479,3882.3706,755.8047,3920.8022,813.60266,0.25118178
+4479,3933.572,757.3545,3962.3977,818.2761,0.2029091
+4480,2884.2449,590.7593,2910.244,653.126,0.9994917
+4480,2979.0986,378.3752,3006.6533,432.8173,0.9992441
+4480,2148.9827,417.39658,2168.3015,482.27945,0.9991652
+4480,3103.9966,586.94714,3128.2188,650.1039,0.9990526
+4480,3000.0125,308.63165,3020.7522,355.83197,0.9990121
+4480,2918.041,417.51917,2944.7266,474.29822,0.99889416
+4480,2100.738,198.71844,2123.682,246.7945,0.99888486
+4480,2054.2996,204.16608,2073.7673,254.29453,0.99883884
+4480,1316.0985,316.01923,1336.8033,372.539,0.9987614
+4480,3022.3323,347.41733,3042.3118,394.6334,0.9987301
+4480,2697.344,250.52054,2722.2366,295.85037,0.9987147
+4480,3155.6235,483.83655,3184.6777,536.9366,0.9987118
+4480,3808.9602,746.57837,3844.0784,803.433,0.9986457
+4480,3007.4395,455.59952,3031.787,510.42105,0.99863666
+4480,2488.5027,323.22742,2506.2024,375.16315,0.99863464
+4480,2460.2085,448.03662,2486.5488,509.10535,0.9985518
+4480,2510.5588,315.66043,2530.617,368.29654,0.99852175
+4480,3027.8865,405.00336,3057.4075,456.9914,0.99834824
+4480,2103.5027,298.54742,2129.2014,354.29602,0.99822646
+4480,2890.6343,232.22284,2915.1191,274.0989,0.99812
+4480,3062.2622,360.83966,3087.1938,409.75,0.998021
+4480,2605.9902,386.7782,2628.6714,441.273,0.9979412
+4480,1977.3723,881.4747,2013.0088,973.63544,0.99793494
+4480,2894.9697,361.00925,2922.0825,413.64926,0.99762076
+4480,3254.5247,317.3363,3276.5417,361.8567,0.99572575
+4480,2010.9828,123.2779,2029.4725,162.92699,0.99353325
+4480,3518.1985,813.689,3548.239,881.43146,0.9508208
+4480,3693.554,800.10846,3732.8274,865.31244,0.23233998
+4480,3933.2844,758.05054,3962.322,818.18384,0.12929873
+4480,3885.6594,756.399,3919.3796,813.2891,0.12273322
+4481,2917.3958,416.93097,2944.0837,472.64337,0.9994137
+4481,2881.3433,592.1583,2906.5195,652.9723,0.99938285
+4481,2979.067,378.442,3006.2817,432.7537,0.9992224
+4481,2696.821,249.83568,2720.3054,295.61392,0.9991228
+4481,3806.0122,746.9344,3843.2944,804.3963,0.9990952
+4481,3100.1436,586.06866,3124.8784,649.98615,0.99896634
+4481,2145.5327,416.94623,2167.7734,482.65118,0.9989586
+4481,2100.092,198.75745,2122.8845,247.02686,0.9989033
+4481,2102.696,298.42987,2127.3176,354.33337,0.99882346
+4481,3021.444,347.84488,3041.8406,395.1041,0.9988103
+4481,2999.7334,308.60242,3020.1997,355.13782,0.9987676
+4481,2510.53,315.26654,2530.7888,368.1081,0.99875426
+4481,2053.8972,203.84708,2073.0334,253.60089,0.99875295
+4481,2486.061,322.53287,2504.961,375.53928,0.99875236
+4481,1316.2256,315.94223,1336.843,372.56985,0.9986759
+4481,2459.6118,448.25916,2486.0977,509.1397,0.9986594
+4481,3061.2947,361.0262,3085.9358,410.15433,0.9985896
+4481,3154.905,484.0587,3183.8303,537.39954,0.9985379
+4481,3007.2573,455.68246,3030.628,510.60098,0.9984863
+4481,3027.8438,405.0112,3057.4106,457.29318,0.9982603
+4481,2891.6077,360.5323,2921.26,413.70386,0.99810284
+4481,2887.2512,232.43556,2914.3376,274.33203,0.9980877
+4481,2605.9434,386.6872,2628.4526,441.26648,0.9980167
+4481,1977.1965,881.35864,2012.4482,973.60535,0.9978733
+4481,2010.9814,123.236916,2029.4521,162.91707,0.993497
+4481,3252.3264,317.49515,3276.174,362.16605,0.9906632
+4481,3513.9238,813.974,3546.832,884.16125,0.8033623
+4481,3693.6602,798.4988,3731.267,865.12415,0.2738003
+4481,3933.4111,758.9862,3962.2793,818.2534,0.13147554
+4481,3886.9348,755.5566,3918.863,813.2122,0.10236013
+4482,2917.663,416.34003,2942.878,471.3789,0.9994497
+4482,3803.689,747.0948,3842.3228,804.79144,0.9993451
+4482,3095.0044,585.2739,3122.0532,650.0808,0.9991241
+4482,2510.7107,314.97913,2531.2786,368.18866,0.9989561
+4482,3020.0183,348.3297,3041.6013,395.2904,0.9989221
+4482,2696.8638,248.77322,2717.7217,294.86935,0.9989166
+4482,2877.9001,592.33484,2902.9978,652.71924,0.9989166
+4482,2999.6018,308.65826,3019.489,355.35577,0.998874
+4482,2978.9128,378.2197,3006.0354,432.71048,0.99886346
+4482,2101.6372,298.41632,2126.6436,354.30835,0.99868065
+4482,3007.0654,455.34824,3029.98,511.01376,0.9986444
+4482,2458.9685,448.81116,2485.4114,509.27283,0.9986386
+4482,2099.753,198.30612,2121.7646,246.35184,0.99863654
+4482,1316.2456,315.94217,1336.8533,372.58658,0.9986181
+4482,2053.7747,203.0926,2072.6033,253.50749,0.99859554
+4482,2482.8813,322.67017,2502.8013,375.38422,0.9985869
+4482,3155.1218,484.5043,3181.9885,537.7334,0.99856526
+4482,3060.2268,361.03815,3084.9675,410.45264,0.99851197
+4482,2605.6135,386.63034,2627.9426,441.2686,0.9984028
+4482,2143.0164,416.83646,2167.2322,482.56393,0.9983991
+4482,3027.8604,404.97964,3057.44,457.08957,0.9983369
+4482,2890.5945,360.60425,2920.095,413.55823,0.9980117
+4482,1976.9736,881.22784,2012.2146,973.6606,0.99778676
+4482,2886.2207,232.19362,2913.461,273.94687,0.9977343
+4482,3252.23,317.34793,3276.1226,361.13998,0.99457175
+4482,2010.9834,123.18569,2029.4541,162.90015,0.99351513
+4482,3507.9097,814.07465,3544.9023,886.4914,0.9249228
+4482,3693.6042,797.48987,3729.986,864.9142,0.40493667
+4482,3886.5,755.8101,3917.5366,813.0179,0.25974613
+4482,3933.288,759.9722,3962.7026,818.3129,0.16167817
+4483,2917.4014,416.1271,2942.207,471.28238,0.9993814
+4483,3087.9373,584.266,3120.8708,650.3745,0.9993025
+4483,3801.3696,747.6929,3841.5156,804.1278,0.9991887
+4483,2511.214,314.7824,2531.9504,368.20215,0.9989849
+4483,2978.9204,378.11813,3005.6978,432.5942,0.9989735
+4483,2999.815,308.2204,3019.128,355.26166,0.9989411
+4483,3059.9873,361.06366,3083.417,410.35126,0.9989386
+4483,2100.997,298.08118,2125.2158,353.3252,0.99881613
+4483,2138.4067,416.16855,2167.3389,482.54813,0.99874616
+4483,2458.4607,449.07245,2484.9197,509.4624,0.998681
+4483,3007.0237,455.42114,3029.1653,511.14478,0.99867475
+4483,2697.0261,248.59155,2714.513,294.63452,0.9986588
+4483,2478.1267,322.62402,2501.9333,375.53503,0.9986572
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+4483,1976.8296,880.85974,2012.0032,973.71875,0.99716383
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+4483,2010.9935,123.18802,2029.4454,162.89227,0.99351984
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+4484,1976.6158,880.9146,2011.777,973.737,0.9969567
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+4484,2010.9941,123.188644,2029.4182,162.89064,0.99347067
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+4485,2914.6938,414.9086,2940.996,471.62112,0.99899274
+4485,2695.0305,248.71439,2713.0002,294.61282,0.99896574
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+4485,2468.6694,323.20413,2500.9526,375.02023,0.9989115
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+4485,1976.5271,880.732,2011.2915,973.74225,0.99709105
+4485,2888.7517,359.37387,2917.9724,412.26517,0.99686414
+4485,2010.9889,123.18819,2029.4017,162.88939,0.9934157
+4485,3248.4033,318.0604,3274.5186,360.94388,0.9888377
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+4485,3639.638,812.7787,3669.056,881.66394,0.11906395
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+4486,3078.3667,587.26843,3113.7139,652.19055,0.99916935
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+4486,3018.5127,350.99036,3038.6387,396.34497,0.998869
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+4486,1976.3856,880.805,2011.0704,973.7719,0.9973349
+4486,2010.9836,123.19078,2029.395,162.88496,0.9934437
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+4487,1976.1493,880.7515,2010.8202,973.76044,0.9967259
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+4488,2885.5195,230.23753,2901.4907,271.64987,0.9983059
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+4488,1975.8651,880.34546,2010.7179,973.87585,0.99721813
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+4489,1975.6627,880.3659,2010.3666,973.9692,0.997291
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+4522,2992.5042,381.83167,3015.7102,433.8694,0.9983292
+4522,2986.9692,296.57727,3009.127,341.9878,0.99823004
+4522,2654.0093,237.12125,2673.2046,281.91745,0.9981664
+4522,2009.3383,125.86402,2027.8888,162.61694,0.99804664
+4522,3025.9246,405.83084,3050.2751,455.13486,0.997976
+4522,2832.3328,222.32417,2853.074,263.26016,0.9968527
+4522,2029.2463,198.80756,2046.1299,248.02664,0.9964567
+4522,3007.557,361.84384,3026.5652,411.5007,0.99267274
+4522,1969.5431,881.7176,2006.6854,976.9058,0.98677814
+4522,3021.0493,355.5898,3035.79,407.01883,0.9797681
+4522,3239.4414,333.12897,3256.766,363.41113,0.9636426
+4522,3428.985,819.46545,3468.9236,894.2726,0.8534717
+4522,3853.6125,761.3513,3888.9827,820.48535,0.74354255
+4522,3947.1543,753.48834,3993.9878,811.20685,0.43961442
+4522,3504.4917,811.72424,3543.6064,875.88745,0.41176546
+4522,2580.018,881.3357,2630.401,973.7091,0.12623894
+4523,2749.3835,634.2952,2776.799,701.52545,0.9995351
+4523,3761.016,751.01276,3801.1416,808.58014,0.9994398
+4523,2419.6594,445.48688,2450.9856,505.83978,0.99931824
+4523,2085.0452,295.90253,2103.8943,352.90125,0.9992488
+4523,2990.0754,473.24442,3022.2737,532.2473,0.9991616
+4523,2532.8232,311.3236,2558.3555,364.24835,0.99914825
+4523,2894.3364,402.32556,2921.2295,458.96594,0.9990933
+4523,2095.1082,410.01917,2125.6824,476.4098,0.9990897
+4523,2604.294,383.7786,2625.148,439.24246,0.99908626
+4523,2061.6228,189.12769,2082.8513,234.04044,0.9990695
+4523,2936.4521,593.1852,2971.1445,659.0054,0.99906164
+4523,2401.5684,314.31406,2433.1587,367.8347,0.99905366
+4523,3132.6182,498.26382,3156.666,554.9431,0.99903417
+4523,2861.4924,344.44937,2885.4768,396.24582,0.9990307
+4523,2987.601,296.10077,3009.1016,341.60675,0.99893826
+4523,1309.9932,319.60696,1334.0745,374.01163,0.99848783
+4523,2651.893,236.61601,2672.8105,282.2026,0.9984053
+4523,2992.9658,380.67636,3016.558,434.11972,0.99830663
+4523,2009.2847,125.917755,2027.4961,162.41699,0.9982488
+4523,3025.4436,405.57077,3050.1292,455.0806,0.99798477
+4523,2027.7081,198.84396,2045.1757,247.70438,0.9968681
+4523,2833.1804,221.95958,2850.8362,263.08768,0.9961882
+4523,1969.1204,882.07904,2006.5923,977.20685,0.99152815
+4523,3021.446,355.76898,3034.4773,407.21686,0.97928154
+4523,3237.5735,332.85895,3256.2185,363.53052,0.97853196
+4523,3007.037,362.29645,3026.899,412.83795,0.9765331
+4523,3425.5237,818.61035,3468.3508,894.1714,0.91469854
+4523,3511.2705,811.7767,3548.5752,873.4876,0.8454811
+4523,3852.4631,761.64935,3888.411,820.794,0.42664194
+4523,3947.961,751.4868,3994.8486,810.6737,0.1350189
+4524,2534.1208,311.2703,2558.7136,363.89493,0.99938226
+4524,2743.782,634.69434,2775.0427,704.82666,0.9993586
+4524,3760.9792,751.2739,3799.797,808.8163,0.999315
+4524,2419.657,445.3765,2450.7683,505.62277,0.9992945
+4524,2934.105,593.806,2962.8335,659.18555,0.9992908
+4524,2085.0688,295.89502,2105.6343,352.93878,0.99922276
+4524,3131.8335,498.33627,3156.1445,554.99677,0.99918324
+4524,2604.635,383.8568,2625.6086,439.0879,0.99916804
+4524,2989.9832,474.25867,3022.741,534.0887,0.9990916
+4524,2859.4226,344.2268,2884.8972,396.0796,0.99906355
+4524,2401.453,314.02133,2431.6428,367.71548,0.99901223
+4524,2894.3933,402.10416,2921.0725,457.92422,0.9989938
+4524,2094.019,409.80032,2125.2324,476.6559,0.9989889
+4524,2988.0427,295.48157,3009.117,341.37665,0.99894196
+4524,2061.1658,188.53786,2081.654,233.44713,0.9988751
+4524,2649.0776,236.49374,2671.9067,282.33432,0.9986891
+4524,2993.4854,380.40625,3017.8286,433.92316,0.99862635
+4524,1310.2448,319.42404,1334.1102,374.0206,0.99861634
+4524,3024.244,405.228,3049.6414,455.29837,0.9983063
+4524,2008.9465,125.948166,2027.0476,162.3806,0.9981047
+4524,2832.317,221.27682,2849.0537,263.3,0.99788755
+4524,2026.6494,198.90042,2044.4429,247.46365,0.9975319
+4524,3236.9045,332.8752,3256.115,363.85526,0.99006385
+4524,1968.7565,882.3507,2006.4425,977.26025,0.9882208
+4524,3422.863,818.84186,3467.4167,894.05194,0.96334124
+4524,3020.455,355.65674,3033.3608,406.26398,0.93943954
+4524,3517.7275,811.814,3552.6748,873.41534,0.9359221
+4524,3008.1323,362.18,3028.6387,410.60773,0.89901537
+4524,3852.357,762.30804,3888.8633,821.15106,0.33375776
+4524,3953.8037,753.3747,3994.912,810.5335,0.21286806
+4525,2737.875,635.8309,2773.565,707.40717,0.9994716
+4525,2534.3684,310.87418,2559.0447,363.7468,0.99931324
+4525,2894.5632,401.66153,2920.1038,457.4434,0.99931026
+4525,2604.7097,383.84753,2626.8342,439.24115,0.99929154
+4525,2419.364,445.26816,2450.9949,505.5582,0.9992662
+4525,2093.8064,409.71103,2124.5935,476.48026,0.99919194
+4525,2085.0776,295.8937,2105.5752,352.95126,0.99915314
+4525,3761.4402,751.62726,3798.947,809.00555,0.9991509
+4525,3131.1582,498.1905,3155.6426,554.8123,0.99914694
+4525,2989.9307,475.1277,3022.815,535.13806,0.99910766
+4525,2060.8616,188.59436,2080.7537,232.83948,0.99908125
+4525,2930.4478,593.0376,2959.1147,659.16736,0.9990324
+4525,2988.288,295.1873,3009.1753,340.91263,0.9989849
+4525,2646.5525,236.70735,2670.193,282.2503,0.9988048
+4525,1310.3193,319.48724,1334.1416,374.0276,0.99874544
+4525,2857.2102,343.9175,2884.5955,396.95224,0.9986524
+4525,2992.9104,379.90424,3018.1633,434.10028,0.99848145
+4525,2401.5977,313.87482,2429.438,366.13605,0.99830663
+4525,2025.0709,198.99347,2043.9984,247.82468,0.99793977
+4525,2008.7872,126.129654,2026.6656,162.36848,0.99778676
+4525,3022.319,404.7731,3049.5798,455.0189,0.99776745
+4525,2831.4211,220.56673,2847.675,264.30148,0.9975522
+4525,3237.406,332.71597,3255.5715,362.99075,0.9912382
+4525,3421.9004,819.8661,3465.6719,894.48914,0.98787296
+4525,1968.5249,882.5936,2006.3577,977.3507,0.9855101
+4525,3008.6506,355.64432,3033.2449,407.5903,0.97863364
+4525,3519.6064,810.988,3557.3384,873.8322,0.9185088
+4525,3852.4487,762.3157,3888.4531,821.15173,0.24336621
+4525,3955.2644,753.6622,3994.9763,810.84564,0.22706915
+4525,3608.5195,805.71783,3666.648,872.33417,0.20991302
+4526,2894.6208,401.2339,2919.7976,457.12158,0.99945647
+4526,2927.1067,592.57385,2955.6238,659.1732,0.99939907
+4526,2735.0078,637.3119,2772.291,709.7446,0.99933964
+4526,2060.6418,188.13951,2079.1838,232.77922,0.9993049
+4526,2857.061,343.84238,2884.1235,397.24606,0.9992563
+4526,2085.2058,295.93823,2104.1565,352.96338,0.99918586
+4526,2536.2515,310.94113,2559.5674,363.7769,0.9991449
+4526,2988.7258,295.1469,3009.422,340.7475,0.99914074
+4526,2093.6826,409.49255,2123.8062,475.91644,0.99909544
+4526,2643.4717,235.96465,2669.0093,282.16763,0.9990839
+4526,2604.8096,384.04538,2627.495,438.93082,0.9990827
+4526,2990.0732,476.18674,3023.0244,536.14746,0.99906653
+4526,2419.36,445.68607,2449.8855,505.37344,0.99905413
+4526,3761.5073,752.2164,3798.0786,809.37555,0.9990103
+4526,2401.2349,312.92175,2425.6118,365.63702,0.99878937
+4526,1310.4631,319.46143,1334.2129,373.96033,0.99876523
+4526,3129.7703,498.4823,3154.4875,554.6056,0.9984964
+4526,2992.8972,380.08392,3018.5706,434.06952,0.99848026
+4526,2024.6318,198.93495,2044.0488,247.886,0.99844164
+4526,2828.8923,220.60188,2845.9666,263.56714,0.9980538
+4526,3020.7693,404.41464,3049.4963,455.20187,0.99773204
+4526,2008.2327,126.37239,2026.4912,162.42375,0.99756765
+4526,3237.8252,331.9429,3255.224,362.71286,0.9947444
+4526,1968.421,882.7193,2006.2897,977.43536,0.99255073
+4526,3008.3457,358.4126,3032.5903,406.98547,0.97264165
+4526,3421.6865,820.8964,3464.4087,895.23883,0.971981
+4526,3524.1472,809.1974,3561.2205,873.9592,0.32298166
+4526,3852.3645,762.322,3887.6804,821.2438,0.24469548
+4526,3608.6587,804.52454,3643.312,868.9718,0.12979281
+4526,3640.4314,820.0387,3670.2117,879.276,0.10257056
+4527,2894.5645,400.9919,2919.8555,456.65903,0.9995234
+4527,2922.9312,592.1074,2952.1655,660.0803,0.9993938
+4527,2855.8755,343.59482,2883.3975,397.1481,0.999316
+4527,2731.8557,640.02136,2769.863,711.7018,0.99927634
+4527,2604.9075,384.34824,2628.7131,438.86655,0.9992374
+4527,2085.1736,296.02438,2104.055,352.93655,0.9991685
+4527,2401.1558,312.65488,2422.213,365.0024,0.9991448
+4527,2988.4688,295.02588,3009.8481,340.9859,0.9991216
+4527,2990.1436,477.2738,3023.4126,536.9667,0.9991062
+4527,2419.2961,445.51486,2449.4583,505.0159,0.9990663
+4527,2642.118,235.82715,2667.9705,281.9245,0.9990501
+4527,2060.6157,187.65904,2077.313,232.52516,0.9990415
+4527,2538.4797,310.84344,2560.3064,362.4262,0.99893934
+4527,3127.6936,499.12976,3154.0793,555.4546,0.9989347
+4527,2093.7256,409.48895,2122.941,475.722,0.9987796
+4527,2993.1038,380.9516,3019.4324,434.3062,0.99873775
+4527,3761.3767,752.9479,3797.0095,809.67737,0.9987349
+4527,1310.6614,319.55157,1334.3044,373.9796,0.99871993
+4527,2023.7422,198.80252,2044.1008,247.89536,0.99857056
+4527,2824.3962,220.40555,2844.886,263.57358,0.9979664
+4527,2007.8717,126.47284,2026.1923,162.37479,0.9977035
+4527,3018.3364,403.73413,3048.5796,455.1466,0.9975043
+4527,3238.3552,331.93088,3255.0544,361.55063,0.99395025
+4527,1968.0972,882.9019,2006.3091,977.4671,0.99353445
+4527,3421.6558,822.431,3462.9614,894.76086,0.96210974
+4527,3007.8123,361.1995,3028.6975,408.96542,0.8933708
+4527,3012.9111,354.0579,3030.7515,405.79465,0.8412232
+4527,3608.6367,803.82336,3641.4858,868.48804,0.3569214
+4527,3008.0884,354.31738,3028.2842,392.8629,0.34294227
+4527,3639.9377,820.0086,3670.278,879.8714,0.23422322
+4527,3520.8225,806.609,3565.6106,873.57837,0.1732864
+4527,3852.2617,762.2243,3885.8257,821.2901,0.14567998
+4528,2894.484,400.55063,2919.5806,456.26083,0.9996873
+4528,2914.461,591.80743,2948.567,660.8923,0.9996032
+4528,2730.7847,643.5702,2765.9263,712.9254,0.9993774
+4528,3124.7668,500.16486,3153.3762,556.3482,0.99921036
+4528,2085.3606,296.04156,2104.4182,352.93427,0.99918956
+4528,2989.037,294.854,3010.3457,341.29382,0.9991641
+4528,2990.6372,478.1418,3023.2583,536.9055,0.99908745
+4528,2399.9639,312.50696,2419.3008,364.75287,0.99902534
+4528,2419.2666,445.5595,2448.084,504.84753,0.9990144
+4528,2059.585,187.59036,2076.1318,232.41785,0.99899894
+4528,2855.4795,343.60907,2882.499,396.50995,0.99899596
+4528,2605.0627,384.32303,2629.6917,438.75967,0.9989905
+4528,2993.1387,381.13223,3020.7227,434.50797,0.99891984
+4528,3759.7637,753.36804,3795.6045,810.1979,0.99880064
+4528,2093.6494,409.3973,2122.5796,475.3428,0.9987313
+4528,2641.6484,235.69304,2666.6504,281.56244,0.99868256
+4528,2023.7034,198.68219,2044.1111,247.87646,0.99861026
+4528,2539.853,310.57016,2560.8262,362.0704,0.99859565
+4528,1310.732,319.6797,1334.4904,374.00616,0.99846053
+4528,3016.6519,403.04572,3048.6836,455.31287,0.99835473
+4528,2821.0566,220.85599,2844.1343,263.56155,0.99778795
+4528,2007.1663,126.44313,2025.5493,162.44032,0.9970721
+4528,1967.994,883.0588,2006.2445,977.3299,0.99578995
+4528,3237.7134,331.6691,3254.6987,363.06516,0.9938883
+4528,3421.5918,822.5735,3462.4087,894.9519,0.97927
+4528,3004.2798,363.36633,3028.246,405.31396,0.9204291
+4528,3014.167,353.99945,3029.1812,402.40076,0.6712059
+4528,3005.7,354.71356,3026.9163,389.88824,0.3704462
+4528,3640.7664,819.64545,3670.385,880.1452,0.18777074
+4528,3954.5784,753.5922,3995.8274,811.13995,0.12229064
+4528,3852.4102,762.25793,3884.8833,821.24866,0.10742172
+4529,2894.0452,400.28003,2919.404,455.28278,0.9997216
+4529,2906.4084,592.02325,2946.5823,660.95953,0.99952483
+4529,2989.2578,294.7252,3010.7285,341.10776,0.99929893
+4529,1311.27,318.53342,1335.3425,372.77798,0.99922335
+4529,2058.1238,187.58186,2075.777,232.39876,0.9992119
+4529,2993.2732,381.27505,3024.4211,434.67978,0.9991841
+4529,2991.6204,478.15656,3023.3489,536.74133,0.9991744
+4529,2085.352,296.19492,2104.7148,352.95248,0.9991184
+4529,3123.9282,500.5133,3152.1748,556.3263,0.99909866
+4529,2729.3591,645.878,2761.4436,712.9339,0.9990715
+4529,2398.0544,312.26318,2417.8875,365.03662,0.998998
+4529,2419.3318,445.2706,2445.5955,504.33585,0.99887294
+4529,2605.0713,384.09528,2629.8564,438.7342,0.9988319
+4529,2854.8767,343.44342,2882.3118,396.39703,0.99880683
+4529,2541.228,310.19125,2561.4067,361.75974,0.998713
+4529,3758.483,753.58545,3794.6353,810.7903,0.9986614
+4529,2093.606,408.83875,2121.581,475.43372,0.9986185
+4529,2641.4326,235.34074,2665.915,281.07248,0.99849355
+4529,3015.8672,403.00702,3045.831,455.49072,0.99838436
+4529,2816.787,220.66765,2843.4526,263.61356,0.9983187
+4529,2023.6794,198.68991,2043.4014,247.82446,0.9976949
+4529,2006.9218,126.38852,2024.8673,162.31616,0.9964042
+4529,1967.8943,883.14233,2006.1877,977.3922,0.99583805
+4529,3237.5432,331.13776,3254.5286,363.55103,0.9915019
+4529,3421.5034,822.35016,3461.4224,894.5429,0.9878878
+4529,3003.7908,364.12485,3025.097,406.24326,0.85011184
+4529,3002.9927,353.58453,3026.1118,390.98224,0.74629873
+4529,3640.491,818.3726,3670.5227,879.3912,0.25685716
+4529,3608.1118,801.8678,3638.6675,869.0768,0.24385524
+4529,3013.6787,353.744,3026.3633,391.5469,0.232965
+4530,2893.44,400.06036,2919.2593,454.6034,0.99969876
+4530,2993.904,381.1491,3024.6033,434.23877,0.9995334
+4530,2057.7434,187.3968,2075.7278,232.51073,0.9995134
+4530,2901.6733,592.88074,2942.8042,661.647,0.9994666
+4530,2987.8936,294.6586,3010.9326,341.06003,0.9993166
+4530,2085.3823,296.35663,2105.019,353.00238,0.9992968
+4530,2992.2017,478.0959,3023.7139,536.5488,0.9992735
+4530,1311.6078,318.35162,1335.2523,372.7973,0.9991955
+4530,3122.6694,500.89038,3150.7754,556.62915,0.99918336
+4530,2542.6084,309.59772,2562.1816,362.5619,0.9991454
+4530,2726.4495,646.8033,2756.5374,713.31036,0.99904174
+4530,2605.1426,384.60614,2629.8545,438.66858,0.9989397
+4530,3014.0598,402.766,3045.138,455.7611,0.9989242
+4530,2419.4858,445.54327,2442.229,504.59296,0.9989058
+4530,2641.102,235.65385,2664.9883,280.85535,0.998803
+4530,2093.2961,408.90222,2118.6355,475.49933,0.99869496
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+4532,3010.6152,352.73996,3024.0884,388.83572,0.28530207
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+4538,2992.3315,292.25323,3013.7964,337.43463,0.99895895
+4538,2004.5527,126.05144,2019.1631,162.12251,0.99887913
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+4538,3755.8113,752.5249,3787.0154,812.20996,0.9982425
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+4538,1966.0416,883.97156,2006.3334,977.7339,0.9976069
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+4539,2995.535,481.98883,3020.4172,540.98737,0.99897814
+4539,2049.9016,185.35776,2069.782,231.14441,0.99885845
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+4539,3753.588,752.4391,3786.6528,812.63354,0.9986124
+4539,2004.4042,126.0316,2018.6996,162.68666,0.99856865
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+4539,2023.2255,198.51012,2038.4205,247.21895,0.99781513
+4539,1965.9229,883.92316,2006.314,977.7463,0.9977702
+4539,2633.5771,233.89528,2656.1338,279.6131,0.99766576
+4539,2998.3787,365.46555,3019.8982,416.64542,0.9975594
+4539,3236.2842,328.52798,3256.524,368.11642,0.99752
+4539,2988.0254,351.75204,3015.995,400.60056,0.9965405
+4539,2083.5796,409.3313,2109.2832,473.18884,0.9961998
+4539,3009.8403,381.33746,3031.6006,434.43475,0.9955745
+4539,2086.9526,297.01285,2109.3423,353.25925,0.98351616
+4539,3422.8137,819.11444,3462.782,891.45746,0.83693886
+4539,2585.6594,873.73566,2626.2239,956.3388,0.8032778
+4539,3640.3713,816.2213,3670.7854,882.81384,0.4632652
+4539,3573.4653,794.14764,3623.0073,869.31305,0.41203728
+4539,3838.3342,765.7739,3871.694,821.70984,0.22894283
+4539,3583.383,809.81586,3623.4993,878.6718,0.1494073
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+4540,2993.4993,292.2325,3014.5085,337.0258,0.9993499
+4540,3119.1057,504.22754,3144.6794,561.72095,0.9992731
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+4540,2546.4934,308.84116,2572.2336,361.45236,0.99915695
+4540,2855.0354,339.19183,2880.8352,390.14093,0.998963
+4540,2995.7554,482.49835,3020.0742,542.30365,0.9989172
+4540,3751.4126,752.93365,3786.088,813.8328,0.9988354
+4540,2691.5195,660.0214,2719.8774,726.9301,0.9988146
+4540,2048.779,185.04953,2067.3723,230.31055,0.9987141
+4540,2607.4546,384.9997,2634.94,438.45465,0.9985481
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+4540,2403.2812,443.8753,2433.4321,505.13733,0.99834716
+4540,2632.5254,233.60269,2655.9053,279.4341,0.99831223
+4540,2801.8801,219.08922,2826.1262,260.5046,0.9982065
+4540,2003.9893,126.619865,2017.3167,162.76802,0.99792516
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+4540,3236.1934,328.21085,3256.5288,368.4816,0.9977405
+4540,1965.7118,884.00726,2005.9244,978.43805,0.99764746
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+4540,2987.8706,351.57422,3014.996,400.65204,0.99660337
+4540,2997.5952,365.8956,3019.9644,416.83237,0.9958406
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+4540,2087.2888,297.11172,2107.7087,353.16983,0.95889145
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+4540,2087.3071,296.2405,2121.5503,353.30637,0.6529307
+4540,3582.3384,793.4448,3628.167,865.7804,0.6170549
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+4540,3838.4429,765.86163,3870.5908,821.5269,0.2241903
+4541,2894.8726,397.75162,2921.6655,451.58585,0.9994603
+4541,3118.074,505.0952,3144.0989,562.4081,0.99940515
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+4541,2378.0437,310.1367,2396.6238,362.59293,0.9992219
+4541,2688.4558,659.96735,2716.3225,726.98346,0.9992111
+4541,2863.3433,597.74396,2898.3076,666.0412,0.99915123
+4541,3750.0068,753.2257,3785.3447,814.2379,0.9990687
+4541,2854.4036,339.09546,2879.6409,389.86774,0.99905926
+4541,2995.7812,482.96912,3019.7437,542.4928,0.99898213
+4541,2546.9878,308.31927,2572.6167,361.39642,0.99894065
+4541,2630.3748,233.843,2655.0208,279.58508,0.99891984
+4541,2609.297,384.98126,2635.2854,438.4541,0.9988912
+4541,2049.0464,184.25916,2066.8706,229.19238,0.99877244
+4541,2003.071,126.88763,2016.7834,161.95688,0.99855334
+4541,2087.3652,297.06467,2106.3809,353.20157,0.99850947
+4541,2402.893,444.00046,2433.1968,505.15475,0.99843854
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+4541,2022.7329,197.92654,2038.1799,247.43832,0.9981997
+4541,3235.8362,328.01028,3256.5994,368.2062,0.9981242
+4541,1965.7069,884.0062,2005.9308,978.44104,0.99764854
+4541,2081.9084,409.9217,2108.2542,473.58344,0.9972898
+4541,2987.5085,351.3086,3013.7805,400.64355,0.99704045
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+4541,3010.7888,398.90613,3032.4514,452.4513,0.9957326
+4541,3010.7756,380.84424,3033.5984,434.6775,0.942102
+4541,3423.3298,818.7096,3464.8577,890.4861,0.84738004
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+4541,2585.7969,873.3174,2626.3008,956.1555,0.27903107
+4541,3838.4363,766.09686,3869.6765,821.5118,0.2502325
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+4542,2996.0156,482.9286,3019.8877,542.0242,0.9992919
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+4542,3749.9263,753.33167,3784.9019,814.6371,0.999108
+4542,2375.9407,310.04105,2394.8113,362.50882,0.9991032
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+4542,2048.5525,183.67648,2065.8025,229.23071,0.9989347
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+4542,2080.0247,409.98364,2108.09,473.89056,0.99805826
+4542,2002.3367,126.403915,2016.5798,162.15111,0.99798787
+4542,1965.6746,883.98865,2005.9153,978.4707,0.9979836
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+4543,2857.7957,598.8694,2886.5042,666.1277,0.99877423
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+4543,2047.8313,183.34789,2064.553,228.485,0.9986688
+4543,2000.2023,126.00693,2016.431,162.62344,0.9982368
+4543,2079.7861,410.28906,2107.8296,474.06665,0.99819297
+4543,1965.5835,883.94666,2005.9141,978.46643,0.99818206
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+4543,2987.196,351.02625,3010.7551,400.42773,0.99790275
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+4543,2998.4082,367.86462,3018.9268,417.02545,0.9962657
+4543,2087.3289,297.12628,2106.473,353.1858,0.9953792
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+4543,3423.8523,816.81995,3467.5808,887.70984,0.9620054
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+4544,2853.7205,338.19778,2876.9417,389.2205,0.9992368
+4544,2854.1343,598.3443,2883.4248,665.638,0.9991973
+4544,2995.8503,483.34665,3020.2595,542.11255,0.99912554
+4544,2087.3254,297.17984,2106.0667,353.2189,0.99911934
+4544,3114.4678,506.98508,3142.1118,565.1064,0.999064
+4544,2365.6775,309.3077,2391.9695,362.57504,0.99902725
+4544,2798.9033,218.91121,2822.9268,259.96188,0.9990208
+4544,2994.6746,292.54184,3016.4084,336.3535,0.99898905
+4544,3747.2173,753.5624,3783.9321,814.7395,0.99897337
+4544,2629.6116,234.02264,2653.7654,279.65424,0.998909
+4544,2401.893,444.44952,2432.5942,504.91736,0.99887806
+4544,2046.8851,183.32751,2063.4714,228.4802,0.9988142
+4544,2614.4258,384.98367,2636.689,438.29904,0.99881184
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+4544,3236.9612,327.59418,3257.3489,368.04218,0.998615
+4544,2079.614,410.44373,2106.7468,473.86816,0.9984205
+4544,1965.4984,883.8425,2005.9132,978.4607,0.9983582
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+4544,2022.3871,197.1153,2038.2765,247.56293,0.9978383
+4544,1999.293,124.553406,2016.1475,162.65671,0.9956536
+4544,2998.3738,368.5171,3019.0017,417.54657,0.9905263
+4544,3010.4292,381.69543,3036.4004,435.938,0.97811586
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+4544,3424.9604,816.5043,3468.7183,887.3672,0.9292791
+4544,3640.7593,818.7846,3673.394,884.5124,0.70828825
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+4545,2897.77,396.5103,2923.2837,449.87756,0.99953103
+4545,2853.1436,337.60123,2875.9448,388.17673,0.99951744
+4545,2044.4717,183.18842,2062.8315,228.30652,0.99928457
+4545,2667.807,661.97107,2707.657,735.69434,0.99925727
+4545,2087.2456,297.18665,2105.8784,353.25354,0.99924374
+4545,2850.1633,597.1271,2880.2185,667.2761,0.99918
+4545,3114.5571,507.1206,3141.3647,565.0873,0.99914443
+4545,1312.2416,317.83026,1337.0377,373.3772,0.99910766
+4545,2995.434,483.7469,3020.3535,542.5444,0.9990396
+4545,2361.7178,309.71066,2391.4775,362.41068,0.9990283
+4545,2401.6719,444.26065,2432.4097,504.69064,0.99901986
+4545,2629.5103,233.94147,2653.7422,279.40747,0.9989888
+4545,2798.1272,218.16734,2820.0813,258.32156,0.9989304
+4545,3745.3806,754.7757,3783.4358,814.5732,0.99883276
+4545,2994.7507,292.34363,3017.2878,336.37158,0.9988166
+4545,2615.3586,384.96796,2637.2483,438.4397,0.9987301
+4545,2556.426,308.1057,2576.2,360.90485,0.99869734
+4545,1965.4348,883.87976,2005.9087,978.5166,0.9983381
+4545,2987.1294,350.53113,3008.7349,400.32758,0.9982351
+4545,3236.975,327.2427,3257.5269,368.52872,0.998228
+4545,2079.6707,410.86838,2106.3958,473.65604,0.9980679
+4545,2022.1243,196.92984,2038.1301,247.58295,0.9977506
+4545,1998.591,124.47211,2016.1027,162.67694,0.99685717
+4545,3008.1824,399.86328,3026.1995,450.07928,0.9855158
+4545,2998.1555,368.91647,3018.3396,418.1658,0.98431844
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+4545,3640.0342,818.8965,3673.9473,884.49304,0.37094957
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+4546,2087.171,297.2038,2105.8467,353.24286,0.9992343
+4546,2995.021,292.6209,3018.313,336.46283,0.9991258
+4546,1312.1953,317.88693,1336.6904,373.64017,0.9990778
+4546,2359.7844,309.69955,2390.9832,362.17966,0.9990565
+4546,2401.372,444.01846,2431.6724,504.19943,0.99899787
+4546,2616.5188,385.17123,2638.095,438.60385,0.9989221
+4546,2629.2258,234.07208,2652.3767,279.39117,0.99890995
+4546,2797.5117,218.23221,2817.5078,257.59528,0.9989091
+4546,3745.613,754.9345,3783.119,814.63934,0.9987048
+4546,2987.1084,350.29022,3007.38,400.55792,0.9985677
+4546,2558.7317,308.1186,2577.4495,360.90393,0.99852175
+4546,2990.6897,484.073,3020.3171,542.98206,0.9983948
+4546,2079.4048,410.797,2105.9902,473.66327,0.99819356
+4546,1965.2473,883.8795,2005.8899,978.5044,0.99808973
+4546,3237.3428,327.42697,3257.6016,370.0505,0.9978942
+4546,2022.1487,196.76064,2037.6914,247.48122,0.99775094
+4546,1998.3381,124.25542,2015.6926,162.72206,0.9965857
+4546,3007.038,399.07736,3026.4692,450.64743,0.99603623
+4546,2998.1519,369.73535,3016.579,418.4782,0.99573696
+4546,3020.8042,382.3192,3038.1992,436.8246,0.9861383
+4546,3576.965,808.9639,3617.9646,879.4727,0.9802248
+4546,3639.829,819.5689,3675.1606,885.43744,0.75124246
+4546,3427.202,814.8311,3470.548,883.9362,0.3836473
+4546,3609.2122,785.3732,3661.696,837.9289,0.25348556
+4546,3010.0671,381.43384,3037.657,437.34387,0.24023877
+4547,2899.5928,395.94806,2924.7642,449.31378,0.9996489
+4547,2852.8977,336.55838,2875.2947,386.69186,0.99959284
+4547,2662.1292,667.14703,2702.2458,740.55597,0.99950814
+4547,3114.3965,507.1984,3140.1807,565.4343,0.99935013
+4547,2834.2903,597.3536,2873.4417,668.8504,0.9993462
+4547,2995.0476,292.61932,3018.844,337.08527,0.9992926
+4547,2041.0854,183.15718,2062.5088,228.57832,0.99923193
+4547,2360.0261,309.27112,2390.2864,362.27838,0.9992009
+4547,2617.3438,385.14737,2638.6157,438.4868,0.9991435
+4547,2401.3376,443.33664,2430.18,503.6633,0.9990969
+4547,1311.761,317.9216,1336.5222,373.58102,0.99905795
+4547,2087.0322,297.17725,2105.7188,353.21387,0.99904925
+4547,2559.8962,308.38452,2577.9119,360.8664,0.9988207
+4547,2796.8828,218.25363,2813.9438,257.61652,0.99876666
+4547,2079.559,410.70557,2105.3535,473.18768,0.99847776
+4547,2629.1477,234.31075,2649.7415,279.5193,0.9984713
+4547,3746.5845,755.06,3782.6812,814.7022,0.99844164
+4547,3237.9526,327.99088,3257.8936,372.13333,0.9983854
+4547,2989.1077,484.94498,3020.3318,544.54126,0.99837625
+4547,2986.9277,349.96774,3005.9795,400.60812,0.9982253
+4547,2021.814,197.01994,2037.7029,247.89812,0.99809736
+4547,2998.1106,370.6351,3015.5388,419.35233,0.9980724
+4547,1965.2067,883.9126,2005.9056,978.56775,0.9979888
+4547,1998.1372,124.33588,2015.0728,162.67096,0.9969933
+4547,3006.5554,399.59622,3026.6711,450.01804,0.99573636
+4547,3021.225,382.31525,3039.0112,437.43555,0.99556375
+4547,3572.8892,811.5211,3616.5688,881.53284,0.984643
+4547,3640.4375,819.2607,3676.0117,886.13104,0.8762107
+4547,3428.3577,814.9096,3471.674,883.2182,0.7331924
+4547,3625.1836,785.8412,3667.4536,838.56494,0.10282362
+4548,2900.882,395.47473,2924.9807,449.56494,0.9995788
+4548,2828.8538,598.3322,2869.572,669.7846,0.9994764
+4548,2041.1304,183.21521,2062.1558,228.27524,0.99945956
+4548,2851.937,336.24435,2874.7153,385.35696,0.9994524
+4548,3113.784,507.3202,3139.3054,565.6139,0.9993573
+4548,2996.382,292.4102,3019.5007,336.80215,0.9993179
+4548,2358.7046,309.0381,2389.52,362.1725,0.9993081
+4548,2661.8833,669.75995,2696.9321,740.46075,0.9992449
+4548,2559.8806,308.39276,2579.527,360.70526,0.99923265
+4548,2618.1614,385.3206,2639.2195,438.59335,0.9992181
+4548,2401.441,443.60712,2429.0425,503.518,0.9991461
+4548,2629.1372,234.34341,2647.755,279.54135,0.99875104
+4548,2079.6335,410.53198,2104.973,472.92706,0.99864036
+4548,2795.0886,217.30743,2810.5476,257.82938,0.9986261
+4548,3004.4888,399.29126,3025.641,449.8717,0.9986022
+4548,2989.3484,485.72528,3020.0354,545.97845,0.9985475
+4548,1311.7723,318.00464,1336.2128,373.67426,0.9985274
+4548,3238.0117,327.9796,3258.0498,372.17792,0.99852365
+4548,2086.9004,297.15198,2105.747,353.23383,0.9985019
+4548,3747.5312,755.06854,3782.3975,814.62054,0.99849
+4548,2997.2227,371.1764,3015.109,419.71307,0.9981584
+4548,3020.8997,382.4134,3040.3484,437.39325,0.9980964
+4548,2986.5298,349.7131,3005.0244,401.16727,0.99793696
+4548,1965.0038,884.02295,2005.8483,978.48987,0.9979067
+4548,2021.8558,196.58661,2037.9567,247.28696,0.9977551
+4548,1998.0463,124.38045,2015.0099,162.64742,0.9965102
+4548,3639.5308,819.474,3676.9058,887.0327,0.8737967
+4548,3431.031,814.036,3473.4543,883.06006,0.83195376
+4548,3569.6528,813.53253,3614.771,882.0954,0.81843966
+4548,3632.3394,786.99384,3672.4468,840.00055,0.25692374
+4548,3832.5862,767.3549,3861.8484,824.1015,0.11410526
+4549,2827.0564,599.90576,2865.6633,671.59814,0.99959785
+4549,2901.1045,395.32352,2925.2612,449.62723,0.9995883
+4549,2358.5854,308.45178,2387.5664,361.37695,0.9995109
+4549,2851.439,335.96576,2874.6406,385.29413,0.9994492
+4549,2040.7404,183.20432,2061.7344,228.36908,0.9994247
+4549,3113.319,507.2589,3139.0994,565.697,0.99931
+4549,2560.0059,308.3277,2581.167,360.86542,0.99923503
+4549,2659.062,672.3002,2691.6533,740.3539,0.99919087
+4549,2401.2595,443.39697,2427.0764,503.3369,0.999154
+4549,2997.9333,291.79004,3020.6946,336.00116,0.9990553
+4549,2628.853,233.83492,2645.0132,279.81137,0.99886465
+4549,3018.0444,382.32828,3041.1357,437.32645,0.9988643
+4549,2079.7246,410.41196,2104.212,472.65927,0.998857
+4549,2792.3176,216.77692,2809.2126,257.64114,0.99869424
+4549,3237.9138,328.12396,3257.9856,372.40082,0.99862945
+4549,2989.2913,486.39026,3019.6838,546.9834,0.9984958
+4549,2617.9434,385.36517,2639.879,438.71295,0.9984849
+4549,1311.5809,318.1103,1335.5153,373.982,0.99846375
+4549,3748.9548,755.22687,3782.036,814.6524,0.99840575
+4549,2999.9446,399.08618,3025.4802,449.87976,0.9981713
+4549,1964.97,884.05914,2005.8398,978.5014,0.9978886
+4549,2021.5459,196.63266,2037.8918,247.22864,0.99785894
+4549,2984.1907,349.00827,3003.8933,400.2715,0.9969807
+4549,1998.0256,124.694084,2013.8472,162.5487,0.9960819
+4549,2996.5742,371.30066,3014.9795,419.58063,0.9958248
+4549,3639.5989,819.8405,3678.2434,887.0562,0.98290575
+4549,3566.5654,816.05225,3613.8877,882.7837,0.91378206
+4549,2086.8113,297.0473,2106.9924,353.2874,0.90703243
+4549,3632.489,784.524,3679.2722,841.55475,0.89093316
+4549,2086.7656,294.78915,2124.371,353.24966,0.6904391
+4549,3432.401,813.5943,3474.145,883.2564,0.44968495
+4550,2852.0017,335.51974,2874.735,385.02286,0.9995561
+4550,2901.1519,395.138,2925.5317,449.6084,0.99952996
+4550,3019.7744,382.4773,3041.9072,437.21118,0.9995172
+4550,2040.4564,183.01495,2060.735,227.43188,0.9995053
+4550,2358.943,308.58313,2384.994,360.37384,0.9994948
+4550,3112.3384,507.76,3138.6348,564.4492,0.9994624
+4550,2559.6965,308.3728,2582.9363,360.83582,0.9993962
+4550,2656.081,672.9845,2686.919,740.41516,0.99928904
+4550,2826.798,601.4157,2859.153,671.5422,0.9992238
+4550,2997.4604,398.89417,3024.7676,449.44562,0.9990728
+4550,2790.0203,216.26985,2808.446,257.08945,0.99901545
+4550,2401.191,443.10718,2425.1396,503.3725,0.9989191
+4550,2998.4504,291.4196,3021.571,335.53043,0.9987589
+4550,2079.5833,410.31622,2103.7263,472.93024,0.9987545
+4550,2626.7437,233.64067,2643.8657,279.71417,0.998722
+4550,3237.9412,328.057,3258.172,372.03802,0.9986093
+4550,2988.0671,487.0952,3019.2834,547.8281,0.9986084
+4550,3750.4834,755.1654,3781.7446,814.8562,0.9982003
+4550,1311.3843,318.28677,1335.0564,374.11435,0.9981797
+4550,2021.3126,196.63612,2037.8837,246.61598,0.9981388
+4550,2980.6125,349.1487,3003.1052,399.40317,0.998133
+4550,2618.246,385.2883,2640.5376,438.3997,0.9980945
+4550,1964.7195,884.0787,2005.8376,978.4764,0.99781007
+4550,2995.6042,371.69882,3015.0178,420.2275,0.995344
+4550,1997.521,124.26904,2013.513,162.58835,0.99374557
+4550,2086.4595,294.9944,2124.88,353.24695,0.97378856
+4550,3639.2996,819.3144,3680.0544,887.2424,0.9632148
+4550,3564.4758,817.3141,3612.389,883.23413,0.9049511
+4550,3636.0674,783.2534,3685.5093,842.68835,0.7640693
+4550,3434.9565,813.25244,3475.004,883.3341,0.6801371
+4550,3637.6543,782.15674,3685.5332,884.68677,0.25784236
+4551,2851.3274,335.24683,2874.5051,384.8911,0.99954766
+4551,2901.2937,394.86716,2925.9836,449.2715,0.99950993
+4551,2040.051,182.66458,2059.799,227.25467,0.99945825
+4551,3021.4143,382.44427,3042.7263,437.09753,0.9993869
+4551,2995.8865,398.9625,3024.2913,449.66257,0.99929357
+4551,2560.057,308.45285,2584.6902,360.88193,0.99928254
+4551,2826.3938,601.8242,2851.4788,671.54114,0.9991798
+4551,2653.1904,673.4199,2683.9966,740.548,0.9991504
+4551,2786.633,215.34244,2807.5847,257.0696,0.9991455
+4551,2400.8643,442.41772,2423.3657,503.37018,0.99913317
+4551,3000.2192,291.69574,3022.1309,335.18604,0.99905187
+4551,2358.7456,308.02704,2381.9375,360.3095,0.9990422
+4551,3110.2659,507.68106,3138.2424,564.4797,0.9990103
+4551,2079.4727,410.26288,2103.398,472.8446,0.9988834
+4551,2975.6064,348.54434,3002.3442,399.60458,0.9988048
+4551,2988.086,488.43927,3019.0752,548.4776,0.9987142
+4551,1311.1973,318.4564,1335.1646,374.17203,0.9985562
+4551,3238.1897,328.02243,3258.5378,371.39566,0.998489
+4551,2021.1273,196.52647,2038.0145,246.256,0.99835074
+4551,2623.417,232.8066,2642.915,279.63904,0.9981962
+4551,3750.8005,755.4088,3781.6204,815.60645,0.9981426
+4551,2618.1487,385.4348,2641.2336,438.26886,0.9979189
+4551,1964.7184,884.10675,2005.8417,978.47186,0.9977927
+4551,2994.3481,372.61475,3014.684,421.92303,0.996795
+4551,1997.3213,124.066536,2013.1604,162.6865,0.9925109
+4551,2086.4167,295.01648,2124.9128,353.25232,0.9735516
+4551,3437.309,813.26416,3476.437,883.27893,0.8690086
+4551,3563.9438,817.9417,3615.983,885.3045,0.83705467
+4551,3641.156,816.9996,3681.9036,888.1551,0.77866995
+4551,3641.9941,782.63416,3692.0972,839.3551,0.3805219
+4551,3640.6272,782.276,3692.6887,888.5569,0.36581323
+4551,3828.628,769.24603,3858.5283,826.5018,0.24683414
+4552,2995.581,398.8321,3022.7925,449.56543,0.99968696
+4552,2650.5413,673.0867,2680.6042,743.3862,0.99954104
+4552,2039.8439,182.3663,2059.406,227.17929,0.99951875
+4552,2901.3005,394.73032,2926.1824,449.48282,0.99949586
+4552,2850.3884,335.01288,2874.5051,384.6405,0.9994949
+4552,2560.4565,308.7246,2585.601,360.81427,0.9993254
+4552,2358.4055,307.69952,2378.1565,359.96967,0.9992654
+4552,3002.7175,291.12183,3023.0535,335.0567,0.9992367
+4552,3021.9292,382.52493,3044.2422,437.08725,0.9991648
+4552,2820.5613,601.8514,2845.2834,671.03595,0.99914956
+4552,3107.7197,508.3452,3137.6216,565.8463,0.9990503
+4552,2400.581,442.45776,2422.794,503.21674,0.99900264
+4552,2079.2766,410.1229,2103.1458,472.83627,0.9987846
+4552,2780.9023,214.3494,2806.3188,255.99951,0.9986619
+4552,2621.748,232.87212,2642.294,279.68695,0.99856526
+4552,2988.3264,488.93332,3019.0364,550.04694,0.9984976
+4552,1310.7096,318.69476,1334.9559,374.1637,0.99849296
+4552,3238.2417,327.89633,3258.9868,371.52243,0.9984428
+4552,2020.6262,196.30562,2037.7766,245.98631,0.99827814
+4552,2972.9966,348.47983,3001.3057,399.12772,0.99825186
+4552,2617.8533,385.35358,2642.6907,438.34106,0.9979329
+4552,2993.928,372.54352,3014.3728,422.8556,0.99787843
+4552,1964.5544,884.1373,2005.8477,978.4143,0.9977564
+4552,3751.864,755.3101,3781.3523,815.674,0.99775165
+4552,1996.8885,123.92602,2012.7592,162.77197,0.99104017
+4552,2086.2075,295.0681,2124.8413,353.20966,0.9789533
+4552,3642.121,781.4627,3699.309,887.3035,0.9345473
+4552,3440.168,812.96704,3477.3281,882.9569,0.92297643
+4552,3563.4724,819.0893,3616.6868,887.3045,0.8007272
+4553,2995.2988,399.2461,3022.3018,449.47986,0.9995702
+4553,2848.8354,334.79327,2874.33,384.73328,0.9995454
+4553,2901.3618,394.73233,2926.459,449.35764,0.9995154
+4553,2560.7527,308.62427,2586.7893,360.65424,0.9994659
+4553,2356.734,308.07172,2376.0679,360.39398,0.9994369
+4553,2646.552,673.40265,2677.119,748.544,0.99935037
+4553,2038.9181,182.08502,2057.3723,226.32675,0.99918705
+4553,2779.114,213.95322,2805.657,256.25455,0.9990374
+4553,3105.975,509.27097,3136.6333,565.8298,0.9989398
+4553,3002.971,290.9503,3024.1184,335.20767,0.9989017
+4553,2079.3425,409.9066,2102.926,472.9581,0.99886644
+4553,2815.1816,600.8065,2841.0405,670.7234,0.99878174
+4553,1310.3314,318.88986,1334.9652,374.22678,0.99875057
+4553,2399.5535,442.25418,2421.802,502.82986,0.9986523
+4553,2620.0442,232.99454,2641.6838,279.7554,0.9986292
+4553,2988.4646,489.50635,3019.5637,550.567,0.99861896
+4553,2972.2812,348.53854,2999.7212,399.17392,0.99855167
+4553,3023.334,382.40317,3044.9702,437.10294,0.9984206
+4553,2617.7112,385.73508,2644.134,438.25858,0.99824274
+4553,2020.394,196.12842,2037.6458,245.55444,0.9979227
+4553,3238.0266,328.05917,3259.3171,372.4177,0.99778616
+4553,1964.5665,884.1437,2005.8524,978.4647,0.9977488
+4553,3751.9282,755.4787,3781.142,815.71747,0.9974607
+4553,2993.886,372.6594,3014.2703,423.24026,0.9971204
+4553,1996.5494,124.14712,2012.1696,162.47421,0.99118304
+4553,2086.177,295.0546,2124.8894,353.1946,0.97818995
+4553,3441.2786,812.8006,3478.1487,883.085,0.9583558
+4553,3642.142,823.4827,3683.602,887.51984,0.83505523
+4553,3651.1643,782.3105,3705.2336,841.5516,0.73715496
+4553,3562.7556,819.8172,3620.6072,887.1972,0.66055214
+4553,3828.2969,769.0433,3856.518,826.71356,0.11726768
+4554,2848.667,334.52167,2874.2432,384.5866,0.99958354
+4554,2901.4724,394.40555,2926.797,449.1633,0.9995666
+4554,2811.5945,600.5009,2839.0017,670.5966,0.9994824
+4554,2779.7825,214.36859,2803.4812,255.6944,0.9994729
+4554,2994.3352,399.67105,3021.8386,449.56796,0.9994155
+4554,2561.1116,308.0666,2587.3582,360.6397,0.99938035
+4554,2355.0073,307.5199,2374.416,360.0522,0.9993305
+4554,2638.4365,675.2597,2674.5562,750.762,0.99920255
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+4562,2387.7659,443.18353,2414.9177,502.77264,0.9988662
+4562,2337.3489,306.34546,2366.5164,358.0323,0.9988481
+4562,3008.3867,290.155,3030.6895,333.42422,0.99884427
+4562,2614.994,232.36981,2632.95,278.7758,0.99881065
+4562,2079.0308,407.76337,2102.4854,473.14404,0.99879944
+4562,2085.2905,296.9312,2106.4028,353.23578,0.9985152
+4562,2626.1367,384.56583,2650.999,438.6674,0.9983114
+4562,2019.8845,195.04465,2036.2175,243.63449,0.99801826
+4562,2969.8083,344.3421,2989.8699,394.901,0.9980172
+4562,2987.305,374.1199,3010.5251,422.99588,0.99780434
+4562,1964.4932,884.1925,2005.9417,978.4935,0.9977964
+4562,3237.5693,331.02692,3261.4766,378.69598,0.997764
+4562,1989.719,124.902275,2007.7844,162.50624,0.9945357
+4562,3447.4458,807.9996,3488.3765,876.83966,0.9940031
+4562,3751.8767,755.4727,3779.553,815.1236,0.83989555
+4562,3808.639,771.4407,3844.2444,827.82837,0.15500341
+4562,3548.975,818.98083,3590.6973,891.6168,0.12537801
+4563,2773.792,602.55457,2804.0356,675.80566,0.99964416
+4563,2907.415,391.3706,2930.2695,445.84802,0.99940383
+4563,2605.5874,685.16205,2638.317,759.7996,0.99938726
+4563,3028.6687,383.49216,3056.5208,437.28403,0.99929416
+4563,1304.9977,320.18307,1327.472,374.54056,0.99927896
+4563,2987.8867,492.98978,3015.3145,552.26495,0.999203
+4563,3103.51,512.06,3129.324,569.89276,0.9991979
+4563,2993.5208,398.37268,3014.9397,447.52136,0.9991347
+4563,2850.34,331.5837,2873.5554,382.00302,0.9990693
+4563,2573.0962,307.23254,2592.4595,359.6889,0.9990281
+4563,2758.8386,211.6276,2784.7195,252.91782,0.9988864
+4563,2337.3376,305.83698,2362.3118,357.99774,0.9988348
+4563,2387.4175,442.8823,2414.6694,502.7863,0.9988072
+4563,2968.5603,344.1626,2988.569,394.7752,0.99871266
+4563,2627.9377,384.45978,2651.6472,438.4557,0.99870324
+4563,2028.7913,180.28152,2051.6235,225.72134,0.99855846
+4563,2079.0837,407.63828,2101.408,473.2156,0.99828535
+4563,2615.254,232.31203,2631.7383,278.79254,0.99827707
+4563,3009.8381,289.5384,3031.0286,333.0191,0.99798125
+4563,2019.9415,194.68039,2036.6998,243.67896,0.99793124
+4563,1964.4923,884.2045,2005.9774,978.4996,0.99785715
+4563,3238.3542,331.7777,3261.9436,379.1848,0.99779224
+4563,2986.0994,374.3199,3010.491,423.29974,0.9973997
+4563,1989.7745,124.935844,2007.3729,162.5492,0.9949837
+4563,3448.6157,805.4435,3491.045,875.5563,0.9923488
+4563,2085.445,296.453,2108.7942,353.3507,0.97354376
+4563,3543.4558,820.85406,3590.3254,891.17487,0.5789039
+4563,3731.4155,754.90295,3779.7495,832.54126,0.4346463
+4563,3808.219,771.5639,3842.7883,828.2325,0.16829512
+4563,3722.852,779.0253,3772.436,841.28925,0.13675407
+4564,2765.977,602.522,2801.7866,676.7937,0.99969447
+4564,2907.4175,391.3294,2930.5083,445.80627,0.9994265
+4564,2598.8733,686.4802,2636.5608,763.0078,0.99932206
+4564,2337.3542,305.4348,2358.763,357.89478,0.9993206
+4564,2851.947,331.4289,2872.489,381.29657,0.99927646
+4564,2758.94,211.59094,2781.626,253.57489,0.99925035
+4564,1304.8702,320.43518,1325.9244,374.727,0.99921274
+4564,2987.187,494.069,3014.8892,552.96967,0.99909616
+4564,3103.3315,512.6447,3129.1318,570.7895,0.99908805
+4564,3030.84,383.43835,3057.4543,437.1396,0.9989793
+4564,2573.095,307.48553,2593.8044,359.1192,0.998931
+4564,2387.2625,443.47702,2414.5325,502.84097,0.99886847
+4564,2992.9346,398.1635,3014.7378,447.60858,0.9988669
+4564,2966.9607,344.12646,2987.4358,395.34656,0.9988219
+4564,2630.042,384.71365,2652.2583,439.18747,0.9987852
+4564,3013.1938,289.23755,3031.8618,331.73273,0.9985568
+4564,2079.2412,407.4442,2101.0542,473.25488,0.9982893
+4564,2614.1863,232.68214,2630.674,278.47385,0.9982868
+4564,2020.1403,194.86151,2036.7875,243.63974,0.9981622
+4564,3239.5168,331.91907,3262.3108,378.6344,0.99806076
+4564,2026.8997,180.30449,2050.116,225.17998,0.9979673
+4564,1964.5293,884.22516,2006.0076,978.5092,0.9979037
+4564,2986.031,375.05728,3009.5662,423.5482,0.9968964
+4564,1989.7391,124.928375,2007.2362,162.46082,0.9957534
+4564,3448.9082,803.6819,3492.7368,875.10706,0.9939429
+4564,2085.0063,295.7935,2121.0713,353.22763,0.9649903
+4564,3539.195,821.7341,3587.5315,891.3785,0.5730554
+4564,3736.282,755.04346,3779.327,824.8804,0.4875502
+4564,3732.6216,782.3121,3771.8687,843.12213,0.35109302
+4564,3808.529,771.7854,3841.2566,828.1704,0.2011003
+4565,2756.9382,602.6083,2799.9773,676.6588,0.99976045
+4565,2907.7327,391.21442,2931.1467,445.2677,0.99946755
+4565,1304.9534,320.56067,1324.8816,374.75372,0.9993339
+4565,2594.4622,687.9395,2635.739,765.5108,0.9992538
+4565,3102.936,513.20276,3128.4995,573.7074,0.99925035
+4565,2851.9011,331.26645,2872.3572,381.07553,0.99924266
+4565,2337.012,304.6012,2355.6492,357.76868,0.99919707
+4565,3034.7144,383.1349,3058.7876,436.86804,0.9990814
+4565,2758.0312,211.88828,2777.7612,252.59357,0.9990741
+4565,2573.0974,307.6361,2596.3333,358.89288,0.99900836
+4565,2085.4211,296.75446,2107.0603,353.25623,0.9989876
+4565,2386.9434,443.18906,2414.46,502.90103,0.99898237
+4565,2992.244,397.65735,3013.7893,447.7663,0.99894184
+4565,2987.0532,494.18533,3013.94,553.81866,0.998896
+4565,2963.4033,344.15616,2986.875,394.76694,0.9987929
+4565,3240.0383,332.1455,3262.8523,377.9964,0.99852043
+4565,3013.596,288.9336,3032.5017,332.04083,0.9984126
+4565,2079.4465,407.19592,2100.6594,473.32452,0.99827075
+4565,2631.296,384.60535,2653.082,439.7334,0.99822396
+4565,2020.8356,195.2168,2036.2572,243.29132,0.9981933
+4565,2611.7246,232.55212,2629.8716,278.0367,0.99811316
+4565,1964.511,884.23846,2005.9763,978.4936,0.99801767
+4565,2985.5007,375.2874,3008.5642,424.6291,0.9978168
+4565,3449.1155,802.615,3495.5505,874.68994,0.9976162
+4565,2026.4739,179.96933,2048.7888,225.21405,0.9974337
+4565,1989.9519,124.38975,2006.7783,162.44342,0.99691474
+4565,3535.5906,821.5608,3602.366,892.20447,0.5908821
+4565,3741.459,782.77386,3776.187,843.48895,0.42615336
+4565,3808.5925,771.40674,3839.5706,828.04333,0.25198913
+4565,3741.129,755.4018,3779.376,823.7465,0.21559142
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+4566,2907.9363,391.25845,2931.7131,445.19382,0.9995259
+4566,2591.9734,690.43884,2632.1448,767.76086,0.9993562
+4566,2852.1143,331.15417,2872.4385,380.62854,0.99932444
+4566,3037.1145,383.43707,3059.5515,437.26282,0.99923944
+4566,2573.76,308.64764,2598.4407,359.0183,0.9991805
+4566,2336.2205,304.58472,2354.0286,358.12628,0.9991535
+4566,1305.0094,320.41946,1324.0013,374.6998,0.9991387
+4566,3102.1487,514.06885,3128.0637,574.12244,0.99912435
+4566,2959.693,343.63245,2985.8542,394.57172,0.99904865
+4566,2386.8748,442.92398,2414.4104,502.9142,0.99897826
+4566,2631.62,384.92505,2653.5508,439.14722,0.99894065
+4566,2991.022,397.0906,3012.7227,447.49094,0.99883825
+4566,2986.9727,494.6894,3013.205,554.0567,0.99882144
+4566,3013.607,288.79697,3033.088,332.696,0.99871933
+4566,2079.4543,407.20035,2100.2678,473.2872,0.9985128
+4566,2756.5195,211.91374,2773.562,252.47551,0.99822503
+4566,1964.513,884.25433,2005.9824,978.5052,0.99811053
+4566,2020.4108,195.493,2036.7589,243.52773,0.99810624
+4566,2984.1948,375.6042,3008.1426,425.16028,0.99786633
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+4566,3240.902,332.70596,3262.9685,378.74222,0.99733883
+4566,1989.4556,124.658554,2007.0107,162.49477,0.9967062
+4566,2026.9819,179.59515,2047.1418,224.32602,0.995312
+4566,2085.5864,296.79205,2107.252,353.20587,0.992251
+4566,3451.5483,802.8547,3497.2637,873.81366,0.98265517
+4566,3533.9001,822.0514,3598.405,892.59595,0.8796675
+4566,3751.3994,755.44696,3779.1685,813.02484,0.7393133
+4566,3748.2632,782.34265,3781.5356,844.37354,0.49007463
+4566,3808.549,771.02997,3838.0208,828.5855,0.26436523
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+4567,2589.8425,693.26215,2630.0413,769.9644,0.999564
+4567,2573.5686,308.0731,2598.9602,358.9479,0.99950886
+4567,3100.828,514.85876,3127.7917,574.7936,0.99947876
+4567,2907.8547,391.33875,2932.3704,445.1748,0.99940884
+4567,2085.8047,296.80215,2106.3438,353.2598,0.9993771
+4567,2852.1775,331.12317,2872.5964,380.35803,0.999321
+4567,2989.1318,397.23782,3012.3853,447.2769,0.9992818
+4567,2333.7266,304.3159,2352.6401,357.97244,0.9991472
+4567,3039.9514,383.2121,3061.4749,436.57147,0.99912685
+4567,2631.906,384.82217,2654.5266,439.39822,0.999059
+4567,2957.8813,343.8115,2984.8638,394.51132,0.9990533
+4567,2987.0127,495.22485,3012.5703,554.1478,0.99891186
+4567,1304.5542,319.86005,1323.9731,374.62,0.99890816
+4567,3013.8418,288.83597,3033.5898,333.1484,0.9988494
+4567,2386.6033,442.68347,2414.2644,502.79242,0.99884367
+4567,2079.88,407.22327,2100.1558,473.35895,0.99862206
+4567,2607.331,232.4962,2628.7056,278.3965,0.9983734
+4567,3241.0598,332.51877,3263.763,379.47067,0.9982103
+4567,1964.4741,884.277,2005.9556,978.495,0.99819094
+4567,2754.2554,211.64333,2770.232,252.6841,0.9981754
+4567,2020.3569,196.02145,2036.1592,243.59766,0.9977514
+4567,2983.7446,375.37692,3007.7295,425.16232,0.99757856
+4567,1989.6039,125.252625,2005.9218,162.26892,0.99736446
+4567,2026.7979,179.02414,2044.406,222.75024,0.9938651
+4567,3452.3323,803.14453,3498.6526,872.78076,0.9708581
+4567,3751.1384,755.4994,3778.639,814.1511,0.96176726
+4567,3532.9043,822.2438,3595.9478,894.35144,0.90723395
+4567,3749.5898,781.0952,3786.4766,843.833,0.39605603
+4567,3808.369,770.0118,3837.2058,828.3243,0.22051325
+4567,1659.8798,961.9916,1694.2606,1028.3462,0.11622333
+4568,2749.6638,606.9216,2784.3337,678.1224,0.9996655
+4568,2588.7686,696.3275,2624.745,769.94714,0.99952054
+4568,2085.824,296.8285,2105.594,353.24835,0.99943215
+4568,3099.9749,515.37646,3126.909,575.4934,0.9993616
+4568,2631.9663,385.15128,2655.9023,439.07407,0.9993222
+4568,2574.0552,306.86417,2599.295,357.79977,0.9992919
+4568,2852.2344,331.2479,2872.774,380.42105,0.99926645
+4568,2986.2751,397.92758,3012.2273,447.33267,0.9992532
+4568,2907.486,391.13306,2933.1428,445.0678,0.9991841
+4568,2955.8323,343.21915,2983.488,394.84262,0.9991073
+4568,3014.0554,288.8306,3034.41,332.91867,0.9991047
+4568,3039.2449,383.44974,3062.4324,436.5082,0.99907625
+4568,2386.444,443.04257,2414.1306,502.84018,0.99903595
+4568,2329.0488,304.18704,2350.9702,357.71506,0.99901927
+4568,2986.7256,495.10385,3012.1333,553.9894,0.9990163
+4568,1303.465,320.0289,1323.6271,374.58463,0.9989574
+4568,2605.872,232.3055,2628.1182,278.44083,0.99878556
+4568,2079.943,407.16968,2100.3,472.78412,0.99867356
+4568,1964.4806,884.3115,2005.9786,978.5266,0.9982261
+4568,2751.4182,210.82918,2768.3933,252.52324,0.9980217
+4568,2020.9882,195.83426,2036.9259,243.41968,0.99795616
+4568,3241.7703,332.67972,3263.6619,380.22116,0.9978896
+4568,1989.5371,125.38827,2005.6338,162.1131,0.997396
+4568,2983.5386,375.923,3007.5518,425.2361,0.99722135
+4568,3750.0764,755.7179,3778.4114,814.0294,0.9969879
+4568,2026.6053,178.65608,2042.6332,221.4463,0.9941461
+4568,3455.5588,804.1534,3499.6594,873.1746,0.99263024
+4568,3531.5835,823.8746,3593.8857,894.331,0.9431663
+4568,3753.0933,781.6501,3792.938,841.2132,0.7922302
+4568,3807.7803,769.023,3836.413,828.0224,0.24402435
+4569,3099.775,515.99005,3126.5396,576.31757,0.99944264
+4569,2747.6682,607.7879,2776.9817,679.21783,0.9994398
+4569,2584.9795,697.7814,2618.7075,769.7339,0.999411
+4569,2908.1748,390.79996,2934.3433,444.7914,0.9993716
+4569,2985.3933,396.5556,3011.4866,447.53693,0.99936885
+4569,2604.632,232.76791,2628.0217,278.41187,0.9992772
+4569,2322.7483,304.53265,2350.1619,357.64496,0.99924016
+4569,2852.5222,331.0179,2873.052,380.16898,0.9992322
+4569,2748.5994,210.02887,2767.47,251.55081,0.9992194
+4569,2574.08,307.23904,2599.2827,357.76248,0.99919003
+4569,3039.4146,383.3766,3063.1084,436.5583,0.9991774
+4569,3014.2979,289.13086,3035.713,332.86688,0.99917006
+4569,2085.7063,296.841,2107.8445,353.24695,0.999091
+4569,2631.9883,385.2941,2658.5576,439.56558,0.99896765
+4569,2986.3994,495.16495,3012.0972,554.1551,0.9989427
+4569,1302.9419,319.7154,1323.4954,374.4673,0.9989177
+4569,2386.2751,443.30542,2413.8528,502.96576,0.99887997
+4569,2021.3008,195.17609,2036.6301,243.36276,0.9987011
+4569,2080.0518,407.17206,2100.605,471.5868,0.9986104
+4569,1989.2474,125.191376,2005.462,162.15872,0.9982815
+4569,2954.6611,343.06882,2982.2148,394.567,0.9981432
+4569,1964.4382,884.3315,2005.9932,978.48834,0.9980793
+4569,3746.7834,756.53735,3778.0002,813.6654,0.99761367
+4569,3241.9595,333.36365,3263.8955,380.11176,0.9970862
+4569,2983.4932,375.94644,3007.1523,425.1033,0.99672335
+4569,2026.3967,178.30959,2040.9185,222.2329,0.98645335
+4569,3459.1826,805.44543,3500.6592,874.4658,0.982379
+4569,3757.0579,781.813,3799.5676,842.1974,0.9164198
+4569,3530.8098,823.9148,3588.9602,894.0486,0.86430025
+4569,3806.987,769.10065,3834.8386,828.76996,0.45638478
+4569,1659.2723,963.4331,1694.1505,1029.8661,0.13632792
+4570,2909.0808,390.505,2935.9026,444.786,0.99945706
+4570,2085.8806,296.85236,2105.3694,353.27393,0.9993969
+4570,2604.5505,232.70122,2627.7717,278.4038,0.99937975
+4570,2984.3516,396.41943,3010.3403,447.79315,0.9993351
+4570,2744.918,209.6954,2766.2236,250.79349,0.9993261
+4570,3099.9421,516.2697,3125.9973,576.42914,0.99923265
+4570,2582.017,698.4257,2614.5103,769.91974,0.9991228
+4570,2986.2766,495.59726,3011.4832,554.5336,0.9990865
+4570,2632.049,385.68912,2658.803,439.66806,0.9990741
+4570,2742.5967,607.28687,2768.7085,678.58704,0.9990621
+4570,1302.234,319.75647,1323.5187,374.21564,0.9989863
+4570,2573.6387,307.55096,2598.892,358.1488,0.9989755
+4570,2320.2825,304.27048,2349.781,357.73746,0.99897385
+4570,2852.4626,331.04996,2873.3596,380.2996,0.9989134
+4570,3039.069,383.53268,3064.7654,437.55356,0.99888855
+4570,2386.1287,443.33707,2413.6018,502.99368,0.9987949
+4570,3014.5696,289.6981,3036.8562,332.8085,0.9987639
+4570,3744.3215,756.51355,3777.6033,813.9448,0.9984675
+4570,1989.0968,125.16296,2005.4535,162.24274,0.9984438
+4570,2080.0635,406.60904,2101.2954,470.994,0.9981529
+4570,2954.5713,342.45895,2981.287,393.93784,0.99809486
+4570,1964.5258,884.405,2006.0114,978.49194,0.9980045
+4570,2020.8679,194.94864,2036.5967,243.40717,0.9977198
+4570,3242.625,333.4829,3263.9941,381.0484,0.99752325
+4570,2983.3298,375.60104,3006.464,424.988,0.99643576
+4570,3463.473,805.8537,3502.236,874.5267,0.9900358
+4570,2026.2203,177.98232,2039.7587,221.66695,0.9571845
+4570,3758.6836,781.7621,3804.2354,843.97046,0.89108515
+4570,3804.6162,769.07697,3833.085,827.02655,0.3486501
+4570,3531.2434,823.8563,3571.7517,894.0659,0.3146496
+4570,1659.1133,963.479,1694.2246,1029.9199,0.15625967
+4571,2909.1045,390.25412,2935.8657,444.38962,0.9995074
+4571,2737.9487,606.0271,2764.1582,677.57715,0.99947625
+4571,2086.0173,296.85562,2105.4856,353.28787,0.99937755
+4571,2604.481,232.6459,2627.2798,278.4203,0.99934345
+4571,3099.732,516.4707,3125.4429,576.6018,0.999228
+4571,2631.8787,385.98367,2659.1042,440.0009,0.999161
+4571,2986.1748,495.8497,3011.0288,555.4709,0.9990669
+4571,2983.2307,395.67468,3009.5427,447.9372,0.99906427
+4571,3039.1494,383.98126,3065.9058,437.77478,0.9990375
+4571,2579.216,698.71326,2611.4749,770.16296,0.99902546
+4571,2319.285,304.60202,2349.5593,357.67953,0.9989196
+4571,1301.8905,319.5809,1323.7228,374.43454,0.99883574
+4571,2386.0354,443.50208,2412.9856,503.0412,0.9988072
+4571,2852.4456,330.87323,2873.8025,380.4084,0.99875355
+4571,3741.8315,756.2185,3777.08,814.44507,0.9987494
+4571,3014.612,289.33395,3037.8284,332.9503,0.9987205
+4571,2574.3555,307.5417,2599.314,357.76593,0.9984957
+4571,1988.9742,125.011696,2005.4591,162.44641,0.99849045
+4571,2953.5923,342.16046,2980.373,393.68768,0.9983613
+4571,2021.3538,193.4206,2037.5461,243.0488,0.99827385
+4571,2738.3008,209.0571,2765.6284,250.47964,0.99807334
+4571,1964.623,884.47797,2006.0308,978.49725,0.9978326
+4571,2080.335,406.57117,2101.8003,471.05054,0.99770516
+4571,3242.759,333.95422,3264.176,380.98096,0.99764663
+4571,2982.967,375.6134,3006.0159,425.3365,0.99670404
+4571,3464.6694,805.6177,3504.2349,874.35315,0.9901675
+4571,2025.7554,177.7127,2039.2915,220.4039,0.92870474
+4571,3531.3499,823.8859,3571.3552,894.4277,0.6879654
+4571,3766.1545,781.8372,3811.0828,843.9051,0.35878706
+4571,1659.0116,963.50104,1694.4628,1029.7458,0.12405188
+4572,2733.8413,605.1145,2762.458,677.5571,0.99956125
+4572,2576.9312,699.37976,2607.8257,774.8058,0.99934417
+4572,2086.0483,296.9249,2105.3613,353.40854,0.9993298
+4572,2604.2905,232.27902,2626.4458,278.16425,0.99927086
+4572,2910.461,390.12103,2936.918,443.63965,0.99921894
+4572,3099.842,516.6897,3125.2239,577.0692,0.9992142
+4572,2318.2114,304.22415,2349.0396,357.52933,0.9991155
+4572,3039.0935,384.39545,3067.2673,438.12885,0.99910957
+4572,2982.2466,396.61063,3008.961,447.9376,0.99909115
+4572,2735.8674,209.26913,2763.593,249.64972,0.99907076
+4572,2852.591,331.03314,2874.4207,379.5003,0.999046
+4572,2631.9421,386.1179,2659.4612,439.93222,0.9990243
+4572,1301.4277,319.49878,1323.9832,374.4621,0.9989594
+4572,3741.6658,756.2844,3776.9734,814.6726,0.99890685
+4572,2385.9612,444.2771,2411.3616,502.9026,0.9988067
+4572,3014.7288,289.62686,3038.64,332.7205,0.9987087
+4572,2984.9905,496.11694,3010.5305,556.3303,0.99869615
+4572,1988.9314,125.22795,2005.1638,162.26414,0.99866045
+4572,2953.654,341.4693,2978.9744,392.9513,0.9982552
+4572,3242.7124,334.3597,3264.245,382.95468,0.998125
+4572,2021.8455,194.66672,2037.7537,243.19717,0.9980336
+4572,2575.1594,306.9616,2599.7102,357.59882,0.9979634
+4572,2080.493,406.48056,2102.5042,471.94058,0.99767643
+4572,1964.7684,884.74194,2005.6534,978.0497,0.99761105
+4572,2982.3599,375.6616,3006.022,425.70084,0.99598503
+4572,3465.5193,804.2931,3506.508,873.786,0.9933369
+4572,2024.4727,178.2893,2038.177,218.182,0.88929343
+4572,3531.224,822.91516,3585.8457,894.6422,0.7974665
+4572,3773.287,781.41266,3816.9697,844.8892,0.17884754
+4572,2609.1472,864.9738,2656.9163,944.6685,0.16251493
+4573,2724.881,604.1435,2760.8767,677.76874,0.9995059
+4573,2981.0806,395.8226,3008.2285,447.9211,0.9994821
+4573,2086.0618,296.89484,2105.1907,353.40582,0.99935275
+4573,2633.0542,386.21957,2659.8994,439.84207,0.9992667
+4573,2912.6533,389.8185,2937.435,443.1367,0.99926287
+4573,2735.2068,209.74269,2760.6086,249.80336,0.9992174
+4573,2852.776,330.7562,2874.9043,379.14236,0.99917907
+4573,2570.461,700.3143,2604.4893,778.11346,0.9991677
+4573,2318.124,303.87268,2347.6177,357.53192,0.9991455
+4573,3098.865,516.8295,3124.9011,577.4329,0.9991436
+4573,3039.2393,384.76135,3068.1504,438.32965,0.9991097
+4573,2604.2798,232.19083,2625.6108,278.01782,0.9990134
+4573,3740.4082,755.9783,3776.4102,814.724,0.9990069
+4573,1301.019,319.44745,1324.2283,374.36267,0.99897563
+4573,1988.927,125.228355,2005.1584,162.27658,0.99865735
+4573,3014.894,290.24384,3040.0215,333.10638,0.9986325
+4573,2982.6194,496.72687,3010.4202,557.5207,0.99850476
+4573,2021.402,195.1437,2039.1547,243.0026,0.9983937
+4573,3242.6409,334.50586,3264.672,383.47552,0.9981687
+4573,2575.2646,306.81207,2600.544,357.62305,0.99800926
+4573,2080.6614,406.2894,2103.3406,471.71817,0.99769044
+4573,2385.9917,445.42014,2410.1729,502.88333,0.9976342
+4573,2953.8896,341.10254,2977.5894,391.8634,0.997612
+4573,1964.7684,884.74194,2005.6534,978.0497,0.99761105
+4573,2981.674,376.1751,3005.555,425.7741,0.9931677
+4573,3466.471,802.87585,3507.971,873.3998,0.98765403
+4573,2020.4104,178.09091,2037.0571,221.68573,0.96468586
+4573,3531.0278,822.43463,3585.6528,894.4798,0.54291856
+4573,2611.7974,864.81177,2658.25,944.18677,0.32115477
+4573,3781.4763,781.8099,3823.1804,845.4394,0.3120958
+4573,3784.9194,771.3442,3826.169,832.51935,0.11626173
+4574,2981.0369,395.45093,3007.1584,447.8567,0.99960726
+4574,2717.1033,603.86035,2760.0886,678.4225,0.9995733
+4574,2915.3235,389.64664,2938.347,442.9537,0.9995158
+4574,2561.528,701.25745,2601.2336,778.52966,0.999419
+4574,3039.5664,384.8645,3069.3154,438.24725,0.9993315
+4574,2086.2485,296.8218,2105.0088,353.44528,0.99926835
+4574,2317.9688,303.46747,2346.129,356.99872,0.99917954
+4574,2853.2664,330.24417,2875.1785,378.072,0.99914587
+4574,3098.5632,517.1037,3124.6882,577.4202,0.9990226
+4574,2633.7993,386.24664,2660.1475,439.75018,0.9989428
+4574,2735.4844,209.79453,2757.1377,249.08221,0.99890685
+4574,3739.0288,756.20984,3776.2905,814.4636,0.99877733
+4574,2603.9639,231.78735,2625.1304,277.96042,0.9987557
+4574,2021.82,193.6795,2039.7645,243.02005,0.9986725
+4574,2954.1523,340.6206,2976.6553,390.4491,0.9985209
+4574,1300.563,319.40573,1324.5723,374.40543,0.99843174
+4574,2981.1543,497.5528,3010.3882,557.15314,0.9983376
+4574,1988.7416,125.33931,2005.2926,162.3286,0.99822015
+4574,3015.9717,290.42664,3040.933,332.52942,0.9981957
+4574,2080.345,406.68127,2104.6687,472.1134,0.9981901
+4574,3243.0232,334.88702,3264.8318,383.48682,0.9980083
+4574,2577.3384,306.78418,2601.8164,357.66974,0.9979711
+4574,1964.7684,884.74194,2005.6534,978.0497,0.99761057
+4574,2385.9775,445.1039,2409.3818,502.83652,0.9973494
+4574,2981.7322,376.4227,3005.4348,425.92447,0.9936608
+4574,3469.0715,800.93036,3510.4114,872.6186,0.99361575
+4574,2017.7943,178.2255,2036.132,221.1954,0.99155515
+4574,3530.0217,821.2947,3584.3289,893.7628,0.7061259
+4574,3785.605,772.28436,3824.2207,827.4161,0.2961561
+4574,3788.9336,783.2726,3825.3438,846.46436,0.16635162
+4574,3985.0725,747.18677,4017.968,801.4723,0.12189783
+4575,2712.4133,604.9433,2757.1965,678.9882,0.9997751
+4575,2917.265,389.2822,2939.1501,442.98495,0.99967444
+4575,2318.753,301.662,2343.3896,354.3285,0.99966156
+4575,2980.935,395.87476,3006.586,447.5871,0.99965286
+4575,2555.6384,703.28937,2598.906,780.0229,0.99937063
+4575,3040.018,384.809,3070.1533,438.18454,0.9993042
+4575,3097.6233,517.4166,3124.1824,576.9927,0.999278
+4575,2086.378,296.7343,2104.983,353.44354,0.9992611
+4575,2853.44,330.15875,2875.712,377.64752,0.9989618
+4575,2734.5564,209.62953,2753.9338,248.76308,0.9988288
+4575,2022.1392,193.5349,2040.0586,243.20546,0.9988048
+4575,3738.513,756.02435,3775.4329,814.8022,0.9987882
+4575,2603.9993,232.01166,2623.9866,277.82596,0.99873334
+4575,1299.9523,319.43146,1324.4696,374.36566,0.99868244
+4575,2634.79,386.20102,2660.9727,439.85593,0.9986072
+4575,2579.596,306.54605,2602.8396,357.69852,0.9984818
+4575,2954.1116,340.07056,2976.1477,390.38928,0.9984428
+4575,2981.0312,498.25854,3010.3447,557.9038,0.9981515
+4575,1988.8314,125.21855,2005.2491,162.38683,0.99800795
+4575,2385.6538,445.02145,2408.5688,502.7106,0.99792635
+4575,3243.6104,335.6382,3265.334,383.77072,0.9978744
+4575,1964.7684,884.74194,2005.6534,978.0497,0.99761224
+4575,2080.4172,406.67325,2106.1296,472.35666,0.9975235
+4575,3017.118,290.2898,3042.1218,332.90186,0.9972088
+4575,3469.597,800.4684,3512.2405,870.1737,0.9952975
+4575,2981.771,376.6903,3004.8223,426.37817,0.99223137
+4575,2015.5543,178.84003,2035.2933,221.39417,0.9909053
+4575,3527.4375,822.5532,3583.3022,893.9796,0.87794167
+4575,3984.6167,747.2592,4018.413,801.6818,0.175115
+4575,3788.222,773.4903,3826.0427,828.9766,0.11675267
+4576,2710.7224,606.62823,2753.2913,680.1785,0.9997534
+4576,2980.1467,396.189,3006.0037,447.2595,0.9996704
+4576,2917.7146,389.37918,2939.8845,442.95047,0.99962974
+4576,2318.178,302.591,2339.828,354.95807,0.9994135
+4576,2553.6626,705.33264,2595.0093,782.22754,0.99939525
+4576,3040.776,384.92346,3070.893,438.2066,0.9993625
+4576,3097.104,517.9544,3123.1357,576.60504,0.99918896
+4576,2086.5654,296.96567,2104.9082,353.4507,0.999124
+4576,2854.3337,329.73523,2876.1482,377.04553,0.99910945
+4576,2022.4075,194.1036,2040.7454,243.03558,0.999033
+4576,2637.3257,385.56625,2661.9365,439.68857,0.9988863
+4576,1299.1445,319.4293,1323.771,374.42917,0.99882513
+4576,2581.8293,306.5094,2603.8933,357.6178,0.99869746
+4576,3737.6455,755.9628,3774.7192,814.86115,0.998628
+4576,2604.1694,231.9223,2622.6123,277.6282,0.99848026
+4576,2980.1584,499.44016,3010.1238,560.1368,0.9983606
+4576,3019.1646,289.41357,3043.2075,332.2754,0.9980691
+4576,2733.5894,209.13586,2749.5234,248.69174,0.9980566
+4576,2080.6917,406.73242,2107.262,472.65094,0.99798524
+4576,2954.0383,340.06085,2975.7395,390.60718,0.9978733
+4576,2385.3657,444.80414,2408.4438,502.72827,0.9976635
+4576,3244.397,335.65298,3265.3647,382.96378,0.9976324
+4576,1988.1455,125.552124,2004.9019,162.49646,0.9975057
+4576,1964.7668,884.7407,2005.6362,978.05164,0.9974698
+4576,3472.8342,800.72095,3513.3625,866.0686,0.9961299
+4576,2015.3347,179.01634,2034.2129,221.56447,0.9944541
+4576,2981.032,377.00974,3004.6204,427.44614,0.9934095
+4576,3523.4805,822.97455,3581.9897,895.00336,0.5009222
+4576,2623.2307,864.922,2663.89,945.37354,0.2098975
+4576,3984.3586,746.8458,4018.0745,801.73425,0.15883029
+4577,2710.3179,608.3382,2747.5156,680.4427,0.9996959
+4577,2917.1975,389.12347,2940.6458,442.65808,0.99955136
+4577,2552.8364,707.8346,2589.5933,783.5845,0.9994759
+4577,2978.9246,395.81912,3005.0203,447.16354,0.99945873
+4577,3042.3306,384.77798,3072.3574,438.38718,0.99938333
+4577,2317.2861,302.10068,2336.6875,354.79263,0.9991302
+4577,2022.6248,194.36111,2041.1426,242.08359,0.99909604
+4577,2854.8555,329.3042,2876.6533,376.88934,0.99904877
+4577,2086.7502,297.10776,2104.7288,353.4446,0.99904114
+4577,2638.8777,385.22522,2662.3914,439.57867,0.9990319
+4577,1298.4259,319.3751,1323.1503,374.21304,0.9990282
+4577,2730.2969,208.21684,2745.9248,249.02843,0.9988182
+4577,3095.216,518.4532,3122.6711,576.9921,0.99880993
+4577,2979.6565,500.16077,3009.988,561.6571,0.99873525
+4577,2604.1272,231.59286,2622.008,277.71844,0.9985469
+4577,2954.0085,339.61905,2974.7678,390.1992,0.99851686
+4577,3021.8984,289.28174,3044.2104,332.97217,0.9985084
+4577,2584.1797,306.0288,2604.5532,357.44305,0.9984498
+4577,3737.7383,756.2584,3774.2622,814.83093,0.99835724
+4577,2385.0334,445.36526,2407.926,502.67264,0.99818003
+4577,2081.1558,406.4976,2107.835,472.6771,0.99796104
+4577,1988.0404,125.46837,2004.943,162.48463,0.99764806
+4577,1964.7909,884.7484,2005.6315,978.05994,0.9975024
+4577,2015.0834,177.83592,2033.8373,221.60548,0.99716717
+4577,3244.7288,336.07812,3265.8406,383.5417,0.9967706
+4577,2980.5525,378.08722,3004.2146,427.5739,0.9936054
+4577,3475.3608,801.7307,3514.6807,866.2584,0.9495736
+4577,3517.7158,824.6846,3582.2168,896.021,0.8408722
+4577,2626.8594,864.6542,2666.1836,944.313,0.3344073
+4577,3984.5352,747.02026,4016.8364,801.73804,0.10122267
+4578,2707.5088,609.33777,2739.876,680.55505,0.9995921
+4578,2552.5007,709.78015,2583.922,783.36755,0.99958986
+4578,2917.034,388.59476,2941.4724,442.7466,0.9995689
+4578,2979.118,395.5235,3004.0862,446.37848,0.9994748
+4578,2315.8774,302.01202,2334.005,354.5946,0.99929523
+4578,2640.772,385.10318,2663.3618,439.3184,0.9992576
+4578,2855.29,328.6742,2877.1738,376.86798,0.9991665
+4578,2086.9875,297.16382,2104.6145,353.4325,0.99896526
+4578,1297.8944,319.26276,1321.9703,374.23206,0.9989513
+4578,3045.169,384.99146,3073.2803,438.31897,0.9988638
+4578,2586.697,306.29767,2605.9158,357.4945,0.99883026
+4578,2979.5244,500.71442,3009.9717,562.1319,0.9987153
+4578,2023.1598,194.37732,2041.128,242.26266,0.9985875
+4578,2952.799,339.44574,2974.3186,390.45233,0.99853814
+4578,2603.9749,231.60852,2621.528,277.9226,0.9984841
+4578,3737.0579,756.59515,3773.6199,815.71136,0.99825317
+4578,3094.1384,519.1528,3122.1384,577.53375,0.99823594
+4578,3023.5479,289.11737,3045.0547,332.50092,0.99806327
+4578,2384.8315,445.40305,2407.829,502.83414,0.9978811
+4578,1988.0186,125.48032,2004.8113,162.20116,0.99757963
+4578,1964.7803,884.7515,2005.6533,978.0633,0.99739873
+4578,2727.6777,206.73714,2744.9585,248.5605,0.9973979
+4578,2081.136,407.34454,2109.154,472.60162,0.996995
+4578,2015.1674,177.70067,2033.4337,221.81035,0.99649256
+4578,3244.7603,336.32007,3266.2407,383.76166,0.99637955
+4578,2979.8167,378.65646,3003.161,428.47696,0.9911464
+4578,3512.1672,825.5011,3580.0032,896.3784,0.98438245
+4578,3479.16,802.7489,3515.5989,866.329,0.9682142
+4578,2632.6204,864.75867,2667.9084,944.34534,0.1451473
+4579,2916.7878,388.41983,2941.8333,442.62643,0.99957985
+4579,2856.525,328.45773,2878.101,376.8253,0.9994973
+4579,2550.0857,711.03534,2578.909,783.2403,0.9994351
+4579,2978.9346,395.21408,3003.3687,446.22098,0.9994205
+4579,2701.759,608.98755,2732.0447,680.70044,0.9993969
+4579,2313.2146,301.70337,2333.0334,355.09662,0.9992306
+4579,3049.5881,384.91168,3074.9207,438.55634,0.99917704
+4579,2086.9663,297.2174,2104.602,353.41412,0.99899334
+4579,2642.6582,385.10458,2664.859,439.81912,0.998847
+4579,1297.3728,319.18814,1321.0049,374.03214,0.9988312
+4579,2979.4512,500.93597,3010.204,562.4947,0.99876344
+4579,2723.323,205.15584,2744.411,248.3181,0.9987582
+4579,3026.7163,289.17535,3045.5938,332.11646,0.99866736
+4579,2586.4492,306.37933,2606.8525,357.57294,0.9986596
+4579,2603.7937,231.58458,2620.1716,278.0541,0.9986513
+4579,3736.8638,756.38055,3772.8433,815.8798,0.99851876
+4579,3094.27,520.8582,3121.6372,577.998,0.99851745
+4579,2951.1196,339.22656,2973.665,390.16034,0.99851054
+4579,2080.5862,407.2127,2109.7913,472.71942,0.99835676
+4579,2023.3466,194.48062,2041.2284,242.24792,0.99834013
+4579,3245.1082,336.81982,3266.367,384.2937,0.9974185
+4579,1964.7886,884.7648,2005.6277,978.0647,0.99740016
+4579,2384.3623,445.46466,2407.5269,502.94745,0.9973986
+4579,1987.8894,125.46746,2004.573,161.92972,0.99707663
+4579,2015.1228,177.63078,2033.0315,222.05418,0.9968913
+4579,2979.3887,379.18365,3002.2373,429.43,0.9881429
+4579,3506.4817,827.02856,3580.0872,896.4946,0.97883874
+4579,3479.4463,803.5824,3516.5186,866.28613,0.9370712
+4579,3783.9568,771.7486,3816.2449,830.4089,0.17668504
+4579,2637.8967,864.67456,2669.9724,943.98206,0.14356951
+4580,2916.9136,388.45898,2942.521,442.65533,0.9996227
+4580,2857.1091,328.23923,2879.0725,376.95023,0.9994369
+4580,2310.204,301.3998,2331.6577,355.05002,0.9994172
+4580,2545.7444,710.905,2574.1028,783.12195,0.9994011
+4580,2697.6528,608.3588,2727.7046,680.5599,0.9993692
+4580,2644.4846,385.1194,2665.611,439.58563,0.9992706
+4580,3051.7268,384.9093,3076.2087,438.4386,0.9991217
+4580,2978.6936,395.55707,3002.0261,446.2884,0.9991019
+4580,2087.0322,297.31326,2104.538,353.40042,0.9990533
+4580,2717.6895,204.78674,2743.0479,247.67654,0.99903464
+4580,1296.445,319.20163,1320.1832,374.28275,0.9989047
+4580,2979.5376,501.15536,3010.0708,562.49866,0.99881613
+4580,2950.0405,339.33075,2973.621,390.23102,0.9987111
+4580,2080.806,407.54523,2110.182,472.6717,0.99865687
+4580,3245.5999,337.84277,3266.6409,384.6305,0.99863017
+4580,3093.2283,521.32526,3121.3333,580.8984,0.99860245
+4580,2024.2146,194.38275,2041.5654,242.17926,0.998456
+4580,3027.0298,289.05994,3046.3423,332.43958,0.99842876
+4580,2586.7085,306.2442,2608.3003,357.40082,0.99826163
+4580,3736.7888,755.5999,3772.1345,814.6639,0.99813676
+4580,2602.3682,231.49158,2619.4697,278.0935,0.99792516
+4580,2014.9834,177.86357,2032.3486,222.2119,0.9976604
+4580,1987.8821,125.458725,2004.5942,161.93538,0.99734515
+4580,1964.7728,884.72485,2005.6066,978.07715,0.99731535
+4580,2384.0886,445.37354,2406.9001,503.0675,0.9972379
+4580,2979.67,379.21466,3002.0625,429.2942,0.979807
+4580,3479.8313,802.4683,3519.347,865.86664,0.95997983
+4580,3502.061,827.657,3576.7725,896.30383,0.91785246
+4580,2638.8662,864.968,2672.2485,943.05383,0.6280599
+4580,3782.8413,772.5118,3814.5742,832.6917,0.15228078
+4581,2693.9985,607.45435,2724.8398,680.70056,0.9996284
+4581,2917.238,388.63754,2942.7688,442.6888,0.9996068
+4581,2856.4346,328.00436,2880.0488,377.09537,0.9993175
+4581,2646.0198,385.3207,2666.3792,439.56296,0.99931645
+4581,2541.5986,709.94446,2570.579,783.10754,0.99922836
+4581,2303.8892,301.6547,2330.4644,355.10593,0.9991824
+4581,2087.056,297.41064,2104.512,353.40686,0.9990715
+4581,3055.1387,385.01407,3077.2935,438.51566,0.9990293
+4581,2979.5833,501.1969,3009.9802,562.5731,0.99894685
+4581,2080.9475,407.68344,2110.5027,472.6788,0.99894077
+4581,3245.8435,338.92682,3266.7415,384.8012,0.9988722
+4581,2586.8193,305.90897,2609.9897,357.50253,0.99886274
+4581,3092.1394,521.9797,3120.193,582.6788,0.9987606
+4581,2949.1511,339.0238,2972.9983,390.30292,0.9986946
+4581,1295.4733,319.53598,1318.7638,374.9966,0.9986921
+4581,2978.3604,395.13666,3001.276,446.29773,0.9985038
+4581,2024.705,194.38916,2041.851,241.48483,0.9984738
+4581,3027.0916,289.09613,3047.3235,332.43158,0.9984276
+4581,3736.767,754.9468,3771.046,814.67615,0.9979144
+4581,2713.263,204.16447,2742.2434,247.10222,0.9978575
+4581,2014.2737,177.6858,2031.5159,221.76997,0.9976525
+4581,2383.6685,445.3271,2406.5024,503.13312,0.9975278
+4581,2599.714,230.9834,2618.7263,278.28925,0.99738306
+4581,1964.7598,884.7092,2005.6133,978.07605,0.9973189
+4581,1987.8477,125.49768,2004.6091,161.8534,0.9972373
+4581,3498.6943,827.8007,3573.5562,896.3012,0.9910158
+4581,3479.6616,801.71387,3521.7954,866.1139,0.9847056
+4581,2979.55,379.39853,3002.034,428.57425,0.9709362
+4581,2639.0,865.79816,2674.224,943.13873,0.7390825
+4581,3780.7637,774.13257,3813.9487,833.32385,0.5688999
+4582,2917.5286,388.39584,2943.4968,442.06168,0.99955875
+4582,2686.9602,606.9979,2722.3083,682.38525,0.99954164
+4582,2537.0667,709.7803,2566.7004,783.9376,0.99953675
+4582,2646.4563,385.15854,2667.3186,439.76248,0.99930835
+4582,2854.639,327.65256,2880.381,377.18045,0.99922097
+4582,3056.1719,385.29657,3078.4668,438.49506,0.99908066
+4582,2087.0593,297.66586,2104.5315,353.39157,0.9990687
+4582,3245.438,339.33908,3267.293,384.61343,0.9990024
+4582,2979.8557,501.18408,3009.5188,562.38184,0.9989447
+4582,2586.7563,306.45743,2612.037,357.30502,0.9988992
+4582,2948.7444,338.7558,2972.9753,390.06152,0.9988938
+4582,3091.9941,522.17236,3119.8442,582.9535,0.9988728
+4582,2300.6445,301.4669,2329.9062,355.08542,0.9988599
+4582,1295.1934,319.6612,1318.0347,374.8841,0.99882454
+4582,2081.0256,407.76645,2110.8323,472.67416,0.99870324
+4582,2712.928,204.36781,2740.8171,247.28157,0.9986981
+4582,2025.497,194.18675,2042.0248,241.72899,0.9984144
+4582,3736.808,755.0504,3770.749,814.3778,0.9980817
+4582,2978.0388,394.73615,3000.8728,445.02997,0.99795544
+4582,3027.0562,289.5459,3048.6675,332.4239,0.99778533
+4582,2382.7996,445.20447,2406.325,503.1159,0.99747616
+4582,1964.7671,884.69006,2005.645,978.058,0.99744475
+4582,1987.8334,125.533646,2004.6007,161.7484,0.9973441
+4582,2014.1208,177.49474,2030.6755,221.61406,0.9972524
+4582,2597.4207,230.64827,2617.7063,278.4297,0.99722195
+4582,3481.0132,800.0785,3524.585,866.71436,0.98049647
+4582,2979.8381,380.26147,3001.7112,429.28967,0.9626121
+4582,3496.3604,827.72327,3571.7847,896.8601,0.87653697
+4582,3780.784,775.0314,3811.05,833.89404,0.8543822
+4582,2981.4834,380.2906,3000.954,410.9433,0.30064547
+4582,3830.764,780.5966,3860.722,838.7402,0.10799852
+4582,2638.956,865.8725,2675.7798,945.3825,0.1055329
+4583,2677.4404,607.07214,2720.7422,681.5746,0.9996511
+4583,2529.7388,709.93036,2565.6084,787.4246,0.9996038
+4583,2917.9146,387.9002,2944.3647,441.6419,0.99949396
+4583,2853.7712,327.48895,2881.113,377.28156,0.99912685
+4583,2947.8503,338.43314,2972.2449,389.5997,0.99911684
+4583,2646.497,384.95538,2668.2383,439.4372,0.99908566
+4583,2087.0193,297.66953,2104.5232,353.4034,0.9990274
+4583,2980.3228,501.3477,3009.3657,562.23065,0.99901736
+4583,3056.3755,385.17697,3079.3516,438.55124,0.9990157
+4583,2586.9146,306.48148,2613.462,357.2264,0.9988599
+4583,2081.4666,408.08334,2111.1384,472.58664,0.9988405
+4583,3091.752,522.60376,3119.4927,583.3091,0.99881244
+4583,3245.3096,339.90952,3268.046,385.0015,0.9988115
+4583,2298.1445,301.62503,2329.7188,354.89536,0.9986278
+4583,2025.5428,194.03851,2042.2511,241.38751,0.9984598
+4583,2712.2158,205.00197,2737.5596,246.4087,0.9984106
+4583,1294.8875,319.39148,1316.1956,375.14325,0.99830353
+4583,2596.8564,230.88803,2616.9424,278.3127,0.9982169
+4583,3736.6924,754.8418,3770.2583,814.14355,0.9978593
+4583,2978.1416,394.56937,3000.4624,444.36783,0.9978071
+4583,3027.3447,290.00534,3049.788,332.5167,0.9977059
+4583,2381.1104,445.2253,2406.19,503.11203,0.99768865
+4583,1987.7942,125.541016,2004.656,161.56754,0.9974438
+4583,1964.7628,884.6596,2005.6129,978.0622,0.9973441
+4583,2012.3298,176.81357,2030.8665,221.66254,0.9963176
+4583,3780.0122,777.2189,3809.7178,834.50793,0.98238176
+4583,2979.8765,379.6286,3001.6382,428.04584,0.95758694
+4583,3481.8691,799.31165,3527.8828,867.46924,0.8758567
+4583,3495.3132,828.0931,3567.6077,897.0307,0.8388785
+4583,2981.6265,380.3735,3000.767,411.72397,0.30398205
+4583,3838.3115,780.3365,3863.564,838.7177,0.1000947
+4584,2672.47,608.0482,2717.861,680.853,0.99970067
+4584,2523.17,710.98706,2564.3198,789.562,0.99957997
+4584,2918.471,387.44687,2944.656,441.01376,0.9993475
+4584,2853.453,327.2207,2881.1257,377.16516,0.999169
+4584,2087.1323,297.68735,2104.5503,353.43246,0.9990515
+4584,2586.9612,306.68423,2614.5867,357.42062,0.99904066
+4584,3056.2974,385.26462,3080.9487,438.91605,0.99902904
+4584,2298.0342,301.84775,2329.2915,354.66794,0.99902594
+4584,2647.08,385.2108,2668.829,439.27798,0.9989613
+4584,2946.9001,337.70795,2971.136,388.80634,0.9989542
+4584,2980.373,501.49203,3008.6216,562.34076,0.99882406
+4584,2081.9248,408.20392,2111.5967,472.53625,0.9987715
+4584,3091.701,522.79553,3119.4548,583.198,0.9987112
+4584,2712.139,205.1505,2732.9353,246.11864,0.9985923
+4584,1294.7264,319.435,1315.8558,374.8031,0.99848735
+4584,2595.7378,230.33563,2616.7075,278.11084,0.99847335
+4584,3027.5095,290.05914,3051.7615,332.60162,0.99842525
+4584,3246.062,340.8089,3268.9644,386.0205,0.99825066
+4584,1964.7814,884.66406,2005.5873,978.1074,0.99781835
+4584,2978.4045,394.70343,3000.3967,443.2334,0.9976687
+4584,3736.815,754.7538,3769.713,814.21484,0.9976636
+4584,2025.8485,193.915,2042.6232,241.91399,0.9975389
+4584,1987.7133,125.56544,2004.6637,161.32854,0.99743456
+4584,2010.9253,176.61562,2030.1394,221.3939,0.9973506
+4584,2378.7305,445.25528,2405.9502,503.0919,0.9964862
+4584,3777.8518,777.857,3808.777,835.9069,0.9801334
+4584,3482.7383,799.22504,3530.0322,870.0064,0.97102904
+4584,2979.8657,379.72632,3001.5786,426.3927,0.9517924
+4584,3495.8762,828.6258,3564.9597,897.1829,0.90534323
+4584,3841.412,779.39453,3866.6328,838.73865,0.28734887
+4585,2669.98,609.534,2712.6772,683.0627,0.99958235
+4585,2518.7092,712.19556,2562.7668,791.3478,0.9995289
+4585,2587.4077,306.53296,2615.538,357.15283,0.99934965
+4585,3056.2424,385.6257,3081.7908,439.59006,0.999303
+4585,2919.2073,387.02094,2945.1716,440.66376,0.9992204
+4585,2980.3213,501.86884,3008.122,562.1866,0.99907994
+4585,2297.5237,301.8462,2328.5598,354.7082,0.99904686
+4585,2087.2747,297.91205,2104.6624,353.42126,0.9990177
+4585,2853.1663,327.04233,2881.9275,377.10367,0.9989686
+4585,2647.1138,385.1101,2669.359,439.37018,0.9988801
+4585,3091.7349,522.9648,3119.52,582.5838,0.9987674
+4585,1294.596,319.48883,1315.426,375.28503,0.99873465
+4585,3027.71,289.91968,3053.0596,332.49768,0.99867994
+4585,2946.3757,336.97195,2969.947,387.95425,0.99855
+4585,2083.0596,408.01685,2111.5308,472.47827,0.9985403
+4585,2594.7861,230.50694,2616.5327,278.00153,0.9981274
+4585,1964.799,884.5547,2005.5751,978.0989,0.99801636
+4585,2711.5334,204.75494,2729.1736,245.58514,0.9978582
+4585,2977.6863,394.69052,3000.3,442.92538,0.99757355
+4585,1987.7118,125.56308,2004.6237,161.04004,0.9975007
+4585,3246.0745,341.1707,3269.576,386.86118,0.997361
+4585,2010.2578,175.72499,2028.6885,220.69203,0.99726796
+4585,3736.9575,754.6208,3769.0254,813.8614,0.99715686
+4585,2026.0541,193.79228,2042.9374,242.0215,0.99662787
+4585,2378.0554,445.2641,2405.7083,503.11877,0.99656194
+4585,3776.1565,777.89124,3807.5764,836.9374,0.973749
+4585,3484.7668,799.4073,3532.074,869.01434,0.9550317
+4585,2979.602,380.20737,3001.3613,426.68655,0.95370936
+4585,3495.064,828.3331,3566.6587,897.76855,0.6371935
+4585,3842.1833,776.9061,3871.9314,837.83765,0.14289008
+4586,2516.0244,715.4866,2560.8965,793.93994,0.9995808
+4586,2669.7422,611.0498,2706.081,682.37634,0.9994641
+4586,2980.5608,502.3977,3007.1614,562.15173,0.9992418
+4586,3056.2842,385.7744,3083.5815,439.35083,0.9991935
+4586,2919.989,386.6745,2945.468,440.1538,0.9991442
+4586,2588.2373,306.43732,2616.502,357.29272,0.9991277
+4586,3091.831,523.1916,3119.3672,582.87164,0.9989806
+4586,2853.5964,326.8028,2881.9944,377.0011,0.9989348
+4586,1294.3921,319.477,1315.1582,375.47852,0.9988657
+4586,2087.3438,298.03094,2104.9844,353.47046,0.9988606
+4586,2647.3054,385.05212,2669.6492,439.3089,0.9988482
+4586,2297.524,301.18045,2327.4995,353.18332,0.9987249
+4586,3245.8909,341.36346,3270.052,387.5603,0.99865216
+4586,2594.6511,230.24725,2616.4011,278.0703,0.9986014
+4586,3029.0874,289.75775,3054.3882,333.10144,0.9984542
+4586,2946.261,336.10916,2969.6125,387.0142,0.9982646
+4586,1964.833,884.493,2005.5657,978.1115,0.99820125
+4586,2709.3416,204.2916,2725.1511,245.70023,0.99810946
+4586,1987.7241,125.59273,2004.4412,160.97679,0.99754864
+4586,2978.0178,394.7368,3000.1418,442.74026,0.99717206
+4586,2009.4819,176.57037,2029.0613,220.56335,0.99710995
+4586,2376.1616,445.17987,2405.435,503.1137,0.99704945
+4586,2026.6989,193.50568,2043.278,242.33133,0.99700135
+4586,3736.6245,754.5865,3768.212,813.77264,0.99692106
+4586,2086.6577,408.1117,2112.5498,472.59625,0.9951513
+4586,2979.9006,380.40457,3001.2136,426.63907,0.93919796
+4586,3776.342,777.8635,3806.7922,836.8248,0.8110187
+4586,3488.513,800.80817,3533.8303,867.7336,0.70139956
+4586,3494.8647,828.13727,3554.815,896.9323,0.5191329
+4586,2639.726,866.4067,2685.1338,945.3796,0.11529662
+4587,2669.7173,612.1293,2698.4023,683.03296,0.99939394
+4587,2514.6367,718.0695,2557.3315,796.3564,0.9993098
+4587,2921.498,386.5138,2945.6,439.6234,0.99918324
+4587,2979.7063,503.49673,3006.5793,562.10693,0.9991007
+4587,2853.9636,326.54175,2881.9177,376.89557,0.9990651
+4587,2647.511,385.19254,2669.9988,439.31363,0.9990563
+4587,2593.9316,230.33948,2616.247,277.94403,0.99902725
+4587,3091.6487,523.3994,3119.2405,583.67896,0.9989532
+4587,3056.2622,385.98734,3085.0962,439.65164,0.99893147
+4587,2297.5747,300.42618,2324.17,352.0712,0.9988379
+4587,1293.9999,319.29465,1314.8661,375.7462,0.9988167
+4587,2087.2673,298.15536,2105.2058,353.4697,0.9987686
+4587,3246.043,341.3137,3270.6821,387.6373,0.9987639
+4587,2706.7522,203.90424,2722.9285,245.935,0.9987067
+4587,3029.6965,289.46088,3054.78,333.0882,0.99867034
+4587,2946.4539,335.7229,2969.2395,386.1725,0.99824166
+4587,2589.4543,305.94293,2617.5027,356.9751,0.9982211
+4587,1964.8335,884.43756,2005.5825,978.07965,0.9981415
+4587,1987.7534,125.597404,2004.3787,160.9718,0.9976267
+4587,2026.8652,193.36633,2043.508,242.40884,0.9974208
+4587,2977.6482,394.29877,3000.048,442.1751,0.99739957
+4587,2375.6184,445.20316,2405.0837,503.05392,0.9972178
+4587,2009.229,176.70255,2028.9304,220.4313,0.99719524
+4587,3736.5205,754.5695,3767.837,813.7585,0.9969465
+4587,2090.0193,408.00748,2113.447,472.34317,0.9931885
+4587,2978.9023,380.70438,3000.9746,428.01846,0.9518788
+4587,3776.4243,777.8985,3806.2432,836.7498,0.79721445
+4587,3495.7322,827.8728,3551.8665,897.40906,0.46065512
+4587,3498.2793,802.0564,3535.086,863.6294,0.27049202
+4587,2640.0273,866.4029,2686.9004,945.77325,0.14237252
+4587,3846.8518,773.6621,3878.4744,833.2772,0.11385655
+4588,2513.9678,720.4171,2552.5166,796.43506,0.99937904
+4588,2922.6208,386.2093,2946.2268,440.05084,0.99934465
+4588,2297.449,299.79648,2320.8835,352.20303,0.99931777
+4588,2647.6875,385.3579,2670.463,439.30804,0.99916315
+4588,2854.4006,325.39252,2881.6262,376.5708,0.99912053
+4588,2593.6946,230.48343,2616.1252,277.6564,0.9990139
+4588,3056.353,386.02167,3086.2134,439.71832,0.9989893
+4588,2665.0686,611.7837,2690.8289,682.92334,0.9989761
+4588,3091.2883,523.1297,3118.7122,583.7598,0.99883085
+4588,2703.0488,202.82082,2721.7573,245.08519,0.99878234
+4588,3246.9707,341.97137,3271.3325,387.72137,0.99877983
+4588,2978.991,504.04507,3005.6985,562.73486,0.9987035
+4588,1293.6556,319.12872,1314.6171,375.9745,0.99866223
+4588,2087.3132,298.26962,2105.3557,353.5621,0.9985984
+4588,3031.0999,289.392,3054.9167,332.36075,0.9984237
+4588,2089.9014,408.35562,2114.4863,472.41794,0.9983504
+4588,2590.9143,305.78455,2617.904,356.95966,0.9981294
+4588,1964.791,884.42725,2005.5625,978.0768,0.9980258
+4588,2009.0155,176.82042,2027.3629,220.29341,0.99796104
+4588,2977.5325,394.40405,2999.8645,442.76062,0.99779886
+4588,1987.9082,125.506065,2004.2666,160.94244,0.99767476
+4588,2026.8905,193.31714,2043.7972,242.41806,0.99753594
+4588,2946.0383,335.21048,2968.452,385.0917,0.9973827
+4588,2375.255,445.05298,2404.81,503.05212,0.997323
+4588,3736.0625,754.5888,3767.4492,814.5295,0.99665195
+4588,2979.605,380.88174,3000.67,427.18497,0.92840713
+4588,3496.8625,826.00696,3555.4333,899.3755,0.71339655
+4588,3776.6562,777.86395,3805.8672,836.8746,0.54818827
+4588,3501.0747,802.952,3535.9785,847.3026,0.53166026
+4588,3848.5444,772.7135,3881.1924,833.0819,0.17791791
+4589,2924.0464,385.97302,2946.7324,440.1576,0.9994117
+4589,2510.8606,721.56946,2546.265,796.25037,0.99933165
+4589,2647.8918,384.6113,2670.8074,439.0037,0.9992687
+4589,2854.7622,324.94806,2881.3867,375.75763,0.99922454
+4589,2658.7468,610.8882,2685.1384,682.72,0.9991985
+4589,2297.2778,299.50055,2317.6504,352.28796,0.9991954
+4589,1293.4834,319.31876,1314.4409,376.0941,0.9991543
+4589,3091.7424,523.7599,3118.3777,584.3684,0.99907804
+4589,2593.4382,230.1239,2614.3044,277.4686,0.9990778
+4589,2087.6943,298.25308,2105.6035,353.62003,0.9988998
+4589,3056.5627,386.1842,3087.9514,439.7279,0.9988708
+4589,3247.6775,341.10712,3272.4788,388.01556,0.9987238
+4589,2978.1128,504.48737,3004.9414,563.0247,0.9986884
+4589,2698.0469,202.08733,2720.7837,244.95863,0.9985139
+4589,2090.5505,407.97147,2115.5667,472.7601,0.9983839
+4589,2593.6504,305.55286,2618.3306,356.65802,0.99833053
+4589,1988.0131,123.513214,2004.4882,161.21729,0.9981775
+4589,2976.4038,394.25772,2999.6772,443.01913,0.99814796
+4589,3033.4812,289.21484,3055.487,332.45056,0.99806124
+4589,1964.7997,884.4214,2005.5646,978.08057,0.99796224
+4589,2008.8566,176.64479,2026.0455,220.22676,0.99756867
+4589,2375.5198,444.89655,2404.6077,502.99017,0.997544
+4589,2945.4734,334.59744,2967.9822,385.1212,0.9971511
+4589,3735.8594,754.58765,3767.3306,815.1079,0.99658895
+4589,2025.769,192.97305,2044.1514,242.17078,0.9964593
+4589,3496.3105,826.5599,3555.2612,900.75116,0.93488705
+4589,2979.4487,381.3964,3000.4312,427.54642,0.92488736
+4589,3501.0786,803.0426,3538.4624,861.20715,0.67329425
+4589,3777.2747,777.6762,3805.7615,836.6871,0.5351141
+4589,3850.9373,772.6204,3884.0378,831.86676,0.18238142
+4590,2925.028,385.68106,2947.0398,439.41965,0.9994691
+4590,2855.4927,324.32013,2880.4238,375.31482,0.9994228
+4590,2297.0269,299.3441,2315.1772,352.40463,0.9992489
+4590,2088.386,298.48587,2106.464,353.5706,0.9991786
+4590,2692.4448,201.21022,2719.624,244.69319,0.9991742
+4590,2653.922,609.9633,2682.5002,682.1341,0.99916804
+4590,2595.8987,305.1737,2618.4446,356.34363,0.99906904
+4590,2507.072,722.38544,2541.118,796.1092,0.998998
+4590,2977.786,504.76337,3004.2078,564.32855,0.99896574
+4590,2647.9578,384.6464,2672.4436,439.186,0.9989641
+4590,3091.5388,524.0522,3117.695,585.23834,0.9989292
+4590,3249.5251,341.2043,3273.1267,388.3621,0.99874496
+4590,3057.0334,386.47635,3089.2405,439.44992,0.9987237
+4590,1293.2977,319.47513,1314.245,376.1937,0.9986957
+4590,2593.3335,229.63344,2613.232,277.31873,0.9985392
+4590,1964.8014,884.45703,2005.5966,978.10693,0.99789596
+4590,2975.1511,394.26505,2999.3245,443.29422,0.9977763
+4590,2008.453,176.06354,2025.8505,219.71579,0.99769586
+4590,2375.6946,444.57544,2404.1116,502.96063,0.99747
+4590,2945.0244,334.49603,2967.1885,385.84222,0.9974037
+4590,3735.2563,754.76196,3766.98,815.6681,0.99730504
+4590,2091.6567,408.4429,2117.0786,472.67136,0.99714464
+4590,3035.3127,289.04416,3056.4827,332.60806,0.99699926
+4590,2025.93,192.71283,2044.2604,241.98367,0.9967205
+4590,1988.3094,123.99771,2006.1395,161.18314,0.9949715
+4590,2979.7166,381.44757,3000.3196,427.59338,0.89598954
+4590,3501.9492,801.6849,3540.6958,854.24713,0.88392276
+4590,3496.9055,824.6987,3553.6912,901.0023,0.56356823
+4590,3777.8887,777.6282,3805.6514,836.8712,0.2955755
+4590,3852.4714,772.91925,3886.7078,831.0821,0.22896641
+4591,2856.7039,324.16492,2879.5178,374.14874,0.99953413
+4591,2646.946,609.18353,2681.8484,682.40594,0.9994542
+4591,2925.5134,386.00708,2947.2195,438.86414,0.9994148
+4591,2296.1106,299.01392,2313.953,352.33746,0.99926394
+4591,2089.126,298.25214,2106.9263,353.5678,0.9991641
+4591,2598.5093,305.14297,2618.9658,356.57632,0.9991555
+4591,2504.8325,722.4263,2538.5244,798.567,0.99914324
+4591,2977.6223,505.48163,3002.7654,565.3676,0.99908006
+4591,3090.9688,524.5954,3117.063,585.9716,0.9989986
+4591,1293.0409,319.59988,1314.2308,376.19962,0.99880385
+4591,3059.2014,386.51315,3090.5623,439.17886,0.99858534
+4591,3251.2808,341.29596,3273.5884,388.37115,0.9985826
+4591,2593.1702,229.91296,2611.3743,276.33517,0.9985374
+4591,2094.2527,408.37033,2118.2039,472.81778,0.99845195
+4591,2008.3964,175.81049,2025.65,219.33112,0.9984345
+4591,2689.8047,201.314,2717.9888,243.08585,0.9982811
+4591,2649.3982,384.60568,2673.0183,438.75632,0.9982444
+4591,3733.2014,754.78186,3766.802,815.57385,0.99803346
+4591,3037.6858,289.24197,3057.5266,332.54294,0.9979079
+4591,1964.7773,884.39954,2005.6138,978.0897,0.99769324
+4591,2944.6633,334.24454,2966.8186,385.07883,0.9976078
+4591,2375.7068,444.30737,2403.8005,502.93164,0.99756634
+4591,2974.9016,394.23422,2998.7698,443.34274,0.9975314
+4591,2025.1527,192.52367,2044.506,241.76921,0.9965395
+4591,1988.4543,123.97164,2006.356,161.18886,0.99506843
+4591,2979.1082,381.68246,3000.1418,428.22702,0.91181076
+4591,3488.7214,822.32544,3553.2244,902.02185,0.5001701
+4591,3501.738,800.06244,3543.577,852.2973,0.47750783
+4591,2647.0364,864.185,2693.471,944.3411,0.4080542
+4591,3779.6453,778.34204,3805.4817,838.5846,0.3872081
+4591,3852.533,772.9788,3890.488,831.43536,0.37282056
+4592,2638.531,608.4802,2680.093,682.40784,0.999493
+4592,2857.231,323.88446,2879.1147,374.0703,0.9994554
+4592,2925.8376,385.81723,2947.6213,438.74875,0.99935263
+4592,2294.0146,298.72714,2312.9395,351.81448,0.9992957
+4592,2502.626,722.7467,2535.602,801.63403,0.9992588
+4592,2089.905,298.46338,2106.988,353.391,0.9991153
+4592,1292.7578,319.83084,1314.23,376.2103,0.9990375
+4592,2095.1958,408.7002,2119.0796,472.49847,0.9989159
+4592,2977.7935,506.25616,3002.1416,566.22186,0.99890745
+4592,2600.3232,305.01404,2619.7837,356.72162,0.99887305
+4592,3252.2188,342.21872,3273.9058,388.58786,0.99876106
+4592,2376.0547,443.90045,2403.1025,502.91718,0.9987275
+4592,3061.704,386.49777,3092.0835,439.18027,0.99868435
+4592,2593.426,229.34543,2610.2283,276.06137,0.99859923
+4592,2689.773,202.08116,2714.7979,243.08394,0.9985971
+4592,2007.9255,175.31517,2025.3569,219.2253,0.99855596
+4592,3732.1829,754.62366,3766.7986,815.9686,0.99831676
+4592,2650.5686,384.9544,2673.5232,439.31732,0.99827826
+4592,3091.2114,525.32446,3116.6968,586.7522,0.99826413
+4592,2942.9893,333.76627,2966.2734,385.39975,0.9980385
+4592,1964.743,884.40106,2005.601,978.0931,0.99771225
+4592,2025.6212,192.18967,2044.7389,241.61343,0.9971103
+4592,3039.4592,288.95978,3058.4426,332.56635,0.9970741
+4592,2974.921,394.44894,2998.189,443.31027,0.99654466
+4592,1988.7013,123.688,2005.1332,161.26189,0.9953818
+4592,2979.411,382.09247,2999.6858,428.2234,0.90779907
+4592,3486.816,823.9013,3552.9429,902.257,0.83314127
+4592,3779.047,779.90027,3804.8728,841.068,0.64767724
+4592,2650.1619,864.0052,2694.9973,943.84515,0.5171615
+4592,3857.3076,772.9059,3894.401,831.2311,0.28501052
+4592,3504.313,799.7658,3546.0264,867.034,0.16359228
+4592,3512.2078,799.7688,3545.0872,848.79736,0.116591506
+4593,2632.5981,609.40436,2678.3857,682.4753,0.99967706
+4593,2496.0557,723.091,2532.8262,804.44293,0.99951947
+4593,2858.0034,323.60873,2879.0435,373.51468,0.99936706
+4593,1292.6865,320.12994,1314.0688,376.29602,0.9993044
+4593,2089.8926,298.5245,2107.2964,353.5112,0.9992095
+4593,2290.0999,298.7472,2311.5164,351.60046,0.999062
+4593,2925.9617,385.50275,2948.4026,438.93073,0.99904376
+4593,3090.5483,526.4009,3116.1787,588.07654,0.9990165
+4593,3253.763,342.31277,3274.557,388.97922,0.9989888
+4593,2376.57,443.1233,2402.361,502.9002,0.9988152
+4593,2095.3198,408.69666,2120.7632,472.32227,0.9987382
+4593,2977.5762,506.42828,3001.601,567.3477,0.99869233
+4593,3062.94,386.28503,3092.7603,439.7798,0.9986596
+4593,2601.711,305.1429,2620.5132,357.1384,0.9985832
+4593,2593.092,229.13911,2609.0027,275.74612,0.9985275
+4593,3732.183,754.7295,3766.6196,815.8688,0.9984818
+4593,2007.7079,175.07874,2025.0548,219.13794,0.99847466
+4593,2689.4983,201.91757,2712.0002,243.13232,0.99846244
+4593,2651.828,384.67764,2674.0618,437.86307,0.99827945
+4593,2941.8452,333.56323,2965.6685,385.3448,0.99783033
+4593,1964.733,884.3685,2005.5746,978.08826,0.99752706
+4593,2026.3073,192.05685,2044.7198,241.45648,0.99751496
+4593,3039.5615,288.62405,3059.1553,334.86386,0.99571925
+4593,2974.909,394.8643,2997.7913,443.34714,0.99551696
+4593,1988.9465,123.4198,2005.3264,161.16159,0.9936785
+4593,2979.7065,382.7113,2999.7227,427.69006,0.8961133
+4593,3779.3296,781.1967,3803.6885,843.5811,0.8233674
+4593,3484.9841,826.2621,3529.0603,902.0785,0.6911268
+4593,3502.9126,799.0404,3548.5894,882.9518,0.61231726
+4593,2653.2244,863.8858,2696.557,944.14166,0.4290727
+4593,3863.6736,771.6746,3897.9465,831.3785,0.3187638
+4594,2630.4434,610.266,2674.274,684.1843,0.9996445
+4594,2489.3286,724.4968,2529.8516,804.64795,0.999571
+4594,1292.4161,320.46472,1314.0177,376.43408,0.99936193
+4594,2859.2312,323.44644,2878.2083,372.19186,0.999315
+4594,2286.3586,298.36847,2310.6677,351.33508,0.99906605
+4594,2688.5168,202.1293,2707.6052,243.10489,0.99904746
+4594,3090.6646,526.89923,3116.0156,588.1098,0.9989768
+4594,2089.8965,298.26138,2107.7852,353.63187,0.99896383
+4594,2977.3047,506.65414,3001.4492,567.7008,0.99891675
+4594,2376.4285,442.97943,2402.0789,502.98517,0.998896
+4594,2926.148,385.5078,2948.915,438.36542,0.9988959
+4594,2654.2266,384.55713,2674.1592,437.4427,0.9987931
+4594,3254.3018,342.924,3275.104,389.9458,0.9987625
+4594,3065.6807,386.32504,3093.814,440.3195,0.9987293
+4594,2593.1548,228.76254,2608.2544,275.39905,0.99859244
+4594,2007.6113,174.50575,2024.8933,219.0317,0.99854964
+4594,3732.0483,754.70795,3766.5127,815.70905,0.9984403
+4594,2095.9656,408.97885,2121.3308,472.11325,0.9983815
+4594,2602.7007,305.1624,2621.6938,357.17926,0.99819475
+4594,3040.0786,288.71216,3060.7222,333.3935,0.99781764
+4594,2941.1912,332.91107,2965.097,384.84686,0.99770796
+4594,2027.7197,192.02107,2045.1499,241.64809,0.9975799
+4594,1964.6753,884.3266,2005.5862,978.11005,0.9974897
+4594,1989.2885,123.29907,2004.4188,161.18567,0.99716216
+4594,2974.957,394.68146,2997.7373,443.32642,0.9955273
+4594,2979.536,382.64566,2999.5369,427.5519,0.9006271
+4594,3779.4688,783.1388,3802.316,846.6836,0.7462001
+4594,3519.2363,799.7028,3549.0796,846.71686,0.37302876
+4594,3482.7773,827.4751,3530.6655,902.7693,0.32368487
+4594,3868.8035,769.69275,3902.465,831.5664,0.19909325
+4594,2658.1602,864.1773,2697.9946,944.20197,0.14932159
+4595,2484.7605,726.3434,2527.0256,805.7252,0.9995345
+4595,2630.15,611.5942,2668.726,684.1114,0.999433
+4595,2859.1855,323.1878,2878.296,372.2602,0.99933064
+4595,1292.0778,320.68173,1314.2164,376.4257,0.99927765
+4595,3068.6682,386.37473,3095.2366,439.77652,0.99927753
+4595,2977.3867,506.51578,3001.354,567.17487,0.99904364
+4595,2376.3457,442.81284,2401.8887,502.96298,0.99904007
+4595,2655.221,384.32227,2674.847,437.64172,0.99900335
+4595,2283.5696,298.29312,2310.0886,351.0731,0.99896
+4595,2926.2354,385.20892,2949.7764,438.13403,0.9989471
+4595,3090.6467,527.33154,3115.802,588.4835,0.9988865
+4595,2089.9636,297.8537,2108.5623,353.57837,0.9988625
+4595,2602.6213,305.13995,2623.3503,357.1446,0.9988411
+4595,2007.1617,174.12836,2024.756,218.77182,0.99882394
+4595,3040.195,289.03854,3062.2722,333.42422,0.99874675
+4595,2095.8616,408.88873,2122.408,472.56567,0.99867994
+4595,3732.0156,754.675,3766.4248,815.67694,0.9984816
+4595,2687.3755,202.05621,2703.961,243.13501,0.99842274
+4595,2591.095,228.5599,2607.9075,274.81525,0.9983851
+4595,3254.4019,343.95654,3275.8604,390.31525,0.9982539
+4595,2941.238,332.66516,2964.8713,383.96857,0.9980938
+4595,1989.4083,123.44022,2004.4027,161.24048,0.9977246
+4595,1964.7189,884.324,2005.5629,978.12866,0.9975393
+4595,2028.2217,191.86272,2045.5886,240.69731,0.99711215
+4595,2974.9812,394.35443,2997.3044,443.23853,0.9948715
+4595,2979.348,382.7096,2999.1936,427.92432,0.9199991
+4595,3778.6033,784.1914,3802.0251,849.34924,0.6715875
+4595,3482.7493,828.7924,3532.3313,902.7225,0.34841546
+4595,2981.0867,383.30127,2999.3298,409.6313,0.24651091
+4595,3519.0664,800.853,3550.9858,853.6328,0.22380233
+4596,2481.3096,728.3158,2523.5654,808.89355,0.9995883
+4596,2629.3423,613.4725,2661.974,683.6066,0.99944574
+4596,1291.608,320.7635,1314.1749,376.4131,0.99938464
+4596,2859.3252,323.21832,2878.2227,372.12335,0.99937516
+4596,2977.083,506.51636,3001.292,567.6752,0.9992403
+4596,2376.1135,442.06558,2401.4377,503.00412,0.99923575
+4596,2656.3176,383.95575,2675.7937,437.7815,0.99920017
+4596,2926.4844,384.997,2950.609,437.93445,0.9990994
+4596,3090.5046,527.84064,3115.9368,588.56903,0.9990746
+4596,2282.4797,298.5347,2309.0115,351.07858,0.99903786
+4596,3069.302,386.28174,3096.4749,439.9151,0.9989561
+4596,2006.8602,173.8908,2024.7225,218.8271,0.99895597
+4596,3254.3113,344.65253,3276.465,390.85352,0.99891293
+4596,2090.025,297.85754,2109.0332,353.42175,0.99880373
+4596,2095.315,409.10226,2123.9546,472.63907,0.9987968
+4596,2685.1333,200.80547,2700.8662,243.15813,0.9987909
+4596,3040.171,289.04535,3064.2783,333.56378,0.9986987
+4596,2602.4436,305.19708,2625.9133,357.12622,0.9984536
+4596,2588.1165,228.92348,2607.366,274.62726,0.9984036
+4596,3731.9875,754.69055,3766.36,815.7689,0.99839455
+4596,2941.0615,332.48987,2964.564,383.88763,0.99821126
+4596,1989.3563,123.3297,2004.3954,161.48186,0.9976109
+4596,1964.6975,884.32904,2005.4963,978.1337,0.99744666
+4596,2028.452,191.81342,2045.7728,240.3334,0.9971425
+4596,2975.017,394.25812,2997.1514,443.20422,0.9954919
+4596,2979.3855,382.84164,2999.0159,428.13052,0.92421573
+4596,3482.9658,829.242,3529.1475,902.6816,0.58438367
+4596,3516.9993,802.2628,3552.696,866.406,0.5239953
+4596,3777.9763,785.44965,3801.3865,850.1733,0.5154528
+4596,2981.0068,383.46432,2999.2256,410.55057,0.25736174
+4596,3985.6108,749.27136,4009.2988,801.5751,0.13600853
+4597,2481.0708,730.6791,2519.8599,809.3966,0.9995498
+4597,2859.4275,322.74738,2878.3005,371.8286,0.9993838
+4597,1291.3918,320.86005,1314.0874,376.40625,0.99935645
+4597,2376.0332,441.80103,2401.374,502.9782,0.9993383
+4597,2281.7798,297.97134,2308.067,351.2758,0.9992982
+4597,2926.7385,383.96542,2951.6345,437.9581,0.9992818
+4597,2657.0417,384.04797,2676.7527,438.1936,0.99921656
+4597,2682.1543,200.22304,2699.6948,242.48286,0.99912566
+4597,2976.9636,506.4843,3001.3704,567.36444,0.99911743
+4597,2624.5957,613.5923,2654.0405,683.96094,0.9991105
+4597,2006.3701,173.55513,2024.3364,218.78944,0.99907196
+4597,2090.0356,297.67212,2109.855,353.46167,0.998955
+4597,3090.6458,528.23865,3116.4895,588.8463,0.9989002
+4597,3254.2812,344.9963,3277.0352,391.58817,0.9988751
+4597,3070.1301,386.5939,3097.8083,439.75076,0.998643
+4597,3731.9797,754.6364,3766.4495,815.9841,0.99856853
+4597,2941.248,332.29346,2963.774,382.9038,0.99852365
+4597,2602.4277,305.10132,2628.4722,356.8108,0.9984848
+4597,2586.292,228.75237,2607.4844,274.59665,0.9982455
+4597,2095.667,409.43094,2125.7969,472.6014,0.99823207
+4597,1989.2207,123.53043,2004.5752,161.33446,0.9978465
+4597,1964.6998,884.4067,2005.4554,978.1149,0.9976562
+4597,2028.9393,191.48389,2045.8783,240.03445,0.99749124
+4597,3040.429,289.1609,3065.4807,333.63397,0.9973877
+4597,2975.0542,394.1132,2996.9194,443.20767,0.9943932
+4597,2979.4753,382.86542,2998.9807,427.95825,0.924257
+4597,3516.6248,803.329,3554.4695,871.3334,0.76976615
+4597,3776.8018,785.731,3800.4604,850.6126,0.6823244
+4597,3483.142,828.8761,3524.7246,902.87476,0.5401974
+4597,3984.5815,749.1934,4009.0415,802.1086,0.27580374
+4597,2980.8184,383.54474,2999.247,411.5097,0.2743613
+4598,2480.7925,732.3113,2516.3306,809.38477,0.99947375
+4598,2375.9932,441.28833,2401.2437,503.0509,0.99941504
+4598,2859.3425,322.6919,2878.3713,372.00293,0.9993789
+4598,1291.1879,320.94678,1314.0824,376.41925,0.99924135
+4598,2657.1367,383.61734,2677.1743,438.1282,0.9991794
+4598,2281.267,297.1147,2306.6382,350.69788,0.9990883
+4598,2090.1355,297.49484,2110.3425,353.45914,0.99908006
+4598,2976.0737,507.19128,3001.2544,567.67444,0.9990797
+4598,2926.7417,383.522,2952.9814,437.4341,0.99903405
+4598,2006.2635,173.44809,2024.4847,218.83054,0.99902594
+4598,2678.2969,199.55223,2698.9233,242.46373,0.9990109
+4598,2620.0872,612.49945,2649.824,684.16925,0.9989963
+4598,3070.3599,386.79132,3099.0303,439.77197,0.9989188
+4598,2584.4165,228.38327,2607.2676,274.36993,0.99889934
+4598,3254.4023,345.89658,3277.2256,392.2413,0.9988036
+4598,3088.6772,528.483,3117.1235,588.8702,0.9986187
+4598,3040.4255,289.0473,3067.5193,334.30585,0.9984841
+4598,3731.9377,754.7348,3766.4084,815.8791,0.99837154
+4598,2941.143,331.936,2963.1855,382.2598,0.9983524
+4598,2602.555,305.08545,2629.9685,356.6399,0.99819154
+4598,2095.3215,409.44492,2128.2585,472.61057,0.99804676
+4598,1989.3616,123.23919,2004.8345,161.53804,0.997951
+4598,1964.7102,884.3939,2005.2488,978.1653,0.9975418
+4598,2029.2605,191.45839,2045.8315,239.33714,0.99741554
+4598,2974.9675,393.52524,2996.6868,443.46756,0.9946315
+4598,2979.7375,382.89764,2999.0833,428.57733,0.9162213
+4598,3518.1785,803.29736,3556.45,868.3534,0.7498946
+4598,3483.122,828.57367,3520.4214,902.972,0.70275104
+4598,3774.5298,785.65533,3799.6152,850.78815,0.58325773
+4598,2981.272,383.55875,2999.3638,410.6212,0.25990346
+4598,2672.541,863.58685,2703.9512,946.20355,0.19910294
+4598,3983.1523,749.00024,4008.33,801.3684,0.1687421
+4599,2479.5234,732.60675,2511.898,809.26105,0.9993986
+4599,2859.2886,322.64777,2878.4546,372.08008,0.9993899
+4599,2375.8843,441.06436,2401.0986,503.04575,0.99936515
+4599,2616.3293,611.8497,2646.5598,684.14484,0.9993057
+4599,2280.8252,296.24835,2305.0498,349.76782,0.9992785
+4599,1290.9976,320.8966,1313.9641,376.43213,0.9992685
+4599,2927.3247,383.66293,2953.6265,436.7108,0.99923396
+4599,2090.1814,297.21793,2110.7341,353.37283,0.99917394
+4599,2006.0878,173.5574,2024.4362,218.84042,0.9991049
+4599,2975.7292,507.59766,3001.2693,568.81323,0.9989587
+4599,2671.9573,198.78252,2698.4587,242.09145,0.9988863
+4599,2602.7737,305.38232,2629.6506,356.50787,0.99871624
+4599,2657.2043,383.56866,2677.8093,437.8764,0.9986009
+4599,3732.097,754.7982,3766.4875,815.9071,0.99853027
+4599,3254.8574,346.24997,3277.9521,392.56143,0.9985251
+4599,2583.3855,228.63867,2607.3352,274.19098,0.998519
+4599,3070.4775,386.78802,3100.455,439.7033,0.9982332
+4599,2940.92,331.50275,2962.8765,380.94006,0.99805766
+4599,2095.4407,409.49512,2128.1086,472.67615,0.99805105
+4599,1989.6213,123.58137,2004.8044,161.54956,0.9980337
+4599,3089.4312,528.8988,3117.8672,588.8534,0.99800545
+4599,3040.216,289.23953,3068.5618,334.28934,0.9979073
+4599,1964.8179,884.4001,2005.2302,978.1215,0.9974481
+4599,2029.727,191.22113,2045.6975,238.65726,0.99710196
+4599,2974.7166,393.55563,2996.7375,443.49326,0.9956864
+4599,2978.8042,382.83905,2998.7861,428.82098,0.91850007
+4599,3519.3015,802.24603,3559.7175,867.2696,0.6441945
+4599,3482.988,828.19446,3518.3318,903.006,0.5554237
+4599,3981.293,751.1422,4006.9487,800.93115,0.42192128
+4599,3772.3901,785.7096,3798.609,850.8823,0.35155404
+4599,3885.68,765.56744,3912.6252,817.16754,0.13440615
+4600,2474.8958,732.5248,2508.932,809.3104,0.9995092
+4600,2859.1787,322.59424,2878.6538,372.26196,0.9994068
+4600,2375.521,440.6567,2400.75,503.04996,0.99938595
+4600,1290.5674,321.06894,1313.9148,376.47092,0.9993542
+4600,2280.6404,296.13315,2302.7024,348.68173,0.99934965
+4600,2609.635,611.27686,2642.844,685.80237,0.9993268
+4600,2657.1743,383.26077,2680.1953,437.1331,0.99928087
+4600,2929.7422,383.44803,2954.7695,436.27444,0.999252
+4600,2090.276,296.88742,2111.291,353.38132,0.999153
+4600,2603.0247,305.7242,2629.9636,356.1119,0.99884063
+4600,2006.1292,173.51439,2024.4868,218.61476,0.99884003
+4600,2582.6243,228.63957,2607.1614,274.74347,0.9987962
+4600,2972.742,508.8771,3001.4724,569.9379,0.9987363
+4600,3255.3616,346.342,3278.4158,392.71213,0.99871135
+4600,3070.977,386.88986,3101.6655,439.21182,0.99858093
+4600,3731.8896,754.86096,3766.5278,815.7527,0.99849844
+4600,2669.1992,199.02126,2696.496,241.32796,0.9984762
+4600,3040.0938,289.00146,3069.2998,334.19983,0.99842274
+4600,3090.0784,529.19196,3118.6624,588.9107,0.998323
+4600,2940.7612,330.92496,2962.543,379.35324,0.9981279
+4600,2095.9917,409.60144,2128.0234,472.73322,0.99809486
+4600,1989.5428,123.28736,2004.8207,161.59819,0.9980003
+4600,1964.8407,884.3847,2005.2233,978.0993,0.9975883
+4600,2974.817,393.65143,2996.8926,443.3969,0.996447
+4600,2030.0002,191.19977,2045.6245,238.32599,0.9964051
+4600,2978.4036,382.78464,2998.8655,428.7076,0.9177205
+4600,3482.9028,827.70483,3517.0786,902.32446,0.821996
+4600,3976.833,751.53705,4006.0571,801.8769,0.26077667
+4600,3769.204,785.31024,3797.9849,851.1209,0.24584682
+4600,2979.9612,383.75223,2999.1174,411.80893,0.20709975
+4600,3518.473,801.1995,3563.2063,867.8708,0.19666438
+4600,3887.9312,765.073,3913.7026,816.68555,0.15314339
+4601,2599.5698,611.2239,2641.275,685.95056,0.9994704
+4601,2859.0479,322.6629,2878.9702,370.83972,0.9994438
+4601,2932.0144,383.39746,2955.3572,436.32312,0.9994311
+4601,2473.1736,732.5055,2506.1848,809.5858,0.9993444
+4601,2375.3733,440.6213,2400.5408,502.981,0.99934095
+4601,1290.3354,321.17462,1314.0559,376.44568,0.99930906
+4601,2279.4775,295.94037,2301.0063,348.2929,0.99928397
+4601,2667.4722,198.48788,2694.5977,240.52924,0.9992353
+4601,2657.7095,383.77283,2682.0112,437.2812,0.9991097
+4601,3071.3442,387.87253,3102.7026,438.87228,0.999087
+4601,2090.593,296.62155,2111.5486,353.18942,0.99899966
+4601,2006.1609,173.04482,2024.2756,218.79512,0.998976
+4601,2582.166,228.62708,2605.9014,274.75757,0.9989409
+4601,2603.1914,305.42188,2630.5132,356.33203,0.9989135
+4601,3090.89,529.5428,3119.843,589.987,0.9987826
+4601,3256.2432,346.67,3278.872,393.53677,0.998705
+4601,2970.4812,509.15833,3001.4563,571.93555,0.99855846
+4601,3731.1558,754.51996,3766.5503,815.86554,0.9984958
+4601,2096.305,409.49393,2129.6204,473.03543,0.9981211
+4601,1989.6727,123.24225,2005.0939,161.07065,0.9978492
+4601,1964.9233,884.4168,2005.2505,978.1069,0.9978417
+4601,3042.9297,289.57556,3068.8389,332.5509,0.9977869
+4601,2941.1628,330.70004,2961.9192,378.34616,0.99759966
+4601,2030.6492,191.13194,2045.5525,238.27974,0.9972926
+4601,2975.1565,393.42038,2997.7092,443.4523,0.9963702
+4601,2977.6008,383.11227,2998.7703,430.1442,0.9464006
+4601,3481.9463,827.9901,3515.5215,902.7711,0.74027365
+4601,3765.883,785.6925,3796.0017,850.7898,0.60334915
+4601,3519.218,800.678,3565.744,867.43115,0.36998668
+4601,3972.7974,751.79626,4004.7144,802.4999,0.24003635
+4601,2980.0403,383.80875,2998.4495,414.1365,0.22109573
+4601,3890.754,764.35547,3916.5396,817.1333,0.10794217
+4602,2594.4062,611.8738,2638.6016,686.2982,0.9994966
+4602,2858.952,322.00232,2879.0247,371.12323,0.99945
+4602,2668.3623,199.34154,2692.2979,240.70326,0.99941266
+4602,2467.2292,732.4107,2503.3792,809.32404,0.99938214
+4602,2934.1077,383.25616,2956.0847,436.2198,0.99933165
+4602,2279.2676,295.67505,2299.7437,348.38098,0.99928963
+4602,1290.2279,321.20425,1314.0514,376.38638,0.99924505
+4602,2375.2222,440.3603,2400.2207,502.661,0.9991819
+4602,2582.0522,228.63942,2604.6626,274.54425,0.99917233
+4602,2657.801,383.7899,2682.4954,437.24588,0.9990331
+4602,2090.8457,296.11108,2111.629,353.19397,0.9989618
+4602,3071.6052,387.77316,3103.098,438.55222,0.99895823
+4602,2006.298,173.11218,2024.2748,218.79245,0.99891794
+4602,3091.2615,530.13385,3120.8743,590.7956,0.9987301
+4602,3257.196,346.73254,3279.2195,394.24872,0.9986374
+4602,2603.4597,304.74667,2631.0178,356.32553,0.9985851
+4602,2970.0269,509.77847,3001.4585,572.3396,0.998514
+4602,3044.653,289.56216,3070.4558,332.87796,0.998494
+4602,3731.024,754.1674,3766.5068,816.0307,0.99838614
+4602,2096.7134,409.33853,2129.8398,472.9279,0.99804354
+4602,1964.9417,884.4046,2005.2834,978.16425,0.9977609
+4602,2030.662,191.08989,2045.8021,238.81963,0.9977222
+4602,2941.1306,330.0537,2961.6497,377.88593,0.9976809
+4602,1989.7748,123.08136,2005.0367,160.92358,0.9974642
+4602,2975.2893,393.56415,2997.4543,443.54077,0.9943078
+4602,2977.7727,383.56924,2998.8137,431.15335,0.9474981
+4602,3480.0344,828.0088,3514.8806,902.9441,0.89787555
+4602,3520.7964,799.63794,3569.0405,870.25574,0.76294583
+4602,3760.3916,786.0194,3794.5483,851.32996,0.66201156
+4602,3891.7935,762.2703,3916.8887,816.11127,0.18656793
+4602,3970.2407,752.71686,4003.13,801.91766,0.11863332
+4603,2456.4775,733.2114,2500.9937,812.6875,0.9994185
+4603,2859.3135,321.4059,2878.9849,369.92075,0.9994137
+4603,2592.1738,612.76324,2634.1265,687.6425,0.9993267
+4603,2374.8552,440.12988,2399.9084,502.4391,0.9993019
+4603,2278.2188,295.64218,2298.5596,348.9177,0.9992543
+4603,1289.776,321.5377,1313.9034,376.37234,0.9992199
+4603,2935.1052,383.23135,2956.6863,436.51053,0.9991431
+4603,2581.983,228.34604,2603.892,274.15964,0.99914217
+4603,2667.743,199.58005,2688.4133,240.13916,0.99912673
+4603,3090.7014,531.56134,3121.694,593.53534,0.9989048
+4603,2006.2712,173.05806,2024.2932,218.73546,0.99880445
+4603,2970.1238,510.59454,3001.4055,573.51544,0.9987809
+4603,3072.5422,387.7969,3104.593,438.782,0.9987625
+4603,2657.8906,383.827,2682.9102,437.37088,0.99874485
+4603,2091.2366,295.79678,2112.557,353.10092,0.99872714
+4603,3730.915,754.2101,3766.5889,816.21155,0.99841917
+4603,2605.915,304.78494,2631.5361,356.36624,0.9981358
+4603,2030.7605,190.93425,2046.4053,238.86388,0.9979703
+4603,2941.0232,329.2159,2961.5916,377.89218,0.9978417
+4603,1964.9417,884.40466,2005.2834,978.1642,0.99776137
+4603,3047.2446,288.97302,3070.0967,332.36688,0.99766976
+4603,3257.629,347.11856,3279.3223,395.07974,0.9974954
+4603,1989.8832,123.086655,2005.4911,160.3759,0.9973348
+4603,2099.263,408.36627,2130.1648,472.51898,0.99451786
+4603,2975.2537,393.44345,2996.2346,443.6122,0.99012226
+4603,3521.6333,800.46423,3571.333,869.67444,0.9781948
+4603,3474.4404,828.2306,3514.023,902.9724,0.9622709
+4603,2978.7305,384.37088,2999.3784,432.46603,0.9344307
+4603,3754.644,787.12317,3792.5693,851.6699,0.5536526
+4603,3968.6875,753.1041,4001.4653,802.17505,0.15598008
+4603,3891.2136,760.8332,3920.927,815.63934,0.14692478
+4603,2980.2046,385.4449,2999.292,409.51556,0.120720156
+4604,2452.4656,734.14056,2498.2283,814.27814,0.9994917
+4604,2859.1765,320.6078,2879.2615,369.64856,0.9994666
+4604,2591.752,615.13885,2628.8584,687.4001,0.99944943
+4604,2374.7207,439.5042,2399.6792,502.24982,0.9993032
+4604,1289.6714,321.58035,1313.8059,376.3915,0.999263
+4604,2935.0908,382.98572,2957.4883,436.68787,0.9992254
+4604,2581.8516,228.54572,2601.9287,274.08035,0.99922204
+4604,3090.7046,531.9638,3122.0684,593.8566,0.9990206
+4604,2970.1611,511.5831,3001.2227,574.6404,0.9989102
+4604,2006.2054,172.5896,2023.8104,217.50156,0.99887604
+4604,2666.3755,199.5932,2684.2764,240.62747,0.99887484
+4604,2274.8655,295.00333,2297.2913,348.41977,0.99878913
+4604,2606.8726,304.46066,2632.2446,355.73593,0.99876666
+4604,2658.097,383.93564,2682.404,437.63797,0.9987117
+4604,2092.3384,294.99124,2113.4688,352.2034,0.998531
+4604,3731.0022,754.19904,3766.5598,815.9383,0.99850535
+4604,2031.0082,190.82309,2047.6312,238.62604,0.9984145
+4604,2941.1226,328.86655,2961.6987,377.233,0.9981567
+4604,3074.9907,387.66998,3105.9136,438.32355,0.9980825
+4604,3049.7554,288.59738,3071.1587,332.81833,0.99772507
+4604,1965.0469,884.66205,2005.7368,978.46906,0.997686
+4604,1989.9417,122.94344,2005.9922,160.13919,0.99742067
+4604,2100.3582,408.06152,2130.596,472.71765,0.99568695
+4604,3258.259,347.48923,3279.572,394.27347,0.99150836
+4604,2974.9648,393.4898,2995.3643,443.78082,0.99136496
+4604,3522.8982,801.0314,3573.5198,870.35864,0.98901373
+4604,2979.6033,385.5465,2999.8933,433.8723,0.9411052
+4604,3470.592,828.3745,3512.5447,902.969,0.9109677
+4604,3748.2507,788.4163,3790.1975,852.52106,0.70918864
+4604,3968.5833,756.14056,4001.3977,803.4488,0.36238897
+4604,3891.9492,758.7567,3922.666,813.5387,0.2240706
+4604,2981.3442,385.99008,3000.0522,413.41306,0.20917487
+4605,2449.9275,735.40784,2495.6526,816.578,0.9995066
+4605,2859.0293,320.26636,2879.3604,368.50916,0.99944454
+4605,2374.6428,439.19614,2399.7717,502.38596,0.9992681
+4605,1289.5857,321.7234,1313.7427,376.39288,0.9992466
+4605,2581.9126,228.50053,2600.1636,274.2711,0.9992347
+4605,2935.064,382.7744,2958.4126,436.73608,0.9991984
+4605,2591.47,616.11456,2621.8958,687.0445,0.9991609
+4605,3090.7087,532.4006,3122.6199,594.4007,0.9991524
+4605,2969.9812,512.036,3001.4875,575.224,0.99881256
+4605,2658.104,384.06836,2682.6655,437.65894,0.99880826
+4605,2006.207,172.74937,2023.7292,217.00279,0.9988078
+4605,2269.7954,294.82962,2295.849,348.4611,0.99875975
+4605,3731.1465,754.2659,3766.5718,815.9289,0.998569
+4605,2030.98,190.82446,2047.9346,238.71594,0.99845994
+4605,2094.2432,295.06918,2113.8276,351.57333,0.99832565
+4605,2609.4023,303.91315,2633.5723,356.07495,0.9983115
+4605,2663.069,199.02039,2680.2537,240.48477,0.99799937
+4605,1965.0193,884.65155,2005.7441,978.44354,0.99773175
+4605,2940.8953,328.18668,2961.805,377.21213,0.9977077
+4605,3077.9314,387.50613,3107.2869,438.2327,0.99756575
+4605,1989.989,122.93123,2005.6753,160.00633,0.99743277
+4605,3050.9229,288.72098,3072.4014,333.2474,0.9968706
+4605,2103.84,408.24274,2130.8015,472.79926,0.9944845
+4605,3257.917,346.75543,3279.8115,395.60382,0.9914294
+4605,2974.887,393.64005,2995.4739,443.86844,0.9908676
+4605,3526.197,802.38245,3576.2883,870.37744,0.98860276
+4605,3467.6726,828.56354,3510.947,903.0644,0.9613614
+4605,2979.9023,385.76318,3000.0825,434.156,0.94967604
+4605,3744.8215,789.68176,3788.1267,852.4713,0.9114728
+4605,3968.1328,755.90625,4000.185,804.3357,0.27830628
+4605,3893.4204,757.8166,3925.0015,813.52716,0.120119855
+4606,2448.2207,737.14197,2494.1582,818.7831,0.9995907
+4606,2858.8503,319.5807,2879.5042,368.524,0.9994081
+4606,1289.4995,321.8676,1313.8015,376.38477,0.9992393
+4606,2935.0317,382.47244,2959.0137,436.8569,0.99918276
+4606,3090.686,532.90424,3123.2705,595.1601,0.9991671
+4606,2612.0132,303.94638,2634.9702,355.7676,0.9990748
+4606,2581.8083,228.20326,2597.987,273.78748,0.9990626
+4606,2588.5532,616.4061,2613.3374,687.0492,0.9990257
+4606,2374.6301,439.02408,2397.7976,501.821,0.9990121
+4606,2658.0854,383.9583,2683.397,437.52917,0.9989631
+4606,2660.6855,198.31412,2677.9429,240.3183,0.9989427
+4606,2265.7153,294.76004,2295.0283,348.20505,0.99880886
+4606,2970.6926,512.5886,3001.2683,575.7958,0.9987141
+4606,2006.2242,172.32799,2023.525,216.41396,0.9986332
+4606,3731.0728,754.26715,3766.6094,816.01227,0.9986229
+4606,2095.5664,294.39114,2114.2441,350.42734,0.9985519
+4606,2031.2504,190.79811,2049.0398,238.4616,0.9981299
+4606,3081.508,387.41257,3108.887,437.68295,0.99811506
+4606,1965.0286,884.64856,2005.7314,978.4485,0.997741
+4606,1989.6246,122.79134,2007.0428,160.11653,0.9977077
+4606,2940.9675,328.24435,2961.6296,376.7369,0.9976654
+4606,3052.6199,288.86572,3073.2068,333.25458,0.99737513
+4606,3258.6628,347.50546,3280.2268,396.038,0.99561846
+4606,2106.1448,408.13327,2131.3318,472.8165,0.995063
+4606,3530.349,803.4939,3578.1484,870.27783,0.9949569
+4606,2974.761,393.09723,2995.3865,443.86212,0.9912043
+4606,2980.567,386.39282,3000.622,435.98138,0.9448231
+4606,3743.1665,789.8143,3786.2437,852.31146,0.62235326
+4606,3466.5166,828.78,3510.088,903.4872,0.5843943
+4606,3968.334,755.83435,3998.917,804.41125,0.24868587
+4607,2447.0947,738.7117,2492.121,820.70715,0.999509
+4607,2858.2532,319.24643,2879.8928,368.53745,0.9994844
+4607,2582.5884,615.82153,2608.0771,687.09534,0.99929833
+4607,1289.2821,321.82086,1313.7482,376.39923,0.9992507
+4607,3090.5923,533.3299,3124.4453,595.49335,0.9992186
+4607,2934.9624,382.4896,2959.2432,436.71182,0.99916494
+4607,2614.344,304.03384,2635.265,355.75778,0.9991295
+4607,2581.9824,228.41397,2596.1182,274.484,0.9990947
+4607,2006.529,171.92989,2023.0454,216.1722,0.99900347
+4607,2374.5105,438.77423,2397.0764,500.89893,0.9989579
+4607,3085.1697,387.07452,3111.2014,437.53632,0.99884844
+4607,2970.0823,512.75543,3001.1106,575.8161,0.99883753
+4607,2264.46,294.6069,2294.166,348.05966,0.998779
+4607,2658.5652,383.8922,2684.5813,437.34802,0.99873143
+4607,2096.3264,294.19702,2114.5896,349.47272,0.9986633
+4607,3731.1963,754.3309,3766.5981,816.12274,0.99852836
+4607,2031.3081,190.77396,2049.6406,238.50357,0.9984291
+4607,2658.3718,197.27159,2676.5432,239.84254,0.9983367
+4607,3257.9722,348.26,3280.8857,396.07373,0.9978933
+4607,1989.6125,122.806915,2006.9392,160.29102,0.9978715
+4607,2941.354,328.19443,2961.6294,376.83597,0.9978683
+4607,1965.0199,884.64185,2005.7233,978.44507,0.99771
+4607,3054.5486,288.9135,3074.3381,333.30798,0.99744165
+4607,2110.1592,407.97372,2131.8667,472.89175,0.9939435
+4607,2974.788,392.98746,2995.7666,443.81833,0.99280655
+4607,3535.3206,803.9171,3580.6077,870.1968,0.97436553
+4607,2981.4158,386.28378,3000.6467,435.69254,0.94878566
+4607,3466.2166,828.4621,3508.4788,903.06415,0.6165729
+4607,3743.1167,789.45337,3784.1719,852.2422,0.28551027
+4607,3893.7263,756.3004,3929.5466,812.32043,0.18319614
+4607,3968.0813,755.6146,3997.9749,804.21204,0.15417649
+4608,2653.054,196.81125,2675.7239,240.10785,0.9995765
+4608,2856.4436,319.01093,2879.9841,368.35455,0.99943334
+4608,2447.074,740.0332,2489.907,820.5791,0.9994042
+4608,1289.1079,321.8049,1313.8091,376.37625,0.9993032
+4608,3090.5974,534.30096,3125.286,595.1972,0.9991535
+4608,2935.1316,382.3896,2960.3645,436.57898,0.999148
+4608,2971.2808,512.8214,3000.8867,575.7728,0.9990615
+4608,2617.0112,303.58896,2636.2148,355.73196,0.999025
+4608,2578.143,614.6668,2605.292,685.5993,0.99894994
+4608,2006.1803,171.56267,2023.205,215.90611,0.9989465
+4608,2263.3552,294.4521,2293.3923,347.73212,0.9988619
+4608,2374.3035,438.6478,2396.781,499.8279,0.9988368
+4608,2659.5066,383.9027,2685.3728,437.33105,0.99873155
+4608,3085.8157,387.49274,3112.5793,438.45807,0.9986854
+4608,2097.1172,294.1081,2115.0078,349.56824,0.998596
+4608,2579.7324,228.52359,2594.5327,274.45596,0.99854577
+4608,3258.4253,349.07587,3281.0493,396.12903,0.9983708
+4608,2031.8326,190.79527,2049.8274,238.37917,0.9982907
+4608,3731.155,754.28564,3766.5613,816.0591,0.99811554
+4608,2941.381,328.1154,2961.5286,377.295,0.9979995
+4608,1989.5636,122.785805,2006.9496,160.52887,0.9978398
+4608,1965.0355,884.6321,2005.7611,978.45435,0.9976615
+4608,2110.9624,408.08936,2133.0898,472.5454,0.9975139
+4608,3054.9707,288.44635,3075.2827,333.08527,0.9964778
+4608,2974.7104,392.8305,2995.9482,443.83173,0.9934604
+4608,3540.7078,803.4471,3583.8357,869.9964,0.99059606
+4608,2981.898,386.12933,3000.892,435.58923,0.94813055
+4608,3466.5107,829.6797,3507.6553,903.0869,0.5460873
+4608,3894.9563,756.23676,3932.2903,812.0739,0.37670785
+4608,3742.975,789.0271,3783.0361,852.8126,0.29807842
+4608,3967.844,755.94366,3996.9397,803.99603,0.29597357
+4609,2572.5774,614.3919,2603.507,686.6496,0.9995259
+4609,2856.1157,318.7705,2879.713,368.02673,0.9994449
+4609,2263.6162,294.50714,2291.996,346.77356,0.99943465
+4609,2447.0251,741.29877,2487.389,820.57465,0.99940395
+4609,1289.0977,321.82562,1313.8213,376.36377,0.9993079
+4609,2972.5068,512.84814,3000.5361,575.7489,0.99922144
+4609,3091.3162,535.09894,3126.1965,595.59686,0.9990545
+4609,2648.1208,196.54015,2674.3044,239.15941,0.9990538
+4609,2618.798,303.70132,2637.6448,355.84268,0.9990482
+4609,2935.2805,382.0799,2961.3865,436.56396,0.9989869
+4609,2005.9556,171.07843,2023.4744,215.48639,0.9989753
+4609,2374.121,438.51486,2396.6094,499.1779,0.99895537
+4609,3085.7812,387.93106,3114.2788,439.30283,0.99891436
+4609,2097.2979,294.18753,2115.4082,349.09842,0.9988147
+4609,2661.1294,383.87024,2686.0142,437.48303,0.9987613
+4609,2113.5444,408.29538,2134.2559,472.53787,0.9983267
+4609,2941.0217,328.11298,2961.443,377.82593,0.9983165
+4609,3258.256,349.72897,3281.361,396.1619,0.9982748
+4609,2032.2286,190.78769,2049.906,238.37497,0.998164
+4609,3731.274,754.32465,3766.498,816.2247,0.9981352
+4609,1989.6108,122.80956,2007.0842,160.5697,0.99800736
+4609,3054.6833,288.93567,3076.9265,333.12177,0.99779737
+4609,1965.0305,884.5947,2005.7961,978.45483,0.9977932
+4609,2576.1067,228.36313,2593.6584,274.3764,0.99778664
+4609,3541.9524,802.2486,3586.7683,870.285,0.99459076
+4609,2974.7935,393.01593,2995.8052,443.8097,0.9933119
+4609,2982.0356,386.34637,3000.9258,435.7025,0.9495005
+4609,3466.6543,829.6709,3507.295,902.48083,0.8299429
+4609,3966.6511,756.0059,3995.9758,804.24554,0.21903457
+4609,3742.8179,788.2298,3779.6338,851.45856,0.20885737
+4610,2563.5483,613.8262,2602.2456,688.25476,0.9994435
+4610,2446.5854,742.0396,2484.7837,820.8366,0.99938786
+4610,2855.4077,318.49487,2879.7524,367.8305,0.99937505
+4610,2263.8098,294.15543,2290.5417,346.0967,0.9993729
+4610,1289.0817,321.82635,1313.7614,376.36157,0.9993154
+4610,2935.7332,381.90262,2961.5945,436.2518,0.999172
+4610,3085.7668,388.91064,3116.4202,440.19537,0.9991523
+4610,2618.8157,303.53098,2639.3567,354.70694,0.999129
+4610,2005.7888,171.23901,2022.9263,215.55182,0.9989963
+4610,2373.8955,437.35217,2396.3413,498.4098,0.9989637
+4610,3093.4348,535.8144,3126.9885,596.07697,0.9989512
+4610,2972.0493,512.9242,2999.809,575.51135,0.99891794
+4610,2097.3274,294.15228,2116.3274,348.79816,0.9988452
+4610,2661.8374,383.83627,2686.9404,437.3232,0.99875844
+4610,3055.1523,288.8131,3079.3477,332.89008,0.99863833
+4610,2115.409,408.0408,2136.0383,472.85233,0.99854434
+4610,2940.9211,328.0823,2961.4768,377.88278,0.99831855
+4610,3731.2485,754.3611,3766.4995,816.4076,0.99821496
+4610,3258.6897,350.59827,3282.0173,396.59418,0.9982017
+4610,1989.5823,122.74549,2007.2178,160.53818,0.99807346
+4610,2573.6619,228.23787,2593.3132,274.313,0.9979419
+4610,2645.785,196.38257,2673.908,238.7475,0.99776804
+4610,1965.0271,884.6277,2005.7024,978.47363,0.9977617
+4610,2032.5314,190.88788,2049.9253,238.35498,0.99776125
+4610,2974.8738,392.5199,2995.9382,443.84412,0.9944728
+4610,3544.1594,801.0598,3589.239,870.1532,0.9922461
+4610,2982.464,386.1568,3000.739,435.00427,0.9433144
+4610,3466.7156,829.235,3507.4363,902.104,0.9094255
+4610,3742.6604,787.04095,3776.3875,851.2806,0.4432311
+4610,3964.1003,756.50104,3994.598,803.5809,0.11912383
+4611,2557.3765,614.1398,2601.7188,689.48816,0.9995283
+4611,2446.359,742.12823,2482.6284,820.67804,0.9994444
+4611,2855.0166,318.012,2879.8237,367.9216,0.99942696
+4611,2936.7925,381.59213,2962.3076,435.6362,0.9993506
+4611,1289.0146,321.83188,1313.741,376.3525,0.9993293
+4611,2971.9426,513.13,2999.172,575.6605,0.9992704
+4611,2263.4778,293.32056,2289.4456,344.7058,0.99924684
+4611,3085.9502,389.77194,3118.4077,440.62527,0.9992353
+4611,2644.7437,196.88486,2670.0098,238.6037,0.99917644
+4611,2618.8577,304.23505,2640.7664,355.63953,0.9990909
+4611,2373.7327,437.40805,2395.8806,498.34225,0.9990563
+4611,3095.9915,535.90533,3127.5486,595.6716,0.9990101
+4611,2663.688,383.79712,2688.1714,437.07068,0.99897826
+4611,2005.496,170.73859,2022.8593,215.59134,0.99893934
+4611,2097.4153,293.97983,2117.341,348.88443,0.99888986
+4611,2940.894,327.77322,2961.2744,377.16458,0.99850994
+4611,3055.3606,289.39557,3080.7786,333.1577,0.99848086
+4611,3258.7747,351.62576,3282.508,398.05612,0.9984627
+4611,2115.1233,408.4663,2137.0862,472.82416,0.99841344
+4611,2570.792,228.66934,2593.211,274.62396,0.99816114
+4611,2032.684,190.87334,2049.958,238.38716,0.99801457
+4611,1989.4901,122.925674,2007.3038,160.53603,0.99790764
+4611,1964.9999,884.6228,2005.7001,978.44543,0.9978751
+4611,3731.1858,754.32275,3766.6233,816.2224,0.99739206
+4611,2974.902,392.58826,2995.8972,443.85052,0.9948624
+4611,3545.8176,800.5752,3592.6296,868.44995,0.9846315
+4611,2982.6501,386.2357,3000.679,434.69687,0.94030684
+4611,3742.3704,785.59393,3774.2454,850.36017,0.62780523
+4611,3466.9717,828.9959,3507.602,901.95746,0.55150956
+4611,3961.7585,757.88715,3993.5774,805.9808,0.14558023
+4612,2555.5547,615.5963,2598.3022,690.58765,0.9996995
+4612,2937.8918,381.40903,2962.5154,435.02444,0.9994574
+4612,2443.6128,742.5343,2480.622,820.90955,0.9994554
+4612,2854.9973,317.90472,2879.9138,367.68896,0.9993777
+4612,1289.0284,321.84464,1313.8312,376.35233,0.9993255
+4612,2098.0354,293.79904,2119.1316,348.6945,0.9992617
+4612,2263.6052,293.253,2287.7473,344.464,0.9991812
+4612,3085.8604,390.18756,3120.5146,440.6623,0.9991768
+4612,2619.1245,304.0462,2642.1812,355.7307,0.99913067
+4612,2373.6865,436.8471,2395.4614,498.1739,0.99905986
+4612,2005.4149,169.85492,2022.8812,215.60117,0.99901056
+4612,2115.2651,408.42593,2139.2822,473.3762,0.9990055
+4612,3098.2017,536.22656,3128.5327,595.81384,0.99885917
+4612,2644.8074,197.51448,2667.0222,238.56123,0.9988564
+4612,2666.1692,383.72046,2688.553,437.16388,0.99884474
+4612,2970.9255,513.5212,2998.6375,576.049,0.998762
+4612,3056.2295,289.18622,3082.0474,333.69702,0.998657
+4612,3258.9014,351.73248,3283.0254,398.469,0.9986511
+4612,2033.0254,190.77338,2050.0051,237.91412,0.9984028
+4612,2569.4219,228.69429,2593.1206,274.56702,0.9982065
+4612,1989.4441,122.88305,2007.4749,160.5642,0.9979373
+4612,1965.0055,884.63855,2005.6793,978.4496,0.99781114
+4612,2940.5967,327.00665,2961.2197,377.0719,0.99770457
+4612,3731.3242,754.2918,3766.688,816.42816,0.9975805
+4612,2974.9153,392.6516,2995.7932,443.81183,0.99465793
+4612,3547.442,800.55945,3594.8823,867.0609,0.9943534
+4612,2982.8574,386.1996,3000.6665,434.3855,0.9419101
+4612,3741.1567,784.2627,3773.128,849.59106,0.83148295
+4612,3466.8242,829.0585,3507.9302,902.77234,0.2921717
+4613,2555.113,616.87646,2593.5808,691.0679,0.9996767
+4613,2440.7854,742.906,2478.888,824.39233,0.99946696
+4613,2263.6409,292.41116,2287.092,344.5223,0.999448
+4613,2854.7366,317.4266,2879.6018,367.4213,0.9994112
+4613,2938.9688,381.0574,2962.8794,434.27408,0.99937695
+4613,1289.0345,321.85364,1313.7672,376.3662,0.99933845
+4613,3085.7664,390.09235,3121.9226,441.0609,0.9992781
+4613,3100.2725,536.6614,3129.2217,596.0762,0.99918205
+4613,2619.124,304.11877,2644.2197,355.86072,0.9991762
+4613,2373.711,436.84763,2395.3657,498.01706,0.99910295
+4613,2098.4856,293.6027,2120.5525,348.4811,0.9990765
+4613,2005.3213,170.01584,2022.6721,215.54532,0.99898547
+4613,2115.2139,408.6171,2141.3677,473.27042,0.9989103
+4613,2644.3262,197.07611,2662.1714,238.38953,0.9988985
+4613,2568.5864,228.77057,2592.8574,274.29086,0.9987325
+4613,2666.9067,383.77325,2689.4043,436.89203,0.998657
+4613,2941.0168,326.7598,2961.0715,375.77567,0.99863416
+4613,2970.4368,513.8395,2997.9973,576.4435,0.9985769
+4613,3056.0256,289.11578,3083.2834,334.2917,0.99842846
+4613,2033.4257,190.8135,2050.2295,237.81955,0.99831283
+4613,3258.6377,350.76404,3284.6382,399.30383,0.998075
+4613,1989.4714,122.92186,2007.4365,160.43965,0.9980275
+4613,1965.0157,884.64014,2005.7479,978.4923,0.99780256
+4613,3731.594,754.3032,3766.8928,816.16675,0.9970643
+4613,2974.934,392.56332,2995.958,443.85422,0.9952459
+4613,3550.651,801.2217,3596.934,866.1178,0.9903295
+4613,2982.6726,386.15103,3000.7146,435.40372,0.9467351
+4613,3740.5588,782.5288,3770.285,847.62305,0.7935939
+4613,3466.8826,828.7348,3507.987,904.2918,0.178114
+4613,3655.515,825.33203,3687.1917,892.12256,0.112496875
+4614,2437.7732,743.67126,2478.578,828.23975,0.99953926
+4614,2554.3105,618.6564,2586.9834,691.44116,0.9994771
+4614,2263.4094,291.782,2286.1633,344.59702,0.9994748
+4614,2854.7007,317.04797,2879.4512,367.09076,0.99941707
+4614,2940.836,380.6168,2963.4272,433.4194,0.9994118
+4614,1289.0621,321.74075,1313.892,376.35416,0.99936384
+4614,2373.3665,436.7336,2395.4329,498.103,0.9993212
+4614,3086.51,390.3098,3123.0989,441.61157,0.99919313
+4614,2619.3245,303.93015,2645.76,355.7009,0.9991334
+4614,3101.033,536.7413,3129.5945,597.5015,0.9991208
+4614,2005.1687,170.20264,2022.595,215.52948,0.9989791
+4614,2567.9856,228.47423,2592.5876,273.52643,0.99896693
+4614,2098.7695,293.56528,2121.1538,348.275,0.99892634
+4614,2641.738,196.8084,2658.0818,238.59044,0.9988912
+4614,3057.9258,289.09595,3083.5479,332.9673,0.99878603
+4614,2115.3054,408.97772,2145.2024,473.27264,0.99870276
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+4614,2941.3982,325.5489,2960.9587,374.74207,0.99848324
+4614,2969.92,514.818,2997.1367,576.23376,0.9984328
+4614,2033.8588,190.82626,2050.342,237.63263,0.99832803
+4614,3259.4607,351.66742,3285.18,399.13873,0.9982425
+4614,1989.5165,122.926186,2007.0194,160.6265,0.99805236
+4614,1965.0162,884.637,2005.7372,978.4936,0.9977429
+4614,2975.0098,392.73407,2996.1611,443.74542,0.9954632
+4614,3731.8606,754.35504,3766.8044,815.84174,0.99504787
+4614,3553.3372,802.30896,3599.1003,865.88403,0.98630154
+4614,2982.7068,386.2926,3000.7097,435.47565,0.93432206
+4614,3737.1987,782.04596,3767.438,847.05316,0.7224975
+4614,3466.557,828.75415,3507.616,905.26404,0.22162178
+4614,3655.533,825.3819,3687.1858,892.1097,0.11131823
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+4615,2619.6775,304.2501,2646.6301,355.68918,0.99947065
+4615,2435.212,744.57697,2477.7031,830.3913,0.9994686
+4615,2854.5476,317.0341,2879.3103,366.82492,0.99944407
+4615,2262.4075,291.7311,2285.2542,344.70267,0.99941456
+4615,2373.182,436.67587,2395.8445,498.42746,0.9993734
+4615,1289.1261,321.76694,1313.9242,376.36636,0.99935776
+4615,2942.384,380.4112,2964.571,433.8689,0.9991584
+4615,2115.2402,409.612,2147.5044,473.2345,0.9991437
+4615,3101.8752,537.64404,3130.136,597.48816,0.9990163
+4615,2567.6921,228.57712,2590.82,273.24002,0.99900013
+4615,3088.1248,390.82233,3124.8108,441.62183,0.9989863
+4615,2098.8813,293.40884,2121.29,348.00278,0.9989794
+4615,2005.0879,170.12971,2022.53,215.5128,0.9989035
+4615,2669.815,383.7515,2690.9297,437.42703,0.9987595
+4615,3059.4915,289.43634,3084.3064,332.47754,0.998718
+4615,2033.9042,190.74615,2050.4814,237.60406,0.9985454
+4615,2639.067,195.95146,2655.5752,238.46,0.9984309
+4615,3260.317,351.67795,3286.083,398.91592,0.99837875
+4615,2967.5728,515.37256,2996.2827,576.83264,0.9982652
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+4615,1989.5533,122.91134,2006.9918,160.65332,0.99806565
+4615,1965.0198,884.63776,2005.7432,978.50446,0.9977239
+4615,2974.8457,392.56183,2996.3022,443.69128,0.99591523
+4615,3559.2046,802.9391,3601.6938,865.984,0.99288905
+4615,3731.8513,754.36725,3766.6726,815.31415,0.99178493
+4615,2981.8677,386.54193,3000.8726,435.5506,0.9321764
+4615,3731.0466,781.3245,3765.6023,846.2419,0.905998
+4615,3466.891,828.9769,3507.9473,905.77795,0.22429977
+4615,3655.6135,825.3766,3687.175,892.1831,0.1752731
+4616,2854.7485,316.47772,2878.995,366.67383,0.99945694
+4616,2620.642,304.37378,2647.3271,355.52478,0.99940944
+4616,2433.397,746.24646,2477.0083,832.12866,0.9994062
+4616,1289.1666,321.7365,1313.9034,376.36768,0.99935335
+4616,2373.0022,436.60468,2396.0637,498.5744,0.9993412
+4616,2545.8743,618.4945,2574.5168,691.2063,0.9992735
+4616,2261.3418,291.11502,2284.9478,344.90182,0.99917644
+4616,2115.381,409.76373,2148.6204,473.22095,0.999099
+4616,3102.8577,538.15985,3130.4949,599.0943,0.9990707
+4616,2099.183,293.51352,2121.2505,347.95383,0.99905723
+4616,2942.8254,380.02823,2964.8113,433.67752,0.9989655
+4616,2567.2087,227.83035,2589.0637,272.83563,0.99894243
+4616,2004.7036,169.89131,2022.3098,215.3621,0.9988984
+4616,2670.3647,383.81006,2691.4644,437.2995,0.9988747
+4616,3090.8733,390.97375,3126.824,441.8869,0.9988237
+4616,2636.984,195.03302,2654.7349,237.33194,0.9988192
+4616,2966.498,515.7988,2995.4287,577.3745,0.99862385
+4616,3062.015,288.9736,3085.132,332.4,0.99851626
+4616,2034.0459,190.43289,2050.9092,237.44763,0.9982912
+4616,2941.2437,324.47217,2961.0615,372.62732,0.99828374
+4616,3262.245,352.10468,3286.4185,399.3465,0.9982033
+4616,1989.561,122.874985,2006.9565,160.82353,0.9979528
+4616,1965.0281,884.6291,2005.7239,978.55,0.9977428
+4616,2974.8699,392.59268,2996.5193,443.68448,0.9962147
+4616,3563.0986,804.0199,3603.37,866.2234,0.99307317
+4616,2981.4126,386.516,3000.9507,435.62915,0.9397903
+4616,3735.9438,754.3882,3766.5269,810.18884,0.88853514
+4616,3723.6074,781.60046,3763.4507,846.08105,0.8350842
+4616,3466.8525,829.31323,3507.667,907.58887,0.16988567
+4616,3655.5925,825.4009,3687.2078,892.07214,0.13766295
+4616,2704.2642,864.8114,2738.8604,949.7079,0.106637575
+4617,2541.8772,618.21936,2571.1106,691.67126,0.9994654
+4617,2621.2327,304.04288,2648.1091,355.45218,0.9994286
+4617,2854.7659,316.01157,2878.6692,366.30832,0.999398
+4617,2372.8728,436.55212,2396.179,498.6371,0.99939
+4617,1289.2959,321.7624,1313.8804,376.3672,0.99933475
+4617,2099.2295,292.7467,2121.8823,347.6292,0.9993236
+4617,2260.0535,291.19373,2284.551,344.94812,0.99930704
+4617,2431.1072,747.74634,2477.4626,833.6614,0.999231
+4617,2567.1638,227.46236,2587.6018,272.51654,0.9991924
+4617,3103.566,538.5618,3130.6328,599.1762,0.9991392
+4617,2632.6567,194.13165,2653.3804,237.09015,0.9991364
+4617,2943.45,380.05396,2964.8875,433.26904,0.99910396
+4617,3094.7683,390.7078,3128.1619,441.71884,0.9990564
+4617,2116.2444,410.08914,2150.131,473.54538,0.9990324
+4617,3263.27,351.9439,3287.1768,399.30072,0.9989104
+4617,2004.5603,170.02982,2022.1931,214.06036,0.9989028
+4617,2670.7654,383.63266,2691.7253,437.42715,0.99866104
+4617,2941.29,324.1222,2961.083,372.81573,0.9986442
+4617,3064.1047,288.79547,3084.8289,332.49774,0.99860245
+4617,2966.3345,516.5692,2994.4883,577.53796,0.99849796
+4617,2034.1885,190.42883,2051.3657,237.36752,0.9981822
+4617,1989.527,122.90322,2006.9879,160.7172,0.9979631
+4617,1965.0393,884.6021,2005.7407,978.5463,0.99781275
+4617,2974.8352,392.6477,2996.3367,443.69916,0.9959669
+4617,3565.1711,803.0954,3607.5017,866.3897,0.962996
+4617,2981.601,386.4929,3000.9126,435.60092,0.9425794
+4617,3738.209,754.5898,3766.5547,804.3942,0.87931925
+4617,3717.1404,782.2121,3761.551,846.1806,0.26508936
+4618,2537.3015,617.7947,2568.07,693.72107,0.99955827
+4618,2372.7224,436.48553,2396.4128,498.65118,0.9994106
+4618,2854.7305,315.7168,2878.42,363.39978,0.9993793
+4618,3105.5017,539.27515,3131.2078,600.4181,0.99936444
+4618,2260.317,290.9588,2284.204,344.75488,0.9993343
+4618,2430.179,749.1424,2476.3704,835.2338,0.99928147
+4618,2944.0269,379.76794,2965.372,433.03003,0.99926764
+4618,1289.6125,321.6622,1314.0239,376.366,0.99926656
+4618,2099.7058,292.51505,2122.5422,347.65286,0.99926645
+4618,2627.232,193.63815,2652.65,237.20059,0.99919635
+4618,2622.6333,304.01425,2648.3984,355.03366,0.9991897
+4618,2567.1523,227.22145,2585.58,272.63092,0.99903023
+4618,3265.7896,352.09985,3288.1567,399.72308,0.9990024
+4618,3098.8474,390.26062,3129.8079,441.84943,0.99892104
+4618,2004.2773,169.80054,2022.104,213.98953,0.99885166
+4618,2116.7622,410.09988,2149.7627,473.50443,0.9987954
+4618,2941.1414,323.75668,2961.1335,372.76498,0.99873596
+4618,3066.755,288.89865,3085.6426,332.31345,0.99872005
+4618,2034.5479,190.35704,2052.1445,237.11525,0.99855894
+4618,2966.3242,517.1458,2993.645,578.4725,0.9983981
+4618,2670.9448,383.4465,2692.1323,437.1805,0.99832255
+4618,1965.037,884.6292,2005.7792,978.54767,0.99779403
+4618,1989.6371,123.1126,2007.3075,158.68317,0.9969554
+4618,2974.9912,392.69952,2996.2168,443.69617,0.9955694
+4618,3565.8623,801.7563,3611.3682,865.9527,0.9931444
+4618,2981.5742,387.0528,3000.787,436.04724,0.9374853
+4618,3741.1377,754.9414,3766.4316,802.70374,0.9052439
+4618,3710.5933,782.1355,3758.7026,845.9385,0.7528508
+4618,3466.823,829.90686,3508.2678,907.7125,0.25120503
+4618,3655.6726,825.3851,3687.1912,892.0041,0.17633472
+4619,2528.4407,617.80054,2566.5378,694.70447,0.99961877
+4619,2372.6772,436.4741,2396.6152,498.62845,0.9994728
+4619,3106.2566,540.3102,3131.673,600.1908,0.99947196
+4619,2429.1594,750.43677,2473.5618,837.489,0.99945015
+4619,2259.56,290.95932,2283.7637,344.4766,0.9994017
+4619,2943.893,379.8239,2966.0483,432.76654,0.99929535
+4619,2100.514,292.0217,2123.389,347.40353,0.9992654
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+4619,2854.8865,315.3797,2877.7078,363.05536,0.99917245
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+4619,2004.1865,168.5705,2021.6233,213.76376,0.9989961
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+4619,1965.0417,884.61237,2005.835,978.5347,0.99784493
+4619,1990.119,121.63931,2008.2721,158.73123,0.99683666
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+4621,1965.0302,884.6258,2005.7682,978.5723,0.9978769
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+4621,1990.121,121.80271,2008.2594,159.17303,0.99713814
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+4622,2003.4778,167.86122,2021.4324,212.80855,0.9990445
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+4622,2622.5295,193.97455,2642.8977,236.06061,0.9989685
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+4622,2966.3447,517.8251,2991.9038,580.3209,0.9988072
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+4622,2104.688,290.73425,2125.3872,345.24023,0.99865204
+4622,2036.3026,190.29346,2052.0845,236.78302,0.9985989
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+4622,1965.0421,884.62744,2005.7863,978.57324,0.9978649
+4622,1990.1583,121.69218,2008.4034,158.7073,0.9970693
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+4622,2974.9639,392.28452,2995.912,443.7488,0.9951198
+4622,3737.689,754.04095,3766.1104,814.5484,0.97627205
+4622,2982.0232,386.91843,3000.486,436.6567,0.94766
+4622,3656.4297,825.4477,3687.2627,892.1712,0.853216
+4622,3469.5645,829.6066,3508.8228,907.94366,0.2368649
+4622,3705.264,784.26715,3748.6482,845.5256,0.1738392
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+4623,2372.6497,436.52402,2398.032,498.30344,0.9995216
+4623,2516.44,622.60486,2551.046,696.49744,0.9994979
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+4623,1290.7634,321.01886,1314.2244,376.41278,0.9991627
+4623,3106.9521,542.2748,3132.919,602.0226,0.9991549
+4623,2944.2463,380.02542,2969.3464,432.1924,0.99912876
+4623,2003.3973,167.95589,2021.1656,212.67616,0.9991234
+4623,2106.0732,290.59332,2126.4526,345.07855,0.99903584
+4623,2855.6553,313.9411,2877.13,361.3756,0.9990233
+4623,2966.319,517.9829,2991.889,580.41125,0.99895906
+4623,3108.9907,392.31458,3138.5015,442.40997,0.99881685
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+4623,1990.3156,121.562065,2008.7633,158.76004,0.9971667
+4623,2121.7698,410.10873,2151.2874,473.20267,0.99706894
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+4623,3576.71,800.88513,3629.248,866.6173,0.9934103
+4623,3735.4797,754.11334,3765.8523,816.044,0.98940617
+4623,2982.196,386.94492,3000.4885,436.81516,0.9460642
+4623,3656.3948,825.45526,3687.301,892.1597,0.8138057
+4623,3705.555,783.3629,3747.3025,843.9524,0.22968577
+4623,3469.4478,829.18005,3508.7007,907.84924,0.19308092
+4624,2372.462,436.53674,2398.06,498.34705,0.99951947
+4624,2515.0269,622.7879,2542.9502,696.6768,0.99936587
+4624,2257.5955,289.6811,2280.8792,340.5468,0.9993425
+4624,2425.718,754.06726,2458.5671,837.2999,0.9993349
+4624,3107.6133,542.28564,3133.2764,602.12646,0.9993125
+4624,1291.3174,321.28647,1314.3333,376.3975,0.9992418
+4624,2944.5632,379.8336,2970.4407,431.8381,0.9991879
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+4624,2003.2819,167.7386,2021.0997,212.5594,0.9990307
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+4624,1990.7281,121.43066,2009.1051,159.03603,0.9969824
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+4624,3580.3765,802.12024,3631.9414,867.0044,0.9937994
+4624,3732.773,754.14886,3765.7544,816.1342,0.9897864
+4624,2982.4927,386.90646,3000.3706,436.78098,0.940085
+4624,3656.3792,825.4469,3687.3728,892.0669,0.59183437
+4624,3469.4204,829.1745,3509.0361,907.9449,0.27765632
+4624,3705.2036,782.29706,3745.4038,844.0201,0.17953251
+4625,2372.5205,436.4945,2398.7954,498.4035,0.9996543
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+4625,2036.7013,190.40338,2053.2554,236.98633,0.9987704
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+4625,1991.0444,121.50918,2009.4028,159.03412,0.99669564
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+4625,2982.2854,387.00143,3000.3562,436.9103,0.9406223
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+4626,2420.522,755.4729,2455.462,837.30334,0.99956006
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+4626,2945.9873,379.33987,2972.375,431.45847,0.9991811
+4626,2674.535,381.9905,2695.5154,434.86703,0.9991033
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+4626,2855.5667,313.26785,2877.0906,361.27603,0.99904627
+4626,2003.3947,167.2652,2020.9247,212.51822,0.9989513
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+4626,2941.2961,321.73657,2961.3967,369.41815,0.99850595
+4626,1965.051,884.62585,2005.8755,978.5928,0.9979085
+4626,1991.2693,121.49982,2009.7495,159.0553,0.99762386
+4626,2133.7986,410.00226,2155.4036,473.22156,0.9972812
+4626,2974.7698,391.93933,2995.8035,443.7436,0.9962626
+4626,3733.9514,754.00275,3765.4343,815.7275,0.9956365
+4626,3592.5437,803.01605,3635.8586,867.6192,0.9728072
+4626,2982.0688,387.18744,3000.379,436.8022,0.92365766
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+4627,2372.6824,436.48627,2398.7131,498.46063,0.99963725
+4627,2256.1948,288.2492,2279.0845,340.55652,0.99957377
+4627,2501.7305,620.6584,2530.3525,695.61395,0.9995609
+4627,3108.9536,542.3663,3134.7246,603.2493,0.99945503
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+4627,2855.6387,313.15396,2877.0527,361.11826,0.99906677
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+4627,3070.1033,290.03018,3094.8005,333.58414,0.9990387
+4627,2966.084,518.8297,2992.6606,582.25574,0.99902177
+4627,2606.71,191.9852,2630.9053,234.60043,0.9989864
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+4627,2003.4053,167.10648,2020.844,212.45154,0.9989128
+4627,2036.6707,190.20609,2052.9834,236.85553,0.9987948
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+4627,3110.9685,394.30023,3145.1428,444.24188,0.99849963
+4627,1991.9419,121.26826,2010.1973,158.97098,0.99827564
+4627,3270.024,355.78735,3293.044,404.1861,0.9982126
+4627,2133.028,409.9608,2157.2664,473.5629,0.99813557
+4627,1965.0571,884.6391,2005.8713,978.58575,0.99794906
+4627,3733.5518,753.95966,3765.1733,815.8376,0.9970258
+4627,2974.8171,391.8846,2995.8699,443.7164,0.9961171
+4627,2981.625,387.68674,3000.247,437.08353,0.9023123
+4627,3703.0515,781.43884,3736.3801,845.42944,0.808344
+4627,3597.6619,802.0274,3640.0012,867.7824,0.7173141
+4627,3469.4028,829.48474,3510.9556,908.73364,0.21238098
+4627,2709.2283,864.1594,2760.3987,950.1864,0.12068207
+4628,2411.8215,757.0947,2452.929,845.199,0.99967587
+4628,2372.6897,436.47485,2398.992,498.4629,0.99967325
+4628,2493.8843,620.8525,2529.6045,696.5994,0.9995782
+4628,2255.1104,287.7945,2278.725,340.54504,0.9994848
+4628,3109.413,542.9784,3135.2256,602.81885,0.9994771
+4628,1292.9832,320.4907,1314.6929,376.3549,0.99937963
+4628,2554.0354,227.03079,2575.9768,271.6825,0.99932575
+4628,2136.4614,410.37183,2159.004,473.45447,0.9993055
+4628,2966.055,519.84076,2992.7776,583.4838,0.99919623
+4628,2948.6746,378.99814,2974.2078,431.32553,0.9990452
+4628,2855.568,313.1479,2877.0144,361.143,0.99903893
+4628,2677.0835,381.63168,2696.5967,435.15512,0.9989766
+4628,2003.4054,167.0091,2020.743,212.44681,0.998889
+4628,3070.7373,290.0961,3096.1484,334.10135,0.99887544
+4628,2631.4048,302.2645,2652.6914,353.77396,0.99879766
+4628,2036.609,190.20686,2053.1868,236.92323,0.9987425
+4628,2106.9448,289.6774,2130.6377,345.0621,0.998613
+4628,2941.3977,320.60736,2961.4119,367.93292,0.99848396
+4628,1992.0582,121.67331,2010.1017,158.93301,0.9984469
+4628,2601.3113,191.76138,2629.222,234.0839,0.9982376
+4628,3270.8418,355.7601,3293.546,404.8582,0.9980665
+4628,3114.002,394.25177,3147.0435,444.46997,0.9980108
+4628,1965.0681,884.62366,2005.867,978.5929,0.9979583
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+4628,2974.7612,391.78177,2995.9004,443.70944,0.99640584
+4628,3600.2852,800.9202,3643.5283,867.6269,0.92287683
+4628,2981.9827,387.4103,3000.3757,436.70047,0.8813687
+4628,3700.0957,781.84393,3733.9727,845.0444,0.45239326
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+4629,2372.864,436.3825,2399.1296,498.3802,0.99966645
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+4629,2254.2585,287.6079,2278.4187,340.45514,0.9993924
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+4629,1293.4248,320.33545,1314.7412,376.3687,0.99930096
+4629,2951.5342,378.90027,2975.0986,431.1997,0.999263
+4629,2677.8743,381.56952,2697.3513,434.89673,0.9992524
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+4629,2966.0898,520.1493,2993.2373,585.557,0.9991154
+4629,2136.6533,410.33914,2160.851,473.28714,0.9989629
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+4629,2855.3809,313.1608,2876.9897,361.31015,0.9988869
+4629,2003.327,166.90515,2020.8453,212.48026,0.99888605
+4629,2631.5566,302.49084,2652.7495,353.719,0.9988488
+4629,1991.6425,121.96452,2010.5026,159.14397,0.9986833
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+4629,3072.9717,289.93008,3097.085,334.21457,0.9986168
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+4629,2599.0337,191.56892,2627.3403,233.48076,0.9984164
+4629,1965.0693,884.62885,2005.9138,978.5761,0.9978946
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+4629,2974.6726,391.7142,2995.9197,443.74332,0.9949669
+4629,3117.0361,394.09497,3149.794,444.54602,0.99485815
+4629,3602.6526,799.2978,3646.4465,866.5903,0.9634133
+4629,2981.1746,388.1117,3000.2166,437.52362,0.8770678
+4629,3695.817,782.9283,3731.5352,844.73663,0.31314978
+4629,3470.1096,829.87976,3513.6301,909.199,0.21224086
+4630,2373.2349,436.62457,2399.2573,498.1521,0.99966276
+4630,2408.223,760.0653,2451.549,849.6504,0.99965656
+4630,2482.0447,622.3342,2526.03,698.12573,0.9996352
+4630,3109.2087,543.957,3135.8342,604.27985,0.99948525
+4630,2553.9604,225.71082,2571.8052,272.23645,0.999387
+4630,1293.9717,320.0252,1314.8857,376.28125,0.99935764
+4630,2678.3042,381.46445,2697.7944,434.94382,0.99933016
+4630,2253.746,287.44785,2278.1304,340.42953,0.9993118
+4630,2965.9685,520.6307,2993.2834,586.1346,0.9991893
+4630,2632.024,302.56,2652.4897,353.41455,0.99903643
+4630,2854.9465,313.13687,2877.0974,361.6229,0.9989415
+4630,2953.7302,378.94046,2976.112,431.34464,0.998938
+4630,2003.6273,166.74658,2021.0721,212.26172,0.99889934
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+4630,3272.2268,356.79346,3294.1453,404.73846,0.99876153
+4630,1992.0007,122.17555,2010.7188,159.06584,0.99871385
+4630,2036.6531,189.3483,2053.2986,237.01636,0.9987043
+4630,2136.8237,410.48724,2162.3833,473.61835,0.9985667
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+4630,2598.8306,192.17851,2624.5464,233.20976,0.99828917
+4630,1965.0754,884.62836,2005.936,978.60065,0.9978345
+4630,3732.772,754.01764,3764.6895,816.74506,0.99746096
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+4630,2974.8777,392.0205,2996.1887,443.72778,0.9944226
+4630,3604.6755,798.5917,3649.9055,864.78125,0.96666515
+4630,2982.2913,387.7052,3000.7053,436.58636,0.80908984
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+4631,2678.3606,381.48346,2698.431,434.26654,0.99934894
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+4631,1294.4604,319.75714,1315.0562,376.1538,0.99910504
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+4631,2036.6655,188.87784,2052.6384,237.03925,0.9987081
+4631,1992.1897,122.33571,2010.9551,159.14415,0.9986493
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+4631,2108.9072,288.59927,2132.5493,344.6503,0.99817586
+4631,1965.0913,884.63477,2005.9326,978.60754,0.997729
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+4631,2982.2612,387.9199,3000.6846,436.6365,0.7938919
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+4632,2373.6475,436.47006,2399.42,497.9938,0.99970573
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+4632,2678.6047,381.3156,2699.11,433.71857,0.9992812
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+4632,2965.8398,521.97815,2993.9326,587.08105,0.99900514
+4632,2954.6824,378.6343,2978.007,431.3983,0.99898034
+4632,2003.7776,166.28662,2021.7571,211.12906,0.9989525
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+4632,3079.0374,290.0205,3100.556,334.14435,0.99880373
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+4632,1992.6035,122.207825,2011.5203,159.07495,0.9984566
+4632,2136.9302,410.21,2166.2734,473.5853,0.9983882
+4632,3273.6804,356.73077,3294.5017,406.01068,0.9979032
+4632,1965.1492,884.6389,2005.9312,978.6155,0.9977889
+4632,2110.5623,287.592,2132.8357,344.10452,0.99742055
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+4632,3610.3328,798.0163,3656.114,861.2668,0.99054116
+4632,3470.8796,829.68,3516.99,904.03094,0.7235808
+4632,2980.939,388.35504,3000.544,437.48114,0.7192292
+4632,3693.6438,785.9469,3724.258,842.22656,0.5352656
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+4633,2003.9531,166.27551,2021.7852,210.28122,0.9989359
+4633,2036.9247,188.58015,2051.865,237.03194,0.9989121
+4633,3108.3198,544.93567,3138.0513,606.74585,0.9988702
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+4633,1992.7904,121.85307,2012.0409,159.13432,0.9981457
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+4633,2974.9055,391.27344,2998.5774,443.55994,0.99683714
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+4633,3615.2827,798.936,3658.0293,862.029,0.9652704
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+4633,2983.5957,388.9053,3000.0542,436.59598,0.381364
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+4634,2593.9104,191.46939,2609.9377,233.30487,0.9984156
+4634,1992.8976,122.34173,2012.4608,159.23056,0.99783784
+4634,1965.171,884.6515,2005.9235,978.56287,0.99778396
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+4634,3732.1624,754.00275,3764.575,816.5667,0.9971349
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+4635,2254.8162,287.23196,2276.2112,338.43094,0.9993812
+4635,2471.9177,625.7646,2503.0447,700.0733,0.9992273
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+4635,3731.7632,753.986,3764.5078,816.48846,0.9972589
+4635,1993.3091,122.05229,2012.5579,159.03822,0.99725276
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+4636,2469.7847,625.6882,2500.0747,701.7153,0.99938095
+4636,2678.7874,381.23093,2702.0457,433.76303,0.9992322
+4636,2004.6774,164.92076,2021.9928,209.64583,0.9991979
+4636,2635.1807,301.2644,2654.936,352.1822,0.9991424
+4636,2405.873,765.9493,2445.3564,850.6706,0.999087
+4636,3107.4187,547.0017,3140.6672,608.95166,0.99907386
+4636,2036.7792,187.92325,2051.315,237.16034,0.9988876
+4636,2965.8813,524.23944,2994.0903,587.5007,0.99887353
+4636,3273.7234,358.37247,3296.2888,407.40848,0.9988274
+4636,3131.1772,395.40216,3163.1191,447.72375,0.9986492
+4636,2954.7708,378.18073,2981.381,430.7691,0.99863464
+4636,3084.0278,289.89697,3104.8872,334.1223,0.99861467
+4636,2541.0789,225.57803,2566.3855,271.93387,0.9985796
+4636,2586.7932,188.69992,2608.181,232.63815,0.99841774
+4636,1965.3188,884.4864,2005.5312,977.97614,0.99837273
+4636,2942.2695,318.34753,2962.1074,367.28308,0.99819547
+4636,2116.6538,286.75372,2134.88,340.5943,0.9981877
+4636,2974.8887,390.56494,3000.002,443.43622,0.9981818
+4636,2138.0881,410.2333,2171.6829,474.24527,0.9980829
+4636,3731.3425,754.0027,3764.5164,816.69006,0.9970962
+4636,1993.8999,122.702415,2013.5427,158.70255,0.99664116
+4636,3480.385,827.82776,3522.549,902.9243,0.9806111
+4636,3630.0176,799.4171,3665.7954,862.0862,0.26189345
+4636,3691.2327,784.21423,3718.618,842.5796,0.14190283
+4637,1297.4713,317.75116,1318.9098,373.32538,0.99960464
+4637,2855.712,311.3835,2878.625,357.889,0.99952257
+4637,2254.6714,286.7555,2275.4092,336.44836,0.999466
+4637,2461.0867,625.94666,2497.3953,702.8683,0.99944335
+4637,2004.8151,164.87152,2021.8966,209.63101,0.99938214
+4637,2374.6858,436.56335,2401.4065,498.15527,0.99934703
+4637,2678.8535,381.34894,2702.3462,433.72217,0.9992169
+4637,2405.605,767.1031,2442.5732,850.4919,0.99910104
+4637,3107.2883,547.4937,3141.9167,610.3372,0.9990977
+4637,3084.3591,290.01114,3106.074,333.9708,0.999044
+4637,2635.3335,300.85608,2655.686,352.19543,0.9990355
+4637,3131.2131,395.97824,3166.5193,448.09793,0.9990189
+4637,2965.5403,524.29626,2993.6746,587.6166,0.998934
+4637,2955.1746,378.17972,2982.1907,430.5585,0.9989104
+4637,2117.895,286.44006,2135.331,340.37872,0.9988668
+4637,3274.3772,358.96747,3297.0247,407.5609,0.9988518
+4637,2581.5037,188.47246,2607.1292,232.60524,0.9988096
+4637,2539.6724,225.6431,2565.9814,271.9822,0.9988055
+4637,2036.7023,188.01367,2051.2354,237.23056,0.9988024
+4637,2974.7068,390.6742,2999.971,443.45935,0.9985494
+4637,1965.3188,884.4864,2005.5312,977.97614,0.99837273
+4637,2942.2488,318.29575,2962.1829,367.27463,0.9981735
+4637,2140.279,410.2578,2171.8772,474.2287,0.9979449
+4637,3731.4739,754.05237,3764.5657,816.6725,0.99714595
+4637,1994.0748,122.644165,2014.4398,158.79065,0.9940183
+4637,3481.9858,827.9941,3522.772,902.73773,0.9733965
+4637,3629.6848,798.09296,3671.1448,863.795,0.21357353
+4637,3690.876,783.13416,3717.6958,842.6598,0.20985901
+4638,1297.8246,317.76285,1319.6539,373.42538,0.9996344
+4638,2855.4084,311.2521,2878.6106,357.79172,0.999483
+4638,2254.6511,285.91373,2275.2747,336.43167,0.9994406
+4638,2004.7838,164.83853,2021.9901,209.43594,0.99935704
+4638,2956.718,377.92572,2982.8098,429.72833,0.99931324
+4638,2403.9175,767.5465,2438.585,850.4999,0.99931157
+4638,2375.2305,436.55695,2401.832,497.8407,0.9993051
+4638,2679.0803,381.35806,2702.9119,433.64194,0.99929845
+4638,2454.1,626.2561,2494.3403,704.1366,0.9992853
+4638,3107.9004,548.2118,3142.725,611.2002,0.99919206
+4638,3084.7898,290.95316,3107.4304,333.68277,0.9991755
+4638,3131.5686,397.26675,3168.8948,448.59384,0.9990675
+4638,2538.8567,225.35959,2565.949,271.88025,0.999037
+4638,2637.473,300.17374,2656.303,352.0038,0.99895513
+4638,2036.8086,187.97923,2051.217,237.06438,0.9989177
+4638,2965.8596,524.4623,2993.5637,587.4426,0.99890065
+4638,2974.6028,390.68115,3000.0535,443.13123,0.99880457
+4638,2118.2693,286.47156,2136.3362,341.00275,0.9985422
+4638,1965.3188,884.5009,2005.5308,977.9754,0.99838364
+4638,3274.3562,359.6748,3297.4583,408.0641,0.9983444
+4638,2577.6357,188.37813,2605.706,231.21904,0.9982285
+4638,3731.3618,754.02997,3764.5425,816.7952,0.9976514
+4638,2942.808,317.8286,2962.5225,365.94232,0.9976412
+4638,2142.7776,409.6466,2172.6174,473.9895,0.9968149
+4638,1994.2347,122.53482,2014.8712,158.68454,0.99505675
+4638,3483.1702,827.87317,3523.9534,902.4877,0.9532401
+4638,3630.9993,797.433,3675.218,863.8263,0.75238323
+4638,3689.5427,782.17535,3716.818,844.58417,0.1668463
+4638,3653.0454,824.5403,3689.458,891.7927,0.10188008
+4639,1298.1167,318.54828,1319.9375,374.04596,0.99954563
+4639,2399.5054,768.42535,2437.0117,850.39166,0.9995358
+4639,2855.1843,311.2228,2878.547,357.7405,0.9995048
+4639,2254.569,285.47607,2275.2625,336.29065,0.99945194
+4639,2450.5476,627.8391,2491.2004,704.9269,0.99941397
+4639,2375.4502,436.50967,2402.046,497.61093,0.9993748
+4639,2004.8103,164.91992,2022.0425,209.28491,0.99925655
+4639,2957.76,377.74557,2984.1697,429.24924,0.9991799
+4639,3108.3071,548.76556,3144.1782,611.582,0.9991738
+4639,2974.1619,390.7309,2999.7805,443.12097,0.9991697
+4639,3132.1782,397.2665,3170.0571,448.57913,0.99908733
+4639,2679.4116,381.0927,2703.8804,433.7558,0.9990791
+4639,2638.354,300.02808,2657.0586,351.94403,0.99903095
+4639,2538.3862,225.09529,2565.4707,271.35214,0.9989519
+4639,2118.61,286.38937,2137.3044,340.51266,0.99891937
+4639,3084.6982,290.8259,3108.8184,333.57462,0.9989021
+4639,2965.1543,524.68066,2993.5742,587.4474,0.99879056
+4639,2036.6792,187.98424,2051.23,236.94185,0.9987826
+4639,2576.4019,189.14905,2604.5557,231.08517,0.998697
+4639,1965.3215,884.517,2005.529,977.9721,0.9983778
+4639,2943.057,317.15765,2962.7664,366.0697,0.9982343
+4639,3274.198,360.89456,3298.1443,408.7205,0.9975802
+4639,3731.7715,754.04706,3764.5806,816.6171,0.99746287
+4639,2144.8337,409.7359,2173.2214,473.92065,0.996951
+4639,1994.5535,122.2206,2016.8662,158.69487,0.99442357
+4639,3483.32,828.2705,3524.846,902.6515,0.97138476
+4639,3633.5542,795.4618,3678.6733,860.8988,0.35031733
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+4640,2396.3752,769.0537,2434.4905,850.0077,0.99950767
+4640,2855.1804,311.1573,2878.3347,357.8546,0.99945956
+4640,2448.8435,629.45776,2487.2273,705.5011,0.99943334
+4640,1298.4869,318.60422,1320.6954,373.94394,0.9993741
+4640,2375.6511,436.8241,2402.302,497.53284,0.99937266
+4640,2254.5593,284.4122,2275.4504,336.3492,0.99934405
+4640,2959.6243,377.54984,2984.7004,429.42477,0.9992077
+4640,2004.8801,164.83592,2021.8506,208.95625,0.99916077
+4640,3134.0967,397.4368,3172.0293,448.60428,0.9991416
+4640,3108.6099,549.43933,3144.8594,611.3412,0.99908006
+4640,2638.7544,300.33673,2657.4678,351.77887,0.9990785
+4640,2680.511,380.90897,2705.032,433.67264,0.9990308
+4640,2118.9297,286.2662,2138.3936,340.39847,0.9989367
+4640,3085.736,290.70435,3109.8987,333.67535,0.99889815
+4640,2974.9915,390.94098,2999.7239,442.4339,0.99885046
+4640,2965.1643,524.8403,2993.5896,587.4831,0.9987336
+4640,2036.265,188.06635,2051.2483,236.91519,0.99861753
+4640,2537.9631,225.0107,2564.9973,271.34998,0.99860626
+4640,2943.3613,316.71878,2962.9658,365.10117,0.9984843
+4640,1965.3386,884.5065,2005.5212,977.97314,0.99843913
+4640,2576.6511,190.87036,2601.7888,230.5534,0.9982445
+4640,3732.0188,754.03613,3764.736,816.54663,0.9974995
+4640,1995.6567,122.25611,2015.47,158.3378,0.99741083
+4640,3274.4346,361.73782,3298.831,408.90085,0.99725384
+4640,2150.5635,409.76904,2173.7925,473.9688,0.9954787
+4640,3483.347,827.98694,3526.0476,902.2085,0.92223126
+4640,3635.4854,795.1229,3681.9346,861.9098,0.89828074
+4640,3686.7195,783.6082,3713.9153,846.30865,0.29149574
+4640,3654.167,821.751,3689.2593,891.338,0.24328001
+4641,2394.5667,769.9851,2432.2024,850.2362,0.9995443
+4641,2119.1418,286.03616,2140.1077,339.85666,0.9993949
+4641,2254.6165,284.3107,2275.5554,336.3251,0.99938536
+4641,2855.018,311.24417,2878.1929,357.869,0.99938476
+4641,2375.8103,436.68326,2402.4358,497.4012,0.99936074
+4641,3136.8413,397.17273,3172.8623,448.81982,0.999337
+4641,1298.8591,318.84576,1321.3218,374.28278,0.9992132
+4641,2448.2964,631.1686,2483.19,705.52893,0.99919647
+4641,2639.0354,300.32272,2658.3113,351.90695,0.99914646
+4641,3108.9045,549.7843,3145.923,611.5797,0.99912304
+4641,2004.8854,164.96893,2021.6764,208.71835,0.99907637
+4641,2975.1553,390.9408,2999.7212,442.13928,0.9990073
+4641,2681.9346,381.04724,2705.6792,433.62177,0.99898523
+4641,2962.2788,377.32446,2985.503,427.96252,0.99890256
+4641,2538.3115,224.56027,2562.2734,270.94086,0.9988839
+4641,3086.2432,290.53024,3110.6763,334.25943,0.9988393
+4641,2943.7446,316.3012,2963.6016,363.76215,0.99879277
+4641,2965.8276,525.06836,2993.5156,587.4415,0.99874204
+4641,2036.018,188.0197,2051.3794,236.91786,0.998701
+4641,2575.6526,190.19055,2596.9802,230.75778,0.9984896
+4641,1965.3337,884.5277,2005.5256,977.979,0.9984419
+4641,2151.4172,410.0165,2175.1174,474.20377,0.9979938
+4641,1997.908,122.059525,2015.7727,158.46931,0.9977627
+4641,3731.8228,754.0557,3764.8916,816.5937,0.9975434
+4641,3274.238,361.75644,3299.5286,409.5732,0.99688977
+4641,3638.364,793.92053,3686.7927,860.2495,0.9714469
+4641,3483.3145,828.13055,3527.5117,902.22174,0.82902855
+4641,3655.9211,823.48,3691.9788,890.86646,0.33346358
+4641,3678.9182,783.492,3711.8333,845.3964,0.27742493
+4641,3905.0999,758.25073,3935.8333,814.98486,0.16846833
+4642,2391.7192,770.73474,2429.963,851.5713,0.9995937
+4642,2375.9905,436.76233,2402.7185,497.35492,0.9995028
+4642,2254.6655,284.30438,2275.689,336.37756,0.999411
+4642,2119.1519,285.68503,2143.1929,339.71896,0.9993709
+4642,2639.129,300.25946,2658.8696,351.9212,0.99920744
+4642,2004.6677,164.78285,2021.5686,208.73454,0.9991837
+4642,2447.6084,632.0365,2478.7642,705.2295,0.999141
+4642,2964.034,377.051,2986.135,427.69577,0.9991196
+4642,3140.2305,397.58365,3173.8271,449.01834,0.99911314
+4642,2975.0178,390.93497,2999.7527,442.04355,0.9990939
+4642,2855.213,311.1256,2878.0806,357.79498,0.99908817
+4642,2682.4404,380.80054,2706.6914,433.36548,0.9990834
+4642,3109.1035,549.9293,3146.8364,611.6963,0.9990435
+4642,1299.2609,318.9726,1322.4203,374.29498,0.99897087
+4642,2574.607,189.75224,2593.9058,230.88234,0.99879575
+4642,2966.2651,525.2411,2993.3853,587.64124,0.9987684
+4642,2944.246,316.02197,2963.6777,363.83325,0.99870074
+4642,2035.7052,187.94322,2051.2903,236.67369,0.9986094
+4642,3087.4978,289.86612,3112.0862,334.3815,0.9985582
+4642,2538.2253,223.73221,2559.3694,270.6529,0.998547
+4642,2154.301,409.88507,2176.4998,473.98676,0.998522
+4642,1998.1827,122.01757,2015.6176,158.4293,0.9984016
+4642,1965.3354,884.55396,2005.5249,977.97485,0.99835145
+4642,3733.659,754.1351,3765.2131,816.4611,0.99761754
+4642,3275.4983,362.34354,3299.924,409.22018,0.99709356
+4642,3483.5728,828.0249,3528.5059,902.20557,0.847699
+4642,3641.3013,795.18835,3693.3062,863.78345,0.8282825
+4642,3675.3547,785.0677,3709.9753,847.73846,0.49886236
+4642,3903.979,758.21857,3934.0503,814.19366,0.30625123
+4642,3665.5105,786.24646,3711.614,864.80334,0.2097925
+4642,3653.6748,816.2848,3694.4556,890.17224,0.13122182
+4643,2389.553,771.23413,2427.511,853.89795,0.9996388
+4643,2376.0518,436.65717,2403.1787,497.33795,0.999567
+4643,2254.7014,284.16266,2275.8455,336.42786,0.99940956
+4643,1299.7388,319.03052,1324.2297,374.25598,0.9993969
+4643,2119.517,285.82117,2144.667,339.48413,0.9992889
+4643,2638.8132,299.94476,2659.633,351.8683,0.9992046
+4643,2965.4758,376.89767,2986.9792,427.49753,0.99919826
+4643,2975.4436,391.05176,2999.6008,442.34033,0.99918085
+4643,2683.6025,380.7415,2707.3271,433.19894,0.9991786
+4643,2004.3486,164.56386,2021.4404,208.72148,0.99911255
+4643,3112.6,550.0,3147.628,611.61804,0.999052
+4643,2538.2878,223.31728,2556.634,270.27356,0.999044
+4643,2444.14,631.938,2474.5173,705.35657,0.99897707
+4643,2855.44,311.06476,2878.1377,357.79895,0.9988839
+4643,2944.3281,315.29556,2964.2046,364.04965,0.9987985
+4643,2157.7053,409.7199,2177.5403,474.74646,0.99876034
+4643,2965.9438,525.44775,2993.183,587.6094,0.99874663
+4643,3144.7559,397.7356,3174.1455,448.7975,0.9987098
+4643,2035.4062,187.84225,2051.3875,236.49731,0.99849045
+4643,3089.675,289.4301,3112.3088,334.4784,0.9984401
+4643,3276.8428,362.12735,3300.7598,409.35892,0.9982089
+4643,1965.3262,884.5742,2005.5164,977.94763,0.9981462
+4643,2572.5762,189.07869,2589.0278,231.36638,0.99797374
+4643,1998.6521,122.138626,2015.7739,158.58885,0.99795294
+4643,3734.6846,754.1219,3765.5361,816.38043,0.9966742
+4643,3483.263,827.83356,3529.9353,901.7513,0.84862876
+4643,3645.5195,796.30945,3694.8652,866.2759,0.699982
+4643,3672.6284,787.23456,3708.31,852.28644,0.4093369
+4643,3901.43,758.9521,3933.388,815.8049,0.12532654
+4643,3654.6233,788.3926,3709.138,860.24084,0.115071915
+4644,2386.7192,771.57086,2425.8452,855.2684,0.9996044
+4644,2376.1423,436.67578,2403.9294,497.38898,0.9995183
+4644,2254.783,284.19846,2275.9524,336.392,0.99937636
+4644,1300.5431,319.45944,1325.1239,374.1138,0.9993691
+4644,2964.8923,376.76544,2987.8489,427.95154,0.9992644
+4644,2119.6584,285.64352,2145.3196,339.23575,0.99921954
+4644,2685.774,380.5375,2708.4736,433.26596,0.9992083
+4644,2157.9172,409.94305,2179.3708,474.81573,0.9991518
+4644,2004.2925,164.21909,2021.251,208.74158,0.9991192
+4644,3114.268,550.37537,3148.0889,611.3639,0.99910384
+4644,2856.4807,310.89484,2879.3357,357.36554,0.9990528
+4644,2638.7153,299.68326,2660.7417,351.9413,0.9990005
+4644,2975.889,390.72864,2999.4817,442.797,0.9989994
+4644,2944.2395,314.20605,2964.5583,363.90002,0.9989747
+4644,2538.2473,223.55269,2555.069,269.70764,0.99896586
+4644,2569.7646,188.64557,2586.7202,230.88882,0.99883634
+4644,2440.3137,631.4794,2470.99,704.6284,0.9987978
+4644,2035.1501,188.11395,2051.301,235.80685,0.9987257
+4644,3149.6777,397.39206,3175.1895,448.96945,0.9987143
+4644,2965.8225,525.88513,2993.1575,587.7003,0.99870825
+4644,3091.75,289.84503,3112.6323,333.53607,0.99850994
+4644,1965.2687,884.5662,2005.5299,977.9379,0.9979144
+4644,1999.2373,122.177124,2016.0269,158.6532,0.99788034
+4644,3277.5938,362.38498,3301.5098,409.3549,0.99749255
+4644,3735.0945,754.1681,3765.7507,816.13916,0.9968923
+4644,3484.398,827.8388,3530.749,901.66266,0.6986691
+4644,3649.903,790.35175,3708.321,871.0971,0.64377284
+4644,3897.8853,760.03534,3931.606,816.9318,0.28771967
+4644,3651.3882,807.65295,3696.981,888.4258,0.2195606
+4644,3960.202,730.4686,3988.702,784.5647,0.105063654
+4645,2384.797,772.22144,2424.9978,856.2108,0.9995523
+4645,2376.2356,436.73218,2404.3748,497.3429,0.9993038
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+4645,1301.1243,319.5883,1326.8955,374.25296,0.9992618
+4645,3116.7292,550.8502,3148.41,611.26904,0.99922824
+4645,2120.0632,285.66495,2145.0452,339.1124,0.9992083
+4645,2433.9072,630.63635,2468.5889,705.04895,0.9991352
+4645,2687.4014,380.35562,2708.957,433.2535,0.99913126
+4645,2944.0383,314.06387,2965.0208,363.53445,0.99910885
+4645,2004.0645,163.8656,2021.2654,208.78076,0.99909353
+4645,2857.3083,310.8154,2880.0354,357.3759,0.9990476
+4645,2638.679,299.61508,2661.1414,351.8732,0.9990445
+4645,2975.8345,390.75327,2999.287,443.4147,0.99903834
+4645,2157.9253,409.91913,2181.0405,474.40747,0.9990336
+4645,2966.4485,526.3071,2993.0803,589.1704,0.99901354
+4645,2965.6016,376.7699,2988.5884,427.9226,0.9990036
+4645,2035.1024,188.0703,2051.1643,235.47765,0.9988857
+4645,3094.2888,289.8551,3113.2468,333.72723,0.9984676
+4645,2537.643,223.37448,2553.129,269.8236,0.9983523
+4645,3152.98,397.35165,3176.9365,448.82895,0.9981629
+4645,2567.081,188.29834,2585.7314,230.52722,0.99815136
+4645,1965.2551,884.5178,2005.5818,977.9425,0.9979068
+4645,3279.215,362.5516,3302.4114,409.3436,0.99780077
+4645,1999.6628,122.16616,2016.6006,158.8224,0.9972952
+4645,3735.4563,754.1372,3766.0159,816.15405,0.9970079
+4645,3895.2454,760.3473,3928.952,817.5514,0.75720996
+4645,3656.405,791.1837,3705.5408,862.6638,0.5594141
+4645,3485.6694,826.9433,3531.5488,901.7243,0.49226025
+4645,3962.2534,732.2184,3988.2173,784.1316,0.13878146
+4645,2969.6165,383.66904,2993.1995,434.09988,0.10799969
+4646,2384.1475,773.4768,2424.272,856.3734,0.999602
+4646,2688.3704,380.31528,2709.5334,433.28995,0.99937326
+4646,3119.2087,551.1911,3149.6262,611.0972,0.99932694
+4646,2255.0159,284.1124,2276.6663,336.39993,0.9992661
+4646,2376.7825,436.6361,2405.0623,497.1712,0.9992453
+4646,2427.2852,630.4542,2465.6094,705.81616,0.99921
+4646,2158.1882,409.96927,2184.119,475.1003,0.9991886
+4646,2003.96,163.91144,2021.052,208.6442,0.99916756
+4646,2120.3608,285.5547,2145.412,338.6123,0.9991591
+4646,2944.5903,313.83228,2965.0366,362.78204,0.9991013
+4646,1301.6115,319.75916,1327.1132,374.53613,0.9990559
+4646,2966.166,526.783,2992.9077,589.10675,0.9990544
+4646,2639.0874,299.43387,2661.291,351.67178,0.99905163
+4646,2976.524,390.96222,2999.4106,443.04108,0.99900836
+4646,2965.8555,377.09604,2989.666,427.4539,0.9989679
+4646,2857.4377,310.7668,2879.1028,357.36154,0.9989619
+4646,2034.9574,188.00412,2051.1553,235.23056,0.9988411
+4646,3095.0093,289.85626,3114.2808,334.05804,0.9987348
+4646,3280.8347,362.8555,3302.8367,409.26608,0.9987342
+4646,3154.3152,397.74457,3178.9978,449.0905,0.9984884
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+4646,2534.1694,223.30203,2552.3872,269.67337,0.99788046
+4646,1965.2445,884.4866,2005.5865,977.9237,0.9978265
+4646,2000.2003,122.01372,2017.0458,158.55736,0.99736905
+4646,3735.9075,754.16254,3766.2751,813.88385,0.9972651
+4646,3891.2976,762.097,3927.16,817.9395,0.5652758
+4646,3487.4014,825.70496,3533.6123,899.1996,0.3139834
+4647,2383.3525,774.3001,2423.809,856.85504,0.9995432
+4647,2689.8335,380.5076,2709.8716,433.16806,0.999461
+4647,1301.8462,319.85397,1327.4612,374.69797,0.9994049
+4647,2255.0837,284.13016,2276.8567,336.37692,0.99932086
+4647,2423.3855,630.3058,2463.4153,707.5016,0.9993024
+4647,3121.9653,551.58716,3151.748,611.11865,0.99924433
+4647,2158.1533,410.04575,2187.441,475.14713,0.99923885
+4647,2003.7754,163.89513,2021.06,208.52464,0.9991215
+4647,2377.1843,436.35495,2405.5525,497.01608,0.9991062
+4647,2965.945,527.0591,2992.8293,590.07056,0.9990921
+4647,2639.286,299.44647,2661.7014,351.66022,0.9990682
+4647,2120.5896,285.18057,2145.5286,338.90842,0.99904734
+4647,2858.8193,310.80545,2878.5972,357.30167,0.99894625
+4647,2976.2683,391.3467,2999.1926,443.8823,0.9989145
+4647,2966.3064,377.35046,2990.7996,427.83563,0.9988319
+4647,3095.774,290.14114,3115.1582,334.92947,0.99875975
+4647,2557.4539,186.60109,2583.2786,229.50539,0.9987192
+4647,3154.2302,398.65167,3180.9158,449.73547,0.9987148
+4647,2034.4889,187.80649,2050.9595,234.70831,0.9986727
+4647,3282.344,362.9945,3303.1921,409.3695,0.9985996
+4647,2531.0876,223.13074,2551.2322,269.3514,0.9980622
+4647,1965.2495,884.5398,2005.5745,977.92285,0.99782085
+4647,2000.6442,121.89303,2017.0956,159.06335,0.9977198
+4647,3736.2153,754.13763,3766.462,814.107,0.99771667
+4647,2942.8345,313.6673,2965.3223,362.92438,0.99763703
+4647,3892.2505,762.2388,3926.565,818.85876,0.54511184
+4647,3491.5305,823.9603,3534.9993,898.5366,0.32609397
+4647,3965.7441,733.90283,3989.2637,782.3696,0.1853486
+4647,3656.3462,791.4554,3705.9287,891.31415,0.15231659
+4648,2383.437,775.0427,2423.522,857.2406,0.9995474
+4648,1302.027,320.05746,1327.7545,374.60458,0.9994881
+4648,2690.139,380.3365,2710.1926,433.24762,0.99946755
+4648,2158.3591,410.69916,2190.6853,474.799,0.9992964
+4648,2254.676,284.22894,2277.385,335.9087,0.99929047
+4648,2003.8015,164.029,2020.7212,208.31181,0.9992617
+4648,2421.5957,631.0619,2460.521,708.70483,0.99920756
+4648,2639.3843,299.46024,2661.9028,351.64328,0.9991202
+4648,2965.8953,527.49005,2992.737,590.0041,0.9991015
+4648,3096.3655,290.26868,3116.0974,335.16547,0.9990025
+4648,2858.5803,310.64825,2878.4778,357.62903,0.99891317
+4648,3154.0134,399.1498,3182.7927,449.9686,0.9988663
+4648,2377.7273,436.47693,2406.0515,497.0614,0.99886465
+4648,3124.2905,552.2727,3154.2183,610.79724,0.9988555
+4648,2122.1619,284.76337,2146.0413,338.37567,0.99871886
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+4648,2554.9917,186.32895,2581.4404,229.58911,0.9986853
+4648,2033.8561,187.91429,2050.7744,234.69072,0.998615
+4648,3282.5854,363.00687,3303.8008,410.80923,0.9985911
+4648,2966.5845,377.5009,2992.0269,427.6638,0.9981608
+4648,2529.4404,222.87207,2550.3794,269.3852,0.9978282
+4648,2001.0485,121.984795,2017.6791,158.99689,0.99772924
+4648,1965.2871,884.5264,2005.5684,977.877,0.99762195
+4648,3736.6445,754.10736,3766.7466,813.8497,0.9974642
+4648,2943.5173,313.40344,2965.3733,363.5432,0.9972572
+4648,3494.9817,822.7382,3536.9353,896.2126,0.76615787
+4648,3890.098,761.78235,3923.9592,819.2643,0.7202794
+4648,3655.915,790.2705,3709.128,890.52246,0.1547526
+4648,3966.6157,729.4798,3991.5269,782.00336,0.12377137
+4649,2690.1636,380.32654,2710.421,433.0605,0.99948585
+4649,2383.0083,775.4784,2421.838,857.5442,0.99948525
+4649,2255.0251,283.92206,2278.299,335.3188,0.9994106
+4649,1302.3394,320.08554,1327.8987,374.43497,0.9993444
+4649,2158.5317,410.4756,2191.1235,474.8844,0.99933976
+4649,2421.1406,632.4387,2457.9858,708.3445,0.999321
+4649,2003.6893,163.3622,2020.4542,208.47096,0.9992791
+4649,2639.5728,299.30743,2662.2378,351.18286,0.9992774
+4649,3154.189,399.65982,3185.7651,449.96103,0.9992167
+4649,2965.5422,527.9697,2992.661,590.7328,0.99916303
+4649,2378.3347,436.6209,2406.5286,496.9798,0.9991321
+4649,3096.6936,290.34976,3116.8704,335.21585,0.99901116
+4649,2858.6318,310.3669,2878.5884,357.55173,0.9989802
+4649,2975.881,392.1882,2998.9006,444.8983,0.99884343
+4649,2553.9321,187.27997,2578.6953,228.57965,0.99881315
+4649,3125.3362,552.6269,3156.8865,611.0854,0.99864024
+4649,3282.6853,363.38727,3304.3,411.8838,0.9986167
+4649,2122.6682,283.92468,2146.595,338.153,0.99859565
+4649,2032.6165,187.72055,2050.6445,234.5886,0.99837685
+4649,2001.1918,122.11696,2017.6705,159.26927,0.99807215
+4649,2527.1196,223.2852,2549.456,269.3994,0.99806076
+4649,2967.087,377.59915,2993.9697,427.64542,0.9974341
+4649,1965.2935,884.497,2005.5618,977.8669,0.9974317
+4649,3737.0483,754.1669,3766.978,814.57153,0.99595696
+4649,2941.216,313.20877,2965.155,363.07132,0.99382365
+4649,3496.6912,820.8691,3538.951,895.3825,0.797283
+4649,3889.377,762.1715,3922.9233,820.86414,0.6855745
+4649,3664.506,790.3337,3711.9177,879.5784,0.31705403
+4649,3967.658,726.43604,3992.9607,781.02527,0.24993838
+4649,3671.6519,788.62946,3704.4336,852.7292,0.1927276
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+4650,2383.1108,775.4826,2419.4556,857.6337,0.99956256
+4650,2690.3008,379.94693,2710.734,433.085,0.99939287
+4650,2003.5876,163.02994,2020.3569,208.327,0.9993405
+4650,2378.8513,436.86334,2406.973,497.0868,0.9993129
+4650,2158.6675,410.5997,2190.8345,474.97787,0.9992514
+4650,1302.2454,319.9772,1327.8608,374.2673,0.99917334
+4650,3153.9792,400.25665,3187.966,450.2379,0.99915946
+4650,2420.7612,634.31525,2454.504,708.435,0.9991547
+4650,2640.4585,299.18295,2662.4253,350.22037,0.99912125
+4650,2965.8792,528.11035,2992.5403,590.30774,0.9991179
+4650,2858.6726,310.16028,2878.426,357.62762,0.99911565
+4650,3097.1052,290.89444,3117.6443,334.93515,0.9990681
+4650,2124.5464,284.08893,2147.6826,337.41382,0.99902964
+4650,2525.7441,223.19081,2549.2744,269.33746,0.9988311
+4650,2553.7085,187.77682,2575.2598,228.75671,0.99881965
+4650,2975.6445,393.1147,2998.5283,444.893,0.99881625
+4650,2001.1519,122.18871,2018.1079,159.67169,0.998492
+4650,3125.3945,553.07245,3158.246,611.0949,0.99848723
+4650,3282.9478,363.46216,3304.7534,411.50812,0.99847406
+4650,2031.7429,187.96469,2050.3696,234.97556,0.99772376
+4650,3737.0222,754.1776,3767.3313,814.21716,0.9975101
+4650,2942.9844,313.21362,2965.1982,362.44458,0.9974305
+4650,2967.0532,377.56305,2994.629,427.10718,0.9973732
+4650,1965.2635,884.45465,2005.5958,977.8825,0.99733067
+4650,3498.203,819.6829,3540.3943,894.36053,0.82058936
+4650,3657.832,790.58484,3716.7578,888.7167,0.8153537
+4650,3889.0513,762.46173,3919.831,819.85284,0.6424198
+4650,3968.044,727.90906,3992.293,781.0038,0.2739091
+4650,2977.8896,382.80112,2998.7686,431.61063,0.10639934
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+4651,2256.108,283.97028,2279.8306,332.98462,0.9995202
+4651,2003.5552,162.41895,2020.2183,208.11102,0.999406
+4651,2419.783,635.30383,2451.3503,709.11475,0.9993599
+4651,2379.499,437.22195,2407.5645,497.0862,0.9993587
+4651,1303.1494,320.00095,1327.9421,374.31052,0.9993549
+4651,2690.5435,380.0161,2710.8828,432.88812,0.99934906
+4651,3154.658,400.14038,3190.2522,451.3485,0.99932337
+4651,2524.9204,223.37473,2549.019,269.15753,0.9992781
+4651,2159.2202,410.21777,2191.4336,474.9718,0.9991984
+4651,2858.6682,309.96707,2878.3245,357.58908,0.9991154
+4651,2965.9653,528.12286,2992.4487,591.59625,0.999073
+4651,2641.1733,299.15613,2662.5005,349.63556,0.99899143
+4651,3097.4475,291.14636,3119.1238,334.6765,0.9988142
+4651,2551.6875,187.94638,2571.796,228.85107,0.9986187
+4651,2001.1825,122.39797,2018.1407,160.02222,0.9985385
+4651,2126.5586,283.75888,2148.5415,336.816,0.99849
+4651,2029.671,188.25323,2050.0554,235.29462,0.99835914
+4651,3282.9607,363.51865,3305.453,411.96848,0.99834526
+4651,3125.413,553.0952,3159.6401,611.1437,0.99822396
+4651,2975.6702,393.49277,2998.408,444.82394,0.99819416
+4651,3737.2869,754.06604,3767.7903,814.2772,0.99804085
+4651,2967.4458,377.50473,2995.9868,427.5325,0.99738985
+4651,1965.265,884.47534,2005.6022,977.8845,0.99732226
+4651,2940.931,312.95435,2965.179,362.29108,0.99530965
+4651,3498.7646,819.297,3542.749,894.33057,0.53993714
+4651,3889.1548,761.9848,3918.2637,819.8195,0.50088674
+4651,3968.295,727.6415,3992.354,781.4149,0.3690296
+4651,3677.989,793.7993,3720.9988,869.1422,0.24401943
+4651,3661.664,788.55225,3706.7632,877.93616,0.2077046
+4652,2382.43,775.44366,2416.8215,858.16815,0.9996935
+4652,2379.9268,437.21207,2408.0508,497.19614,0.99951124
+4652,2003.5907,162.39769,2020.2582,207.56105,0.99942905
+4652,1303.6567,319.95898,1327.739,374.25677,0.99939907
+4652,2417.312,635.47833,2448.1763,709.2071,0.9993748
+4652,2690.896,379.91754,2711.1626,432.71844,0.9993519
+4652,2257.0615,284.0094,2280.3486,332.61987,0.99926573
+4652,2524.585,223.78075,2548.5493,269.32693,0.99925536
+4652,3156.7334,401.07782,3190.8818,451.497,0.9992212
+4652,2642.2043,298.81573,2662.703,348.7555,0.9991416
+4652,2858.478,309.77002,2878.393,357.3753,0.99910057
+4652,2129.1897,283.73743,2149.632,336.73126,0.9989945
+4652,2966.1611,528.69446,2992.2817,592.542,0.9989843
+4652,2160.1262,410.30206,2191.699,474.9983,0.99894947
+4652,2975.934,393.71756,2998.4585,444.8892,0.9983524
+4652,2001.6595,122.466156,2018.3812,160.05716,0.9983493
+4652,2028.7039,188.53236,2049.6504,235.31212,0.9983475
+4652,3097.1978,291.58218,3120.7437,334.62485,0.998334
+4652,2549.4812,186.87428,2567.488,229.17192,0.9981065
+4652,3125.4182,553.40607,3159.8918,611.00055,0.9980476
+4652,3282.7852,363.81708,3305.7617,413.07654,0.9978941
+4652,3737.448,754.1715,3768.324,814.35583,0.9978187
+4652,1965.3407,884.48456,2005.5931,977.8929,0.9974993
+4652,2939.7854,312.55136,2964.6165,361.47824,0.9954607
+4652,2967.8457,377.3685,2997.483,428.1124,0.9950724
+4652,3499.9739,819.07245,3545.1526,893.6429,0.87388515
+4652,3968.625,727.084,3993.0518,781.8176,0.655188
+4652,3889.6465,762.32623,3917.1104,819.0743,0.44328663
+4653,2379.878,775.68176,2417.5825,858.4796,0.9997224
+4653,2415.0073,635.03534,2445.6865,711.7576,0.9994962
+4653,1304.3903,319.29636,1327.726,373.34842,0.99945885
+4653,2003.6067,162.43793,2020.2698,207.19235,0.99940777
+4653,2524.3542,223.52031,2547.8826,269.0883,0.9993343
+4653,2690.847,379.5717,2711.4714,432.5024,0.99928087
+4653,2381.3271,437.17776,2408.8389,497.20444,0.99925655
+4653,2131.304,283.79074,2150.6497,336.7161,0.9991142
+4653,2257.2869,283.812,2280.7932,332.52747,0.999111
+4653,2857.7542,309.62805,2878.5173,357.02173,0.99909806
+4653,2643.0752,299.0968,2662.7314,348.44513,0.99902725
+4653,3159.602,400.89545,3192.2007,452.11176,0.99895644
+4653,2966.0767,529.1384,2992.1514,593.32947,0.9989042
+4653,3125.5369,553.7161,3160.097,611.1639,0.9988078
+4653,2976.6187,393.93344,2998.5645,444.90988,0.9986753
+4653,3282.937,365.09778,3306.1865,413.58728,0.99865603
+4653,2027.6204,188.23535,2049.1492,235.12848,0.9985885
+4653,2161.112,410.20786,2192.1877,475.06754,0.99846554
+4653,3097.1997,291.906,3121.376,334.73566,0.99843174
+4653,2001.7875,122.61122,2018.2748,160.03874,0.99819106
+4653,3737.237,754.24927,3768.8728,814.4431,0.9981172
+4653,2548.023,186.1998,2564.6084,228.95511,0.997795
+4653,1965.3938,884.4955,2005.6135,977.89795,0.9974316
+4653,2939.4429,312.3595,2964.649,360.583,0.9961966
+4653,2969.3965,377.07343,2998.151,427.51172,0.9945157
+4653,3500.41,820.30695,3546.3713,893.15607,0.7885479
+4653,3968.7026,727.29315,3992.9766,781.76605,0.6547813
+4653,3889.6553,761.79065,3916.1025,817.5393,0.21386543
+4653,3692.4658,797.56665,3724.162,860.1599,0.14270718
+4654,2376.4338,776.8264,2417.8113,860.45886,0.99960166
+4654,2412.736,634.2698,2443.1243,713.90735,0.9995067
+4654,2003.5116,162.32687,2020.2631,207.35611,0.99936813
+4654,2691.0059,379.19135,2711.6377,432.36157,0.9991893
+4654,3163.3037,400.66882,3193.7432,451.7437,0.999186
+4654,2382.3079,437.35236,2409.11,497.09058,0.99918574
+4654,2132.2126,282.91766,2151.5574,336.71613,0.99918467
+4654,2857.545,309.6171,2878.6523,356.8154,0.99914825
+4654,2257.3555,282.97995,2281.0596,332.23605,0.999116
+4654,2524.3057,223.19655,2545.9116,269.27554,0.99903226
+4654,1306.3787,319.55304,1327.603,374.36383,0.99902725
+4654,2976.2793,394.5033,2998.6138,444.95453,0.9990061
+4654,2966.0095,529.3144,2991.907,593.56573,0.99894994
+4654,2545.72,185.78632,2562.9387,228.08798,0.9988902
+4654,2643.4902,299.15637,2662.7183,348.54224,0.99886274
+4654,3125.2566,554.28644,3160.2307,611.5074,0.9987419
+4654,3283.5425,365.00708,3306.2983,413.8733,0.99859136
+4654,2027.5483,188.26129,2048.8115,235.21109,0.99836475
+4654,2162.3247,410.2329,2192.7998,474.99536,0.99833125
+4654,3097.1506,291.72684,3121.902,335.1721,0.998319
+4654,2002.0686,122.60679,2018.3989,160.09879,0.9981515
+4654,3737.066,754.52026,3769.8174,814.3347,0.9979779
+4654,1965.4403,884.5024,2005.6178,977.8704,0.9974468
+4654,2940.6174,312.12457,2965.0442,358.93927,0.9942755
+4654,2970.2554,377.35236,2998.5977,427.85028,0.9888475
+4654,3500.6438,822.46423,3547.3328,892.999,0.8695686
+4654,3886.5264,762.3071,3913.2021,818.9773,0.5589623
+4654,3968.4639,727.6133,3993.228,782.78577,0.5082207
+4655,2372.8855,777.46387,2417.986,862.7201,0.99955434
+4655,2003.3665,162.29886,2020.3262,207.03189,0.9993548
+4655,2541.97,184.40819,2562.1516,228.18626,0.99925977
+4655,2407.5894,634.3055,2441.5513,715.1141,0.9992174
+4655,2691.1895,378.9715,2712.171,432.09344,0.99920267
+4655,2382.7388,437.375,2409.29,497.0724,0.9991202
+4655,2856.7476,309.55896,2878.7246,356.66998,0.9990945
+4655,2132.8523,282.75937,2153.7283,336.7927,0.99906224
+4655,1307.5454,319.6625,1327.8044,374.395,0.9990563
+4655,2257.33,283.49435,2281.4736,332.36264,0.99898344
+4655,3125.1116,554.6708,3160.4753,612.14844,0.9989341
+4655,3166.989,400.33026,3195.8708,451.77484,0.99893314
+4655,2976.121,394.9762,2998.2847,444.93097,0.99891055
+4655,2965.8816,529.6167,2991.8508,593.76697,0.9988859
+4655,2524.2578,222.55836,2544.2217,268.88123,0.9987841
+4655,2643.7996,298.67703,2662.7834,348.64114,0.9986222
+4655,3284.0066,365.15045,3306.6633,414.28375,0.99858403
+4655,2027.4027,188.35484,2048.45,235.17134,0.99833506
+4655,3097.4792,291.87595,3123.8474,335.58878,0.9983053
+4655,2002.3331,122.57909,2018.5626,160.0833,0.998197
+4655,3737.272,754.4638,3770.2744,814.5538,0.9979672
+4655,1965.4413,884.5188,2005.6339,977.85913,0.9975337
+4655,2163.8074,410.10565,2193.6428,474.97894,0.9974744
+4655,2938.8132,311.51492,2964.1292,359.84384,0.9938599
+4655,2973.8997,377.40283,2999.738,425.94257,0.9820796
+4655,3499.8015,823.9689,3548.4368,892.87805,0.87811023
+4655,3968.5796,728.70087,3993.664,783.6285,0.44298732
+4655,3883.8237,763.2944,3911.0938,820.9693,0.4278421
+4655,3699.714,798.38837,3729.3767,860.1457,0.10811329
+4656,2368.5793,778.25256,2418.2732,864.18665,0.99944705
+4656,2405.6033,634.91766,2439.8206,716.6872,0.99928904
+4656,2003.4551,162.24074,2020.3572,207.21577,0.9992836
+4656,2383.9211,437.30066,2409.4548,497.07013,0.99924433
+4656,2856.4712,309.39862,2878.8262,356.5965,0.99916565
+4656,2257.5498,283.80994,2281.6333,332.5078,0.999138
+4656,2691.5066,378.88135,2712.4163,432.2704,0.99905235
+4656,2966.058,529.8968,2991.481,593.72064,0.9989993
+4656,3125.0679,555.0122,3161.267,612.84937,0.99895644
+4656,3098.7625,292.11246,3124.055,335.35104,0.9988426
+4656,1308.855,319.71954,1328.3081,374.50427,0.998828
+4656,2132.964,282.5666,2154.9177,336.64246,0.9987029
+4656,2643.9304,298.73645,2663.0198,348.59296,0.998616
+4656,3171.6282,400.4051,3197.7458,451.63232,0.9985588
+4656,2976.847,395.18588,2997.9832,445.04886,0.99845576
+4656,2027.4358,188.40213,2048.362,235.24344,0.99826056
+4656,2942.5605,310.9827,2964.731,358.8632,0.99824023
+4656,2524.1423,221.28717,2542.0808,269.08124,0.9982268
+4656,3285.2834,364.70816,3306.8445,413.60318,0.9982054
+4656,2002.534,122.57793,2019.236,160.16759,0.99815303
+4656,2537.5579,183.83789,2561.4558,227.80933,0.99795365
+4656,1965.4778,884.4855,2005.6135,977.8528,0.9975891
+4656,2167.6255,409.62045,2194.9526,474.89218,0.99751127
+4656,3736.7627,754.518,3770.8394,814.7298,0.997212
+4656,3499.5679,825.8099,3549.6865,892.92413,0.966342
+4656,2982.6243,377.29773,3000.4626,423.5916,0.87475586
+4656,3700.828,798.3699,3732.2922,859.95593,0.4171844
+4656,3968.579,727.9953,3994.0952,785.2773,0.29905835
+4656,3879.2224,765.03046,3910.8708,822.37335,0.10746474
+4657,2365.953,778.07965,2417.3499,865.889,0.9995301
+4657,2404.074,635.7702,2438.536,716.43976,0.99941456
+4657,2385.2366,437.30273,2409.3665,497.00977,0.9992774
+4657,2003.4558,161.98384,2020.5042,207.24065,0.99924517
+4657,3125.0708,555.01373,3161.0908,613.2819,0.99924505
+4657,2855.9236,309.24374,2878.9426,356.6406,0.9991341
+4657,2258.083,283.59616,2282.1,332.33652,0.999074
+4657,2966.1907,530.1328,2991.2283,593.62463,0.9989969
+4657,2644.0457,298.64343,2663.2278,348.62885,0.9989491
+4657,2943.9734,310.75882,2964.7595,358.30167,0.9988525
+4657,3101.3481,292.08194,3124.9023,335.24722,0.9988483
+4657,2027.2433,188.12268,2048.0386,235.28137,0.99880695
+4657,3175.5298,400.88467,3198.706,451.4424,0.9987828
+4657,2692.0295,378.64267,2712.6106,431.98337,0.99873453
+4657,2534.7375,183.7406,2559.2463,226.70972,0.998667
+4657,1309.5254,319.65347,1328.6848,374.42282,0.9986077
+4657,3286.4695,365.41455,3307.8484,413.96185,0.99855417
+4657,2133.7244,282.85825,2156.26,336.5765,0.998374
+4657,2170.715,409.5903,2196.1296,474.9857,0.99833703
+4657,2524.0405,220.4791,2540.1118,269.25198,0.99818146
+4657,2976.9792,395.5842,2998.5154,445.1418,0.9980604
+4657,2002.7742,122.62995,2019.4551,160.3761,0.99757725
+4657,1965.4714,884.4777,2005.6328,977.8465,0.9974377
+4657,3736.6506,754.8924,3771.307,814.9886,0.99507165
+4657,3500.5398,826.3052,3552.5457,892.6316,0.98857635
+4657,3701.1353,797.01953,3737.1084,860.4507,0.9280788
+4657,2984.6812,377.0762,3001.4556,422.67166,0.86566114
+4657,3968.3745,727.8672,3994.1143,786.2529,0.12493549
+4657,3869.5105,765.1526,3909.7869,822.7329,0.100671016
+4658,2363.5305,780.47876,2415.7854,867.30566,0.99962723
+4658,2400.287,637.5024,2437.4873,716.2138,0.9993892
+4658,3125.5293,555.16394,3161.2578,614.00635,0.99931
+4658,2259.645,283.06723,2282.8262,332.36288,0.9992493
+4658,2386.0303,437.20584,2409.3926,497.14145,0.9991672
+4658,2003.4193,161.94347,2020.5563,207.03111,0.99915874
+4658,2855.3318,309.12827,2878.8093,356.51828,0.9990896
+4658,2966.2598,530.20734,2991.2266,593.5689,0.9990355
+4658,1310.2933,319.5945,1329.389,374.8494,0.99901915
+4658,2944.695,310.66513,2964.6418,358.392,0.9990157
+4658,2027.012,188.09247,2048.0344,235.33148,0.9988487
+4658,2693.4248,378.62067,2712.9692,431.502,0.99884415
+4658,2644.1182,298.39127,2663.9824,348.65384,0.99880326
+4658,3175.5918,401.62216,3200.6318,452.9838,0.99868554
+4658,2134.2114,282.71814,2157.2065,336.576,0.99868065
+4658,3103.2966,292.08304,3125.4001,334.64713,0.99862456
+4658,2532.2493,183.9679,2557.319,226.63477,0.99825305
+4658,2172.2893,409.92337,2197.5833,475.019,0.9981988
+4658,3287.1855,365.41544,3308.4424,414.86484,0.997945
+4658,2977.1272,395.46326,2997.3948,445.39362,0.99778605
+4658,2003.0554,122.514984,2020.0398,160.51266,0.99767643
+4658,1965.4729,884.488,2005.6318,977.86053,0.99742043
+4658,3735.7178,755.4856,3772.0244,814.6367,0.9972767
+4658,2522.7327,219.56435,2539.618,269.42282,0.9960233
+4658,3501.9688,826.0584,3554.9736,893.03314,0.9935795
+4658,3702.0742,796.1548,3741.209,859.4883,0.985336
+4658,2985.4517,377.59772,3003.3457,424.44513,0.8970645
+4658,3867.9922,765.7254,3910.3394,824.1922,0.60260326
+4658,3665.8726,784.63635,3703.2095,871.25745,0.11013345
+4659,2362.3513,781.33154,2413.4182,868.9929,0.9995938
+4659,2393.9045,638.6749,2436.5876,716.41956,0.9994904
+4659,2261.218,282.49182,2283.1448,332.15826,0.9994861
+4659,3125.8752,555.03186,3160.9788,614.1743,0.99934345
+4659,2386.5051,437.11343,2409.4187,497.1913,0.9991679
+4659,1310.6859,319.71768,1330.5585,374.90054,0.9991423
+4659,2026.726,188.07547,2048.118,235.37595,0.99911743
+4659,2855.209,309.0849,2878.755,356.5061,0.9991148
+4659,2003.3025,161.90015,2020.6936,206.8081,0.99908686
+4659,2966.3665,530.2901,2991.2961,593.207,0.99905366
+4659,2944.73,310.5112,2964.6655,358.317,0.99903274
+4659,2532.0017,184.74945,2554.5789,227.28998,0.9989641
+4659,2134.5327,282.57104,2158.524,336.57983,0.99883634
+4659,2693.9578,378.59753,2713.3743,431.19806,0.99878794
+4659,2174.8386,410.0548,2199.5447,475.13098,0.99857926
+4659,3175.1812,402.03174,3202.144,453.52307,0.99848455
+4659,2644.1926,298.33014,2664.7932,348.46338,0.99829143
+4659,3105.3057,291.8145,3126.2568,335.61554,0.99796224
+4659,2977.161,395.40872,2997.1926,445.77396,0.99773884
+4659,2003.3983,122.62189,2020.2108,160.56506,0.9976603
+4659,1965.4484,884.5136,2005.6676,977.8648,0.9971264
+4659,3287.262,366.0664,3308.9421,415.03296,0.99708587
+4659,2519.1926,220.74522,2537.589,269.77774,0.995621
+4659,3737.1323,755.31104,3772.689,814.85474,0.99398077
+4659,3703.8325,793.99445,3744.8867,860.5915,0.98863995
+4659,3503.9902,826.1794,3558.6704,893.0599,0.9865297
+4659,3862.029,766.25525,3909.7727,825.0293,0.90786064
+4659,2984.3132,377.3271,3004.7952,426.5246,0.85535157
+4660,2385.9214,638.92554,2434.957,716.114,0.99954337
+4660,2361.5444,781.9897,2411.3296,870.1679,0.99946636
+4660,2262.157,282.1987,2283.551,332.4006,0.9994553
+4660,2026.645,188.39984,2047.9551,235.34195,0.99922454
+4660,3127.2332,555.5394,3161.491,613.9457,0.99912876
+4660,2966.4758,530.59863,2991.5579,592.8541,0.9991202
+4660,2387.139,437.0411,2409.4495,497.32913,0.9991097
+4660,2855.1853,308.81952,2878.6868,356.53174,0.9991066
+4660,2944.9343,310.3978,2964.7097,358.37924,0.9990583
+4660,2176.4812,409.8829,2200.784,475.01303,0.99903417
+4660,1311.3271,319.76263,1331.7161,374.76984,0.99901307
+4660,2003.3282,161.09372,2020.9032,206.8306,0.9989712
+4660,3175.198,402.85358,3205.6472,454.84656,0.9989077
+4660,2694.7097,378.5027,2713.6208,431.40442,0.9987903
+4660,2530.9287,185.14456,2551.0044,226.20432,0.9985416
+4660,2003.9026,122.6356,2021.2598,160.5235,0.99844295
+4660,2977.3735,395.37915,2997.435,445.9956,0.99832696
+4660,2644.3801,298.24738,2665.2961,348.47156,0.9982741
+4660,2134.7488,282.59415,2159.2512,336.21036,0.99822444
+4660,3107.6423,292.03555,3126.9539,334.76254,0.9980611
+4660,1965.5148,884.48474,2005.6696,977.85034,0.997378
+4660,2515.816,220.86447,2536.3447,269.74097,0.9972101
+4660,3287.8816,366.64847,3309.528,415.83066,0.997015
+4660,3505.8423,826.11475,3561.6035,892.6682,0.993455
+4660,3738.2517,755.41187,3773.264,815.6533,0.9899262
+4660,3704.958,792.4424,3749.6665,858.8325,0.9726557
+4660,3861.9934,765.83875,3908.7107,826.24475,0.9619929
+4660,2985.797,377.17407,3006.2512,426.9718,0.94547844
+4660,2980.5798,390.41635,3003.7205,442.38052,0.13047482
+4661,2381.101,639.4941,2432.0625,716.3597,0.99964
+4661,2361.3784,782.9514,2408.6104,870.921,0.99960965
+4661,2262.6204,282.20773,2283.617,332.57462,0.99943036
+4661,2026.5958,188.38167,2047.61,235.35509,0.99927956
+4661,2387.5938,437.0201,2409.3926,497.3514,0.99914074
+4661,2966.547,530.91846,2991.8347,592.9419,0.99914026
+4661,2177.5645,409.8888,2201.5518,474.75037,0.99909186
+4661,2855.106,308.53424,2878.4258,356.53973,0.9990909
+4661,3175.4216,403.73,3209.2683,454.82248,0.9990471
+4661,1312.1614,319.6661,1333.0698,374.6393,0.9989807
+4661,2003.4541,160.93692,2021.1245,206.64938,0.99895585
+4661,2695.5837,378.37628,2713.889,431.95148,0.9988686
+4661,3129.2378,555.13214,3162.3022,614.064,0.9988292
+4661,2004.2188,122.71492,2021.488,160.52095,0.9987979
+4661,2945.0579,310.04056,2964.2883,357.53012,0.9987287
+4661,2529.317,184.92726,2547.372,225.99535,0.9985039
+4661,3109.7588,292.4153,3127.9463,335.1135,0.99840075
+4661,2644.811,298.34885,2665.4497,348.44284,0.99836963
+4661,2977.4133,395.01498,2997.7517,446.67215,0.9980634
+4661,3288.154,366.96075,3309.8552,416.01184,0.9976826
+4661,2135.7366,282.2587,2160.131,335.83878,0.99762255
+4661,1965.5494,884.4328,2005.6798,977.88983,0.9975011
+4661,2513.7156,221.88196,2535.5852,269.60562,0.99724686
+4661,3737.103,755.3244,3774.0933,818.4953,0.995522
+4661,3509.9949,825.4162,3564.536,892.0346,0.9911737
+4661,2986.923,377.287,3009.0945,428.0072,0.97951496
+4661,3860.7646,766.26263,3909.6187,826.22723,0.92348003
+4661,3708.5774,793.04236,3749.9167,857.05347,0.7851257
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+4662,2376.6692,639.68713,2428.199,717.0244,0.9994974
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+4662,2026.4727,188.45166,2047.2959,235.27777,0.99928564
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+4662,2696.0088,378.3754,2714.1157,432.08975,0.9990959
+4662,2854.9587,308.43045,2878.198,356.57724,0.99909544
+4662,2177.7964,409.69708,2203.6465,474.852,0.9990953
+4662,2387.6912,436.9227,2409.09,497.3876,0.99906665
+4662,2003.9163,160.47874,2021.4607,205.65556,0.9989189
+4662,2004.3829,122.614006,2021.7152,160.59961,0.998838
+4662,3131.4812,554.69196,3163.408,613.8525,0.998816
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+4662,2977.4895,394.15164,2998.8748,447.4648,0.998054
+4662,1965.518,884.42773,2005.693,977.8978,0.9975681
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+4662,2987.375,377.42535,3010.5068,427.44592,0.9894091
+4662,3737.6462,755.4391,3774.576,819.8684,0.98267317
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+4662,3860.4275,766.7353,3908.1384,825.9565,0.8817095
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+4663,2363.8018,785.8423,2403.708,871.63916,0.9996082
+4663,2374.4907,639.4374,2423.3403,718.9934,0.9995034
+4663,2026.4042,188.43591,2046.9625,235.12756,0.99938834
+4663,2966.634,531.3825,2992.2214,594.33636,0.9993691
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+4663,2263.3157,282.51968,2284.3796,332.3588,0.9991466
+4663,2004.2279,160.35901,2021.3453,205.09558,0.9990903
+4663,1312.9993,319.1937,1336.8762,373.88675,0.99907136
+4663,2854.8252,308.2061,2878.0854,356.53043,0.9990594
+4663,2696.0396,378.3406,2714.3223,431.57498,0.9990514
+4663,2387.7605,436.89316,2408.9436,497.43094,0.99903274
+4663,3134.0063,554.7563,3164.5947,613.9805,0.9989794
+4663,3110.6692,291.90024,3130.469,335.0198,0.9988557
+4663,2004.7875,122.48918,2021.9352,160.53847,0.998781
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+4663,1965.5529,884.43494,2005.6881,977.8834,0.9975909
+4663,2139.7158,281.46918,2160.7476,335.20703,0.9966641
+4663,2988.9326,376.86304,3011.8486,427.32184,0.9864006
+4663,3744.862,755.21063,3775.382,815.0367,0.96166646
+4663,3520.1882,823.458,3570.4958,891.78235,0.9408102
+4663,3860.8892,765.7654,3906.954,825.6957,0.8228134
+4663,3716.0095,796.49146,3756.7888,858.3843,0.5783183
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+4664,2372.9941,639.63007,2419.061,719.74664,0.9994217
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+4664,2696.4348,378.1975,2714.675,431.3236,0.999323
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+4664,2263.4773,282.00668,2284.5896,332.40305,0.99914193
+4664,2004.355,160.03242,2021.5986,204.52837,0.9991333
+4664,1313.4642,319.71893,1339.4357,374.49023,0.9991186
+4664,2854.7698,308.20267,2877.819,356.10165,0.9990792
+4664,2387.8757,436.73065,2408.7854,497.45294,0.9990363
+4664,3110.9512,291.92285,3131.184,334.7614,0.9989613
+4664,3136.9136,554.86707,3165.3823,614.18066,0.9988438
+4664,2004.9579,122.48739,2022.5729,160.37914,0.99878675
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+4664,3287.7344,368.4708,3310.7227,417.36923,0.9986951
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+4664,1965.5844,884.4044,2005.6783,977.8981,0.997488
+4664,2142.841,281.1188,2161.6414,333.79495,0.9971814
+4664,2989.3108,376.99585,3012.8406,427.59656,0.99479586
+4664,3745.2031,755.2175,3775.5566,815.2825,0.9764009
+4664,3721.2163,797.1242,3760.041,858.0384,0.85741246
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+4664,3861.2185,765.3397,3899.0999,825.92017,0.5128806
+4664,3678.9907,778.8486,3709.0796,831.28436,0.2510017
+4665,2367.1194,787.4496,2400.1765,872.1067,0.99952126
+4665,2696.6921,378.2477,2715.845,431.78806,0.9994641
+4665,3179.927,405.38126,3216.886,457.2302,0.99945694
+4665,2372.0586,640.1442,2417.274,720.0485,0.9994259
+4665,1313.888,319.7873,1340.7305,374.40076,0.9992968
+4665,2026.2224,187.94456,2045.5073,234.88164,0.9992198
+4665,2966.2195,532.5907,2993.032,596.58093,0.9992113
+4665,2854.7776,308.11356,2877.5784,355.8539,0.9990765
+4665,2645.6943,298.7822,2667.338,348.5165,0.9990721
+4665,2004.4719,159.70421,2021.9124,204.47615,0.99905306
+4665,2263.5503,282.19803,2285.0352,332.2689,0.9990004
+4665,2388.0188,436.62698,2408.6775,497.4739,0.9989649
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+4665,3138.7097,554.8968,3166.096,614.0976,0.9986713
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+4665,2005.1361,122.51718,2022.5319,160.39655,0.9983748
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+4665,3287.9756,368.78845,3311.3486,417.37372,0.99826163
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+4665,1965.5708,884.3819,2005.6821,977.8912,0.9973991
+4665,2989.8508,377.17105,3014.075,427.96353,0.9939937
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+4665,3726.0198,794.48816,3767.2317,858.3866,0.8269367
+4665,3741.4172,755.4002,3776.345,825.9963,0.7176412
+4665,3860.8699,764.54755,3894.8416,825.8217,0.5346939
+4665,3726.839,759.88446,3776.4739,855.5246,0.23666804
+4665,3679.705,778.71875,3709.1372,832.6301,0.13509946
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+4666,2368.042,788.0068,2398.4097,872.2161,0.999461
+4666,2508.9023,222.02205,2532.5464,268.55286,0.9993401
+4666,2696.6433,378.1275,2716.6213,431.85834,0.9993237
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+4666,2004.5292,159.48473,2022.027,204.44398,0.9991667
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+4666,2966.2695,532.8678,2993.084,597.5156,0.9990803
+4666,2854.767,308.2504,2877.3213,355.85715,0.99905735
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+4666,2388.0947,436.51825,2408.5864,497.46466,0.9989936
+4666,2026.2932,187.93663,2044.4468,234.85039,0.9989237
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+4666,2005.2587,122.48859,2022.9874,160.25229,0.9982607
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+4666,1965.5891,884.38513,2005.6743,977.8906,0.9973822
+4666,2939.8118,308.69427,2964.846,356.01923,0.9961986
+4666,2990.2297,377.96353,3014.6785,428.0156,0.9948655
+4666,3738.3516,755.51154,3776.5645,829.8046,0.94418436
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+4667,2371.5596,641.4038,2414.4165,719.9147,0.99930954
+4667,2263.672,281.95493,2285.968,332.47012,0.9992999
+4667,2696.744,378.344,2716.64,431.8661,0.9992879
+4667,2509.032,221.95006,2530.282,268.2985,0.9992065
+4667,2004.5217,158.96979,2022.0076,204.33624,0.99916613
+4667,2966.3257,533.38086,2993.2676,598.43994,0.999074
+4667,2854.778,308.2601,2877.323,355.85214,0.9990646
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+4667,2026.3285,187.97728,2043.056,234.64677,0.9990313
+4667,3187.3374,405.17178,3218.024,457.19785,0.99902785
+4667,2388.148,436.49234,2408.5977,497.4315,0.99898535
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+4667,2509.365,183.95001,2536.6414,224.52603,0.99881494
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+4667,2179.5583,409.3406,2207.5237,474.59225,0.99853134
+4667,3142.2715,555.4707,3168.872,615.7064,0.9983487
+4667,2005.6444,122.91614,2023.2833,160.32321,0.998099
+4667,2977.433,394.53638,3000.1265,449.90527,0.9977931
+4667,1965.6042,884.3814,2005.676,977.88275,0.9974318
+4667,3287.3857,369.27115,3311.8086,419.50858,0.9971245
+4667,2938.7136,308.00082,2965.1824,354.6901,0.9954288
+4667,2990.4214,377.20215,3014.2988,428.24237,0.9887884
+4667,3744.6968,755.2657,3777.379,818.27496,0.92244846
+4667,3733.3584,787.54016,3769.8237,857.8406,0.80823755
+4667,3533.4543,816.3147,3581.9246,888.78467,0.61364985
+4667,3857.2004,763.3878,3893.9836,824.46106,0.58086956
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+4668,2264.4275,281.4163,2286.6248,332.11307,0.9994368
+4668,2004.8838,158.5999,2021.7554,203.95854,0.9993374
+4668,2696.9392,378.31183,2716.8982,431.87305,0.9992286
+4668,2371.1562,640.3734,2407.209,719.58813,0.99915767
+4668,1315.2999,319.60748,1342.889,374.4272,0.99914575
+4668,2509.6907,183.69626,2533.6418,224.50864,0.9991072
+4668,2854.8093,308.31607,2877.1755,355.8209,0.9990791
+4668,2388.2603,435.7869,2408.4946,497.4291,0.9990656
+4668,2180.2659,408.76978,2207.8845,474.6151,0.9990188
+4668,2148.128,279.99005,2166.0347,333.92676,0.9990107
+4668,2966.2437,534.17426,2993.5513,599.34393,0.9989912
+4668,3111.1382,292.05112,3134.9668,335.1722,0.99896336
+4668,2026.3011,187.83015,2041.7584,234.97362,0.99878937
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+4668,2508.9543,221.43803,2528.1335,267.92032,0.9985569
+4668,2646.7642,298.8,2669.4521,348.57123,0.99849725
+4668,3142.346,555.3432,3169.8894,616.28033,0.99832326
+4668,2005.8792,122.86493,2023.6282,160.34665,0.99825996
+4668,1966.1967,884.30774,2005.5563,978.1836,0.9974023
+4668,2977.171,392.71545,3000.6436,450.00208,0.99732333
+4668,3288.781,370.38403,3313.5554,420.06555,0.9959934
+4668,2937.5166,307.05893,2965.1597,354.15146,0.9922077
+4668,2992.817,376.01764,3014.204,428.62976,0.9862099
+4668,3747.8748,755.286,3777.596,814.937,0.9685429
+4668,3535.2046,816.6282,3583.5571,887.10913,0.9460888
+4668,3735.3813,787.68115,3771.9053,857.7389,0.87867177
+4668,3852.9236,763.28503,3892.2107,824.8157,0.3078049
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+4669,1315.6472,319.7102,1343.0056,374.44385,0.9993765
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+4669,2264.2727,281.0692,2286.924,332.88098,0.9992889
+4669,2005.0012,158.74692,2021.7676,203.52808,0.9992817
+4669,2697.0144,378.2921,2717.2678,431.9043,0.9992531
+4669,2388.3694,435.67142,2408.493,497.3989,0.99914026
+4669,2370.7712,639.61566,2402.2312,719.7668,0.9990478
+4669,2148.197,279.93704,2167.2937,333.54422,0.99902153
+4669,2854.7664,308.2164,2877.0447,355.84122,0.99900347
+4669,2966.185,534.364,2993.8345,600.1804,0.9988518
+4669,3142.3643,556.0098,3170.7524,616.7232,0.9987205
+4669,2182.147,408.86334,2208.2285,474.07196,0.998684
+4669,2026.2344,187.80644,2041.6182,234.95638,0.9986469
+4669,2647.274,299.2257,2670.2104,348.62616,0.9985702
+4669,3111.73,292.07507,3136.1235,335.3282,0.99836713
+4669,3195.5574,404.935,3220.7449,457.17377,0.9983606
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+4669,2977.1167,393.6539,3000.8428,449.82217,0.9975967
+4669,1966.1967,884.30774,2005.5563,978.1836,0.9974023
+4669,2006.0566,123.05589,2023.0984,160.60683,0.99720407
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+4669,3751.1526,755.3877,3778.1594,811.0834,0.9120429
+4669,3737.583,786.16205,3773.5903,856.29865,0.85323966
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+4670,2004.8473,158.38808,2021.6432,203.3051,0.9993315
+4670,2697.038,378.55353,2717.75,431.88495,0.9993068
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+4670,3142.5684,556.7959,3172.546,617.3169,0.99883276
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+4679,2003.4248,157.79112,2020.2988,201.59077,0.9992513
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+4680,2008.8005,122.91139,2026.9531,160.53337,0.99685526
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+4681,1966.1392,884.23083,2005.5996,978.2849,0.9965688
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+4687,2194.6199,406.39102,2222.1506,470.17728,0.99832004
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+4687,3760.3762,752.3329,3794.448,810.85974,0.9978848
+4687,3014.7458,379.17206,3038.027,431.11426,0.9974789
+4687,1966.1483,884.24146,2005.5294,978.2599,0.996761
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+4687,3298.9043,374.0578,3319.3496,424.66144,0.99667263
+4687,2009.3264,122.934296,2027.564,159.86447,0.99628276
+4687,2981.3735,397.42072,3009.2422,448.64447,0.9915828
+4687,3587.9297,809.38715,3634.5283,880.60846,0.9483813
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+4688,2002.9906,156.24701,2020.0663,201.21329,0.9991454
+4688,2323.4668,638.33014,2365.7139,716.63947,0.9990896
+4688,2976.921,538.209,3002.5015,600.8507,0.9990729
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+4688,2471.891,179.87773,2491.8638,222.86595,0.99899846
+4688,2279.9417,278.66516,2300.322,329.31165,0.99899703
+4688,2698.6514,379.0883,2719.7944,432.09146,0.9989936
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+4688,2479.4133,219.7793,2504.9646,266.20804,0.998035
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+4688,3762.2527,752.5142,3794.8381,811.3945,0.99784863
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+4688,1966.1066,884.211,2005.5319,978.28595,0.99651814
+4688,2009.1785,122.99685,2027.637,159.89268,0.99622005
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+4688,3781.64,785.4084,3817.7185,851.04584,0.4382705
+4688,3854.8923,767.6182,3891.0598,826.0114,0.40493062
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+4689,2976.6516,538.2194,3002.5227,600.83844,0.9991215
+4689,2164.8225,279.66568,2190.507,332.37436,0.999119
+4689,2003.1233,156.11703,2020.0159,201.17575,0.9991054
+4689,3165.4097,562.5955,3197.83,622.01044,0.9990466
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+4689,3134.5762,294.5661,3152.1782,337.12897,0.9977825
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+4689,3298.7156,374.16907,3319.847,425.82373,0.99771154
+4689,3014.5906,379.35846,3041.3699,430.57758,0.9975655
+4689,2023.4167,184.46912,2037.5627,234.89044,0.9971825
+4689,1966.1127,884.1852,2005.5343,978.27966,0.9967055
+4689,2009.2559,123.07492,2027.6643,159.87927,0.99624145
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+4690,2977.8137,538.27075,3002.9236,600.84033,0.99914336
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+4690,2003.2119,156.04567,2020.0203,201.12877,0.99907815
+4690,2165.2964,279.58417,2191.064,332.21613,0.9989876
+4690,2478.8447,220.1025,2503.474,265.12024,0.99897647
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+4690,1966.1237,884.2055,2005.5316,978.2863,0.99651235
+4690,2009.1001,123.08906,2027.6577,159.76898,0.99605477
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+4690,3786.9377,785.414,3820.7068,850.6285,0.5924117
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+4691,3229.703,414.50073,3261.8728,465.17383,0.9991616
+4691,2003.3032,155.90405,2020.1826,200.49805,0.99911064
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+4691,1966.1095,884.2242,2005.5402,978.2861,0.9965088
+4691,2009.1384,123.11224,2027.658,159.65881,0.99607456
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+4691,3789.0645,785.9891,3822.667,849.7131,0.8777607
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+4692,2003.7026,155.81775,2020.146,200.20535,0.999067
+4692,2459.8503,180.2472,2484.568,223.23233,0.99889344
+4692,2977.8604,538.3296,3004.2856,601.07043,0.99888414
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+4692,3015.945,379.90213,3042.223,430.45145,0.9986304
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+4692,3137.3179,294.12875,3154.626,337.03586,0.9984848
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+4692,2197.9526,405.27518,2227.3145,469.64157,0.99830914
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+4692,2023.5012,184.45737,2037.031,234.91223,0.9974728
+4692,2009.1323,123.05329,2027.5415,159.81677,0.99652773
+4692,1966.1129,884.2062,2005.5465,978.2848,0.9964824
+4692,3764.1892,752.02454,3796.215,811.4243,0.9946951
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+4692,3790.8547,787.4851,3824.1658,849.6858,0.9369209
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+4692,3725.9314,782.09546,3762.7307,844.1107,0.80404645
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+4693,3016.4146,379.38586,3043.2837,430.36298,0.99937904
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+4693,2003.7755,155.72754,2020.2975,199.36777,0.99897325
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+4693,2285.8247,278.33087,2304.2754,329.21265,0.9988048
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+4693,2977.8381,538.29205,3004.6858,600.9622,0.99862134
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+4693,2023.5302,184.36414,2036.9752,234.87405,0.9973206
+4693,2009.1593,123.00513,2027.4896,159.7911,0.9965364
+4693,1966.1177,884.1964,2005.5447,978.30444,0.99642533
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+4694,2004.2273,155.06519,2020.4668,199.33173,0.99896526
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+4694,2286.1372,278.33688,2306.2788,329.26425,0.99812824
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+4694,2023.5511,184.37804,2037.0157,234.86378,0.997373
+4694,2170.9475,278.52032,2193.43,331.95294,0.99670863
+4694,2009.3868,123.01433,2027.3531,159.79823,0.9965849
+4694,1966.1173,884.19366,2005.5526,978.2955,0.996333
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+4694,3796.5117,787.3689,3825.833,846.6345,0.94678235
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+4695,2004.557,154.65202,2020.5507,199.12209,0.9989969
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+4695,1966.1143,884.1775,2005.53,978.2981,0.99650997
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+4696,2004.7324,154.41197,2020.5537,198.95161,0.9989594
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+4696,1966.1271,884.09625,2005.5135,978.2695,0.9962857
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+4697,1966.123,884.0969,2005.5173,978.26587,0.9961373
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+4698,1966.1272,884.10754,2005.51,978.2667,0.996225
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+4699,2004.5669,154.18016,2020.7024,198.07231,0.9988355
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+4699,1966.1293,884.09845,2005.5062,978.262,0.9963223
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+4700,2201.0596,404.2633,2231.5557,469.39966,0.99882764
+4700,2004.5719,154.18077,2020.7389,198.15883,0.99882644
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+4700,2009.0457,122.73481,2026.5676,160.16858,0.9968208
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+4700,1966.1287,884.1084,2005.5046,978.25146,0.9959715
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+4701,2201.4265,404.33875,2231.1077,469.57184,0.9988907
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+4701,2977.8997,538.85284,3009.6643,601.14044,0.9987268
+4701,2004.1804,154.1311,2021.529,197.81287,0.99859136
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+4701,2023.3578,184.05269,2036.6571,234.60182,0.9979132
+4701,2009.2191,122.75453,2026.4169,159.97575,0.9969965
+4701,1966.2524,884.08844,2005.4932,977.86774,0.995877
+4701,3299.2876,377.62244,3325.692,425.3626,0.99556434
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+4702,2003.9515,154.28647,2021.2201,197.76743,0.9986694
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+4703,2003.6188,154.41283,2020.9471,197.76955,0.9988361
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+4703,1966.2524,884.08844,2005.4932,977.86774,0.995877
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+4704,3185.2722,568.99176,3219.9202,628.00214,0.9993736
+4704,2696.3208,380.4453,2717.1099,433.59918,0.9992113
+4704,2293.2737,642.41833,2332.116,717.676,0.9992085
+4704,3029.4739,380.77515,3056.869,432.0511,0.9991516
+4704,2853.743,308.1638,2872.7742,355.4407,0.99914575
+4704,2391.1313,428.97342,2418.338,487.18332,0.99911326
+4704,2462.974,217.69165,2486.6948,263.52902,0.9990606
+4704,1338.3578,319.01575,1356.2028,374.14172,0.99900216
+4704,2290.5034,276.62848,2311.2764,327.82196,0.99897516
+4704,2003.4766,154.38821,2020.9707,197.74094,0.99889827
+4704,2202.523,403.63623,2230.1733,469.56555,0.9988807
+4704,2442.4675,176.47017,2465.8767,219.17314,0.9985979
+4704,3138.2354,294.5714,3166.1885,340.01196,0.99848527
+4704,2650.878,300.43777,2674.151,348.42404,0.99844307
+4704,2979.0806,538.6489,3010.7842,600.65137,0.99840003
+4704,3254.3499,417.71713,3283.3464,467.87064,0.9983529
+4704,2023.4568,183.98218,2036.5588,234.50336,0.9981147
+4704,2947.6619,298.7322,2965.6462,344.56418,0.99804413
+4704,2180.1255,279.32916,2209.6807,332.7798,0.9976641
+4704,2009.038,123.36035,2025.4567,159.54065,0.99713194
+4704,1966.2517,884.0758,2005.4917,977.9033,0.9955245
+4704,2994.7808,398.90527,3013.9712,451.52942,0.99472696
+4704,3302.109,377.66916,3326.0005,425.27762,0.9881209
+4704,3774.5398,749.05,3803.404,806.1757,0.98713374
+4704,3003.5757,399.78476,3027.6401,451.6727,0.9829327
+4704,3756.3152,778.5841,3783.182,842.5935,0.8773338
+4704,3811.2278,785.4085,3836.1345,843.665,0.43027166
+4704,3885.6553,763.108,3920.8657,821.19257,0.11219125
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+4705,2291.8962,276.4242,2312.007,327.9524,0.99918777
+4705,3188.7883,569.3153,3220.7258,628.65356,0.9991499
+4705,2853.668,308.12,2872.747,355.43872,0.9991394
+4705,2391.0706,428.97565,2419.261,486.92584,0.99912864
+4705,3029.5088,380.63675,3057.5107,432.1852,0.999108
+4705,2003.2366,154.25015,2021.1665,197.68793,0.9989629
+4705,1338.8568,318.87955,1357.2255,374.31522,0.9988907
+4705,2202.5386,403.59225,2229.8086,469.57663,0.9988877
+4705,2462.8298,217.17645,2484.4504,263.51492,0.9988765
+4705,2442.411,176.29071,2465.0798,219.16342,0.9984694
+4705,3138.7317,294.3972,3166.5476,340.08127,0.99842995
+4705,2651.2036,300.2709,2673.7437,348.4081,0.9982589
+4705,3258.8264,417.46466,3284.8777,467.6303,0.99821067
+4705,2947.9685,298.6476,2965.6628,344.29202,0.9981464
+4705,2023.5062,183.97037,2036.5394,234.32675,0.9980506
+4705,2979.9053,538.34595,3011.148,600.5969,0.9978364
+4705,2180.6045,279.56125,2209.9478,333.24155,0.9977575
+4705,2008.8229,122.9836,2025.1759,159.54588,0.9973195
+4705,1966.248,884.0616,2005.4976,977.9191,0.9957123
+4705,2994.842,398.6918,3014.554,451.90768,0.9950059
+4705,3298.9382,377.59494,3326.3083,425.5388,0.9937664
+4705,3007.8303,400.2761,3028.4573,451.58096,0.9816513
+4705,3774.933,749.1986,3803.9526,806.6124,0.98144746
+4705,3756.465,777.4601,3785.4177,842.0149,0.85991627
+4705,3811.2205,786.3383,3835.703,844.0933,0.40224758
+4705,3890.6208,761.6239,3922.2542,818.47546,0.16978176
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+4706,2291.1267,643.10693,2331.4558,717.89246,0.9994566
+4706,2293.9219,276.2969,2312.6147,328.04013,0.9993229
+4706,2853.564,308.10947,2872.6685,355.42215,0.9991491
+4706,2442.3623,176.17073,2463.3096,218.38902,0.9991351
+4706,2696.2703,381.17236,2716.49,433.7256,0.9990853
+4706,2391.2651,428.92117,2419.546,486.7784,0.9990386
+4706,2003.078,154.12285,2021.1168,197.76228,0.99902964
+4706,2202.4558,403.39288,2229.2825,469.53833,0.9990079
+4706,3190.9377,569.76385,3221.9915,628.94867,0.9990006
+4706,3030.323,381.01462,3058.7205,432.9059,0.9988568
+4706,3140.61,294.3124,3166.7449,339.88644,0.9987447
+4706,1339.3276,318.98874,1359.7146,374.2321,0.9986375
+4706,2462.8428,217.2266,2481.4297,263.71976,0.9984749
+4706,2651.3591,299.1767,2672.9739,348.3468,0.99838114
+4706,2947.9907,298.59088,2965.8687,344.32825,0.9983429
+4706,2023.3403,183.96838,2036.6445,234.137,0.9980229
+4706,2181.0466,279.68375,2210.139,333.21164,0.99779356
+4706,3263.2239,418.17276,3286.0374,468.40207,0.9976757
+4706,2008.957,123.07912,2025.1985,159.68437,0.99708706
+4706,2981.495,538.445,3011.9814,599.8913,0.99704915
+4706,2995.0125,398.8796,3014.8943,452.8134,0.99592483
+4706,3298.2903,377.58484,3326.381,425.5326,0.9958539
+4706,1966.2473,884.07263,2005.4976,977.9315,0.9957748
+4706,3008.169,401.37146,3029.4019,451.75073,0.9827697
+4706,3774.6572,749.15314,3804.624,806.8294,0.9479543
+4706,3756.5613,776.4221,3785.303,841.4226,0.9423185
+4706,2999.0632,400.5213,3023.6814,452.3822,0.41110632
+4706,3811.9707,785.481,3835.7437,843.8354,0.28668836
+4706,3636.4065,803.9559,3680.6252,875.13153,0.1912631
+4706,3891.8467,761.1006,3924.6294,817.83167,0.10683735
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+4707,2290.6362,642.3503,2330.6128,717.97076,0.9992945
+4707,2202.3857,403.20285,2228.4048,469.48624,0.99919707
+4707,2295.5186,276.38422,2313.2769,328.60852,0.99918336
+4707,2002.853,154.05919,2020.8745,197.66023,0.9991779
+4707,3031.2598,380.9272,3059.3276,433.10437,0.999161
+4707,2853.4932,308.05643,2872.6738,355.40268,0.9991609
+4707,2391.4202,428.2088,2420.0002,486.60132,0.99906904
+4707,1339.8552,319.06595,1361.1604,373.98483,0.99891233
+4707,3192.048,570.09534,3222.1614,629.0427,0.998911
+4707,2696.1743,381.5318,2716.268,433.77972,0.9988952
+4707,2441.819,175.6387,2463.7273,217.2958,0.9988746
+4707,3140.882,294.2238,3167.261,340.27762,0.9987947
+4707,2462.6284,217.37914,2479.9238,263.81412,0.998408
+4707,2651.435,299.0498,2672.5068,348.33618,0.99835676
+4707,3265.9082,418.01663,3288.583,468.78464,0.99819046
+4707,2947.777,298.36203,2965.698,344.10202,0.9980716
+4707,2023.443,183.9942,2036.683,233.9992,0.9979778
+4707,2182.7515,279.5973,2210.3838,333.1432,0.9967974
+4707,3297.8438,377.68216,3326.4258,425.37003,0.9963386
+4707,1966.2384,884.08405,2005.4879,977.8973,0.996068
+4707,2983.329,538.3169,3013.2153,599.7527,0.9957105
+4707,2995.0652,399.18567,3015.617,452.2558,0.9951231
+4707,2007.3937,122.68211,2025.1864,160.0703,0.99236506
+4707,3776.9834,748.9314,3805.0723,806.28564,0.9863712
+4707,3005.4463,402.41885,3030.0435,452.02615,0.9823596
+4707,3758.6091,774.7938,3786.9875,838.2859,0.9516215
+4707,3811.5918,783.45966,3836.08,844.01483,0.60289645
+4707,3639.509,804.4483,3682.6682,873.9522,0.31092748
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+4708,2296.2688,276.58563,2314.013,328.5127,0.9993911
+4708,1340.1053,319.03528,1364.1383,373.9792,0.999298
+4708,2202.4182,402.88568,2228.0027,469.33405,0.99925214
+4708,2290.3333,642.0858,2329.256,718.0469,0.9992519
+4708,2391.5315,428.02383,2419.5159,486.5383,0.999209
+4708,2002.9274,153.74005,2020.5673,196.97382,0.9991966
+4708,2853.4648,308.0554,2872.6694,355.41183,0.99916804
+4708,3032.4824,380.94,3060.1606,433.04614,0.9991066
+4708,2461.9185,217.46431,2479.628,263.74292,0.9989517
+4708,2441.0034,175.1493,2461.249,217.11217,0.99890065
+4708,2695.9238,381.68173,2715.8555,433.84158,0.9988452
+4708,3193.1501,570.8323,3222.4001,628.37024,0.99878937
+4708,3141.1611,294.40097,3167.6235,339.78836,0.9986401
+4708,3265.7825,418.42902,3290.0593,469.18384,0.99860126
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+4708,2023.4637,183.97797,2036.6925,233.81644,0.99817383
+4708,2947.635,297.98273,2965.3772,343.8484,0.9980908
+4708,2185.0945,279.7841,2210.866,333.17722,0.9979906
+4708,3296.5757,377.7016,3326.5166,425.30243,0.9976047
+4708,1966.2484,884.09186,2005.5011,977.9107,0.9960556
+4708,2995.1096,399.48453,3016.073,452.07004,0.99599373
+4708,2985.716,538.6521,3014.0413,599.886,0.9958431
+4708,2007.6,122.99132,2025.0734,160.19737,0.9917217
+4708,3778.287,749.16675,3805.9512,806.4249,0.98656404
+4708,3005.4912,402.89056,3030.7998,452.15778,0.9839587
+4708,3759.835,773.0824,3790.7402,836.98987,0.9264601
+4708,3811.5967,782.28033,3835.9888,844.15546,0.5698486
+4708,3893.8127,760.6613,3929.2444,817.64594,0.23851767
+4708,3642.7896,804.63074,3683.6123,877.1583,0.22533892
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+4709,2296.7322,276.5895,2314.5867,328.07675,0.99939775
+4709,2290.4885,642.1102,2326.0784,717.8424,0.9993162
+4709,2391.4788,427.97684,2419.693,486.4008,0.9993125
+4709,2439.1997,174.60919,2458.1553,216.64236,0.99920875
+4709,3194.7468,571.6188,3222.577,632.03186,0.99917775
+4709,2002.9922,153.34541,2020.6147,196.69856,0.99917525
+4709,2853.439,308.05267,2872.662,355.42145,0.99917346
+4709,2202.4731,402.3614,2227.085,468.89868,0.99907935
+4709,1340.6749,319.04807,1365.6583,373.99667,0.999015
+4709,2695.6924,381.7727,2715.3457,433.8166,0.99888116
+4709,3265.453,419.10583,3292.7664,470.1363,0.9988525
+4709,2651.6748,298.27487,2671.914,348.26608,0.99859315
+4709,2988.3054,538.4467,3015.0745,600.72406,0.9985788
+4709,3033.7197,381.45773,3061.017,433.01926,0.9984888
+4709,3141.9631,294.60425,3168.0393,339.89667,0.99813324
+4709,2023.3635,183.94516,2036.7561,233.68405,0.9981072
+4709,2458.6719,217.44122,2478.4448,263.6625,0.99807346
+4709,2947.3171,297.46826,2964.8904,343.5829,0.9979169
+4709,3296.5679,377.7742,3326.565,425.1913,0.9976884
+4709,2995.2224,399.59247,3016.1028,452.62555,0.9975459
+4709,2186.928,279.6821,2211.181,333.2111,0.9971661
+4709,1966.2584,884.0894,2005.5165,977.91327,0.99607664
+4709,3008.0024,402.6838,3031.9297,452.04904,0.9944001
+4709,2007.6771,123.09456,2024.2865,160.036,0.9887655
+4709,3778.6504,749.1042,3806.8228,805.3691,0.9857114
+4709,3760.2136,772.3925,3793.9456,834.4362,0.79777205
+4709,3811.449,781.63617,3836.2214,843.83136,0.6201049
+4709,3893.0234,761.83264,3930.5835,817.63306,0.24024561
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+4710,2309.4756,804.06464,2353.981,887.77924,0.99937195
+4710,2297.2468,276.6308,2315.3909,327.96973,0.99936885
+4710,2290.2412,642.0952,2322.1943,717.8981,0.9992964
+4710,2391.825,427.86377,2419.0593,486.3579,0.9992593
+4710,2853.3784,308.07123,2872.6797,355.427,0.99917823
+4710,2988.341,538.4342,3016.2068,601.5139,0.99913365
+4710,2002.9646,152.83072,2020.5762,196.23566,0.9991092
+4710,2202.4492,401.9511,2226.1104,468.49475,0.99900144
+4710,3265.107,419.98526,3295.1558,470.24106,0.9989875
+4710,2695.7004,382.002,2715.0964,433.8589,0.9988747
+4710,2436.8892,174.38995,2456.1865,216.47586,0.9988298
+4710,1341.3197,318.9652,1366.0865,374.0042,0.9988035
+4710,3037.287,381.8777,3061.751,433.08466,0.9987482
+4710,2651.7693,297.57407,2671.7268,348.26163,0.99866915
+4710,2455.141,217.29659,2477.5571,263.9572,0.9983215
+4710,2946.788,296.86673,2964.9282,343.0156,0.9980842
+4710,2023.1527,184.0493,2036.6864,233.47188,0.9980591
+4710,2995.2266,399.50754,3016.2646,452.491,0.9979907
+4710,3296.4934,377.87393,3326.6443,425.012,0.99752635
+4710,3143.429,294.6345,3168.3386,339.99966,0.99724376
+4710,2190.1577,279.55322,2211.954,333.1176,0.9970796
+4710,1966.2543,884.0851,2005.5023,977.93823,0.99627006
+4710,3009.0876,403.07773,3032.7546,452.33908,0.9962405
+4710,2007.2102,122.94424,2024.459,160.56343,0.9920374
+4710,3779.6501,749.3828,3807.9207,804.3977,0.9847547
+4710,3811.4302,781.19403,3836.7666,843.4411,0.7338741
+4710,3762.6897,772.13074,3795.6228,831.92773,0.3926411
+4710,3893.566,761.75085,3931.9766,817.60535,0.20003372
+4711,2307.9065,804.3244,2352.8503,887.83026,0.99944633
+4711,2297.6858,276.5767,2316.694,327.68442,0.9994342
+4711,3197.5361,572.9206,3223.6123,634.63763,0.9993806
+4711,2392.5037,427.55542,2418.4714,486.37738,0.9993773
+4711,2853.397,308.08917,2872.6836,355.4068,0.9991861
+4711,2202.2969,401.6825,2225.419,468.3031,0.999154
+4711,2290.0872,642.17755,2318.47,718.5004,0.9991385
+4711,2988.282,538.5693,3017.639,601.96515,0.9990754
+4711,2002.9243,152.52618,2020.1731,196.43732,0.99903524
+4711,2453.1343,217.20749,2476.6528,263.60593,0.99886215
+4711,3040.0334,381.39325,3063.2346,433.16962,0.9987482
+4711,2695.4402,382.2179,2714.8953,434.00647,0.9987097
+4711,3264.9722,420.6353,3298.3823,470.937,0.9986754
+4711,1341.8004,318.98117,1366.5275,373.9595,0.9986638
+4711,2433.582,174.57352,2454.4653,216.71118,0.9986218
+4711,2652.0056,297.45242,2671.5251,348.249,0.9985241
+4711,2946.207,296.65985,2964.4653,343.055,0.9983632
+4711,2995.142,399.4572,3016.3179,452.82104,0.99832946
+4711,2023.0624,184.08018,2036.7198,233.56728,0.997997
+4711,3011.6145,402.85822,3034.8083,452.6789,0.9975809
+4711,3146.8157,294.71347,3168.5242,340.40347,0.9974916
+4711,2193.5652,279.6905,2212.89,333.09753,0.9974718
+4711,3296.7554,378.0022,3326.6914,424.97943,0.9969963
+4711,1966.2524,884.0848,2005.4893,977.9366,0.9963594
+4711,2007.0935,122.918564,2024.6047,160.30302,0.99184954
+4711,3780.6692,749.8919,3808.263,804.29926,0.97092986
+4711,3811.6506,781.0509,3836.761,843.76306,0.7713632
+4711,3763.264,772.0428,3796.2288,830.1663,0.30035
+4711,3892.9543,761.8839,3934.2434,818.08325,0.19573611
+4712,2297.9502,276.40204,2318.663,327.75873,0.9995689
+4712,2307.7383,803.7119,2351.2656,887.879,0.9995604
+4712,3198.8909,573.30145,3224.406,634.2342,0.999421
+4712,2289.6396,641.93665,2317.479,718.8142,0.9992028
+4712,2853.3838,308.10303,2872.6724,355.3628,0.9991818
+4712,2392.3132,427.04144,2418.381,486.39404,0.999172
+4712,2988.9688,539.2308,3018.7607,602.5031,0.9991629
+4712,2202.3223,401.0962,2225.0386,467.2663,0.9990682
+4712,1341.546,318.028,1366.5529,373.49915,0.99901867
+4712,2002.9255,152.33922,2020.2654,196.41747,0.9990038
+4712,2450.4194,217.46358,2476.683,263.5628,0.9989644
+4712,3265.448,420.19992,3300.5618,471.52438,0.9989243
+4712,2695.2722,382.2962,2714.7888,434.04706,0.9987386
+4712,2995.1274,399.44745,3016.6812,452.47675,0.99870193
+4712,3148.6604,294.8167,3168.8176,340.44263,0.9986578
+4712,2652.1191,297.3843,2671.4512,348.13614,0.99851006
+4712,2430.3523,174.77911,2453.322,216.90076,0.9984394
+4712,2946.224,296.49765,2963.9663,342.1428,0.9983688
+4712,2023.0022,184.16597,2036.7312,233.641,0.99823594
+4712,2195.5444,279.75818,2214.2178,333.19122,0.9981536
+4712,3042.2063,381.6784,3064.6199,433.37616,0.99752825
+4712,3013.0112,403.14435,3035.167,452.50897,0.9973097
+4712,3300.5693,377.851,3326.6514,425.3543,0.99630874
+4712,1966.2645,884.0693,2005.5006,977.9107,0.9961838
+4712,2006.9067,122.93509,2024.4668,160.1647,0.9924973
+4712,3781.5396,750.00616,3809.103,804.6296,0.95340896
+4712,3811.3833,781.1481,3836.6528,844.04724,0.74678093
+4712,3763.837,771.6415,3796.2334,830.5382,0.42664886
\ No newline at end of file
diff --git a/debug.py b/debug.py
new file mode 100644
index 0000000..7184155
--- /dev/null
+++ b/debug.py
@@ -0,0 +1,403 @@
+import tools
+import cv2
+from scipy.ndimage import rotate
+import numpy as np
+from random import randrange
+from scipy.spatial import distance
+import operator
+
+import matplotlib.pyplot as plt
+import helper
+
+import ast
+
+import mmcv
+
+def get_patch_by_id(tracker, track_num, detections, images, features):
+    # get hypothesis file
+    # extract all lines with field == track_num
+    frame_infos = []
+    with open('./hypothesis.txt') as fp:
+        for line in fp:
+            line_lst = line.split(',')
+
+            if int(line_lst[1]) == track_num:
+                frame_infos.append( ( int(line_lst[0]), float(line_lst[2]), float(line_lst[3]) ) )
+
+    det_indices = []
+    for (frame_num, x, y) in frame_infos:
+        for i,d in enumerate(detections[str(frame_num)]):
+            if d[0] == x and d[1] == y:
+                det_indices.append((frame_num, i))
+                break
+
+    # save link costs 
+    for index in range(1, len(det_indices)):
+        prev_det_data =  det_indices[index-1]
+        prev_img_name = str(prev_det_data[0])
+        prev_det_idx = prev_det_data[1]
+
+        cur_det_data = det_indices[index]
+        cur_img_name = str(cur_det_data[0])
+        cur_det_idx = cur_det_data[1]
+
+        cd = distance.cosine(features[prev_img_name][prev_det_idx], features[cur_img_name][cur_det_idx])
+
+        rot_img = rotate(images[cur_img_name][cur_det_idx], 90)
+        h = rot_img.shape[0]
+        w = rot_img.shape[1]
+
+        cv2.putText(rot_img, str(round(cd, 6)), (1, int(h/2)+3), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
+        out_img = rotate(rot_img, -90)
+        cv2.imwrite('./id_%d_f_%s.jpg' % (track_num, cur_img_name), out_img)
+
+# def visualise_hypothesis_with_detections(path2video, detections_all, slice_start, slice_end):
+def visualise_hypothesis_with_detections(path2video, data, slice_start, slice_end):
+
+    hypothesis = np.loadtxt("./hypothesis.txt", delimiter=',')
+    frame_indices = hypothesis[:, 0].astype(np.int)
+    
+    out_size = (1800, 550)
+    vout = cv2.VideoWriter('./out.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 25, out_size)
+
+    video = mmcv.VideoReader(path2video)
+
+    fnum = 0
+
+    for frame_idx in range(slice_start, slice_end):
+        frame = video[frame_idx]
+
+        # skip frame
+        if (frame_idx-slice_start+1) % 2 == 0 and frame_idx != slice_end-1:
+            continue
+
+        fnum = fnum+1
+
+        if fnum % 500 == 0:
+            print("Frame %05d/%05d" % (fnum, slice_end))    
+        # if (frame_idx+1) % 500 == 0:
+        #     print("Frame %05d/%05d" % (frame_idx+1, slice_end))
+
+        mask_h = frame_indices == (frame_idx - slice_start + 1)
+        rows = hypothesis[mask_h]
+
+        cv2.putText(frame, str(frame_idx+1), (150, 200), cv2.FONT_HERSHEY_PLAIN, 4, (0,0,255), 4)
+
+        for r in rows:	
+            tid, x1, y1, w, h = int(r[1]), int(r[2]), int(r[3]), int(r[4]), int(r[5])
+            cv2.putText(frame, str(tid), (x1, y1), cv2.FONT_HERSHEY_PLAIN, 2, (0,0,255), 2)
+
+        node_lst = data[str(fnum)]
+        # node_lst = data[str(frame_idx+1)]
+
+        for node in node_lst:
+            d = node._bb
+            x1, y1, x2, y2 = int(d[0]), int(d[1]), int(d[2]), int(d[3])
+            cv2.circle(frame, (x1+int((x2-x1)/2), y1+int((y2-y1)/2)), 2, (0,0,255), 5)
+
+        cv2.putText(frame, '# dets ' + str(len(node_lst)), (150, 400), cv2.FONT_HERSHEY_PLAIN, 4, (0,255,0), 4)
+        vout.write(cv2.resize(frame, out_size))
+
+    vout.release()
+
+def visualise_tracks(filename, path2video, slice_start, slice_end, _wc, transform, size, video_name, draw_mask=True):
+    hypothesis = np.loadtxt(filename, delimiter=',')
+    frame_indices = hypothesis[:, 0].astype(np.int)
+    
+    if draw_mask:
+        fmask=open('./masks.txt','r')
+        fmaskLst = [line.rstrip() for line in fmask]
+        print (len(fmaskLst))
+        print (type(fmaskLst[0]))
+
+    color_mask = np.array(mmcv.color_val('green')[::-1], dtype=np.uint8)
+
+    out_size = (1800, 450)
+    vout = cv2.VideoWriter(video_name, cv2.VideoWriter_fourcc('M','J','P','G'), 25, out_size)
+
+    video = mmcv.VideoReader(path2video)
+
+    for frame_idx in range(slice_start, slice_end):
+        frame = video[frame_idx]
+        
+        if (frame_idx+1) % 500 == 0:
+            print("Frame %05d/%05d" % (frame_idx+1, slice_end))
+
+        mask_h = frame_indices == (frame_idx - slice_start + 1)
+        rows = hypothesis[mask_h]
+
+        cv2.putText(frame, str(frame_idx-slice_start+1), (150, 200), cv2.FONT_HERSHEY_PLAIN, 4, (0,0,255), 4)
+
+        for i,r in enumerate(rows):	
+            tid, x1, y1, w, h, s = int(r[1]), int(r[2]), int(r[3]), int(r[4]), int(r[5]), int(r[9])
+            cv2.putText(frame, str(tid), (x1, y1), cv2.FONT_HERSHEY_PLAIN, 2, (0,0,255), 2)
+
+            if draw_mask:
+                mask = ast.literal_eval(fmaskLst[i])
+                mask_np = np.array(mask).astype(bool)
+
+                mh, mw = mask_np.shape[0], mask_np.shape[1]
+                crop = frame[int(y1):int(y1)+mh, int(x1):int(x1)+mw, :]
+                crop[mask_np] = crop[mask_np] * 0.5 + color_mask * 0.5
+
+        if frame_idx in _wc:
+            for c in _wc[frame_idx]:
+                vx, vy = transform.ground_to_video(c[0], c[1])
+                cv2.circle(frame, (int(vx * size[1]), int(vy * size[0])), 2, (255,0,0), 5)
+
+        vout.write(cv2.resize(frame, out_size))
+
+    vout.release()
+
+def visualise_hypothesis(path2video, path2det, frame_offset, frame_count):
+    hypothesis = np.loadtxt("./hypothesis.txt", delimiter=',')
+    detections = np.loadtxt(path2det, delimiter=',')
+
+    frame_indices = hypothesis[:, 0].astype(np.int)
+    frame_indices_dets = detections[:, 0].astype(np.int)
+
+    min_frame_idx = frame_indices.astype(np.int).min()
+    max_frame_idx = frame_indices.astype(np.int).max()
+    
+    out_size = (1800, 550)
+    vout = cv2.VideoWriter('./out.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 25, out_size)
+
+    video = mmcv.VideoReader(path2video)
+
+    slice_start = 0 if frame_offset == 0 else frame_offset-1
+    slice_end = min(frame_offset+frame_count, frame_offset+max_frame_idx)
+
+    for frame_idx in range(slice_start, slice_end):
+        frame = video[frame_idx]
+        
+        if (frame_idx+1) % 500 == 0:
+            print("Frame %05d/%05d" % (frame_idx+1, slice_end))
+
+        mask_h = frame_indices == (frame_idx - slice_start + 1)
+        mask_d = frame_indices_dets == (frame_idx - slice_start + 1)
+        
+        rows = hypothesis[mask_h]
+        dets = detections[mask_d]
+
+        cv2.putText(frame, str(frame_idx+1), (150, 200), cv2.FONT_HERSHEY_PLAIN, 4, (0,0,255), 4)
+
+        # log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0], l[1], l[2], l[3], l[4], l[5], l[6]))
+
+        for r in rows:	
+            tid, x1, y1, w, h, s = int(r[1]), int(r[2]), int(r[3]), int(r[4]), int(r[5]), int(r[9])
+
+            if s == 1:
+                cv2.rectangle(frame, (x1,y1), (x1+int(w), y1+int(h)), (255,0,0), 2)
+            elif s == 2:
+                cv2.rectangle(frame, (x1,y1), (x1+int(w), y1+int(h)), (0,255,0), 2)
+
+            cv2.putText(frame, str(tid), (x1, y1), cv2.FONT_HERSHEY_PLAIN, 2, (0,0,255), 2)
+        
+        for d in dets:	
+            _, x1, y1, x2, y2,_ = int(d[0]), int(d[1]), int(d[2]), int(d[3]), int(d[4]), int(d[5])
+            cv2.circle(frame, (x1+int((x2-x1)/2), y1+int((y2-y1)/2)), 2, (255,0,0), 5)
+
+        cv2.putText(frame, '# dets ' + str(len(dets)), (150, 400), cv2.FONT_HERSHEY_PLAIN, 4, (0,255,0), 4)
+
+        vout.write(cv2.resize(frame, out_size))
+
+    vout.release()
+
+def validate_appereance_model(image, detections, features):
+    n = len(detections)
+    anchor_idx = randrange(n-1)
+
+    x = {}
+
+    for i, det in enumerate(detections):
+
+        if i == anchor_idx:
+            continue
+
+        cos_diff = distance.cosine(features[anchor_idx], features[i])
+        # x[det] = cos_diff
+        x[i] = cos_diff
+
+    sorted_x = sorted(x.items(), key=operator.itemgetter(1))
+    group_blue = []
+
+    for item in sorted_x:
+        if item[1] <= 0.400:
+            group_blue.append((detections[item[0]], item[1]))
+
+    col = 255, 0, 0
+    _col = 0, 255, 0
+    thick = 3
+
+    for ind, tup in enumerate(group_blue):
+        bbox, score = tup[0], tup[1]
+        pt1 = int(bbox[0]), int(bbox[1])
+        pt2 = int(bbox[2]), int(bbox[3])
+        center = pt1[0] + 5, pt1[1] + 5
+        cv2.rectangle(image, pt1, pt2, col, thick)
+        # cv2.putText(image, str(det.confidence), center, cv2.FONT_HERSHEY_PLAIN, 5, (0, 0, 255), 3)
+        cv2.putText(image, str(round(score, 6)), center, cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 1)
+    
+    _pt1 = int(detections[anchor_idx][0]), int(detections[anchor_idx][1]) 
+    _pt2 = int(detections[anchor_idx][2]), int(detections[anchor_idx][3]) 
+
+    cv2.rectangle(image, _pt1, _pt2, _col, thick)
+    cv2.imwrite('appearance_eval.jpg', image)
+
+
+def return_closest_box_index(cur_box, prev_nodes, frame, cur_box_index):
+    max_iou = 0.
+    max_index = -1
+
+    for j, node in enumerate(prev_nodes):
+        iou = tools.calc_overlap(cur_box, node._bb)
+
+        if iou > max_iou:
+            max_iou = iou
+            max_index = j
+
+    if max_index == -1:
+        print ("[DBG] no nearby box for box index: %d in the previous frame" % (cur_box_index))
+        cv2.rectangle(frame, (int(cur_box[0]), int(cur_box[1])), (int(cur_box[2]), int(cur_box[3])), (0, 0, 255), 3)
+        return -1
+
+    return max_index
+
+def return_closest_box_index_extrinsic(transform, cur_box, ref_frame_dets, frame, cur_box_index):
+    min_dist = 100.
+    min_index = -1
+
+    for j, det in enumerate(ref_frame_dets):
+
+        p1 = helper.box2midpoint_normalised(cur_box, frame.shape[1], frame.shape[0])
+        p2 = helper.box2midpoint_normalised(det, frame.shape[1], frame.shape[0])
+
+        cx, cy = transform.video_to_ground(p1[0], p1[1])
+        rx, ry = transform.video_to_ground(p2[0], p2[1])
+
+        dist = helper.calc_eucl_dist([cx*transform.parameter.get("ground_width"),cy*transform.parameter.get("ground_height")], 
+                            [rx*transform.parameter.get("ground_width"),ry*transform.parameter.get("ground_height")])
+
+        if dist < min_dist:
+            min_dist = dist
+            min_index = j
+
+    if min_index == -1:
+        print ("[DBG] no nearby box for box index: %d in the previous frame" % (cur_box_index))
+        # cv2.rectangle(frame, (int(cur_box[0]), int(cur_box[1])), (int(cur_box[2]), int(cur_box[3])), (0, 0, 255), 3)
+        return -1
+
+    return min_index
+
+def validate_cosine_with_detections(path2video, frame_num_lst, detections, features, images):
+    # frame_num - frame number of anchor frame: cos distance between this frame and previous will be visualised
+
+    for frame_num in frame_num_lst:
+        video = mmcv.VideoReader(path2video)
+        frame = video[frame_num-1]
+
+        image_name = str(frame_num)
+        prev_img_name = str(frame_num-1)
+
+        # dbg_img_lst = []
+
+        for i, det in enumerate(detections[image_name]):
+            ref_index = return_closest_box_index(det, detections[prev_img_name], frame, i)
+
+            if ref_index == -1:
+                continue
+            
+
+            cd = distance.cosine(features[image_name][i], features[prev_img_name][ref_index])
+
+            # rot_img = rotate(images[image_name][i], 90)
+            # h = rot_img.shape[0]
+            # w = rot_img.shape[1]
+            # cv2.putText(rot_img, str(round(cd, 6)), (1, int(h/2)+3), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
+            # out_img = rotate(rot_img, -90)
+
+            # dbg_img_lst.append(out_img)
+
+            pt1 = int(det[0]), int(det[1])
+            pt2 = int(det[2]), int(det[3])
+            center = pt1[0] + 5, pt1[1] + 5
+            cv2.putText(frame, str(round(cd, 6)), center, cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 1)
+
+        cv2.imwrite('./%d_validate_cosine_with_dets.jpg' % (frame_num), frame)
+
+        fig, axarr = plt.subplots(4, 10)
+        # for ax,im in zip(axarr.ravel(), dbg_img_lst):
+        for ax,im in zip(axarr.ravel(), images[image_name]):
+            ax.axis('off')
+            ax.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
+        fig.savefig('%d_current_crops.jpg' % (frame_num))
+        plt.clf()
+
+
+
+def validate_hist_mask_bhattacharyya(frame_data, image):
+    n = len(frame_data)
+    anchor_idx = randrange(n-1)
+
+    x = {}
+
+    for i, node in enumerate(frame_data):
+        if i == anchor_idx:
+            continue
+
+        bhat_dist = tools.calc_bhattacharyya_distance(node._hist, frame_data[anchor_idx]._hist)
+        x[i] = bhat_dist
+
+    sorted_x = sorted(x.items(), key=operator.itemgetter(1))
+    group_blue = []
+
+    for item in sorted_x:
+        group_blue.append((frame_data[item[0]], item[1]))
+
+    col = 255, 0, 0
+    _col = 0, 255, 0
+    thick = 3
+
+    for ind, tup in enumerate(group_blue):
+        node, score = tup[0], tup[1]
+        bbox = node._bb
+        pt1 = int(bbox[0]), int(bbox[1])
+        pt2 = int(bbox[2]), int(bbox[3])
+        center = pt1[0] + 5, pt1[1] + 5
+        cv2.rectangle(image, pt1, pt2, col, thick)
+        cv2.putText(image, str(round(score, 6)), center, cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 1)
+    
+    _pt1 = int(frame_data[anchor_idx]._bb[0]), int(frame_data[anchor_idx]._bb[1]) 
+    _pt2 = int(frame_data[anchor_idx]._bb[2]), int(frame_data[anchor_idx]._bb[3]) 
+    cv2.rectangle(image, _pt1, _pt2, _col, thick)
+
+    cv2.imwrite('./_validate_cosine_with_dets.jpg', image)
+
+    # # frame_num - frame number of anchor frame: cos distance between this frame and previous will be visualised
+    # for frame_num in frame_num_lst:
+    #     video = mmcv.VideoReader(path2video)
+    #     frame = video[frame_num-1]
+
+    #     # image_name = str(frame_num)
+    #     # prev_img_name = str(frame_num-1)
+
+    #     image_name = str(int(frame_num/2))
+    #     prev_img_name = str(int(image_name)-1)
+
+    #     for i, node in enumerate(data[image_name]):
+    #         ref_index = return_closest_box_index(node._bb, data[prev_img_name], frame, i)
+
+    #         if ref_index == -1:
+    #             continue
+        
+    #         bhat_dist = tools.calc_bhattacharyya_distance(node._hist, data[prev_img_name][ref_index]._hist)
+
+    #         pt1 = int(node._bb[0]), int(node._bb[1])
+    #         pt2 = int(node._bb[2]), int(node._bb[3])
+    #         center = pt1[0] + 5, pt1[1] + 5
+    #         cv2.putText(frame, str(round(bhat_dist, 6)), center, cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 1)
+
+    #     # _pt1 = int(detections[anchor_idx][0]), int(detections[anchor_idx][1]) 
+    #     # _pt2 = int(detections[anchor_idx][2]), int(detections[anchor_idx][3]) 
+    #     # cv2.rectangle(image, _pt1, _pt2, _col, thick)
+    #     cv2.imwrite('./%d_validate_cosine_with_dets.jpg' % (frame_num), frame)
diff --git a/helper.py b/helper.py
new file mode 100644
index 0000000..ae62405
--- /dev/null
+++ b/helper.py
@@ -0,0 +1,680 @@
+import tools
+from scipy.optimize import linear_sum_assignment
+import numpy as np
+
+import sys
+
+import torch
+
+from bepy.transform import Transform
+from bepy.models import MatchVideo
+
+import cv2
+from scipy.spatial import distance
+
+import debug
+import copy
+import utils
+
+import matplotlib.pyplot as plt
+from scipy.misc import face
+
+import math
+
+from node import GraphNode
+from scipy import interpolate
+from bbox import Box
+
+from pfe.torchreid.utils import FeatureExtractor
+from pfe.scripts.extract_fetures import network_feed_from_list
+
+sys.path.append("../")
+from maskrcnn_mmdet.demo.inference_mask import init_segmentor, get_masks_from_image_lst
+
+import mmcv
+
+import KalmanFilter
+from Tracker import OnlineTracker
+
+def isGoalArea(transform, xwc, size=None, frame=None):
+
+    gp = [[0, 54.16, 0], [16.5, 54.16, 0], [0, 13.84, 0], [16.5, 13.84, 0],
+            [105, 54.16, 0], [88.5, 54.16, 0], [105, 13.84, 0], [88.5, 13.84, 0]]
+
+    # for p in gp:
+    #     x,y = transform.ground_to_video(p[0]/105., p[1]/68., 0)
+    #     x = x*size[1]
+    #     y = y*size[0]
+    #     cv2.circle(frame, (int(x), int(y)), 5, (0,0,255), 5)
+
+    # cv2.imwrite('_check_goal_area.jpg', frame)
+
+    x, y = xwc[0], xwc[1]
+
+    x = x * transform.parameter.get("ground_width")
+    y = y * transform.parameter.get("ground_height")
+
+    if (x >= 0 and x<= 16.5 and y>=13.84 and y<=54.16) or (x>=88.5 and x<=105 and y>=13.84 and y<=54.16):
+        return True
+
+    return False
+
+def is_box_occluded(tlbr, boxes, t_iou=1.0):
+    # calculate iou with all boxes in current frame
+    for box in boxes:
+        _, x1, y1, x2, y2, s = int(box[0]), float(box[1]), float(box[2]), float(box[3]), float(box[4]), float(box[5])
+        cb = [x1,y1,x2,y2]
+        
+        if cb == tlbr:
+            continue
+
+        if tools.calc_overlap(tlbr, cb) > t_iou:
+            return True
+
+    return False
+
+def is_patch_complex_scene(index, wc, transform, tdist=5.0, tcrowd=5):
+    crowd = 0
+    cb = wc[index]
+    cx, cy = cb[0], cb[1]
+
+    for i, b in enumerate(wc):
+        if i == index:
+            continue
+
+        rx, ry = b[0], b[1]
+        dist = calc_eucl_dist([cx*transform.parameter.get("ground_width"),cy*transform.parameter.get("ground_height")], 
+                            [rx*transform.parameter.get("ground_width"),ry*transform.parameter.get("ground_height")])
+
+        if dist <= tdist:
+            crowd = crowd+1
+
+    if crowd >= tcrowd:
+        return True
+
+    return False
+
+def find_prev_imgbox(transform, box_cur, detections, images, name, frame, bi):
+    prev_index = str(int(name)-1)
+    # max_iou_index = debug.return_closest_box_index(box_cur, detections[prev_index], frame, bi)
+    min_dist_index = debug.return_closest_box_index_extrinsic(transform, box_cur, detections[prev_index], frame, bi)
+
+    if min_dist_index == -1:
+        return False, None
+
+    patch = copy.deepcopy(images[prev_index][min_dist_index])
+    return True, patch
+
+def _test_tracker_online(path2det, path2video, slice_start, slice_end, det_in, frame_indices, match_video_id, 
+                    out_file, min_confidence=0.4, max_iou=0.98):
+
+    video = mmcv.VideoReader(path2video)
+    match_video = MatchVideo.load(match_video_id)
+
+    transform = Transform(
+        match_video.video.camera_recording["parameter"],
+        match_video.video.camera_recording["extrinsic_json"],
+        match_video.video.camera_recording["stitching_json"],
+    )
+
+    parity = False
+
+    if slice_start %2 != 0 and (slice_end-1) % 2 != 0: # odd - odd
+        parity = True
+    elif slice_start %2 == 0 and (slice_end-1) % 2 == 0: # even - even
+        parity = True
+    
+    tracker = OnlineTracker()
+
+    fnum = 0
+    all_lines = []
+    for index in range(slice_start, slice_end):
+        frame = video[index]
+
+        if index == slice_start:
+            size = frame.shape
+            cv2.imwrite("./frame.jpg", frame)
+
+        # skip frame
+        # note: if index of last frame in chunk is even, throw away odd indexes and vice versa
+        # frame of last index is needed for inter-chunk connection
+        if parity:
+            if (index-slice_start) % 2 != 0:
+                continue
+        else:
+            # instead of skipping 0 index skip 1 index
+            if (index-slice_start) != 0 and (index-slice_start) % 2 == 0:
+                continue
+            if (index-slice_start) == 1:
+                continue
+
+        fnum = fnum+1
+
+        if fnum % 10 == 0:
+            print ('-> reading frame %d / %d' % (fnum, slice_end-slice_start))
+
+        mask = frame_indices == (index - slice_start + 1)
+        rows = det_in[mask]
+
+        boxes = []
+
+        for _,r in enumerate(rows):
+            _, x1, y1, x2, y2, s = int(r[0]), float(r[1]), float(r[2]), float(r[3]), float(r[4]), float(r[5])
+            boxes.append(Box([x1,y1,x2,y2], s, transform, size))
+
+        boxes = [b for b in boxes if b.confidence >= min_confidence]
+
+        # Run non-maxima suppression.
+        blst = np.array([b.to_tlwh() for b in boxes])
+        scrlst = np.array([b.confidence for b in boxes])
+        indices = utils.non_max_suppression(blst, max_iou, scrlst)
+        boxes_nms = [boxes[i] for i in indices]
+
+        if fnum == 1:
+            tracker.onlineTrackerInit(boxes_nms)
+            continue
+ 
+        matches, unmatched_track_ids, unmatched_det_ids = tracker.onlineTrackerAssign(boxes_nms, index-slice_start+1)
+
+        # for t in unmatched_track_ids:
+        #     print ('[%d] unmatched track id %d' % (index-slice_start+1, tracker.tracks[t].id))
+
+        # for d in unmatched_det_ids:
+        #     print ('[%d] unmatched det idx %d'%(index-slice_start+1, d))
+
+        lines = []
+        for match in matches:
+            tidx, didx = match[0], match[1]
+            box, track = boxes_nms[didx], tracker.tracks[tidx]
+            l = [index-slice_start+1, track.id, box.tlbr[0], box.tlbr[1], box.tlbr[2]-box.tlbr[0], box.tlbr[3]-box.tlbr[1], 0.99]
+            lines.append(l)
+
+        all_lines.append(lines)
+
+        tracker.onlineTrackerUpdate(matches, boxes_nms)
+
+    interpolate_lines(out_file, all_lines, parity)
+    
+    return [], transform, size
+
+def read_input_data(path2det, path2video, slice_start, slice_end, det_in, frame_indices, match_video_id,
+                        min_confidence=0.4, max_iou=0.98, segment=True, ckpt_path='/root/py-mcftracker/pfe/checkpoints/market_combined_120e.pth'):
+    
+    input_data = {}
+    video = mmcv.VideoReader(path2video)
+
+    if segment:
+        segmentor = init_segmentor()
+    else:
+        extractor = FeatureExtractor(
+            model_name='osnet_x1_0',
+            model_path=ckpt_path,
+            device='cuda'
+        )
+
+    match_video = MatchVideo.load(match_video_id)
+
+    transform = Transform(
+        match_video.video.camera_recording["parameter"],
+        match_video.video.camera_recording["extrinsic_json"],
+        match_video.video.camera_recording["stitching_json"],
+    )
+
+    size = np.zeros(2)
+    fnum = 0
+
+    print ('-> video frame interval [%s-%s]' % (slice_start, slice_end-1))
+
+    parity = False
+
+    if slice_start %2 != 0 and (slice_end-1) % 2 != 0: # odd - odd
+        parity = True
+    elif slice_start %2 == 0 and (slice_end-1) % 2 == 0: # even - even
+        parity = True
+    
+    last_frame = ""
+    wc_d = {}
+
+    # online tracker to extract motion model
+    tracker = OnlineTracker()
+
+    for index in range(slice_start, slice_end):
+        frame = video[index]
+
+        if index == slice_start:
+            size = frame.shape
+            cv2.imwrite("./frame.jpg", frame)
+
+        # skip frame
+        # note: if index of last frame in chunk is even, throw away odd indexes and vice versa
+        # frame of last index is needed for inter-chunk connection
+        if parity:
+            if (index-slice_start) % 2 != 0:
+                continue
+        else:
+            # instead of skipping 0 index skip 1 index
+            if (index-slice_start) != 0 and (index-slice_start) % 2 == 0:
+                continue
+            if (index-slice_start) == 1:
+                continue
+
+        fnum = fnum+1
+
+        if fnum % 100 == 0:
+            print ('-> reading frame %d / %d' % (fnum, slice_end-slice_start))
+
+        mask = frame_indices == (index - slice_start + 1)
+        rows = det_in[mask]
+
+        image_name = "%d" % (fnum)
+        last_frame = image_name
+
+        bbimgs = []
+        node_lst = []
+        _wc = []
+        boxes = []
+
+        for n,r in enumerate(rows):
+            _, x1, y1, x2, y2, s = int(r[0]), float(r[1]), float(r[2]), float(r[3]), float(r[4]), float(r[5])
+            boxes.append(Box([x1,y1,x2,y2], s, transform, size))
+
+        boxes = [b for b in boxes if b.confidence >= min_confidence]
+
+        # Run non-maxima suppression.
+        blst = np.array([b.to_tlwh() for b in boxes])
+        scrlst = np.array([b.confidence for b in boxes])
+        indices = utils.non_max_suppression(blst, max_iou, scrlst)
+        boxes_nms = [boxes[i] for i in indices]
+
+        for box in boxes_nms:
+            imgbox = frame[int(box.tlbr[1]):int(box.tlbr[3]), int(box.tlbr[0]):int(box.tlbr[2]), :]
+            p = box.to_world()
+            _wc.append(p)
+
+            mskt_ = np.full((imgbox.shape[0],imgbox.shape[1]), True, dtype=bool)
+            node = GraphNode(p, box.tlbr, box.confidence, 0, mask=mskt_)
+
+            bbimgs.append(imgbox)
+            node_lst.append(node)
+        
+        wc_d[index] = _wc.copy()
+
+        if len(bbimgs) == 0:
+            print ('[index %d] empty detection list fnum %d' % (index,fnum))
+            continue
+
+        if segment:    
+            masks = get_masks_from_image_lst(segmentor, bbimgs)
+            masks_p = utils.preprocess_masks(masks, bbimgs)
+
+            for m in masks_p:
+                idx = m[0]
+                mask = m[1]
+                node_lst[idx]._mask = mask
+                    
+            for i,node in enumerate(node_lst):
+                node._hist = tools.calc_RGB_histogram(bbimgs[i], node._mask)
+
+        else:
+            feats = network_feed_from_list(bbimgs, extractor)
+            for i,node in enumerate(node_lst):
+                node._feat = feats[i]
+
+        if fnum == 1:
+            tracker.onlineTrackerInit(boxes_nms)
+        else:
+            matches, _, undet_idx = tracker.onlineTrackerAssign(boxes_nms, index-slice_start+1)
+
+            for match in matches:
+                tidx, didx = match[0], match[1]
+                dnode, track = node_lst[didx], tracker.tracks[tidx]
+                
+                dnode._observed = True
+                dnode._kf = track.kf
+                dnode._mean = track.mean
+                dnode._covar = track.covariance
+            
+            # init new track for unmatched dets
+            unmatched_boxes = [boxes_nms[i] for i in undet_idx]
+            
+            if len(unmatched_boxes)>0:
+                tracker.onlineTrackerInit(unmatched_boxes)
+
+            tracker.onlineTrackerUpdate(matches, boxes_nms)
+
+        input_data[image_name] = node_lst
+
+    print ('-> %d images have been read & processed' % (len(input_data)))
+
+    return input_data, transform, size, parity, wc_d, last_frame
+
+def interpolate_lines(log_filename, all_lines, parity, draw_mask=False):
+    # write to file
+    log_file = open(log_filename, 'w')
+
+    if draw_mask:
+        log_file_mask = open('./masks.txt', 'w')
+
+    if not parity:
+        for i in range(len(all_lines)-1):
+            fl = sorted(all_lines[i], key=lambda x: x[1])
+            sl = sorted(all_lines[i+1], key=lambda x: x[1])
+
+            al = []
+
+            for l in fl:
+                for m in sl:
+                    if m[1] == l[1]:
+                        al.append([(l[k] + m[k])/2 if k!=len(l)-1 else l[k] for k in range(len(l))])
+
+            for l in fl:
+                log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0], l[1], l[2], l[3], l[4], l[5], l[6]))
+                if draw_mask:
+                    log_file_mask.write(str(l[7])+'\n')
+
+            for l in al:
+                log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0], l[1], l[2], l[3], l[4], l[5], l[6]))
+                if draw_mask:
+                    log_file_mask.write(str(l[7])+'\n')
+        
+        # last index
+        li = len(all_lines)-1
+        for l in all_lines[li]:
+            log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0], l[1], l[2], l[3], l[4], l[5], l[6]))
+            if draw_mask:
+                log_file_mask.write(str(l[7])+'\n')
+
+        for l in all_lines[li]:
+            log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0]+1, l[1], l[2], l[3], l[4], l[5], l[6]))
+            if draw_mask:
+                log_file_mask.write(str(l[7])+'\n')
+
+    else:
+        for i in range(len(all_lines)-1):
+            fl = sorted(all_lines[i], key=lambda x: x[1])
+            sl = sorted(all_lines[i+1], key=lambda x: x[1])
+
+            al = []
+
+            for l in fl:
+                for m in sl:
+                    if m[1] == l[1]:
+                        al.append([(l[k] + m[k])/2 if k!=len(l)-1 else l[k] for k in range(len(l))])
+
+            for l in fl:
+                log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0], l[1], l[2], l[3], l[4], l[5], l[6]))
+                if draw_mask:
+                    log_file_mask.write(str(l[7])+'\n')
+
+            for l in al:
+                log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0], l[1], l[2], l[3], l[4], l[5], l[6]))
+                if draw_mask:
+                    log_file_mask.write(str(l[7])+'\n')
+
+        # write last line
+        li = len(all_lines)-1
+        for l in all_lines[li]:
+            log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (l[0], l[1], l[2], l[3], l[4], l[5], l[6]))
+            if draw_mask:
+                log_file_mask.write(str(l[7])+'\n')
+
+    return 
+
+def write_output_data(log_filename, track_hypot, path2det, data, iend, frame_offset, iid, parity, draw_mask=True):
+
+    f = 1
+    all_lines = []
+
+    for n in range(1, iend):
+        lines = []
+        for id, track in enumerate(track_hypot):
+            for i, t in enumerate(track):
+                if i % 2 == 0:
+                    
+                    if int(t[0]) == n:
+                        bi = int(t[1])
+                        b = data[t[0]][bi]._bb
+                        s = data[t[0]][bi]._status
+
+                        if draw_mask:
+                            wc = data[t[0]][bi]._3dc
+                            if wc[0] == 0. and wc[1] == 0.: # interpolated in tracklet matching
+                                mask = [[]]
+                            else:
+                                mask = data[t[0]][bi]._mask.astype(np.uint8).tolist()
+                            lines.append([f, (iid-1)*10000+(id+1), b[0], b[1], b[2]-b[0], b[3]-b[1], s, mask])
+                        else:
+                            lines.append([f, (iid-1)*10000+(id+1), b[0], b[1], b[2]-b[0], b[3]-b[1], s])
+
+        all_lines.append(lines)
+        f = f+2
+    
+    interpolate_lines(log_filename, all_lines, parity)
+
+    return
+
+def extract_patch_block(patch):
+    h, w = patch.shape[0], patch.shape[1]
+    half_p = patch[:int(h/2), :, ]
+    s = int(0.3 * w)
+    roi = half_p[:, s:w-s, :]
+    return roi
+
+def recursive_get_track(elem, dct, lst):
+    if elem in dct:
+        lst.append(elem)
+        k = dct[elem].items()
+        recursive_get_track(k[0][0], dct, lst)
+    return
+
+def loop_get_track(elem, dct):
+    lst = []
+    # while dict has key
+    while elem in dct:
+        lst.append(elem)
+        elem = list(dct[elem].keys())[0]
+    return lst
+
+def calc_eucl_dist(det1, det2):
+    dist = 0
+    pt1 = det1[:2]
+    pt2 = det2[:2]
+    pt1_np = np.asarray(pt1, dtype=np.float)
+    pt2_np = np.asarray(pt2, dtype=np.float)
+    dist = np.linalg.norm(pt1_np-pt2_np)
+    return dist
+
+def build_hypothesis_lst(flow_dict, source_idx, sink_idx):
+    track_hypot = []
+
+    nh = [] # no head
+    nt = [] # no tail
+    nht = [] # no head no tail
+    ht = []
+
+    print ('source frame %s sink frame %s' % (source_idx, sink_idx))
+
+    for n, (k, _) in enumerate(flow_dict["source"].items()):
+        tr_lst = loop_get_track(k, flow_dict)
+        track_hypot.append(tr_lst)
+
+        s_node = tr_lst[0]
+        t_node = tr_lst[-1]
+
+        if s_node[0] != source_idx and t_node[0] == sink_idx:
+            nh.append(n)
+        elif s_node[0] == source_idx and t_node[0] != sink_idx:
+            nt.append(n)
+        elif s_node[0] != source_idx and t_node[0] != sink_idx:
+            nht.append(n)
+        elif s_node[0] == source_idx and t_node[0] == sink_idx:
+            ht.append(n)
+                
+    print ('# tracklets not ending at last frame: %d' % (len(nt)))
+    print ('# tracklets not starting at first frame: %d' % (len(nh)))
+    print ('# tracklets not starting at first and not ending at last frame: %d' %(len(nht)))
+    print ('# tracklets complete: %d' % (len(ht)))
+
+    return track_hypot, nt, nh, nht
+
+def remove_compex_scene_id(path2hypothesis, transform, size):
+    hypothesis = np.loadtxt("./hypothesis_.txt", delimiter=',')
+    frame_indices = hypothesis[:, 0].astype(np.int)
+
+    min_frame_idx = frame_indices.astype(np.int).min()
+    max_frame_idx = frame_indices.astype(np.int).max()
+
+    log_filename = './hypothesis.txt'
+    log_file = open(log_filename, 'w')
+
+    print ("==> removing complex scene ids")
+    count = 0
+
+    for frame_idx in range(min_frame_idx, max_frame_idx):
+        rows = hypothesis[frame_indices == frame_idx]
+        
+        _wc = convert2world_post(rows, size, transform)
+
+        for n,r in enumerate(rows):
+            if is_patch_complex_scene(n, _wc, transform, tdist=3.5, tcrowd=6):
+                count = count + 1
+                continue
+
+            tid, x1, y1, w, h, s = int(r[1]), float(r[2]), float(r[3]), float(r[4]), float(r[5]), int(r[9])
+            # write new result
+            log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (frame_idx, tid, x1, y1, w, h, s))
+
+def remove_compex_scene_id_grid(file_in, file_out, transform, size):
+    v_t = 20
+    h_t = 30
+
+    gw = float(transform.parameter.get("ground_width"))
+    gh = float(transform.parameter.get("ground_height"))
+
+    x_w = gw / h_t
+    y_w = gh / v_t
+
+    x_range = [x * x_w for x in range(0, h_t)]
+    y_range = [y * y_w for y in range(0, v_t)]
+
+    grid = {}
+    for i,y in enumerate(y_range):
+        ty = [y, y+y_w]
+        for j,x in enumerate(x_range):
+            tx = [x, x+x_w]
+            pos = (i,j)
+            grid[pos] = [ty,tx]
+
+    hypothesis = np.loadtxt(file_in, delimiter=',')
+    frame_indices = hypothesis[:, 0].astype(np.int)
+
+    min_frame_idx = frame_indices.astype(np.int).min()
+    max_frame_idx = frame_indices.astype(np.int).max()
+
+    # log_filename = './hypothesis.txt'
+    log_file = open(file_out, 'w')
+
+    print ("==> removing complex scene ids")
+    b2t = {}
+
+    for frame_idx in range(min_frame_idx, max_frame_idx+1):
+        rows = hypothesis[frame_indices == frame_idx]
+        _wc = convert2world_post(rows, size, transform)
+        
+        for item in grid.items():
+            # item[0] -> (i,j)
+            # item[1] -> [[y1 y2],[x1 x2]]
+            pos = item[0]
+            yran = item[1][0] # [y1 y2]
+            xran = item[1][1] # [x1 x2]
+
+            b2t[pos] = []
+
+            for n,p in enumerate(_wc):
+                # p is normalised
+                xp = min(p[0]*transform.parameter.get("ground_width"), float(transform.parameter.get("ground_width")))
+                yp = min(p[1]*transform.parameter.get("ground_height"), float(transform.parameter.get("ground_height")))
+
+                if yp >= yran[0] and yp < yran[1] and xp >= xran[0] and xp < xran[1]:
+                    b2t[pos].append(n)
+
+        for item in b2t.items():
+            # item[0] -> (i,j) grid pos
+            # item[1] -> [] lst of indices in row
+            if len(item[1]) < 4:
+                for i in item[1]:
+                    r = rows[i]
+                    tid, x1, y1, w, h, s = int(r[1]), float(r[2]), float(r[3]), float(r[4]), float(r[5]), int(r[9])
+                    # write new result
+                    log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (frame_idx, tid, x1, y1, w, h, s))
+
+        # writing id's outside of pitch
+        for n,p in enumerate(_wc):
+            # p is normalised
+            xp = p[0]*transform.parameter.get("ground_width")
+            yp = p[1]*transform.parameter.get("ground_height")
+
+            if xp<=0. or yp<=0. or xp>=float(transform.parameter.get("ground_width")) or yp>=float(transform.parameter.get("ground_height")):
+                r = rows[n]
+                tid, x1, y1, w, h, s = int(r[1]), float(r[2]), float(r[3]), float(r[4]), float(r[5]), int(r[9])
+                # write new result
+                log_file.write('%d, %d, %f, %f, %f, %f, 1,-1,-1, %d \n' % (frame_idx, tid, x1, y1, w, h, s))
+
+
+def interpolate_gap(tracks, data):
+    
+    for j,track in enumerate(tracks):
+        # track -> [('1', 24, 'u'), ... ]
+        new_track = []
+
+        new_track.append(track[0])
+        new_track.append(track[1])
+
+        for i in range(2, len(track), 2):
+            tpre = track[i-2]
+            tcur = track[i]
+
+            new_track.append(track[i])
+            new_track.append(track[i+1])
+
+            fpre = int(tpre[0])
+            fcur = int(tcur[0])
+
+            if (fcur-fpre)>1 and (fcur-fpre)<=20:
+                bs = data[tpre[0]][tpre[1]]._bb
+                be = data[tcur[0]][tcur[1]]._bb
+
+                # print ('%d -> %d > %s -> %s' % (fpre,fcur,bs,be))
+
+                fpx1 = [bs[0], be[0]]
+                fpy1 = [bs[1], be[1]]
+                fpx2 = [bs[2], be[2]]
+                fpy2 = [bs[3], be[3]]
+
+                xp = [fpre, fcur]
+
+                for x in range(fpre+1, fcur):
+                    pix1 = np.interp(x, xp, fpx1)
+                    piy1 = np.interp(x, xp, fpy1)
+                    pix2 = np.interp(x, xp, fpx2)
+                    piy2 = np.interp(x, xp, fpy2)
+
+                    # print ('%d -> %s' % (x, [pix1,piy1,pix2,piy2]))
+                    pi = [pix1,piy1,pix2,piy2]
+
+                    gn = GraphNode([0.,0.], pi, 0., 0)
+                    fn = str(x)
+
+                    index = len(data[fn])
+                    data[fn].append(gn)
+
+                    node_u = (fn, index, "u")
+                    node_v = (fn, index, "v")
+
+                    new_track.append(node_u)
+                    new_track.append(node_v)
+
+        # sort new_track by fn
+        new_track_s = sorted(new_track, key=lambda x: int(x[0]))
+        # assign new_track
+        tracks[j] = new_track_s    
\ No newline at end of file
diff --git a/mcftracker.py b/mcftracker.py
index 51cd6eb..1cb51a6 100644
--- a/mcftracker.py
+++ b/mcftracker.py
@@ -9,167 +9,261 @@
 import math
 from ortools.graph import pywrapgraph
 import sys
-
+import cv2
 import tools
+import numpy as np
 
+from sklearn.metrics.pairwise import cosine_similarity
+import helper
 
 class MinCostFlowTracker:
-	"""
-		Object tracking based on data association via minimum cost flow algorithm
-		L. Zhang et al.,
-		"Global data association for multi-object tracking using network flows",
-		CVPR 2008
-	"""
-
-	def __init__(self, detections, tags, min_thresh, P_enter, P_exit, beta):
-		self._detections = detections
-		self._min_thresh = min_thresh
-
-		self.P_enter = P_enter
-		self.P_exit = self.P_enter
-		self.beta = beta
-
-		self._id2name = tools.map_id2name(tags)
-		self._name2id = tools.map_name2id(tags)
-		self._id2node = tools.map_id2node(detections)
-		self._node2id = tools.map_node2id(detections)
-		self._fib_cache = {0: 0, 1: 1}
-
-	def _fib(self, n):
-		if n in self._fib_cache:
-			return self._fib_cache[n]
-		elif n > 1:
-			return self._fib_cache.setdefault(n, self._fib(n - 1) + self._fib(n - 2))
-		return n
-
-	def _find_nearest_fib(self, num):
-		for n in range(num):
-			if num < self._fib(n):
-				return (n - 1, self._fib(n - 1))
-		return (num, self._fib(num))
-
-	def _calc_cost_enter(self):
-		return -math.log(self.P_enter)
-
-	def _calc_cost_exit(self):
-		return -math.log(self.P_exit)
-
-	def _calc_cost_detection(self, beta):
-		return math.log(beta / (1.0 - beta))
-
-	def _calc_cost_link(self, rect1, rect2, image1=None, image2=None, eps=1e-7):
-		prob_iou = tools.calc_overlap(rect1, rect2)
-		hist1 = tools.calc_HS_histogram(image1, rect1)
-		hist2 = tools.calc_HS_histogram(image2, rect2)
-		prob_color = 1.0 - tools.calc_bhattacharyya_distance(hist1, hist2)
-
-		prob_sim = prob_iou * prob_color
-		return -math.log(prob_sim + eps)
-
-	def build_network(self, images={}, f2i_factor=10000):
-		self.mcf = pywrapgraph.SimpleMinCostFlow()
-
-		for image_name, rects in sorted(self._detections.items()):
-			for i, rect in enumerate(rects):
-				self.mcf.AddArcWithCapacityAndUnitCost(self._node2id["source"], self._node2id[(image_name, i, "u")], 1, int(self._calc_cost_enter() * f2i_factor))
-				self.mcf.AddArcWithCapacityAndUnitCost(self._node2id[(image_name, i, "u")], self._node2id[(image_name, i, "v")], 1, int(self._calc_cost_detection(rect[4]) * f2i_factor))
-				self.mcf.AddArcWithCapacityAndUnitCost(self._node2id[(image_name, i, "v")], self._node2id["sink"], 1, int(self._calc_cost_exit() * f2i_factor))
-
-			frame_id = self._name2id[image_name]
-			if frame_id == 0:
-				continue
-			prev_image_name = self._id2name[frame_id - 1]
-			if prev_image_name not in self._detections:
-				continue
-
-			for i, i_rect in enumerate(self._detections[prev_image_name]):
-				for j, j_rect in enumerate(rects):
-					self.mcf.AddArcWithCapacityAndUnitCost(self._node2id[(prev_image_name, i, "v")], self._node2id[(image_name, j, "u")], 1, int(self._calc_cost_link(images[prev_image_name], i_rect, images[image_name], j_rect) * 1000))
-
-	def _make_flow_dict(self):
-		self.flow_dict = {}
-		for i in range(self.mcf.NumArcs()):
-			if self.mcf.Flow(i) > 0:
-				tail = self.mcf.Tail(i)
-				head = self.mcf.Head(i)
-				if self._id2node[tail] in self.flow_dict:
-					self.flow_dict[self._id2node[tail]][self._id2node[head]] = 1
-				else:
-					self.flow_dict[self._id2node[tail]] = {self._id2node[head]: 1}
-
-	def _fibonacci_search(self, search_range=200):
-		s = 0
-		k_max, t = self._find_nearest_fib(self.mcf.NumNodes() // search_range)
-		cost = {}
-
-		for k in range(k_max, 1, -1):
-			# s < u < v < t
-			u = s + self._fib(k - 2)
-			v = s + self._fib(k - 1)
-
-			if u not in cost:
-				self.mcf.SetNodeSupply(self._node2id["source"], u)
-				self.mcf.SetNodeSupply(self._node2id["sink"], -u)
-
-				if self.mcf.Solve() == self.mcf.OPTIMAL:
-					cost[u] = self.mcf.OptimalCost()
-				else:
-					print("There was an issue with the min cost flow input.")
-					sys.exit()
-
-			if v not in cost:
-				self.mcf.SetNodeSupply(self._node2id["source"], v)
-				self.mcf.SetNodeSupply(self._node2id["sink"], -v)
-
-				if self.mcf.Solve() == self.mcf.OPTIMAL:
-					cost[v] = self.mcf.OptimalCost()
-				else:
-					print("There was an issue with the min cost flow input.")
-					sys.exit()
-
-			if cost[u] < cost[v]:
-				t = v
-			elif cost[u] == cost[v]:
-				s = u
-				t = v
-			else:
-				s = u
-
-		self.mcf.SetNodeSupply(self._node2id["source"], s)
-		self.mcf.SetNodeSupply(self._node2id["sink"], -s)
-
-		if self.mcf.Solve() == self.mcf.OPTIMAL:
-			optimal_cost = self.mcf.OptimalCost()
-		else:
-			print("There was an issue with the min cost flow input.")
-			sys.exit()
-		self._make_flow_dict()
-		return (s, optimal_cost)
-
-	def _brute_force(self, search_range=100):
-		max_flow = self.mcf.NumNodes() // search_range
-		print("Search: 0 < num_flow <", max_flow)
-
-		optimal_flow = 0
-		optimal_cost = float("inf")
-		for flow in range(max_flow):
-			self.mcf.SetNodeSupply(self._node2id["source"], flow)
-			self.mcf.SetNodeSupply(self._node2id["sink"], -flow)
-
-			if self.mcf.Solve() == self.mcf.OPTIMAL:
-				cost = self.mcf.OptimalCost()
-			else:
-				print("There was an issue with the min cost flow input.")
-				sys.exit()
-
-			if cost < optimal_cost:
-				optimal_flow = flow
-				optimal_cost = cost
-				self._make_flow_dict()
-		return (optimal_flow, optimal_cost)
-
-	def run(self, fib=False):
-		if fib:
-			return self._fibonacci_search()
-		else:
-			return self._brute_force()
+    """
+        Object tracking based on data association via minimum cost flow algorithm
+        L. Zhang et al.,
+        "Global data association for multi-object tracking using network flows",
+        CVPR 2008
+    """
+
+    def __init__(self, data, min_thresh, P_enter, P_exit):
+        self._data = data
+        self._min_thresh = min_thresh
+
+        self.P_enter = P_enter
+        self.P_exit = P_exit
+
+        self._id2name = tools.map_id2name(data)
+        self._name2id = tools.map_name2id(data)
+
+        self._id2node = tools.map_id2node(data)
+        self._node2id = tools.map_node2id(data)
+        
+        self._fib_cache = {0: 0, 1: 1}
+        
+        self._npast = 30
+        self._penalty_skp = 10
+
+    def _fib(self, n):
+        if n in self._fib_cache:
+            return self._fib_cache[n]
+        elif n > 1:
+            return self._fib_cache.setdefault(n, self._fib(n - 1) + self._fib(n - 2))
+        return n
+
+    def _return_max_dist(self,x):
+        if x <= 10:
+            return 4.0
+        return 4.8*math.log10(x)
+
+    def _find_nearest_fib(self, num):
+        for n in range(num):
+            if num < self._fib(n):
+                return (n - 1, self._fib(n - 1))
+        return (num, self._fib(num))
+
+    def _calc_cost_enter(self):
+        return -math.log(self.P_enter)
+
+    def _calc_cost_exit(self):
+        return -math.log(self.P_exit)
+
+    def _calc_cost_detection(self, beta):
+    	return math.log(beta / (1.0 - beta))
+
+    def _calc_cost_link(self, rect1, rect2, image1, image2, dbgLog=False, eps=1e-9):
+        prob_iou = tools.calc_overlap(rect1, rect2, dbgLog)
+        hist1 = tools.calc_HS_histogram(image1)
+        hist2 = tools.calc_HS_histogram(image2)
+        prob_color = 1.0 - tools.calc_bhattacharyya_distance(hist1, hist2)
+
+        if prob_color > 0. and prob_iou > 0.:
+            prob_sim = prob_iou * prob_color
+            return -math.log(prob_sim + eps)
+        else:
+            return 10000
+    
+    def _calc_cost_link_appearance(self, prev_node, cur_node, transform, size, dbgLog=False, dst_max=2.4, segment=True):
+
+        if segment: 
+            u = prev_node._hist
+            v = cur_node._hist
+            prob_color = 1.0-tools.calc_bhattacharyya_distance(u, v) 
+        else:
+            u = prev_node._feat
+            v = cur_node._feat            
+            prob_color = cosine_similarity([u],[v])[0][0]
+
+        pxy = (prev_node._3dc[0], prev_node._3dc[1])
+        cxy = (cur_node._3dc[0], cur_node._3dc[1])
+
+        a = np.array(pxy)
+        b = np.array(cxy)
+
+        dst_eucl = np.linalg.norm(a-b)
+
+        if dst_eucl >= dst_max:
+            return -1
+
+        if prev_node._observed:
+            u_mean = prev_node._mean
+            u_cov = prev_node._covar
+            u_kf = prev_node._kf
+
+            sqmah_dist = u_kf.gating_distance(u_mean, u_cov, cur_node._3dc)
+
+            if prob_color == 0:
+                return -1
+
+            cost_colour = -math.log(prob_color)
+            cost = sqmah_dist + cost_colour*1000
+            # cost = sqmah_dist
+
+            return cost
+        else:
+            prob_dst = np.float32(1.0 - dst_eucl / dst_max)
+
+            bu = prev_node._bb
+            bv = cur_node._bb
+            prob_iou = tools.calc_overlap(bu,bv)
+
+            if prob_iou != 0.:
+                c1, c2, c3 = 0.20, 0.60, 0.20
+                prob_sim = c1*prob_dst + c2*prob_color + c3*prob_iou
+            else:
+                c1, c2, c3 = 0.2, 0.8, 0.0
+                prob_sim = c1*prob_dst + c2*prob_color + c3*prob_iou
+
+            return -math.log(prob_sim) * 1000
+
+        return -1
+
+    def build_network(self, last_img_name, transform, size, f2i_factor=100000):
+        self.mcf = pywrapgraph.SimpleMinCostFlow()
+
+        for n, (image_name, node_lst) in enumerate(sorted(self._data.items(), key=lambda t: tools.get_key(t[0]))):
+            if n % 100 == 0:
+                print ('-> processing image %s / %s' % (image_name, last_img_name))
+
+            frame_id = self._name2id[image_name]
+
+            pnlty_en = 1 if frame_id == 0 else 100
+            
+            for i, node in enumerate(node_lst):
+                self.mcf.AddArcWithCapacityAndUnitCost(self._node2id["source"], self._node2id[(image_name, i, "u")], 1, int(self._calc_cost_enter() * f2i_factor * pnlty_en))
+                self.mcf.AddArcWithCapacityAndUnitCost(self._node2id[(image_name, i, "u")], self._node2id[(image_name, i, "v")], 1, int(self._calc_cost_detection(1.0-node._score)*f2i_factor))
+                self.mcf.AddArcWithCapacityAndUnitCost(self._node2id[(image_name, i, "v")], self._node2id["sink"], 1, int(self._calc_cost_exit() * f2i_factor * pnlty_en))
+            
+            if frame_id > 0:
+                prev_image_name = self._id2name[frame_id - 1]
+                
+                for i, i_node in enumerate(self._data[prev_image_name]):
+                    for j, j_node in enumerate(node_lst):
+                        unit_cost = self._calc_cost_link_appearance(i_node, j_node, transform, size)
+                        if unit_cost >= 0.:
+                            self.mcf.AddArcWithCapacityAndUnitCost(self._node2id[(prev_image_name, i, "v")], self._node2id[(image_name, j, "u")], 1, int(unit_cost))
+
+                # connect N previous frames to current frame's nodes
+                if frame_id >= 2:
+                    npast = self._npast if frame_id >= self._npast else frame_id
+
+                    for step in range(npast, 1, -1):
+                        fpast = self._id2name[frame_id-step]
+                        for i, i_node in enumerate(self._data[fpast]):
+                            for j, j_node in enumerate(node_lst):
+                                unit_cost = self._calc_cost_link_appearance(i_node, j_node, transform, size)
+                                if unit_cost >= 0.:
+                                    self.mcf.AddArcWithCapacityAndUnitCost(self._node2id[(fpast, i, "v")], self._node2id[(image_name, j, "u")], 1, int(unit_cost*self._penalty_skp))
+
+        return
+
+    def _make_flow_dict(self):
+        self.flow_dict = {}
+        for i in range(self.mcf.NumArcs()):
+            if self.mcf.Flow(i) > 0:
+                tail = self.mcf.Tail(i)
+                head = self.mcf.Head(i)
+                if self._id2node[tail] in self.flow_dict:
+                    self.flow_dict[self._id2node[tail]][self._id2node[head]] = 1
+                else:
+                    self.flow_dict[self._id2node[tail]] = {self._id2node[head]: 1}
+
+    def _fibonacci_search(self, search_range=200):
+        s = 0
+        k_max, t = self._find_nearest_fib(self.mcf.NumNodes() // search_range)
+
+        print ('number of nodes: %d | maximum flows: %d' % (self.mcf.NumNodes(), k_max))
+        cost = {}
+
+        for k in range(k_max, 1, -1):
+            # s < u < v < t
+            u = s + self._fib(k - 2)
+            v = s + self._fib(k - 1)
+
+            if u not in cost:
+                self.mcf.SetNodeSupply(self._node2id["source"], u)
+                self.mcf.SetNodeSupply(self._node2id["sink"], -u)
+
+                if self.mcf.Solve() == self.mcf.OPTIMAL:
+                    cost[u] = self.mcf.OptimalCost()
+                else:
+                    print("There was an issue with the min cost flow input.")
+                    sys.exit()
+
+            if v not in cost:
+                self.mcf.SetNodeSupply(self._node2id["source"], v)
+                self.mcf.SetNodeSupply(self._node2id["sink"], -v)
+
+                if self.mcf.Solve() == self.mcf.OPTIMAL:
+                    cost[v] = self.mcf.OptimalCost()
+                else:
+                    print("There was an issue with the min cost flow input.")
+                    sys.exit()
+
+            if cost[u] < cost[v]:
+                t = v
+            elif cost[u] == cost[v]:
+                s = u
+                t = v
+            else:
+                s = u
+
+        self.mcf.SetNodeSupply(self._node2id["source"], s)
+        self.mcf.SetNodeSupply(self._node2id["sink"], -s)
+
+        if self.mcf.Solve() == self.mcf.OPTIMAL:
+            optimal_cost = self.mcf.OptimalCost()
+        else:
+            print("There was an issue with the min cost flow input.")
+            sys.exit()
+        self._make_flow_dict()
+        return (s, optimal_cost)
+
+    def _brute_force(self, min_flow, max_flow, search_range=100):
+        optimal_flow = -1
+        optimal_cost = float("inf")
+
+        print ('min flow: %d max flow: %d' % (min_flow, max_flow))
+
+        for flow in range(min_flow, max_flow):
+            self.mcf.SetNodeSupply(self._node2id["source"], flow)
+            self.mcf.SetNodeSupply(self._node2id["sink"], -flow)
+
+            if self.mcf.Solve() == self.mcf.OPTIMAL:
+                cost = self.mcf.OptimalCost()
+            else:
+                continue
+
+            if cost < optimal_cost:
+                optimal_flow = flow
+                optimal_cost = cost
+                self._make_flow_dict()
+
+            # print ('flow: %d optimal flow: %d' % (flow, optimal_flow))
+        
+        return (optimal_flow, optimal_cost)
+
+    def run(self, min_flow, max_flow, fib=False):
+        if fib:
+            return self._fibonacci_search()
+        else:
+            return self._brute_force(min_flow, max_flow)
+
diff --git a/nh.json b/nh.json
new file mode 100644
index 0000000..e889c42
--- /dev/null
+++ b/nh.json
@@ -0,0 +1 @@
+[22, 59, 66, 93, 96, 98, 102, 103, 105, 107, 109, 110, 113, 115, 116, 117, 119, 120, 122, 124]
\ No newline at end of file
diff --git a/nht.json b/nht.json
new file mode 100644
index 0000000..5ca2c07
--- /dev/null
+++ b/nht.json
@@ -0,0 +1 @@
+[17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 100, 101, 104, 106, 108, 111, 112, 114, 118, 121, 123]
\ No newline at end of file
diff --git a/node.py b/node.py
new file mode 100644
index 0000000..2482bc2
--- /dev/null
+++ b/node.py
@@ -0,0 +1,14 @@
+class GraphNode:
+    def __init__(self, wc, bb, score, status, feat_v=None, mask=None, hist=None):
+        self._3dc = wc
+        self._bb = bb
+        self._score = score
+        self._status = status
+        self._feat = feat_v
+        self._mask = mask
+        self._hist = hist
+        
+        self._observed = False
+        self._mean = None
+        self._covar = None
+        self._kf = None
diff --git a/nt.json b/nt.json
new file mode 100644
index 0000000..6760850
--- /dev/null
+++ b/nt.json
@@ -0,0 +1 @@
+[0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 14, 15, 16]
\ No newline at end of file
diff --git a/pfe/.flake8 b/pfe/.flake8
new file mode 100644
index 0000000..4fc103c
--- /dev/null
+++ b/pfe/.flake8
@@ -0,0 +1,18 @@
+[flake8]
+ignore =
+    # At least two spaces before inline comment
+    E261,
+    # Line lengths are recommended to be no greater than 79 characters
+    E501,
+    # Missing whitespace around arithmetic operator 
+    E226,
+    # Blank line contains whitespace
+    W293,
+    # Do not use bare 'except'
+    E722,
+    # Line break after binary operator
+    W504,
+    # isort found an import in the wrong position
+    I001
+max-line-length = 79
+exclude = __init__.py, build, torchreid/metrics/rank_cylib/
\ No newline at end of file
diff --git a/pfe/.gitignore b/pfe/.gitignore
new file mode 100644
index 0000000..6c093a6
--- /dev/null
+++ b/pfe/.gitignore
@@ -0,0 +1,140 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Cython eval code
+*.c
+*.html
+
+# OS X
+.DS_Store
+.Spotlight-V100
+.Trashes
+._*
+
+# ReID
+reid-data/
+log/
+saved-models/
+model-zoo/
+debug*
diff --git a/pfe/.isort.cfg b/pfe/.isort.cfg
new file mode 100644
index 0000000..8039326
--- /dev/null
+++ b/pfe/.isort.cfg
@@ -0,0 +1,10 @@
+[isort]
+line_length=79
+multi_line_output=3
+length_sort=true
+known_standard_library=numpy,setuptools
+known_myself=torchreid
+known_third_party=matplotlib,cv2,torch,torchvision,PIL,yacs
+no_lines_before=STDLIB,THIRDPARTY
+sections=FUTURE,STDLIB,THIRDPARTY,myself,FIRSTPARTY,LOCALFOLDER
+default_section=FIRSTPARTY
\ No newline at end of file
diff --git a/pfe/.style.yapf b/pfe/.style.yapf
new file mode 100644
index 0000000..29d8e52
--- /dev/null
+++ b/pfe/.style.yapf
@@ -0,0 +1,7 @@
+[style]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
+DEDENT_CLOSING_BRACKETS = true
+SPACES_BEFORE_COMMENT = 1
+ARITHMETIC_PRECEDENCE_INDICATION = true
\ No newline at end of file
diff --git a/pfe/LICENSE b/pfe/LICENSE
new file mode 100644
index 0000000..d2bcb88
--- /dev/null
+++ b/pfe/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Kaiyang Zhou
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/pfe/bepro_data_collect.py b/pfe/bepro_data_collect.py
new file mode 100644
index 0000000..6a395f7
--- /dev/null
+++ b/pfe/bepro_data_collect.py
@@ -0,0 +1,149 @@
+import shutil
+import os
+import glob
+import cv2
+
+training_data_path = '/home/dmitriy.khvan/pytorch-beginner/training/'
+validation_data_path = '/home/dmitriy.khvan/pytorch-beginner/validation/'
+
+training_data_dest = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro/bounding_box_train'
+query_data_dest = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro/query'
+test_data_dest = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro/test'
+
+copy_data_path = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro_tmp'
+
+train_dir_list = os.listdir(training_data_path)
+valid_dir_list = os.listdir(validation_data_path)
+tmp_dir_list = os.listdir(copy_data_path)
+
+def copy_files():
+    count = 0
+
+    for d in train_dir_list:
+        folder_indices = [0]
+
+        copy_dir = os.path.join(copy_data_path, d)
+        os.mkdir(copy_dir)
+
+        for fi in folder_indices:
+            path2dir = os.path.join(training_data_path, d, str(fi))
+            
+            if os.path.isdir(path2dir):
+                # pid_list = os.listdir(path2dir)
+                pid_list = ['0', '1']
+
+                for p in pid_list:
+                    copy_dir_low = os.path.join(copy_dir, p)
+                    os.mkdir(copy_dir_low)
+
+                    path2pid = os.path.join(path2dir, p)
+
+                    if os.path.isdir(path2pid):
+                        for filename in os.listdir(path2pid):
+                            path2file = os.path.join(path2pid, filename)
+
+                            imgidx = int(filename.split('.')[0])
+                            img_indices = [0, 10, 15, 20, 25, 29]
+
+                            if imgidx in img_indices:
+                                print (path2file)
+                                image = cv2.imread(path2file)
+                                
+                                if image.shape[0] < 110 and image.shape[1] < 40:
+                                    continue
+            
+                                path2cp_dest = os.path.join(copy_dir_low, filename)
+
+                                image_res = cv2.resize(image, (64,128))
+                                cv2.imwrite(path2cp_dest, image_res)
+                                count = count + 1
+
+    print (count)
+    
+
+def create_test_dataset(pid):
+    pid = pid +1 
+
+    count_q = 0
+    count_t = 0
+
+    for d in valid_dir_list:
+
+        folder_indices = [0,1,2,3]
+
+        for fi in folder_indices:
+            path2dir = os.path.join(validation_data_path, d, str(fi))
+
+            if os.path.isdir(path2dir):
+                pid_list = os.listdir(path2dir)
+
+                for p in pid_list:
+                    path2pid = os.path.join(path2dir, p)
+                    
+                    pid = pid + 1
+                    for filename in os.listdir(path2pid):
+                        path2file = os.path.join(path2pid, filename)
+
+                        imgidx = int(filename.split('.')[0])
+                        img_indices = [5, 10, 15, 20, 25, 29]
+
+                        image = cv2.imread(path2file)
+                        
+                        if imgidx == 0:
+                            #copy to query
+                            new_filename = '%d_%s_%d.jpg' % (pid, '0', imgidx)
+                            path2dest = os.path.join(query_data_dest, new_filename)
+                            count_q = count_q + 1
+                        elif imgidx in img_indices:
+                            #copy to test
+                            new_filename = '%d_%s_%d.jpg' % (pid, '1', imgidx)
+                            path2dest = os.path.join(test_data_dest, new_filename)
+                            count_t = count_t + 1
+                        else:
+                            continue
+
+                        print (path2dest)
+
+                        image_res = cv2.resize(image, (64,128))
+                        cv2.imwrite(path2dest, image_res)
+
+    print (count_q)
+    print (count_t)
+
+def create_training_dataset():
+    pid = 1
+    
+    for d in tmp_dir_list:
+        
+        path2dir = os.path.join(copy_data_path, d)
+        pid_list = os.listdir(path2dir)
+
+        print (d)
+        print (pid_list)
+        
+        for p in pid_list:
+            path2pid = os.path.join(path2dir, p)
+            print (path2pid)
+            pid = pid + 1
+            fcount = 0
+
+            for filename in os.listdir(path2pid):
+                path2file = os.path.join(path2pid, filename)
+                print (path2file)
+
+                image = cv2.imread(path2file)
+
+                new_filename = '%d_%s_%d.jpg' % (pid, '1', fcount)
+                path2dest = os.path.join(training_data_dest, new_filename)
+
+                # print (path2dest)
+                cv2.imwrite(path2dest, image)
+
+                fcount = fcount + 1
+
+    return pid
+
+if __name__ == "__main__":
+    # copy_files()
+    pid = create_training_dataset()
+    create_test_dataset(pid)
diff --git a/pfe/checkpoints/market_100e.pth b/pfe/checkpoints/market_100e.pth
new file mode 100644
index 0000000..beadaa6
Binary files /dev/null and b/pfe/checkpoints/market_100e.pth differ
diff --git a/pfe/checkpoints/market_combined_120e.pth b/pfe/checkpoints/market_combined_120e.pth
new file mode 100644
index 0000000..f9f0573
Binary files /dev/null and b/pfe/checkpoints/market_combined_120e.pth differ
diff --git a/pfe/checkpoints/market_cuhk03_100e.pth b/pfe/checkpoints/market_cuhk03_100e.pth
new file mode 100644
index 0000000..3ac3699
Binary files /dev/null and b/pfe/checkpoints/market_cuhk03_100e.pth differ
diff --git a/pfe/checkpoints/market_cuhk_prid_88e.pth b/pfe/checkpoints/market_cuhk_prid_88e.pth
new file mode 100644
index 0000000..62d6b68
Binary files /dev/null and b/pfe/checkpoints/market_cuhk_prid_88e.pth differ
diff --git a/pfe/checkpoints/osnet_ain_x10_100e_market_64x32.pth b/pfe/checkpoints/osnet_ain_x10_100e_market_64x32.pth
new file mode 100644
index 0000000..1844a59
Binary files /dev/null and b/pfe/checkpoints/osnet_ain_x10_100e_market_64x32.pth differ
diff --git a/pfe/configs/im_osnet_ain_x1_0_softmax_256x128_amsgrad_cosine.yaml b/pfe/configs/im_osnet_ain_x1_0_softmax_256x128_amsgrad_cosine.yaml
new file mode 100644
index 0000000..c8b63ec
--- /dev/null
+++ b/pfe/configs/im_osnet_ain_x1_0_softmax_256x128_amsgrad_cosine.yaml
@@ -0,0 +1,35 @@
+model:
+  name: 'osnet_ain_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501', 'dukemtmcreid']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip', 'color_jitter']
+  save_dir: 'log/osnet_ain_x1_0_market1501_softmax_cosinelr'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 100
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'cosine'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_osnet_ibn_x1_0_softmax_256x128_amsgrad.yaml b/pfe/configs/im_osnet_ibn_x1_0_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..b4f8903
--- /dev/null
+++ b/pfe/configs/im_osnet_ibn_x1_0_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_ibn_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['dukemtmcreid']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip', 'color_jitter']
+  save_dir: 'log/osnet_ibn_x1_0_market2duke_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 150
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [60]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_osnet_x0_25_softmax_256x128_amsgrad.yaml b/pfe/configs/im_osnet_x0_25_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..d256a6b
--- /dev/null
+++ b/pfe/configs/im_osnet_x0_25_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x0_25'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x0_25_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.003
+  max_epoch: 180
+  batch_size: 128
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [80]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_osnet_x0_5_softmax_256x128_amsgrad.yaml b/pfe/configs/im_osnet_x0_5_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..467305f
--- /dev/null
+++ b/pfe/configs/im_osnet_x0_5_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x0_5'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x0_5_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.003
+  max_epoch: 180
+  batch_size: 128
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [80]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_osnet_x0_75_softmax_256x128_amsgrad.yaml b/pfe/configs/im_osnet_x0_75_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..04203fd
--- /dev/null
+++ b/pfe/configs/im_osnet_x0_75_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x0_75'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x0_75_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 150
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [60]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml b/pfe/configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..f1c970f
--- /dev/null
+++ b/pfe/configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x1_0_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 150
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [60]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml b/pfe/configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml
new file mode 100644
index 0000000..29750bc
--- /dev/null
+++ b/pfe/configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml
@@ -0,0 +1,35 @@
+model:
+  name: 'osnet_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x1_0_market1501_softmax_cosinelr'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 250
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_r50_softmax_256x128_amsgrad.yaml b/pfe/configs/im_r50_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..6173589
--- /dev/null
+++ b/pfe/configs/im_r50_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'resnet50_fc512'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/resnet50_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0003
+  max_epoch: 60
+  batch_size: 32
+  fixbase_epoch: 5
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [20]
+
+test:
+  batch_size: 100
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/configs/im_r50fc512_softmax_256x128_amsgrad.yaml b/pfe/configs/im_r50fc512_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..5ec2b70
--- /dev/null
+++ b/pfe/configs/im_r50fc512_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'resnet50_fc512'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/resnet50_fc512_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0003
+  max_epoch: 60
+  batch_size: 32
+  fixbase_epoch: 5
+  open_layers: ['fc', 'classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [20]
+
+test:
+  batch_size: 100
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/docs/AWESOME_REID.md b/pfe/docs/AWESOME_REID.md
new file mode 100644
index 0000000..16c75cc
--- /dev/null
+++ b/pfe/docs/AWESOME_REID.md
@@ -0,0 +1,69 @@
+# Awesome-ReID
+Here is a collection of ReID-related research with links to papers and code. You are welcome to submit [PR](https://help.github.com/articles/creating-a-pull-request/)s if you find something missing.
+
+
+- [ArXiv20] Deep Learning for Person Re-identification: A Survey and Outlook [[paper](https://arxiv.org/abs/2001.04193)] [[code](https://github.com/mangye16/ReID-Survey)]
+
+- [ArXiv19] Learning Generalisable Omni-Scale Representations for Person Re-Identification [[paper](https://arxiv.org/abs/1910.06827)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [ICCV19] RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_RGB-Infrared_Cross-Modality_Person_Re-Identification_via_Joint_Pixel_and_Feature_Alignment_ICCV_2019_paper.pdf)] [[code](https://github.com/wangguanan/AlignGAN)]
+
+- [ICCV19] Unsupervised Graph Association for Person Re-identification. [[paper](https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification)] [[code](https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification)]
+
+- [ICCV19] Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf)] [[code](https://github.com/OasisYang/SSG)]
+
+- [ICCV19] Spectral Feature Transformation for Person Re-Identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_Spectral_Feature_Transformation_for_Person_Re-Identification_ICCV_2019_paper.pdf)] [[code](https://github.com/LuckyDC/SFT_REID)]
+
+- [ICCV19] Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Guo_Beyond_Human_Parts_Dual_Part-Aligned_Representations_for_Person_Re-Identification_ICCV_2019_paper.pdf)] [[code](https://github.com/ggjy/P2Net.pytorch)]
+
+- [ICCV19] Co-segmentation Inspired Attention Networks for Video-based Person Re-identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Subramaniam_Co-Segmentation_Inspired_Attention_Networks_for_Video-Based_Person_Re-Identification_ICCV_2019_paper.pdf)][[code](https://github.com/InnovArul/vidreid_cosegmentation)]
+
+- [ICCV19] Mixed High-Order Attention Network for Person Re-Identification. [[paper](https://arxiv.org/abs/1908.05819)][[code](https://github.com/chenbinghui1/MHN)]
+
+- [ICCV19] ABD-Net: Attentive but Diverse Person Re-Identification. [[paper](https://arxiv.org/abs/1908.01114)] [[code](https://github.com/TAMU-VITA/ABD-Net)]
+
+- [ICCV19] Omni-Scale Feature Learning for Person Re-Identification. [[paper](https://arxiv.org/abs/1905.00953)] [[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [CVPR19] Joint Discriminative and Generative Learning for Person Re-identification. [[paper](https://arxiv.org/abs/1904.07223)][[code](https://github.com/NVlabs/DG-Net)]
+- [CVPR19] Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. [[paper](https://arxiv.org/abs/1904.01990)][[code](https://github.com/zhunzhong07/ECN)]
+- [CVPR19] Dissecting Person Re-identification from the Viewpoint of Viewpoint. [[paper](https://arxiv.org/abs/1812.02162)][[code](https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint)]
+- [CVPR19] Unsupervised Person Re-identification by Soft Multilabel Learning. [[paper](https://arxiv.org/abs/1903.06325)][[code](https://github.com/KovenYu/MAR)]
+- [CVPR19] Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification. [[paper](https://kovenyu.com/publication/2019-cvpr-pedal/)][[code](https://github.com/QizeYang/PAUL)]
+
+- [AAAI19] Spatial and Temporal Mutual Promotion for Video-based Person Re-identification. [[paper](https://arxiv.org/abs/1812.10305)][[code](https://github.com/yolomax/person-reid-lib)]
+
+- [AAAI19] Spatial-Temporal Person Re-identification. [[paper](https://arxiv.org/abs/1812.03282)][[code](https://github.com/Wanggcong/Spatial-Temporal-Re-identification)]
+
+- [AAAI19] Horizontal Pyramid Matching for Person Re-identification. [[paper](https://arxiv.org/abs/1804.05275)][[code](https://github.com/OasisYang/HPM)]
+
+- [AAAI19] Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-identification. [[paper](https://arxiv.org/abs/1901.06140)][[code](https://github.com/youngminPIL/rollback)]
+
+- [AAAI19] A Bottom-Up Clustering Approach to Unsupervised Person Re-identification. [[paper](https://vana77.github.io/vana77.github.io/images/AAAI19.pdf)][[code](https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification)]
+
+- [NIPS18] FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification. [[paper](https://arxiv.org/abs/1810.02936)][[code](https://github.com/yxgeee/FD-GAN)]
+
+- [ECCV18] Generalizing A Person Retrieval Model Hetero- and Homogeneously. [[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.pdf)][[code](https://github.com/zhunzhong07/HHL)]
+
+- [ECCV18] Pose-Normalized Image Generation for Person Re-identification. [[paper](https://arxiv.org/abs/1712.02225)][[code](https://github.com/naiq/PN_GAN)]
+
+- [CVPR18] Camera Style Adaptation for Person Re-Identification. [[paper](https://arxiv.org/abs/1711.10295)][[code](https://github.com/zhunzhong07/CamStyle)]
+
+- [CVPR18] Deep Group-Shuffling Random Walk for Person Re-Identification. [[paper](https://arxiv.org/abs/1807.11178)][[code](https://github.com/YantaoShen/kpm_rw_person_reid)]
+
+- [CVPR18] End-to-End Deep Kronecker-Product Matching for Person Re-identification. [[paper](https://arxiv.org/abs/1807.11182)][[code](https://github.com/YantaoShen/kpm_rw_person_reid)]
+
+- [CVPR18] Features for Multi-Target Multi-Camera Tracking and Re-Identification. [[paper](https://arxiv.org/abs/1803.10859)][[code](https://github.com/ergysr/DeepCC)]
+
+- [CVPR18] Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Group_Consistent_Similarity_CVPR_2018_paper.pdf)][[code](https://github.com/dapengchen123/crf_affinity)]
+
+- [CVPR18] Harmonious Attention Network for Person Re-Identification. [[paper](https://arxiv.org/abs/1802.08122)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [CVPR18] Human Semantic Parsing for Person Re-Identification. [[paper](https://arxiv.org/abs/1804.00216)][[code](https://github.com/emrahbasaran/SPReID)]
+
+- [CVPR18] Multi-Level Factorisation Net for Person Re-Identification. [[paper](https://arxiv.org/abs/1803.09132)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [CVPR18] Resource Aware Person Re-identification across Multiple Resolutions. [[paper](https://arxiv.org/abs/1805.08805)][[code](https://github.com/mileyan/DARENet)]
+
+- [CVPR18] Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. [[paper](https://yu-wu.net/pdf/CVPR2018_Exploit-Unknown-Gradually.pdf)][[code](https://github.com/Yu-Wu/Exploit-Unknown-Gradually)]
+
+- [ArXiv18] Revisiting Temporal Modeling for Video-based Person ReID. [[paper](https://arxiv.org/abs/1805.02104)][[code](https://github.com/jiyanggao/Video-Person-ReID)]
diff --git a/pfe/docs/MODEL_ZOO.md b/pfe/docs/MODEL_ZOO.md
new file mode 100644
index 0000000..a363bab
--- /dev/null
+++ b/pfe/docs/MODEL_ZOO.md
@@ -0,0 +1,74 @@
+# Model Zoo
+
+- Results are presented in the format of *<Rank-1 (mAP)>*.
+- When computing model size and FLOPs, only layers that are used at test time are considered (see `torchreid.utils.compute_model_complexity`).
+- Asterisk (\*) means the model is trained from scratch.
+- `combineall=True` means all images in the dataset are used for model training.
+
+
+## ImageNet pretrained models
+
+
+| Model | Download |
+| :--- | :---: |
+| shufflenet | [model](https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrgfiJO9kiidz9fZM) |
+| mobilenetv2_x1_0 | [model](https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NUOWvvGPHhaNr-fASF6c) |
+| mobilenetv2_x1_4 | [model](https://mega.nz/#!RGhgEIwS!xN2s2ZdyqI6vQ3EwgmRXLEW3khr9tpXg96G9SUJugGk) |
+| mlfn | [model](https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk) |
+| osnet_x1_0 | [model](https://mega.nz/#!YK5GRARL!F90NsNB2XHjXGZFC3Lrw1GMic0oMw4fnfuDUnSrPAYM) |
+| osnet_x0_75 | [model](https://mega.nz/#!NPxilYBA!Se414Wtts__7eY6J5FIrowynvjUUG7a8Z5zUPfJN33s) |
+| osnet_x0_5 | [model](https://mega.nz/#!NO4ihQSJ!oMIRSZ0HlJF_8FKUbXT8Ei0vzH0xUYs5tWaf_KLrODg) |
+| osnet_x0_25 | [model](https://mega.nz/#!IDwQwaxT!TbQ_33gPK-ZchPFTf43UMc45rlNKWiWMqH4rTXB1T7k) |
+| osnet_ibn_x1_0 | [model](https://mega.nz/#!8Wo2kSDR!bNvgu4V0VkCQp_L2ZUDaudYKYRCkkSNdzcA1CcZGZTE) |
+| osnet_ain_x1_0 | [model](https://drive.google.com/open?id=1-CaioD9NaqbHK_kzSMW8VE4_3KcsRjEo) |
+
+
+## Same-domain ReID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | market1501  | dukemtmcreid | msmt17 |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [87.9 (70.4)](https://mega.nz/#!FKZjVKaZ!4v_FR8pTvuHoMQIKdstJ_YCsRrtZW2hwWxc-T0JIlHE) | [78.3 (58.9)](https://mega.nz/#!JPZjCYhK!YVJbE_4vTc8DX19Rt_FB77YY4BaEA1P6Xb5sNJGep2M) | [63.2 (33.9)](https://mega.nz/#!APAxDY4Z!Iou9x8s3ATdYS2SlK2oiJbHrhvlzH7F1gE2qjM-GJGw) |
+| resnet50_fc512 | 24.6 | 4.1 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [90.8 (75.3)](https://mega.nz/#!EaZjhKyS!lBvD3vAJ4DOmElZkNa7gyPM1RE661GUd2v9kK84gSZE) | [81.0 (64.0)](https://mega.nz/#!lXYDSKZa!lumiXkY2H5Sm8gEgTWPBdWKv3ujy4zjrffjERaXkc9I) | [69.6 (38.4)](https://mega.nz/#!9PQTXIpL!iI5wgieTCn0Jm-pyg9RCu0RkH43pV3ntHhr1PeqSyT4) |
+| mlfn | 32.5 | 2.8 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [90.1 (74.3)](https://mega.nz/#!kHQ3ESLT!NoGc8eHEBZOJZM19THh3DFfRBXIPXzM-sdLmF1mvTXA) | [81.1 (63.2)](https://mega.nz/#!8PQXUCaI!mJO1vD9tI739hkNBj2QWUt0VPcZ-s89fSMMGPPP1msc) | [66.4 (37.2)](https://mega.nz/#!paIXFQCS!W3ZGkxyF1idwvQzTRDE2p0DhNDki2SBJRfp7S_Cwphk) |
+| hacnn<sup>*</sup> | 4.5 | 0.5 | softmax | (160, 64) | `random_flip`, `random_crop` | `euclidean` | [90.9 (75.6)](https://mega.nz/#!ULQXUQBK!S-8v_pR2xBD3ZpuY0I7Bqift-eX_V84gajHMDG6zUac) | [80.1 (63.2)](https://mega.nz/#!wPJTkAQR!XkKd39lsmBZMrCh3JjF6vnNafBZkouVIVdeBqQKdSzA) | [64.7 (37.2)](https://mega.nz/#!AXAziKjL!JtMwHz2UYy58gDMQLGakSmF3JOr72o8zmkqlQA-LIpQ) |
+| mobilenetv2_x1_0 | 2.2 | 0.2 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [85.6 (67.3)](https://mega.nz/#!8KYTFAIB!3dL35WQLxSoTSClDTv0kxa81k3fh5hXmAWA4_a3qiOI) | [74.2 (54.7)](https://mega.nz/#!hbRXDSCL!YYgqJ6PVUf4clgtUuK2s5FRhYJdU3yTibLscwOTNnDk) | [57.4 (29.3)](https://mega.nz/#!5SJTmCYb!ZQ8O2MN9JF4-WDAeX04Xex1KyuBYQ_o2aoMIsTgQ748) |
+| mobilenetv2_x1_4 | 4.3 | 0.4 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [87.0 (68.5)](https://mega.nz/#!4XZhEKCS!6lTuTRbHIWU5nzJzTPDGykA7sPME8_1ISGsUYFJXZWA) | [76.2 (55.8)](https://mega.nz/#!JbQVDIYQ!-7pnjIfpIDt1EoQOvpvuIEcTj3Qg8SE6o_3ZPGWrIcw) | [60.1 (31.5)](https://mega.nz/#!gOYDAQrK!sMJO7c_X4iIxoVfV_tXYdzeDJByPo5XkUjEN7Z2JTmM) |
+| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip` | `euclidean` | [94.2 (82.6)](https://mega.nz/#!hLoyTSba!fqt7GcKrHJhwe9BtuK0ozgVAQcrlMG8Pm6JsSfr5HEI) | [87.0 (70.2)](https://mega.nz/#!ETwGhQYB!h2gHN-H3J4X4WqcJXy2b0pPKl28paydkiS-PDHsEgPM) | [74.9 (43.8)](https://mega.nz/#!hWxE2aJA!NGcxu5uYH1qI6DfBTu0KFoi_NfoA0TJcBFW-g43pC0I) |
+| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | `random_flip` | `euclidean` | [93.7 (81.2)](https://mega.nz/#!JO4WAaJa!nQuoqZnYfy0xu7vs2mp28AFceya-ZhrXTry837jvoDQ) | [85.8 (69.8)](https://mega.nz/#!lOgkEIoI!fQ5vuYIABIOcRxF-OK-6YxtEufWhyVkYkGB4qPoRYJ4) | [72.8 (41.4)](https://mega.nz/#!0exGXI5a!rxtzBayyRK0on0HFq9XO0UtWEBhbV86dFitljhjeWcs) |
+| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | `random_flip` | `euclidean` | [92.5 (79.8)](https://mega.nz/#!QCx0RArD!hqz3Mh0Iif5d8PpQW0frxa-Tepn2a2g24aei7du4MFs) | [85.1 (67.4)](https://mega.nz/#!QTxCDIbT!eOZxj4dHl0uFnjKEB-J3YBY98blXZvppgWGA3CGa-tk) | [69.7 (37.5)](https://mega.nz/#!ETpiECDa!CCkq4JryztHqgw7spL5zDw0usJpAfEsSd5gPlkMufCc) |
+| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | `random_flip` | `euclidean` | [91.2 (75.0)](https://mega.nz/#!VWxCgSqY!Q4WaQ3j9D7HMhK3jsbvMuwaZ7yBY80T2Zj5V8JAlAKU) | [82.0 (61.4)](https://mega.nz/#!5TpwnATK!UvU_Asdy_aJ9SNzuvqhEFoemxSSB8vm_Gm8Xe03jqiA) | [61.4 (29.5)](https://mega.nz/#!AWgE3SzD!DngUaNyA7VIqOd2gq10Aty_-ER0CmG0xTJLHLj6_36g) |
+
+
+## Cross-domain ReID
+
+#### Market1501 -> DukeMTMC-reID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance  | Rank-1 | Rank-5 | Rank-10 | mAP | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| osnet_ibn_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 48.5 | 62.3 | 67.4 | 26.7 | [model](https://mega.nz/#!wXwGxKxK!f8EMk8hBt6AjxU3JIPGMFSMvX7j-Nt5Lp1Gpbqso1Ts) |
+| osnet_ain_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `cosine` | 52.4 | 66.1 | 71.2 | 30.5 | [model](https://mega.nz/#!QLJE2CRI!FXYc3Vm6Y5Scwx0xvRwBJxId56kf06fIXNLwA_b_1FE) |
+
+
+#### DukeMTMC-reID -> Market1501
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance  | Rank-1 | Rank-5 | Rank-10 | mAP | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| osnet_ibn_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 57.7 | 73.7 | 80.0 | 26.1 | [model](https://mega.nz/#!FD4WEKJS!ZGgI-2IwVuX6re09xylChR03o6Dkjpi6KSebrbS0fAA) |
+| osnet_ain_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `cosine` | 61.0 | 77.0 | 82.5 | 30.6 | [model](https://mega.nz/#!4PBQlCCL!9yMHu1WyyBVxqssubLAEyoEfHUiNP4Ggg5On0nCX2S4) |
+
+
+#### MSMT17 (`combineall=True`) -> Market1501 & DukeMTMC-reID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | msmt17 -> market1501 | msmt17 -> dukemtmcreid | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: |
+| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 46.3 (22.8) | 52.3 (32.1) | [model](https://mega.nz/#!VTpkWSbS!Y8gDnmg7u-sPwnZDhWXrtZNYOj7UYL4QzZkhDf1qWW4) |
+| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 66.6 (37.5) | 66.0 (45.3) | [model](https://mega.nz/#!MepG3QRC!Lb-C9d7rdS_YJjGSoJ5cRlzjYcP28P_1Cm5S5WSslW0) |
+| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 63.6 (35.5) | 65.3 (44.5) | [model](https://mega.nz/#!tO4WDagL!8Tl6kdJWRXRHQb16GeUHR008tJqW3N7_3fyVMu-LcKM) |
+| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 64.3 (34.9) | 65.2 (43.3) | [model](https://mega.nz/#!papSWQhY!IId-QfcHj7nXQ_muUubgv9_n0SsnZzarmb5mQgcMv74) |
+| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 59.9 (31.0) | 61.5 (39.6) | [model](https://mega.nz/#!QCoE0Kpa!BITLANumgjiR68TUFteL__N_RIoDKkL0M5Bl3Q8LC3U) |
+| osnet_ibn_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 66.5 (37.2) | 67.4 (45.6) | [model](https://mega.nz/#!dL4Q2K5B!ZdHQ_X_rs2T-xmggigM5YvzJhmT1orkr6aQ1_fHgunM) |
+| osnet_ain_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `cosine` | 70.1 (43.3) | 71.1 (52.7) | [model](https://mega.nz/#!YTZFnSJY!wlbo_5oa2TpDAGyWCTKTX1hh4d6DvJhh_RUA2z6i_so) |
diff --git a/pfe/docs/Makefile b/pfe/docs/Makefile
new file mode 100644
index 0000000..298ea9e
--- /dev/null
+++ b/pfe/docs/Makefile
@@ -0,0 +1,19 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
diff --git a/pfe/docs/conf.py b/pfe/docs/conf.py
new file mode 100644
index 0000000..4d27eed
--- /dev/null
+++ b/pfe/docs/conf.py
@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+#
+# Configuration file for the Sphinx documentation builder.
+#
+# This file does only contain a selection of the most common options. For a
+# full list see the documentation:
+# http://www.sphinx-doc.org/en/master/config
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath('..'))
+
+# -- Project information -----------------------------------------------------
+
+project = u'torchreid'
+copyright = u'2019, Kaiyang Zhou'
+author = u'Kaiyang Zhou'
+
+version_file = '../torchreid/__init__.py'
+with open(version_file, 'r') as f:
+    exec(compile(f.read(), version_file, 'exec'))
+__version__ = locals()['__version__']
+
+# The short X.Y version
+version = __version__
+# The full version, including alpha/beta/rc tags
+release = __version__
+
+# -- General configuration ---------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinxcontrib.napoleon',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.githubpages',
+    'sphinx_markdown_tables',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+source_suffix = ['.rst', '.md']
+# source_suffix = '.rst'
+source_parsers = {'.md': 'recommonmark.parser.CommonMarkParser'}
+
+# The master toctree document.
+master_doc = 'index'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = None
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# The default sidebars (for documents that don't match any pattern) are
+# defined by theme itself.  Builtin themes are using these templates by
+# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
+# 'searchbox.html']``.
+#
+# html_sidebars = {}
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'torchreiddoc'
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (
+        master_doc, 'torchreid.tex', u'torchreid Documentation',
+        u'Kaiyang Zhou', 'manual'
+    ),
+]
+
+# -- Options for manual page output ------------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'torchreid', u'torchreid Documentation', [author], 1)
+]
+
+# -- Options for Texinfo output ----------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        master_doc, 'torchreid', u'torchreid Documentation', author,
+        'torchreid', 'One line description of project.', 'Miscellaneous'
+    ),
+]
+
+# -- Options for Epub output -------------------------------------------------
+
+# Bibliographic Dublin Core info.
+epub_title = project
+
+# The unique identifier of the text. This can be a ISBN number
+# or the project homepage.
+#
+# epub_identifier = ''
+
+# A unique identification for the text.
+#
+# epub_uid = ''
+
+# A list of files that should not be packed into the epub file.
+epub_exclude_files = ['search.html']
+
+# -- Extension configuration -------------------------------------------------
diff --git a/pfe/docs/datasets.rst b/pfe/docs/datasets.rst
new file mode 100644
index 0000000..5a1d601
--- /dev/null
+++ b/pfe/docs/datasets.rst
@@ -0,0 +1,249 @@
+.. _datasets:
+
+Datasets
+=========
+
+Here we provide a guide on how to prepare datasets.
+
+Suppose you wanna store the reid data in a directory called "path/to/reid-data/", you need to specify the ``root`` as *root='path/to/reid-data/'* when initializing ``DataManager``. Below we use ``$REID`` to denote "path/to/reid-data".
+
+Please refer to :ref:`torchreid_data` for details regarding the arguments.
+
+
+.. note::
+    Dataset with a :math:`\dagger` superscript means that the process is automated, so you can directly call the dataset in ``DataManager`` (which automatically downloads the dataset and organizes the data structure). However, we also provide a way below to help the manual setup in case the automation fails.
+
+
+.. note::
+    The keys to use specific datasets are enclosed in the parantheses beside the datasets.
+
+
+.. note::
+    You are suggested to use the provided names for dataset folders such as "market1501" for Market1501 and "dukemtmcreid" for DukeMTMC-reID when doing the manual setup, otherwise you need to modify the source code accordingly (i.e. the ``dataset_dir`` attribute).
+
+
+.. contents::
+   :local:
+
+
+Image Datasets
+--------------
+
+Market1501 :math:`^\dagger` (``market1501``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a directory named "market1501" under ``$REID``.
+- Download the dataset to "market1501" from http://www.liangzheng.org/Project/project_reid.html and extract the files.
+- The data structure should look like
+
+.. code-block:: none
+    
+    market1501/
+        Market-1501-v15.09.15/
+            query/
+            bounding_box_train/
+            bounding_box_test/
+
+- To use the extra 500K distractors (i.e. Market1501 + 500K), go to the **Market-1501+500k Dataset** section at http://www.liangzheng.org/Project/project_reid.html, download the zip file "distractors_500k.zip" and extract it under "market1501/Market-1501-v15.09.15". The argument to use these 500K distrctors is ``market1501_500k`` in ``ImageDataManager``.
+
+
+CUHK03 (``cuhk03``)
+^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "cuhk03" under ``$REID``.
+- Download the dataset to "cuhk03/" from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and extract "cuhk03_release.zip", resulting in "cuhk03/cuhk03_release/".
+- Download the new split (767/700) from `person-re-ranking <https://github.com/zhunzhong07/person-re-ranking/tree/master/evaluation/data/CUHK03>`_. What you need are "cuhk03_new_protocol_config_detected.mat" and "cuhk03_new_protocol_config_labeled.mat". Put these two mat files under "cuhk03/".
+- The data structure should look like
+
+.. code-block:: none
+    
+    cuhk03/
+        cuhk03_release/
+        cuhk03_new_protocol_config_detected.mat
+        cuhk03_new_protocol_config_labeled.mat
+
+
+- In the default mode, we load data using the new split (767/700). If you wanna use the original (20) splits (1367/100), please set ``cuhk03_classic_split`` to True in ``ImageDataManager``. As the CMC is computed differently from Market1501 for the 1367/100 split (see `here <http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html>`_), you need to enable ``use_metric_cuhk03`` in ``ImageDataManager`` to activate the *single-gallery-shot* metric for fair comparison with some methods that adopt the old splits (*do not need to report mAP*). In addition, we support both *labeled* and *detected* modes. The default mode loads *detected* images. Enable ``cuhk03_labeled`` in ``ImageDataManager`` if you wanna train and test on *labeled* images.
+
+.. note::
+    The code will extract images in "cuhk-03.mat" and save them under "cuhk03/images_detected" and "cuhk03/images_labeled". Also, four json files will be automatically generated, i.e. "splits_classic_detected.json", "splits_classic_labeled.json", "splits_new_detected.json" and "splits_new_labeled.json". If the parent path of ``$REID`` is changed, these json files should be manually deleted. The code can automatically generate new json files to match the new path.
+    
+
+DukeMTMC-reID :math:`^\dagger` (``dukemtmcreid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a directory called "dukemtmc-reid" under ``$REID``.
+- Download "DukeMTMC-reID" from http://vision.cs.duke.edu/DukeMTMC/ and extract it under "dukemtmc-reid".
+- The data structure should look like
+
+.. code-block:: none
+    
+    dukemtmc-reid/
+        DukeMTMC-reID/
+            query/
+            bounding_box_train/
+            bounding_box_test/
+            ...
+
+MSMT17 (``msmt17``)
+^^^^^^^^^^^^^^^^^^^^^
+- Create a directory called "msmt17" under ``$REID``.
+- Download the dataset from http://www.pkuvmc.com/publications/msmt17.html to "msmt17" and extract the files.
+- The data structure should look like
+
+.. code-block:: none
+    
+    msmt17/
+        MSMT17_V1/ # or MSMT17_V2
+            train/
+            test/
+            list_train.txt
+            list_query.txt
+            list_gallery.txt
+            list_val.txt
+
+VIPeR :math:`^\dagger` (``viper``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- The download link is http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip.
+- Organize the dataset in a folder named "viper" as follows
+
+.. code-block:: none
+    
+    viper/
+        VIPeR/
+            cam_a/
+            cam_b/
+
+GRID :math:`^\dagger` (``grid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- The download link is http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip.
+- Organize the dataset in a folder named "grid" as follows
+
+.. code-block:: none
+    
+    grid/
+        underground_reid/
+            probe/
+            gallery/
+            ...
+
+CUHK01 (``cuhk01``)
+^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "cuhk01" under ``$REID``.
+- Download "CUHK01.zip" from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and place it under "cuhk01/".
+- The code can automatically extract the files, or you can do it yourself.
+- The data structure should look like
+
+.. code-block:: none
+    
+    cuhk01/
+        campus/
+
+SenseReID (``sensereid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create "sensereid" under ``$REID``.
+- Download the dataset from this `link <https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view>`_ and extract it to "sensereid".
+- Organize the data to be like
+
+.. code-block:: none
+
+    sensereid/
+        SenseReID/
+            test_probe/
+            test_gallery/
+
+QMUL-iLIDS :math:`^\dagger` (``ilids``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "ilids" under ``$REID``.
+- Download the dataset from http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz and organize it to look like
+
+.. code-block:: none
+    
+    ilids/
+        i-LIDS_Pedestrian/
+            Persons/
+
+PRID (``prid``)
+^^^^^^^^^^^^^^^^^^^
+- Create a directory named "prid2011" under ``$REID``.
+- Download the dataset from https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ and extract it under "prid2011".
+- The data structure should end up with
+
+.. code-block:: none
+
+    prid2011/
+        prid_2011/
+            single_shot/
+            multi_shot/
+
+CUHK02 (``cuhk02``)
+^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "cuhk02" under ``$REID``.
+- Download the data from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and put it under "cuhk02/".
+- Extract the file so the data structure looks like
+
+.. code-block:: none
+    
+    cuhk02/
+        Dataset/
+            P1/
+            P2/
+            P3/
+            P4/
+            P5/
+
+Video Datasets
+--------------
+
+MARS (``mars``)
+^^^^^^^^^^^^^^^^^
+- Create "mars/" under ``$REID``.
+- Download the dataset from http://www.liangzheng.com.cn/Project/project_mars.html and place it in "mars/".
+- Extract "bbox_train.zip" and "bbox_test.zip".
+- Download the split metadata from https://github.com/liangzheng06/MARS-evaluation/tree/master/info and put "info/" in "mars/".
+- The data structure should end up with
+
+.. code-block:: none
+    
+    mars/
+        bbox_test/
+        bbox_train/
+        info/
+
+iLIDS-VID :math:`^\dagger` (``ilidsvid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create "ilids-vid" under ``$REID``.
+- Download the dataset from http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html to "ilids-vid".
+- Organize the data structure to match
+
+.. code-block:: none
+    
+    ilids-vid/
+        i-LIDS-VID/
+        train-test people splits/
+
+PRID2011 (``prid2011``)
+^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a directory named "prid2011" under ``$REID``.
+- Download the dataset from https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ and extract it under "prid2011".
+- Download the split created by *iLIDS-VID* from `this google drive <https://drive.google.com/open?id=1qw7SI7YdIgfHetIQO7LLW4SHpL_qkieT>`_ and put it under "prid2011/". Following the standard protocol, only 178 persons whose sequences are more than a threshold are used.
+- The data structure should end up with
+
+.. code-block:: none
+
+    prid2011/
+        splits_prid2011.json
+        prid_2011/
+            single_shot/
+            multi_shot/
+
+DukeMTMC-VideoReID :math:`^\dagger` (``dukemtmcvidreid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create "dukemtmc-vidreid" under ``$REID``.
+- Download "DukeMTMC-VideoReID" from http://vision.cs.duke.edu/DukeMTMC/ and unzip the file to "dukemtmc-vidreid/".
+- The data structure should look like
+
+.. code-block:: none
+    
+    dukemtmc-vidreid/
+        DukeMTMC-VideoReID/
+            train/
+            query/
+            gallery/
diff --git a/pfe/docs/evaluation.rst b/pfe/docs/evaluation.rst
new file mode 100644
index 0000000..979ec75
--- /dev/null
+++ b/pfe/docs/evaluation.rst
@@ -0,0 +1,21 @@
+Evaluation
+==========
+
+Image ReID
+-----------
+- **Market1501**, **DukeMTMC-reID**, **CUHK03 (767/700 split)** and **MSMT17** have fixed split so keeping ``split_id=0`` is fine.
+- **CUHK03 (classic split)** has 20 fixed splits, so do ``split_id=0~19``.
+- **VIPeR** contains 632 identities each with 2 images under two camera views. Evaluation should be done for 10 random splits. Each split randomly divides 632 identities to 316 train ids (632 images) and the other 316 test ids (632 images). Note that, in each random split, there are two sub-splits, one using camera-A as query and camera-B as gallery while the other one using camera-B as query and camera-A as gallery. Thus, there are totally 20 splits generated with ``split_id`` starting from 0 to 19. Models can be trained on ``split_id=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]`` (because ``split_id=0`` and ``split_id=1`` share the same train set, and so on and so forth.). At test time, models trained on ``split_id=0`` can be directly evaluated on ``split_id=1``, models trained on ``split_id=2`` can be directly evaluated on ``split_id=3``, and so on and so forth.
+- **CUHK01** is similar to VIPeR in the split generation.
+- **GRID** , **iLIDS** and **PRID** have 10 random splits, so evaluation should be done by varying ``split_id`` from 0 to 9.
+- **SenseReID** has no training images and is used for evaluation only.
+
+
+.. note::
+    The ``split_id`` argument is defined in ``ImageDataManager`` and ``VideoDataManager``. Please refer to :ref:`torchreid_data`.
+
+
+Video ReID
+-----------
+- **MARS** and **DukeMTMC-VideoReID** have fixed single split so using ``split_id=0`` is ok.
+- **iLIDS-VID** and **PRID2011** have 10 predefined splits so evaluation should be done by varying ``split_id`` from 0 to 9.
\ No newline at end of file
diff --git a/pfe/docs/figures/actmap.jpg b/pfe/docs/figures/actmap.jpg
new file mode 100644
index 0000000..1a0522b
Binary files /dev/null and b/pfe/docs/figures/actmap.jpg differ
diff --git a/pfe/docs/figures/ranking_results.jpg b/pfe/docs/figures/ranking_results.jpg
new file mode 100644
index 0000000..a759dfc
Binary files /dev/null and b/pfe/docs/figures/ranking_results.jpg differ
diff --git a/pfe/docs/index.rst b/pfe/docs/index.rst
new file mode 100644
index 0000000..c437dea
--- /dev/null
+++ b/pfe/docs/index.rst
@@ -0,0 +1,35 @@
+.. include:: ../README.rst
+
+
+.. toctree::
+    :hidden:
+
+    user_guide
+    datasets
+    evaluation
+
+.. toctree::
+    :caption: Package Reference
+    :hidden:
+
+    pkg/data
+    pkg/engine
+    pkg/losses
+    pkg/metrics
+    pkg/models
+    pkg/optim
+    pkg/utils
+
+.. toctree::
+    :caption: Resources
+    :hidden:
+
+    AWESOME_REID.md
+    MODEL_ZOO.md
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
\ No newline at end of file
diff --git a/pfe/docs/pkg/data.rst b/pfe/docs/pkg/data.rst
new file mode 100644
index 0000000..3dc47d6
--- /dev/null
+++ b/pfe/docs/pkg/data.rst
@@ -0,0 +1,86 @@
+.. _torchreid_data:
+
+torchreid.data
+==============
+
+
+Data Manager
+---------------------------
+
+.. automodule:: torchreid.data.datamanager
+    :members:
+
+
+Sampler
+-----------------------
+
+.. automodule:: torchreid.data.sampler
+    :members:
+
+
+Transforms
+---------------------------
+
+.. automodule:: torchreid.data.transforms
+    :members:
+
+
+Dataset
+---------------------------
+
+.. automodule:: torchreid.data.datasets.dataset
+    :members:
+
+
+.. automodule:: torchreid.data.datasets.__init__
+    :members:
+
+
+Image Datasets
+------------------------------
+
+.. automodule:: torchreid.data.datasets.image.market1501
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.cuhk03
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.dukemtmcreid
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.msmt17
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.viper
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.grid
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.cuhk01
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.ilids
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.sensereid
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.prid
+    :members:
+
+
+Video Datasets
+------------------------------
+
+.. automodule:: torchreid.data.datasets.video.mars
+    :members:
+
+.. automodule:: torchreid.data.datasets.video.ilidsvid
+    :members:
+
+.. automodule:: torchreid.data.datasets.video.prid2011
+    :members:
+
+.. automodule:: torchreid.data.datasets.video.dukemtmcvidreid
+    :members:
\ No newline at end of file
diff --git a/pfe/docs/pkg/engine.rst b/pfe/docs/pkg/engine.rst
new file mode 100644
index 0000000..ae2bc68
--- /dev/null
+++ b/pfe/docs/pkg/engine.rst
@@ -0,0 +1,31 @@
+.. _torchreid_engine:
+
+torchreid.engine
+==================
+
+
+Base Engine
+------------
+
+.. autoclass:: torchreid.engine.engine.Engine
+    :members:
+
+
+Image Engines
+-------------
+
+.. autoclass:: torchreid.engine.image.softmax.ImageSoftmaxEngine
+    :members:
+
+
+.. autoclass:: torchreid.engine.image.triplet.ImageTripletEngine
+    :members:
+
+
+Video Engines
+-------------
+
+.. autoclass:: torchreid.engine.video.softmax.VideoSoftmaxEngine
+
+
+.. autoclass:: torchreid.engine.video.triplet.VideoTripletEngine
\ No newline at end of file
diff --git a/pfe/docs/pkg/losses.rst b/pfe/docs/pkg/losses.rst
new file mode 100644
index 0000000..33fd9bc
--- /dev/null
+++ b/pfe/docs/pkg/losses.rst
@@ -0,0 +1,18 @@
+.. _torchreid_losses:
+
+torchreid.losses
+=================
+
+
+Softmax
+--------
+
+.. automodule:: torchreid.losses.cross_entropy_loss
+    :members:
+
+
+Triplet
+-------
+
+.. automodule:: torchreid.losses.hard_mine_triplet_loss
+    :members:
\ No newline at end of file
diff --git a/pfe/docs/pkg/metrics.rst b/pfe/docs/pkg/metrics.rst
new file mode 100644
index 0000000..5a52a90
--- /dev/null
+++ b/pfe/docs/pkg/metrics.rst
@@ -0,0 +1,25 @@
+.. _torchreid_metrics:
+
+torchreid.metrics
+=================
+
+
+Distance
+---------
+
+.. automodule:: torchreid.metrics.distance
+    :members:
+
+
+Accuracy
+--------
+
+.. automodule:: torchreid.metrics.accuracy
+    :members:
+
+
+Rank
+-----
+
+.. automodule:: torchreid.metrics.rank
+    :members: evaluate_rank
\ No newline at end of file
diff --git a/pfe/docs/pkg/models.rst b/pfe/docs/pkg/models.rst
new file mode 100644
index 0000000..685bc73
--- /dev/null
+++ b/pfe/docs/pkg/models.rst
@@ -0,0 +1,43 @@
+.. _torchreid_models:
+
+torchreid.models
+=================
+
+Interface
+---------
+
+.. automodule:: torchreid.models.__init__
+    :members:
+
+
+ImageNet Classification Models
+-------------------------------
+
+.. autoclass:: torchreid.models.resnet.ResNet
+.. autoclass:: torchreid.models.senet.SENet
+.. autoclass:: torchreid.models.densenet.DenseNet
+.. autoclass:: torchreid.models.inceptionresnetv2.InceptionResNetV2
+.. autoclass:: torchreid.models.inceptionv4.InceptionV4
+.. autoclass:: torchreid.models.xception.Xception
+
+
+Lightweight Models
+------------------
+
+.. autoclass:: torchreid.models.nasnet.NASNetAMobile
+.. autoclass:: torchreid.models.mobilenetv2.MobileNetV2
+.. autoclass:: torchreid.models.shufflenet.ShuffleNet
+.. autoclass:: torchreid.models.squeezenet.SqueezeNet
+.. autoclass:: torchreid.models.shufflenetv2.ShuffleNetV2
+
+
+ReID-specific Models
+--------------------
+
+.. autoclass:: torchreid.models.mudeep.MuDeep
+.. autoclass:: torchreid.models.resnetmid.ResNetMid
+.. autoclass:: torchreid.models.hacnn.HACNN
+.. autoclass:: torchreid.models.pcb.PCB
+.. autoclass:: torchreid.models.mlfn.MLFN
+.. autoclass:: torchreid.models.osnet.OSNet
+.. autoclass:: torchreid.models.osnet_ain.OSNet
\ No newline at end of file
diff --git a/pfe/docs/pkg/optim.rst b/pfe/docs/pkg/optim.rst
new file mode 100644
index 0000000..5601623
--- /dev/null
+++ b/pfe/docs/pkg/optim.rst
@@ -0,0 +1,18 @@
+.. _torchreid_optim:
+
+torchreid.optim
+=================
+
+
+Optimizer
+----------
+
+.. automodule:: torchreid.optim.optimizer
+    :members: build_optimizer
+
+
+LR Scheduler
+-------------
+
+.. automodule:: torchreid.optim.lr_scheduler
+    :members: build_lr_scheduler
\ No newline at end of file
diff --git a/pfe/docs/pkg/utils.rst b/pfe/docs/pkg/utils.rst
new file mode 100644
index 0000000..1545bc2
--- /dev/null
+++ b/pfe/docs/pkg/utils.rst
@@ -0,0 +1,41 @@
+.. _torchreid_utils:
+
+torchreid.utils
+=================
+
+Average Meter
+--------------
+
+.. automodule:: torchreid.utils.avgmeter
+    :members:
+
+
+Loggers
+-------
+
+.. automodule:: torchreid.utils.loggers
+    :members:
+
+
+Generic Tools
+---------------
+.. automodule:: torchreid.utils.tools
+    :members:
+
+
+ReID Tools
+----------
+
+.. automodule:: torchreid.utils.reidtools
+    :members:
+
+
+Torch Tools
+------------
+
+.. automodule:: torchreid.utils.torchtools
+    :members:
+
+
+.. automodule:: torchreid.utils.model_complexity
+    :members:
diff --git a/pfe/docs/requirements.txt b/pfe/docs/requirements.txt
new file mode 100644
index 0000000..c5937b9
--- /dev/null
+++ b/pfe/docs/requirements.txt
@@ -0,0 +1,6 @@
+sphinx
+sphinx-markdown-tables
+sphinx-rtd-theme
+sphinxcontrib-napoleon
+sphinxcontrib-websupport
+recommonmark
\ No newline at end of file
diff --git a/pfe/docs/user_guide.rst b/pfe/docs/user_guide.rst
new file mode 100644
index 0000000..5415109
--- /dev/null
+++ b/pfe/docs/user_guide.rst
@@ -0,0 +1,351 @@
+How-to
+============
+
+.. contents::
+   :local:
+
+
+Prepare datasets
+-----------------
+See :ref:`datasets`.
+
+
+Find model keys
+-----------------
+Keys are listed under the *Public keys* section within each model class in :ref:`torchreid_models`.
+
+
+Show available models
+----------------------
+
+.. code-block:: python
+    
+    import torchreid
+    torchreid.models.show_avai_models()
+
+
+Change the training sampler
+-----------------------------
+The default ``train_sampler`` is "RandomSampler". You can give the specific sampler name as input to ``train_sampler``, e.g. ``train_sampler='RandomIdentitySampler'`` for triplet loss.
+
+
+Choose an optimizer/lr_scheduler
+----------------------------------
+Please refer to the source code of ``build_optimizer``/``build_lr_scheduler`` in :ref:`torchreid_optim` for details.
+
+
+Resume training
+----------------
+Suppose the checkpoint is saved in "log/resnet50/model.pth.tar-30", you can do
+
+.. code-block:: python
+    
+    start_epoch = torchreid.utils.resume_from_checkpoint(
+        'log/resnet50/model.pth.tar-30',
+        model,
+        optimizer
+    )
+
+    engine.run(
+        save_dir='log/resnet50',
+        max_epoch=60,
+        start_epoch=start_epoch
+    )
+
+
+Compute model complexity
+--------------------------
+We provide a tool in ``torchreid.utils.model_complexity.py`` to automatically compute the model complexity, i.e. number of parameters and FLOPs.
+
+.. code-block:: python
+    
+    from torchreid import models, utils
+    
+    model = models.build_model(name='resnet50', num_classes=1000)
+    num_params, flops = utils.compute_model_complexity(model, (1, 3, 256, 128))
+
+    # show detailed complexity for each module
+    utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True)
+
+    # count flops for all layers including ReLU and BatchNorm
+    utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True, only_conv_linear=False)
+
+Note that (1) this function only provides an estimate of the theoretical time complexity rather than the actual running time which depends on implementations and hardware; (2) the FLOPs is only counted for layers that are used at test time. This means that redundant layers such as person ID classification layer will be ignored. The inference graph depends on how you define the computations in ``forward()``.
+
+
+Combine multiple datasets
+---------------------------
+Easy. Just give whatever datasets (keys) you want to the ``sources`` argument when instantiating a data manager. For example,
+
+.. code-block:: python
+    
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources=['market1501', 'dukemtmcreid', 'cuhk03', 'msmt17'],
+        height=256,
+        width=128,
+        batch_size=32
+    )
+
+In this example, the target datasets are Market1501, DukeMTMC-reID, CUHK03 and MSMT17 as the ``targets`` argument is not specified. Please refer to ``Engine.test()`` in :ref:`torchreid_engine` for details regarding how evaluation is performed.
+
+
+Do cross-dataset evaluation
+-----------------------------
+Easy. Just give whatever datasets (keys) you want to the argument ``targets``, like
+
+.. code-block:: python
+    
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources='market1501',
+        targets='dukemtmcreid', # or targets='cuhk03' or targets=['dukemtmcreid', 'cuhk03']
+        height=256,
+        width=128,
+        batch_size=32
+    )
+
+
+Combine train, query and gallery
+---------------------------------
+This can be easily done by setting ``combineall=True`` when instantiating a data manager. Below is an example of using Market1501,
+
+.. code-block:: python
+    
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources='market1501',
+        height=256,
+        width=128,
+        batch_size=32,
+        market1501_500k=False,
+        combineall=True # it's me, here
+    )
+
+More specifically, with ``combineall=False``, you will get
+
+.. code-block:: none
+    
+    => Loaded Market1501
+      ----------------------------------------
+      subset   | # ids | # images | # cameras
+      ----------------------------------------
+      train    |   751 |    12936 |         6
+      query    |   750 |     3368 |         6
+      gallery  |   751 |    15913 |         6
+      ---------------------------------------
+
+with ``combineall=True``, you will get
+
+.. code-block:: none
+    
+    => Loaded Market1501
+      ----------------------------------------
+      subset   | # ids | # images | # cameras
+      ----------------------------------------
+      train    |  1501 |    29419 |         6
+      query    |   750 |     3368 |         6
+      gallery  |   751 |    15913 |         6
+      ---------------------------------------
+
+
+Optimize layers with different learning rates
+-----------------------------------------------
+A common practice for fine-tuning pretrained models is to use a smaller learning rate for base layers and a large learning rate for randomly initialized layers (referred to as ``new_layers``). ``torchreid.optim.optimizer`` has implemented such feature. What you need to do is to set ``staged_lr=True`` and give the names of ``new_layers`` such as "classifier".
+
+Below is an example of setting different learning rates for base layers and new layers in ResNet50,
+
+.. code-block:: python
+    
+    # New layer "classifier" has a learning rate of 0.01
+    # The base layers have a learning rate of 0.001
+    optimizer = torchreid.optim.build_optimizer(
+        model,
+        optim='sgd',
+        lr=0.01,
+        staged_lr=True,
+        new_layers='classifier',
+        base_lr_mult=0.1
+    )
+
+Please refer to :ref:`torchreid_optim` for more details.
+
+
+Do two-stepped transfer learning
+-------------------------------------
+To prevent the pretrained layers from being damaged by harmful gradients back-propagated from randomly initialized layers, one can adopt the *two-stepped transfer learning strategy* presented in `Deep Transfer Learning for Person Re-identification <https://arxiv.org/abs/1611.05244>`_. The basic idea is to pretrain the randomly initialized layers for few epochs while keeping the base layers frozen before training all layers end-to-end.
+
+This has been implemented in ``Engine.train()`` (see :ref:`torchreid_engine`). The arguments related to this feature are ``fixbase_epoch`` and ``open_layers``. Intuitively, ``fixbase_epoch`` denotes the number of epochs to keep the base layers frozen; ``open_layers`` means which layer is open for training.
+
+For example, say you want to pretrain the classification layer named "classifier" in ResNet50 for 5 epochs before training all layers, you can do
+
+.. code-block:: python
+    
+    engine.run(
+        save_dir='log/resnet50',
+        max_epoch=60,
+        eval_freq=10,
+        print_freq=10,
+        test_only=False,
+        fixbase_epoch=5,
+        open_layers='classifier'
+    )
+    # or open_layers=['fc', 'classifier'] if there is another fc layer that
+    # is randomly initialized, like resnet50_fc512
+
+Note that ``fixbase_epoch`` is counted into ``max_epoch``. In the above example, the base network will be fixed for 5 epochs and then open for training for 55 epochs. Thus, if you want to freeze some layers throughout the training, what you can do is to set ``fixbase_epoch`` equal to ``max_epoch`` and put the layer names in ``open_layers`` which you want to train.
+
+
+Test a trained model
+----------------------
+You can load a trained model using :code:`torchreid.utils.load_pretrained_weights(model, weight_path)` and set ``test_only=True`` in ``engine.run()``.
+
+
+Fine-tune a model pre-trained on reid datasets
+-----------------------------------------------
+Use :code:`torchreid.utils.load_pretrained_weights(model, weight_path)` to load the pre-trained weights and then fine-tune on the dataset you want.
+
+
+Visualize learning curves with tensorboard
+--------------------------------------------
+The ``SummaryWriter()`` for tensorboard will be automatically initialized in ``engine.run()`` when you are training your model. Therefore, you do not need to do extra jobs. After the training is done, the ``*tf.events*`` file will be saved in ``save_dir``. Then, you just call ``tensorboard --logdir=your_save_dir`` in your terminal and visit ``http://localhost:6006/`` in a web browser. See `pytorch tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ for further information.
+
+
+Visualize ranking results
+---------------------------
+This can be achieved by setting ``visrank`` to true in ``engine.run()``. ``visrank_topk`` determines the top-k images to be visualized (Default is ``visrank_topk=10``). Note that ``visrank`` can only be used in test mode, i.e. ``test_only=True`` in ``engine.run()``. The output will be saved under ``save_dir/visrank_DATASETNAME`` where each plot contains the top-k similar gallery images given a query. An example is shown below where red and green denote incorrect and correct matches respectively.
+
+.. image:: figures/ranking_results.jpg
+    :width: 800px
+    :align: center
+
+
+Visualize activation maps
+--------------------------
+To understand where the CNN focuses on to extract features for ReID, you can visualize the activation maps as in `OSNet <https://arxiv.org/abs/1905.00953>`_. This is implemented in ``tools/visualize_actmap.py`` (check the code for more details). An example running command is
+
+.. code-block:: shell
+    
+    python tools/visualize_actmap.py \
+    --root $DATA/reid \
+    -d market1501 \
+    -m osnet_x1_0 \
+    --weights PATH_TO_PRETRAINED_WEIGHTS \
+    --save-dir log/visactmap_osnet_x1_0_market1501
+
+The output will look like (from left to right: image, activation map, overlapped image)
+
+.. image:: figures/actmap.jpg
+    :width: 300px
+    :align: center
+
+
+.. note::
+    In order to visualize activation maps, the CNN needs to output the last convolutional feature maps at eval mode. See ``torchreid/models/osnet.py`` for example.
+
+
+Use your own dataset
+----------------------
+1. Write your own dataset class. Below is a template for image dataset. However, it can also be applied to a video dataset class, for which you simply change ``ImageDataset`` to ``VideoDataset``.
+
+.. code-block:: python
+    
+    from __future__ import absolute_import
+    from __future__ import print_function
+    from __future__ import division
+
+    import sys
+    import os
+    import os.path as osp
+
+    from torchreid.data import ImageDataset
+
+
+    class NewDataset(ImageDataset):
+        dataset_dir = 'new_dataset'
+
+        def __init__(self, root='', **kwargs):
+            self.root = osp.abspath(osp.expanduser(root))
+            self.dataset_dir = osp.join(self.root, self.dataset_dir)
+
+            # All you need to do here is to generate three lists,
+            # which are train, query and gallery.
+            # Each list contains tuples of (img_path, pid, camid),
+            # where
+            # - img_path (str): absolute path to an image.
+            # - pid (int): person ID, e.g. 0, 1.
+            # - camid (int): camera ID, e.g. 0, 1.
+            # Note that
+            # - pid and camid should be 0-based.
+            # - query and gallery should share the same pid scope (e.g.
+            #   pid=0 in query refers to the same person as pid=0 in gallery).
+            # - train, query and gallery share the same camid scope (e.g.
+            #   camid=0 in train refers to the same camera as camid=0
+            #   in query/gallery).
+            train = ...
+            query = ...
+            gallery = ...
+
+            super(NewDataset, self).__init__(train, query, gallery, **kwargs)
+
+
+2. Register your dataset.
+
+.. code-block:: python
+    
+    import torchreid
+    torchreid.data.register_image_dataset('new_dataset', NewDataset)
+
+
+3. Initialize a data manager with your dataset.
+
+.. code-block:: python
+    
+    # use your own dataset only
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources='new_dataset'
+    )
+    # combine with other datasets
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources=['new_dataset', 'dukemtmcreid']
+    )
+    # cross-dataset evaluation
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources=['new_dataset', 'dukemtmcreid'],
+        targets='market1501' # or targets=['market1501', 'cuhk03']
+    )
+
+
+
+Design your own Engine
+------------------------
+A new Engine should be designed if you have your own loss function. The base Engine class ``torchreid.engine.Engine`` has implemented some generic methods which you can inherit to avoid re-writing. Please refer to the source code for more details. You are suggested to see how ``ImageSoftmaxEngine`` and ``ImageTripletEngine`` are constructed (also ``VideoSoftmaxEngine`` and ``VideoTripletEngine``). All you need to implement might be just a ``forward_backward()`` function.
+
+
+Use Torchreid as a feature extractor in your projects
+-------------------------------------------------------
+We have provided a simple API for feature extraction, which accepts input of various types such as a list of image paths or numpy arrays. More details can be found in the code at ``torchreid/utils/feature_extractor.py``. Here we show a simple example of how to extract features given a list of image paths.
+
+.. code-block:: python
+
+    from torchreid.utils import FeatureExtractor
+
+    extractor = FeatureExtractor(
+        model_name='osnet_x1_0',
+        model_path='a/b/c/model.pth.tar',
+        device='cuda'
+    )
+
+    image_list = [
+        'a/b/c/image001.jpg',
+        'a/b/c/image002.jpg',
+        'a/b/c/image003.jpg',
+        'a/b/c/image004.jpg',
+        'a/b/c/image005.jpg'
+    ]
+
+    features = extractor(image_list)
+    print(features.shape) # output (5, 512)
diff --git a/pfe/linter.sh b/pfe/linter.sh
new file mode 100644
index 0000000..9db34f9
--- /dev/null
+++ b/pfe/linter.sh
@@ -0,0 +1,11 @@
+echo "Running isort"
+isort -y -sp .
+echo "Done"
+
+echo "Running yapf"
+yapf -i -r -vv -e build .
+echo "Done"
+
+echo "Running flake8"
+flake8 .
+echo "Done"
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/.flake8 b/pfe/player-feature-extractor/.flake8
new file mode 100644
index 0000000..4fc103c
--- /dev/null
+++ b/pfe/player-feature-extractor/.flake8
@@ -0,0 +1,18 @@
+[flake8]
+ignore =
+    # At least two spaces before inline comment
+    E261,
+    # Line lengths are recommended to be no greater than 79 characters
+    E501,
+    # Missing whitespace around arithmetic operator 
+    E226,
+    # Blank line contains whitespace
+    W293,
+    # Do not use bare 'except'
+    E722,
+    # Line break after binary operator
+    W504,
+    # isort found an import in the wrong position
+    I001
+max-line-length = 79
+exclude = __init__.py, build, torchreid/metrics/rank_cylib/
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/.gitignore b/pfe/player-feature-extractor/.gitignore
new file mode 100644
index 0000000..6c093a6
--- /dev/null
+++ b/pfe/player-feature-extractor/.gitignore
@@ -0,0 +1,140 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Cython eval code
+*.c
+*.html
+
+# OS X
+.DS_Store
+.Spotlight-V100
+.Trashes
+._*
+
+# ReID
+reid-data/
+log/
+saved-models/
+model-zoo/
+debug*
diff --git a/pfe/player-feature-extractor/.isort.cfg b/pfe/player-feature-extractor/.isort.cfg
new file mode 100644
index 0000000..8039326
--- /dev/null
+++ b/pfe/player-feature-extractor/.isort.cfg
@@ -0,0 +1,10 @@
+[isort]
+line_length=79
+multi_line_output=3
+length_sort=true
+known_standard_library=numpy,setuptools
+known_myself=torchreid
+known_third_party=matplotlib,cv2,torch,torchvision,PIL,yacs
+no_lines_before=STDLIB,THIRDPARTY
+sections=FUTURE,STDLIB,THIRDPARTY,myself,FIRSTPARTY,LOCALFOLDER
+default_section=FIRSTPARTY
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/.style.yapf b/pfe/player-feature-extractor/.style.yapf
new file mode 100644
index 0000000..29d8e52
--- /dev/null
+++ b/pfe/player-feature-extractor/.style.yapf
@@ -0,0 +1,7 @@
+[style]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
+DEDENT_CLOSING_BRACKETS = true
+SPACES_BEFORE_COMMENT = 1
+ARITHMETIC_PRECEDENCE_INDICATION = true
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/LICENSE b/pfe/player-feature-extractor/LICENSE
new file mode 100644
index 0000000..d2bcb88
--- /dev/null
+++ b/pfe/player-feature-extractor/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Kaiyang Zhou
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/pfe/player-feature-extractor/bepro_data_collect.py b/pfe/player-feature-extractor/bepro_data_collect.py
new file mode 100644
index 0000000..6a395f7
--- /dev/null
+++ b/pfe/player-feature-extractor/bepro_data_collect.py
@@ -0,0 +1,149 @@
+import shutil
+import os
+import glob
+import cv2
+
+training_data_path = '/home/dmitriy.khvan/pytorch-beginner/training/'
+validation_data_path = '/home/dmitriy.khvan/pytorch-beginner/validation/'
+
+training_data_dest = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro/bounding_box_train'
+query_data_dest = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro/query'
+test_data_dest = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro/test'
+
+copy_data_path = '/home/dmitriy.khvan/deep-person-reid/reid-data/bepro_tmp'
+
+train_dir_list = os.listdir(training_data_path)
+valid_dir_list = os.listdir(validation_data_path)
+tmp_dir_list = os.listdir(copy_data_path)
+
+def copy_files():
+    count = 0
+
+    for d in train_dir_list:
+        folder_indices = [0]
+
+        copy_dir = os.path.join(copy_data_path, d)
+        os.mkdir(copy_dir)
+
+        for fi in folder_indices:
+            path2dir = os.path.join(training_data_path, d, str(fi))
+            
+            if os.path.isdir(path2dir):
+                # pid_list = os.listdir(path2dir)
+                pid_list = ['0', '1']
+
+                for p in pid_list:
+                    copy_dir_low = os.path.join(copy_dir, p)
+                    os.mkdir(copy_dir_low)
+
+                    path2pid = os.path.join(path2dir, p)
+
+                    if os.path.isdir(path2pid):
+                        for filename in os.listdir(path2pid):
+                            path2file = os.path.join(path2pid, filename)
+
+                            imgidx = int(filename.split('.')[0])
+                            img_indices = [0, 10, 15, 20, 25, 29]
+
+                            if imgidx in img_indices:
+                                print (path2file)
+                                image = cv2.imread(path2file)
+                                
+                                if image.shape[0] < 110 and image.shape[1] < 40:
+                                    continue
+            
+                                path2cp_dest = os.path.join(copy_dir_low, filename)
+
+                                image_res = cv2.resize(image, (64,128))
+                                cv2.imwrite(path2cp_dest, image_res)
+                                count = count + 1
+
+    print (count)
+    
+
+def create_test_dataset(pid):
+    pid = pid +1 
+
+    count_q = 0
+    count_t = 0
+
+    for d in valid_dir_list:
+
+        folder_indices = [0,1,2,3]
+
+        for fi in folder_indices:
+            path2dir = os.path.join(validation_data_path, d, str(fi))
+
+            if os.path.isdir(path2dir):
+                pid_list = os.listdir(path2dir)
+
+                for p in pid_list:
+                    path2pid = os.path.join(path2dir, p)
+                    
+                    pid = pid + 1
+                    for filename in os.listdir(path2pid):
+                        path2file = os.path.join(path2pid, filename)
+
+                        imgidx = int(filename.split('.')[0])
+                        img_indices = [5, 10, 15, 20, 25, 29]
+
+                        image = cv2.imread(path2file)
+                        
+                        if imgidx == 0:
+                            #copy to query
+                            new_filename = '%d_%s_%d.jpg' % (pid, '0', imgidx)
+                            path2dest = os.path.join(query_data_dest, new_filename)
+                            count_q = count_q + 1
+                        elif imgidx in img_indices:
+                            #copy to test
+                            new_filename = '%d_%s_%d.jpg' % (pid, '1', imgidx)
+                            path2dest = os.path.join(test_data_dest, new_filename)
+                            count_t = count_t + 1
+                        else:
+                            continue
+
+                        print (path2dest)
+
+                        image_res = cv2.resize(image, (64,128))
+                        cv2.imwrite(path2dest, image_res)
+
+    print (count_q)
+    print (count_t)
+
+def create_training_dataset():
+    pid = 1
+    
+    for d in tmp_dir_list:
+        
+        path2dir = os.path.join(copy_data_path, d)
+        pid_list = os.listdir(path2dir)
+
+        print (d)
+        print (pid_list)
+        
+        for p in pid_list:
+            path2pid = os.path.join(path2dir, p)
+            print (path2pid)
+            pid = pid + 1
+            fcount = 0
+
+            for filename in os.listdir(path2pid):
+                path2file = os.path.join(path2pid, filename)
+                print (path2file)
+
+                image = cv2.imread(path2file)
+
+                new_filename = '%d_%s_%d.jpg' % (pid, '1', fcount)
+                path2dest = os.path.join(training_data_dest, new_filename)
+
+                # print (path2dest)
+                cv2.imwrite(path2dest, image)
+
+                fcount = fcount + 1
+
+    return pid
+
+if __name__ == "__main__":
+    # copy_files()
+    pid = create_training_dataset()
+    create_test_dataset(pid)
diff --git a/pfe/player-feature-extractor/checkpoints/market_100e.pth b/pfe/player-feature-extractor/checkpoints/market_100e.pth
new file mode 100644
index 0000000..beadaa6
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diff --git a/pfe/player-feature-extractor/checkpoints/market_combined_120e.pth b/pfe/player-feature-extractor/checkpoints/market_combined_120e.pth
new file mode 100644
index 0000000..f9f0573
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diff --git a/pfe/player-feature-extractor/checkpoints/market_cuhk03_100e.pth b/pfe/player-feature-extractor/checkpoints/market_cuhk03_100e.pth
new file mode 100644
index 0000000..3ac3699
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diff --git a/pfe/player-feature-extractor/checkpoints/market_cuhk_prid_88e.pth b/pfe/player-feature-extractor/checkpoints/market_cuhk_prid_88e.pth
new file mode 100644
index 0000000..62d6b68
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diff --git a/pfe/player-feature-extractor/configs/im_osnet_ain_x1_0_softmax_256x128_amsgrad_cosine.yaml b/pfe/player-feature-extractor/configs/im_osnet_ain_x1_0_softmax_256x128_amsgrad_cosine.yaml
new file mode 100644
index 0000000..c8b63ec
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_osnet_ain_x1_0_softmax_256x128_amsgrad_cosine.yaml
@@ -0,0 +1,35 @@
+model:
+  name: 'osnet_ain_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501', 'dukemtmcreid']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip', 'color_jitter']
+  save_dir: 'log/osnet_ain_x1_0_market1501_softmax_cosinelr'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 100
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'cosine'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_osnet_ibn_x1_0_softmax_256x128_amsgrad.yaml b/pfe/player-feature-extractor/configs/im_osnet_ibn_x1_0_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..b4f8903
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_osnet_ibn_x1_0_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_ibn_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['dukemtmcreid']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip', 'color_jitter']
+  save_dir: 'log/osnet_ibn_x1_0_market2duke_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 150
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [60]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_osnet_x0_25_softmax_256x128_amsgrad.yaml b/pfe/player-feature-extractor/configs/im_osnet_x0_25_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..d256a6b
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_osnet_x0_25_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x0_25'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x0_25_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.003
+  max_epoch: 180
+  batch_size: 128
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [80]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_osnet_x0_5_softmax_256x128_amsgrad.yaml b/pfe/player-feature-extractor/configs/im_osnet_x0_5_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..467305f
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_osnet_x0_5_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x0_5'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x0_5_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.003
+  max_epoch: 180
+  batch_size: 128
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [80]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_osnet_x0_75_softmax_256x128_amsgrad.yaml b/pfe/player-feature-extractor/configs/im_osnet_x0_75_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..04203fd
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_osnet_x0_75_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x0_75'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x0_75_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 150
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [60]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml b/pfe/player-feature-extractor/configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..f1c970f
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'osnet_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x1_0_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 150
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [60]
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml b/pfe/player-feature-extractor/configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml
new file mode 100644
index 0000000..29750bc
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml
@@ -0,0 +1,35 @@
+model:
+  name: 'osnet_x1_0'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/osnet_x1_0_market1501_softmax_cosinelr'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 250
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_r50_softmax_256x128_amsgrad.yaml b/pfe/player-feature-extractor/configs/im_r50_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..6173589
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_r50_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'resnet50_fc512'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/resnet50_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0003
+  max_epoch: 60
+  batch_size: 32
+  fixbase_epoch: 5
+  open_layers: ['classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [20]
+
+test:
+  batch_size: 100
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/configs/im_r50fc512_softmax_256x128_amsgrad.yaml b/pfe/player-feature-extractor/configs/im_r50fc512_softmax_256x128_amsgrad.yaml
new file mode 100644
index 0000000..5ec2b70
--- /dev/null
+++ b/pfe/player-feature-extractor/configs/im_r50fc512_softmax_256x128_amsgrad.yaml
@@ -0,0 +1,36 @@
+model:
+  name: 'resnet50_fc512'
+  pretrained: True
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip']
+  save_dir: 'log/resnet50_fc512_market1501_softmax'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0003
+  max_epoch: 60
+  batch_size: 32
+  fixbase_epoch: 5
+  open_layers: ['fc', 'classifier']
+  lr_scheduler: 'single_step'
+  stepsize: [20]
+
+test:
+  batch_size: 100
+  dist_metric: 'euclidean'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/AWESOME_REID.md b/pfe/player-feature-extractor/docs/AWESOME_REID.md
new file mode 100644
index 0000000..16c75cc
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/AWESOME_REID.md
@@ -0,0 +1,69 @@
+# Awesome-ReID
+Here is a collection of ReID-related research with links to papers and code. You are welcome to submit [PR](https://help.github.com/articles/creating-a-pull-request/)s if you find something missing.
+
+
+- [ArXiv20] Deep Learning for Person Re-identification: A Survey and Outlook [[paper](https://arxiv.org/abs/2001.04193)] [[code](https://github.com/mangye16/ReID-Survey)]
+
+- [ArXiv19] Learning Generalisable Omni-Scale Representations for Person Re-Identification [[paper](https://arxiv.org/abs/1910.06827)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [ICCV19] RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_RGB-Infrared_Cross-Modality_Person_Re-Identification_via_Joint_Pixel_and_Feature_Alignment_ICCV_2019_paper.pdf)] [[code](https://github.com/wangguanan/AlignGAN)]
+
+- [ICCV19] Unsupervised Graph Association for Person Re-identification. [[paper](https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification)] [[code](https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification)]
+
+- [ICCV19] Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf)] [[code](https://github.com/OasisYang/SSG)]
+
+- [ICCV19] Spectral Feature Transformation for Person Re-Identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_Spectral_Feature_Transformation_for_Person_Re-Identification_ICCV_2019_paper.pdf)] [[code](https://github.com/LuckyDC/SFT_REID)]
+
+- [ICCV19] Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Guo_Beyond_Human_Parts_Dual_Part-Aligned_Representations_for_Person_Re-Identification_ICCV_2019_paper.pdf)] [[code](https://github.com/ggjy/P2Net.pytorch)]
+
+- [ICCV19] Co-segmentation Inspired Attention Networks for Video-based Person Re-identification. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Subramaniam_Co-Segmentation_Inspired_Attention_Networks_for_Video-Based_Person_Re-Identification_ICCV_2019_paper.pdf)][[code](https://github.com/InnovArul/vidreid_cosegmentation)]
+
+- [ICCV19] Mixed High-Order Attention Network for Person Re-Identification. [[paper](https://arxiv.org/abs/1908.05819)][[code](https://github.com/chenbinghui1/MHN)]
+
+- [ICCV19] ABD-Net: Attentive but Diverse Person Re-Identification. [[paper](https://arxiv.org/abs/1908.01114)] [[code](https://github.com/TAMU-VITA/ABD-Net)]
+
+- [ICCV19] Omni-Scale Feature Learning for Person Re-Identification. [[paper](https://arxiv.org/abs/1905.00953)] [[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [CVPR19] Joint Discriminative and Generative Learning for Person Re-identification. [[paper](https://arxiv.org/abs/1904.07223)][[code](https://github.com/NVlabs/DG-Net)]
+- [CVPR19] Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. [[paper](https://arxiv.org/abs/1904.01990)][[code](https://github.com/zhunzhong07/ECN)]
+- [CVPR19] Dissecting Person Re-identification from the Viewpoint of Viewpoint. [[paper](https://arxiv.org/abs/1812.02162)][[code](https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint)]
+- [CVPR19] Unsupervised Person Re-identification by Soft Multilabel Learning. [[paper](https://arxiv.org/abs/1903.06325)][[code](https://github.com/KovenYu/MAR)]
+- [CVPR19] Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification. [[paper](https://kovenyu.com/publication/2019-cvpr-pedal/)][[code](https://github.com/QizeYang/PAUL)]
+
+- [AAAI19] Spatial and Temporal Mutual Promotion for Video-based Person Re-identification. [[paper](https://arxiv.org/abs/1812.10305)][[code](https://github.com/yolomax/person-reid-lib)]
+
+- [AAAI19] Spatial-Temporal Person Re-identification. [[paper](https://arxiv.org/abs/1812.03282)][[code](https://github.com/Wanggcong/Spatial-Temporal-Re-identification)]
+
+- [AAAI19] Horizontal Pyramid Matching for Person Re-identification. [[paper](https://arxiv.org/abs/1804.05275)][[code](https://github.com/OasisYang/HPM)]
+
+- [AAAI19] Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-identification. [[paper](https://arxiv.org/abs/1901.06140)][[code](https://github.com/youngminPIL/rollback)]
+
+- [AAAI19] A Bottom-Up Clustering Approach to Unsupervised Person Re-identification. [[paper](https://vana77.github.io/vana77.github.io/images/AAAI19.pdf)][[code](https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification)]
+
+- [NIPS18] FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification. [[paper](https://arxiv.org/abs/1810.02936)][[code](https://github.com/yxgeee/FD-GAN)]
+
+- [ECCV18] Generalizing A Person Retrieval Model Hetero- and Homogeneously. [[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.pdf)][[code](https://github.com/zhunzhong07/HHL)]
+
+- [ECCV18] Pose-Normalized Image Generation for Person Re-identification. [[paper](https://arxiv.org/abs/1712.02225)][[code](https://github.com/naiq/PN_GAN)]
+
+- [CVPR18] Camera Style Adaptation for Person Re-Identification. [[paper](https://arxiv.org/abs/1711.10295)][[code](https://github.com/zhunzhong07/CamStyle)]
+
+- [CVPR18] Deep Group-Shuffling Random Walk for Person Re-Identification. [[paper](https://arxiv.org/abs/1807.11178)][[code](https://github.com/YantaoShen/kpm_rw_person_reid)]
+
+- [CVPR18] End-to-End Deep Kronecker-Product Matching for Person Re-identification. [[paper](https://arxiv.org/abs/1807.11182)][[code](https://github.com/YantaoShen/kpm_rw_person_reid)]
+
+- [CVPR18] Features for Multi-Target Multi-Camera Tracking and Re-Identification. [[paper](https://arxiv.org/abs/1803.10859)][[code](https://github.com/ergysr/DeepCC)]
+
+- [CVPR18] Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Group_Consistent_Similarity_CVPR_2018_paper.pdf)][[code](https://github.com/dapengchen123/crf_affinity)]
+
+- [CVPR18] Harmonious Attention Network for Person Re-Identification. [[paper](https://arxiv.org/abs/1802.08122)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [CVPR18] Human Semantic Parsing for Person Re-Identification. [[paper](https://arxiv.org/abs/1804.00216)][[code](https://github.com/emrahbasaran/SPReID)]
+
+- [CVPR18] Multi-Level Factorisation Net for Person Re-Identification. [[paper](https://arxiv.org/abs/1803.09132)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+
+- [CVPR18] Resource Aware Person Re-identification across Multiple Resolutions. [[paper](https://arxiv.org/abs/1805.08805)][[code](https://github.com/mileyan/DARENet)]
+
+- [CVPR18] Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. [[paper](https://yu-wu.net/pdf/CVPR2018_Exploit-Unknown-Gradually.pdf)][[code](https://github.com/Yu-Wu/Exploit-Unknown-Gradually)]
+
+- [ArXiv18] Revisiting Temporal Modeling for Video-based Person ReID. [[paper](https://arxiv.org/abs/1805.02104)][[code](https://github.com/jiyanggao/Video-Person-ReID)]
diff --git a/pfe/player-feature-extractor/docs/MODEL_ZOO.md b/pfe/player-feature-extractor/docs/MODEL_ZOO.md
new file mode 100644
index 0000000..a363bab
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/MODEL_ZOO.md
@@ -0,0 +1,74 @@
+# Model Zoo
+
+- Results are presented in the format of *<Rank-1 (mAP)>*.
+- When computing model size and FLOPs, only layers that are used at test time are considered (see `torchreid.utils.compute_model_complexity`).
+- Asterisk (\*) means the model is trained from scratch.
+- `combineall=True` means all images in the dataset are used for model training.
+
+
+## ImageNet pretrained models
+
+
+| Model | Download |
+| :--- | :---: |
+| shufflenet | [model](https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrgfiJO9kiidz9fZM) |
+| mobilenetv2_x1_0 | [model](https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NUOWvvGPHhaNr-fASF6c) |
+| mobilenetv2_x1_4 | [model](https://mega.nz/#!RGhgEIwS!xN2s2ZdyqI6vQ3EwgmRXLEW3khr9tpXg96G9SUJugGk) |
+| mlfn | [model](https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk) |
+| osnet_x1_0 | [model](https://mega.nz/#!YK5GRARL!F90NsNB2XHjXGZFC3Lrw1GMic0oMw4fnfuDUnSrPAYM) |
+| osnet_x0_75 | [model](https://mega.nz/#!NPxilYBA!Se414Wtts__7eY6J5FIrowynvjUUG7a8Z5zUPfJN33s) |
+| osnet_x0_5 | [model](https://mega.nz/#!NO4ihQSJ!oMIRSZ0HlJF_8FKUbXT8Ei0vzH0xUYs5tWaf_KLrODg) |
+| osnet_x0_25 | [model](https://mega.nz/#!IDwQwaxT!TbQ_33gPK-ZchPFTf43UMc45rlNKWiWMqH4rTXB1T7k) |
+| osnet_ibn_x1_0 | [model](https://mega.nz/#!8Wo2kSDR!bNvgu4V0VkCQp_L2ZUDaudYKYRCkkSNdzcA1CcZGZTE) |
+| osnet_ain_x1_0 | [model](https://drive.google.com/open?id=1-CaioD9NaqbHK_kzSMW8VE4_3KcsRjEo) |
+
+
+## Same-domain ReID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | market1501  | dukemtmcreid | msmt17 |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [87.9 (70.4)](https://mega.nz/#!FKZjVKaZ!4v_FR8pTvuHoMQIKdstJ_YCsRrtZW2hwWxc-T0JIlHE) | [78.3 (58.9)](https://mega.nz/#!JPZjCYhK!YVJbE_4vTc8DX19Rt_FB77YY4BaEA1P6Xb5sNJGep2M) | [63.2 (33.9)](https://mega.nz/#!APAxDY4Z!Iou9x8s3ATdYS2SlK2oiJbHrhvlzH7F1gE2qjM-GJGw) |
+| resnet50_fc512 | 24.6 | 4.1 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [90.8 (75.3)](https://mega.nz/#!EaZjhKyS!lBvD3vAJ4DOmElZkNa7gyPM1RE661GUd2v9kK84gSZE) | [81.0 (64.0)](https://mega.nz/#!lXYDSKZa!lumiXkY2H5Sm8gEgTWPBdWKv3ujy4zjrffjERaXkc9I) | [69.6 (38.4)](https://mega.nz/#!9PQTXIpL!iI5wgieTCn0Jm-pyg9RCu0RkH43pV3ntHhr1PeqSyT4) |
+| mlfn | 32.5 | 2.8 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [90.1 (74.3)](https://mega.nz/#!kHQ3ESLT!NoGc8eHEBZOJZM19THh3DFfRBXIPXzM-sdLmF1mvTXA) | [81.1 (63.2)](https://mega.nz/#!8PQXUCaI!mJO1vD9tI739hkNBj2QWUt0VPcZ-s89fSMMGPPP1msc) | [66.4 (37.2)](https://mega.nz/#!paIXFQCS!W3ZGkxyF1idwvQzTRDE2p0DhNDki2SBJRfp7S_Cwphk) |
+| hacnn<sup>*</sup> | 4.5 | 0.5 | softmax | (160, 64) | `random_flip`, `random_crop` | `euclidean` | [90.9 (75.6)](https://mega.nz/#!ULQXUQBK!S-8v_pR2xBD3ZpuY0I7Bqift-eX_V84gajHMDG6zUac) | [80.1 (63.2)](https://mega.nz/#!wPJTkAQR!XkKd39lsmBZMrCh3JjF6vnNafBZkouVIVdeBqQKdSzA) | [64.7 (37.2)](https://mega.nz/#!AXAziKjL!JtMwHz2UYy58gDMQLGakSmF3JOr72o8zmkqlQA-LIpQ) |
+| mobilenetv2_x1_0 | 2.2 | 0.2 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [85.6 (67.3)](https://mega.nz/#!8KYTFAIB!3dL35WQLxSoTSClDTv0kxa81k3fh5hXmAWA4_a3qiOI) | [74.2 (54.7)](https://mega.nz/#!hbRXDSCL!YYgqJ6PVUf4clgtUuK2s5FRhYJdU3yTibLscwOTNnDk) | [57.4 (29.3)](https://mega.nz/#!5SJTmCYb!ZQ8O2MN9JF4-WDAeX04Xex1KyuBYQ_o2aoMIsTgQ748) |
+| mobilenetv2_x1_4 | 4.3 | 0.4 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [87.0 (68.5)](https://mega.nz/#!4XZhEKCS!6lTuTRbHIWU5nzJzTPDGykA7sPME8_1ISGsUYFJXZWA) | [76.2 (55.8)](https://mega.nz/#!JbQVDIYQ!-7pnjIfpIDt1EoQOvpvuIEcTj3Qg8SE6o_3ZPGWrIcw) | [60.1 (31.5)](https://mega.nz/#!gOYDAQrK!sMJO7c_X4iIxoVfV_tXYdzeDJByPo5XkUjEN7Z2JTmM) |
+| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip` | `euclidean` | [94.2 (82.6)](https://mega.nz/#!hLoyTSba!fqt7GcKrHJhwe9BtuK0ozgVAQcrlMG8Pm6JsSfr5HEI) | [87.0 (70.2)](https://mega.nz/#!ETwGhQYB!h2gHN-H3J4X4WqcJXy2b0pPKl28paydkiS-PDHsEgPM) | [74.9 (43.8)](https://mega.nz/#!hWxE2aJA!NGcxu5uYH1qI6DfBTu0KFoi_NfoA0TJcBFW-g43pC0I) |
+| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | `random_flip` | `euclidean` | [93.7 (81.2)](https://mega.nz/#!JO4WAaJa!nQuoqZnYfy0xu7vs2mp28AFceya-ZhrXTry837jvoDQ) | [85.8 (69.8)](https://mega.nz/#!lOgkEIoI!fQ5vuYIABIOcRxF-OK-6YxtEufWhyVkYkGB4qPoRYJ4) | [72.8 (41.4)](https://mega.nz/#!0exGXI5a!rxtzBayyRK0on0HFq9XO0UtWEBhbV86dFitljhjeWcs) |
+| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | `random_flip` | `euclidean` | [92.5 (79.8)](https://mega.nz/#!QCx0RArD!hqz3Mh0Iif5d8PpQW0frxa-Tepn2a2g24aei7du4MFs) | [85.1 (67.4)](https://mega.nz/#!QTxCDIbT!eOZxj4dHl0uFnjKEB-J3YBY98blXZvppgWGA3CGa-tk) | [69.7 (37.5)](https://mega.nz/#!ETpiECDa!CCkq4JryztHqgw7spL5zDw0usJpAfEsSd5gPlkMufCc) |
+| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | `random_flip` | `euclidean` | [91.2 (75.0)](https://mega.nz/#!VWxCgSqY!Q4WaQ3j9D7HMhK3jsbvMuwaZ7yBY80T2Zj5V8JAlAKU) | [82.0 (61.4)](https://mega.nz/#!5TpwnATK!UvU_Asdy_aJ9SNzuvqhEFoemxSSB8vm_Gm8Xe03jqiA) | [61.4 (29.5)](https://mega.nz/#!AWgE3SzD!DngUaNyA7VIqOd2gq10Aty_-ER0CmG0xTJLHLj6_36g) |
+
+
+## Cross-domain ReID
+
+#### Market1501 -> DukeMTMC-reID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance  | Rank-1 | Rank-5 | Rank-10 | mAP | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| osnet_ibn_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 48.5 | 62.3 | 67.4 | 26.7 | [model](https://mega.nz/#!wXwGxKxK!f8EMk8hBt6AjxU3JIPGMFSMvX7j-Nt5Lp1Gpbqso1Ts) |
+| osnet_ain_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `cosine` | 52.4 | 66.1 | 71.2 | 30.5 | [model](https://mega.nz/#!QLJE2CRI!FXYc3Vm6Y5Scwx0xvRwBJxId56kf06fIXNLwA_b_1FE) |
+
+
+#### DukeMTMC-reID -> Market1501
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance  | Rank-1 | Rank-5 | Rank-10 | mAP | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| osnet_ibn_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 57.7 | 73.7 | 80.0 | 26.1 | [model](https://mega.nz/#!FD4WEKJS!ZGgI-2IwVuX6re09xylChR03o6Dkjpi6KSebrbS0fAA) |
+| osnet_ain_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `cosine` | 61.0 | 77.0 | 82.5 | 30.6 | [model](https://mega.nz/#!4PBQlCCL!9yMHu1WyyBVxqssubLAEyoEfHUiNP4Ggg5On0nCX2S4) |
+
+
+#### MSMT17 (`combineall=True`) -> Market1501 & DukeMTMC-reID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | msmt17 -> market1501 | msmt17 -> dukemtmcreid | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: |
+| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 46.3 (22.8) | 52.3 (32.1) | [model](https://mega.nz/#!VTpkWSbS!Y8gDnmg7u-sPwnZDhWXrtZNYOj7UYL4QzZkhDf1qWW4) |
+| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 66.6 (37.5) | 66.0 (45.3) | [model](https://mega.nz/#!MepG3QRC!Lb-C9d7rdS_YJjGSoJ5cRlzjYcP28P_1Cm5S5WSslW0) |
+| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 63.6 (35.5) | 65.3 (44.5) | [model](https://mega.nz/#!tO4WDagL!8Tl6kdJWRXRHQb16GeUHR008tJqW3N7_3fyVMu-LcKM) |
+| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 64.3 (34.9) | 65.2 (43.3) | [model](https://mega.nz/#!papSWQhY!IId-QfcHj7nXQ_muUubgv9_n0SsnZzarmb5mQgcMv74) |
+| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 59.9 (31.0) | 61.5 (39.6) | [model](https://mega.nz/#!QCoE0Kpa!BITLANumgjiR68TUFteL__N_RIoDKkL0M5Bl3Q8LC3U) |
+| osnet_ibn_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 66.5 (37.2) | 67.4 (45.6) | [model](https://mega.nz/#!dL4Q2K5B!ZdHQ_X_rs2T-xmggigM5YvzJhmT1orkr6aQ1_fHgunM) |
+| osnet_ain_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `cosine` | 70.1 (43.3) | 71.1 (52.7) | [model](https://mega.nz/#!YTZFnSJY!wlbo_5oa2TpDAGyWCTKTX1hh4d6DvJhh_RUA2z6i_so) |
diff --git a/pfe/player-feature-extractor/docs/Makefile b/pfe/player-feature-extractor/docs/Makefile
new file mode 100644
index 0000000..298ea9e
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/Makefile
@@ -0,0 +1,19 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/conf.py b/pfe/player-feature-extractor/docs/conf.py
new file mode 100644
index 0000000..4d27eed
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/conf.py
@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+#
+# Configuration file for the Sphinx documentation builder.
+#
+# This file does only contain a selection of the most common options. For a
+# full list see the documentation:
+# http://www.sphinx-doc.org/en/master/config
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath('..'))
+
+# -- Project information -----------------------------------------------------
+
+project = u'torchreid'
+copyright = u'2019, Kaiyang Zhou'
+author = u'Kaiyang Zhou'
+
+version_file = '../torchreid/__init__.py'
+with open(version_file, 'r') as f:
+    exec(compile(f.read(), version_file, 'exec'))
+__version__ = locals()['__version__']
+
+# The short X.Y version
+version = __version__
+# The full version, including alpha/beta/rc tags
+release = __version__
+
+# -- General configuration ---------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinxcontrib.napoleon',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.githubpages',
+    'sphinx_markdown_tables',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+source_suffix = ['.rst', '.md']
+# source_suffix = '.rst'
+source_parsers = {'.md': 'recommonmark.parser.CommonMarkParser'}
+
+# The master toctree document.
+master_doc = 'index'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = None
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# The default sidebars (for documents that don't match any pattern) are
+# defined by theme itself.  Builtin themes are using these templates by
+# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
+# 'searchbox.html']``.
+#
+# html_sidebars = {}
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'torchreiddoc'
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (
+        master_doc, 'torchreid.tex', u'torchreid Documentation',
+        u'Kaiyang Zhou', 'manual'
+    ),
+]
+
+# -- Options for manual page output ------------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'torchreid', u'torchreid Documentation', [author], 1)
+]
+
+# -- Options for Texinfo output ----------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        master_doc, 'torchreid', u'torchreid Documentation', author,
+        'torchreid', 'One line description of project.', 'Miscellaneous'
+    ),
+]
+
+# -- Options for Epub output -------------------------------------------------
+
+# Bibliographic Dublin Core info.
+epub_title = project
+
+# The unique identifier of the text. This can be a ISBN number
+# or the project homepage.
+#
+# epub_identifier = ''
+
+# A unique identification for the text.
+#
+# epub_uid = ''
+
+# A list of files that should not be packed into the epub file.
+epub_exclude_files = ['search.html']
+
+# -- Extension configuration -------------------------------------------------
diff --git a/pfe/player-feature-extractor/docs/datasets.rst b/pfe/player-feature-extractor/docs/datasets.rst
new file mode 100644
index 0000000..5a1d601
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/datasets.rst
@@ -0,0 +1,249 @@
+.. _datasets:
+
+Datasets
+=========
+
+Here we provide a guide on how to prepare datasets.
+
+Suppose you wanna store the reid data in a directory called "path/to/reid-data/", you need to specify the ``root`` as *root='path/to/reid-data/'* when initializing ``DataManager``. Below we use ``$REID`` to denote "path/to/reid-data".
+
+Please refer to :ref:`torchreid_data` for details regarding the arguments.
+
+
+.. note::
+    Dataset with a :math:`\dagger` superscript means that the process is automated, so you can directly call the dataset in ``DataManager`` (which automatically downloads the dataset and organizes the data structure). However, we also provide a way below to help the manual setup in case the automation fails.
+
+
+.. note::
+    The keys to use specific datasets are enclosed in the parantheses beside the datasets.
+
+
+.. note::
+    You are suggested to use the provided names for dataset folders such as "market1501" for Market1501 and "dukemtmcreid" for DukeMTMC-reID when doing the manual setup, otherwise you need to modify the source code accordingly (i.e. the ``dataset_dir`` attribute).
+
+
+.. contents::
+   :local:
+
+
+Image Datasets
+--------------
+
+Market1501 :math:`^\dagger` (``market1501``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a directory named "market1501" under ``$REID``.
+- Download the dataset to "market1501" from http://www.liangzheng.org/Project/project_reid.html and extract the files.
+- The data structure should look like
+
+.. code-block:: none
+    
+    market1501/
+        Market-1501-v15.09.15/
+            query/
+            bounding_box_train/
+            bounding_box_test/
+
+- To use the extra 500K distractors (i.e. Market1501 + 500K), go to the **Market-1501+500k Dataset** section at http://www.liangzheng.org/Project/project_reid.html, download the zip file "distractors_500k.zip" and extract it under "market1501/Market-1501-v15.09.15". The argument to use these 500K distrctors is ``market1501_500k`` in ``ImageDataManager``.
+
+
+CUHK03 (``cuhk03``)
+^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "cuhk03" under ``$REID``.
+- Download the dataset to "cuhk03/" from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and extract "cuhk03_release.zip", resulting in "cuhk03/cuhk03_release/".
+- Download the new split (767/700) from `person-re-ranking <https://github.com/zhunzhong07/person-re-ranking/tree/master/evaluation/data/CUHK03>`_. What you need are "cuhk03_new_protocol_config_detected.mat" and "cuhk03_new_protocol_config_labeled.mat". Put these two mat files under "cuhk03/".
+- The data structure should look like
+
+.. code-block:: none
+    
+    cuhk03/
+        cuhk03_release/
+        cuhk03_new_protocol_config_detected.mat
+        cuhk03_new_protocol_config_labeled.mat
+
+
+- In the default mode, we load data using the new split (767/700). If you wanna use the original (20) splits (1367/100), please set ``cuhk03_classic_split`` to True in ``ImageDataManager``. As the CMC is computed differently from Market1501 for the 1367/100 split (see `here <http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html>`_), you need to enable ``use_metric_cuhk03`` in ``ImageDataManager`` to activate the *single-gallery-shot* metric for fair comparison with some methods that adopt the old splits (*do not need to report mAP*). In addition, we support both *labeled* and *detected* modes. The default mode loads *detected* images. Enable ``cuhk03_labeled`` in ``ImageDataManager`` if you wanna train and test on *labeled* images.
+
+.. note::
+    The code will extract images in "cuhk-03.mat" and save them under "cuhk03/images_detected" and "cuhk03/images_labeled". Also, four json files will be automatically generated, i.e. "splits_classic_detected.json", "splits_classic_labeled.json", "splits_new_detected.json" and "splits_new_labeled.json". If the parent path of ``$REID`` is changed, these json files should be manually deleted. The code can automatically generate new json files to match the new path.
+    
+
+DukeMTMC-reID :math:`^\dagger` (``dukemtmcreid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a directory called "dukemtmc-reid" under ``$REID``.
+- Download "DukeMTMC-reID" from http://vision.cs.duke.edu/DukeMTMC/ and extract it under "dukemtmc-reid".
+- The data structure should look like
+
+.. code-block:: none
+    
+    dukemtmc-reid/
+        DukeMTMC-reID/
+            query/
+            bounding_box_train/
+            bounding_box_test/
+            ...
+
+MSMT17 (``msmt17``)
+^^^^^^^^^^^^^^^^^^^^^
+- Create a directory called "msmt17" under ``$REID``.
+- Download the dataset from http://www.pkuvmc.com/publications/msmt17.html to "msmt17" and extract the files.
+- The data structure should look like
+
+.. code-block:: none
+    
+    msmt17/
+        MSMT17_V1/ # or MSMT17_V2
+            train/
+            test/
+            list_train.txt
+            list_query.txt
+            list_gallery.txt
+            list_val.txt
+
+VIPeR :math:`^\dagger` (``viper``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- The download link is http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip.
+- Organize the dataset in a folder named "viper" as follows
+
+.. code-block:: none
+    
+    viper/
+        VIPeR/
+            cam_a/
+            cam_b/
+
+GRID :math:`^\dagger` (``grid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- The download link is http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip.
+- Organize the dataset in a folder named "grid" as follows
+
+.. code-block:: none
+    
+    grid/
+        underground_reid/
+            probe/
+            gallery/
+            ...
+
+CUHK01 (``cuhk01``)
+^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "cuhk01" under ``$REID``.
+- Download "CUHK01.zip" from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and place it under "cuhk01/".
+- The code can automatically extract the files, or you can do it yourself.
+- The data structure should look like
+
+.. code-block:: none
+    
+    cuhk01/
+        campus/
+
+SenseReID (``sensereid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create "sensereid" under ``$REID``.
+- Download the dataset from this `link <https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view>`_ and extract it to "sensereid".
+- Organize the data to be like
+
+.. code-block:: none
+
+    sensereid/
+        SenseReID/
+            test_probe/
+            test_gallery/
+
+QMUL-iLIDS :math:`^\dagger` (``ilids``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "ilids" under ``$REID``.
+- Download the dataset from http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz and organize it to look like
+
+.. code-block:: none
+    
+    ilids/
+        i-LIDS_Pedestrian/
+            Persons/
+
+PRID (``prid``)
+^^^^^^^^^^^^^^^^^^^
+- Create a directory named "prid2011" under ``$REID``.
+- Download the dataset from https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ and extract it under "prid2011".
+- The data structure should end up with
+
+.. code-block:: none
+
+    prid2011/
+        prid_2011/
+            single_shot/
+            multi_shot/
+
+CUHK02 (``cuhk02``)
+^^^^^^^^^^^^^^^^^^^^^
+- Create a folder named "cuhk02" under ``$REID``.
+- Download the data from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and put it under "cuhk02/".
+- Extract the file so the data structure looks like
+
+.. code-block:: none
+    
+    cuhk02/
+        Dataset/
+            P1/
+            P2/
+            P3/
+            P4/
+            P5/
+
+Video Datasets
+--------------
+
+MARS (``mars``)
+^^^^^^^^^^^^^^^^^
+- Create "mars/" under ``$REID``.
+- Download the dataset from http://www.liangzheng.com.cn/Project/project_mars.html and place it in "mars/".
+- Extract "bbox_train.zip" and "bbox_test.zip".
+- Download the split metadata from https://github.com/liangzheng06/MARS-evaluation/tree/master/info and put "info/" in "mars/".
+- The data structure should end up with
+
+.. code-block:: none
+    
+    mars/
+        bbox_test/
+        bbox_train/
+        info/
+
+iLIDS-VID :math:`^\dagger` (``ilidsvid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create "ilids-vid" under ``$REID``.
+- Download the dataset from http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html to "ilids-vid".
+- Organize the data structure to match
+
+.. code-block:: none
+    
+    ilids-vid/
+        i-LIDS-VID/
+        train-test people splits/
+
+PRID2011 (``prid2011``)
+^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create a directory named "prid2011" under ``$REID``.
+- Download the dataset from https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ and extract it under "prid2011".
+- Download the split created by *iLIDS-VID* from `this google drive <https://drive.google.com/open?id=1qw7SI7YdIgfHetIQO7LLW4SHpL_qkieT>`_ and put it under "prid2011/". Following the standard protocol, only 178 persons whose sequences are more than a threshold are used.
+- The data structure should end up with
+
+.. code-block:: none
+
+    prid2011/
+        splits_prid2011.json
+        prid_2011/
+            single_shot/
+            multi_shot/
+
+DukeMTMC-VideoReID :math:`^\dagger` (``dukemtmcvidreid``)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- Create "dukemtmc-vidreid" under ``$REID``.
+- Download "DukeMTMC-VideoReID" from http://vision.cs.duke.edu/DukeMTMC/ and unzip the file to "dukemtmc-vidreid/".
+- The data structure should look like
+
+.. code-block:: none
+    
+    dukemtmc-vidreid/
+        DukeMTMC-VideoReID/
+            train/
+            query/
+            gallery/
diff --git a/pfe/player-feature-extractor/docs/evaluation.rst b/pfe/player-feature-extractor/docs/evaluation.rst
new file mode 100644
index 0000000..979ec75
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/evaluation.rst
@@ -0,0 +1,21 @@
+Evaluation
+==========
+
+Image ReID
+-----------
+- **Market1501**, **DukeMTMC-reID**, **CUHK03 (767/700 split)** and **MSMT17** have fixed split so keeping ``split_id=0`` is fine.
+- **CUHK03 (classic split)** has 20 fixed splits, so do ``split_id=0~19``.
+- **VIPeR** contains 632 identities each with 2 images under two camera views. Evaluation should be done for 10 random splits. Each split randomly divides 632 identities to 316 train ids (632 images) and the other 316 test ids (632 images). Note that, in each random split, there are two sub-splits, one using camera-A as query and camera-B as gallery while the other one using camera-B as query and camera-A as gallery. Thus, there are totally 20 splits generated with ``split_id`` starting from 0 to 19. Models can be trained on ``split_id=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]`` (because ``split_id=0`` and ``split_id=1`` share the same train set, and so on and so forth.). At test time, models trained on ``split_id=0`` can be directly evaluated on ``split_id=1``, models trained on ``split_id=2`` can be directly evaluated on ``split_id=3``, and so on and so forth.
+- **CUHK01** is similar to VIPeR in the split generation.
+- **GRID** , **iLIDS** and **PRID** have 10 random splits, so evaluation should be done by varying ``split_id`` from 0 to 9.
+- **SenseReID** has no training images and is used for evaluation only.
+
+
+.. note::
+    The ``split_id`` argument is defined in ``ImageDataManager`` and ``VideoDataManager``. Please refer to :ref:`torchreid_data`.
+
+
+Video ReID
+-----------
+- **MARS** and **DukeMTMC-VideoReID** have fixed single split so using ``split_id=0`` is ok.
+- **iLIDS-VID** and **PRID2011** have 10 predefined splits so evaluation should be done by varying ``split_id`` from 0 to 9.
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/figures/actmap.jpg b/pfe/player-feature-extractor/docs/figures/actmap.jpg
new file mode 100644
index 0000000..1a0522b
Binary files /dev/null and b/pfe/player-feature-extractor/docs/figures/actmap.jpg differ
diff --git a/pfe/player-feature-extractor/docs/figures/ranking_results.jpg b/pfe/player-feature-extractor/docs/figures/ranking_results.jpg
new file mode 100644
index 0000000..a759dfc
Binary files /dev/null and b/pfe/player-feature-extractor/docs/figures/ranking_results.jpg differ
diff --git a/pfe/player-feature-extractor/docs/index.rst b/pfe/player-feature-extractor/docs/index.rst
new file mode 100644
index 0000000..c437dea
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/index.rst
@@ -0,0 +1,35 @@
+.. include:: ../README.rst
+
+
+.. toctree::
+    :hidden:
+
+    user_guide
+    datasets
+    evaluation
+
+.. toctree::
+    :caption: Package Reference
+    :hidden:
+
+    pkg/data
+    pkg/engine
+    pkg/losses
+    pkg/metrics
+    pkg/models
+    pkg/optim
+    pkg/utils
+
+.. toctree::
+    :caption: Resources
+    :hidden:
+
+    AWESOME_REID.md
+    MODEL_ZOO.md
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/pkg/data.rst b/pfe/player-feature-extractor/docs/pkg/data.rst
new file mode 100644
index 0000000..3dc47d6
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/pkg/data.rst
@@ -0,0 +1,86 @@
+.. _torchreid_data:
+
+torchreid.data
+==============
+
+
+Data Manager
+---------------------------
+
+.. automodule:: torchreid.data.datamanager
+    :members:
+
+
+Sampler
+-----------------------
+
+.. automodule:: torchreid.data.sampler
+    :members:
+
+
+Transforms
+---------------------------
+
+.. automodule:: torchreid.data.transforms
+    :members:
+
+
+Dataset
+---------------------------
+
+.. automodule:: torchreid.data.datasets.dataset
+    :members:
+
+
+.. automodule:: torchreid.data.datasets.__init__
+    :members:
+
+
+Image Datasets
+------------------------------
+
+.. automodule:: torchreid.data.datasets.image.market1501
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.cuhk03
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.dukemtmcreid
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.msmt17
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.viper
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.grid
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.cuhk01
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.ilids
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.sensereid
+    :members:
+
+.. automodule:: torchreid.data.datasets.image.prid
+    :members:
+
+
+Video Datasets
+------------------------------
+
+.. automodule:: torchreid.data.datasets.video.mars
+    :members:
+
+.. automodule:: torchreid.data.datasets.video.ilidsvid
+    :members:
+
+.. automodule:: torchreid.data.datasets.video.prid2011
+    :members:
+
+.. automodule:: torchreid.data.datasets.video.dukemtmcvidreid
+    :members:
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/pkg/engine.rst b/pfe/player-feature-extractor/docs/pkg/engine.rst
new file mode 100644
index 0000000..ae2bc68
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/pkg/engine.rst
@@ -0,0 +1,31 @@
+.. _torchreid_engine:
+
+torchreid.engine
+==================
+
+
+Base Engine
+------------
+
+.. autoclass:: torchreid.engine.engine.Engine
+    :members:
+
+
+Image Engines
+-------------
+
+.. autoclass:: torchreid.engine.image.softmax.ImageSoftmaxEngine
+    :members:
+
+
+.. autoclass:: torchreid.engine.image.triplet.ImageTripletEngine
+    :members:
+
+
+Video Engines
+-------------
+
+.. autoclass:: torchreid.engine.video.softmax.VideoSoftmaxEngine
+
+
+.. autoclass:: torchreid.engine.video.triplet.VideoTripletEngine
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/pkg/losses.rst b/pfe/player-feature-extractor/docs/pkg/losses.rst
new file mode 100644
index 0000000..33fd9bc
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/pkg/losses.rst
@@ -0,0 +1,18 @@
+.. _torchreid_losses:
+
+torchreid.losses
+=================
+
+
+Softmax
+--------
+
+.. automodule:: torchreid.losses.cross_entropy_loss
+    :members:
+
+
+Triplet
+-------
+
+.. automodule:: torchreid.losses.hard_mine_triplet_loss
+    :members:
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/pkg/metrics.rst b/pfe/player-feature-extractor/docs/pkg/metrics.rst
new file mode 100644
index 0000000..5a52a90
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/pkg/metrics.rst
@@ -0,0 +1,25 @@
+.. _torchreid_metrics:
+
+torchreid.metrics
+=================
+
+
+Distance
+---------
+
+.. automodule:: torchreid.metrics.distance
+    :members:
+
+
+Accuracy
+--------
+
+.. automodule:: torchreid.metrics.accuracy
+    :members:
+
+
+Rank
+-----
+
+.. automodule:: torchreid.metrics.rank
+    :members: evaluate_rank
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/pkg/models.rst b/pfe/player-feature-extractor/docs/pkg/models.rst
new file mode 100644
index 0000000..685bc73
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/pkg/models.rst
@@ -0,0 +1,43 @@
+.. _torchreid_models:
+
+torchreid.models
+=================
+
+Interface
+---------
+
+.. automodule:: torchreid.models.__init__
+    :members:
+
+
+ImageNet Classification Models
+-------------------------------
+
+.. autoclass:: torchreid.models.resnet.ResNet
+.. autoclass:: torchreid.models.senet.SENet
+.. autoclass:: torchreid.models.densenet.DenseNet
+.. autoclass:: torchreid.models.inceptionresnetv2.InceptionResNetV2
+.. autoclass:: torchreid.models.inceptionv4.InceptionV4
+.. autoclass:: torchreid.models.xception.Xception
+
+
+Lightweight Models
+------------------
+
+.. autoclass:: torchreid.models.nasnet.NASNetAMobile
+.. autoclass:: torchreid.models.mobilenetv2.MobileNetV2
+.. autoclass:: torchreid.models.shufflenet.ShuffleNet
+.. autoclass:: torchreid.models.squeezenet.SqueezeNet
+.. autoclass:: torchreid.models.shufflenetv2.ShuffleNetV2
+
+
+ReID-specific Models
+--------------------
+
+.. autoclass:: torchreid.models.mudeep.MuDeep
+.. autoclass:: torchreid.models.resnetmid.ResNetMid
+.. autoclass:: torchreid.models.hacnn.HACNN
+.. autoclass:: torchreid.models.pcb.PCB
+.. autoclass:: torchreid.models.mlfn.MLFN
+.. autoclass:: torchreid.models.osnet.OSNet
+.. autoclass:: torchreid.models.osnet_ain.OSNet
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/pkg/optim.rst b/pfe/player-feature-extractor/docs/pkg/optim.rst
new file mode 100644
index 0000000..5601623
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/pkg/optim.rst
@@ -0,0 +1,18 @@
+.. _torchreid_optim:
+
+torchreid.optim
+=================
+
+
+Optimizer
+----------
+
+.. automodule:: torchreid.optim.optimizer
+    :members: build_optimizer
+
+
+LR Scheduler
+-------------
+
+.. automodule:: torchreid.optim.lr_scheduler
+    :members: build_lr_scheduler
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/pkg/utils.rst b/pfe/player-feature-extractor/docs/pkg/utils.rst
new file mode 100644
index 0000000..1545bc2
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/pkg/utils.rst
@@ -0,0 +1,41 @@
+.. _torchreid_utils:
+
+torchreid.utils
+=================
+
+Average Meter
+--------------
+
+.. automodule:: torchreid.utils.avgmeter
+    :members:
+
+
+Loggers
+-------
+
+.. automodule:: torchreid.utils.loggers
+    :members:
+
+
+Generic Tools
+---------------
+.. automodule:: torchreid.utils.tools
+    :members:
+
+
+ReID Tools
+----------
+
+.. automodule:: torchreid.utils.reidtools
+    :members:
+
+
+Torch Tools
+------------
+
+.. automodule:: torchreid.utils.torchtools
+    :members:
+
+
+.. automodule:: torchreid.utils.model_complexity
+    :members:
diff --git a/pfe/player-feature-extractor/docs/requirements.txt b/pfe/player-feature-extractor/docs/requirements.txt
new file mode 100644
index 0000000..c5937b9
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/requirements.txt
@@ -0,0 +1,6 @@
+sphinx
+sphinx-markdown-tables
+sphinx-rtd-theme
+sphinxcontrib-napoleon
+sphinxcontrib-websupport
+recommonmark
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/docs/user_guide.rst b/pfe/player-feature-extractor/docs/user_guide.rst
new file mode 100644
index 0000000..5415109
--- /dev/null
+++ b/pfe/player-feature-extractor/docs/user_guide.rst
@@ -0,0 +1,351 @@
+How-to
+============
+
+.. contents::
+   :local:
+
+
+Prepare datasets
+-----------------
+See :ref:`datasets`.
+
+
+Find model keys
+-----------------
+Keys are listed under the *Public keys* section within each model class in :ref:`torchreid_models`.
+
+
+Show available models
+----------------------
+
+.. code-block:: python
+    
+    import torchreid
+    torchreid.models.show_avai_models()
+
+
+Change the training sampler
+-----------------------------
+The default ``train_sampler`` is "RandomSampler". You can give the specific sampler name as input to ``train_sampler``, e.g. ``train_sampler='RandomIdentitySampler'`` for triplet loss.
+
+
+Choose an optimizer/lr_scheduler
+----------------------------------
+Please refer to the source code of ``build_optimizer``/``build_lr_scheduler`` in :ref:`torchreid_optim` for details.
+
+
+Resume training
+----------------
+Suppose the checkpoint is saved in "log/resnet50/model.pth.tar-30", you can do
+
+.. code-block:: python
+    
+    start_epoch = torchreid.utils.resume_from_checkpoint(
+        'log/resnet50/model.pth.tar-30',
+        model,
+        optimizer
+    )
+
+    engine.run(
+        save_dir='log/resnet50',
+        max_epoch=60,
+        start_epoch=start_epoch
+    )
+
+
+Compute model complexity
+--------------------------
+We provide a tool in ``torchreid.utils.model_complexity.py`` to automatically compute the model complexity, i.e. number of parameters and FLOPs.
+
+.. code-block:: python
+    
+    from torchreid import models, utils
+    
+    model = models.build_model(name='resnet50', num_classes=1000)
+    num_params, flops = utils.compute_model_complexity(model, (1, 3, 256, 128))
+
+    # show detailed complexity for each module
+    utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True)
+
+    # count flops for all layers including ReLU and BatchNorm
+    utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True, only_conv_linear=False)
+
+Note that (1) this function only provides an estimate of the theoretical time complexity rather than the actual running time which depends on implementations and hardware; (2) the FLOPs is only counted for layers that are used at test time. This means that redundant layers such as person ID classification layer will be ignored. The inference graph depends on how you define the computations in ``forward()``.
+
+
+Combine multiple datasets
+---------------------------
+Easy. Just give whatever datasets (keys) you want to the ``sources`` argument when instantiating a data manager. For example,
+
+.. code-block:: python
+    
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources=['market1501', 'dukemtmcreid', 'cuhk03', 'msmt17'],
+        height=256,
+        width=128,
+        batch_size=32
+    )
+
+In this example, the target datasets are Market1501, DukeMTMC-reID, CUHK03 and MSMT17 as the ``targets`` argument is not specified. Please refer to ``Engine.test()`` in :ref:`torchreid_engine` for details regarding how evaluation is performed.
+
+
+Do cross-dataset evaluation
+-----------------------------
+Easy. Just give whatever datasets (keys) you want to the argument ``targets``, like
+
+.. code-block:: python
+    
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources='market1501',
+        targets='dukemtmcreid', # or targets='cuhk03' or targets=['dukemtmcreid', 'cuhk03']
+        height=256,
+        width=128,
+        batch_size=32
+    )
+
+
+Combine train, query and gallery
+---------------------------------
+This can be easily done by setting ``combineall=True`` when instantiating a data manager. Below is an example of using Market1501,
+
+.. code-block:: python
+    
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources='market1501',
+        height=256,
+        width=128,
+        batch_size=32,
+        market1501_500k=False,
+        combineall=True # it's me, here
+    )
+
+More specifically, with ``combineall=False``, you will get
+
+.. code-block:: none
+    
+    => Loaded Market1501
+      ----------------------------------------
+      subset   | # ids | # images | # cameras
+      ----------------------------------------
+      train    |   751 |    12936 |         6
+      query    |   750 |     3368 |         6
+      gallery  |   751 |    15913 |         6
+      ---------------------------------------
+
+with ``combineall=True``, you will get
+
+.. code-block:: none
+    
+    => Loaded Market1501
+      ----------------------------------------
+      subset   | # ids | # images | # cameras
+      ----------------------------------------
+      train    |  1501 |    29419 |         6
+      query    |   750 |     3368 |         6
+      gallery  |   751 |    15913 |         6
+      ---------------------------------------
+
+
+Optimize layers with different learning rates
+-----------------------------------------------
+A common practice for fine-tuning pretrained models is to use a smaller learning rate for base layers and a large learning rate for randomly initialized layers (referred to as ``new_layers``). ``torchreid.optim.optimizer`` has implemented such feature. What you need to do is to set ``staged_lr=True`` and give the names of ``new_layers`` such as "classifier".
+
+Below is an example of setting different learning rates for base layers and new layers in ResNet50,
+
+.. code-block:: python
+    
+    # New layer "classifier" has a learning rate of 0.01
+    # The base layers have a learning rate of 0.001
+    optimizer = torchreid.optim.build_optimizer(
+        model,
+        optim='sgd',
+        lr=0.01,
+        staged_lr=True,
+        new_layers='classifier',
+        base_lr_mult=0.1
+    )
+
+Please refer to :ref:`torchreid_optim` for more details.
+
+
+Do two-stepped transfer learning
+-------------------------------------
+To prevent the pretrained layers from being damaged by harmful gradients back-propagated from randomly initialized layers, one can adopt the *two-stepped transfer learning strategy* presented in `Deep Transfer Learning for Person Re-identification <https://arxiv.org/abs/1611.05244>`_. The basic idea is to pretrain the randomly initialized layers for few epochs while keeping the base layers frozen before training all layers end-to-end.
+
+This has been implemented in ``Engine.train()`` (see :ref:`torchreid_engine`). The arguments related to this feature are ``fixbase_epoch`` and ``open_layers``. Intuitively, ``fixbase_epoch`` denotes the number of epochs to keep the base layers frozen; ``open_layers`` means which layer is open for training.
+
+For example, say you want to pretrain the classification layer named "classifier" in ResNet50 for 5 epochs before training all layers, you can do
+
+.. code-block:: python
+    
+    engine.run(
+        save_dir='log/resnet50',
+        max_epoch=60,
+        eval_freq=10,
+        print_freq=10,
+        test_only=False,
+        fixbase_epoch=5,
+        open_layers='classifier'
+    )
+    # or open_layers=['fc', 'classifier'] if there is another fc layer that
+    # is randomly initialized, like resnet50_fc512
+
+Note that ``fixbase_epoch`` is counted into ``max_epoch``. In the above example, the base network will be fixed for 5 epochs and then open for training for 55 epochs. Thus, if you want to freeze some layers throughout the training, what you can do is to set ``fixbase_epoch`` equal to ``max_epoch`` and put the layer names in ``open_layers`` which you want to train.
+
+
+Test a trained model
+----------------------
+You can load a trained model using :code:`torchreid.utils.load_pretrained_weights(model, weight_path)` and set ``test_only=True`` in ``engine.run()``.
+
+
+Fine-tune a model pre-trained on reid datasets
+-----------------------------------------------
+Use :code:`torchreid.utils.load_pretrained_weights(model, weight_path)` to load the pre-trained weights and then fine-tune on the dataset you want.
+
+
+Visualize learning curves with tensorboard
+--------------------------------------------
+The ``SummaryWriter()`` for tensorboard will be automatically initialized in ``engine.run()`` when you are training your model. Therefore, you do not need to do extra jobs. After the training is done, the ``*tf.events*`` file will be saved in ``save_dir``. Then, you just call ``tensorboard --logdir=your_save_dir`` in your terminal and visit ``http://localhost:6006/`` in a web browser. See `pytorch tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ for further information.
+
+
+Visualize ranking results
+---------------------------
+This can be achieved by setting ``visrank`` to true in ``engine.run()``. ``visrank_topk`` determines the top-k images to be visualized (Default is ``visrank_topk=10``). Note that ``visrank`` can only be used in test mode, i.e. ``test_only=True`` in ``engine.run()``. The output will be saved under ``save_dir/visrank_DATASETNAME`` where each plot contains the top-k similar gallery images given a query. An example is shown below where red and green denote incorrect and correct matches respectively.
+
+.. image:: figures/ranking_results.jpg
+    :width: 800px
+    :align: center
+
+
+Visualize activation maps
+--------------------------
+To understand where the CNN focuses on to extract features for ReID, you can visualize the activation maps as in `OSNet <https://arxiv.org/abs/1905.00953>`_. This is implemented in ``tools/visualize_actmap.py`` (check the code for more details). An example running command is
+
+.. code-block:: shell
+    
+    python tools/visualize_actmap.py \
+    --root $DATA/reid \
+    -d market1501 \
+    -m osnet_x1_0 \
+    --weights PATH_TO_PRETRAINED_WEIGHTS \
+    --save-dir log/visactmap_osnet_x1_0_market1501
+
+The output will look like (from left to right: image, activation map, overlapped image)
+
+.. image:: figures/actmap.jpg
+    :width: 300px
+    :align: center
+
+
+.. note::
+    In order to visualize activation maps, the CNN needs to output the last convolutional feature maps at eval mode. See ``torchreid/models/osnet.py`` for example.
+
+
+Use your own dataset
+----------------------
+1. Write your own dataset class. Below is a template for image dataset. However, it can also be applied to a video dataset class, for which you simply change ``ImageDataset`` to ``VideoDataset``.
+
+.. code-block:: python
+    
+    from __future__ import absolute_import
+    from __future__ import print_function
+    from __future__ import division
+
+    import sys
+    import os
+    import os.path as osp
+
+    from torchreid.data import ImageDataset
+
+
+    class NewDataset(ImageDataset):
+        dataset_dir = 'new_dataset'
+
+        def __init__(self, root='', **kwargs):
+            self.root = osp.abspath(osp.expanduser(root))
+            self.dataset_dir = osp.join(self.root, self.dataset_dir)
+
+            # All you need to do here is to generate three lists,
+            # which are train, query and gallery.
+            # Each list contains tuples of (img_path, pid, camid),
+            # where
+            # - img_path (str): absolute path to an image.
+            # - pid (int): person ID, e.g. 0, 1.
+            # - camid (int): camera ID, e.g. 0, 1.
+            # Note that
+            # - pid and camid should be 0-based.
+            # - query and gallery should share the same pid scope (e.g.
+            #   pid=0 in query refers to the same person as pid=0 in gallery).
+            # - train, query and gallery share the same camid scope (e.g.
+            #   camid=0 in train refers to the same camera as camid=0
+            #   in query/gallery).
+            train = ...
+            query = ...
+            gallery = ...
+
+            super(NewDataset, self).__init__(train, query, gallery, **kwargs)
+
+
+2. Register your dataset.
+
+.. code-block:: python
+    
+    import torchreid
+    torchreid.data.register_image_dataset('new_dataset', NewDataset)
+
+
+3. Initialize a data manager with your dataset.
+
+.. code-block:: python
+    
+    # use your own dataset only
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources='new_dataset'
+    )
+    # combine with other datasets
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources=['new_dataset', 'dukemtmcreid']
+    )
+    # cross-dataset evaluation
+    datamanager = torchreid.data.ImageDataManager(
+        root='reid-data',
+        sources=['new_dataset', 'dukemtmcreid'],
+        targets='market1501' # or targets=['market1501', 'cuhk03']
+    )
+
+
+
+Design your own Engine
+------------------------
+A new Engine should be designed if you have your own loss function. The base Engine class ``torchreid.engine.Engine`` has implemented some generic methods which you can inherit to avoid re-writing. Please refer to the source code for more details. You are suggested to see how ``ImageSoftmaxEngine`` and ``ImageTripletEngine`` are constructed (also ``VideoSoftmaxEngine`` and ``VideoTripletEngine``). All you need to implement might be just a ``forward_backward()`` function.
+
+
+Use Torchreid as a feature extractor in your projects
+-------------------------------------------------------
+We have provided a simple API for feature extraction, which accepts input of various types such as a list of image paths or numpy arrays. More details can be found in the code at ``torchreid/utils/feature_extractor.py``. Here we show a simple example of how to extract features given a list of image paths.
+
+.. code-block:: python
+
+    from torchreid.utils import FeatureExtractor
+
+    extractor = FeatureExtractor(
+        model_name='osnet_x1_0',
+        model_path='a/b/c/model.pth.tar',
+        device='cuda'
+    )
+
+    image_list = [
+        'a/b/c/image001.jpg',
+        'a/b/c/image002.jpg',
+        'a/b/c/image003.jpg',
+        'a/b/c/image004.jpg',
+        'a/b/c/image005.jpg'
+    ]
+
+    features = extractor(image_list)
+    print(features.shape) # output (5, 512)
diff --git a/pfe/player-feature-extractor/linter.sh b/pfe/player-feature-extractor/linter.sh
new file mode 100644
index 0000000..9db34f9
--- /dev/null
+++ b/pfe/player-feature-extractor/linter.sh
@@ -0,0 +1,11 @@
+echo "Running isort"
+isort -y -sp .
+echo "Done"
+
+echo "Running yapf"
+yapf -i -r -vv -e build .
+echo "Done"
+
+echo "Running flake8"
+flake8 .
+echo "Done"
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/projects/DML/README.md b/pfe/player-feature-extractor/projects/DML/README.md
new file mode 100644
index 0000000..e81be7f
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/DML/README.md
@@ -0,0 +1,16 @@
+# Deep mutual learning
+
+This repo implements [Deep Mutual Learning (CVPR'18)](https://zpascal.net/cvpr2018/Zhang_Deep_Mutual_Learning_CVPR_2018_paper.pdf) (DML) for person re-id.
+
+We used this code in our [OSNet](https://arxiv.org/pdf/1905.00953.pdf) paper (see Supp. B). The training command to reproduce the result of "triplet + DML" (Table 12f in the paper) is
+```bash
+python main.py \
+--config-file im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml \
+--root $DATA
+```
+
+`$DATA` corresponds to the path to your dataset folder.
+
+Change `model.deploy` to `both` if you wanna enable model ensembling.
+
+If you have any questions, please raise an issue in the Issues area.
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/projects/DML/default_config.py b/pfe/player-feature-extractor/projects/DML/default_config.py
new file mode 100644
index 0000000..9b15e35
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/DML/default_config.py
@@ -0,0 +1,207 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'resnet50'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights1 = '' # path to model-1 weights
+    cfg.model.load_weights2 = '' # path to model-2 weights
+    cfg.model.resume1 = '' # path to checkpoint for resume training
+    cfg.model.resume2 = '' # path to checkpoint for resume training
+    cfg.model.deploy = 'model1' # model1, model2 or both
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['market1501']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 256 # image height
+    cfg.data.width = 128 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler'
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0003
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 60
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 20 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'triplet'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+    cfg.loss.dml = CN()
+    cfg.loss.dml.weight_ml = 1. # weight for mutual learning loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'euclidean' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = False # test only
+    cfg.test.eval_freq = -1 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 0 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        'test_only': cfg.test.evaluate,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': cfg.test.dist_metric,
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': cfg.test.visrank,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/player-feature-extractor/projects/DML/dml.py b/pfe/player-feature-extractor/projects/DML/dml.py
new file mode 100644
index 0000000..546e573
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/DML/dml.py
@@ -0,0 +1,149 @@
+from __future__ import division, print_function, absolute_import
+import torch
+from torch.nn import functional as F
+
+from torchreid.utils import open_all_layers, open_specified_layers
+from torchreid.engine import Engine
+from torchreid.losses import TripletLoss, CrossEntropyLoss
+
+
+class ImageDMLEngine(Engine):
+
+    def __init__(
+        self,
+        datamanager,
+        model1,
+        optimizer1,
+        scheduler1,
+        model2,
+        optimizer2,
+        scheduler2,
+        margin=0.3,
+        weight_t=0.5,
+        weight_x=1.,
+        weight_ml=1.,
+        use_gpu=True,
+        label_smooth=True,
+        deploy='model1'
+    ):
+        super(ImageDMLEngine, self).__init__(datamanager, use_gpu)
+
+        self.model1 = model1
+        self.optimizer1 = optimizer1
+        self.scheduler1 = scheduler1
+        self.register_model('model1', model1, optimizer1, scheduler1)
+
+        self.model2 = model2
+        self.optimizer2 = optimizer2
+        self.scheduler2 = scheduler2
+        self.register_model('model2', model2, optimizer2, scheduler2)
+
+        self.weight_t = weight_t
+        self.weight_x = weight_x
+        self.weight_ml = weight_ml
+
+        assert deploy in ['model1', 'model2', 'both']
+        self.deploy = deploy
+
+        self.criterion_t = TripletLoss(margin=margin)
+        self.criterion_x = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        outputs1, features1 = self.model1(imgs)
+        loss1_x = self.compute_loss(self.criterion_x, outputs1, pids)
+        loss1_t = self.compute_loss(self.criterion_t, features1, pids)
+
+        outputs2, features2 = self.model2(imgs)
+        loss2_x = self.compute_loss(self.criterion_x, outputs2, pids)
+        loss2_t = self.compute_loss(self.criterion_t, features2, pids)
+
+        loss1_ml = self.compute_kl_div(
+            outputs2.detach(), outputs1, is_logit=True
+        )
+        loss2_ml = self.compute_kl_div(
+            outputs1.detach(), outputs2, is_logit=True
+        )
+
+        loss1 = 0
+        loss1 += loss1_x * self.weight_x
+        loss1 += loss1_t * self.weight_t
+        loss1 += loss1_ml * self.weight_ml
+
+        loss2 = 0
+        loss2 += loss2_x * self.weight_x
+        loss2 += loss2_t * self.weight_t
+        loss2 += loss2_ml * self.weight_ml
+
+        self.optimizer1.zero_grad()
+        loss1.backward()
+        self.optimizer1.step()
+
+        self.optimizer2.zero_grad()
+        loss2.backward()
+        self.optimizer2.step()
+
+        loss_dict = {
+            'loss1_x': loss1_x.item(),
+            'loss1_t': loss1_t.item(),
+            'loss1_ml': loss1_ml.item(),
+            'loss2_x': loss1_x.item(),
+            'loss2_t': loss1_t.item(),
+            'loss2_ml': loss1_ml.item()
+        }
+
+        return loss_dict
+
+    @staticmethod
+    def compute_kl_div(p, q, is_logit=True):
+        if is_logit:
+            p = F.softmax(p, dim=1)
+            q = F.softmax(q, dim=1)
+        return -(p * torch.log(q + 1e-8)).sum(1).mean()
+
+    def two_stepped_transfer_learning(
+        self, epoch, fixbase_epoch, open_layers, model=None
+    ):
+        """Two stepped transfer learning.
+
+        The idea is to freeze base layers for a certain number of epochs
+        and then open all layers for training.
+
+        Reference: https://arxiv.org/abs/1611.05244
+        """
+        model1 = self.model1
+        model2 = self.model2
+
+        if (epoch + 1) <= fixbase_epoch and open_layers is not None:
+            print(
+                '* Only train {} (epoch: {}/{})'.format(
+                    open_layers, epoch + 1, fixbase_epoch
+                )
+            )
+            open_specified_layers(model1, open_layers)
+            open_specified_layers(model2, open_layers)
+        else:
+            open_all_layers(model1)
+            open_all_layers(model2)
+
+    def extract_features(self, input):
+        if self.deploy == 'model1':
+            return self.model1(input)
+
+        elif self.deploy == 'model2':
+            return self.model2(input)
+
+        else:
+            features = []
+            features.append(self.model1(input))
+            features.append(self.model2(input))
+            return torch.cat(features, 1)
diff --git a/pfe/player-feature-extractor/projects/DML/im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml b/pfe/player-feature-extractor/projects/DML/im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml
new file mode 100644
index 0000000..3a240b1
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/DML/im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml
@@ -0,0 +1,42 @@
+model:
+  name: 'osnet_x1_0'
+  pretrained: True
+  deploy: 'model1'
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip', 'random_erase']
+  save_dir: 'log/osnet_x1_0_market1501_dml_cosinelr'
+
+loss:
+  name: 'triplet'
+  softmax:
+    label_smooth: True
+  triplet:
+    margin: 0.3
+    weight_t: 0.5
+    weight_x: 1.
+  dml:
+    weight_ml: 1.
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 250
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'cosine'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/projects/DML/main.py b/pfe/player-feature-extractor/projects/DML/main.py
new file mode 100755
index 0000000..9af33d9
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/DML/main.py
@@ -0,0 +1,166 @@
+import sys
+import copy
+import time
+import os.path as osp
+import argparse
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, check_isfile, set_random_seed, collect_env_info,
+    resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
+)
+
+from dml import ImageDMLEngine
+from default_config import (
+    imagedata_kwargs, optimizer_kwargs, engine_run_kwargs, get_default_config,
+    lr_scheduler_kwargs
+)
+
+
+def reset_config(cfg, args):
+    if args.root:
+        cfg.data.root = args.root
+    if args.sources:
+        cfg.data.sources = args.sources
+    if args.targets:
+        cfg.data.targets = args.targets
+    if args.transforms:
+        cfg.data.transforms = args.transforms
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        '--config-file', type=str, default='', help='path to config file'
+    )
+    parser.add_argument(
+        '-s',
+        '--sources',
+        type=str,
+        nargs='+',
+        help='source datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '-t',
+        '--targets',
+        type=str,
+        nargs='+',
+        help='target datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '--transforms', type=str, nargs='+', help='data augmentation'
+    )
+    parser.add_argument(
+        '--root', type=str, default='', help='path to data root'
+    )
+    parser.add_argument(
+        'opts',
+        default=None,
+        nargs=argparse.REMAINDER,
+        help='Modify config options using the command-line'
+    )
+    args = parser.parse_args()
+
+    cfg = get_default_config()
+    cfg.use_gpu = torch.cuda.is_available()
+    if args.config_file:
+        cfg.merge_from_file(args.config_file)
+    reset_config(cfg, args)
+    cfg.merge_from_list(args.opts)
+    set_random_seed(cfg.train.seed)
+
+    log_name = 'test.log' if cfg.test.evaluate else 'train.log'
+    log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
+    sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
+
+    print('Show configuration\n{}\n'.format(cfg))
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if cfg.use_gpu:
+        torch.backends.cudnn.benchmark = True
+
+    datamanager = torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
+
+    print('Building model-1: {}'.format(cfg.model.name))
+    model1 = torchreid.models.build_model(
+        name=cfg.model.name,
+        num_classes=datamanager.num_train_pids,
+        loss=cfg.loss.name,
+        pretrained=cfg.model.pretrained,
+        use_gpu=cfg.use_gpu
+    )
+    num_params, flops = compute_model_complexity(
+        model1, (1, 3, cfg.data.height, cfg.data.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    print('Copying model-1 to model-2')
+    model2 = copy.deepcopy(model1)
+
+    if cfg.model.load_weights1 and check_isfile(cfg.model.load_weights1):
+        load_pretrained_weights(model1, cfg.model.load_weights1)
+
+    if cfg.model.load_weights2 and check_isfile(cfg.model.load_weights2):
+        load_pretrained_weights(model2, cfg.model.load_weights2)
+
+    if cfg.use_gpu:
+        model1 = nn.DataParallel(model1).cuda()
+        model2 = nn.DataParallel(model2).cuda()
+
+    optimizer1 = torchreid.optim.build_optimizer(
+        model1, **optimizer_kwargs(cfg)
+    )
+    scheduler1 = torchreid.optim.build_lr_scheduler(
+        optimizer1, **lr_scheduler_kwargs(cfg)
+    )
+
+    optimizer2 = torchreid.optim.build_optimizer(
+        model2, **optimizer_kwargs(cfg)
+    )
+    scheduler2 = torchreid.optim.build_lr_scheduler(
+        optimizer2, **lr_scheduler_kwargs(cfg)
+    )
+
+    if cfg.model.resume1 and check_isfile(cfg.model.resume1):
+        cfg.train.start_epoch = resume_from_checkpoint(
+            cfg.model.resume1,
+            model1,
+            optimizer=optimizer1,
+            scheduler=scheduler1
+        )
+
+    if cfg.model.resume2 and check_isfile(cfg.model.resume2):
+        resume_from_checkpoint(
+            cfg.model.resume2,
+            model2,
+            optimizer=optimizer2,
+            scheduler=scheduler2
+        )
+
+    print('Building DML-engine for image-reid')
+    engine = ImageDMLEngine(
+        datamanager,
+        model1,
+        optimizer1,
+        scheduler1,
+        model2,
+        optimizer2,
+        scheduler2,
+        margin=cfg.loss.triplet.margin,
+        weight_t=cfg.loss.triplet.weight_t,
+        weight_x=cfg.loss.triplet.weight_x,
+        weight_ml=cfg.loss.dml.weight_ml,
+        use_gpu=cfg.use_gpu,
+        label_smooth=cfg.loss.softmax.label_smooth,
+        deploy=cfg.model.deploy
+    )
+    engine.run(**engine_run_kwargs(cfg))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/player-feature-extractor/projects/OSNet_AIN/README.md b/pfe/player-feature-extractor/projects/OSNet_AIN/README.md
new file mode 100644
index 0000000..2020b43
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/OSNet_AIN/README.md
@@ -0,0 +1,38 @@
+# Differentiable NAS for OSNet-AIN
+
+## Introduction
+This repository contains the neural architecture search (NAS) code (based on [Torchreid](https://arxiv.org/abs/1910.10093)) for [OSNet-AIN](https://arxiv.org/abs/1910.06827), an extension of [OSNet](https://arxiv.org/abs/1905.00953) that achieves strong performance on cross-domain person re-identification (re-ID) benchmarks (*without using any target data*). OSNet-AIN builds on the idea of using [instance normalisation](https://arxiv.org/abs/1607.08022) (IN) layers to eliminate instance-specific contrast in images for domain-generalisable representation learning. This is inspired by the [neural style transfer](https://arxiv.org/abs/1703.06868) works that use IN to remove image styles. Though IN naturally suits the cross-domain person re-ID task, it still remains unclear that where to insert IN to a re-ID CNN can maximise the performance gain. To avoid exhaustively evaluating all possible designs, OSNet-AIN learns to search for the optimal OSNet+IN design from data using a differentiable NAS algorithm. For technical details, please refer to our paper at https://arxiv.org/abs/1910.06827.
+
+<div align="center">
+  <img src="https://drive.google.com/uc?export=view&id=1yvVIi2Ml7WBe85Uhaa54qyG4g8z-MGEB" width="500px" />
+</div>
+
+## Training
+Assume the reid data is stored at `$DATA`. Run
+```
+python main.py --config-file nas.yaml --root $DATA
+```
+
+The structure of the found architecture will be shown at the end of training.
+
+The default config was designed for 8 Tesla V100 32GB GPUs. You can modify the batch size based on your device memory.
+
+**Note** that the test result obtained at the end of architecture search is not meaningful (due to the stochastic sampling layers). Therefore, do not rely on the result to judge the model performance. Instead, you should construct the found architecture in `osnet_child.py` and re-train and evaluate the model on the reid datasets.
+
+## Citation
+If you find this code useful to your research, please consider citing the following papers.
+```
+@article{zhou2019learning,
+  title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
+  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
+  journal={arXiv preprint arXiv:1910.06827},
+  year={2019}
+}
+
+@inproceedings{zhou2019osnet,
+  title={Omni-Scale Feature Learning for Person Re-Identification},
+  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
+  booktitle={ICCV},
+  year={2019}
+}
+```
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/projects/OSNet_AIN/default_config.py b/pfe/player-feature-extractor/projects/OSNet_AIN/default_config.py
new file mode 100644
index 0000000..733a9f0
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/OSNet_AIN/default_config.py
@@ -0,0 +1,210 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'resnet50'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '' # path to model weights
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # NAS
+    cfg.nas = CN()
+    cfg.nas.mc_iter = 1 # Monte Carlo sampling
+    cfg.nas.init_lmda = 10. # initial lambda value
+    cfg.nas.min_lmda = 1. # minimum lambda value
+    cfg.nas.lmda_decay_step = 20 # decay step for lambda
+    cfg.nas.lmda_decay_rate = 0.5 # decay rate for lambda
+    cfg.nas.fixed_lmda = False # keep lambda unchanged
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['market1501']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 256 # image height
+    cfg.data.width = 128 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler'
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0003
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 60
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 20 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'euclidean' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = False # test only
+    cfg.test.eval_freq = -1 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 0 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+    cfg.test.visactmap = False # visualize CNN activation maps
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        'test_only': cfg.test.evaluate,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': cfg.test.dist_metric,
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': cfg.test.visrank,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/player-feature-extractor/projects/OSNet_AIN/main.py b/pfe/player-feature-extractor/projects/OSNet_AIN/main.py
new file mode 100755
index 0000000..f591770
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/OSNet_AIN/main.py
@@ -0,0 +1,145 @@
+import os
+import sys
+import time
+import os.path as osp
+import argparse
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, check_isfile, set_random_seed, collect_env_info,
+    resume_from_checkpoint, compute_model_complexity
+)
+
+import osnet_search as osnet_models
+from softmax_nas import ImageSoftmaxNASEngine
+from default_config import (
+    imagedata_kwargs, optimizer_kwargs, engine_run_kwargs, get_default_config,
+    lr_scheduler_kwargs
+)
+
+
+def reset_config(cfg, args):
+    if args.root:
+        cfg.data.root = args.root
+    if args.sources:
+        cfg.data.sources = args.sources
+    if args.targets:
+        cfg.data.targets = args.targets
+    if args.transforms:
+        cfg.data.transforms = args.transforms
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        '--config-file', type=str, default='', help='path to config file'
+    )
+    parser.add_argument(
+        '-s',
+        '--sources',
+        type=str,
+        nargs='+',
+        help='source datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '-t',
+        '--targets',
+        type=str,
+        nargs='+',
+        help='target datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '--transforms', type=str, nargs='+', help='data augmentation'
+    )
+    parser.add_argument(
+        '--root', type=str, default='', help='path to data root'
+    )
+    parser.add_argument(
+        '--gpu-devices',
+        type=str,
+        default='',
+    )
+    parser.add_argument(
+        'opts',
+        default=None,
+        nargs=argparse.REMAINDER,
+        help='Modify config options using the command-line'
+    )
+    args = parser.parse_args()
+
+    cfg = get_default_config()
+    cfg.use_gpu = torch.cuda.is_available()
+    if args.config_file:
+        cfg.merge_from_file(args.config_file)
+    reset_config(cfg, args)
+    cfg.merge_from_list(args.opts)
+    set_random_seed(cfg.train.seed)
+
+    if cfg.use_gpu and args.gpu_devices:
+        # if gpu_devices is not specified, all available gpus will be used
+        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
+    log_name = 'test.log' if cfg.test.evaluate else 'train.log'
+    log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
+    sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
+
+    print('Show configuration\n{}\n'.format(cfg))
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if cfg.use_gpu:
+        torch.backends.cudnn.benchmark = True
+
+    datamanager = torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
+
+    print('Building model: {}'.format(cfg.model.name))
+    model = osnet_models.build_model(
+        cfg.model.name, num_classes=datamanager.num_train_pids
+    )
+    num_params, flops = compute_model_complexity(
+        model, (1, 3, cfg.data.height, cfg.data.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    if cfg.use_gpu:
+        model = nn.DataParallel(model).cuda()
+
+    optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
+    scheduler = torchreid.optim.build_lr_scheduler(
+        optimizer, **lr_scheduler_kwargs(cfg)
+    )
+
+    if cfg.model.resume and check_isfile(cfg.model.resume):
+        cfg.train.start_epoch = resume_from_checkpoint(
+            cfg.model.resume, model, optimizer=optimizer
+        )
+
+    print('Building NAS engine')
+    engine = ImageSoftmaxNASEngine(
+        datamanager,
+        model,
+        optimizer,
+        scheduler=scheduler,
+        use_gpu=cfg.use_gpu,
+        label_smooth=cfg.loss.softmax.label_smooth,
+        mc_iter=cfg.nas.mc_iter,
+        init_lmda=cfg.nas.init_lmda,
+        min_lmda=cfg.nas.min_lmda,
+        lmda_decay_step=cfg.nas.lmda_decay_step,
+        lmda_decay_rate=cfg.nas.lmda_decay_rate,
+        fixed_lmda=cfg.nas.fixed_lmda
+    )
+    engine.run(**engine_run_kwargs(cfg))
+
+    print('*** Display the found architecture ***')
+    if cfg.use_gpu:
+        model.module.build_child_graph()
+    else:
+        model.build_child_graph()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/player-feature-extractor/projects/OSNet_AIN/nas.yaml b/pfe/player-feature-extractor/projects/OSNet_AIN/nas.yaml
new file mode 100644
index 0000000..507b2ec
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/OSNet_AIN/nas.yaml
@@ -0,0 +1,44 @@
+model:
+  name: 'osnet_nas'
+  pretrained: False
+
+nas:
+  mc_iter: 1
+  init_lmda: 10.
+  min_lmda: 1.
+  lmda_decay_step: 20
+  lmda_decay_rate: 0.5
+  fixed_lmda: False
+
+data:
+  type: 'image'
+  sources: ['msmt17']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: True
+  transforms: ['random_flip', 'color_jitter']
+  save_dir: 'log/osnet_nas'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'sgd'
+  lr: 0.1
+  max_epoch: 120
+  batch_size: 512
+  fixbase_epoch: 0
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'cosine'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
+  visactmap: False
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/projects/OSNet_AIN/osnet_child.py b/pfe/player-feature-extractor/projects/OSNet_AIN/osnet_child.py
new file mode 100644
index 0000000..167f9dd
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/OSNet_AIN/osnet_child.py
@@ -0,0 +1,535 @@
+from __future__ import division, absolute_import
+from torch import nn
+from torch.nn import functional as F
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1, bn=True):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = None
+        if bn:
+            self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConvStream(nn.Module):
+    """Lightweight convolution stream."""
+
+    def __init__(self, in_channels, out_channels, depth):
+        super(LightConvStream, self).__init__()
+        assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
+            depth
+        )
+        layers = []
+        layers += [LightConv3x3(in_channels, out_channels)]
+        for i in range(depth - 1):
+            layers += [LightConv3x3(out_channels, out_channels)]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlock, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv1(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv1, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv2(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv2, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN(out) # IN outside residual
+        return F.relu(out)
+
+
+class OSBlockINv3(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv3, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN_in = nn.InstanceNorm2d(out_channels, affine=True)
+        self.IN_out = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN_in(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN_out(out) # IN outside residual
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        conv1_IN=True,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+        self.feature_dim = feature_dim
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(
+            3, channels[0], 7, stride=2, padding=3, IN=conv1_IN
+        )
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0], layers[0], channels[0], channels[1]
+        )
+        self.pool2 = nn.Sequential(
+            Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv3 = self._make_layer(
+            blocks[1], layers[1], channels[1], channels[2]
+        )
+        self.pool3 = nn.Sequential(
+            Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv4 = self._make_layer(
+            blocks[2], layers[2], channels[2], channels[3]
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            self.feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, blocks, layer, in_channels, out_channels):
+        layers = []
+        layers += [blocks[0](in_channels, out_channels)]
+        for i in range(1, len(blocks)):
+            layers += [blocks[i](out_channels, out_channels)]
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.InstanceNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.pool2(x)
+        x = self.conv3(x)
+        x = self.pool3(x)
+        x = self.conv4(x)
+        return self.conv5(x)
+
+    def forward(self, x, return_featuremaps=False, **kwargs):
+        x = self.featuremaps(x)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+##########
+# Instantiation
+##########
+def osnet_ain_x1_0(
+    num_classes=1000, pretrained=True, loss='softmax', **kwargs
+):
+    model = OSNet(
+        num_classes,
+        blocks=[
+            [OSBlockINv1, OSBlockINv1], [OSBlock, OSBlockINv1],
+            [OSBlockINv1, OSBlock]
+        ],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        conv1_IN=True,
+        **kwargs
+    )
+    return model
+
+
+__models = {'osnet_ain_x1_0': osnet_ain_x1_0}
+
+
+def build_model(name, num_classes=100):
+    avai_models = list(__models.keys())
+    if name not in avai_models:
+        raise KeyError(
+            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
+        )
+    return __models[name](num_classes=num_classes)
diff --git a/pfe/player-feature-extractor/projects/OSNet_AIN/osnet_search.py b/pfe/player-feature-extractor/projects/OSNet_AIN/osnet_search.py
new file mode 100644
index 0000000..a2a2cfa
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/OSNet_AIN/osnet_search.py
@@ -0,0 +1,584 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+EPS = 1e-12
+NORM_AFFINE = False # enable affine transformations for normalization layer
+
+
+##########
+# Basic layers
+##########
+class IBN(nn.Module):
+    """Instance + Batch Normalization."""
+
+    def __init__(self, num_channels):
+        super(IBN, self).__init__()
+        half1 = int(num_channels / 2)
+        self.half = half1
+        half2 = num_channels - half1
+        self.IN = nn.InstanceNorm2d(half1, affine=NORM_AFFINE)
+        self.BN = nn.BatchNorm2d(half2, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        split = torch.split(x, self.half, 1)
+        out1 = self.IN(split[0].contiguous())
+        out2 = self.BN(split[1].contiguous())
+        return torch.cat((out1, out2), 1)
+
+
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(
+        self, in_channels, out_channels, stride=1, groups=1, ibn=False
+    ):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        if ibn:
+            self.bn = IBN(out_channels)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1, bn=True):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = None
+        if bn:
+            self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConvStream(nn.Module):
+    """Lightweight convolution stream."""
+
+    def __init__(self, in_channels, out_channels, depth):
+        super(LightConvStream, self).__init__()
+        assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
+            depth
+        )
+        layers = []
+        layers += [LightConv3x3(in_channels, out_channels)]
+        for i in range(depth - 1):
+            layers += [LightConv3x3(out_channels, out_channels)]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlock, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv1(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv1, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv2(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv2, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN(out) # IN outside residual
+        return F.relu(out)
+
+
+class OSBlockINv3(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv3, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN_in = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+        self.IN_out = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN_in(x3) # inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN_out(out) # IN outside residual
+        return F.relu(out)
+
+
+class NASBlock(nn.Module):
+    """Neural architecture search layer."""
+
+    def __init__(self, in_channels, out_channels, search_space=None):
+        super(NASBlock, self).__init__()
+        self._is_child_graph = False
+        self.search_space = search_space
+        if self.search_space is None:
+            raise ValueError('search_space is None')
+
+        self.os_block = nn.ModuleList()
+        for block in self.search_space:
+            self.os_block += [block(in_channels, out_channels)]
+        self.weights = nn.Parameter(torch.ones(len(self.search_space)))
+
+    def build_child_graph(self):
+        if self._is_child_graph:
+            raise RuntimeError('build_child_graph() can only be called once')
+
+        idx = self.weights.data.max(dim=0)[1].item()
+        self.os_block = self.os_block[idx]
+        self.weights = None
+        self._is_child_graph = True
+        return self.search_space[idx]
+
+    def forward(self, x, lmda=1.):
+        if self._is_child_graph:
+            return self.os_block(x)
+
+        uniform = torch.rand_like(self.weights)
+        gumbel = -torch.log(-torch.log(uniform + EPS))
+        nonneg_weights = F.relu(self.weights)
+        logits = torch.log(nonneg_weights + EPS) + gumbel
+        exp = torch.exp(logits / lmda)
+        weights_softmax = exp / (exp.sum() + EPS)
+
+        output = 0
+        for i, weight in enumerate(weights_softmax):
+            output = output + weight * self.os_block[i](x)
+        return output
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        search_space=None,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        # no matter what loss is specified, the model only returns the ID predictions
+        self.loss = loss
+        self.feature_dim = feature_dim
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=True)
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0], layers[0], channels[0], channels[1], search_space
+        )
+        self.pool2 = nn.Sequential(
+            Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv3 = self._make_layer(
+            blocks[1], layers[1], channels[1], channels[2], search_space
+        )
+        self.pool3 = nn.Sequential(
+            Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv4 = self._make_layer(
+            blocks[2], layers[2], channels[2], channels[3], search_space
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            self.feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+    def _make_layer(
+        self, block, layer, in_channels, out_channels, search_space
+    ):
+        layers = nn.ModuleList()
+        layers += [block(in_channels, out_channels, search_space=search_space)]
+        for i in range(1, layer):
+            layers += [
+                block(out_channels, out_channels, search_space=search_space)
+            ]
+        return layers
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim, affine=NORM_AFFINE))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def build_child_graph(self):
+        print('Building child graph')
+        for i, conv in enumerate(self.conv2):
+            block = conv.build_child_graph()
+            print('- conv2-{} Block={}'.format(i + 1, block.__name__))
+        for i, conv in enumerate(self.conv3):
+            block = conv.build_child_graph()
+            print('- conv3-{} Block={}'.format(i + 1, block.__name__))
+        for i, conv in enumerate(self.conv4):
+            block = conv.build_child_graph()
+            print('- conv4-{} Block={}'.format(i + 1, block.__name__))
+
+    def featuremaps(self, x, lmda):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        for conv in self.conv2:
+            x = conv(x, lmda)
+        x = self.pool2(x)
+        for conv in self.conv3:
+            x = conv(x, lmda)
+        x = self.pool3(x)
+        for conv in self.conv4:
+            x = conv(x, lmda)
+        return self.conv5(x)
+
+    def forward(self, x, lmda=1., return_featuremaps=False):
+        # lmda (float): temperature parameter for concrete distribution
+        x = self.featuremaps(x, lmda)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        return self.classifier(v)
+
+
+##########
+# Instantiation
+##########
+def osnet_nas(num_classes=1000, loss='softmax', **kwargs):
+    # standard size (width x1.0)
+    return OSNet(
+        num_classes,
+        blocks=[NASBlock, NASBlock, NASBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        search_space=[OSBlock, OSBlockINv1, OSBlockINv2, OSBlockINv3],
+        **kwargs
+    )
+
+
+__NAS_models = {'osnet_nas': osnet_nas}
+
+
+def build_model(name, num_classes=100):
+    avai_models = list(__NAS_models.keys())
+    if name not in avai_models:
+        raise KeyError(
+            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
+        )
+    return __NAS_models[name](num_classes=num_classes)
diff --git a/pfe/player-feature-extractor/projects/OSNet_AIN/softmax_nas.py b/pfe/player-feature-extractor/projects/OSNet_AIN/softmax_nas.py
new file mode 100644
index 0000000..e2a03b6
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/OSNet_AIN/softmax_nas.py
@@ -0,0 +1,73 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid import metrics
+from torchreid.engine import Engine
+from torchreid.losses import CrossEntropyLoss
+
+
+class ImageSoftmaxNASEngine(Engine):
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        scheduler=None,
+        use_gpu=False,
+        label_smooth=True,
+        mc_iter=1,
+        init_lmda=1.,
+        min_lmda=1.,
+        lmda_decay_step=20,
+        lmda_decay_rate=0.5,
+        fixed_lmda=False
+    ):
+        super(ImageSoftmaxNASEngine, self).__init__(datamanager, use_gpu)
+        self.mc_iter = mc_iter
+        self.init_lmda = init_lmda
+        self.min_lmda = min_lmda
+        self.lmda_decay_step = lmda_decay_step
+        self.lmda_decay_rate = lmda_decay_rate
+        self.fixed_lmda = fixed_lmda
+
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.register_model('model', model, optimizer, scheduler)
+
+        self.criterion = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        # softmax temporature
+        if self.fixed_lmda or self.lmda_decay_step == -1:
+            lmda = self.init_lmda
+        else:
+            lmda = self.init_lmda * self.lmda_decay_rate**(
+                self.epoch // self.lmda_decay_step
+            )
+            if lmda < self.min_lmda:
+                lmda = self.min_lmda
+
+        for k in range(self.mc_iter):
+            outputs = self.model(imgs, lmda=lmda)
+            loss = self.compute_loss(self.criterion, outputs, pids)
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+
+        loss_dict = {
+            'loss': loss.item(),
+            'acc': metrics.accuracy(outputs, pids)[0].item()
+        }
+
+        return loss_dict
diff --git a/pfe/player-feature-extractor/projects/README.md b/pfe/player-feature-extractor/projects/README.md
new file mode 100644
index 0000000..fa63919
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/README.md
@@ -0,0 +1,5 @@
+Here are some research projects built on [Torchreid](https://arxiv.org/abs/1910.10093).
+
++ `OSNet_AIN`: [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1910.06827)
++ `DML`: [Deep Mutual Learning (CVPR'18)](https://arxiv.org/abs/1706.00384)
++ `attribute_recognition`: [Omni-Scale Feature Learning for Person Re-Identification (ICCV'19)](https://arxiv.org/abs/1905.00953)
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/README.md b/pfe/player-feature-extractor/projects/attribute_recognition/README.md
new file mode 100644
index 0000000..a20b6e7
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/README.md
@@ -0,0 +1,18 @@
+# Person Attribute Recognition
+This code was developed for the experiment of person attribute recognition in [Omni-Scale Feature Learning for Person Re-Identification (ICCV'19)](https://arxiv.org/abs/1905.00953).
+
+## Download data
+Download the PA-100K dataset from [https://github.com/xh-liu/HydraPlus-Net](https://github.com/xh-liu/HydraPlus-Net), and extract the file under the folder where you store your data (say $DATASET). The folder structure should look like
+```bash
+$DATASET/
+    pa100k/
+        data/ # images
+        annotation/
+            annotation.mat
+```
+
+## Train
+The training command is provided in `train.sh`. Run `bash train.sh $DATASET` to start training.
+
+## Test
+To test a pretrained model, add the following two arguments to `train.sh`: `--load-weights $PATH_TO_WEIGHTS --evaluate`.
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/datasets/__init__.py b/pfe/player-feature-extractor/projects/attribute_recognition/datasets/__init__.py
new file mode 100644
index 0000000..664598c
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/datasets/__init__.py
@@ -0,0 +1,15 @@
+from __future__ import division, print_function, absolute_import
+
+from .pa100k import PA100K
+
+__datasets = {'pa100k': PA100K}
+
+
+def init_dataset(name, **kwargs):
+    avai_datasets = list(__datasets.keys())
+    if name not in avai_datasets:
+        raise ValueError(
+            'Invalid dataset name. Received "{}", '
+            'but expected to be one of {}'.format(name, avai_datasets)
+        )
+    return __datasets[name](**kwargs)
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/datasets/dataset.py b/pfe/player-feature-extractor/projects/attribute_recognition/datasets/dataset.py
new file mode 100644
index 0000000..7ce4ebf
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/datasets/dataset.py
@@ -0,0 +1,87 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+
+from torchreid.utils import read_image
+
+
+class Dataset(object):
+
+    def __init__(
+        self,
+        train,
+        val,
+        test,
+        attr_dict,
+        transform=None,
+        mode='train',
+        verbose=True,
+        **kwargs
+    ):
+        self.train = train
+        self.val = val
+        self.test = test
+        self._attr_dict = attr_dict
+        self._num_attrs = len(self.attr_dict)
+        self.transform = transform
+
+        if mode == 'train':
+            self.data = self.train
+        elif mode == 'val':
+            self.data = self.val
+        else:
+            self.data = self.test
+
+        if verbose:
+            self.show_summary()
+
+    @property
+    def num_attrs(self):
+        return self._num_attrs
+
+    @property
+    def attr_dict(self):
+        return self._attr_dict
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        img_path, attrs = self.data[index]
+        img = read_image(img_path)
+        if self.transform is not None:
+            img = self.transform(img)
+        return img, attrs, img_path
+
+    def check_before_run(self, required_files):
+        """Checks if required files exist before going deeper.
+        Args:
+            required_files (str or list): string file name(s).
+        """
+        if isinstance(required_files, str):
+            required_files = [required_files]
+
+        for fpath in required_files:
+            if not osp.exists(fpath):
+                raise RuntimeError('"{}" is not found'.format(fpath))
+
+    def show_summary(self):
+        num_train = len(self.train)
+        num_val = len(self.val)
+        num_test = len(self.test)
+        num_total = num_train + num_val + num_test
+
+        print('=> Loaded {}'.format(self.__class__.__name__))
+        print("  ------------------------------")
+        print("  subset   | # images")
+        print("  ------------------------------")
+        print("  train    | {:8d}".format(num_train))
+        print("  val      | {:8d}".format(num_val))
+        print("  test     | {:8d}".format(num_test))
+        print("  ------------------------------")
+        print("  total    | {:8d}".format(num_total))
+        print("  ------------------------------")
+        print("  # attributes: {}".format(len(self.attr_dict)))
+        print("  attributes:")
+        for label, attr in self.attr_dict.items():
+            print('    {:3d}: {}'.format(label, attr))
+        print("  ------------------------------")
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/datasets/pa100k.py b/pfe/player-feature-extractor/projects/attribute_recognition/datasets/pa100k.py
new file mode 100644
index 0000000..61dd26c
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/datasets/pa100k.py
@@ -0,0 +1,59 @@
+from __future__ import division, print_function, absolute_import
+import numpy as np
+import os.path as osp
+from scipy.io import loadmat
+
+from .dataset import Dataset
+
+
+class PA100K(Dataset):
+    """Pedestrian attribute dataset.
+
+    80k training images + 20k test images.
+
+    The folder structure should be:
+        pa100k/
+            data/ # images
+            annotation/
+                annotation.mat
+    """
+    dataset_dir = 'pa100k'
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.data_dir = osp.join(self.dataset_dir, 'data')
+        self.anno_mat_path = osp.join(
+            self.dataset_dir, 'annotation', 'annotation.mat'
+        )
+
+        required_files = [self.data_dir, self.anno_mat_path]
+        self.check_before_run(required_files)
+
+        train, val, test, attr_dict = self.extract_data()
+        super(PA100K, self).__init__(train, val, test, attr_dict, **kwargs)
+
+    def extract_data(self):
+        # anno_mat is a dictionary with keys: ['test_images_name', 'val_images_name',
+        # 'train_images_name', 'val_label', 'attributes', 'test_label', 'train_label']
+        anno_mat = loadmat(self.anno_mat_path)
+
+        def _extract(key_name, key_label):
+            names = anno_mat[key_name]
+            labels = anno_mat[key_label]
+            num_imgs = names.shape[0]
+            data = []
+            for i in range(num_imgs):
+                name = names[i, 0][0]
+                attrs = labels[i, :].astype(np.float32)
+                img_path = osp.join(self.data_dir, name)
+                data.append((img_path, attrs))
+            return data
+
+        train = _extract('train_images_name', 'train_label')
+        val = _extract('val_images_name', 'val_label')
+        test = _extract('test_images_name', 'test_label')
+        attrs = anno_mat['attributes']
+        attr_dict = {i: str(attr[0][0]) for i, attr in enumerate(attrs)}
+
+        return train, val, test, attr_dict
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/default_parser.py b/pfe/player-feature-extractor/projects/attribute_recognition/default_parser.py
new file mode 100644
index 0000000..d19a8be
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/default_parser.py
@@ -0,0 +1,243 @@
+from __future__ import print_function, absolute_import
+import argparse
+
+
+def init_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    # ************************************************************
+    # Datasets
+    # ************************************************************
+    parser.add_argument(
+        '--root',
+        type=str,
+        default='',
+        required=True,
+        help='root path to data directory'
+    )
+    parser.add_argument(
+        '-d',
+        '--dataset',
+        type=str,
+        required=True,
+        help='which dataset to choose'
+    )
+    parser.add_argument(
+        '-j',
+        '--workers',
+        type=int,
+        default=4,
+        help='number of data loading workers (tips: 4 or 8 times number of gpus)'
+    )
+    parser.add_argument(
+        '--height', type=int, default=256, help='height of an image'
+    )
+    parser.add_argument(
+        '--width', type=int, default=128, help='width of an image'
+    )
+
+    # ************************************************************
+    # Optimization options
+    # ************************************************************
+    parser.add_argument(
+        '--optim',
+        type=str,
+        default='adam',
+        help='optimization algorithm (see optimizers.py)'
+    )
+    parser.add_argument(
+        '--lr', type=float, default=0.0003, help='initial learning rate'
+    )
+    parser.add_argument(
+        '--weight-decay', type=float, default=5e-04, help='weight decay'
+    )
+    # sgd
+    parser.add_argument(
+        '--momentum',
+        type=float,
+        default=0.9,
+        help='momentum factor for sgd and rmsprop'
+    )
+    parser.add_argument(
+        '--sgd-dampening',
+        type=float,
+        default=0,
+        help='sgd\'s dampening for momentum'
+    )
+    parser.add_argument(
+        '--sgd-nesterov',
+        action='store_true',
+        help='whether to enable sgd\'s Nesterov momentum'
+    )
+    # rmsprop
+    parser.add_argument(
+        '--rmsprop-alpha',
+        type=float,
+        default=0.99,
+        help='rmsprop\'s smoothing constant'
+    )
+    # adam/amsgrad
+    parser.add_argument(
+        '--adam-beta1',
+        type=float,
+        default=0.9,
+        help='exponential decay rate for adam\'s first moment'
+    )
+    parser.add_argument(
+        '--adam-beta2',
+        type=float,
+        default=0.999,
+        help='exponential decay rate for adam\'s second moment'
+    )
+
+    # ************************************************************
+    # Training hyperparameters
+    # ************************************************************
+    parser.add_argument(
+        '--max-epoch', type=int, default=60, help='maximum epochs to run'
+    )
+    parser.add_argument(
+        '--start-epoch',
+        type=int,
+        default=0,
+        help='manual epoch number (useful when restart)'
+    )
+    parser.add_argument(
+        '--batch-size', type=int, default=32, help='batch size'
+    )
+
+    parser.add_argument(
+        '--fixbase-epoch',
+        type=int,
+        default=0,
+        help='number of epochs to fix base layers'
+    )
+    parser.add_argument(
+        '--open-layers',
+        type=str,
+        nargs='+',
+        default=['classifier'],
+        help='open specified layers for training while keeping others frozen'
+    )
+
+    parser.add_argument(
+        '--staged-lr',
+        action='store_true',
+        help='set different lr to different layers'
+    )
+    parser.add_argument(
+        '--new-layers',
+        type=str,
+        nargs='+',
+        default=['classifier'],
+        help='newly added layers with default lr'
+    )
+    parser.add_argument(
+        '--base-lr-mult',
+        type=float,
+        default=0.1,
+        help='learning rate multiplier for base layers'
+    )
+
+    # ************************************************************
+    # Learning rate scheduler options
+    # ************************************************************
+    parser.add_argument(
+        '--lr-scheduler',
+        type=str,
+        default='multi_step',
+        help='learning rate scheduler (see lr_schedulers.py)'
+    )
+    parser.add_argument(
+        '--stepsize',
+        type=int,
+        default=[20, 40],
+        nargs='+',
+        help='stepsize to decay learning rate'
+    )
+    parser.add_argument(
+        '--gamma', type=float, default=0.1, help='learning rate decay'
+    )
+
+    # ************************************************************
+    # Architecture
+    # ************************************************************
+    parser.add_argument(
+        '-a', '--arch', type=str, default='', help='model architecture'
+    )
+    parser.add_argument(
+        '--no-pretrained',
+        action='store_true',
+        help='do not load pretrained weights'
+    )
+
+    # ************************************************************
+    # Loss
+    # ************************************************************
+    parser.add_argument(
+        '--weighted-bce', action='store_true', help='use weighted BCELoss'
+    )
+
+    # ************************************************************
+    # Test settings
+    # ************************************************************
+    parser.add_argument(
+        '--load-weights', type=str, default='', help='load pretrained weights'
+    )
+    parser.add_argument(
+        '--evaluate', action='store_true', help='evaluate only'
+    )
+    parser.add_argument(
+        '--save-prediction', action='store_true', help='save prediction'
+    )
+
+    # ************************************************************
+    # Miscs
+    # ************************************************************
+    parser.add_argument(
+        '--print-freq', type=int, default=20, help='print frequency'
+    )
+    parser.add_argument('--seed', type=int, default=1, help='manual seed')
+    parser.add_argument(
+        '--resume',
+        type=str,
+        default='',
+        metavar='PATH',
+        help='resume from a checkpoint'
+    )
+    parser.add_argument(
+        '--save-dir',
+        type=str,
+        default='log',
+        help='path to save log and model weights'
+    )
+    parser.add_argument('--use-cpu', action='store_true', help='use cpu')
+
+    return parser
+
+
+def optimizer_kwargs(parsed_args):
+    return {
+        'optim': parsed_args.optim,
+        'lr': parsed_args.lr,
+        'weight_decay': parsed_args.weight_decay,
+        'momentum': parsed_args.momentum,
+        'sgd_dampening': parsed_args.sgd_dampening,
+        'sgd_nesterov': parsed_args.sgd_nesterov,
+        'rmsprop_alpha': parsed_args.rmsprop_alpha,
+        'adam_beta1': parsed_args.adam_beta1,
+        'adam_beta2': parsed_args.adam_beta2,
+        'staged_lr': parsed_args.staged_lr,
+        'new_layers': parsed_args.new_layers,
+        'base_lr_mult': parsed_args.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(parsed_args):
+    return {
+        'lr_scheduler': parsed_args.lr_scheduler,
+        'stepsize': parsed_args.stepsize,
+        'gamma': parsed_args.gamma
+    }
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/main.py b/pfe/player-feature-extractor/projects/attribute_recognition/main.py
new file mode 100644
index 0000000..1fb4238
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/main.py
@@ -0,0 +1,399 @@
+from __future__ import division, print_function
+import sys
+import copy
+import time
+import numpy as np
+import os.path as osp
+import datetime
+import warnings
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, AverageMeter, check_isfile, open_all_layers, save_checkpoint,
+    set_random_seed, collect_env_info, open_specified_layers,
+    load_pretrained_weights, compute_model_complexity
+)
+from torchreid.data.transforms import (
+    Resize, Compose, ToTensor, Normalize, Random2DTranslation,
+    RandomHorizontalFlip
+)
+
+import models
+import datasets
+from default_parser import init_parser, optimizer_kwargs, lr_scheduler_kwargs
+
+parser = init_parser()
+args = parser.parse_args()
+
+
+def init_dataset(use_gpu):
+    normalize = Normalize(
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+    )
+
+    transform_tr = Compose(
+        [
+            Random2DTranslation(args.height, args.width, p=0.5),
+            RandomHorizontalFlip(),
+            ToTensor(), normalize
+        ]
+    )
+
+    transform_te = Compose(
+        [Resize([args.height, args.width]),
+         ToTensor(), normalize]
+    )
+
+    trainset = datasets.init_dataset(
+        args.dataset,
+        root=args.root,
+        transform=transform_tr,
+        mode='train',
+        verbose=True
+    )
+
+    valset = datasets.init_dataset(
+        args.dataset,
+        root=args.root,
+        transform=transform_te,
+        mode='val',
+        verbose=False
+    )
+
+    testset = datasets.init_dataset(
+        args.dataset,
+        root=args.root,
+        transform=transform_te,
+        mode='test',
+        verbose=False
+    )
+
+    num_attrs = trainset.num_attrs
+    attr_dict = trainset.attr_dict
+
+    trainloader = torch.utils.data.DataLoader(
+        trainset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        num_workers=args.workers,
+        pin_memory=use_gpu,
+        drop_last=True
+    )
+
+    valloader = torch.utils.data.DataLoader(
+        valset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=args.workers,
+        pin_memory=use_gpu,
+        drop_last=False
+    )
+
+    testloader = torch.utils.data.DataLoader(
+        testset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=args.workers,
+        pin_memory=use_gpu,
+        drop_last=False
+    )
+
+    return trainloader, valloader, testloader, num_attrs, attr_dict
+
+
+def main():
+    global args
+
+    set_random_seed(args.seed)
+    use_gpu = torch.cuda.is_available() and not args.use_cpu
+    log_name = 'test.log' if args.evaluate else 'train.log'
+    sys.stdout = Logger(osp.join(args.save_dir, log_name))
+
+    print('** Arguments **')
+    arg_keys = list(args.__dict__.keys())
+    arg_keys.sort()
+    for key in arg_keys:
+        print('{}: {}'.format(key, args.__dict__[key]))
+    print('\n')
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if use_gpu:
+        torch.backends.cudnn.benchmark = True
+    else:
+        warnings.warn(
+            'Currently using CPU, however, GPU is highly recommended'
+        )
+
+    dataset_vars = init_dataset(use_gpu)
+    trainloader, valloader, testloader, num_attrs, attr_dict = dataset_vars
+
+    if args.weighted_bce:
+        print('Use weighted binary cross entropy')
+        print('Computing the weights ...')
+        bce_weights = torch.zeros(num_attrs, dtype=torch.float)
+        for _, attrs, _ in trainloader:
+            bce_weights += attrs.sum(0) # sum along the batch dim
+        bce_weights /= len(trainloader) * args.batch_size
+        print('Sample ratio for each attribute: {}'.format(bce_weights))
+        bce_weights = torch.exp(-1 * bce_weights)
+        print('BCE weights: {}'.format(bce_weights))
+        bce_weights = bce_weights.expand(args.batch_size, num_attrs)
+        criterion = nn.BCEWithLogitsLoss(weight=bce_weights)
+
+    else:
+        print('Use plain binary cross entropy')
+        criterion = nn.BCEWithLogitsLoss()
+
+    print('Building model: {}'.format(args.arch))
+    model = models.build_model(
+        args.arch,
+        num_attrs,
+        pretrained=not args.no_pretrained,
+        use_gpu=use_gpu
+    )
+    num_params, flops = compute_model_complexity(
+        model, (1, 3, args.height, args.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    if args.load_weights and check_isfile(args.load_weights):
+        load_pretrained_weights(model, args.load_weights)
+
+    if use_gpu:
+        model = nn.DataParallel(model).cuda()
+        criterion = criterion.cuda()
+
+    if args.evaluate:
+        test(model, testloader, attr_dict, use_gpu)
+        return
+
+    optimizer = torchreid.optim.build_optimizer(
+        model, **optimizer_kwargs(args)
+    )
+    scheduler = torchreid.optim.build_lr_scheduler(
+        optimizer, **lr_scheduler_kwargs(args)
+    )
+
+    start_epoch = args.start_epoch
+    best_result = -np.inf
+    if args.resume and check_isfile(args.resume):
+        checkpoint = torch.load(args.resume)
+        model.load_state_dict(checkpoint['state_dict'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        start_epoch = checkpoint['epoch']
+        best_result = checkpoint['label_mA']
+        print('Loaded checkpoint from "{}"'.format(args.resume))
+        print('- start epoch: {}'.format(start_epoch))
+        print('- label_mA: {}'.format(best_result))
+
+    time_start = time.time()
+
+    for epoch in range(start_epoch, args.max_epoch):
+        train(
+            epoch, model, criterion, optimizer, scheduler, trainloader, use_gpu
+        )
+        test_outputs = test(model, testloader, attr_dict, use_gpu)
+        label_mA = test_outputs[0]
+        is_best = label_mA > best_result
+        if is_best:
+            best_result = label_mA
+
+        save_checkpoint(
+            {
+                'state_dict': model.state_dict(),
+                'epoch': epoch + 1,
+                'label_mA': label_mA,
+                'optimizer': optimizer.state_dict(),
+            },
+            args.save_dir,
+            is_best=is_best
+        )
+
+    elapsed = round(time.time() - time_start)
+    elapsed = str(datetime.timedelta(seconds=elapsed))
+    print('Elapsed {}'.format(elapsed))
+
+
+def train(epoch, model, criterion, optimizer, scheduler, trainloader, use_gpu):
+    losses = AverageMeter()
+    batch_time = AverageMeter()
+    data_time = AverageMeter()
+    model.train()
+
+    if (epoch + 1) <= args.fixbase_epoch and args.open_layers is not None:
+        print(
+            '* Only train {} (epoch: {}/{})'.format(
+                args.open_layers, epoch + 1, args.fixbase_epoch
+            )
+        )
+        open_specified_layers(model, args.open_layers)
+    else:
+        open_all_layers(model)
+
+    end = time.time()
+    for batch_idx, data in enumerate(trainloader):
+        data_time.update(time.time() - end)
+
+        imgs, attrs = data[0], data[1]
+        if use_gpu:
+            imgs = imgs.cuda()
+            attrs = attrs.cuda()
+
+        optimizer.zero_grad()
+        outputs = model(imgs)
+        loss = criterion(outputs, attrs)
+        loss.backward()
+        optimizer.step()
+
+        batch_time.update(time.time() - end)
+
+        losses.update(loss.item(), imgs.size(0))
+
+        if (batch_idx+1) % args.print_freq == 0:
+            # estimate remaining time
+            num_batches = len(trainloader)
+            eta_seconds = batch_time.avg * (
+                num_batches - (batch_idx+1) + (args.max_epoch -
+                                               (epoch+1)) * num_batches
+            )
+            eta_str = str(datetime.timedelta(seconds=int(eta_seconds)))
+            print(
+                'Epoch: [{0}/{1}][{2}/{3}]\t'
+                'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
+                'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                'Lr {lr:.6f}\t'
+                'Eta {eta}'.format(
+                    epoch + 1,
+                    args.max_epoch,
+                    batch_idx + 1,
+                    len(trainloader),
+                    batch_time=batch_time,
+                    data_time=data_time,
+                    loss=losses,
+                    lr=optimizer.param_groups[0]['lr'],
+                    eta=eta_str
+                )
+            )
+
+        end = time.time()
+
+    scheduler.step()
+
+
+@torch.no_grad()
+def test(model, testloader, attr_dict, use_gpu):
+    batch_time = AverageMeter()
+    model.eval()
+
+    num_persons = 0
+    prob_thre = 0.5
+    ins_acc = 0
+    ins_prec = 0
+    ins_rec = 0
+    mA_history = {
+        'correct_pos': 0,
+        'real_pos': 0,
+        'correct_neg': 0,
+        'real_neg': 0
+    }
+
+    print('Testing ...')
+
+    for batch_idx, data in enumerate(testloader):
+        imgs, attrs, img_paths = data
+        if use_gpu:
+            imgs = imgs.cuda()
+
+        end = time.time()
+        orig_outputs = model(imgs)
+        batch_time.update(time.time() - end)
+
+        orig_outputs = orig_outputs.data.cpu().numpy()
+        attrs = attrs.data.numpy()
+
+        # transform raw outputs to attributes (binary codes)
+        outputs = copy.deepcopy(orig_outputs)
+        outputs[outputs < prob_thre] = 0
+        outputs[outputs >= prob_thre] = 1
+
+        # compute label-based metric
+        overlaps = outputs * attrs
+        mA_history['correct_pos'] += overlaps.sum(0)
+        mA_history['real_pos'] += attrs.sum(0)
+        inv_overlaps = (1-outputs) * (1-attrs)
+        mA_history['correct_neg'] += inv_overlaps.sum(0)
+        mA_history['real_neg'] += (1 - attrs).sum(0)
+
+        outputs = outputs.astype(bool)
+        attrs = attrs.astype(bool)
+
+        # compute instabce-based accuracy
+        intersect = (outputs & attrs).astype(float)
+        union = (outputs | attrs).astype(float)
+        ins_acc += (intersect.sum(1) / union.sum(1)).sum()
+        ins_prec += (intersect.sum(1) / outputs.astype(float).sum(1)).sum()
+        ins_rec += (intersect.sum(1) / attrs.astype(float).sum(1)).sum()
+
+        num_persons += imgs.size(0)
+
+        if (batch_idx+1) % args.print_freq == 0:
+            print(
+                'Processed batch {}/{}'.format(batch_idx + 1, len(testloader))
+            )
+
+        if args.save_prediction:
+            txtfile = open(osp.join(args.save_dir, 'prediction.txt'), 'a')
+            for idx in range(imgs.size(0)):
+                img_path = img_paths[idx]
+                probs = orig_outputs[idx, :]
+                labels = attrs[idx, :]
+                txtfile.write('{}\n'.format(img_path))
+                txtfile.write('*** Correct prediction ***\n')
+                for attr_idx, (label, prob) in enumerate(zip(labels, probs)):
+                    if label:
+                        attr_name = attr_dict[attr_idx]
+                        info = '{}: {:.1%}  '.format(attr_name, prob)
+                        txtfile.write(info)
+                txtfile.write('\n*** Incorrect prediction ***\n')
+                for attr_idx, (label, prob) in enumerate(zip(labels, probs)):
+                    if not label and prob > 0.5:
+                        attr_name = attr_dict[attr_idx]
+                        info = '{}: {:.1%}  '.format(attr_name, prob)
+                        txtfile.write(info)
+                txtfile.write('\n\n')
+            txtfile.close()
+
+    print(
+        '=> BatchTime(s)/BatchSize(img): {:.4f}/{}'.format(
+            batch_time.avg, args.batch_size
+        )
+    )
+
+    ins_acc /= num_persons
+    ins_prec /= num_persons
+    ins_rec /= num_persons
+    ins_f1 = (2*ins_prec*ins_rec) / (ins_prec+ins_rec)
+
+    term1 = mA_history['correct_pos'] / mA_history['real_pos']
+    term2 = mA_history['correct_neg'] / mA_history['real_neg']
+    label_mA_verbose = (term1+term2) * 0.5
+    label_mA = label_mA_verbose.mean()
+
+    print('* Results *')
+    print('  # test persons: {}'.format(num_persons))
+    print('  (instance-based)  accuracy:      {:.1%}'.format(ins_acc))
+    print('  (instance-based)  precition:     {:.1%}'.format(ins_prec))
+    print('  (instance-based)  recall:        {:.1%}'.format(ins_rec))
+    print('  (instance-based)  f1-score:      {:.1%}'.format(ins_f1))
+    print('  (label-based)     mean accuracy: {:.1%}'.format(label_mA))
+    print('  mA for each attribute: {}'.format(label_mA_verbose))
+
+    return label_mA, ins_acc, ins_prec, ins_rec, ins_f1
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/models/__init__.py b/pfe/player-feature-extractor/projects/attribute_recognition/models/__init__.py
new file mode 100644
index 0000000..ff0f0ed
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/models/__init__.py
@@ -0,0 +1,17 @@
+from __future__ import absolute_import
+
+from .osnet import *
+
+__model_factory = {
+    'osnet_avgpool': osnet_avgpool,
+    'osnet_maxpool': osnet_maxpool
+}
+
+
+def build_model(name, num_classes, pretrained=True, use_gpu=True):
+    avai_models = list(__model_factory.keys())
+    if name not in avai_models:
+        raise KeyError
+    return __model_factory[name](
+        num_classes=num_classes, pretrained=pretrained, use_gpu=use_gpu
+    )
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/models/osnet.py b/pfe/player-feature-extractor/projects/attribute_recognition/models/osnet.py
new file mode 100644
index 0000000..12569da
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/models/osnet.py
@@ -0,0 +1,414 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['osnet_avgpool', 'osnet_maxpool']
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution without non-linearity."""
+
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, **kwargs):
+        super(OSBlock, self).__init__()
+        mid_channels = out_channels // 4
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2a = LightConv3x3(mid_channels, mid_channels)
+        self.conv2b = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2c = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2d = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        residual = x
+        x1 = self.conv1(x)
+        x2a = self.conv2a(x1)
+        x2b = self.conv2b(x1)
+        x2c = self.conv2c(x1)
+        x2d = self.conv2d(x1)
+        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+        out = x3 + residual
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class BaseNet(nn.Module):
+
+    def _make_layer(
+        self, block, layer, in_channels, out_channels, reduce_spatial_size
+    ):
+        layers = []
+
+        layers.append(block(in_channels, out_channels))
+        for i in range(1, layer):
+            layers.append(block(out_channels, out_channels))
+
+        if reduce_spatial_size:
+            layers.append(
+                nn.Sequential(
+                    Conv1x1(out_channels, out_channels),
+                    nn.AvgPool2d(2, stride=2)
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+
+class OSNet(BaseNet):
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        pool='avg',
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3)
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0],
+            layers[0],
+            channels[0],
+            channels[1],
+            reduce_spatial_size=True
+        )
+        self.conv3 = self._make_layer(
+            blocks[1],
+            layers[1],
+            channels[1],
+            channels[2],
+            reduce_spatial_size=True
+        )
+        self.conv4 = self._make_layer(
+            blocks[2],
+            layers[2],
+            channels[2],
+            channels[3],
+            reduce_spatial_size=False
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        if pool == 'avg':
+            self.global_pool = nn.AdaptiveAvgPool2d(1)
+        else:
+            self.global_pool = nn.AdaptiveMaxPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            feature_dim, channels[3], dropout_p=None
+        )
+        # classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self.init_params()
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x):
+        x = self.featuremaps(x)
+        v = self.global_pool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        y = self.classifier(v)
+        if not self.training:
+            y = torch.sigmoid(y)
+        return y
+
+
+def osnet_avgpool(num_classes=1000, loss='softmax', **kwargs):
+    return OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        pool='avg',
+        **kwargs
+    )
+
+
+def osnet_maxpool(num_classes=1000, loss='softmax', **kwargs):
+    return OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        pool='max',
+        **kwargs
+    )
diff --git a/pfe/player-feature-extractor/projects/attribute_recognition/train.sh b/pfe/player-feature-extractor/projects/attribute_recognition/train.sh
new file mode 100644
index 0000000..9080a66
--- /dev/null
+++ b/pfe/player-feature-extractor/projects/attribute_recognition/train.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+# DATASET points to the directory containing pa100k/
+DATASET=$1
+
+python main.py \
+--root ${DATASET} \
+-d pa100k \
+-a osnet_maxpool \
+--max-epoch 50 \
+--stepsize 30 40 \
+--batch-size 32 \
+--lr 0.065 \
+--optim sgd \
+--weighted-bce \
+--save-dir log/pa100k-osnet_maxpool
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/requirements.txt b/pfe/player-feature-extractor/requirements.txt
new file mode 100644
index 0000000..8b35c0e
--- /dev/null
+++ b/pfe/player-feature-extractor/requirements.txt
@@ -0,0 +1,16 @@
+numpy
+Cython
+h5py
+Pillow
+six
+scipy
+opencv-python
+matplotlib
+tb-nightly
+future
+yacs
+gdown
+flake8
+yapf
+isort
+imageio
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/scripts/cuhk03_config.py b/pfe/player-feature-extractor/scripts/cuhk03_config.py
new file mode 100644
index 0000000..783a17f
--- /dev/null
+++ b/pfe/player-feature-extractor/scripts/cuhk03_config.py
@@ -0,0 +1,211 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    # cfg.model.name = 'resnet50_fc512'
+    cfg.model.name = 'osnet_x1_0'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '' # path to model weights
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['bepro']
+
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 128 # image height
+    cfg.data.width = 64 # image width
+    # cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.combineall = True # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.k_tfm = 1 # number of times to apply augmentation to an image independently
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False # load training set from target dataset
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = True # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler' # sampler for source train loader
+    cfg.sampler.train_sampler_t = 'RandomSampler' # sampler for target train loader
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0001
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 120
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 60 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'cosine' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = True # test only
+    cfg.test.eval_freq = 10 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 1 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'k_tfm': cfg.data.k_tfm,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        'train_sampler_t': cfg.sampler.train_sampler_t,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        # 'test_only': cfg.test.evaluate,
+        'test_only': False,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': 'cosine',
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': False,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/player-feature-extractor/scripts/default_config.py b/pfe/player-feature-extractor/scripts/default_config.py
new file mode 100644
index 0000000..ce4ef5f
--- /dev/null
+++ b/pfe/player-feature-extractor/scripts/default_config.py
@@ -0,0 +1,206 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'resnet50'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '' # path to model weights
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['market1501']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 256 # image height
+    cfg.data.width = 128 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.k_tfm = 1 # number of times to apply augmentation to an image independently
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False # load training set from target dataset
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler' # sampler for source train loader
+    cfg.sampler.train_sampler_t = 'RandomSampler' # sampler for target train loader
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0003
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 60
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 20 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'euclidean' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = False # test only
+    cfg.test.eval_freq = -1 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 0 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'k_tfm': cfg.data.k_tfm,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        'train_sampler_t': cfg.sampler.train_sampler_t,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        'test_only': cfg.test.evaluate,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': cfg.test.dist_metric,
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': cfg.test.visrank,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/player-feature-extractor/scripts/extract_fetures.py b/pfe/player-feature-extractor/scripts/extract_fetures.py
new file mode 100644
index 0000000..92b2c76
--- /dev/null
+++ b/pfe/player-feature-extractor/scripts/extract_fetures.py
@@ -0,0 +1,178 @@
+from torchreid.utils import FeatureExtractor
+import torch
+
+import os
+import sys
+import numpy as np
+import cv2
+
+from PIL import Image
+import errno
+
+import mmcv 
+
+def test_sanity():
+    features = extractor('/home/dmitriy.khvan/deep-person-reid/tmp/debug/dbg_orig_0.jpg')
+    print(features)
+
+def extract_image_patch(image, bbox):
+    x, y, w, h = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
+    patch = image[y:y+h, x:x+w]
+    return patch
+
+def network_feed(image, boxes, extractor):
+    patches_np_lst = []
+
+    for num, box in enumerate(boxes):
+        patch = extract_image_patch(image, box)
+        # cv2.imwrite('./tmp/debug/dbg_orig_%d.jpg' % (num), patch)
+        patch_rgb = cv2.cvtColor(patch, cv2.COLOR_BGR2RGB)
+
+        if patch_rgb is None:
+            print("WARNING: Failed to extract image patch: %s." % str(box))
+            patch_rgb = np.random.uniform(0., 255., image_shape).astype(np.uint8)
+
+        patch_np = np.array(patch_rgb)
+        patches_np_lst.append(patch_np)
+
+    features = extractor(patches_np_lst)
+    features_np = features.cpu().detach().numpy()
+    
+    return features_np
+
+def network_feed_from_list(image_list, extractor):
+    patches_np_lst = []
+
+    for patch in image_list:
+        patch_rgb = cv2.cvtColor(patch, cv2.COLOR_BGR2RGB)
+
+        if patch_rgb is None:
+            print("WARNING: Failed to extract image patch: %s." % str(box))
+            patch_rgb = np.random.uniform(0., 255., image_shape).astype(np.uint8)
+
+        patch_np = np.array(patch_rgb)
+        patches_np_lst.append(patch_np)
+
+    features = extractor(patches_np_lst)
+    features_np = features.cpu().detach().numpy()
+
+    return features_np
+
+def generate_detections(ckpt_path, mot_dir, output_dir, detection_dir=None):
+
+    if detection_dir is None:
+        detection_dir = mot_dir
+    try:
+        os.makedirs(output_dir)
+    except OSError as exception:
+        if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
+            pass
+        else:
+            raise ValueError(
+                "Failed to created output directory '%s'" % output_dir)
+
+    extractor = FeatureExtractor(
+        model_name='osnet_x1_0',
+        model_path=ckpt_path,
+        device='cuda'
+    )
+
+    for sequence in os.listdir(mot_dir):
+        print("Processing %s" % sequence)
+        sequence_dir = os.path.join(mot_dir, sequence)
+
+        image_dir = os.path.join(mot_dir, "img1")
+
+        image_filenames = {
+            int(os.path.splitext(f)[0]): os.path.join(image_dir, f)
+            for f in os.listdir(image_dir)}
+
+        detection_file = os.path.join(mot_dir, "det/det.txt")
+        detections_in = np.loadtxt(detection_file, delimiter=',')
+        detections_out = []
+
+        frame_indices = detections_in[:, 0].astype(np.int)
+        min_frame_idx = frame_indices.astype(np.int).min()
+        max_frame_idx = frame_indices.astype(np.int).max()
+
+        # output_filename = os.path.join(output_dir, "%s.npy" % sequence)
+        output_filename = os.path.join(output_dir, "features.npy")
+
+        for frame_idx in range(min_frame_idx, max_frame_idx + 1):
+            print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
+            mask = frame_indices == frame_idx
+            rows = detections_in[mask]
+
+            if frame_idx not in image_filenames:
+                print("WARNING could not find image for frame %d" % frame_idx)
+                continue
+        
+            bgr_image = cv2.imread(image_filenames[frame_idx], cv2.IMREAD_COLOR)
+
+            features = network_feed(bgr_image, rows[:, 2:6].copy(), extractor)
+            detections_out += [np.r_[(row, feature)] for row, feature in zip(rows, features)]
+
+        np.save(output_filename, np.asarray(detections_out), allow_pickle=False)
+        break
+
+def generate_detections_from_video(frame_offset, frame_count, ckpt_path, video_path, det_file_path, output_dir):
+
+    try:
+        os.makedirs(output_dir)
+    
+    except OSError as exception:
+        if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
+            pass
+        else:
+            raise ValueError(
+                "Failed to created output directory '%s'" % output_dir)
+
+    extractor = FeatureExtractor(
+        model_name='osnet_x1_0',
+        model_path=ckpt_path,
+        device='cuda'
+    )
+
+    frame_offset = int(frame_offset)
+    frame_count = int(frame_count)
+
+    video = mmcv.VideoReader(video_path)
+
+    slice_start = 0 if frame_offset == 0 else frame_offset-1
+    slice_end = frame_offset+frame_count
+
+    output_filename = os.path.join(output_dir, "features.npy")
+    detections_in = np.loadtxt(det_file_path, delimiter=',')
+    frame_indices = detections_in[:, 0].astype(np.int)
+
+    print('[DSORT] processing frames from {} - {}'.format(range(slice_start,slice_end)[0], range(slice_start,slice_end)[-1]))
+
+    detections_out = []
+
+    for index in range(slice_start, slice_end):
+        print("Frame %05d/%05d" % (index, slice_end))
+
+        mask = frame_indices == (index - slice_start + 1)
+        rows = detections_in[mask]
+
+        frame = video[index]
+        
+        if frame is None:
+            print('[DBG] Empty frame received!')
+            break
+
+        features = network_feed(frame, rows[:, 2:6].copy(), extractor)
+        detections_out += [np.r_[(row, feature)] for row, feature in zip(rows, features)]
+
+    np.save(output_filename, np.asarray(detections_out), allow_pickle=False)
+
+if __name__=="__main__": 
+    # sys.argv[1] - starting frame index in video
+    # sys.argv[2] - number of frames within interval
+    # sys.argv[3] - path to feature extractor checkpoint
+    # sys.argv[4] - path to video file
+    # sys.argv[5] - path to detection txt file
+    # sys.argv[6] - path to output directory
+
+    generate_detections_from_video(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])
+
diff --git a/pfe/player-feature-extractor/scripts/main.py b/pfe/player-feature-extractor/scripts/main.py
new file mode 100755
index 0000000..150a3b5
--- /dev/null
+++ b/pfe/player-feature-extractor/scripts/main.py
@@ -0,0 +1,194 @@
+import sys
+import time
+import os.path as osp
+import argparse
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, check_isfile, set_random_seed, collect_env_info,
+    resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
+)
+
+# from default_config import (
+#     imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
+#     get_default_config, lr_scheduler_kwargs
+# )
+
+# from test_config import (
+#     imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
+#     get_default_config, lr_scheduler_kwargs
+# )
+
+from cuhk03_config import (
+    imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
+    get_default_config, lr_scheduler_kwargs
+)
+
+def build_datamanager(cfg):
+    if cfg.data.type == 'image':
+        return torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
+    else:
+        return torchreid.data.VideoDataManager(**videodata_kwargs(cfg))
+
+
+def build_engine(cfg, datamanager, model, optimizer, scheduler):
+    if cfg.data.type == 'image':
+        if cfg.loss.name == 'softmax':
+            engine = torchreid.engine.ImageSoftmaxEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth
+            )
+
+        else:
+            engine = torchreid.engine.ImageTripletEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                margin=cfg.loss.triplet.margin,
+                weight_t=cfg.loss.triplet.weight_t,
+                weight_x=cfg.loss.triplet.weight_x,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth
+            )
+
+    else:
+        if cfg.loss.name == 'softmax':
+            engine = torchreid.engine.VideoSoftmaxEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth,
+                pooling_method=cfg.video.pooling_method
+            )
+
+        else:
+            engine = torchreid.engine.VideoTripletEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                margin=cfg.loss.triplet.margin,
+                weight_t=cfg.loss.triplet.weight_t,
+                weight_x=cfg.loss.triplet.weight_x,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth
+            )
+
+    return engine
+
+
+def reset_config(cfg, args):
+    if args.root:
+        cfg.data.root = args.root
+    if args.sources:
+        cfg.data.sources = args.sources
+    if args.targets:
+        cfg.data.targets = args.targets
+    if args.transforms:
+        cfg.data.transforms = args.transforms
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        '--config-file', type=str, default='', help='path to config file'
+    )
+    parser.add_argument(
+        '-s',
+        '--sources',
+        type=str,
+        nargs='+',
+        help='source datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '-t',
+        '--targets',
+        type=str,
+        nargs='+',
+        help='target datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '--transforms', type=str, nargs='+', help='data augmentation'
+    )
+    parser.add_argument(
+        '--root', type=str, default='', help='path to data root'
+    )
+    parser.add_argument(
+        'opts',
+        default=None,
+        nargs=argparse.REMAINDER,
+        help='Modify config options using the command-line'
+    )
+    args = parser.parse_args()
+
+    cfg = get_default_config()
+    cfg.use_gpu = torch.cuda.is_available()
+    if args.config_file:
+        cfg.merge_from_file(args.config_file)
+    reset_config(cfg, args)
+    cfg.merge_from_list(args.opts)
+    set_random_seed(cfg.train.seed)
+
+    log_name = 'test.log' if cfg.test.evaluate else 'train.log'
+    log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
+    sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
+
+    print('Show configuration\n{}\n'.format(cfg))
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if cfg.use_gpu:
+        torch.backends.cudnn.benchmark = True
+
+    datamanager = build_datamanager(cfg)
+
+    print('Building model: {}'.format(cfg.model.name))
+    model = torchreid.models.build_model(
+        name=cfg.model.name,
+        num_classes=datamanager.num_train_pids,
+        loss=cfg.loss.name,
+        pretrained=cfg.model.pretrained,
+        use_gpu=cfg.use_gpu
+    )
+    num_params, flops = compute_model_complexity(
+        model, (1, 3, cfg.data.height, cfg.data.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
+        load_pretrained_weights(model, cfg.model.load_weights)
+
+    if cfg.use_gpu:
+        model = nn.DataParallel(model).cuda()
+
+    optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
+    scheduler = torchreid.optim.build_lr_scheduler(
+        optimizer, **lr_scheduler_kwargs(cfg)
+    )
+
+    if cfg.model.resume and check_isfile(cfg.model.resume):
+        cfg.train.start_epoch = resume_from_checkpoint(
+            cfg.model.resume, model, optimizer=optimizer, scheduler=scheduler
+        )
+
+    print(
+        'Building {}-engine for {}-reid'.format(cfg.loss.name, cfg.data.type)
+    )
+    engine = build_engine(cfg, datamanager, model, optimizer, scheduler)
+    print (engine_run_kwargs(cfg))
+    engine.run(**engine_run_kwargs(cfg))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/player-feature-extractor/scripts/test_config.py b/pfe/player-feature-extractor/scripts/test_config.py
new file mode 100644
index 0000000..222c3dc
--- /dev/null
+++ b/pfe/player-feature-extractor/scripts/test_config.py
@@ -0,0 +1,215 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'osnet_x1_0'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '/home/dmitriy.khvan/deep-person-reid/log/model_market_100/model.pth.tar-100' # path to model weights
+    # cfg.model.resume = '/home/dmitriy.khvan/deep-person-reid/log/model_market_100/model.pth.tar-100' # path to checkpoint for resume training
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    
+    # cfg.data.sources = ['market1501']
+    # cfg.data.targets = ['market1501']
+    # cfg.data.sources = ['msmt17']
+    # cfg.data.targets = ['msmt17']
+    cfg.data.sources = ['bepro']
+    cfg.data.targets = ['bepro']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    # cfg.data.height = 256 # image height
+    # cfg.data.width = 128 # image width
+    cfg.data.height = 128 # image height
+    cfg.data.width = 64 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.k_tfm = 1 # number of times to apply augmentation to an image independently
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False # load training set from target dataset
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler' # sampler for source train loader
+    cfg.sampler.train_sampler_t = 'RandomSampler' # sampler for target train loader
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0001
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 100
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 60 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'cosine' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = True # test only
+    cfg.test.eval_freq = 4 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 1 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'k_tfm': cfg.data.k_tfm,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        'train_sampler_t': cfg.sampler.train_sampler_t,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        # 'test_only': cfg.test.evaluate,
+        'test_only': True,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': 'cosine',
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': True,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/player-feature-extractor/setup.py b/pfe/player-feature-extractor/setup.py
new file mode 100644
index 0000000..4ab6cf8
--- /dev/null
+++ b/pfe/player-feature-extractor/setup.py
@@ -0,0 +1,57 @@
+import numpy as np
+import os.path as osp
+from setuptools import setup, find_packages
+from distutils.extension import Extension
+from Cython.Build import cythonize
+
+
+def readme():
+    # with open('README.rst') as f:
+        # content = f.read()
+    return ''
+
+
+def find_version():
+    version_file = 'torchreid/__init__.py'
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+def numpy_include():
+    try:
+        numpy_include = np.get_include()
+    except AttributeError:
+        numpy_include = np.get_numpy_include()
+    return numpy_include
+
+
+ext_modules = [
+    Extension(
+        'torchreid.metrics.rank_cylib.rank_cy',
+        ['torchreid/metrics/rank_cylib/rank_cy.pyx'],
+        include_dirs=[numpy_include()],
+    )
+]
+
+
+def get_requirements(filename='requirements.txt'):
+    here = osp.dirname(osp.realpath(__file__))
+    with open(osp.join(here, filename), 'r') as f:
+        requires = [line.replace('\n', '') for line in f.readlines()]
+    return requires
+
+
+setup(
+    name='torchreid',
+    version=find_version(),
+    description='A library for deep learning person re-ID in PyTorch',
+    author='Kaiyang Zhou',
+    license='MIT',
+    long_description=readme(),
+    url='https://github.com/KaiyangZhou/deep-person-reid',
+    packages=find_packages(),
+    install_requires=get_requirements(),
+    keywords=['Person Re-Identification', 'Deep Learning', 'Computer Vision'],
+    ext_modules=cythonize(ext_modules)
+)
diff --git a/pfe/player-feature-extractor/tools/compute_mean_std.py b/pfe/player-feature-extractor/tools/compute_mean_std.py
new file mode 100644
index 0000000..e0a5dbe
--- /dev/null
+++ b/pfe/player-feature-extractor/tools/compute_mean_std.py
@@ -0,0 +1,59 @@
+"""
+Compute channel-wise mean and standard deviation of a dataset.
+
+Usage:
+$ python compute_mean_std.py DATASET_ROOT DATASET_KEY
+
+- The first argument points to the root path where you put the datasets.
+- The second argument means the specific dataset key.
+
+For instance, your datasets are put under $DATA and you wanna
+compute the statistics of Market1501, do
+$ python compute_mean_std.py $DATA market1501
+"""
+import argparse
+
+import torchreid
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('root', type=str)
+    parser.add_argument('sources', type=str)
+    args = parser.parse_args()
+
+    datamanager = torchreid.data.ImageDataManager(
+        root=args.root,
+        sources=args.sources,
+        targets=None,
+        height=256,
+        width=128,
+        batch_size_train=100,
+        batch_size_test=100,
+        transforms=None,
+        norm_mean=[0., 0., 0.],
+        norm_std=[1., 1., 1.],
+        train_sampler='SequentialSampler'
+    )
+    train_loader = datamanager.train_loader
+
+    print('Computing mean and std ...')
+    mean = 0.
+    std = 0.
+    n_samples = 0.
+    for data in train_loader:
+        data = data['img']
+        batch_size = data.size(0)
+        data = data.view(batch_size, data.size(1), -1)
+        mean += data.mean(2).sum(0)
+        std += data.std(2).sum(0)
+        n_samples += batch_size
+
+    mean /= n_samples
+    std /= n_samples
+    print('Mean: {}'.format(mean))
+    print('Std: {}'.format(std))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/player-feature-extractor/tools/visualize_actmap.py b/pfe/player-feature-extractor/tools/visualize_actmap.py
new file mode 100644
index 0000000..ae69991
--- /dev/null
+++ b/pfe/player-feature-extractor/tools/visualize_actmap.py
@@ -0,0 +1,173 @@
+"""Visualizes CNN activation maps to see where the CNN focuses on to extract features.
+
+Reference:
+    - Zagoruyko and Komodakis. Paying more attention to attention: Improving the
+      performance of convolutional neural networks via attention transfer. ICLR, 2017
+    - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+"""
+import numpy as np
+import os.path as osp
+import argparse
+import cv2
+import torch
+from torch.nn import functional as F
+
+import torchreid
+from torchreid.utils import (
+    check_isfile, mkdir_if_missing, load_pretrained_weights
+)
+
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+GRID_SPACING = 10
+
+
+@torch.no_grad()
+def visactmap(
+    model,
+    test_loader,
+    save_dir,
+    width,
+    height,
+    use_gpu,
+    img_mean=None,
+    img_std=None
+):
+    if img_mean is None or img_std is None:
+        # use imagenet mean and std
+        img_mean = IMAGENET_MEAN
+        img_std = IMAGENET_STD
+
+    model.eval()
+
+    for target in list(test_loader.keys()):
+        data_loader = test_loader[target]['query'] # only process query images
+        # original images and activation maps are saved individually
+        actmap_dir = osp.join(save_dir, 'actmap_' + target)
+        mkdir_if_missing(actmap_dir)
+        print('Visualizing activation maps for {} ...'.format(target))
+
+        for batch_idx, data in enumerate(data_loader):
+            imgs, paths = data['img'], data['impath']
+            if use_gpu:
+                imgs = imgs.cuda()
+
+            # forward to get convolutional feature maps
+            try:
+                outputs = model(imgs, return_featuremaps=True)
+            except TypeError:
+                raise TypeError(
+                    'forward() got unexpected keyword argument "return_featuremaps". '
+                    'Please add return_featuremaps as an input argument to forward(). When '
+                    'return_featuremaps=True, return feature maps only.'
+                )
+
+            if outputs.dim() != 4:
+                raise ValueError(
+                    'The model output is supposed to have '
+                    'shape of (b, c, h, w), i.e. 4 dimensions, but got {} dimensions. '
+                    'Please make sure you set the model output at eval mode '
+                    'to be the last convolutional feature maps'.format(
+                        outputs.dim()
+                    )
+                )
+
+            # compute activation maps
+            outputs = (outputs**2).sum(1)
+            b, h, w = outputs.size()
+            outputs = outputs.view(b, h * w)
+            outputs = F.normalize(outputs, p=2, dim=1)
+            outputs = outputs.view(b, h, w)
+
+            if use_gpu:
+                imgs, outputs = imgs.cpu(), outputs.cpu()
+
+            for j in range(outputs.size(0)):
+                # get image name
+                path = paths[j]
+                imname = osp.basename(osp.splitext(path)[0])
+
+                # RGB image
+                img = imgs[j, ...]
+                for t, m, s in zip(img, img_mean, img_std):
+                    t.mul_(s).add_(m).clamp_(0, 1)
+                img_np = np.uint8(np.floor(img.numpy() * 255))
+                img_np = img_np.transpose((1, 2, 0)) # (c, h, w) -> (h, w, c)
+
+                # activation map
+                am = outputs[j, ...].numpy()
+                am = cv2.resize(am, (width, height))
+                am = 255 * (am - np.min(am)) / (
+                    np.max(am) - np.min(am) + 1e-12
+                )
+                am = np.uint8(np.floor(am))
+                am = cv2.applyColorMap(am, cv2.COLORMAP_JET)
+
+                # overlapped
+                overlapped = img_np*0.3 + am*0.7
+                overlapped[overlapped > 255] = 255
+                overlapped = overlapped.astype(np.uint8)
+
+                # save images in a single figure (add white spacing between images)
+                # from left to right: original image, activation map, overlapped image
+                grid_img = 255 * np.ones(
+                    (height, 3*width + 2*GRID_SPACING, 3), dtype=np.uint8
+                )
+                grid_img[:, :width, :] = img_np[:, :, ::-1]
+                grid_img[:,
+                         width + GRID_SPACING:2*width + GRID_SPACING, :] = am
+                grid_img[:, 2*width + 2*GRID_SPACING:, :] = overlapped
+                cv2.imwrite(osp.join(actmap_dir, imname + '.jpg'), grid_img)
+
+            if (batch_idx+1) % 10 == 0:
+                print(
+                    '- done batch {}/{}'.format(
+                        batch_idx + 1, len(data_loader)
+                    )
+                )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--root', type=str)
+    parser.add_argument('-d', '--dataset', type=str, default='market1501')
+    parser.add_argument('-m', '--model', type=str, default='osnet_x1_0')
+    parser.add_argument('--weights', type=str)
+    parser.add_argument('--save-dir', type=str, default='log')
+    parser.add_argument('--height', type=int, default=256)
+    parser.add_argument('--width', type=int, default=128)
+    args = parser.parse_args()
+
+    use_gpu = torch.cuda.is_available()
+
+    datamanager = torchreid.data.ImageDataManager(
+        root=args.root,
+        sources=args.dataset,
+        height=args.height,
+        width=args.width,
+        batch_size_train=100,
+        batch_size_test=100,
+        transforms=None,
+        train_sampler='SequentialSampler'
+    )
+    test_loader = datamanager.test_loader
+
+    model = torchreid.models.build_model(
+        name=args.model,
+        num_classes=datamanager.num_train_pids,
+        use_gpu=use_gpu
+    )
+
+    if use_gpu:
+        model = model.cuda()
+
+    if args.weights and check_isfile(args.weights):
+        load_pretrained_weights(model, args.weights)
+
+    visactmap(
+        model, test_loader, args.save_dir, args.width, args.height, use_gpu
+    )
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/player-feature-extractor/torchreid/__init__.py b/pfe/player-feature-extractor/torchreid/__init__.py
new file mode 100644
index 0000000..2f049be
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/__init__.py
@@ -0,0 +1,9 @@
+from __future__ import print_function, absolute_import
+
+from torchreid import data, optim, utils, engine, losses, models, metrics
+
+__version__ = '1.2.5'
+__author__ = 'Kaiyang Zhou'
+__homepage__ = 'https://kaiyangzhou.github.io/'
+__description__ = 'Deep learning person re-identification in PyTorch'
+__url__ = 'https://github.com/KaiyangZhou/deep-person-reid'
diff --git a/pfe/player-feature-extractor/torchreid/data/__init__.py b/pfe/player-feature-extractor/torchreid/data/__init__.py
new file mode 100644
index 0000000..5318a16
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/__init__.py
@@ -0,0 +1,7 @@
+from __future__ import print_function, absolute_import
+
+from .datasets import (
+    Dataset, ImageDataset, VideoDataset, register_image_dataset,
+    register_video_dataset
+)
+from .datamanager import ImageDataManager, VideoDataManager
diff --git a/pfe/player-feature-extractor/torchreid/data/datamanager.py b/pfe/player-feature-extractor/torchreid/data/datamanager.py
new file mode 100644
index 0000000..7e9f782
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datamanager.py
@@ -0,0 +1,536 @@
+from __future__ import division, print_function, absolute_import
+import torch
+
+from torchreid.data.sampler import build_train_sampler
+from torchreid.data.datasets import init_image_dataset, init_video_dataset
+from torchreid.data.transforms import build_transforms
+
+
+class DataManager(object):
+    r"""Base data manager.
+
+    Args:
+        sources (str or list): source dataset(s).
+        targets (str or list, optional): target dataset(s). If not given,
+            it equals to ``sources``.
+        height (int, optional): target image height. Default is 256.
+        width (int, optional): target image width. Default is 128.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        norm_mean (list or None, optional): data mean. Default is None (use imagenet mean).
+        norm_std (list or None, optional): data std. Default is None (use imagenet std).
+        use_gpu (bool, optional): use gpu. Default is True.
+    """
+
+    def __init__(
+        self,
+        sources=None,
+        targets=None,
+        height=256,
+        width=128,
+        transforms='random_flip',
+        norm_mean=None,
+        norm_std=None,
+        use_gpu=False
+    ):
+        self.sources = sources
+        self.targets = targets
+        self.height = height
+        self.width = width
+
+        if self.sources is None:
+            raise ValueError('sources must not be None')
+
+        if isinstance(self.sources, str):
+            self.sources = [self.sources]
+
+        if self.targets is None:
+            self.targets = self.sources
+
+        if isinstance(self.targets, str):
+            self.targets = [self.targets]
+
+        self.transform_tr, self.transform_te = build_transforms(
+            self.height,
+            self.width,
+            transforms=transforms,
+            norm_mean=norm_mean,
+            norm_std=norm_std
+        )
+
+        self.use_gpu = (torch.cuda.is_available() and use_gpu)
+
+    @property
+    def num_train_pids(self):
+        """Returns the number of training person identities."""
+        return self._num_train_pids
+
+    @property
+    def num_train_cams(self):
+        """Returns the number of training cameras."""
+        return self._num_train_cams
+
+    def fetch_test_loaders(self, name):
+        """Returns query and gallery of a test dataset, each containing
+        tuples of (img_path(s), pid, camid).
+
+        Args:
+            name (str): dataset name.
+        """
+        query_loader = self.test_dataset[name]['query']
+        gallery_loader = self.test_dataset[name]['gallery']
+        return query_loader, gallery_loader
+
+    def preprocess_pil_img(self, img):
+        """Transforms a PIL image to torch tensor for testing."""
+        return self.transform_te(img)
+
+
+class ImageDataManager(DataManager):
+    r"""Image data manager.
+
+    Args:
+        root (str): root path to datasets.
+        sources (str or list): source dataset(s).
+        targets (str or list, optional): target dataset(s). If not given,
+            it equals to ``sources``.
+        height (int, optional): target image height. Default is 256.
+        width (int, optional): target image width. Default is 128.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        k_tfm (int): number of times to apply augmentation to an image
+            independently. If k_tfm > 1, the transform function will be
+            applied k_tfm times to an image. This variable will only be
+            useful for training and is currently valid for image datasets only.
+        norm_mean (list or None, optional): data mean. Default is None (use imagenet mean).
+        norm_std (list or None, optional): data std. Default is None (use imagenet std).
+        use_gpu (bool, optional): use gpu. Default is True.
+        split_id (int, optional): split id (*0-based*). Default is 0.
+        combineall (bool, optional): combine train, query and gallery in a dataset for
+            training. Default is False.
+        load_train_targets (bool, optional): construct train-loader for target datasets.
+            Default is False. This is useful for domain adaptation research.
+        batch_size_train (int, optional): number of images in a training batch. Default is 32.
+        batch_size_test (int, optional): number of images in a test batch. Default is 32.
+        workers (int, optional): number of workers. Default is 4.
+        num_instances (int, optional): number of instances per identity in a batch.
+            Default is 4.
+        train_sampler (str, optional): sampler. Default is RandomSampler.
+        train_sampler_t (str, optional): sampler for target train loader. Default is RandomSampler.
+        cuhk03_labeled (bool, optional): use cuhk03 labeled images.
+            Default is False (defaul is to use detected images).
+        cuhk03_classic_split (bool, optional): use the classic split in cuhk03.
+            Default is False.
+        market1501_500k (bool, optional): add 500K distractors to the gallery
+            set in market1501. Default is False.
+
+    Examples::
+
+        datamanager = torchreid.data.ImageDataManager(
+            root='path/to/reid-data',
+            sources='market1501',
+            height=256,
+            width=128,
+            batch_size_train=32,
+            batch_size_test=100
+        )
+
+        # return train loader of source data
+        train_loader = datamanager.train_loader
+
+        # return test loader of target data
+        test_loader = datamanager.test_loader
+
+        # return train loader of target data
+        train_loader_t = datamanager.train_loader_t
+    """
+    data_type = 'image'
+
+    def __init__(
+        self,
+        root='',
+        sources=None,
+        targets=None,
+        height=256,
+        width=128,
+        transforms='random_flip',
+        k_tfm=1,
+        norm_mean=None,
+        norm_std=None,
+        use_gpu=True,
+        split_id=0,
+        combineall=False,
+        load_train_targets=False,
+        batch_size_train=32,
+        batch_size_test=32,
+        workers=4,
+        num_instances=4,
+        train_sampler='RandomSampler',
+        train_sampler_t='RandomSampler',
+        cuhk03_labeled=False,
+        cuhk03_classic_split=False,
+        market1501_500k=False
+    ):
+
+        super(ImageDataManager, self).__init__(
+            sources=sources,
+            targets=targets,
+            height=height,
+            width=width,
+            transforms=transforms,
+            norm_mean=norm_mean,
+            norm_std=norm_std,
+            use_gpu=use_gpu
+        )
+
+        print('=> Loading train (source) dataset')
+        trainset = []
+        for name in self.sources:
+            trainset_ = init_image_dataset(
+                name,
+                transform=self.transform_tr,
+                k_tfm=k_tfm,
+                mode='train',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                cuhk03_labeled=cuhk03_labeled,
+                cuhk03_classic_split=cuhk03_classic_split,
+                market1501_500k=market1501_500k
+            )
+            trainset.append(trainset_)
+        trainset = sum(trainset)
+
+        self._num_train_pids = trainset.num_train_pids
+        self._num_train_cams = trainset.num_train_cams
+
+        self.train_loader = torch.utils.data.DataLoader(
+            trainset,
+            sampler=build_train_sampler(
+                trainset.train,
+                train_sampler,
+                batch_size=batch_size_train,
+                num_instances=num_instances
+            ),
+            batch_size=batch_size_train,
+            shuffle=False,
+            num_workers=workers,
+            pin_memory=self.use_gpu,
+            drop_last=True
+        )
+
+        self.train_loader_t = None
+        if load_train_targets:
+            # check if sources and targets are identical
+            assert len(set(self.sources) & set(self.targets)) == 0, \
+                'sources={} and targets={} must not have overlap'.format(self.sources, self.targets)
+
+            print('=> Loading train (target) dataset')
+            trainset_t = []
+            for name in self.targets:
+                trainset_t_ = init_image_dataset(
+                    name,
+                    transform=self.transform_tr,
+                    k_tfm=k_tfm,
+                    mode='train',
+                    combineall=False, # only use the training data
+                    root=root,
+                    split_id=split_id,
+                    cuhk03_labeled=cuhk03_labeled,
+                    cuhk03_classic_split=cuhk03_classic_split,
+                    market1501_500k=market1501_500k
+                )
+                trainset_t.append(trainset_t_)
+            trainset_t = sum(trainset_t)
+
+            self.train_loader_t = torch.utils.data.DataLoader(
+                trainset_t,
+                sampler=build_train_sampler(
+                    trainset_t.train,
+                    train_sampler_t,
+                    batch_size=batch_size_train,
+                    num_instances=num_instances
+                ),
+                batch_size=batch_size_train,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=True
+            )
+
+        print('=> Loading test (target) dataset')
+        self.test_loader = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+        self.test_dataset = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+
+        for name in self.targets:
+            # build query loader
+            queryset = init_image_dataset(
+                name,
+                transform=self.transform_te,
+                mode='query',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                cuhk03_labeled=cuhk03_labeled,
+                cuhk03_classic_split=cuhk03_classic_split,
+                market1501_500k=market1501_500k
+            )
+            self.test_loader[name]['query'] = torch.utils.data.DataLoader(
+                queryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            # build gallery loader
+            galleryset = init_image_dataset(
+                name,
+                transform=self.transform_te,
+                mode='gallery',
+                combineall=combineall,
+                verbose=False,
+                root=root,
+                split_id=split_id,
+                cuhk03_labeled=cuhk03_labeled,
+                cuhk03_classic_split=cuhk03_classic_split,
+                market1501_500k=market1501_500k
+            )
+            self.test_loader[name]['gallery'] = torch.utils.data.DataLoader(
+                galleryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            self.test_dataset[name]['query'] = queryset.query
+            self.test_dataset[name]['gallery'] = galleryset.gallery
+
+        print('\n')
+        print('  **************** Summary ****************')
+        print('  source            : {}'.format(self.sources))
+        print('  # source datasets : {}'.format(len(self.sources)))
+        print('  # source ids      : {}'.format(self.num_train_pids))
+        print('  # source images   : {}'.format(len(trainset)))
+        print('  # source cameras  : {}'.format(self.num_train_cams))
+        if load_train_targets:
+            print(
+                '  # target images   : {} (unlabeled)'.format(len(trainset_t))
+            )
+        print('  target            : {}'.format(self.targets))
+        print('  *****************************************')
+        print('\n')
+
+
+class VideoDataManager(DataManager):
+    r"""Video data manager.
+
+    Args:
+        root (str): root path to datasets.
+        sources (str or list): source dataset(s).
+        targets (str or list, optional): target dataset(s). If not given,
+            it equals to ``sources``.
+        height (int, optional): target image height. Default is 256.
+        width (int, optional): target image width. Default is 128.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        norm_mean (list or None, optional): data mean. Default is None (use imagenet mean).
+        norm_std (list or None, optional): data std. Default is None (use imagenet std).
+        use_gpu (bool, optional): use gpu. Default is True.
+        split_id (int, optional): split id (*0-based*). Default is 0.
+        combineall (bool, optional): combine train, query and gallery in a dataset for
+            training. Default is False.
+        batch_size_train (int, optional): number of tracklets in a training batch. Default is 3.
+        batch_size_test (int, optional): number of tracklets in a test batch. Default is 3.
+        workers (int, optional): number of workers. Default is 4.
+        num_instances (int, optional): number of instances per identity in a batch.
+            Default is 4.
+        train_sampler (str, optional): sampler. Default is RandomSampler.
+        seq_len (int, optional): how many images to sample in a tracklet. Default is 15.
+        sample_method (str, optional): how to sample images in a tracklet. Default is "evenly".
+            Choices are ["evenly", "random", "all"]. "evenly" and "random" will sample ``seq_len``
+            images in a tracklet while "all" samples all images in a tracklet, where the batch size
+            needs to be set to 1.
+
+    Examples::
+
+        datamanager = torchreid.data.VideoDataManager(
+            root='path/to/reid-data',
+            sources='mars',
+            height=256,
+            width=128,
+            batch_size_train=3,
+            batch_size_test=3,
+            seq_len=15,
+            sample_method='evenly'
+        )
+
+        # return train loader of source data
+        train_loader = datamanager.train_loader
+
+        # return test loader of target data
+        test_loader = datamanager.test_loader
+
+    .. note::
+        The current implementation only supports image-like training. Therefore, each image in a
+        sampled tracklet will undergo independent transformation functions. To achieve tracklet-aware
+        training, you need to modify the transformation functions for video reid such that each function
+        applies the same operation to all images in a tracklet to keep consistency.
+    """
+    data_type = 'video'
+
+    def __init__(
+        self,
+        root='',
+        sources=None,
+        targets=None,
+        height=256,
+        width=128,
+        transforms='random_flip',
+        norm_mean=None,
+        norm_std=None,
+        use_gpu=True,
+        split_id=0,
+        combineall=False,
+        batch_size_train=3,
+        batch_size_test=3,
+        workers=4,
+        num_instances=4,
+        train_sampler='RandomSampler',
+        seq_len=15,
+        sample_method='evenly'
+    ):
+
+        super(VideoDataManager, self).__init__(
+            sources=sources,
+            targets=targets,
+            height=height,
+            width=width,
+            transforms=transforms,
+            norm_mean=norm_mean,
+            norm_std=norm_std,
+            use_gpu=use_gpu
+        )
+
+        print('=> Loading train (source) dataset')
+        trainset = []
+        for name in self.sources:
+            trainset_ = init_video_dataset(
+                name,
+                transform=self.transform_tr,
+                mode='train',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                seq_len=seq_len,
+                sample_method=sample_method
+            )
+            trainset.append(trainset_)
+        trainset = sum(trainset)
+
+        self._num_train_pids = trainset.num_train_pids
+        self._num_train_cams = trainset.num_train_cams
+
+        train_sampler = build_train_sampler(
+            trainset.train,
+            train_sampler,
+            batch_size=batch_size_train,
+            num_instances=num_instances
+        )
+
+        self.train_loader = torch.utils.data.DataLoader(
+            trainset,
+            sampler=train_sampler,
+            batch_size=batch_size_train,
+            shuffle=False,
+            num_workers=workers,
+            pin_memory=self.use_gpu,
+            drop_last=True
+        )
+
+        print('=> Loading test (target) dataset')
+        self.test_loader = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+        self.test_dataset = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+
+        for name in self.targets:
+            # build query loader
+            queryset = init_video_dataset(
+                name,
+                transform=self.transform_te,
+                mode='query',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                seq_len=seq_len,
+                sample_method=sample_method
+            )
+            self.test_loader[name]['query'] = torch.utils.data.DataLoader(
+                queryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            # build gallery loader
+            galleryset = init_video_dataset(
+                name,
+                transform=self.transform_te,
+                mode='gallery',
+                combineall=combineall,
+                verbose=False,
+                root=root,
+                split_id=split_id,
+                seq_len=seq_len,
+                sample_method=sample_method
+            )
+            self.test_loader[name]['gallery'] = torch.utils.data.DataLoader(
+                galleryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            self.test_dataset[name]['query'] = queryset.query
+            self.test_dataset[name]['gallery'] = galleryset.gallery
+
+        print('\n')
+        print('  **************** Summary ****************')
+        print('  source             : {}'.format(self.sources))
+        print('  # source datasets  : {}'.format(len(self.sources)))
+        print('  # source ids       : {}'.format(self.num_train_pids))
+        print('  # source tracklets : {}'.format(len(trainset)))
+        print('  # source cameras   : {}'.format(self.num_train_cams))
+        print('  target             : {}'.format(self.targets))
+        print('  *****************************************')
+        print('\n')
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/__init__.py b/pfe/player-feature-extractor/torchreid/data/datasets/__init__.py
new file mode 100644
index 0000000..01a7378
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/__init__.py
@@ -0,0 +1,119 @@
+from __future__ import print_function, absolute_import
+
+from .image import (
+    GRID, PRID, CUHK01, CUHK02, CUHK03, MSMT17, VIPeR, SenseReID, Market1501,
+    DukeMTMCreID, iLIDS, BeproTest
+)
+
+from .video import PRID2011, Mars, DukeMTMCVidReID, iLIDSVID
+from .dataset import Dataset, ImageDataset, VideoDataset
+
+__image_datasets = {
+    'market1501': Market1501,
+    'cuhk03': CUHK03,
+    'dukemtmcreid': DukeMTMCreID,
+    'msmt17': MSMT17,
+    'viper': VIPeR,
+    'grid': GRID,
+    'cuhk01': CUHK01,
+    'ilids': iLIDS,
+    'sensereid': SenseReID,
+    'prid': PRID,
+    'cuhk02': CUHK02,
+    'bepro' : BeproTest
+}
+
+__video_datasets = {
+    'mars': Mars,
+    'ilidsvid': iLIDSVID,
+    'prid2011': PRID2011,
+    'dukemtmcvidreid': DukeMTMCVidReID
+}
+
+
+def init_image_dataset(name, **kwargs):
+    """Initializes an image dataset."""
+    avai_datasets = list(__image_datasets.keys())
+    if name not in avai_datasets:
+        raise ValueError(
+            'Invalid dataset name. Received "{}", '
+            'but expected to be one of {}'.format(name, avai_datasets)
+        )
+    return __image_datasets[name](**kwargs)
+
+
+def init_video_dataset(name, **kwargs):
+    """Initializes a video dataset."""
+    avai_datasets = list(__video_datasets.keys())
+    if name not in avai_datasets:
+        raise ValueError(
+            'Invalid dataset name. Received "{}", '
+            'but expected to be one of {}'.format(name, avai_datasets)
+        )
+    return __video_datasets[name](**kwargs)
+
+
+def register_image_dataset(name, dataset):
+    """Registers a new image dataset.
+
+    Args:
+        name (str): key corresponding to the new dataset.
+        dataset (Dataset): the new dataset class.
+
+    Examples::
+        
+        import torchreid
+        import NewDataset
+        torchreid.data.register_image_dataset('new_dataset', NewDataset)
+        # single dataset case
+        datamanager = torchreid.data.ImageDataManager(
+            root='reid-data',
+            sources='new_dataset'
+        )
+        # multiple dataset case
+        datamanager = torchreid.data.ImageDataManager(
+            root='reid-data',
+            sources=['new_dataset', 'dukemtmcreid']
+        )
+    """
+    global __image_datasets
+    curr_datasets = list(__image_datasets.keys())
+    if name in curr_datasets:
+        raise ValueError(
+            'The given name already exists, please choose '
+            'another name excluding {}'.format(curr_datasets)
+        )
+    __image_datasets[name] = dataset
+
+
+def register_video_dataset(name, dataset):
+    """Registers a new video dataset.
+
+    Args:
+        name (str): key corresponding to the new dataset.
+        dataset (Dataset): the new dataset class.
+
+    Examples::
+        
+        import torchreid
+        import NewDataset
+        torchreid.data.register_video_dataset('new_dataset', NewDataset)
+        # single dataset case
+        datamanager = torchreid.data.VideoDataManager(
+            root='reid-data',
+            sources='new_dataset'
+        )
+        # multiple dataset case
+        datamanager = torchreid.data.VideoDataManager(
+            root='reid-data',
+            sources=['new_dataset', 'ilidsvid']
+        )
+    """
+    global __video_datasets
+    curr_datasets = list(__video_datasets.keys())
+    if name in curr_datasets:
+        raise ValueError(
+            'The given name already exists, please choose '
+            'another name excluding {}'.format(curr_datasets)
+        )
+    __video_datasets[name] = dataset
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/dataset.py b/pfe/player-feature-extractor/torchreid/data/datasets/dataset.py
new file mode 100644
index 0000000..3c733ca
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/dataset.py
@@ -0,0 +1,424 @@
+from __future__ import division, print_function, absolute_import
+import copy
+import numpy as np
+import os.path as osp
+import tarfile
+import zipfile
+import torch
+
+from torchreid.utils import read_image, download_url, mkdir_if_missing
+
+
+class Dataset(object):
+    """An abstract class representing a Dataset.
+
+    This is the base class for ``ImageDataset`` and ``VideoDataset``.
+
+    Args:
+        train (list): contains tuples of (img_path(s), pid, camid).
+        query (list): contains tuples of (img_path(s), pid, camid).
+        gallery (list): contains tuples of (img_path(s), pid, camid).
+        transform: transform function.
+        k_tfm (int): number of times to apply augmentation to an image
+            independently. If k_tfm > 1, the transform function will be
+            applied k_tfm times to an image. This variable will only be
+            useful for training and is currently valid for image datasets only.
+        mode (str): 'train', 'query' or 'gallery'.
+        combineall (bool): combines train, query and gallery in a
+            dataset for training.
+        verbose (bool): show information.
+    """
+    _junk_pids = [
+    ] # contains useless person IDs, e.g. background, false detections
+
+    def __init__(
+        self,
+        train,
+        query,
+        gallery,
+        transform=None,
+        k_tfm=1,
+        mode='train',
+        combineall=False,
+        verbose=True,
+        **kwargs
+    ):
+        self.train = train
+        self.query = query
+        self.gallery = gallery
+        self.transform = transform
+        self.k_tfm = k_tfm
+        self.mode = mode
+        self.combineall = combineall
+        self.verbose = verbose
+
+        self.num_train_pids = self.get_num_pids(self.train)
+        self.num_train_cams = self.get_num_cams(self.train)
+
+        if self.combineall:
+            self.combine_all()
+
+        if self.mode == 'train':
+            self.data = self.train
+        elif self.mode == 'query':
+            self.data = self.query
+        elif self.mode == 'gallery':
+            self.data = self.gallery
+        else:
+            raise ValueError(
+                'Invalid mode. Got {}, but expected to be '
+                'one of [train | query | gallery]'.format(self.mode)
+            )
+
+        if self.verbose:
+            self.show_summary()
+
+    def __getitem__(self, index):
+        raise NotImplementedError
+
+    def __len__(self):
+        return len(self.data)
+
+    def __add__(self, other):
+        """Adds two datasets together (only the train set)."""
+        train = copy.deepcopy(self.train)
+
+        for img_path, pid, camid in other.train:
+            pid += self.num_train_pids
+            camid += self.num_train_cams
+            train.append((img_path, pid, camid))
+
+        ###################################
+        # Things to do beforehand:
+        # 1. set verbose=False to avoid unnecessary print
+        # 2. set combineall=False because combineall would have been applied
+        #    if it was True for a specific dataset, setting it to True will
+        #    create new IDs that should have been included
+        ###################################
+        if isinstance(train[0][0], str):
+            return ImageDataset(
+                train,
+                self.query,
+                self.gallery,
+                transform=self.transform,
+                mode=self.mode,
+                combineall=False,
+                verbose=False
+            )
+        else:
+            return VideoDataset(
+                train,
+                self.query,
+                self.gallery,
+                transform=self.transform,
+                mode=self.mode,
+                combineall=False,
+                verbose=False,
+                seq_len=self.seq_len,
+                sample_method=self.sample_method
+            )
+
+    def __radd__(self, other):
+        """Supports sum([dataset1, dataset2, dataset3])."""
+        if other == 0:
+            return self
+        else:
+            return self.__add__(other)
+
+    def parse_data(self, data):
+        """Parses data list and returns the number of person IDs
+        and the number of camera views.
+
+        Args:
+            data (list): contains tuples of (img_path(s), pid, camid)
+        """
+        pids = set()
+        cams = set()
+        for _, pid, camid in data:
+            pids.add(pid)
+            cams.add(camid)
+        return len(pids), len(cams)
+
+    def get_num_pids(self, data):
+        """Returns the number of training person identities."""
+        return self.parse_data(data)[0]
+
+    def get_num_cams(self, data):
+        """Returns the number of training cameras."""
+        return self.parse_data(data)[1]
+
+    def show_summary(self):
+        """Shows dataset statistics."""
+        pass
+
+    def combine_all(self):
+        """Combines train, query and gallery in a dataset for training."""
+        combined = copy.deepcopy(self.train)
+
+        # relabel pids in gallery (query shares the same scope)
+        g_pids = set()
+        for _, pid, _ in self.gallery:
+            if pid in self._junk_pids:
+                continue
+            g_pids.add(pid)
+        pid2label = {pid: i for i, pid in enumerate(g_pids)}
+
+        def _combine_data(data):
+            for img_path, pid, camid in data:
+                if pid in self._junk_pids:
+                    continue
+                pid = pid2label[pid] + self.num_train_pids
+                combined.append((img_path, pid, camid))
+
+        _combine_data(self.query)
+        _combine_data(self.gallery)
+
+        self.train = combined
+        self.num_train_pids = self.get_num_pids(self.train)
+
+    def download_dataset(self, dataset_dir, dataset_url):
+        """Downloads and extracts dataset.
+
+        Args:
+            dataset_dir (str): dataset directory.
+            dataset_url (str): url to download dataset.
+        """
+        if osp.exists(dataset_dir):
+            return
+
+        if dataset_url is None:
+            raise RuntimeError(
+                '{} dataset needs to be manually '
+                'prepared, please follow the '
+                'document to prepare this dataset'.format(
+                    self.__class__.__name__
+                )
+            )
+
+        print('Creating directory "{}"'.format(dataset_dir))
+        mkdir_if_missing(dataset_dir)
+        fpath = osp.join(dataset_dir, osp.basename(dataset_url))
+
+        print(
+            'Downloading {} dataset to "{}"'.format(
+                self.__class__.__name__, dataset_dir
+            )
+        )
+        download_url(dataset_url, fpath)
+
+        print('Extracting "{}"'.format(fpath))
+        try:
+            tar = tarfile.open(fpath)
+            tar.extractall(path=dataset_dir)
+            tar.close()
+        except:
+            zip_ref = zipfile.ZipFile(fpath, 'r')
+            zip_ref.extractall(dataset_dir)
+            zip_ref.close()
+
+        print('{} dataset is ready'.format(self.__class__.__name__))
+
+    def check_before_run(self, required_files):
+        """Checks if required files exist before going deeper.
+
+        Args:
+            required_files (str or list): string file name(s).
+        """
+        if isinstance(required_files, str):
+            required_files = [required_files]
+
+        for fpath in required_files:
+            if not osp.exists(fpath):
+                raise RuntimeError('"{}" is not found'.format(fpath))
+
+    def __repr__(self):
+        num_train_pids, num_train_cams = self.parse_data(self.train)
+        num_query_pids, num_query_cams = self.parse_data(self.query)
+        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
+
+        msg = '  ----------------------------------------\n' \
+              '  subset   | # ids | # items | # cameras\n' \
+              '  ----------------------------------------\n' \
+              '  train    | {:5d} | {:7d} | {:9d}\n' \
+              '  query    | {:5d} | {:7d} | {:9d}\n' \
+              '  gallery  | {:5d} | {:7d} | {:9d}\n' \
+              '  ----------------------------------------\n' \
+              '  items: images/tracklets for image/video dataset\n'.format(
+                  num_train_pids, len(self.train), num_train_cams,
+                  num_query_pids, len(self.query), num_query_cams,
+                  num_gallery_pids, len(self.gallery), num_gallery_cams
+              )
+
+        return msg
+
+    def _transform_image(self, tfm, k_tfm, img0):
+        """Transform a raw image (img0) k_tfm times."""
+        img_list = []
+
+        for k in range(k_tfm):
+            img_list.append(tfm(img0))
+
+        img = img_list
+        if len(img) == 1:
+            img = img[0]
+
+        return img
+
+
+class ImageDataset(Dataset):
+    """A base class representing ImageDataset.
+
+    All other image datasets should subclass it.
+
+    ``__getitem__`` returns an image given index.
+    It will return ``img``, ``pid``, ``camid`` and ``img_path``
+    where ``img`` has shape (channel, height, width). As a result,
+    data in each batch has shape (batch_size, channel, height, width).
+    """
+
+    def __init__(self, train, query, gallery, **kwargs):
+        super(ImageDataset, self).__init__(train, query, gallery, **kwargs)
+
+    def __getitem__(self, index):
+        img_path, pid, camid = self.data[index]
+        img = read_image(img_path)
+        if self.transform is not None:
+            img = self._transform_image(self.transform, self.k_tfm, img)
+        item = {'img': img, 'pid': pid, 'camid': camid, 'impath': img_path}
+        return item
+
+    def show_summary(self):
+        num_train_pids, num_train_cams = self.parse_data(self.train)
+        num_query_pids, num_query_cams = self.parse_data(self.query)
+        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
+
+        print('=> Loaded {}'.format(self.__class__.__name__))
+        print('  ----------------------------------------')
+        print('  subset   | # ids | # images | # cameras')
+        print('  ----------------------------------------')
+        print(
+            '  train    | {:5d} | {:8d} | {:9d}'.format(
+                num_train_pids, len(self.train), num_train_cams
+            )
+        )
+        print(
+            '  query    | {:5d} | {:8d} | {:9d}'.format(
+                num_query_pids, len(self.query), num_query_cams
+            )
+        )
+        print(
+            '  gallery  | {:5d} | {:8d} | {:9d}'.format(
+                num_gallery_pids, len(self.gallery), num_gallery_cams
+            )
+        )
+        print('  ----------------------------------------')
+
+
+class VideoDataset(Dataset):
+    """A base class representing VideoDataset.
+
+    All other video datasets should subclass it.
+
+    ``__getitem__`` returns an image given index.
+    It will return ``imgs``, ``pid`` and ``camid``
+    where ``imgs`` has shape (seq_len, channel, height, width). As a result,
+    data in each batch has shape (batch_size, seq_len, channel, height, width).
+    """
+
+    def __init__(
+        self,
+        train,
+        query,
+        gallery,
+        seq_len=15,
+        sample_method='evenly',
+        **kwargs
+    ):
+        super(VideoDataset, self).__init__(train, query, gallery, **kwargs)
+        self.seq_len = seq_len
+        self.sample_method = sample_method
+
+        if self.transform is None:
+            raise RuntimeError('transform must not be None')
+
+    def __getitem__(self, index):
+        img_paths, pid, camid = self.data[index]
+        num_imgs = len(img_paths)
+
+        if self.sample_method == 'random':
+            # Randomly samples seq_len images from a tracklet of length num_imgs,
+            # if num_imgs is smaller than seq_len, then replicates images
+            indices = np.arange(num_imgs)
+            replace = False if num_imgs >= self.seq_len else True
+            indices = np.random.choice(
+                indices, size=self.seq_len, replace=replace
+            )
+            # sort indices to keep temporal order (comment it to be order-agnostic)
+            indices = np.sort(indices)
+
+        elif self.sample_method == 'evenly':
+            # Evenly samples seq_len images from a tracklet
+            if num_imgs >= self.seq_len:
+                num_imgs -= num_imgs % self.seq_len
+                indices = np.arange(0, num_imgs, num_imgs / self.seq_len)
+            else:
+                # if num_imgs is smaller than seq_len, simply replicate the last image
+                # until the seq_len requirement is satisfied
+                indices = np.arange(0, num_imgs)
+                num_pads = self.seq_len - num_imgs
+                indices = np.concatenate(
+                    [
+                        indices,
+                        np.ones(num_pads).astype(np.int32) * (num_imgs-1)
+                    ]
+                )
+            assert len(indices) == self.seq_len
+
+        elif self.sample_method == 'all':
+            # Samples all images in a tracklet. batch_size must be set to 1
+            indices = np.arange(num_imgs)
+
+        else:
+            raise ValueError(
+                'Unknown sample method: {}'.format(self.sample_method)
+            )
+
+        imgs = []
+        for index in indices:
+            img_path = img_paths[int(index)]
+            img = read_image(img_path)
+            if self.transform is not None:
+                img = self.transform(img)
+            img = img.unsqueeze(0) # img must be torch.Tensor
+            imgs.append(img)
+        imgs = torch.cat(imgs, dim=0)
+
+        item = {'img': imgs, 'pid': pid, 'camid': camid}
+
+        return item
+
+    def show_summary(self):
+        num_train_pids, num_train_cams = self.parse_data(self.train)
+        num_query_pids, num_query_cams = self.parse_data(self.query)
+        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
+
+        print('=> Loaded {}'.format(self.__class__.__name__))
+        print('  -------------------------------------------')
+        print('  subset   | # ids | # tracklets | # cameras')
+        print('  -------------------------------------------')
+        print(
+            '  train    | {:5d} | {:11d} | {:9d}'.format(
+                num_train_pids, len(self.train), num_train_cams
+            )
+        )
+        print(
+            '  query    | {:5d} | {:11d} | {:9d}'.format(
+                num_query_pids, len(self.query), num_query_cams
+            )
+        )
+        print(
+            '  gallery  | {:5d} | {:11d} | {:9d}'.format(
+                num_gallery_pids, len(self.gallery), num_gallery_cams
+            )
+        )
+        print('  -------------------------------------------')
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/__init__.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/__init__.py
new file mode 100644
index 0000000..cadecce
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/__init__.py
@@ -0,0 +1,14 @@
+from __future__ import print_function, absolute_import
+
+from .grid import GRID
+from .prid import PRID
+from .ilids import iLIDS
+from .viper import VIPeR
+from .cuhk01 import CUHK01
+from .cuhk02 import CUHK02
+from .cuhk03 import CUHK03
+from .msmt17 import MSMT17
+from .sensereid import SenseReID
+from .market1501 import Market1501
+from .dukemtmcreid import DukeMTMCreID
+from .bepro import BeproTest
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/bepro.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/bepro.py
new file mode 100644
index 0000000..058c1e9
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/bepro.py
@@ -0,0 +1,93 @@
+from __future__ import division, print_function, absolute_import
+import re
+import glob
+import os.path as osp
+import warnings
+
+from ..dataset import ImageDataset
+
+class BeproTest(ImageDataset):
+    _junk_pids = [0, -1]
+    dataset_dir = 'bepro'
+    # dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'
+
+    def __init__(self, root='', market1501_500k=False, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        # self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        # allow alternative directory structure
+        # self.data_dir = self.dataset_dir
+        # data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')
+        # if osp.isdir(data_dir):
+        self.data_dir = self.dataset_dir
+        # else:
+        #     warnings.warn(
+        #         'The current data structure is deprecated. Please '
+        #         'put data folders such as "bounding_box_train" under '
+        #         '"Market-1501-v15.09.15".'
+        #     )
+
+        self.train_dir = osp.join(self.data_dir, 'bepro_train')
+        self.query_dir = osp.join(self.data_dir, 'bepro_query')
+        self.gallery_dir = osp.join(self.data_dir, 'bepro_test')
+        # self.extra_gallery_dir = osp.join(self.data_dir, 'images')
+        # self.market1501_500k = market1501_500k
+
+        required_files = [
+            self.data_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+
+        # if self.market1501_500k:
+            # required_files.append(self.extra_gallery_dir)
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, relabel=True)
+        query = self.process_dir(self.query_dir, relabel=False)
+        gallery = self.process_dir(self.gallery_dir, relabel=False)
+
+        # if self.market1501_500k:
+        #     gallery += self.process_dir(self.extra_gallery_dir, relabel=False)
+
+        super(BeproTest, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, relabel=False):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        # pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        pid_container = set()
+
+        # print (dir_path)
+
+        for img_path in img_paths:
+            # pid, _ = map(int, pattern.search(img_path).groups())
+            # print (img_path.split('/')[-1].split('.')[0].split('_')[0])
+            # print (img_path.split('/')[0])
+            # print ('pid: %s'%(img_path.split('/')[0]))
+            pid = int(img_path.split('/')[-1].split('.')[0].split('_')[0])
+
+            if pid == -1:
+                continue # junk images are just ignored
+
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        data = []
+        
+        for img_path in img_paths:
+            # pid, _ = map(int, pattern.search(img_path).groups())
+            pid = int(img_path.split('/')[-1].split('.')[0].split('_')[0])
+            camid = int(img_path.split('/')[-1].split('.')[0].split('_')[1])
+            
+            if pid == -1:
+                continue # junk images are just ignored
+
+            # assert 0 <= pid <= 1501 # pid == 0 means background
+            # assert 1 <= camid <= 6
+            # camid -= 1 # index starts from 0
+
+            if relabel:
+                pid = pid2label[pid]
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk01.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk01.py
new file mode 100644
index 0000000..a71e4a4
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk01.py
@@ -0,0 +1,135 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import numpy as np
+import os.path as osp
+import zipfile
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class CUHK01(ImageDataset):
+    """CUHK01.
+
+    Reference:
+        Li et al. Human Reidentification with Transferred Metric Learning. ACCV 2012.
+
+    URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html>`_
+    
+    Dataset statistics:
+        - identities: 971.
+        - images: 3884.
+        - cameras: 4.
+    """
+    dataset_dir = 'cuhk01'
+    dataset_url = None
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.zip_path = osp.join(self.dataset_dir, 'CUHK01.zip')
+        self.campus_dir = osp.join(self.dataset_dir, 'campus')
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        self.extract_file()
+
+        required_files = [self.dataset_dir, self.campus_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query']
+        gallery = split['gallery']
+
+        train = [tuple(item) for item in train]
+        query = [tuple(item) for item in query]
+        gallery = [tuple(item) for item in gallery]
+
+        super(CUHK01, self).__init__(train, query, gallery, **kwargs)
+
+    def extract_file(self):
+        if not osp.exists(self.campus_dir):
+            print('Extracting files')
+            zip_ref = zipfile.ZipFile(self.zip_path, 'r')
+            zip_ref.extractall(self.dataset_dir)
+            zip_ref.close()
+
+    def prepare_split(self):
+        """
+        Image name format: 0001001.png, where first four digits represent identity
+        and last four digits represent cameras. Camera 1&2 are considered the same
+        view and camera 3&4 are considered the same view.
+        """
+        if not osp.exists(self.split_path):
+            print('Creating 10 random splits of train ids and test ids')
+            img_paths = sorted(glob.glob(osp.join(self.campus_dir, '*.png')))
+            img_list = []
+            pid_container = set()
+            for img_path in img_paths:
+                img_name = osp.basename(img_path)
+                pid = int(img_name[:4]) - 1
+                camid = (int(img_name[4:7]) - 1) // 2 # result is either 0 or 1
+                img_list.append((img_path, pid, camid))
+                pid_container.add(pid)
+
+            num_pids = len(pid_container)
+            num_train_pids = num_pids // 2
+
+            splits = []
+            for _ in range(10):
+                order = np.arange(num_pids)
+                np.random.shuffle(order)
+                train_idxs = order[:num_train_pids]
+                train_idxs = np.sort(train_idxs)
+                idx2label = {
+                    idx: label
+                    for label, idx in enumerate(train_idxs)
+                }
+
+                train, test_a, test_b = [], [], []
+                for img_path, pid, camid in img_list:
+                    if pid in train_idxs:
+                        train.append((img_path, idx2label[pid], camid))
+                    else:
+                        if camid == 0:
+                            test_a.append((img_path, pid, camid))
+                        else:
+                            test_b.append((img_path, pid, camid))
+
+                # use cameraA as query and cameraB as gallery
+                split = {
+                    'train': train,
+                    'query': test_a,
+                    'gallery': test_b,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+                # use cameraB as query and cameraA as gallery
+                split = {
+                    'train': train,
+                    'query': test_b,
+                    'gallery': test_a,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file saved to {}'.format(self.split_path))
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk02.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk02.py
new file mode 100644
index 0000000..63e60af
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk02.py
@@ -0,0 +1,95 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+
+class CUHK02(ImageDataset):
+    """CUHK02.
+
+    Reference:
+        Li and Wang. Locally Aligned Feature Transforms across Views. CVPR 2013.
+
+    URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html>`_
+    
+    Dataset statistics:
+        - 5 camera view pairs each with two cameras
+        - 971, 306, 107, 193 and 239 identities from P1 - P5
+        - totally 1,816 identities
+        - image format is png
+
+    Protocol: Use P1 - P4 for training and P5 for evaluation.
+    """
+    dataset_dir = 'cuhk02'
+    cam_pairs = ['P1', 'P2', 'P3', 'P4', 'P5']
+    test_cam_pair = 'P5'
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir, 'Dataset')
+
+        required_files = [self.dataset_dir]
+        self.check_before_run(required_files)
+
+        train, query, gallery = self.get_data_list()
+
+        super(CUHK02, self).__init__(train, query, gallery, **kwargs)
+
+    def get_data_list(self):
+        num_train_pids, camid = 0, 0
+        train, query, gallery = [], [], []
+
+        for cam_pair in self.cam_pairs:
+            cam_pair_dir = osp.join(self.dataset_dir, cam_pair)
+
+            cam1_dir = osp.join(cam_pair_dir, 'cam1')
+            cam2_dir = osp.join(cam_pair_dir, 'cam2')
+
+            impaths1 = glob.glob(osp.join(cam1_dir, '*.png'))
+            impaths2 = glob.glob(osp.join(cam2_dir, '*.png'))
+
+            if cam_pair == self.test_cam_pair:
+                # add images to query
+                for impath in impaths1:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = int(pid)
+                    query.append((impath, pid, camid))
+                camid += 1
+
+                # add images to gallery
+                for impath in impaths2:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = int(pid)
+                    gallery.append((impath, pid, camid))
+                camid += 1
+
+            else:
+                pids1 = [
+                    osp.basename(impath).split('_')[0] for impath in impaths1
+                ]
+                pids2 = [
+                    osp.basename(impath).split('_')[0] for impath in impaths2
+                ]
+                pids = set(pids1 + pids2)
+                pid2label = {
+                    pid: label + num_train_pids
+                    for label, pid in enumerate(pids)
+                }
+
+                # add images to train from cam1
+                for impath in impaths1:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = pid2label[pid]
+                    train.append((impath, pid, camid))
+                camid += 1
+
+                # add images to train from cam2
+                for impath in impaths2:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = pid2label[pid]
+                    train.append((impath, pid, camid))
+                camid += 1
+                num_train_pids += len(pids)
+
+        return train, query, gallery
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk03.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk03.py
new file mode 100644
index 0000000..cd27bc2
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/cuhk03.py
@@ -0,0 +1,307 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+
+from torchreid.utils import read_json, write_json, mkdir_if_missing
+
+from ..dataset import ImageDataset
+
+
+class CUHK03(ImageDataset):
+    """CUHK03.
+
+    Reference:
+        Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014.
+
+    URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html#!>`_
+    
+    Dataset statistics:
+        - identities: 1360.
+        - images: 13164.
+        - cameras: 6.
+        - splits: 20 (classic).
+    """
+    dataset_dir = 'cuhk03'
+    dataset_url = None
+
+    def __init__(
+        self,
+        root='',
+        split_id=0,
+        cuhk03_labeled=False,
+        cuhk03_classic_split=False,
+        **kwargs
+    ):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')
+        self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')
+
+        self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')
+        self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')
+
+        self.split_classic_det_json_path = osp.join(
+            self.dataset_dir, 'splits_classic_detected.json'
+        )
+        self.split_classic_lab_json_path = osp.join(
+            self.dataset_dir, 'splits_classic_labeled.json'
+        )
+
+        self.split_new_det_json_path = osp.join(
+            self.dataset_dir, 'splits_new_detected.json'
+        )
+        self.split_new_lab_json_path = osp.join(
+            self.dataset_dir, 'splits_new_labeled.json'
+        )
+
+        self.split_new_det_mat_path = osp.join(
+            self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat'
+        )
+        self.split_new_lab_mat_path = osp.join(
+            self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat'
+        )
+
+        required_files = [
+            self.dataset_dir, self.data_dir, self.raw_mat_path,
+            self.split_new_det_mat_path, self.split_new_lab_mat_path
+        ]
+        self.check_before_run(required_files)
+
+        self.preprocess_split()
+
+        if cuhk03_labeled:
+            split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
+        else:
+            split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path
+
+        splits = read_json(split_path)
+        assert split_id < len(
+            splits
+        ), 'Condition split_id ({}) < len(splits) ({}) is false'.format(
+            split_id, len(splits)
+        )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query']
+        gallery = split['gallery']
+
+        super(CUHK03, self).__init__(train, query, gallery, **kwargs)
+
+    def preprocess_split(self):
+        # This function is a bit complex and ugly, what it does is
+        # 1. extract data from cuhk-03.mat and save as png images
+        # 2. create 20 classic splits (Li et al. CVPR'14)
+        # 3. create new split (Zhong et al. CVPR'17)
+        if osp.exists(self.imgs_labeled_dir) \
+           and osp.exists(self.imgs_detected_dir) \
+           and osp.exists(self.split_classic_det_json_path) \
+           and osp.exists(self.split_classic_lab_json_path) \
+           and osp.exists(self.split_new_det_json_path) \
+           and osp.exists(self.split_new_lab_json_path):
+            return
+
+        import h5py
+        import imageio
+        from scipy.io import loadmat
+
+        mkdir_if_missing(self.imgs_detected_dir)
+        mkdir_if_missing(self.imgs_labeled_dir)
+
+        print(
+            'Extract image data from "{}" and save as png'.format(
+                self.raw_mat_path
+            )
+        )
+        mat = h5py.File(self.raw_mat_path, 'r')
+
+        def _deref(ref):
+            return mat[ref][:].T
+
+        def _process_images(img_refs, campid, pid, save_dir):
+            img_paths = [] # Note: some persons only have images for one view
+            for imgid, img_ref in enumerate(img_refs):
+                img = _deref(img_ref)
+                if img.size == 0 or img.ndim < 3:
+                    continue # skip empty cell
+                # images are saved with the following format, index-1 (ensure uniqueness)
+                # campid: index of camera pair (1-5)
+                # pid: index of person in 'campid'-th camera pair
+                # viewid: index of view, {1, 2}
+                # imgid: index of image, (1-10)
+                viewid = 1 if imgid < 5 else 2
+                img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(
+                    campid + 1, pid + 1, viewid, imgid + 1
+                )
+                img_path = osp.join(save_dir, img_name)
+                if not osp.isfile(img_path):
+                    imageio.imwrite(img_path, img)
+                img_paths.append(img_path)
+            return img_paths
+
+        def _extract_img(image_type):
+            print('Processing {} images ...'.format(image_type))
+            meta_data = []
+            imgs_dir = self.imgs_detected_dir if image_type == 'detected' else self.imgs_labeled_dir
+            for campid, camp_ref in enumerate(mat[image_type][0]):
+                camp = _deref(camp_ref)
+                num_pids = camp.shape[0]
+                for pid in range(num_pids):
+                    img_paths = _process_images(
+                        camp[pid, :], campid, pid, imgs_dir
+                    )
+                    assert len(img_paths) > 0, \
+                        'campid{}-pid{} has no images'.format(campid, pid)
+                    meta_data.append((campid + 1, pid + 1, img_paths))
+                print(
+                    '- done camera pair {} with {} identities'.format(
+                        campid + 1, num_pids
+                    )
+                )
+            return meta_data
+
+        meta_detected = _extract_img('detected')
+        meta_labeled = _extract_img('labeled')
+
+        def _extract_classic_split(meta_data, test_split):
+            train, test = [], []
+            num_train_pids, num_test_pids = 0, 0
+            num_train_imgs, num_test_imgs = 0, 0
+            for i, (campid, pid, img_paths) in enumerate(meta_data):
+
+                if [campid, pid] in test_split:
+                    for img_path in img_paths:
+                        camid = int(
+                            osp.basename(img_path).split('_')[2]
+                        ) - 1 # make it 0-based
+                        test.append((img_path, num_test_pids, camid))
+                    num_test_pids += 1
+                    num_test_imgs += len(img_paths)
+                else:
+                    for img_path in img_paths:
+                        camid = int(
+                            osp.basename(img_path).split('_')[2]
+                        ) - 1 # make it 0-based
+                        train.append((img_path, num_train_pids, camid))
+                    num_train_pids += 1
+                    num_train_imgs += len(img_paths)
+            return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs
+
+        print('Creating classic splits (# = 20) ...')
+        splits_classic_det, splits_classic_lab = [], []
+        for split_ref in mat['testsets'][0]:
+            test_split = _deref(split_ref).tolist()
+
+            # create split for detected images
+            train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
+                _extract_classic_split(meta_detected, test_split)
+            splits_classic_det.append(
+                {
+                    'train': train,
+                    'query': test,
+                    'gallery': test,
+                    'num_train_pids': num_train_pids,
+                    'num_train_imgs': num_train_imgs,
+                    'num_query_pids': num_test_pids,
+                    'num_query_imgs': num_test_imgs,
+                    'num_gallery_pids': num_test_pids,
+                    'num_gallery_imgs': num_test_imgs
+                }
+            )
+
+            # create split for labeled images
+            train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
+                _extract_classic_split(meta_labeled, test_split)
+            splits_classic_lab.append(
+                {
+                    'train': train,
+                    'query': test,
+                    'gallery': test,
+                    'num_train_pids': num_train_pids,
+                    'num_train_imgs': num_train_imgs,
+                    'num_query_pids': num_test_pids,
+                    'num_query_imgs': num_test_imgs,
+                    'num_gallery_pids': num_test_pids,
+                    'num_gallery_imgs': num_test_imgs
+                }
+            )
+
+        write_json(splits_classic_det, self.split_classic_det_json_path)
+        write_json(splits_classic_lab, self.split_classic_lab_json_path)
+
+        def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):
+            tmp_set = []
+            unique_pids = set()
+            for idx in idxs:
+                img_name = filelist[idx][0]
+                camid = int(img_name.split('_')[2]) - 1 # make it 0-based
+                pid = pids[idx]
+                if relabel:
+                    pid = pid2label[pid]
+                img_path = osp.join(img_dir, img_name)
+                tmp_set.append((img_path, int(pid), camid))
+                unique_pids.add(pid)
+            return tmp_set, len(unique_pids), len(idxs)
+
+        def _extract_new_split(split_dict, img_dir):
+            train_idxs = split_dict['train_idx'].flatten() - 1 # index-0
+            pids = split_dict['labels'].flatten()
+            train_pids = set(pids[train_idxs])
+            pid2label = {pid: label for label, pid in enumerate(train_pids)}
+            query_idxs = split_dict['query_idx'].flatten() - 1
+            gallery_idxs = split_dict['gallery_idx'].flatten() - 1
+            filelist = split_dict['filelist'].flatten()
+            train_info = _extract_set(
+                filelist, pids, pid2label, train_idxs, img_dir, relabel=True
+            )
+            query_info = _extract_set(
+                filelist, pids, pid2label, query_idxs, img_dir, relabel=False
+            )
+            gallery_info = _extract_set(
+                filelist,
+                pids,
+                pid2label,
+                gallery_idxs,
+                img_dir,
+                relabel=False
+            )
+            return train_info, query_info, gallery_info
+
+        print('Creating new split for detected images (767/700) ...')
+        train_info, query_info, gallery_info = _extract_new_split(
+            loadmat(self.split_new_det_mat_path), self.imgs_detected_dir
+        )
+        split = [
+            {
+                'train': train_info[0],
+                'query': query_info[0],
+                'gallery': gallery_info[0],
+                'num_train_pids': train_info[1],
+                'num_train_imgs': train_info[2],
+                'num_query_pids': query_info[1],
+                'num_query_imgs': query_info[2],
+                'num_gallery_pids': gallery_info[1],
+                'num_gallery_imgs': gallery_info[2]
+            }
+        ]
+        write_json(split, self.split_new_det_json_path)
+
+        print('Creating new split for labeled images (767/700) ...')
+        train_info, query_info, gallery_info = _extract_new_split(
+            loadmat(self.split_new_lab_mat_path), self.imgs_labeled_dir
+        )
+        split = [
+            {
+                'train': train_info[0],
+                'query': query_info[0],
+                'gallery': gallery_info[0],
+                'num_train_pids': train_info[1],
+                'num_train_imgs': train_info[2],
+                'num_query_pids': query_info[1],
+                'num_query_imgs': query_info[2],
+                'num_gallery_pids': gallery_info[1],
+                'num_gallery_imgs': gallery_info[2]
+            }
+        ]
+        write_json(split, self.split_new_lab_json_path)
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/dukemtmcreid.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/dukemtmcreid.py
new file mode 100644
index 0000000..5915da5
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/dukemtmcreid.py
@@ -0,0 +1,68 @@
+from __future__ import division, print_function, absolute_import
+import re
+import glob
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+
+class DukeMTMCreID(ImageDataset):
+    """DukeMTMC-reID.
+
+    Reference:
+        - Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016.
+        - Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.
+
+    URL: `<https://github.com/layumi/DukeMTMC-reID_evaluation>`_
+    
+    Dataset statistics:
+        - identities: 1404 (train + query).
+        - images:16522 (train) + 2228 (query) + 17661 (gallery).
+        - cameras: 8.
+    """
+    dataset_dir = 'dukemtmc-reid'
+    dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip'
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+        self.train_dir = osp.join(
+            self.dataset_dir, 'DukeMTMC-reID/bounding_box_train'
+        )
+        self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/query')
+        self.gallery_dir = osp.join(
+            self.dataset_dir, 'DukeMTMC-reID/bounding_box_test'
+        )
+
+        required_files = [
+            self.dataset_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, relabel=True)
+        query = self.process_dir(self.query_dir, relabel=False)
+        gallery = self.process_dir(self.gallery_dir, relabel=False)
+
+        super(DukeMTMCreID, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, relabel=False):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        pid_container = set()
+        for img_path in img_paths:
+            pid, _ = map(int, pattern.search(img_path).groups())
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        data = []
+        for img_path in img_paths:
+            pid, camid = map(int, pattern.search(img_path).groups())
+            assert 1 <= camid <= 8
+            camid -= 1 # index starts from 0
+            if relabel:
+                pid = pid2label[pid]
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/grid.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/grid.py
new file mode 100644
index 0000000..0946ef3
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/grid.py
@@ -0,0 +1,130 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+from scipy.io import loadmat
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class GRID(ImageDataset):
+    """GRID.
+
+    Reference:
+        Loy et al. Multi-camera activity correlation analysis. CVPR 2009.
+
+    URL: `<http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html>`_
+    
+    Dataset statistics:
+        - identities: 250.
+        - images: 1275.
+        - cameras: 8.
+    """
+    dataset_dir = 'grid'
+    dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.probe_path = osp.join(
+            self.dataset_dir, 'underground_reid', 'probe'
+        )
+        self.gallery_path = osp.join(
+            self.dataset_dir, 'underground_reid', 'gallery'
+        )
+        self.split_mat_path = osp.join(
+            self.dataset_dir, 'underground_reid', 'features_and_partitions.mat'
+        )
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        required_files = [
+            self.dataset_dir, self.probe_path, self.gallery_path,
+            self.split_mat_path
+        ]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, '
+                'but expected between 0 and {}'.format(
+                    split_id,
+                    len(splits) - 1
+                )
+            )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query']
+        gallery = split['gallery']
+
+        train = [tuple(item) for item in train]
+        query = [tuple(item) for item in query]
+        gallery = [tuple(item) for item in gallery]
+
+        super(GRID, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating 10 random splits')
+            split_mat = loadmat(self.split_mat_path)
+            trainIdxAll = split_mat['trainIdxAll'][0] # length = 10
+            probe_img_paths = sorted(
+                glob.glob(osp.join(self.probe_path, '*.jpeg'))
+            )
+            gallery_img_paths = sorted(
+                glob.glob(osp.join(self.gallery_path, '*.jpeg'))
+            )
+
+            splits = []
+            for split_idx in range(10):
+                train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist()
+                assert len(train_idxs) == 125
+                idx2label = {
+                    idx: label
+                    for label, idx in enumerate(train_idxs)
+                }
+
+                train, query, gallery = [], [], []
+
+                # processing probe folder
+                for img_path in probe_img_paths:
+                    img_name = osp.basename(img_path)
+                    img_idx = int(img_name.split('_')[0])
+                    camid = int(
+                        img_name.split('_')[1]
+                    ) - 1 # index starts from 0
+                    if img_idx in train_idxs:
+                        train.append((img_path, idx2label[img_idx], camid))
+                    else:
+                        query.append((img_path, img_idx, camid))
+
+                # process gallery folder
+                for img_path in gallery_img_paths:
+                    img_name = osp.basename(img_path)
+                    img_idx = int(img_name.split('_')[0])
+                    camid = int(
+                        img_name.split('_')[1]
+                    ) - 1 # index starts from 0
+                    if img_idx in train_idxs:
+                        train.append((img_path, idx2label[img_idx], camid))
+                    else:
+                        gallery.append((img_path, img_idx, camid))
+
+                split = {
+                    'train': train,
+                    'query': query,
+                    'gallery': gallery,
+                    'num_train_pids': 125,
+                    'num_query_pids': 125,
+                    'num_gallery_pids': 900
+                }
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file saved to {}'.format(self.split_path))
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/ilids.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/ilids.py
new file mode 100644
index 0000000..42971b0
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/ilids.py
@@ -0,0 +1,135 @@
+from __future__ import division, print_function, absolute_import
+import copy
+import glob
+import random
+import os.path as osp
+from collections import defaultdict
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class iLIDS(ImageDataset):
+    """QMUL-iLIDS.
+
+    Reference:
+        Zheng et al. Associating Groups of People. BMVC 2009.
+    
+    Dataset statistics:
+        - identities: 119.
+        - images: 476.
+        - cameras: 8 (not explicitly provided).
+    """
+    dataset_dir = 'ilids'
+    dataset_url = 'http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.data_dir = osp.join(self.dataset_dir, 'i-LIDS_Pedestrian/Persons')
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        required_files = [self.dataset_dir, self.data_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but '
+                'expected between 0 and {}'.format(split_id,
+                                                   len(splits) - 1)
+            )
+        split = splits[split_id]
+
+        train, query, gallery = self.process_split(split)
+
+        super(iLIDS, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating splits ...')
+
+            paths = glob.glob(osp.join(self.data_dir, '*.jpg'))
+            img_names = [osp.basename(path) for path in paths]
+            num_imgs = len(img_names)
+            assert num_imgs == 476, 'There should be 476 images, but ' \
+                                    'got {}, please check the data'.format(num_imgs)
+
+            # store image names
+            # image naming format:
+            #   the first four digits denote the person ID
+            #   the last four digits denote the sequence index
+            pid_dict = defaultdict(list)
+            for img_name in img_names:
+                pid = int(img_name[:4])
+                pid_dict[pid].append(img_name)
+            pids = list(pid_dict.keys())
+            num_pids = len(pids)
+            assert num_pids == 119, 'There should be 119 identities, ' \
+                                    'but got {}, please check the data'.format(num_pids)
+
+            num_train_pids = int(num_pids * 0.5)
+
+            splits = []
+            for _ in range(10):
+                # randomly choose num_train_pids train IDs and the rest for test IDs
+                pids_copy = copy.deepcopy(pids)
+                random.shuffle(pids_copy)
+                train_pids = pids_copy[:num_train_pids]
+                test_pids = pids_copy[num_train_pids:]
+
+                train = []
+                query = []
+                gallery = []
+
+                # for train IDs, all images are used in the train set.
+                for pid in train_pids:
+                    img_names = pid_dict[pid]
+                    train.extend(img_names)
+
+                # for each test ID, randomly choose two images, one for
+                # query and the other one for gallery.
+                for pid in test_pids:
+                    img_names = pid_dict[pid]
+                    samples = random.sample(img_names, 2)
+                    query.append(samples[0])
+                    gallery.append(samples[1])
+
+                split = {'train': train, 'query': query, 'gallery': gallery}
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file is saved to {}'.format(self.split_path))
+
+    def get_pid2label(self, img_names):
+        pid_container = set()
+        for img_name in img_names:
+            pid = int(img_name[:4])
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+        return pid2label
+
+    def parse_img_names(self, img_names, pid2label=None):
+        data = []
+
+        for img_name in img_names:
+            pid = int(img_name[:4])
+            if pid2label is not None:
+                pid = pid2label[pid]
+            camid = int(img_name[4:7]) - 1 # 0-based
+            img_path = osp.join(self.data_dir, img_name)
+            data.append((img_path, pid, camid))
+
+        return data
+
+    def process_split(self, split):
+        train_pid2label = self.get_pid2label(split['train'])
+        train = self.parse_img_names(split['train'], train_pid2label)
+        query = self.parse_img_names(split['query'])
+        gallery = self.parse_img_names(split['gallery'])
+        return train, query, gallery
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/market1501.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/market1501.py
new file mode 100644
index 0000000..0e08896
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/market1501.py
@@ -0,0 +1,90 @@
+from __future__ import division, print_function, absolute_import
+import re
+import glob
+import os.path as osp
+import warnings
+
+from ..dataset import ImageDataset
+
+
+class Market1501(ImageDataset):
+    """Market1501.
+
+    Reference:
+        Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
+
+    URL: `<http://www.liangzheng.org/Project/project_reid.html>`_
+    
+    Dataset statistics:
+        - identities: 1501 (+1 for background).
+        - images: 12936 (train) + 3368 (query) + 15913 (gallery).
+    """
+    _junk_pids = [0, -1]
+    dataset_dir = 'market1501'
+    dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'
+
+    def __init__(self, root='', market1501_500k=False, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        # allow alternative directory structure
+        self.data_dir = self.dataset_dir
+        data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')
+        if osp.isdir(data_dir):
+            self.data_dir = data_dir
+        else:
+            warnings.warn(
+                'The current data structure is deprecated. Please '
+                'put data folders such as "bounding_box_train" under '
+                '"Market-1501-v15.09.15".'
+            )
+
+        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')
+        self.query_dir = osp.join(self.data_dir, 'query')
+        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')
+        self.extra_gallery_dir = osp.join(self.data_dir, 'images')
+        self.market1501_500k = market1501_500k
+
+        required_files = [
+            self.data_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+        if self.market1501_500k:
+            required_files.append(self.extra_gallery_dir)
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, relabel=True)
+        query = self.process_dir(self.query_dir, relabel=False)
+        gallery = self.process_dir(self.gallery_dir, relabel=False)
+        if self.market1501_500k:
+            gallery += self.process_dir(self.extra_gallery_dir, relabel=False)
+
+        super(Market1501, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, relabel=False):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        pid_container = set()
+        for img_path in img_paths:
+            pid, _ = map(int, pattern.search(img_path).groups())
+            if pid == -1:
+                continue # junk images are just ignored
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        data = []
+        
+        for img_path in img_paths:
+            pid, camid = map(int, pattern.search(img_path).groups())
+            if pid == -1:
+                continue # junk images are just ignored
+            assert 0 <= pid <= 1501 # pid == 0 means background
+            assert 1 <= camid <= 6
+            camid -= 1 # index starts from 0
+
+            if relabel:
+                pid = pid2label[pid]
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/msmt17.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/msmt17.py
new file mode 100644
index 0000000..c4741e6
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/msmt17.py
@@ -0,0 +1,98 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+# Log
+# 22.01.2019
+# - add v2
+# - v1 and v2 differ in dir names
+# - note that faces in v2 are blurred
+TRAIN_DIR_KEY = 'train_dir'
+TEST_DIR_KEY = 'test_dir'
+VERSION_DICT = {
+    'MSMT17_V1': {
+        TRAIN_DIR_KEY: 'train',
+        TEST_DIR_KEY: 'test',
+    },
+    'MSMT17_V2': {
+        TRAIN_DIR_KEY: 'mask_train_v2',
+        TEST_DIR_KEY: 'mask_test_v2',
+    }
+}
+
+
+class MSMT17(ImageDataset):
+    """MSMT17.
+
+    Reference:
+        Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.
+
+    URL: `<http://www.pkuvmc.com/publications/msmt17.html>`_
+    
+    Dataset statistics:
+        - identities: 4101.
+        - images: 32621 (train) + 11659 (query) + 82161 (gallery).
+        - cameras: 15.
+    """
+    dataset_dir = 'msmt17'
+    dataset_url = None
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        has_main_dir = False
+        for main_dir in VERSION_DICT:
+            if osp.exists(osp.join(self.dataset_dir, main_dir)):
+                train_dir = VERSION_DICT[main_dir][TRAIN_DIR_KEY]
+                test_dir = VERSION_DICT[main_dir][TEST_DIR_KEY]
+                has_main_dir = True
+                break
+        assert has_main_dir, 'Dataset folder not found'
+
+        self.train_dir = osp.join(self.dataset_dir, main_dir, train_dir)
+        self.test_dir = osp.join(self.dataset_dir, main_dir, test_dir)
+        self.list_train_path = osp.join(
+            self.dataset_dir, main_dir, 'list_train.txt'
+        )
+        self.list_val_path = osp.join(
+            self.dataset_dir, main_dir, 'list_val.txt'
+        )
+        self.list_query_path = osp.join(
+            self.dataset_dir, main_dir, 'list_query.txt'
+        )
+        self.list_gallery_path = osp.join(
+            self.dataset_dir, main_dir, 'list_gallery.txt'
+        )
+
+        required_files = [self.dataset_dir, self.train_dir, self.test_dir]
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, self.list_train_path)
+        val = self.process_dir(self.train_dir, self.list_val_path)
+        query = self.process_dir(self.test_dir, self.list_query_path)
+        gallery = self.process_dir(self.test_dir, self.list_gallery_path)
+
+        # Note: to fairly compare with published methods on the conventional ReID setting,
+        #       do not add val images to the training set.
+        if 'combineall' in kwargs and kwargs['combineall']:
+            train += val
+
+        super(MSMT17, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, list_path):
+        with open(list_path, 'r') as txt:
+            lines = txt.readlines()
+
+        data = []
+
+        for img_idx, img_info in enumerate(lines):
+            img_path, pid = img_info.split(' ')
+            pid = int(pid) # no need to relabel
+            camid = int(img_path.split('_')[2]) - 1 # index starts from 0
+            img_path = osp.join(dir_path, img_path)
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/prid.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/prid.py
new file mode 100644
index 0000000..7fca0ee
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/prid.py
@@ -0,0 +1,106 @@
+from __future__ import division, print_function, absolute_import
+import random
+import os.path as osp
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class PRID(ImageDataset):
+    """PRID (single-shot version of prid-2011)
+
+    Reference:
+        Hirzer et al. Person Re-Identification by Descriptive and Discriminative
+        Classification. SCIA 2011.
+
+    URL: `<https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/>`_
+    
+    Dataset statistics:
+        - Two views.
+        - View A captures 385 identities.
+        - View B captures 749 identities.
+        - 200 identities appear in both views.
+    """
+    dataset_dir = 'prid2011'
+    dataset_url = None
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.cam_a_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'single_shot', 'cam_a'
+        )
+        self.cam_b_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'single_shot', 'cam_b'
+        )
+        self.split_path = osp.join(self.dataset_dir, 'splits_single_shot.json')
+
+        required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+
+        train, query, gallery = self.process_split(split)
+
+        super(PRID, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating splits ...')
+
+            splits = []
+            for _ in range(10):
+                # randomly sample 100 IDs for train and use the rest 100 IDs for test
+                # (note: there are only 200 IDs appearing in both views)
+                pids = [i for i in range(1, 201)]
+                train_pids = random.sample(pids, 100)
+                train_pids.sort()
+                test_pids = [i for i in pids if i not in train_pids]
+                split = {'train': train_pids, 'test': test_pids}
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file is saved to {}'.format(self.split_path))
+
+    def process_split(self, split):
+        train_pids = split['train']
+        test_pids = split['test']
+
+        train_pid2label = {pid: label for label, pid in enumerate(train_pids)}
+
+        # train
+        train = []
+        for pid in train_pids:
+            img_name = 'person_' + str(pid).zfill(4) + '.png'
+            pid = train_pid2label[pid]
+            img_a_path = osp.join(self.cam_a_dir, img_name)
+            train.append((img_a_path, pid, 0))
+            img_b_path = osp.join(self.cam_b_dir, img_name)
+            train.append((img_b_path, pid, 1))
+
+        # query and gallery
+        query, gallery = [], []
+        for pid in test_pids:
+            img_name = 'person_' + str(pid).zfill(4) + '.png'
+            img_a_path = osp.join(self.cam_a_dir, img_name)
+            query.append((img_a_path, pid, 0))
+            img_b_path = osp.join(self.cam_b_dir, img_name)
+            gallery.append((img_b_path, pid, 1))
+        for pid in range(201, 750):
+            img_name = 'person_' + str(pid).zfill(4) + '.png'
+            img_b_path = osp.join(self.cam_b_dir, img_name)
+            gallery.append((img_b_path, pid, 1))
+
+        return train, query, gallery
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/sensereid.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/sensereid.py
new file mode 100644
index 0000000..7cf5f32
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/sensereid.py
@@ -0,0 +1,70 @@
+from __future__ import division, print_function, absolute_import
+import copy
+import glob
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+
+class SenseReID(ImageDataset):
+    """SenseReID.
+
+    This dataset is used for test purpose only.
+
+    Reference:
+        Zhao et al. Spindle Net: Person Re-identification with Human Body
+        Region Guided Feature Decomposition and Fusion. CVPR 2017.
+
+    URL: `<https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view>`_
+
+    Dataset statistics:
+        - query: 522 ids, 1040 images.
+        - gallery: 1717 ids, 3388 images.
+    """
+    dataset_dir = 'sensereid'
+    dataset_url = None
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.query_dir = osp.join(self.dataset_dir, 'SenseReID', 'test_probe')
+        self.gallery_dir = osp.join(
+            self.dataset_dir, 'SenseReID', 'test_gallery'
+        )
+
+        required_files = [self.dataset_dir, self.query_dir, self.gallery_dir]
+        self.check_before_run(required_files)
+
+        query = self.process_dir(self.query_dir)
+        gallery = self.process_dir(self.gallery_dir)
+
+        # relabel
+        g_pids = set()
+        for _, pid, _ in gallery:
+            g_pids.add(pid)
+        pid2label = {pid: i for i, pid in enumerate(g_pids)}
+
+        query = [
+            (img_path, pid2label[pid], camid) for img_path, pid, camid in query
+        ]
+        gallery = [
+            (img_path, pid2label[pid], camid)
+            for img_path, pid, camid in gallery
+        ]
+        train = copy.deepcopy(query) + copy.deepcopy(gallery) # dummy variable
+
+        super(SenseReID, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        data = []
+
+        for img_path in img_paths:
+            img_name = osp.splitext(osp.basename(img_path))[0]
+            pid, camid = img_name.split('_')
+            pid, camid = int(pid), int(camid)
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/image/viper.py b/pfe/player-feature-extractor/torchreid/data/datasets/image/viper.py
new file mode 100644
index 0000000..161dd99
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/image/viper.py
@@ -0,0 +1,128 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import numpy as np
+import os.path as osp
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class VIPeR(ImageDataset):
+    """VIPeR.
+
+    Reference:
+        Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007.
+
+    URL: `<https://vision.soe.ucsc.edu/node/178>`_
+    
+    Dataset statistics:
+        - identities: 632.
+        - images: 632 x 2 = 1264.
+        - cameras: 2.
+    """
+    dataset_dir = 'viper'
+    dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.cam_a_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
+        self.cam_b_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, '
+                'but expected between 0 and {}'.format(
+                    split_id,
+                    len(splits) - 1
+                )
+            )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query'] # query and gallery share the same images
+        gallery = split['gallery']
+
+        train = [tuple(item) for item in train]
+        query = [tuple(item) for item in query]
+        gallery = [tuple(item) for item in gallery]
+
+        super(VIPeR, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating 10 random splits of train ids and test ids')
+
+            cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, '*.bmp')))
+            cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, '*.bmp')))
+            assert len(cam_a_imgs) == len(cam_b_imgs)
+            num_pids = len(cam_a_imgs)
+            print('Number of identities: {}'.format(num_pids))
+            num_train_pids = num_pids // 2
+            """
+            In total, there will be 20 splits because each random split creates two
+            sub-splits, one using cameraA as query and cameraB as gallery
+            while the other using cameraB as query and cameraA as gallery.
+            Therefore, results should be averaged over 20 splits (split_id=0~19).
+            
+            In practice, a model trained on split_id=0 can be applied to split_id=0&1
+            as split_id=0&1 share the same training data (so on and so forth).
+            """
+            splits = []
+            for _ in range(10):
+                order = np.arange(num_pids)
+                np.random.shuffle(order)
+                train_idxs = order[:num_train_pids]
+                test_idxs = order[num_train_pids:]
+                assert not bool(set(train_idxs) & set(test_idxs)), \
+                    'Error: train and test overlap'
+
+                train = []
+                for pid, idx in enumerate(train_idxs):
+                    cam_a_img = cam_a_imgs[idx]
+                    cam_b_img = cam_b_imgs[idx]
+                    train.append((cam_a_img, pid, 0))
+                    train.append((cam_b_img, pid, 1))
+
+                test_a = []
+                test_b = []
+                for pid, idx in enumerate(test_idxs):
+                    cam_a_img = cam_a_imgs[idx]
+                    cam_b_img = cam_b_imgs[idx]
+                    test_a.append((cam_a_img, pid, 0))
+                    test_b.append((cam_b_img, pid, 1))
+
+                # use cameraA as query and cameraB as gallery
+                split = {
+                    'train': train,
+                    'query': test_a,
+                    'gallery': test_b,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+                # use cameraB as query and cameraA as gallery
+                split = {
+                    'train': train,
+                    'query': test_b,
+                    'gallery': test_a,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file saved to {}'.format(self.split_path))
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/video/__init__.py b/pfe/player-feature-extractor/torchreid/data/datasets/video/__init__.py
new file mode 100644
index 0000000..f4e75d3
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/video/__init__.py
@@ -0,0 +1,6 @@
+from __future__ import print_function, absolute_import
+
+from .mars import Mars
+from .ilidsvid import iLIDSVID
+from .prid2011 import PRID2011
+from .dukemtmcvidreid import DukeMTMCVidReID
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/video/dukemtmcvidreid.py b/pfe/player-feature-extractor/torchreid/data/datasets/video/dukemtmcvidreid.py
new file mode 100644
index 0000000..4b4c82f
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/video/dukemtmcvidreid.py
@@ -0,0 +1,128 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+import warnings
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import VideoDataset
+
+
+class DukeMTMCVidReID(VideoDataset):
+    """DukeMTMCVidReID.
+
+    Reference:
+        - Ristani et al. Performance Measures and a Data Set for Multi-Target,
+          Multi-Camera Tracking. ECCVW 2016.
+        - Wu et al. Exploit the Unknown Gradually: One-Shot Video-Based Person
+          Re-Identification by Stepwise Learning. CVPR 2018.
+
+    URL: `<https://github.com/Yu-Wu/DukeMTMC-VideoReID>`_
+    
+    Dataset statistics:
+        - identities: 702 (train) + 702 (test).
+        - tracklets: 2196 (train) + 2636 (test).
+    """
+    dataset_dir = 'dukemtmc-vidreid'
+    dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-VideoReID.zip'
+
+    def __init__(self, root='', min_seq_len=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/train')
+        self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/query')
+        self.gallery_dir = osp.join(
+            self.dataset_dir, 'DukeMTMC-VideoReID/gallery'
+        )
+        self.split_train_json_path = osp.join(
+            self.dataset_dir, 'split_train.json'
+        )
+        self.split_query_json_path = osp.join(
+            self.dataset_dir, 'split_query.json'
+        )
+        self.split_gallery_json_path = osp.join(
+            self.dataset_dir, 'split_gallery.json'
+        )
+        self.min_seq_len = min_seq_len
+
+        required_files = [
+            self.dataset_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+        self.check_before_run(required_files)
+
+        train = self.process_dir(
+            self.train_dir, self.split_train_json_path, relabel=True
+        )
+        query = self.process_dir(
+            self.query_dir, self.split_query_json_path, relabel=False
+        )
+        gallery = self.process_dir(
+            self.gallery_dir, self.split_gallery_json_path, relabel=False
+        )
+
+        super(DukeMTMCVidReID, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, json_path, relabel):
+        if osp.exists(json_path):
+            split = read_json(json_path)
+            return split['tracklets']
+
+        print('=> Generating split json file (** this might take a while **)')
+        pdirs = glob.glob(osp.join(dir_path, '*')) # avoid .DS_Store
+        print(
+            'Processing "{}" with {} person identities'.format(
+                dir_path, len(pdirs)
+            )
+        )
+
+        pid_container = set()
+        for pdir in pdirs:
+            pid = int(osp.basename(pdir))
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        tracklets = []
+        for pdir in pdirs:
+            pid = int(osp.basename(pdir))
+            if relabel:
+                pid = pid2label[pid]
+            tdirs = glob.glob(osp.join(pdir, '*'))
+            for tdir in tdirs:
+                raw_img_paths = glob.glob(osp.join(tdir, '*.jpg'))
+                num_imgs = len(raw_img_paths)
+
+                if num_imgs < self.min_seq_len:
+                    continue
+
+                img_paths = []
+                for img_idx in range(num_imgs):
+                    # some tracklet starts from 0002 instead of 0001
+                    img_idx_name = 'F' + str(img_idx + 1).zfill(4)
+                    res = glob.glob(
+                        osp.join(tdir, '*' + img_idx_name + '*.jpg')
+                    )
+                    if len(res) == 0:
+                        warnings.warn(
+                            'Index name {} in {} is missing, skip'.format(
+                                img_idx_name, tdir
+                            )
+                        )
+                        continue
+                    img_paths.append(res[0])
+                img_name = osp.basename(img_paths[0])
+                if img_name.find('_') == -1:
+                    # old naming format: 0001C6F0099X30823.jpg
+                    camid = int(img_name[5]) - 1
+                else:
+                    # new naming format: 0001_C6_F0099_X30823.jpg
+                    camid = int(img_name[6]) - 1
+                img_paths = tuple(img_paths)
+                tracklets.append((img_paths, pid, camid))
+
+        print('Saving split to {}'.format(json_path))
+        split_dict = {'tracklets': tracklets}
+        write_json(split_dict, json_path)
+
+        return tracklets
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/video/ilidsvid.py b/pfe/player-feature-extractor/torchreid/data/datasets/video/ilidsvid.py
new file mode 100644
index 0000000..c3ac1bb
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/video/ilidsvid.py
@@ -0,0 +1,143 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+from scipy.io import loadmat
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import VideoDataset
+
+
+class iLIDSVID(VideoDataset):
+    """iLIDS-VID.
+
+    Reference:
+        Wang et al. Person Re-Identification by Video Ranking. ECCV 2014.
+
+    URL: `<http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html>`_
+    
+    Dataset statistics:
+        - identities: 300.
+        - tracklets: 600.
+        - cameras: 2.
+    """
+    dataset_dir = 'ilids-vid'
+    dataset_url = 'http://www.eecs.qmul.ac.uk/~xiatian/iLIDS-VID/iLIDS-VID.tar'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.data_dir = osp.join(self.dataset_dir, 'i-LIDS-VID')
+        self.split_dir = osp.join(self.dataset_dir, 'train-test people splits')
+        self.split_mat_path = osp.join(
+            self.split_dir, 'train_test_splits_ilidsvid.mat'
+        )
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+        self.cam_1_path = osp.join(
+            self.dataset_dir, 'i-LIDS-VID/sequences/cam1'
+        )
+        self.cam_2_path = osp.join(
+            self.dataset_dir, 'i-LIDS-VID/sequences/cam2'
+        )
+
+        required_files = [self.dataset_dir, self.data_dir, self.split_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+        train_dirs, test_dirs = split['train'], split['test']
+
+        train = self.process_data(train_dirs, cam1=True, cam2=True)
+        query = self.process_data(test_dirs, cam1=True, cam2=False)
+        gallery = self.process_data(test_dirs, cam1=False, cam2=True)
+
+        super(iLIDSVID, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating splits ...')
+            mat_split_data = loadmat(self.split_mat_path)['ls_set']
+
+            num_splits = mat_split_data.shape[0]
+            num_total_ids = mat_split_data.shape[1]
+            assert num_splits == 10
+            assert num_total_ids == 300
+            num_ids_each = num_total_ids // 2
+
+            # pids in mat_split_data are indices, so we need to transform them
+            # to real pids
+            person_cam1_dirs = sorted(
+                glob.glob(osp.join(self.cam_1_path, '*'))
+            )
+            person_cam2_dirs = sorted(
+                glob.glob(osp.join(self.cam_2_path, '*'))
+            )
+
+            person_cam1_dirs = [
+                osp.basename(item) for item in person_cam1_dirs
+            ]
+            person_cam2_dirs = [
+                osp.basename(item) for item in person_cam2_dirs
+            ]
+
+            # make sure persons in one camera view can be found in the other camera view
+            assert set(person_cam1_dirs) == set(person_cam2_dirs)
+
+            splits = []
+            for i_split in range(num_splits):
+                # first 50% for testing and the remaining for training, following Wang et al. ECCV'14.
+                train_idxs = sorted(
+                    list(mat_split_data[i_split, num_ids_each:])
+                )
+                test_idxs = sorted(
+                    list(mat_split_data[i_split, :num_ids_each])
+                )
+
+                train_idxs = [int(i) - 1 for i in train_idxs]
+                test_idxs = [int(i) - 1 for i in test_idxs]
+
+                # transform pids to person dir names
+                train_dirs = [person_cam1_dirs[i] for i in train_idxs]
+                test_dirs = [person_cam1_dirs[i] for i in test_idxs]
+
+                split = {'train': train_dirs, 'test': test_dirs}
+                splits.append(split)
+
+            print(
+                'Totally {} splits are created, following Wang et al. ECCV\'14'
+                .format(len(splits))
+            )
+            print('Split file is saved to {}'.format(self.split_path))
+            write_json(splits, self.split_path)
+
+    def process_data(self, dirnames, cam1=True, cam2=True):
+        tracklets = []
+        dirname2pid = {dirname: i for i, dirname in enumerate(dirnames)}
+
+        for dirname in dirnames:
+            if cam1:
+                person_dir = osp.join(self.cam_1_path, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 0))
+
+            if cam2:
+                person_dir = osp.join(self.cam_2_path, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 1))
+
+        return tracklets
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/video/mars.py b/pfe/player-feature-extractor/torchreid/data/datasets/video/mars.py
new file mode 100644
index 0000000..4128e1c
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/video/mars.py
@@ -0,0 +1,133 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+import warnings
+from scipy.io import loadmat
+
+from ..dataset import VideoDataset
+
+
+class Mars(VideoDataset):
+    """MARS.
+
+    Reference:
+        Zheng et al. MARS: A Video Benchmark for Large-Scale Person Re-identification. ECCV 2016.
+
+    URL: `<http://www.liangzheng.com.cn/Project/project_mars.html>`_
+    
+    Dataset statistics:
+        - identities: 1261.
+        - tracklets: 8298 (train) + 1980 (query) + 9330 (gallery).
+        - cameras: 6.
+    """
+    dataset_dir = 'mars'
+    dataset_url = None
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.train_name_path = osp.join(
+            self.dataset_dir, 'info/train_name.txt'
+        )
+        self.test_name_path = osp.join(self.dataset_dir, 'info/test_name.txt')
+        self.track_train_info_path = osp.join(
+            self.dataset_dir, 'info/tracks_train_info.mat'
+        )
+        self.track_test_info_path = osp.join(
+            self.dataset_dir, 'info/tracks_test_info.mat'
+        )
+        self.query_IDX_path = osp.join(self.dataset_dir, 'info/query_IDX.mat')
+
+        required_files = [
+            self.dataset_dir, self.train_name_path, self.test_name_path,
+            self.track_train_info_path, self.track_test_info_path,
+            self.query_IDX_path
+        ]
+        self.check_before_run(required_files)
+
+        train_names = self.get_names(self.train_name_path)
+        test_names = self.get_names(self.test_name_path)
+        track_train = loadmat(self.track_train_info_path
+                              )['track_train_info'] # numpy.ndarray (8298, 4)
+        track_test = loadmat(self.track_test_info_path
+                             )['track_test_info'] # numpy.ndarray (12180, 4)
+        query_IDX = loadmat(self.query_IDX_path
+                            )['query_IDX'].squeeze() # numpy.ndarray (1980,)
+        query_IDX -= 1 # index from 0
+        track_query = track_test[query_IDX, :]
+        gallery_IDX = [
+            i for i in range(track_test.shape[0]) if i not in query_IDX
+        ]
+        track_gallery = track_test[gallery_IDX, :]
+
+        train = self.process_data(
+            train_names, track_train, home_dir='bbox_train', relabel=True
+        )
+        query = self.process_data(
+            test_names, track_query, home_dir='bbox_test', relabel=False
+        )
+        gallery = self.process_data(
+            test_names, track_gallery, home_dir='bbox_test', relabel=False
+        )
+
+        super(Mars, self).__init__(train, query, gallery, **kwargs)
+
+    def get_names(self, fpath):
+        names = []
+        with open(fpath, 'r') as f:
+            for line in f:
+                new_line = line.rstrip()
+                names.append(new_line)
+        return names
+
+    def process_data(
+        self, names, meta_data, home_dir=None, relabel=False, min_seq_len=0
+    ):
+        assert home_dir in ['bbox_train', 'bbox_test']
+        num_tracklets = meta_data.shape[0]
+        pid_list = list(set(meta_data[:, 2].tolist()))
+
+        if relabel:
+            pid2label = {pid: label for label, pid in enumerate(pid_list)}
+        tracklets = []
+
+        for tracklet_idx in range(num_tracklets):
+            data = meta_data[tracklet_idx, ...]
+            start_index, end_index, pid, camid = data
+            if pid == -1:
+                continue # junk images are just ignored
+            assert 1 <= camid <= 6
+            if relabel:
+                pid = pid2label[pid]
+            camid -= 1 # index starts from 0
+            img_names = names[start_index - 1:end_index]
+
+            # make sure image names correspond to the same person
+            pnames = [img_name[:4] for img_name in img_names]
+            assert len(
+                set(pnames)
+            ) == 1, 'Error: a single tracklet contains different person images'
+
+            # make sure all images are captured under the same camera
+            camnames = [img_name[5] for img_name in img_names]
+            assert len(
+                set(camnames)
+            ) == 1, 'Error: images are captured under different cameras!'
+
+            # append image names with directory information
+            img_paths = [
+                osp.join(self.dataset_dir, home_dir, img_name[:4], img_name)
+                for img_name in img_names
+            ]
+            if len(img_paths) >= min_seq_len:
+                img_paths = tuple(img_paths)
+                tracklets.append((img_paths, pid, camid))
+
+        return tracklets
+
+    def combine_all(self):
+        warnings.warn(
+            'Some query IDs do not appear in gallery. Therefore, combineall '
+            'does not make any difference to Mars'
+        )
diff --git a/pfe/player-feature-extractor/torchreid/data/datasets/video/prid2011.py b/pfe/player-feature-extractor/torchreid/data/datasets/video/prid2011.py
new file mode 100644
index 0000000..3af2e4d
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/datasets/video/prid2011.py
@@ -0,0 +1,80 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+
+from torchreid.utils import read_json
+
+from ..dataset import VideoDataset
+
+
+class PRID2011(VideoDataset):
+    """PRID2011.
+
+    Reference:
+        Hirzer et al. Person Re-Identification by Descriptive and
+        Discriminative Classification. SCIA 2011.
+
+    URL: `<https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/>`_
+    
+    Dataset statistics:
+        - identities: 200.
+        - tracklets: 400.
+        - cameras: 2.
+    """
+    dataset_dir = 'prid2011'
+    dataset_url = None
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.split_path = osp.join(self.dataset_dir, 'splits_prid2011.json')
+        self.cam_a_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_a'
+        )
+        self.cam_b_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_b'
+        )
+
+        required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_dir]
+        self.check_before_run(required_files)
+
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+        train_dirs, test_dirs = split['train'], split['test']
+
+        train = self.process_dir(train_dirs, cam1=True, cam2=True)
+        query = self.process_dir(test_dirs, cam1=True, cam2=False)
+        gallery = self.process_dir(test_dirs, cam1=False, cam2=True)
+
+        super(PRID2011, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dirnames, cam1=True, cam2=True):
+        tracklets = []
+        dirname2pid = {dirname: i for i, dirname in enumerate(dirnames)}
+
+        for dirname in dirnames:
+            if cam1:
+                person_dir = osp.join(self.cam_a_dir, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 0))
+
+            if cam2:
+                person_dir = osp.join(self.cam_b_dir, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 1))
+
+        return tracklets
diff --git a/pfe/player-feature-extractor/torchreid/data/sampler.py b/pfe/player-feature-extractor/torchreid/data/sampler.py
new file mode 100644
index 0000000..49221cb
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/sampler.py
@@ -0,0 +1,104 @@
+from __future__ import division, absolute_import
+import copy
+import numpy as np
+import random
+from collections import defaultdict
+from torch.utils.data.sampler import Sampler, RandomSampler, SequentialSampler
+
+AVAI_SAMPLERS = ['RandomIdentitySampler', 'SequentialSampler', 'RandomSampler']
+
+
+class RandomIdentitySampler(Sampler):
+    """Randomly samples N identities each with K instances.
+
+    Args:
+        data_source (list): contains tuples of (img_path(s), pid, camid).
+        batch_size (int): batch size.
+        num_instances (int): number of instances per identity in a batch.
+    """
+
+    def __init__(self, data_source, batch_size, num_instances):
+        if batch_size < num_instances:
+            raise ValueError(
+                'batch_size={} must be no less '
+                'than num_instances={}'.format(batch_size, num_instances)
+            )
+
+        self.data_source = data_source
+        self.batch_size = batch_size
+        self.num_instances = num_instances
+        self.num_pids_per_batch = self.batch_size // self.num_instances
+        self.index_dic = defaultdict(list)
+        for index, (_, pid, _) in enumerate(self.data_source):
+            self.index_dic[pid].append(index)
+        self.pids = list(self.index_dic.keys())
+
+        # estimate number of examples in an epoch
+        # TODO: improve precision
+        self.length = 0
+        for pid in self.pids:
+            idxs = self.index_dic[pid]
+            num = len(idxs)
+            if num < self.num_instances:
+                num = self.num_instances
+            self.length += num - num % self.num_instances
+
+    def __iter__(self):
+        batch_idxs_dict = defaultdict(list)
+
+        for pid in self.pids:
+            idxs = copy.deepcopy(self.index_dic[pid])
+            if len(idxs) < self.num_instances:
+                idxs = np.random.choice(
+                    idxs, size=self.num_instances, replace=True
+                )
+            random.shuffle(idxs)
+            batch_idxs = []
+            for idx in idxs:
+                batch_idxs.append(idx)
+                if len(batch_idxs) == self.num_instances:
+                    batch_idxs_dict[pid].append(batch_idxs)
+                    batch_idxs = []
+
+        avai_pids = copy.deepcopy(self.pids)
+        final_idxs = []
+
+        while len(avai_pids) >= self.num_pids_per_batch:
+            selected_pids = random.sample(avai_pids, self.num_pids_per_batch)
+            for pid in selected_pids:
+                batch_idxs = batch_idxs_dict[pid].pop(0)
+                final_idxs.extend(batch_idxs)
+                if len(batch_idxs_dict[pid]) == 0:
+                    avai_pids.remove(pid)
+
+        return iter(final_idxs)
+
+    def __len__(self):
+        return self.length
+
+
+def build_train_sampler(
+    data_source, train_sampler, batch_size=32, num_instances=4, **kwargs
+):
+    """Builds a training sampler.
+
+    Args:
+        data_source (list): contains tuples of (img_path(s), pid, camid).
+        train_sampler (str): sampler name (default: ``RandomSampler``).
+        batch_size (int, optional): batch size. Default is 32.
+        num_instances (int, optional): number of instances per identity in a
+            batch (when using ``RandomIdentitySampler``). Default is 4.
+    """
+    assert train_sampler in AVAI_SAMPLERS, \
+        'train_sampler must be one of {}, but got {}'.format(AVAI_SAMPLERS, train_sampler)
+
+    if train_sampler == 'RandomIdentitySampler':
+        sampler = RandomIdentitySampler(data_source, batch_size, num_instances)
+
+    elif train_sampler == 'SequentialSampler':
+        sampler = SequentialSampler(data_source)
+
+    elif train_sampler == 'RandomSampler':
+        sampler = RandomSampler(data_source)
+
+    return sampler
diff --git a/pfe/player-feature-extractor/torchreid/data/transforms.py b/pfe/player-feature-extractor/torchreid/data/transforms.py
new file mode 100644
index 0000000..f86c8c1
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/data/transforms.py
@@ -0,0 +1,326 @@
+from __future__ import division, print_function, absolute_import
+import math
+import random
+from collections import deque
+import torch
+from PIL import Image
+from torchvision.transforms import (
+    Resize, Compose, ToTensor, Normalize, ColorJitter, RandomHorizontalFlip
+)
+
+
+class Random2DTranslation(object):
+    """Randomly translates the input image with a probability.
+
+    Specifically, given a predefined shape (height, width), the input is first
+    resized with a factor of 1.125, leading to (height*1.125, width*1.125), then
+    a random crop is performed. Such operation is done with a probability.
+
+    Args:
+        height (int): target image height.
+        width (int): target image width.
+        p (float, optional): probability that this operation takes place.
+            Default is 0.5.
+        interpolation (int, optional): desired interpolation. Default is
+            ``PIL.Image.BILINEAR``
+    """
+
+    def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
+        self.height = height
+        self.width = width
+        self.p = p
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        if random.uniform(0, 1) > self.p:
+            return img.resize((self.width, self.height), self.interpolation)
+
+        new_width, new_height = int(round(self.width * 1.125)
+                                    ), int(round(self.height * 1.125))
+        resized_img = img.resize((new_width, new_height), self.interpolation)
+        x_maxrange = new_width - self.width
+        y_maxrange = new_height - self.height
+        x1 = int(round(random.uniform(0, x_maxrange)))
+        y1 = int(round(random.uniform(0, y_maxrange)))
+        croped_img = resized_img.crop(
+            (x1, y1, x1 + self.width, y1 + self.height)
+        )
+        return croped_img
+
+
+class RandomErasing(object):
+    """Randomly erases an image patch.
+
+    Origin: `<https://github.com/zhunzhong07/Random-Erasing>`_
+
+    Reference:
+        Zhong et al. Random Erasing Data Augmentation.
+
+    Args:
+        probability (float, optional): probability that this operation takes place.
+            Default is 0.5.
+        sl (float, optional): min erasing area.
+        sh (float, optional): max erasing area.
+        r1 (float, optional): min aspect ratio.
+        mean (list, optional): erasing value.
+    """
+
+    def __init__(
+        self,
+        probability=0.5,
+        sl=0.02,
+        sh=0.4,
+        r1=0.3,
+        mean=[0.4914, 0.4822, 0.4465]
+    ):
+        self.probability = probability
+        self.mean = mean
+        self.sl = sl
+        self.sh = sh
+        self.r1 = r1
+
+    def __call__(self, img):
+        if random.uniform(0, 1) > self.probability:
+            return img
+
+        for attempt in range(100):
+            area = img.size()[1] * img.size()[2]
+
+            target_area = random.uniform(self.sl, self.sh) * area
+            aspect_ratio = random.uniform(self.r1, 1 / self.r1)
+
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if w < img.size()[2] and h < img.size()[1]:
+                x1 = random.randint(0, img.size()[1] - h)
+                y1 = random.randint(0, img.size()[2] - w)
+                if img.size()[0] == 3:
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
+                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
+                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
+                else:
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
+                return img
+
+        return img
+
+
+class ColorAugmentation(object):
+    """Randomly alters the intensities of RGB channels.
+
+    Reference:
+        Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural
+        Networks. NIPS 2012.
+
+    Args:
+        p (float, optional): probability that this operation takes place.
+            Default is 0.5.
+    """
+
+    def __init__(self, p=0.5):
+        self.p = p
+        self.eig_vec = torch.Tensor(
+            [
+                [0.4009, 0.7192, -0.5675],
+                [-0.8140, -0.0045, -0.5808],
+                [0.4203, -0.6948, -0.5836],
+            ]
+        )
+        self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])
+
+    def _check_input(self, tensor):
+        assert tensor.dim() == 3 and tensor.size(0) == 3
+
+    def __call__(self, tensor):
+        if random.uniform(0, 1) > self.p:
+            return tensor
+        alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
+        quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
+        tensor = tensor + quatity.view(3, 1, 1)
+        return tensor
+
+
+class RandomPatch(object):
+    """Random patch data augmentation.
+
+    There is a patch pool that stores randomly extracted pathces from person images.
+    
+    For each input image, RandomPatch
+        1) extracts a random patch and stores the patch in the patch pool;
+        2) randomly selects a patch from the patch pool and pastes it on the
+           input (at random position) to simulate occlusion.
+
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        prob_happen=0.5,
+        pool_capacity=50000,
+        min_sample_size=100,
+        patch_min_area=0.01,
+        patch_max_area=0.5,
+        patch_min_ratio=0.1,
+        prob_rotate=0.5,
+        prob_flip_leftright=0.5,
+    ):
+        self.prob_happen = prob_happen
+
+        self.patch_min_area = patch_min_area
+        self.patch_max_area = patch_max_area
+        self.patch_min_ratio = patch_min_ratio
+
+        self.prob_rotate = prob_rotate
+        self.prob_flip_leftright = prob_flip_leftright
+
+        self.patchpool = deque(maxlen=pool_capacity)
+        self.min_sample_size = min_sample_size
+
+    def generate_wh(self, W, H):
+        area = W * H
+        for attempt in range(100):
+            target_area = random.uniform(
+                self.patch_min_area, self.patch_max_area
+            ) * area
+            aspect_ratio = random.uniform(
+                self.patch_min_ratio, 1. / self.patch_min_ratio
+            )
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+            if w < W and h < H:
+                return w, h
+        return None, None
+
+    def transform_patch(self, patch):
+        if random.uniform(0, 1) > self.prob_flip_leftright:
+            patch = patch.transpose(Image.FLIP_LEFT_RIGHT)
+        if random.uniform(0, 1) > self.prob_rotate:
+            patch = patch.rotate(random.randint(-10, 10))
+        return patch
+
+    def __call__(self, img):
+        W, H = img.size # original image size
+
+        # collect new patch
+        w, h = self.generate_wh(W, H)
+        if w is not None and h is not None:
+            x1 = random.randint(0, W - w)
+            y1 = random.randint(0, H - h)
+            new_patch = img.crop((x1, y1, x1 + w, y1 + h))
+            self.patchpool.append(new_patch)
+
+        if len(self.patchpool) < self.min_sample_size:
+            return img
+
+        if random.uniform(0, 1) > self.prob_happen:
+            return img
+
+        # paste a randomly selected patch on a random position
+        patch = random.sample(self.patchpool, 1)[0]
+        patchW, patchH = patch.size
+        x1 = random.randint(0, W - patchW)
+        y1 = random.randint(0, H - patchH)
+        patch = self.transform_patch(patch)
+        img.paste(patch, (x1, y1))
+
+        return img
+
+
+def build_transforms(
+    height,
+    width,
+    transforms='random_flip',
+    norm_mean=[0.485, 0.456, 0.406],
+    norm_std=[0.229, 0.224, 0.225],
+    **kwargs
+):
+    """Builds train and test transform functions.
+
+    Args:
+        height (int): target image height.
+        width (int): target image width.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        norm_mean (list or None, optional): normalization mean values. Default is ImageNet means.
+        norm_std (list or None, optional): normalization standard deviation values. Default is
+            ImageNet standard deviation values.
+    """
+    if transforms is None:
+        transforms = []
+
+    if isinstance(transforms, str):
+        transforms = [transforms]
+
+    if not isinstance(transforms, list):
+        raise ValueError(
+            'transforms must be a list of strings, but found to be {}'.format(
+                type(transforms)
+            )
+        )
+
+    if len(transforms) > 0:
+        transforms = [t.lower() for t in transforms]
+
+    if norm_mean is None or norm_std is None:
+        norm_mean = [0.485, 0.456, 0.406] # imagenet mean
+        norm_std = [0.229, 0.224, 0.225] # imagenet std
+    normalize = Normalize(mean=norm_mean, std=norm_std)
+
+    print('Building train transforms ...')
+    transform_tr = []
+
+    print('+ resize to {}x{}'.format(height, width))
+    transform_tr += [Resize((height, width))]
+
+    if 'random_flip' in transforms:
+        print('+ random flip')
+        transform_tr += [RandomHorizontalFlip()]
+
+    if 'random_crop' in transforms:
+        print(
+            '+ random crop (enlarge to {}x{} and '
+            'crop {}x{})'.format(
+                int(round(height * 1.125)), int(round(width * 1.125)), height,
+                width
+            )
+        )
+        transform_tr += [Random2DTranslation(height, width)]
+
+    if 'random_patch' in transforms:
+        print('+ random patch')
+        transform_tr += [RandomPatch()]
+
+    if 'color_jitter' in transforms:
+        print('+ color jitter')
+        transform_tr += [
+            ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)
+        ]
+
+    print('+ to torch tensor of range [0, 1]')
+    transform_tr += [ToTensor()]
+
+    print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
+    transform_tr += [normalize]
+
+    if 'random_erase' in transforms:
+        print('+ random erase')
+        transform_tr += [RandomErasing(mean=norm_mean)]
+
+    transform_tr = Compose(transform_tr)
+
+    print('Building test transforms ...')
+    print('+ resize to {}x{}'.format(height, width))
+    print('+ to torch tensor of range [0, 1]')
+    print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
+
+    transform_te = Compose([
+        Resize((height, width)),
+        ToTensor(),
+        normalize,
+    ])
+
+    return transform_tr, transform_te
diff --git a/pfe/player-feature-extractor/torchreid/engine/__init__.py b/pfe/player-feature-extractor/torchreid/engine/__init__.py
new file mode 100644
index 0000000..a39cc7f
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/__init__.py
@@ -0,0 +1,5 @@
+from __future__ import print_function, absolute_import
+
+from .image import ImageSoftmaxEngine, ImageTripletEngine
+from .video import VideoSoftmaxEngine, VideoTripletEngine
+from .engine import Engine
diff --git a/pfe/player-feature-extractor/torchreid/engine/engine.py b/pfe/player-feature-extractor/torchreid/engine/engine.py
new file mode 100644
index 0000000..0aec558
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/engine.py
@@ -0,0 +1,488 @@
+from __future__ import division, print_function, absolute_import
+import time
+import numpy as np
+import os.path as osp
+import datetime
+from collections import OrderedDict
+import torch
+from torch.nn import functional as F
+from torch.utils.tensorboard import SummaryWriter
+
+from torchreid import metrics
+from torchreid.utils import (
+    MetricMeter, AverageMeter, re_ranking, open_all_layers, save_checkpoint,
+    open_specified_layers, visualize_ranked_results
+)
+from torchreid.losses import DeepSupervision
+
+
+class Engine(object):
+    r"""A generic base Engine class for both image- and video-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+    """
+
+    def __init__(self, datamanager, use_gpu=True):
+        self.datamanager = datamanager
+        self.train_loader = self.datamanager.train_loader
+        self.test_loader = self.datamanager.test_loader
+        self.use_gpu = (torch.cuda.is_available() and use_gpu)
+        self.writer = None
+
+        self.model = None
+        self.optimizer = None
+        self.scheduler = None
+
+        self._models = OrderedDict()
+        self._optims = OrderedDict()
+        self._scheds = OrderedDict()
+
+    def register_model(self, name='model', model=None, optim=None, sched=None):
+        if self.__dict__.get('_models') is None:
+            raise AttributeError(
+                'Cannot assign model before super().__init__() call'
+            )
+
+        if self.__dict__.get('_optims') is None:
+            raise AttributeError(
+                'Cannot assign optim before super().__init__() call'
+            )
+
+        if self.__dict__.get('_scheds') is None:
+            raise AttributeError(
+                'Cannot assign sched before super().__init__() call'
+            )
+
+        self._models[name] = model
+        self._optims[name] = optim
+        self._scheds[name] = sched
+
+    def get_model_names(self, names=None):
+        names_real = list(self._models.keys())
+        if names is not None:
+            if not isinstance(names, list):
+                names = [names]
+            for name in names:
+                assert name in names_real
+            return names
+        else:
+            return names_real
+
+    def save_model(self, epoch, rank1, save_dir, is_best=False):
+        names = self.get_model_names()
+
+        for name in names:
+            save_checkpoint(
+                {
+                    'state_dict': self._models[name].state_dict(),
+                    'epoch': epoch + 1,
+                    'rank1': rank1,
+                    'optimizer': self._optims[name].state_dict(),
+                    'scheduler': self._scheds[name].state_dict()
+                },
+                osp.join(save_dir, name),
+                is_best=is_best
+            )
+
+    def set_model_mode(self, mode='train', names=None):
+        assert mode in ['train', 'eval', 'test']
+        names = self.get_model_names(names)
+
+        for name in names:
+            if mode == 'train':
+                self._models[name].train()
+            else:
+                self._models[name].eval()
+
+    def get_current_lr(self, names=None):
+        names = self.get_model_names(names)
+        name = names[0]
+        return self._optims[name].param_groups[-1]['lr']
+
+    def update_lr(self, names=None):
+        names = self.get_model_names(names)
+
+        for name in names:
+            if self._scheds[name] is not None:
+                self._scheds[name].step()
+
+    def run(
+        self,
+        save_dir='log',
+        max_epoch=0,
+        start_epoch=0,
+        print_freq=10,
+        fixbase_epoch=0,
+        open_layers=None,
+        start_eval=0,
+        eval_freq=-1,
+        test_only=False,
+        dist_metric='euclidean',
+        normalize_feature=False,
+        visrank=False,
+        visrank_topk=10,
+        use_metric_cuhk03=False,
+        ranks=[1, 5, 10, 20],
+        rerank=False
+    ):
+        r"""A unified pipeline for training and evaluating a model.
+
+        Args:
+            save_dir (str): directory to save model.
+            max_epoch (int): maximum epoch.
+            start_epoch (int, optional): starting epoch. Default is 0.
+            print_freq (int, optional): print_frequency. Default is 10.
+            fixbase_epoch (int, optional): number of epochs to train ``open_layers`` (new layers)
+                while keeping base layers frozen. Default is 0. ``fixbase_epoch`` is counted
+                in ``max_epoch``.
+            open_layers (str or list, optional): layers (attribute names) open for training.
+            start_eval (int, optional): from which epoch to start evaluation. Default is 0.
+            eval_freq (int, optional): evaluation frequency. Default is -1 (meaning evaluation
+                is only performed at the end of training).
+            test_only (bool, optional): if True, only runs evaluation on test datasets.
+                Default is False.
+            dist_metric (str, optional): distance metric used to compute distance matrix
+                between query and gallery. Default is "euclidean".
+            normalize_feature (bool, optional): performs L2 normalization on feature vectors before
+                computing feature distance. Default is False.
+            visrank (bool, optional): visualizes ranked results. Default is False. It is recommended to
+                enable ``visrank`` when ``test_only`` is True. The ranked images will be saved to
+                "save_dir/visrank_dataset", e.g. "save_dir/visrank_market1501".
+            visrank_topk (int, optional): top-k ranked images to be visualized. Default is 10.
+            use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
+                Default is False. This should be enabled when using cuhk03 classic split.
+            ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].
+            rerank (bool, optional): uses person re-ranking (by Zhong et al. CVPR'17).
+                Default is False. This is only enabled when test_only=True.
+        """
+
+        if visrank and not test_only:
+            raise ValueError(
+                'visrank can be set to True only if test_only=True'
+            )
+
+        if test_only:
+            self.test(
+                0,
+                dist_metric=dist_metric,
+                normalize_feature=normalize_feature,
+                visrank=visrank,
+                visrank_topk=visrank_topk,
+                save_dir=save_dir,
+                use_metric_cuhk03=use_metric_cuhk03,
+                ranks=ranks,
+                rerank=rerank
+            )
+            return
+
+        if self.writer is None:
+            self.writer = SummaryWriter(log_dir=save_dir)
+
+        time_start = time.time()
+        self.start_epoch = start_epoch
+        self.max_epoch = max_epoch
+        print('=> Start training')
+
+        for self.epoch in range(self.start_epoch, self.max_epoch):
+            self.train(
+                print_freq=print_freq,
+                fixbase_epoch=fixbase_epoch,
+                open_layers=open_layers
+            )
+
+            print ("saving checkpoint to: " + save_dir)
+            self.save_model(self.epoch, 0.5, save_dir)
+
+            if (self.epoch + 1) >= start_eval \
+               and eval_freq > 0 \
+               and (self.epoch+1) % eval_freq == 0 \
+               and (self.epoch + 1) != self.max_epoch:
+                rank1 = self.test(
+                    self.epoch,
+                    dist_metric=dist_metric,
+                    normalize_feature=normalize_feature,
+                    visrank=visrank,
+                    visrank_topk=visrank_topk,
+                    save_dir=save_dir,
+                    use_metric_cuhk03=use_metric_cuhk03,
+                    ranks=ranks
+                )
+                print ("saving checkpoint to: " + save_dir)
+                self.save_model(self.epoch, rank1, save_dir)
+                self.writer.add_scalar('Test/rank1', rank1, self.epoch)
+
+        if self.max_epoch > 0:
+            print('=> Final test')
+            rank1 = self.test(
+                self.epoch,
+                dist_metric=dist_metric,
+                normalize_feature=normalize_feature,
+                visrank=visrank,
+                visrank_topk=visrank_topk,
+                save_dir=save_dir,
+                use_metric_cuhk03=use_metric_cuhk03,
+                ranks=ranks
+            )
+            self.save_model(self.epoch, rank1, save_dir)
+            self.writer.add_scalar('Test/rank1', rank1, self.epoch)
+
+        elapsed = round(time.time() - time_start)
+        elapsed = str(datetime.timedelta(seconds=elapsed))
+        print('Elapsed {}'.format(elapsed))
+        if self.writer is not None:
+            self.writer.close()
+
+    def train(self, print_freq=10, fixbase_epoch=0, open_layers=None):
+        losses = MetricMeter()
+        batch_time = AverageMeter()
+        data_time = AverageMeter()
+
+        self.set_model_mode('train')
+
+        self.two_stepped_transfer_learning(
+            self.epoch, fixbase_epoch, open_layers
+        )
+
+        self.num_batches = len(self.train_loader)
+        end = time.time()
+        for self.batch_idx, data in enumerate(self.train_loader):
+            data_time.update(time.time() - end)
+            loss_summary = self.forward_backward(data)
+            batch_time.update(time.time() - end)
+            losses.update(loss_summary)
+
+            if (self.batch_idx + 1) % print_freq == 0:
+                nb_this_epoch = self.num_batches - (self.batch_idx + 1)
+                nb_future_epochs = (
+                    self.max_epoch - (self.epoch + 1)
+                ) * self.num_batches
+                eta_seconds = batch_time.avg * (nb_this_epoch+nb_future_epochs)
+                eta_str = str(datetime.timedelta(seconds=int(eta_seconds)))
+                print(
+                    'epoch: [{0}/{1}][{2}/{3}]\t'
+                    'time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                    'data {data_time.val:.3f} ({data_time.avg:.3f})\t'
+                    'eta {eta}\t'
+                    '{losses}\t'
+                    'lr {lr:.6f}'.format(
+                        self.epoch + 1,
+                        self.max_epoch,
+                        self.batch_idx + 1,
+                        self.num_batches,
+                        batch_time=batch_time,
+                        data_time=data_time,
+                        eta=eta_str,
+                        losses=losses,
+                        lr=self.get_current_lr()
+                    )
+                )
+
+            if self.writer is not None:
+                n_iter = self.epoch * self.num_batches + self.batch_idx
+                self.writer.add_scalar('Train/time', batch_time.avg, n_iter)
+                self.writer.add_scalar('Train/data', data_time.avg, n_iter)
+                for name, meter in losses.meters.items():
+                    self.writer.add_scalar('Train/' + name, meter.avg, n_iter)
+                self.writer.add_scalar(
+                    'Train/lr', self.get_current_lr(), n_iter
+                )
+
+            end = time.time()
+
+        self.update_lr()
+
+    def forward_backward(self, data):
+        raise NotImplementedError
+
+    def test(
+        self,
+        epoch,
+        dist_metric='euclidean',
+        normalize_feature=False,
+        visrank=False,
+        visrank_topk=10,
+        save_dir='',
+        use_metric_cuhk03=False,
+        ranks=[1, 5, 10, 20],
+        rerank=False
+    ):
+        r"""Tests model on target datasets.
+
+        .. note::
+
+            This function has been called in ``run()``.
+
+        .. note::
+
+            The test pipeline implemented in this function suits both image- and
+            video-reid. In general, a subclass of Engine only needs to re-implement
+            ``extract_features()`` and ``parse_data_for_eval()`` (most of the time),
+            but not a must. Please refer to the source code for more details.
+        """
+        self.set_model_mode('eval')
+        targets = list(self.test_loader.keys())
+
+        for name in targets:
+            domain = 'source' if name in self.datamanager.sources else 'target'
+            print('##### Evaluating {} ({}) #####'.format(name, domain))
+            query_loader = self.test_loader[name]['query']
+            gallery_loader = self.test_loader[name]['gallery']
+            rank1 = self._evaluate(
+                epoch,
+                dataset_name=name,
+                query_loader=query_loader,
+                gallery_loader=gallery_loader,
+                dist_metric=dist_metric,
+                normalize_feature=normalize_feature,
+                visrank=visrank,
+                visrank_topk=visrank_topk,
+                save_dir=save_dir,
+                use_metric_cuhk03=use_metric_cuhk03,
+                ranks=ranks,
+                rerank=rerank
+            )
+
+        return rank1
+
+    @torch.no_grad()
+    def _evaluate(
+        self,
+        epoch,
+        dataset_name='',
+        query_loader=None,
+        gallery_loader=None,
+        dist_metric='euclidean',
+        normalize_feature=False,
+        visrank=False,
+        visrank_topk=10,
+        save_dir='',
+        use_metric_cuhk03=False,
+        ranks=[1, 5, 10, 20],
+        rerank=False
+    ):
+        batch_time = AverageMeter()
+
+        def _feature_extraction(data_loader):
+            f_, pids_, camids_ = [], [], []
+            for batch_idx, data in enumerate(data_loader):
+                imgs, pids, camids = self.parse_data_for_eval(data)
+                if self.use_gpu:
+                    imgs = imgs.cuda()
+                end = time.time()
+                features = self.extract_features(imgs)
+                batch_time.update(time.time() - end)
+                features = features.data.cpu()
+                f_.append(features)
+                pids_.extend(pids)
+                camids_.extend(camids)
+            f_ = torch.cat(f_, 0)
+            pids_ = np.asarray(pids_)
+            camids_ = np.asarray(camids_)
+            return f_, pids_, camids_
+
+        print('Extracting features from query set ...')
+        qf, q_pids, q_camids = _feature_extraction(query_loader)
+        print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
+
+        print('Extracting features from gallery set ...')
+        gf, g_pids, g_camids = _feature_extraction(gallery_loader)
+        print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
+
+        print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
+
+        if normalize_feature:
+            print('Normalzing features with L2 norm ...')
+            qf = F.normalize(qf, p=2, dim=1)
+            gf = F.normalize(gf, p=2, dim=1)
+
+        print(
+            'Computing distance matrix with metric={} ...'.format(dist_metric)
+        )
+        distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
+        distmat = distmat.numpy()
+
+        if rerank:
+            print('Applying person re-ranking ...')
+            distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
+            distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
+            distmat = re_ranking(distmat, distmat_qq, distmat_gg)
+
+        print('Computing CMC and mAP ...')
+        cmc, mAP = metrics.evaluate_rank(
+            distmat,
+            q_pids,
+            g_pids,
+            q_camids,
+            g_camids,
+            use_metric_cuhk03=use_metric_cuhk03
+        )
+
+        print('** Results **')
+        print('mAP: {:.1%}'.format(mAP))
+        print('CMC curve')
+        for r in ranks:
+            print('Rank-{:<3}: {:.1%}'.format(r, cmc[r - 1]))
+
+        if visrank:
+            visualize_ranked_results(
+                distmat,
+                self.datamanager.fetch_test_loaders(dataset_name),
+                self.datamanager.data_type,
+                width=self.datamanager.width,
+                height=self.datamanager.height,
+                save_dir=osp.join(save_dir, 'visrank_' + dataset_name),
+                topk=visrank_topk
+            )
+
+        return cmc[0]
+
+    def compute_loss(self, criterion, outputs, targets):
+        if isinstance(outputs, (tuple, list)):
+            loss = DeepSupervision(criterion, outputs, targets)
+        else:
+            loss = criterion(outputs, targets)
+        return loss
+
+    def extract_features(self, input):
+        return self.model(input)
+
+    def parse_data_for_train(self, data):
+        imgs = data['img']
+        pids = data['pid']
+        return imgs, pids
+
+    def parse_data_for_eval(self, data):
+        imgs = data['img']
+        pids = data['pid']
+        camids = data['camid']
+        return imgs, pids, camids
+
+    def two_stepped_transfer_learning(
+        self, epoch, fixbase_epoch, open_layers, model=None
+    ):
+        """Two-stepped transfer learning.
+
+        The idea is to freeze base layers for a certain number of epochs
+        and then open all layers for training.
+
+        Reference: https://arxiv.org/abs/1611.05244
+        """
+        model = self.model if model is None else model
+        if model is None:
+            return
+
+        if (epoch + 1) <= fixbase_epoch and open_layers is not None:
+            print(
+                '* Only train {} (epoch: {}/{})'.format(
+                    open_layers, epoch + 1, fixbase_epoch
+                )
+            )
+            open_specified_layers(model, open_layers)
+        else:
+            open_all_layers(model)
diff --git a/pfe/player-feature-extractor/torchreid/engine/image/__init__.py b/pfe/player-feature-extractor/torchreid/engine/image/__init__.py
new file mode 100644
index 0000000..08d313a
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/image/__init__.py
@@ -0,0 +1,4 @@
+from __future__ import absolute_import
+
+from .softmax import ImageSoftmaxEngine
+from .triplet import ImageTripletEngine
diff --git a/pfe/player-feature-extractor/torchreid/engine/image/softmax.py b/pfe/player-feature-extractor/torchreid/engine/image/softmax.py
new file mode 100644
index 0000000..5785d4f
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/image/softmax.py
@@ -0,0 +1,97 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid import metrics
+from torchreid.losses import CrossEntropyLoss
+
+from ..engine import Engine
+
+
+class ImageSoftmaxEngine(Engine):
+    r"""Softmax-loss engine for image-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+
+    Examples::
+        
+        import torchreid
+        datamanager = torchreid.data.ImageDataManager(
+            root='path/to/reid-data',
+            sources='market1501',
+            height=256,
+            width=128,
+            combineall=False,
+            batch_size=32
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='softmax'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.ImageSoftmaxEngine(
+            datamanager, model, optimizer, scheduler=scheduler
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-softmax-market1501',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True
+    ):
+        super(ImageSoftmaxEngine, self).__init__(datamanager, use_gpu)
+
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.register_model('model', model, optimizer, scheduler)
+
+        self.criterion = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        outputs = self.model(imgs)
+        loss = self.compute_loss(self.criterion, outputs, pids)
+
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+        loss_summary = {
+            'loss': loss.item(),
+            'acc': metrics.accuracy(outputs, pids)[0].item()
+        }
+
+        return loss_summary
diff --git a/pfe/player-feature-extractor/torchreid/engine/image/triplet.py b/pfe/player-feature-extractor/torchreid/engine/image/triplet.py
new file mode 100644
index 0000000..174d6d2
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/image/triplet.py
@@ -0,0 +1,113 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid import metrics
+from torchreid.losses import TripletLoss, CrossEntropyLoss
+
+from ..engine import Engine
+
+
+class ImageTripletEngine(Engine):
+    r"""Triplet-loss engine for image-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        margin (float, optional): margin for triplet loss. Default is 0.3.
+        weight_t (float, optional): weight for triplet loss. Default is 1.
+        weight_x (float, optional): weight for softmax loss. Default is 1.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+
+    Examples::
+        
+        import torchreid
+        datamanager = torchreid.data.ImageDataManager(
+            root='path/to/reid-data',
+            sources='market1501',
+            height=256,
+            width=128,
+            combineall=False,
+            batch_size=32,
+            num_instances=4,
+            train_sampler='RandomIdentitySampler' # this is important
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='triplet'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.ImageTripletEngine(
+            datamanager, model, optimizer, margin=0.3,
+            weight_t=0.7, weight_x=1, scheduler=scheduler
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-triplet-market1501',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        margin=0.3,
+        weight_t=1,
+        weight_x=1,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True
+    ):
+        super(ImageTripletEngine, self).__init__(datamanager, use_gpu)
+
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.register_model('model', model, optimizer, scheduler)
+
+        self.weight_t = weight_t
+        self.weight_x = weight_x
+
+        self.criterion_t = TripletLoss(margin=margin)
+        self.criterion_x = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        outputs, features = self.model(imgs)
+        loss_t = self.compute_loss(self.criterion_t, features, pids)
+        loss_x = self.compute_loss(self.criterion_x, outputs, pids)
+        loss = self.weight_t * loss_t + self.weight_x * loss_x
+
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+        loss_summary = {
+            'loss_t': loss_t.item(),
+            'loss_x': loss_x.item(),
+            'acc': metrics.accuracy(outputs, pids)[0].item()
+        }
+
+        return loss_summary
diff --git a/pfe/player-feature-extractor/torchreid/engine/video/__init__.py b/pfe/player-feature-extractor/torchreid/engine/video/__init__.py
new file mode 100644
index 0000000..b818bf4
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/video/__init__.py
@@ -0,0 +1,4 @@
+from __future__ import absolute_import
+
+from .softmax import VideoSoftmaxEngine
+from .triplet import VideoTripletEngine
diff --git a/pfe/player-feature-extractor/torchreid/engine/video/softmax.py b/pfe/player-feature-extractor/torchreid/engine/video/softmax.py
new file mode 100644
index 0000000..fe92feb
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/video/softmax.py
@@ -0,0 +1,109 @@
+from __future__ import division, print_function, absolute_import
+import torch
+
+from torchreid.engine.image import ImageSoftmaxEngine
+
+
+class VideoSoftmaxEngine(ImageSoftmaxEngine):
+    """Softmax-loss engine for video-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+        pooling_method (str, optional): how to pool features for a tracklet.
+            Default is "avg" (average). Choices are ["avg", "max"].
+
+    Examples::
+        
+        import torch
+        import torchreid
+        # Each batch contains batch_size*seq_len images
+        datamanager = torchreid.data.VideoDataManager(
+            root='path/to/reid-data',
+            sources='mars',
+            height=256,
+            width=128,
+            combineall=False,
+            batch_size=8, # number of tracklets
+            seq_len=15 # number of images in each tracklet
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='softmax'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.VideoSoftmaxEngine(
+            datamanager, model, optimizer, scheduler=scheduler,
+            pooling_method='avg'
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-softmax-mars',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True,
+        pooling_method='avg'
+    ):
+        super(VideoSoftmaxEngine, self).__init__(
+            datamanager,
+            model,
+            optimizer,
+            scheduler=scheduler,
+            use_gpu=use_gpu,
+            label_smooth=label_smooth
+        )
+        self.pooling_method = pooling_method
+
+    def parse_data_for_train(self, data):
+        imgs = data['img']
+        pids = data['pid']
+        if imgs.dim() == 5:
+            # b: batch size
+            # s: sqeuence length
+            # c: channel depth
+            # h: height
+            # w: width
+            b, s, c, h, w = imgs.size()
+            imgs = imgs.view(b * s, c, h, w)
+            pids = pids.view(b, 1).expand(b, s)
+            pids = pids.contiguous().view(b * s)
+        return imgs, pids
+
+    def extract_features(self, input):
+        # b: batch size
+        # s: sqeuence length
+        # c: channel depth
+        # h: height
+        # w: width
+        b, s, c, h, w = input.size()
+        input = input.view(b * s, c, h, w)
+        features = self.model(input)
+        features = features.view(b, s, -1)
+        if self.pooling_method == 'avg':
+            features = torch.mean(features, 1)
+        else:
+            features = torch.max(features, 1)[0]
+        return features
diff --git a/pfe/player-feature-extractor/torchreid/engine/video/triplet.py b/pfe/player-feature-extractor/torchreid/engine/video/triplet.py
new file mode 100644
index 0000000..5d43b8b
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/engine/video/triplet.py
@@ -0,0 +1,91 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid.engine.image import ImageTripletEngine
+from torchreid.engine.video import VideoSoftmaxEngine
+
+
+class VideoTripletEngine(ImageTripletEngine, VideoSoftmaxEngine):
+    """Triplet-loss engine for video-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        margin (float, optional): margin for triplet loss. Default is 0.3.
+        weight_t (float, optional): weight for triplet loss. Default is 1.
+        weight_x (float, optional): weight for softmax loss. Default is 1.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+        pooling_method (str, optional): how to pool features for a tracklet.
+            Default is "avg" (average). Choices are ["avg", "max"].
+
+    Examples::
+
+        import torch
+        import torchreid
+        # Each batch contains batch_size*seq_len images
+        # Each identity is sampled with num_instances tracklets
+        datamanager = torchreid.data.VideoDataManager(
+            root='path/to/reid-data',
+            sources='mars',
+            height=256,
+            width=128,
+            combineall=False,
+            num_instances=4,
+            train_sampler='RandomIdentitySampler'
+            batch_size=8, # number of tracklets
+            seq_len=15 # number of images in each tracklet
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='triplet'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.VideoTripletEngine(
+            datamanager, model, optimizer, margin=0.3,
+            weight_t=0.7, weight_x=1, scheduler=scheduler,
+            pooling_method='avg'
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-triplet-mars',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        margin=0.3,
+        weight_t=1,
+        weight_x=1,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True,
+        pooling_method='avg'
+    ):
+        super(VideoTripletEngine, self).__init__(
+            datamanager,
+            model,
+            optimizer,
+            margin=margin,
+            weight_t=weight_t,
+            weight_x=weight_x,
+            scheduler=scheduler,
+            use_gpu=use_gpu,
+            label_smooth=label_smooth
+        )
+        self.pooling_method = pooling_method
diff --git a/pfe/player-feature-extractor/torchreid/losses/__init__.py b/pfe/player-feature-extractor/torchreid/losses/__init__.py
new file mode 100644
index 0000000..1625749
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/losses/__init__.py
@@ -0,0 +1,21 @@
+from __future__ import division, print_function, absolute_import
+
+from .cross_entropy_loss import CrossEntropyLoss
+from .hard_mine_triplet_loss import TripletLoss
+
+
+def DeepSupervision(criterion, xs, y):
+    """DeepSupervision
+
+    Applies criterion to each element in a list.
+
+    Args:
+        criterion: loss function
+        xs: tuple of inputs
+        y: ground truth
+    """
+    loss = 0.
+    for x in xs:
+        loss += criterion(x, y)
+    loss /= len(xs)
+    return loss
diff --git a/pfe/player-feature-extractor/torchreid/losses/cross_entropy_loss.py b/pfe/player-feature-extractor/torchreid/losses/cross_entropy_loss.py
new file mode 100644
index 0000000..4cfa5d4
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/losses/cross_entropy_loss.py
@@ -0,0 +1,50 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+
+
+class CrossEntropyLoss(nn.Module):
+    r"""Cross entropy loss with label smoothing regularizer.
+    
+    Reference:
+        Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
+
+    With label smoothing, the label :math:`y` for a class is computed by
+    
+    .. math::
+        \begin{equation}
+        (1 - \eps) \times y + \frac{\eps}{K},
+        \end{equation}
+
+    where :math:`K` denotes the number of classes and :math:`\eps` is a weight. When
+    :math:`\eps = 0`, the loss function reduces to the normal cross entropy.
+    
+    Args:
+        num_classes (int): number of classes.
+        eps (float, optional): weight. Default is 0.1.
+        use_gpu (bool, optional): whether to use gpu devices. Default is True.
+        label_smooth (bool, optional): whether to apply label smoothing. Default is True.
+    """
+
+    def __init__(self, num_classes, eps=0.1, use_gpu=True, label_smooth=True):
+        super(CrossEntropyLoss, self).__init__()
+        self.num_classes = num_classes
+        self.eps = eps if label_smooth else 0
+        self.use_gpu = use_gpu
+        self.logsoftmax = nn.LogSoftmax(dim=1)
+
+    def forward(self, inputs, targets):
+        """
+        Args:
+            inputs (torch.Tensor): prediction matrix (before softmax) with
+                shape (batch_size, num_classes).
+            targets (torch.LongTensor): ground truth labels with shape (batch_size).
+                Each position contains the label index.
+        """
+        log_probs = self.logsoftmax(inputs)
+        zeros = torch.zeros(log_probs.size())
+        targets = zeros.scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
+        if self.use_gpu:
+            targets = targets.cuda()
+        targets = (1 - self.eps) * targets + self.eps / self.num_classes
+        return (-targets * log_probs).mean(0).sum()
diff --git a/pfe/player-feature-extractor/torchreid/losses/hard_mine_triplet_loss.py b/pfe/player-feature-extractor/torchreid/losses/hard_mine_triplet_loss.py
new file mode 100644
index 0000000..102881f
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/losses/hard_mine_triplet_loss.py
@@ -0,0 +1,48 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+
+
+class TripletLoss(nn.Module):
+    """Triplet loss with hard positive/negative mining.
+    
+    Reference:
+        Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
+    
+    Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py>`_.
+    
+    Args:
+        margin (float, optional): margin for triplet. Default is 0.3.
+    """
+
+    def __init__(self, margin=0.3):
+        super(TripletLoss, self).__init__()
+        self.margin = margin
+        self.ranking_loss = nn.MarginRankingLoss(margin=margin)
+
+    def forward(self, inputs, targets):
+        """
+        Args:
+            inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim).
+            targets (torch.LongTensor): ground truth labels with shape (num_classes).
+        """
+        n = inputs.size(0)
+
+        # Compute pairwise distance, replace by the official when merged
+        dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n)
+        dist = dist + dist.t()
+        dist.addmm_(1, -2, inputs, inputs.t())
+        dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
+
+        # For each anchor, find the hardest positive and negative
+        mask = targets.expand(n, n).eq(targets.expand(n, n).t())
+        dist_ap, dist_an = [], []
+        for i in range(n):
+            dist_ap.append(dist[i][mask[i]].max().unsqueeze(0))
+            dist_an.append(dist[i][mask[i] == 0].min().unsqueeze(0))
+        dist_ap = torch.cat(dist_ap)
+        dist_an = torch.cat(dist_an)
+
+        # Compute ranking hinge loss
+        y = torch.ones_like(dist_an)
+        return self.ranking_loss(dist_an, dist_ap, y)
diff --git a/pfe/player-feature-extractor/torchreid/metrics/__init__.py b/pfe/player-feature-extractor/torchreid/metrics/__init__.py
new file mode 100644
index 0000000..5159e1e
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/__init__.py
@@ -0,0 +1,5 @@
+from __future__ import absolute_import
+
+from .rank import evaluate_rank
+from .accuracy import accuracy
+from .distance import compute_distance_matrix
diff --git a/pfe/player-feature-extractor/torchreid/metrics/accuracy.py b/pfe/player-feature-extractor/torchreid/metrics/accuracy.py
new file mode 100644
index 0000000..3161f7b
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/accuracy.py
@@ -0,0 +1,37 @@
+from __future__ import division, print_function, absolute_import
+
+
+def accuracy(output, target, topk=(1, )):
+    """Computes the accuracy over the k top predictions for
+    the specified values of k.
+
+    Args:
+        output (torch.Tensor): prediction matrix with shape (batch_size, num_classes).
+        target (torch.LongTensor): ground truth labels with shape (batch_size).
+        topk (tuple, optional): accuracy at top-k will be computed. For example,
+            topk=(1, 5) means accuracy at top-1 and top-5 will be computed.
+
+    Returns:
+        list: accuracy at top-k.
+
+    Examples::
+        >>> from torchreid import metrics
+        >>> metrics.accuracy(output, target)
+    """
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    if isinstance(output, (tuple, list)):
+        output = output[0]
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        acc = correct_k.mul_(100.0 / batch_size)
+        res.append(acc)
+
+    return res
diff --git a/pfe/player-feature-extractor/torchreid/metrics/distance.py b/pfe/player-feature-extractor/torchreid/metrics/distance.py
new file mode 100644
index 0000000..6278c34
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/distance.py
@@ -0,0 +1,80 @@
+from __future__ import division, print_function, absolute_import
+import torch
+from torch.nn import functional as F
+
+
+def compute_distance_matrix(input1, input2, metric='euclidean'):
+    """A wrapper function for computing distance matrix.
+
+    Args:
+        input1 (torch.Tensor): 2-D feature matrix.
+        input2 (torch.Tensor): 2-D feature matrix.
+        metric (str, optional): "euclidean" or "cosine".
+            Default is "euclidean".
+
+    Returns:
+        torch.Tensor: distance matrix.
+
+    Examples::
+       >>> from torchreid import metrics
+       >>> input1 = torch.rand(10, 2048)
+       >>> input2 = torch.rand(100, 2048)
+       >>> distmat = metrics.compute_distance_matrix(input1, input2)
+       >>> distmat.size() # (10, 100)
+    """
+    # check input
+    assert isinstance(input1, torch.Tensor)
+    assert isinstance(input2, torch.Tensor)
+    assert input1.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(
+        input1.dim()
+    )
+    assert input2.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(
+        input2.dim()
+    )
+    assert input1.size(1) == input2.size(1)
+
+    if metric == 'euclidean':
+        distmat = euclidean_squared_distance(input1, input2)
+    elif metric == 'cosine':
+        distmat = cosine_distance(input1, input2)
+    else:
+        raise ValueError(
+            'Unknown distance metric: {}. '
+            'Please choose either "euclidean" or "cosine"'.format(metric)
+        )
+
+    return distmat
+
+
+def euclidean_squared_distance(input1, input2):
+    """Computes euclidean squared distance.
+
+    Args:
+        input1 (torch.Tensor): 2-D feature matrix.
+        input2 (torch.Tensor): 2-D feature matrix.
+
+    Returns:
+        torch.Tensor: distance matrix.
+    """
+    m, n = input1.size(0), input2.size(0)
+    mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)
+    mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()
+    distmat = mat1 + mat2
+    distmat.addmm_(1, -2, input1, input2.t())
+    return distmat
+
+
+def cosine_distance(input1, input2):
+    """Computes cosine distance.
+
+    Args:
+        input1 (torch.Tensor): 2-D feature matrix.
+        input2 (torch.Tensor): 2-D feature matrix.
+
+    Returns:
+        torch.Tensor: distance matrix.
+    """
+    input1_normed = F.normalize(input1, p=2, dim=1)
+    input2_normed = F.normalize(input2, p=2, dim=1)
+    distmat = 1 - torch.mm(input1_normed, input2_normed.t())
+    return distmat
diff --git a/pfe/player-feature-extractor/torchreid/metrics/rank.py b/pfe/player-feature-extractor/torchreid/metrics/rank.py
new file mode 100644
index 0000000..bf6205b
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/rank.py
@@ -0,0 +1,207 @@
+from __future__ import division, print_function, absolute_import
+import numpy as np
+import warnings
+from collections import defaultdict
+
+try:
+    from torchreid.metrics.rank_cylib.rank_cy import evaluate_cy
+    IS_CYTHON_AVAI = True
+except ImportError:
+    IS_CYTHON_AVAI = False
+    warnings.warn(
+        'Cython evaluation (very fast so highly recommended) is '
+        'unavailable, now use python evaluation.'
+    )
+
+
+def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
+    """Evaluation with cuhk03 metric
+    Key: one image for each gallery identity is randomly sampled for each query identity.
+    Random sampling is performed num_repeats times.
+    """
+    num_repeats = 10
+    num_q, num_g = distmat.shape
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print(
+            'Note: number of gallery samples is quite small, got {}'.
+            format(num_g)
+        )
+
+    indices = np.argsort(distmat, axis=1)
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    num_valid_q = 0. # number of valid query
+
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        order = indices[q_idx]
+        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+        keep = np.invert(remove)
+
+        # compute cmc curve
+        raw_cmc = matches[q_idx][
+            keep] # binary vector, positions with value 1 are correct matches
+        if not np.any(raw_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        kept_g_pids = g_pids[order][keep]
+        g_pids_dict = defaultdict(list)
+        for idx, pid in enumerate(kept_g_pids):
+            g_pids_dict[pid].append(idx)
+
+        cmc = 0.
+        for repeat_idx in range(num_repeats):
+            mask = np.zeros(len(raw_cmc), dtype=np.bool)
+            for _, idxs in g_pids_dict.items():
+                # randomly sample one image for each gallery person
+                rnd_idx = np.random.choice(idxs)
+                mask[rnd_idx] = True
+            masked_raw_cmc = raw_cmc[mask]
+            _cmc = masked_raw_cmc.cumsum()
+            _cmc[_cmc > 1] = 1
+            cmc += _cmc[:max_rank].astype(np.float32)
+
+        cmc /= num_repeats
+        all_cmc.append(cmc)
+        # compute AP
+        num_rel = raw_cmc.sum()
+        tmp_cmc = raw_cmc.cumsum()
+        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+        tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
+        AP = tmp_cmc.sum() / num_rel
+        all_AP.append(AP)
+        num_valid_q += 1.
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+
+    return all_cmc, mAP
+
+
+def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
+    """Evaluation with market1501 metric
+    Key: for each query identity, its gallery images from the same camera view are discarded.
+    """
+    num_q, num_g = distmat.shape
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print(
+            'Note: number of gallery samples is quite small, got {}'.
+            format(num_g)
+        )
+
+    indices = np.argsort(distmat, axis=1)
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    num_valid_q = 0. # number of valid query
+
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        order = indices[q_idx]
+        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+        keep = np.invert(remove)
+
+        # compute cmc curve
+        raw_cmc = matches[q_idx][
+            keep] # binary vector, positions with value 1 are correct matches
+        if not np.any(raw_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        cmc = raw_cmc.cumsum()
+        cmc[cmc > 1] = 1
+
+        all_cmc.append(cmc[:max_rank])
+        num_valid_q += 1.
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        num_rel = raw_cmc.sum()
+        tmp_cmc = raw_cmc.cumsum()
+        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+        tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
+        AP = tmp_cmc.sum() / num_rel
+        all_AP.append(AP)
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+
+    return all_cmc, mAP
+
+
+def evaluate_py(
+    distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03
+):
+    if use_metric_cuhk03:
+        return eval_cuhk03(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank
+        )
+    else:
+        return eval_market1501(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank
+        )
+
+
+def evaluate_rank(
+    distmat,
+    q_pids,
+    g_pids,
+    q_camids,
+    g_camids,
+    max_rank=50,
+    use_metric_cuhk03=False,
+    use_cython=True
+):
+    """Evaluates CMC rank.
+
+    Args:
+        distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
+        q_pids (numpy.ndarray): 1-D array containing person identities
+            of each query instance.
+        g_pids (numpy.ndarray): 1-D array containing person identities
+            of each gallery instance.
+        q_camids (numpy.ndarray): 1-D array containing camera views under
+            which each query instance is captured.
+        g_camids (numpy.ndarray): 1-D array containing camera views under
+            which each gallery instance is captured.
+        max_rank (int, optional): maximum CMC rank to be computed. Default is 50.
+        use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
+            Default is False. This should be enabled when using cuhk03 classic split.
+        use_cython (bool, optional): use cython code for evaluation. Default is True.
+            This is highly recommended as the cython code can speed up the cmc computation
+            by more than 10x. This requires Cython to be installed.
+    """
+    if use_cython and IS_CYTHON_AVAI:
+        return evaluate_cy(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank,
+            use_metric_cuhk03
+        )
+    else:
+        return evaluate_py(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank,
+            use_metric_cuhk03
+        )
diff --git a/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/Makefile b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/Makefile
new file mode 100644
index 0000000..d49e655
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/Makefile
@@ -0,0 +1,6 @@
+all:
+	$(PYTHON) setup.py build_ext --inplace
+	rm -rf build
+clean:
+	rm -rf build
+	rm -f rank_cy.c *.so
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/__init__.py b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/rank_cy.pyx b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/rank_cy.pyx
new file mode 100644
index 0000000..c7ad956
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/rank_cy.pyx
@@ -0,0 +1,249 @@
+# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True
+
+from __future__ import print_function
+
+import cython
+import numpy as np
+cimport numpy as np
+from collections import defaultdict
+import random
+
+
+"""
+Compiler directives:
+https://github.com/cython/cython/wiki/enhancements-compilerdirectives
+
+Cython tutorial:
+https://cython.readthedocs.io/en/latest/src/userguide/numpy_tutorial.html
+
+Credit to https://github.com/luzai
+"""
+
+
+# Main interface
+cpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=False):
+    distmat = np.asarray(distmat, dtype=np.float32)
+    q_pids = np.asarray(q_pids, dtype=np.int64)
+    g_pids = np.asarray(g_pids, dtype=np.int64)
+    q_camids = np.asarray(q_camids, dtype=np.int64)
+    g_camids = np.asarray(g_camids, dtype=np.int64)
+    if use_metric_cuhk03:
+        return eval_cuhk03_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
+    return eval_market1501_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
+
+
+cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
+                     long[:]q_camids, long[:]g_camids, long max_rank):
+    
+    cdef long num_q = distmat.shape[0]
+    cdef long num_g = distmat.shape[1]
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print('Note: number of gallery samples is quite small, got {}'.format(num_g))
+    
+    cdef:
+        long num_repeats = 10
+        long[:,:] indices = np.argsort(distmat, axis=1)
+        long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
+
+        float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)
+        float[:] all_AP = np.zeros(num_q, dtype=np.float32)
+        float num_valid_q = 0. # number of valid query
+
+        long q_idx, q_pid, q_camid, g_idx
+        long[:] order = np.zeros(num_g, dtype=np.int64)
+        long keep
+
+        float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches
+        float[:] masked_raw_cmc = np.zeros(num_g, dtype=np.float32)
+        float[:] cmc, masked_cmc
+        long num_g_real, num_g_real_masked, rank_idx, rnd_idx
+        unsigned long meet_condition
+        float AP
+        long[:] kept_g_pids, mask
+
+        float num_rel
+        float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)
+        float tmp_cmc_sum
+    
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        for g_idx in range(num_g):
+            order[g_idx] = indices[q_idx, g_idx]
+        num_g_real = 0
+        meet_condition = 0
+        kept_g_pids = np.zeros(num_g, dtype=np.int64)
+
+        for g_idx in range(num_g):
+            if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):
+                raw_cmc[num_g_real] = matches[q_idx][g_idx]
+                kept_g_pids[num_g_real] = g_pids[order[g_idx]]
+                num_g_real += 1
+                if matches[q_idx][g_idx] > 1e-31:
+                    meet_condition = 1
+        
+        if not meet_condition:
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        # cuhk03-specific setting
+        g_pids_dict = defaultdict(list) # overhead!
+        for g_idx in range(num_g_real):
+            g_pids_dict[kept_g_pids[g_idx]].append(g_idx)
+
+        cmc = np.zeros(max_rank, dtype=np.float32)
+        for _ in range(num_repeats):
+            mask = np.zeros(num_g_real, dtype=np.int64)
+            
+            for _, idxs in g_pids_dict.items():
+                # randomly sample one image for each gallery person
+                rnd_idx = np.random.choice(idxs)
+                #rnd_idx = idxs[0] # use deterministic for debugging
+                mask[rnd_idx] = 1
+
+            num_g_real_masked = 0
+            for g_idx in range(num_g_real):
+                if mask[g_idx] == 1:
+                    masked_raw_cmc[num_g_real_masked] = raw_cmc[g_idx]
+                    num_g_real_masked += 1
+
+            masked_cmc = np.zeros(num_g, dtype=np.float32)
+            function_cumsum(masked_raw_cmc, masked_cmc, num_g_real_masked)
+            for g_idx in range(num_g_real_masked):
+                if masked_cmc[g_idx] > 1:
+                    masked_cmc[g_idx] = 1
+
+            for rank_idx in range(max_rank):
+                cmc[rank_idx] += masked_cmc[rank_idx] / num_repeats
+        
+        for rank_idx in range(max_rank):
+            all_cmc[q_idx, rank_idx] = cmc[rank_idx]
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        function_cumsum(raw_cmc, tmp_cmc, num_g_real)
+        num_rel = 0
+        tmp_cmc_sum = 0
+        for g_idx in range(num_g_real):
+            tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]
+            num_rel += raw_cmc[g_idx]
+        all_AP[q_idx] = tmp_cmc_sum / num_rel
+        num_valid_q += 1.
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    # compute averaged cmc
+    cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32)
+    for rank_idx in range(max_rank):
+        for q_idx in range(num_q):
+            avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]
+        avg_cmc[rank_idx] /= num_valid_q
+    
+    cdef float mAP = 0
+    for q_idx in range(num_q):
+        mAP += all_AP[q_idx]
+    mAP /= num_valid_q
+
+    return np.asarray(avg_cmc).astype(np.float32), mAP
+
+
+cpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
+                         long[:]q_camids, long[:]g_camids, long max_rank):
+    
+    cdef long num_q = distmat.shape[0]
+    cdef long num_g = distmat.shape[1]
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print('Note: number of gallery samples is quite small, got {}'.format(num_g))
+    
+    cdef:
+        long[:,:] indices = np.argsort(distmat, axis=1)
+        long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
+
+        float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)
+        float[:] all_AP = np.zeros(num_q, dtype=np.float32)
+        float num_valid_q = 0. # number of valid query
+
+        long q_idx, q_pid, q_camid, g_idx
+        long[:] order = np.zeros(num_g, dtype=np.int64)
+        long keep
+
+        float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches
+        float[:] cmc = np.zeros(num_g, dtype=np.float32)
+        long num_g_real, rank_idx
+        unsigned long meet_condition
+
+        float num_rel
+        float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)
+        float tmp_cmc_sum
+    
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        for g_idx in range(num_g):
+            order[g_idx] = indices[q_idx, g_idx]
+        num_g_real = 0
+        meet_condition = 0
+        
+        for g_idx in range(num_g):
+            if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):
+                raw_cmc[num_g_real] = matches[q_idx][g_idx]
+                num_g_real += 1
+                if matches[q_idx][g_idx] > 1e-31:
+                    meet_condition = 1
+        
+        if not meet_condition:
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        # compute cmc
+        function_cumsum(raw_cmc, cmc, num_g_real)
+        for g_idx in range(num_g_real):
+            if cmc[g_idx] > 1:
+                cmc[g_idx] = 1
+
+        for rank_idx in range(max_rank):
+            all_cmc[q_idx, rank_idx] = cmc[rank_idx]
+        num_valid_q += 1.
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        function_cumsum(raw_cmc, tmp_cmc, num_g_real)
+        num_rel = 0
+        tmp_cmc_sum = 0
+        for g_idx in range(num_g_real):
+            tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]
+            num_rel += raw_cmc[g_idx]
+        all_AP[q_idx] = tmp_cmc_sum / num_rel
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    # compute averaged cmc
+    cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32)
+    for rank_idx in range(max_rank):
+        for q_idx in range(num_q):
+            avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]
+        avg_cmc[rank_idx] /= num_valid_q
+    
+    cdef float mAP = 0
+    for q_idx in range(num_q):
+        mAP += all_AP[q_idx]
+    mAP /= num_valid_q
+
+    return np.asarray(avg_cmc).astype(np.float32), mAP
+
+
+# Compute the cumulative sum
+cdef void function_cumsum(cython.numeric[:] src, cython.numeric[:] dst, long n):
+    cdef long i
+    dst[0] = src[0]
+    for i in range(1, n):
+        dst[i] = src[i] + dst[i - 1]
\ No newline at end of file
diff --git a/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/setup.py b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/setup.py
new file mode 100644
index 0000000..ce2aeb7
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/setup.py
@@ -0,0 +1,26 @@
+import numpy as np
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Build import cythonize
+
+
+def numpy_include():
+    try:
+        numpy_include = np.get_include()
+    except AttributeError:
+        numpy_include = np.get_numpy_include()
+    return numpy_include
+
+
+ext_modules = [
+    Extension(
+        'rank_cy',
+        ['rank_cy.pyx'],
+        include_dirs=[numpy_include()],
+    )
+]
+
+setup(
+    name='Cython-based reid evaluation code',
+    ext_modules=cythonize(ext_modules)
+)
diff --git a/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/test_cython.py b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/test_cython.py
new file mode 100644
index 0000000..5d1175d
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/metrics/rank_cylib/test_cython.py
@@ -0,0 +1,83 @@
+from __future__ import print_function
+import sys
+import numpy as np
+import timeit
+import os.path as osp
+
+from torchreid import metrics
+
+sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
+"""
+Test the speed of cython-based evaluation code. The speed improvements
+can be much bigger when using the real reid data, which contains a larger
+amount of query and gallery images.
+
+Note: you might encounter the following error:
+  'AssertionError: Error: all query identities do not appear in gallery'.
+This is normal because the inputs are random numbers. Just try again.
+"""
+
+print('*** Compare running time ***')
+
+setup = '''
+import sys
+import os.path as osp
+import numpy as np
+sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
+from torchreid import metrics
+num_q = 30
+num_g = 300
+max_rank = 5
+distmat = np.random.rand(num_q, num_g) * 20
+q_pids = np.random.randint(0, num_q, size=num_q)
+g_pids = np.random.randint(0, num_g, size=num_g)
+q_camids = np.random.randint(0, 5, size=num_q)
+g_camids = np.random.randint(0, 5, size=num_g)
+'''
+
+print('=> Using market1501\'s metric')
+pytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)',
+    setup=setup,
+    number=20
+)
+cytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)',
+    setup=setup,
+    number=20
+)
+print('Python time: {} s'.format(pytime))
+print('Cython time: {} s'.format(cytime))
+print('Cython is {} times faster than python\n'.format(pytime / cytime))
+
+print('=> Using cuhk03\'s metric')
+pytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=False)',
+    setup=setup,
+    number=20
+)
+cytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=True)',
+    setup=setup,
+    number=20
+)
+print('Python time: {} s'.format(pytime))
+print('Cython time: {} s'.format(cytime))
+print('Cython is {} times faster than python\n'.format(pytime / cytime))
+"""
+print("=> Check precision")
+
+num_q = 30
+num_g = 300
+max_rank = 5
+distmat = np.random.rand(num_q, num_g) * 20
+q_pids = np.random.randint(0, num_q, size=num_q)
+g_pids = np.random.randint(0, num_g, size=num_g)
+q_camids = np.random.randint(0, 5, size=num_q)
+g_camids = np.random.randint(0, 5, size=num_g)
+
+cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)
+print("Python:\nmAP = {} \ncmc = {}\n".format(mAP, cmc))
+cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)
+print("Cython:\nmAP = {} \ncmc = {}\n".format(mAP, cmc))
+"""
diff --git a/pfe/player-feature-extractor/torchreid/models/__init__.py b/pfe/player-feature-extractor/torchreid/models/__init__.py
new file mode 100644
index 0000000..583be15
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/__init__.py
@@ -0,0 +1,119 @@
+from __future__ import absolute_import
+import torch
+
+from .pcb import *
+from .mlfn import *
+from .hacnn import *
+from .osnet import *
+from .senet import *
+from .mudeep import *
+from .nasnet import *
+from .resnet import *
+from .densenet import *
+from .xception import *
+from .osnet_ain import *
+from .resnetmid import *
+from .shufflenet import *
+from .squeezenet import *
+from .inceptionv4 import *
+from .mobilenetv2 import *
+from .resnet_ibn_a import *
+from .resnet_ibn_b import *
+from .shufflenetv2 import *
+from .inceptionresnetv2 import *
+
+__model_factory = {
+    # image classification models
+    'resnet18': resnet18,
+    'resnet34': resnet34,
+    'resnet50': resnet50,
+    'resnet101': resnet101,
+    'resnet152': resnet152,
+    'resnext50_32x4d': resnext50_32x4d,
+    'resnext101_32x8d': resnext101_32x8d,
+    'resnet50_fc512': resnet50_fc512,
+    'se_resnet50': se_resnet50,
+    'se_resnet50_fc512': se_resnet50_fc512,
+    'se_resnet101': se_resnet101,
+    'se_resnext50_32x4d': se_resnext50_32x4d,
+    'se_resnext101_32x4d': se_resnext101_32x4d,
+    'densenet121': densenet121,
+    'densenet169': densenet169,
+    'densenet201': densenet201,
+    'densenet161': densenet161,
+    'densenet121_fc512': densenet121_fc512,
+    'inceptionresnetv2': inceptionresnetv2,
+    'inceptionv4': inceptionv4,
+    'xception': xception,
+    'resnet50_ibn_a': resnet50_ibn_a,
+    'resnet50_ibn_b': resnet50_ibn_b,
+    # lightweight models
+    'nasnsetmobile': nasnetamobile,
+    'mobilenetv2_x1_0': mobilenetv2_x1_0,
+    'mobilenetv2_x1_4': mobilenetv2_x1_4,
+    'shufflenet': shufflenet,
+    'squeezenet1_0': squeezenet1_0,
+    'squeezenet1_0_fc512': squeezenet1_0_fc512,
+    'squeezenet1_1': squeezenet1_1,
+    'shufflenet_v2_x0_5': shufflenet_v2_x0_5,
+    'shufflenet_v2_x1_0': shufflenet_v2_x1_0,
+    'shufflenet_v2_x1_5': shufflenet_v2_x1_5,
+    'shufflenet_v2_x2_0': shufflenet_v2_x2_0,
+    # reid-specific models
+    'mudeep': MuDeep,
+    'resnet50mid': resnet50mid,
+    'hacnn': HACNN,
+    'pcb_p6': pcb_p6,
+    'pcb_p4': pcb_p4,
+    'mlfn': mlfn,
+    'osnet_x1_0': osnet_x1_0,
+    'osnet_x0_75': osnet_x0_75,
+    'osnet_x0_5': osnet_x0_5,
+    'osnet_x0_25': osnet_x0_25,
+    'osnet_ibn_x1_0': osnet_ibn_x1_0,
+    'osnet_ain_x1_0': osnet_ain_x1_0
+}
+
+
+def show_avai_models():
+    """Displays available models.
+
+    Examples::
+        >>> from torchreid import models
+        >>> models.show_avai_models()
+    """
+    print(list(__model_factory.keys()))
+
+
+def build_model(
+    name, num_classes, loss='softmax', pretrained=True, use_gpu=True
+):
+    """A function wrapper for building a model.
+
+    Args:
+        name (str): model name.
+        num_classes (int): number of training identities.
+        loss (str, optional): loss function to optimize the model. Currently
+            supports "softmax" and "triplet". Default is "softmax".
+        pretrained (bool, optional): whether to load ImageNet-pretrained weights.
+            Default is True.
+        use_gpu (bool, optional): whether to use gpu. Default is True.
+
+    Returns:
+        nn.Module
+
+    Examples::
+        >>> from torchreid import models
+        >>> model = models.build_model('resnet50', 751, loss='softmax')
+    """
+    avai_models = list(__model_factory.keys())
+    if name not in avai_models:
+        raise KeyError(
+            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
+        )
+    return __model_factory[name](
+        num_classes=num_classes,
+        loss=loss,
+        pretrained=pretrained,
+        use_gpu=use_gpu
+    )
diff --git a/pfe/player-feature-extractor/torchreid/models/densenet.py b/pfe/player-feature-extractor/torchreid/models/densenet.py
new file mode 100644
index 0000000..a1d9b7e
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/densenet.py
@@ -0,0 +1,380 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import re
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.utils import model_zoo
+
+__all__ = [
+    'densenet121', 'densenet169', 'densenet201', 'densenet161',
+    'densenet121_fc512'
+]
+
+model_urls = {
+    'densenet121':
+    'https://download.pytorch.org/models/densenet121-a639ec97.pth',
+    'densenet169':
+    'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
+    'densenet201':
+    'https://download.pytorch.org/models/densenet201-c1103571.pth',
+    'densenet161':
+    'https://download.pytorch.org/models/densenet161-8d451a50.pth',
+}
+
+
+class _DenseLayer(nn.Sequential):
+
+    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
+        super(_DenseLayer, self).__init__()
+        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
+        self.add_module('relu1', nn.ReLU(inplace=True)),
+        self.add_module(
+            'conv1',
+            nn.Conv2d(
+                num_input_features,
+                bn_size * growth_rate,
+                kernel_size=1,
+                stride=1,
+                bias=False
+            )
+        ),
+        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
+        self.add_module('relu2', nn.ReLU(inplace=True)),
+        self.add_module(
+            'conv2',
+            nn.Conv2d(
+                bn_size * growth_rate,
+                growth_rate,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=False
+            )
+        ),
+        self.drop_rate = drop_rate
+
+    def forward(self, x):
+        new_features = super(_DenseLayer, self).forward(x)
+        if self.drop_rate > 0:
+            new_features = F.dropout(
+                new_features, p=self.drop_rate, training=self.training
+            )
+        return torch.cat([x, new_features], 1)
+
+
+class _DenseBlock(nn.Sequential):
+
+    def __init__(
+        self, num_layers, num_input_features, bn_size, growth_rate, drop_rate
+    ):
+        super(_DenseBlock, self).__init__()
+        for i in range(num_layers):
+            layer = _DenseLayer(
+                num_input_features + i*growth_rate, growth_rate, bn_size,
+                drop_rate
+            )
+            self.add_module('denselayer%d' % (i+1), layer)
+
+
+class _Transition(nn.Sequential):
+
+    def __init__(self, num_input_features, num_output_features):
+        super(_Transition, self).__init__()
+        self.add_module('norm', nn.BatchNorm2d(num_input_features))
+        self.add_module('relu', nn.ReLU(inplace=True))
+        self.add_module(
+            'conv',
+            nn.Conv2d(
+                num_input_features,
+                num_output_features,
+                kernel_size=1,
+                stride=1,
+                bias=False
+            )
+        )
+        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
+
+
+class DenseNet(nn.Module):
+    """Densely connected network.
+    
+    Reference:
+        Huang et al. Densely Connected Convolutional Networks. CVPR 2017.
+
+    Public keys:
+        - ``densenet121``: DenseNet121.
+        - ``densenet169``: DenseNet169.
+        - ``densenet201``: DenseNet201.
+        - ``densenet161``: DenseNet161.
+        - ``densenet121_fc512``: DenseNet121 + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        growth_rate=32,
+        block_config=(6, 12, 24, 16),
+        num_init_features=64,
+        bn_size=4,
+        drop_rate=0,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+
+        super(DenseNet, self).__init__()
+        self.loss = loss
+
+        # First convolution
+        self.features = nn.Sequential(
+            OrderedDict(
+                [
+                    (
+                        'conv0',
+                        nn.Conv2d(
+                            3,
+                            num_init_features,
+                            kernel_size=7,
+                            stride=2,
+                            padding=3,
+                            bias=False
+                        )
+                    ),
+                    ('norm0', nn.BatchNorm2d(num_init_features)),
+                    ('relu0', nn.ReLU(inplace=True)),
+                    (
+                        'pool0',
+                        nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+                    ),
+                ]
+            )
+        )
+
+        # Each denseblock
+        num_features = num_init_features
+        for i, num_layers in enumerate(block_config):
+            block = _DenseBlock(
+                num_layers=num_layers,
+                num_input_features=num_features,
+                bn_size=bn_size,
+                growth_rate=growth_rate,
+                drop_rate=drop_rate
+            )
+            self.features.add_module('denseblock%d' % (i+1), block)
+            num_features = num_features + num_layers*growth_rate
+            if i != len(block_config) - 1:
+                trans = _Transition(
+                    num_input_features=num_features,
+                    num_output_features=num_features // 2
+                )
+                self.features.add_module('transition%d' % (i+1), trans)
+                num_features = num_features // 2
+
+        # Final batch norm
+        self.features.add_module('norm5', nn.BatchNorm2d(num_features))
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.feature_dim = num_features
+        self.fc = self._construct_fc_layer(fc_dims, num_features, dropout_p)
+
+        # Linear layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer.
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        f = self.features(x)
+        f = F.relu(f, inplace=True)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+
+    # '.'s are no longer allowed in module names, but pervious _DenseLayer
+    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
+    # They are also in the checkpoints in model_urls. This pattern is used
+    # to find such keys.
+    pattern = re.compile(
+        r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$'
+    )
+    for key in list(pretrain_dict.keys()):
+        res = pattern.match(key)
+        if res:
+            new_key = res.group(1) + res.group(2)
+            pretrain_dict[new_key] = pretrain_dict[key]
+            del pretrain_dict[key]
+
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+"""
+Dense network configurations:
+--
+densenet121: num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)
+densenet169: num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32)
+densenet201: num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32)
+densenet161: num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24)
+"""
+
+
+def densenet121(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 24, 16),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet121'])
+    return model
+
+
+def densenet169(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 32, 32),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet169'])
+    return model
+
+
+def densenet201(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 48, 32),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet201'])
+    return model
+
+
+def densenet161(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=96,
+        growth_rate=48,
+        block_config=(6, 12, 36, 24),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet161'])
+    return model
+
+
+def densenet121_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 24, 16),
+        fc_dims=[512],
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet121'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/hacnn.py b/pfe/player-feature-extractor/torchreid/models/hacnn.py
new file mode 100644
index 0000000..f21cc82
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/hacnn.py
@@ -0,0 +1,414 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['HACNN']
+
+
+class ConvBlock(nn.Module):
+    """Basic convolutional block.
+    
+    convolution + batch normalization + relu.
+
+    Args:
+        in_c (int): number of input channels.
+        out_c (int): number of output channels.
+        k (int or tuple): kernel size.
+        s (int or tuple): stride.
+        p (int or tuple): padding.
+    """
+
+    def __init__(self, in_c, out_c, k, s=1, p=0):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p)
+        self.bn = nn.BatchNorm2d(out_c)
+
+    def forward(self, x):
+        return F.relu(self.bn(self.conv(x)))
+
+
+class InceptionA(nn.Module):
+
+    def __init__(self, in_channels, out_channels):
+        super(InceptionA, self).__init__()
+        mid_channels = out_channels // 4
+
+        self.stream1 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+        )
+        self.stream2 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+        )
+        self.stream3 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+        )
+        self.stream4 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1),
+            ConvBlock(in_channels, mid_channels, 1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        s4 = self.stream4(x)
+        y = torch.cat([s1, s2, s3, s4], dim=1)
+        return y
+
+
+class InceptionB(nn.Module):
+
+    def __init__(self, in_channels, out_channels):
+        super(InceptionB, self).__init__()
+        mid_channels = out_channels // 4
+
+        self.stream1 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, s=2, p=1),
+        )
+        self.stream2 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+            ConvBlock(mid_channels, mid_channels, 3, s=2, p=1),
+        )
+        self.stream3 = nn.Sequential(
+            nn.MaxPool2d(3, stride=2, padding=1),
+            ConvBlock(in_channels, mid_channels * 2, 1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        y = torch.cat([s1, s2, s3], dim=1)
+        return y
+
+
+class SpatialAttn(nn.Module):
+    """Spatial Attention (Sec. 3.1.I.1)"""
+
+    def __init__(self):
+        super(SpatialAttn, self).__init__()
+        self.conv1 = ConvBlock(1, 1, 3, s=2, p=1)
+        self.conv2 = ConvBlock(1, 1, 1)
+
+    def forward(self, x):
+        # global cross-channel averaging
+        x = x.mean(1, keepdim=True)
+        # 3-by-3 conv
+        x = self.conv1(x)
+        # bilinear resizing
+        x = F.upsample(
+            x, (x.size(2) * 2, x.size(3) * 2),
+            mode='bilinear',
+            align_corners=True
+        )
+        # scaling conv
+        x = self.conv2(x)
+        return x
+
+
+class ChannelAttn(nn.Module):
+    """Channel Attention (Sec. 3.1.I.2)"""
+
+    def __init__(self, in_channels, reduction_rate=16):
+        super(ChannelAttn, self).__init__()
+        assert in_channels % reduction_rate == 0
+        self.conv1 = ConvBlock(in_channels, in_channels // reduction_rate, 1)
+        self.conv2 = ConvBlock(in_channels // reduction_rate, in_channels, 1)
+
+    def forward(self, x):
+        # squeeze operation (global average pooling)
+        x = F.avg_pool2d(x, x.size()[2:])
+        # excitation operation (2 conv layers)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+
+class SoftAttn(nn.Module):
+    """Soft Attention (Sec. 3.1.I)
+    
+    Aim: Spatial Attention + Channel Attention
+    
+    Output: attention maps with shape identical to input.
+    """
+
+    def __init__(self, in_channels):
+        super(SoftAttn, self).__init__()
+        self.spatial_attn = SpatialAttn()
+        self.channel_attn = ChannelAttn(in_channels)
+        self.conv = ConvBlock(in_channels, in_channels, 1)
+
+    def forward(self, x):
+        y_spatial = self.spatial_attn(x)
+        y_channel = self.channel_attn(x)
+        y = y_spatial * y_channel
+        y = torch.sigmoid(self.conv(y))
+        return y
+
+
+class HardAttn(nn.Module):
+    """Hard Attention (Sec. 3.1.II)"""
+
+    def __init__(self, in_channels):
+        super(HardAttn, self).__init__()
+        self.fc = nn.Linear(in_channels, 4 * 2)
+        self.init_params()
+
+    def init_params(self):
+        self.fc.weight.data.zero_()
+        self.fc.bias.data.copy_(
+            torch.tensor(
+                [0, -0.75, 0, -0.25, 0, 0.25, 0, 0.75], dtype=torch.float
+            )
+        )
+
+    def forward(self, x):
+        # squeeze operation (global average pooling)
+        x = F.avg_pool2d(x, x.size()[2:]).view(x.size(0), x.size(1))
+        # predict transformation parameters
+        theta = torch.tanh(self.fc(x))
+        theta = theta.view(-1, 4, 2)
+        return theta
+
+
+class HarmAttn(nn.Module):
+    """Harmonious Attention (Sec. 3.1)"""
+
+    def __init__(self, in_channels):
+        super(HarmAttn, self).__init__()
+        self.soft_attn = SoftAttn(in_channels)
+        self.hard_attn = HardAttn(in_channels)
+
+    def forward(self, x):
+        y_soft_attn = self.soft_attn(x)
+        theta = self.hard_attn(x)
+        return y_soft_attn, theta
+
+
+class HACNN(nn.Module):
+    """Harmonious Attention Convolutional Neural Network.
+
+    Reference:
+        Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.
+
+    Public keys:
+        - ``hacnn``: HACNN.
+    """
+
+    # Args:
+    #    num_classes (int): number of classes to predict
+    #    nchannels (list): number of channels AFTER concatenation
+    #    feat_dim (int): feature dimension for a single stream
+    #    learn_region (bool): whether to learn region features (i.e. local branch)
+
+    def __init__(
+        self,
+        num_classes,
+        loss='softmax',
+        nchannels=[128, 256, 384],
+        feat_dim=512,
+        learn_region=True,
+        use_gpu=True,
+        **kwargs
+    ):
+        super(HACNN, self).__init__()
+        self.loss = loss
+        self.learn_region = learn_region
+        self.use_gpu = use_gpu
+
+        self.conv = ConvBlock(3, 32, 3, s=2, p=1)
+
+        # Construct Inception + HarmAttn blocks
+        # ============== Block 1 ==============
+        self.inception1 = nn.Sequential(
+            InceptionA(32, nchannels[0]),
+            InceptionB(nchannels[0], nchannels[0]),
+        )
+        self.ha1 = HarmAttn(nchannels[0])
+
+        # ============== Block 2 ==============
+        self.inception2 = nn.Sequential(
+            InceptionA(nchannels[0], nchannels[1]),
+            InceptionB(nchannels[1], nchannels[1]),
+        )
+        self.ha2 = HarmAttn(nchannels[1])
+
+        # ============== Block 3 ==============
+        self.inception3 = nn.Sequential(
+            InceptionA(nchannels[1], nchannels[2]),
+            InceptionB(nchannels[2], nchannels[2]),
+        )
+        self.ha3 = HarmAttn(nchannels[2])
+
+        self.fc_global = nn.Sequential(
+            nn.Linear(nchannels[2], feat_dim),
+            nn.BatchNorm1d(feat_dim),
+            nn.ReLU(),
+        )
+        self.classifier_global = nn.Linear(feat_dim, num_classes)
+
+        if self.learn_region:
+            self.init_scale_factors()
+            self.local_conv1 = InceptionB(32, nchannels[0])
+            self.local_conv2 = InceptionB(nchannels[0], nchannels[1])
+            self.local_conv3 = InceptionB(nchannels[1], nchannels[2])
+            self.fc_local = nn.Sequential(
+                nn.Linear(nchannels[2] * 4, feat_dim),
+                nn.BatchNorm1d(feat_dim),
+                nn.ReLU(),
+            )
+            self.classifier_local = nn.Linear(feat_dim, num_classes)
+            self.feat_dim = feat_dim * 2
+        else:
+            self.feat_dim = feat_dim
+
+    def init_scale_factors(self):
+        # initialize scale factors (s_w, s_h) for four regions
+        self.scale_factors = []
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+
+    def stn(self, x, theta):
+        """Performs spatial transform
+        
+        x: (batch, channel, height, width)
+        theta: (batch, 2, 3)
+        """
+        grid = F.affine_grid(theta, x.size())
+        x = F.grid_sample(x, grid)
+        return x
+
+    def transform_theta(self, theta_i, region_idx):
+        """Transforms theta to include (s_w, s_h), resulting in (batch, 2, 3)"""
+        scale_factors = self.scale_factors[region_idx]
+        theta = torch.zeros(theta_i.size(0), 2, 3)
+        theta[:, :, :2] = scale_factors
+        theta[:, :, -1] = theta_i
+        if self.use_gpu:
+            theta = theta.cuda()
+        return theta
+
+    def forward(self, x):
+        assert x.size(2) == 160 and x.size(3) == 64, \
+            'Input size does not match, expected (160, 64) but got ({}, {})'.format(x.size(2), x.size(3))
+        x = self.conv(x)
+
+        # ============== Block 1 ==============
+        # global branch
+        x1 = self.inception1(x)
+        x1_attn, x1_theta = self.ha1(x1)
+        x1_out = x1 * x1_attn
+        # local branch
+        if self.learn_region:
+            x1_local_list = []
+            for region_idx in range(4):
+                x1_theta_i = x1_theta[:, region_idx, :]
+                x1_theta_i = self.transform_theta(x1_theta_i, region_idx)
+                x1_trans_i = self.stn(x, x1_theta_i)
+                x1_trans_i = F.upsample(
+                    x1_trans_i, (24, 28), mode='bilinear', align_corners=True
+                )
+                x1_local_i = self.local_conv1(x1_trans_i)
+                x1_local_list.append(x1_local_i)
+
+        # ============== Block 2 ==============
+        # Block 2
+        # global branch
+        x2 = self.inception2(x1_out)
+        x2_attn, x2_theta = self.ha2(x2)
+        x2_out = x2 * x2_attn
+        # local branch
+        if self.learn_region:
+            x2_local_list = []
+            for region_idx in range(4):
+                x2_theta_i = x2_theta[:, region_idx, :]
+                x2_theta_i = self.transform_theta(x2_theta_i, region_idx)
+                x2_trans_i = self.stn(x1_out, x2_theta_i)
+                x2_trans_i = F.upsample(
+                    x2_trans_i, (12, 14), mode='bilinear', align_corners=True
+                )
+                x2_local_i = x2_trans_i + x1_local_list[region_idx]
+                x2_local_i = self.local_conv2(x2_local_i)
+                x2_local_list.append(x2_local_i)
+
+        # ============== Block 3 ==============
+        # Block 3
+        # global branch
+        x3 = self.inception3(x2_out)
+        x3_attn, x3_theta = self.ha3(x3)
+        x3_out = x3 * x3_attn
+        # local branch
+        if self.learn_region:
+            x3_local_list = []
+            for region_idx in range(4):
+                x3_theta_i = x3_theta[:, region_idx, :]
+                x3_theta_i = self.transform_theta(x3_theta_i, region_idx)
+                x3_trans_i = self.stn(x2_out, x3_theta_i)
+                x3_trans_i = F.upsample(
+                    x3_trans_i, (6, 7), mode='bilinear', align_corners=True
+                )
+                x3_local_i = x3_trans_i + x2_local_list[region_idx]
+                x3_local_i = self.local_conv3(x3_local_i)
+                x3_local_list.append(x3_local_i)
+
+        # ============== Feature generation ==============
+        # global branch
+        x_global = F.avg_pool2d(x3_out,
+                                x3_out.size()[2:]
+                                ).view(x3_out.size(0), x3_out.size(1))
+        x_global = self.fc_global(x_global)
+        # local branch
+        if self.learn_region:
+            x_local_list = []
+            for region_idx in range(4):
+                x_local_i = x3_local_list[region_idx]
+                x_local_i = F.avg_pool2d(x_local_i,
+                                         x_local_i.size()[2:]
+                                         ).view(x_local_i.size(0), -1)
+                x_local_list.append(x_local_i)
+            x_local = torch.cat(x_local_list, 1)
+            x_local = self.fc_local(x_local)
+
+        if not self.training:
+            # l2 normalization before concatenation
+            if self.learn_region:
+                x_global = x_global / x_global.norm(p=2, dim=1, keepdim=True)
+                x_local = x_local / x_local.norm(p=2, dim=1, keepdim=True)
+                return torch.cat([x_global, x_local], 1)
+            else:
+                return x_global
+
+        prelogits_global = self.classifier_global(x_global)
+        if self.learn_region:
+            prelogits_local = self.classifier_local(x_local)
+
+        if self.loss == 'softmax':
+            if self.learn_region:
+                return (prelogits_global, prelogits_local)
+            else:
+                return prelogits_global
+
+        elif self.loss == 'triplet':
+            if self.learn_region:
+                return (prelogits_global, prelogits_local), (x_global, x_local)
+            else:
+                return prelogits_global, x_global
+
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
diff --git a/pfe/player-feature-extractor/torchreid/models/inceptionresnetv2.py b/pfe/player-feature-extractor/torchreid/models/inceptionresnetv2.py
new file mode 100644
index 0000000..03e4034
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/inceptionresnetv2.py
@@ -0,0 +1,361 @@
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['inceptionresnetv2']
+
+pretrained_settings = {
+    'inceptionresnetv2': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000
+        },
+        'imagenet+background': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1001
+        }
+    }
+}
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Mixed_5b(nn.Module):
+
+    def __init__(self):
+        super(Mixed_5b, self).__init__()
+
+        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(192, 48, kernel_size=1, stride=1),
+            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(192, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(192, 64, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Block35(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super(Block35, self).__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Mixed_6a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_6a, self).__init__()
+
+        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Block17(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super(Block17, self).__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 128, kernel_size=1, stride=1),
+            BasicConv2d(
+                128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            )
+        )
+
+        self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Mixed_7a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_7a, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(288, 320, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Block8(nn.Module):
+
+    def __init__(self, scale=1.0, noReLU=False):
+        super(Block8, self).__init__()
+
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(2080, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)
+            ),
+            BasicConv2d(
+                224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
+            )
+        )
+
+        self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+
+# ----------------
+# Model Definition
+# ----------------
+class InceptionResNetV2(nn.Module):
+    """Inception-ResNet-V2.
+
+    Reference:
+        Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
+        Connections on Learning. AAAI 2017.
+
+    Public keys:
+        - ``inceptionresnetv2``: Inception-ResNet-V2.
+    """
+
+    def __init__(self, num_classes, loss='softmax', **kwargs):
+        super(InceptionResNetV2, self).__init__()
+        self.loss = loss
+
+        # Modules
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(
+            32, 64, kernel_size=3, stride=1, padding=1
+        )
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.maxpool_5a = nn.MaxPool2d(3, stride=2)
+        self.mixed_5b = Mixed_5b()
+        self.repeat = nn.Sequential(
+            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
+            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
+            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
+            Block35(scale=0.17)
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_1 = nn.Sequential(
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10)
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_2 = nn.Sequential(
+            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),
+            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),
+            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20)
+        )
+
+        self.block8 = Block8(noReLU=True)
+        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(1536, num_classes)
+
+    def load_imagenet_weights(self):
+        settings = pretrained_settings['inceptionresnetv2']['imagenet']
+        pretrain_dict = model_zoo.load_url(settings['url'])
+        model_dict = self.state_dict()
+        pretrain_dict = {
+            k: v
+            for k, v in pretrain_dict.items()
+            if k in model_dict and model_dict[k].size() == v.size()
+        }
+        model_dict.update(pretrain_dict)
+        self.load_state_dict(model_dict)
+
+    def featuremaps(self, x):
+        x = self.conv2d_1a(x)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.maxpool_5a(x)
+        x = self.mixed_5b(x)
+        x = self.repeat(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_1(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_2(x)
+        x = self.block8(x)
+        x = self.conv2d_7b(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def inceptionresnetv2(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = InceptionResNetV2(num_classes=num_classes, loss=loss, **kwargs)
+    if pretrained:
+        model.load_imagenet_weights()
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/inceptionv4.py b/pfe/player-feature-extractor/torchreid/models/inceptionv4.py
new file mode 100644
index 0000000..b14916f
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/inceptionv4.py
@@ -0,0 +1,381 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['inceptionv4']
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+
+pretrained_settings = {
+    'inceptionv4': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000
+        },
+        'imagenet+background': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1001
+        }
+    }
+}
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Mixed_3a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_3a, self).__init__()
+        self.maxpool = nn.MaxPool2d(3, stride=2)
+        self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
+
+    def forward(self, x):
+        x0 = self.maxpool(x)
+        x1 = self.conv(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Mixed_4a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_4a, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(160, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(160, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
+            BasicConv2d(64, 96, kernel_size=(3, 3), stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Mixed_5a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_5a, self).__init__()
+        self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
+        self.maxpool = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.conv(x)
+        x1 = self.maxpool(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Inception_A(nn.Module):
+
+    def __init__(self):
+        super(Inception_A, self).__init__()
+        self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(384, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(384, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(384, 96, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Reduction_A(nn.Module):
+
+    def __init__(self):
+        super(Reduction_A, self).__init__()
+        self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(384, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(224, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Inception_B(nn.Module):
+
+    def __init__(self):
+        super(Inception_B, self).__init__()
+        self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            )
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            ),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            ),
+            BasicConv2d(
+                224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            )
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(1024, 128, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Reduction_B(nn.Module):
+
+    def __init__(self):
+        super(Reduction_B, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1024, 256, kernel_size=1, stride=1),
+            BasicConv2d(
+                256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            ), BasicConv2d(320, 320, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Inception_C(nn.Module):
+
+    def __init__(self):
+        super(Inception_C, self).__init__()
+
+        self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
+
+        self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
+        self.branch1_1a = BasicConv2d(
+            384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
+        )
+        self.branch1_1b = BasicConv2d(
+            384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
+        )
+
+        self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
+        self.branch2_1 = BasicConv2d(
+            384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0)
+        )
+        self.branch2_2 = BasicConv2d(
+            448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1)
+        )
+        self.branch2_3a = BasicConv2d(
+            512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
+        )
+        self.branch2_3b = BasicConv2d(
+            512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(1536, 256, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+
+        x1_0 = self.branch1_0(x)
+        x1_1a = self.branch1_1a(x1_0)
+        x1_1b = self.branch1_1b(x1_0)
+        x1 = torch.cat((x1_1a, x1_1b), 1)
+
+        x2_0 = self.branch2_0(x)
+        x2_1 = self.branch2_1(x2_0)
+        x2_2 = self.branch2_2(x2_1)
+        x2_3a = self.branch2_3a(x2_2)
+        x2_3b = self.branch2_3b(x2_2)
+        x2 = torch.cat((x2_3a, x2_3b), 1)
+
+        x3 = self.branch3(x)
+
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class InceptionV4(nn.Module):
+    """Inception-v4.
+
+    Reference:
+        Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
+        Connections on Learning. AAAI 2017.
+
+    Public keys:
+        - ``inceptionv4``: InceptionV4.
+    """
+
+    def __init__(self, num_classes, loss, **kwargs):
+        super(InceptionV4, self).__init__()
+        self.loss = loss
+
+        self.features = nn.Sequential(
+            BasicConv2d(3, 32, kernel_size=3, stride=2),
+            BasicConv2d(32, 32, kernel_size=3, stride=1),
+            BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
+            Mixed_3a(),
+            Mixed_4a(),
+            Mixed_5a(),
+            Inception_A(),
+            Inception_A(),
+            Inception_A(),
+            Inception_A(),
+            Reduction_A(), # Mixed_6a
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Reduction_B(), # Mixed_7a
+            Inception_C(),
+            Inception_C(),
+            Inception_C()
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(1536, num_classes)
+
+    def forward(self, x):
+        f = self.features(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = InceptionV4(num_classes, loss, **kwargs)
+    if pretrained:
+        model_url = pretrained_settings['inceptionv4']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/mlfn.py b/pfe/player-feature-extractor/torchreid/models/mlfn.py
new file mode 100644
index 0000000..ac7e126
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/mlfn.py
@@ -0,0 +1,269 @@
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['mlfn']
+
+model_urls = {
+    # training epoch = 5, top1 = 51.6
+    'imagenet':
+    'https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk',
+}
+
+
+class MLFNBlock(nn.Module):
+
+    def __init__(
+        self, in_channels, out_channels, stride, fsm_channels, groups=32
+    ):
+        super(MLFNBlock, self).__init__()
+        self.groups = groups
+        mid_channels = out_channels // 2
+
+        # Factor Modules
+        self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
+        self.fm_bn1 = nn.BatchNorm2d(mid_channels)
+        self.fm_conv2 = nn.Conv2d(
+            mid_channels,
+            mid_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=self.groups
+        )
+        self.fm_bn2 = nn.BatchNorm2d(mid_channels)
+        self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
+        self.fm_bn3 = nn.BatchNorm2d(out_channels)
+
+        # Factor Selection Module
+        self.fsm = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, fsm_channels[0], 1),
+            nn.BatchNorm2d(fsm_channels[0]),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
+            nn.BatchNorm2d(fsm_channels[1]),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(fsm_channels[1], self.groups, 1),
+            nn.BatchNorm2d(self.groups),
+            nn.Sigmoid(),
+        )
+
+        self.downsample = None
+        if in_channels != out_channels or stride > 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(
+                    in_channels, out_channels, 1, stride=stride, bias=False
+                ),
+                nn.BatchNorm2d(out_channels),
+            )
+
+    def forward(self, x):
+        residual = x
+        s = self.fsm(x)
+
+        # reduce dimension
+        x = self.fm_conv1(x)
+        x = self.fm_bn1(x)
+        x = F.relu(x, inplace=True)
+
+        # group convolution
+        x = self.fm_conv2(x)
+        x = self.fm_bn2(x)
+        x = F.relu(x, inplace=True)
+
+        # factor selection
+        b, c = x.size(0), x.size(1)
+        n = c // self.groups
+        ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
+        ss = ss.view(b, n, self.groups, 1, 1)
+        ss = ss.permute(0, 2, 1, 3, 4).contiguous()
+        ss = ss.view(b, c, 1, 1)
+        x = ss * x
+
+        # recover dimension
+        x = self.fm_conv3(x)
+        x = self.fm_bn3(x)
+        x = F.relu(x, inplace=True)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        return F.relu(residual + x, inplace=True), s
+
+
+class MLFN(nn.Module):
+    """Multi-Level Factorisation Net.
+
+    Reference:
+        Chang et al. Multi-Level Factorisation Net for
+        Person Re-Identification. CVPR 2018.
+
+    Public keys:
+        - ``mlfn``: MLFN (Multi-Level Factorisation Net).
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss='softmax',
+        groups=32,
+        channels=[64, 256, 512, 1024, 2048],
+        embed_dim=1024,
+        **kwargs
+    ):
+        super(MLFN, self).__init__()
+        self.loss = loss
+        self.groups = groups
+
+        # first convolutional layer
+        self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
+        self.bn1 = nn.BatchNorm2d(channels[0])
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+
+        # main body
+        self.feature = nn.ModuleList(
+            [
+                # layer 1-3
+                MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
+                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
+                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
+                # layer 4-7
+                MLFNBlock(
+                    channels[1], channels[2], 2, [256, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[2], channels[2], 1, [256, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[2], channels[2], 1, [256, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[2], channels[2], 1, [256, 128], self.groups
+                ),
+                # layer 8-13
+                MLFNBlock(
+                    channels[2], channels[3], 2, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                # layer 14-16
+                MLFNBlock(
+                    channels[3], channels[4], 2, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[4], channels[4], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[4], channels[4], 1, [512, 128], self.groups
+                ),
+            ]
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+
+        # projection functions
+        self.fc_x = nn.Sequential(
+            nn.Conv2d(channels[4], embed_dim, 1, bias=False),
+            nn.BatchNorm2d(embed_dim),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_s = nn.Sequential(
+            nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
+            nn.BatchNorm2d(embed_dim),
+            nn.ReLU(inplace=True),
+        )
+
+        self.classifier = nn.Linear(embed_dim, num_classes)
+
+        self.init_params()
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = F.relu(x, inplace=True)
+        x = self.maxpool(x)
+
+        s_hat = []
+        for block in self.feature:
+            x, s = block(x)
+            s_hat.append(s)
+        s_hat = torch.cat(s_hat, 1)
+
+        x = self.global_avgpool(x)
+        x = self.fc_x(x)
+        s_hat = self.fc_s(s_hat)
+
+        v = (x+s_hat) * 0.5
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def mlfn(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = MLFN(num_classes, loss, **kwargs)
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['imagenet'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['imagenet'])
+        )
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/mobilenetv2.py b/pfe/player-feature-extractor/torchreid/models/mobilenetv2.py
new file mode 100644
index 0000000..c451ef8
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/mobilenetv2.py
@@ -0,0 +1,274 @@
+from __future__ import division, absolute_import
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['mobilenetv2_x1_0', 'mobilenetv2_x1_4']
+
+model_urls = {
+    # 1.0: top-1 71.3
+    'mobilenetv2_x1_0':
+    'https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NUOWvvGPHhaNr-fASF6c',
+    # 1.4: top-1 73.9
+    'mobilenetv2_x1_4':
+    'https://mega.nz/#!RGhgEIwS!xN2s2ZdyqI6vQ3EwgmRXLEW3khr9tpXg96G9SUJugGk',
+}
+
+
+class ConvBlock(nn.Module):
+    """Basic convolutional block.
+    
+    convolution (bias discarded) + batch normalization + relu6.
+
+    Args:
+        in_c (int): number of input channels.
+        out_c (int): number of output channels.
+        k (int or tuple): kernel size.
+        s (int or tuple): stride.
+        p (int or tuple): padding.
+        g (int): number of blocked connections from input channels
+            to output channels (default: 1).
+    """
+
+    def __init__(self, in_c, out_c, k, s=1, p=0, g=1):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Conv2d(
+            in_c, out_c, k, stride=s, padding=p, bias=False, groups=g
+        )
+        self.bn = nn.BatchNorm2d(out_c)
+
+    def forward(self, x):
+        return F.relu6(self.bn(self.conv(x)))
+
+
+class Bottleneck(nn.Module):
+
+    def __init__(self, in_channels, out_channels, expansion_factor, stride=1):
+        super(Bottleneck, self).__init__()
+        mid_channels = in_channels * expansion_factor
+        self.use_residual = stride == 1 and in_channels == out_channels
+        self.conv1 = ConvBlock(in_channels, mid_channels, 1)
+        self.dwconv2 = ConvBlock(
+            mid_channels, mid_channels, 3, stride, 1, g=mid_channels
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(mid_channels, out_channels, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+        )
+
+    def forward(self, x):
+        m = self.conv1(x)
+        m = self.dwconv2(m)
+        m = self.conv3(m)
+        if self.use_residual:
+            return x + m
+        else:
+            return m
+
+
+class MobileNetV2(nn.Module):
+    """MobileNetV2.
+
+    Reference:
+        Sandler et al. MobileNetV2: Inverted Residuals and
+        Linear Bottlenecks. CVPR 2018.
+
+    Public keys:
+        - ``mobilenetv2_x1_0``: MobileNetV2 x1.0.
+        - ``mobilenetv2_x1_4``: MobileNetV2 x1.4.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        width_mult=1,
+        loss='softmax',
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        super(MobileNetV2, self).__init__()
+        self.loss = loss
+        self.in_channels = int(32 * width_mult)
+        self.feature_dim = int(1280 * width_mult) if width_mult > 1 else 1280
+
+        # construct layers
+        self.conv1 = ConvBlock(3, self.in_channels, 3, s=2, p=1)
+        self.conv2 = self._make_layer(
+            Bottleneck, 1, int(16 * width_mult), 1, 1
+        )
+        self.conv3 = self._make_layer(
+            Bottleneck, 6, int(24 * width_mult), 2, 2
+        )
+        self.conv4 = self._make_layer(
+            Bottleneck, 6, int(32 * width_mult), 3, 2
+        )
+        self.conv5 = self._make_layer(
+            Bottleneck, 6, int(64 * width_mult), 4, 2
+        )
+        self.conv6 = self._make_layer(
+            Bottleneck, 6, int(96 * width_mult), 3, 1
+        )
+        self.conv7 = self._make_layer(
+            Bottleneck, 6, int(160 * width_mult), 3, 2
+        )
+        self.conv8 = self._make_layer(
+            Bottleneck, 6, int(320 * width_mult), 1, 1
+        )
+        self.conv9 = ConvBlock(self.in_channels, self.feature_dim, 1)
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = self._construct_fc_layer(
+            fc_dims, self.feature_dim, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, block, t, c, n, s):
+        # t: expansion factor
+        # c: output channels
+        # n: number of blocks
+        # s: stride for first layer
+        layers = []
+        layers.append(block(self.in_channels, c, t, s))
+        self.in_channels = c
+        for i in range(1, n):
+            layers.append(block(self.in_channels, c, t))
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer.
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        x = self.conv6(x)
+        x = self.conv7(x)
+        x = self.conv8(x)
+        x = self.conv9(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def mobilenetv2_x1_0(num_classes, loss, pretrained=True, **kwargs):
+    model = MobileNetV2(
+        num_classes,
+        loss=loss,
+        width_mult=1,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['mobilenetv2_x1_0'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['mobilenetv2_x1_0'])
+        )
+    return model
+
+
+def mobilenetv2_x1_4(num_classes, loss, pretrained=True, **kwargs):
+    model = MobileNetV2(
+        num_classes,
+        loss=loss,
+        width_mult=1.4,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['mobilenetv2_x1_4'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['mobilenetv2_x1_4'])
+        )
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/mudeep.py b/pfe/player-feature-extractor/torchreid/models/mudeep.py
new file mode 100644
index 0000000..f6a4043
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/mudeep.py
@@ -0,0 +1,203 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['MuDeep']
+
+
+class ConvBlock(nn.Module):
+    """Basic convolutional block.
+    
+    convolution + batch normalization + relu.
+
+    Args:
+        in_c (int): number of input channels.
+        out_c (int): number of output channels.
+        k (int or tuple): kernel size.
+        s (int or tuple): stride.
+        p (int or tuple): padding.
+    """
+
+    def __init__(self, in_c, out_c, k, s, p):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p)
+        self.bn = nn.BatchNorm2d(out_c)
+
+    def forward(self, x):
+        return F.relu(self.bn(self.conv(x)))
+
+
+class ConvLayers(nn.Module):
+    """Preprocessing layers."""
+
+    def __init__(self):
+        super(ConvLayers, self).__init__()
+        self.conv1 = ConvBlock(3, 48, k=3, s=1, p=1)
+        self.conv2 = ConvBlock(48, 96, k=3, s=1, p=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.maxpool(x)
+        return x
+
+
+class MultiScaleA(nn.Module):
+    """Multi-scale stream layer A (Sec.3.1)"""
+
+    def __init__(self):
+        super(MultiScaleA, self).__init__()
+        self.stream1 = nn.Sequential(
+            ConvBlock(96, 96, k=1, s=1, p=0),
+            ConvBlock(96, 24, k=3, s=1, p=1),
+        )
+        self.stream2 = nn.Sequential(
+            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
+            ConvBlock(96, 24, k=1, s=1, p=0),
+        )
+        self.stream3 = ConvBlock(96, 24, k=1, s=1, p=0)
+        self.stream4 = nn.Sequential(
+            ConvBlock(96, 16, k=1, s=1, p=0),
+            ConvBlock(16, 24, k=3, s=1, p=1),
+            ConvBlock(24, 24, k=3, s=1, p=1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        s4 = self.stream4(x)
+        y = torch.cat([s1, s2, s3, s4], dim=1)
+        return y
+
+
+class Reduction(nn.Module):
+    """Reduction layer (Sec.3.1)"""
+
+    def __init__(self):
+        super(Reduction, self).__init__()
+        self.stream1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.stream2 = ConvBlock(96, 96, k=3, s=2, p=1)
+        self.stream3 = nn.Sequential(
+            ConvBlock(96, 48, k=1, s=1, p=0),
+            ConvBlock(48, 56, k=3, s=1, p=1),
+            ConvBlock(56, 64, k=3, s=2, p=1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        y = torch.cat([s1, s2, s3], dim=1)
+        return y
+
+
+class MultiScaleB(nn.Module):
+    """Multi-scale stream layer B (Sec.3.1)"""
+
+    def __init__(self):
+        super(MultiScaleB, self).__init__()
+        self.stream1 = nn.Sequential(
+            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
+            ConvBlock(256, 256, k=1, s=1, p=0),
+        )
+        self.stream2 = nn.Sequential(
+            ConvBlock(256, 64, k=1, s=1, p=0),
+            ConvBlock(64, 128, k=(1, 3), s=1, p=(0, 1)),
+            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
+        )
+        self.stream3 = ConvBlock(256, 256, k=1, s=1, p=0)
+        self.stream4 = nn.Sequential(
+            ConvBlock(256, 64, k=1, s=1, p=0),
+            ConvBlock(64, 64, k=(1, 3), s=1, p=(0, 1)),
+            ConvBlock(64, 128, k=(3, 1), s=1, p=(1, 0)),
+            ConvBlock(128, 128, k=(1, 3), s=1, p=(0, 1)),
+            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        s4 = self.stream4(x)
+        return s1, s2, s3, s4
+
+
+class Fusion(nn.Module):
+    """Saliency-based learning fusion layer (Sec.3.2)"""
+
+    def __init__(self):
+        super(Fusion, self).__init__()
+        self.a1 = nn.Parameter(torch.rand(1, 256, 1, 1))
+        self.a2 = nn.Parameter(torch.rand(1, 256, 1, 1))
+        self.a3 = nn.Parameter(torch.rand(1, 256, 1, 1))
+        self.a4 = nn.Parameter(torch.rand(1, 256, 1, 1))
+
+        # We add an average pooling layer to reduce the spatial dimension
+        # of feature maps, which differs from the original paper.
+        self.avgpool = nn.AvgPool2d(kernel_size=4, stride=4, padding=0)
+
+    def forward(self, x1, x2, x3, x4):
+        s1 = self.a1.expand_as(x1) * x1
+        s2 = self.a2.expand_as(x2) * x2
+        s3 = self.a3.expand_as(x3) * x3
+        s4 = self.a4.expand_as(x4) * x4
+        y = self.avgpool(s1 + s2 + s3 + s4)
+        return y
+
+
+class MuDeep(nn.Module):
+    """Multiscale deep neural network.
+
+    Reference:
+        Qian et al. Multi-scale Deep Learning Architectures
+        for Person Re-identification. ICCV 2017.
+
+    Public keys:
+        - ``mudeep``: Multiscale deep neural network.
+    """
+
+    def __init__(self, num_classes, loss='softmax', **kwargs):
+        super(MuDeep, self).__init__()
+        self.loss = loss
+
+        self.block1 = ConvLayers()
+        self.block2 = MultiScaleA()
+        self.block3 = Reduction()
+        self.block4 = MultiScaleB()
+        self.block5 = Fusion()
+
+        # Due to this fully connected layer, input image has to be fixed
+        # in shape, i.e. (3, 256, 128), such that the last convolutional feature
+        # maps are of shape (256, 16, 8). If input shape is changed,
+        # the input dimension of this layer has to be changed accordingly.
+        self.fc = nn.Sequential(
+            nn.Linear(256 * 16 * 8, 4096),
+            nn.BatchNorm1d(4096),
+            nn.ReLU(),
+        )
+        self.classifier = nn.Linear(4096, num_classes)
+        self.feat_dim = 4096
+
+    def featuremaps(self, x):
+        x = self.block1(x)
+        x = self.block2(x)
+        x = self.block3(x)
+        x = self.block4(x)
+        x = self.block5(*x)
+        return x
+
+    def forward(self, x):
+        x = self.featuremaps(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        y = self.classifier(x)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, x
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
diff --git a/pfe/player-feature-extractor/torchreid/models/nasnet.py b/pfe/player-feature-extractor/torchreid/models/nasnet.py
new file mode 100644
index 0000000..b1f31de
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/nasnet.py
@@ -0,0 +1,1131 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['nasnetamobile']
+"""
+NASNet Mobile
+Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!
+
+
+------------------------------------------------------------------------------------
+      Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M)
+------------------------------------------------------------------------------------
+|   NASNet-A (4 @ 1056)  |   74.08%  |   91.74%  |       564 M    |     5.3        |
+------------------------------------------------------------------------------------
+# References:
+ - [Learning Transferable Architectures for Scalable Image Recognition]
+    (https://arxiv.org/abs/1707.07012)
+"""
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+
+pretrained_settings = {
+    'nasnetamobile': {
+        'imagenet': {
+            # 'url': 'https://github.com/veronikayurchuk/pretrained-models.pytorch/releases/download/v1.0/nasnetmobile-7e03cead.pth.tar',
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/nasnetamobile-7e03cead.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224], # resize 256
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000
+        },
+        # 'imagenet+background': {
+        #     # 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
+        #     'input_space': 'RGB',
+        #     'input_size': [3, 224, 224], # resize 256
+        #     'input_range': [0, 1],
+        #     'mean': [0.5, 0.5, 0.5],
+        #     'std': [0.5, 0.5, 0.5],
+        #     'num_classes': 1001
+        # }
+    }
+}
+
+
+class MaxPoolPad(nn.Module):
+
+    def __init__(self):
+        super(MaxPoolPad, self).__init__()
+        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
+        self.pool = nn.MaxPool2d(3, stride=2, padding=1)
+
+    def forward(self, x):
+        x = self.pad(x)
+        x = self.pool(x)
+        x = x[:, :, 1:, 1:].contiguous()
+        return x
+
+
+class AvgPoolPad(nn.Module):
+
+    def __init__(self, stride=2, padding=1):
+        super(AvgPoolPad, self).__init__()
+        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
+        self.pool = nn.AvgPool2d(
+            3, stride=stride, padding=padding, count_include_pad=False
+        )
+
+    def forward(self, x):
+        x = self.pad(x)
+        x = self.pool(x)
+        x = x[:, :, 1:, 1:].contiguous()
+        return x
+
+
+class SeparableConv2d(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        dw_kernel,
+        dw_stride,
+        dw_padding,
+        bias=False
+    ):
+        super(SeparableConv2d, self).__init__()
+        self.depthwise_conv2d = nn.Conv2d(
+            in_channels,
+            in_channels,
+            dw_kernel,
+            stride=dw_stride,
+            padding=dw_padding,
+            bias=bias,
+            groups=in_channels
+        )
+        self.pointwise_conv2d = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, bias=bias
+        )
+
+    def forward(self, x):
+        x = self.depthwise_conv2d(x)
+        x = self.pointwise_conv2d(x)
+        return x
+
+
+class BranchSeparables(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        name=None,
+        bias=False
+    ):
+        super(BranchSeparables, self).__init__()
+        self.relu = nn.ReLU()
+        self.separable_1 = SeparableConv2d(
+            in_channels, in_channels, kernel_size, stride, padding, bias=bias
+        )
+        self.bn_sep_1 = nn.BatchNorm2d(
+            in_channels, eps=0.001, momentum=0.1, affine=True
+        )
+        self.relu1 = nn.ReLU()
+        self.separable_2 = SeparableConv2d(
+            in_channels, out_channels, kernel_size, 1, padding, bias=bias
+        )
+        self.bn_sep_2 = nn.BatchNorm2d(
+            out_channels, eps=0.001, momentum=0.1, affine=True
+        )
+        self.name = name
+
+    def forward(self, x):
+        x = self.relu(x)
+        if self.name == 'specific':
+            x = nn.ZeroPad2d((1, 0, 1, 0))(x)
+        x = self.separable_1(x)
+        if self.name == 'specific':
+            x = x[:, :, 1:, 1:].contiguous()
+
+        x = self.bn_sep_1(x)
+        x = self.relu1(x)
+        x = self.separable_2(x)
+        x = self.bn_sep_2(x)
+        return x
+
+
+class BranchSeparablesStem(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        bias=False
+    ):
+        super(BranchSeparablesStem, self).__init__()
+        self.relu = nn.ReLU()
+        self.separable_1 = SeparableConv2d(
+            in_channels, out_channels, kernel_size, stride, padding, bias=bias
+        )
+        self.bn_sep_1 = nn.BatchNorm2d(
+            out_channels, eps=0.001, momentum=0.1, affine=True
+        )
+        self.relu1 = nn.ReLU()
+        self.separable_2 = SeparableConv2d(
+            out_channels, out_channels, kernel_size, 1, padding, bias=bias
+        )
+        self.bn_sep_2 = nn.BatchNorm2d(
+            out_channels, eps=0.001, momentum=0.1, affine=True
+        )
+
+    def forward(self, x):
+        x = self.relu(x)
+        x = self.separable_1(x)
+        x = self.bn_sep_1(x)
+        x = self.relu1(x)
+        x = self.separable_2(x)
+        x = self.bn_sep_2(x)
+        return x
+
+
+class BranchSeparablesReduction(BranchSeparables):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        z_padding=1,
+        bias=False
+    ):
+        BranchSeparables.__init__(
+            self, in_channels, out_channels, kernel_size, stride, padding, bias
+        )
+        self.padding = nn.ZeroPad2d((z_padding, 0, z_padding, 0))
+
+    def forward(self, x):
+        x = self.relu(x)
+        x = self.padding(x)
+        x = self.separable_1(x)
+        x = x[:, :, 1:, 1:].contiguous()
+        x = self.bn_sep_1(x)
+        x = self.relu1(x)
+        x = self.separable_2(x)
+        x = self.bn_sep_2(x)
+        return x
+
+
+class CellStem0(nn.Module):
+
+    def __init__(self, stem_filters, num_filters=42):
+        super(CellStem0, self).__init__()
+        self.num_filters = num_filters
+        self.stem_filters = stem_filters
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                self.stem_filters, self.num_filters, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                self.num_filters, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            self.num_filters, self.num_filters, 5, 2, 2
+        )
+        self.comb_iter_0_right = BranchSeparablesStem(
+            self.stem_filters, self.num_filters, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_1_right = BranchSeparablesStem(
+            self.stem_filters, self.num_filters, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_2_left = nn.AvgPool2d(
+            3, stride=2, padding=1, count_include_pad=False
+        )
+        self.comb_iter_2_right = BranchSeparablesStem(
+            self.stem_filters, self.num_filters, 5, 2, 2, bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            self.num_filters, self.num_filters, 3, 1, 1, bias=False
+        )
+        self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
+
+    def forward(self, x):
+        x1 = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x1)
+        x_comb_iter_0_right = self.comb_iter_0_right(x)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x1)
+        x_comb_iter_1_right = self.comb_iter_1_right(x)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x1)
+        x_comb_iter_2_right = self.comb_iter_2_right(x)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x1)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class CellStem1(nn.Module):
+
+    def __init__(self, stem_filters, num_filters):
+        super(CellStem1, self).__init__()
+        self.num_filters = num_filters
+        self.stem_filters = stem_filters
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                2 * self.num_filters,
+                self.num_filters,
+                1,
+                stride=1,
+                bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                self.num_filters, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.relu = nn.ReLU()
+        self.path_1 = nn.Sequential()
+        self.path_1.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_1.add_module(
+            'conv',
+            nn.Conv2d(
+                self.stem_filters,
+                self.num_filters // 2,
+                1,
+                stride=1,
+                bias=False
+            )
+        )
+        self.path_2 = nn.ModuleList()
+        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
+        self.path_2.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_2.add_module(
+            'conv',
+            nn.Conv2d(
+                self.stem_filters,
+                self.num_filters // 2,
+                1,
+                stride=1,
+                bias=False
+            )
+        )
+
+        self.final_path_bn = nn.BatchNorm2d(
+            self.num_filters, eps=0.001, momentum=0.1, affine=True
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_1_left = MaxPoolPad()
+        self.comb_iter_1_right = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
+        self.comb_iter_2_left = AvgPoolPad()
+        self.comb_iter_2_right = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            3,
+            1,
+            1,
+            name='specific',
+            bias=False
+        )
+        # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_4_right = MaxPoolPad()
+
+    def forward(self, x_conv0, x_stem_0):
+        x_left = self.conv_1x1(x_stem_0)
+
+        x_relu = self.relu(x_conv0)
+        # path 1
+        x_path1 = self.path_1(x_relu)
+        # path 2
+        x_path2 = self.path_2.pad(x_relu)
+        x_path2 = x_path2[:, :, 1:, 1:]
+        x_path2 = self.path_2.avgpool(x_path2)
+        x_path2 = self.path_2.conv(x_path2)
+        # final path
+        x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_left)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_right)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_right)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_left)
+        x_comb_iter_2_right = self.comb_iter_2_right(x_right)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x_left)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class FirstCell(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(FirstCell, self).__init__()
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.relu = nn.ReLU()
+        self.path_1 = nn.Sequential()
+        self.path_1.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.path_2 = nn.ModuleList()
+        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
+        self.path_2.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_2.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+
+        self.final_path_bn = nn.BatchNorm2d(
+            out_channels_left * 2, eps=0.001, momentum=0.1, affine=True
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            out_channels_right, out_channels_right, 5, 1, 2, bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_1_left = BranchSeparables(
+            out_channels_right, out_channels_right, 5, 1, 2, bias=False
+        )
+        self.comb_iter_1_right = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_2_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_3_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+    def forward(self, x, x_prev):
+        x_relu = self.relu(x_prev)
+        # path 1
+        x_path1 = self.path_1(x_relu)
+        # path 2
+        x_path2 = self.path_2.pad(x_relu)
+        x_path2 = x_path2[:, :, 1:, 1:]
+        x_path2 = self.path_2.avgpool(x_path2)
+        x_path2 = self.path_2.conv(x_path2)
+        # final path
+        x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
+
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2 = x_comb_iter_2_left + x_left
+
+        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
+        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
+        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_right
+
+        x_out = torch.cat(
+            [
+                x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
+                x_comb_iter_3, x_comb_iter_4
+            ], 1
+        )
+        return x_out
+
+
+class NormalCell(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(NormalCell, self).__init__()
+        self.conv_prev_1x1 = nn.Sequential()
+        self.conv_prev_1x1.add_module('relu', nn.ReLU())
+        self.conv_prev_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.conv_prev_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_left, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            out_channels_right, out_channels_right, 5, 1, 2, bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            out_channels_left, out_channels_left, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_1_left = BranchSeparables(
+            out_channels_left, out_channels_left, 5, 1, 2, bias=False
+        )
+        self.comb_iter_1_right = BranchSeparables(
+            out_channels_left, out_channels_left, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_2_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_3_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+    def forward(self, x, x_prev):
+        x_left = self.conv_prev_1x1(x_prev)
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2 = x_comb_iter_2_left + x_left
+
+        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
+        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
+        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_right
+
+        x_out = torch.cat(
+            [
+                x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
+                x_comb_iter_3, x_comb_iter_4
+            ], 1
+        )
+        return x_out
+
+
+class ReductionCell0(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(ReductionCell0, self).__init__()
+        self.conv_prev_1x1 = nn.Sequential()
+        self.conv_prev_1x1.add_module('relu', nn.ReLU())
+        self.conv_prev_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.conv_prev_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_left, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 5, 2, 2, bias=False
+        )
+        self.comb_iter_0_right = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_1_left = MaxPoolPad()
+        self.comb_iter_1_right = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_2_left = AvgPoolPad()
+        self.comb_iter_2_right = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 5, 2, 2, bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+        self.comb_iter_4_right = MaxPoolPad()
+
+    def forward(self, x, x_prev):
+        x_left = self.conv_prev_1x1(x_prev)
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class ReductionCell1(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(ReductionCell1, self).__init__()
+        self.conv_prev_1x1 = nn.Sequential()
+        self.conv_prev_1x1.add_module('relu', nn.ReLU())
+        self.conv_prev_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.conv_prev_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_left, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_1_left = MaxPoolPad()
+        self.comb_iter_1_right = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
+        self.comb_iter_2_left = AvgPoolPad()
+        self.comb_iter_2_right = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            3,
+            1,
+            1,
+            name='specific',
+            bias=False
+        )
+        # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_4_right = MaxPoolPad()
+
+    def forward(self, x, x_prev):
+        x_left = self.conv_prev_1x1(x_prev)
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class NASNetAMobile(nn.Module):
+    """Neural Architecture Search (NAS).
+
+    Reference:
+        Zoph et al. Learning Transferable Architectures
+        for Scalable Image Recognition. CVPR 2018.
+
+    Public keys:
+        - ``nasnetamobile``: NASNet-A Mobile.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        stem_filters=32,
+        penultimate_filters=1056,
+        filters_multiplier=2,
+        **kwargs
+    ):
+        super(NASNetAMobile, self).__init__()
+        self.stem_filters = stem_filters
+        self.penultimate_filters = penultimate_filters
+        self.filters_multiplier = filters_multiplier
+        self.loss = loss
+
+        filters = self.penultimate_filters // 24
+        # 24 is default value for the architecture
+
+        self.conv0 = nn.Sequential()
+        self.conv0.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels=3,
+                out_channels=self.stem_filters,
+                kernel_size=3,
+                padding=0,
+                stride=2,
+                bias=False
+            )
+        )
+        self.conv0.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                self.stem_filters, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.cell_stem_0 = CellStem0(
+            self.stem_filters, num_filters=filters // (filters_multiplier**2)
+        )
+        self.cell_stem_1 = CellStem1(
+            self.stem_filters, num_filters=filters // filters_multiplier
+        )
+
+        self.cell_0 = FirstCell(
+            in_channels_left=filters,
+            out_channels_left=filters // 2, # 1, 0.5
+            in_channels_right=2 * filters,
+            out_channels_right=filters
+        ) # 2, 1
+        self.cell_1 = NormalCell(
+            in_channels_left=2 * filters,
+            out_channels_left=filters, # 2, 1
+            in_channels_right=6 * filters,
+            out_channels_right=filters
+        ) # 6, 1
+        self.cell_2 = NormalCell(
+            in_channels_left=6 * filters,
+            out_channels_left=filters, # 6, 1
+            in_channels_right=6 * filters,
+            out_channels_right=filters
+        ) # 6, 1
+        self.cell_3 = NormalCell(
+            in_channels_left=6 * filters,
+            out_channels_left=filters, # 6, 1
+            in_channels_right=6 * filters,
+            out_channels_right=filters
+        ) # 6, 1
+
+        self.reduction_cell_0 = ReductionCell0(
+            in_channels_left=6 * filters,
+            out_channels_left=2 * filters, # 6, 2
+            in_channels_right=6 * filters,
+            out_channels_right=2 * filters
+        ) # 6, 2
+
+        self.cell_6 = FirstCell(
+            in_channels_left=6 * filters,
+            out_channels_left=filters, # 6, 1
+            in_channels_right=8 * filters,
+            out_channels_right=2 * filters
+        ) # 8, 2
+        self.cell_7 = NormalCell(
+            in_channels_left=8 * filters,
+            out_channels_left=2 * filters, # 8, 2
+            in_channels_right=12 * filters,
+            out_channels_right=2 * filters
+        ) # 12, 2
+        self.cell_8 = NormalCell(
+            in_channels_left=12 * filters,
+            out_channels_left=2 * filters, # 12, 2
+            in_channels_right=12 * filters,
+            out_channels_right=2 * filters
+        ) # 12, 2
+        self.cell_9 = NormalCell(
+            in_channels_left=12 * filters,
+            out_channels_left=2 * filters, # 12, 2
+            in_channels_right=12 * filters,
+            out_channels_right=2 * filters
+        ) # 12, 2
+
+        self.reduction_cell_1 = ReductionCell1(
+            in_channels_left=12 * filters,
+            out_channels_left=4 * filters, # 12, 4
+            in_channels_right=12 * filters,
+            out_channels_right=4 * filters
+        ) # 12, 4
+
+        self.cell_12 = FirstCell(
+            in_channels_left=12 * filters,
+            out_channels_left=2 * filters, # 12, 2
+            in_channels_right=16 * filters,
+            out_channels_right=4 * filters
+        ) # 16, 4
+        self.cell_13 = NormalCell(
+            in_channels_left=16 * filters,
+            out_channels_left=4 * filters, # 16, 4
+            in_channels_right=24 * filters,
+            out_channels_right=4 * filters
+        ) # 24, 4
+        self.cell_14 = NormalCell(
+            in_channels_left=24 * filters,
+            out_channels_left=4 * filters, # 24, 4
+            in_channels_right=24 * filters,
+            out_channels_right=4 * filters
+        ) # 24, 4
+        self.cell_15 = NormalCell(
+            in_channels_left=24 * filters,
+            out_channels_left=4 * filters, # 24, 4
+            in_channels_right=24 * filters,
+            out_channels_right=4 * filters
+        ) # 24, 4
+
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout()
+        self.classifier = nn.Linear(24 * filters, num_classes)
+
+        self._init_params()
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def features(self, input):
+        x_conv0 = self.conv0(input)
+        x_stem_0 = self.cell_stem_0(x_conv0)
+        x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
+
+        x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
+        x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
+        x_cell_2 = self.cell_2(x_cell_1, x_cell_0)
+        x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
+
+        x_reduction_cell_0 = self.reduction_cell_0(x_cell_3, x_cell_2)
+
+        x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_3)
+        x_cell_7 = self.cell_7(x_cell_6, x_reduction_cell_0)
+        x_cell_8 = self.cell_8(x_cell_7, x_cell_6)
+        x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
+
+        x_reduction_cell_1 = self.reduction_cell_1(x_cell_9, x_cell_8)
+
+        x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_9)
+        x_cell_13 = self.cell_13(x_cell_12, x_reduction_cell_1)
+        x_cell_14 = self.cell_14(x_cell_13, x_cell_12)
+        x_cell_15 = self.cell_15(x_cell_14, x_cell_13)
+
+        x_cell_15 = self.relu(x_cell_15)
+        x_cell_15 = F.avg_pool2d(
+            x_cell_15,
+            x_cell_15.size()[2:]
+        ) # global average pool
+        x_cell_15 = x_cell_15.view(x_cell_15.size(0), -1)
+        x_cell_15 = self.dropout(x_cell_15)
+
+        return x_cell_15
+
+    def forward(self, input):
+        v = self.features(input)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def nasnetamobile(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = NASNetAMobile(num_classes, loss, **kwargs)
+    if pretrained:
+        model_url = pretrained_settings['nasnetamobile']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/osnet.py b/pfe/player-feature-extractor/torchreid/models/osnet.py
new file mode 100644
index 0000000..e6273ff
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/osnet.py
@@ -0,0 +1,597 @@
+from __future__ import division, absolute_import
+import warnings
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = [
+    'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
+]
+
+pretrained_urls = {
+    'osnet_x1_0':
+    'https://drive.google.com/uc?id=1LaG1EJpHrxdAxKnSCJ_i0u-nbxSAeiFY',
+    'osnet_x0_75':
+    'https://drive.google.com/uc?id=1uwA9fElHOk3ZogwbeY5GkLI6QPTX70Hq',
+    'osnet_x0_5':
+    'https://drive.google.com/uc?id=16DGLbZukvVYgINws8u8deSaOqjybZ83i',
+    'osnet_x0_25':
+    'https://drive.google.com/uc?id=1rb8UN5ZzPKRc_xvtHlyDh-cSz88YX9hs',
+    'osnet_ibn_x1_0':
+    'https://drive.google.com/uc?id=1sr90V6irlYYDd4_4ISU2iruoRG8J__6l'
+}
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        IN=False,
+        bottleneck_reduction=4,
+        **kwargs
+    ):
+        super(OSBlock, self).__init__()
+        mid_channels = out_channels // bottleneck_reduction
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2a = LightConv3x3(mid_channels, mid_channels)
+        self.conv2b = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2c = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2d = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = None
+        if IN:
+            self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2a = self.conv2a(x1)
+        x2b = self.conv2b(x1)
+        x2c = self.conv2c(x1)
+        x2d = self.conv2d(x1)
+        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        if self.IN is not None:
+            out = self.IN(out)
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        IN=False,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=IN)
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0],
+            layers[0],
+            channels[0],
+            channels[1],
+            reduce_spatial_size=True,
+            IN=IN
+        )
+        self.conv3 = self._make_layer(
+            blocks[1],
+            layers[1],
+            channels[1],
+            channels[2],
+            reduce_spatial_size=True
+        )
+        self.conv4 = self._make_layer(
+            blocks[2],
+            layers[2],
+            channels[2],
+            channels[3],
+            reduce_spatial_size=False
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(
+        self,
+        block,
+        layer,
+        in_channels,
+        out_channels,
+        reduce_spatial_size,
+        IN=False
+    ):
+        layers = []
+
+        layers.append(block(in_channels, out_channels, IN=IN))
+        for i in range(1, layer):
+            layers.append(block(out_channels, out_channels, IN=IN))
+
+        if reduce_spatial_size:
+            layers.append(
+                nn.Sequential(
+                    Conv1x1(out_channels, out_channels),
+                    nn.AvgPool2d(2, stride=2)
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x, return_featuremaps=False):
+        x = self.featuremaps(x)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, key=''):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    import os
+    import errno
+    import gdown
+    from collections import OrderedDict
+
+    def _get_torch_home():
+        ENV_TORCH_HOME = 'TORCH_HOME'
+        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
+        DEFAULT_CACHE_DIR = '~/.cache'
+        torch_home = os.path.expanduser(
+            os.getenv(
+                ENV_TORCH_HOME,
+                os.path.join(
+                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
+                )
+            )
+        )
+        return torch_home
+
+    torch_home = _get_torch_home()
+    model_dir = os.path.join(torch_home, 'checkpoints')
+    try:
+        os.makedirs(model_dir)
+    except OSError as e:
+        if e.errno == errno.EEXIST:
+            # Directory already exists, ignore.
+            pass
+        else:
+            # Unexpected OSError, re-raise.
+            raise
+    filename = key + '_imagenet.pth'
+    cached_file = os.path.join(model_dir, filename)
+
+    if not os.path.exists(cached_file):
+        gdown.download(pretrained_urls[key], cached_file, quiet=False)
+
+    state_dict = torch.load(cached_file)
+    model_dict = model.state_dict()
+    new_state_dict = OrderedDict()
+    matched_layers, discarded_layers = [], []
+
+    for k, v in state_dict.items():
+        if k.startswith('module.'):
+            k = k[7:] # discard module.
+
+        if k in model_dict and model_dict[k].size() == v.size():
+            new_state_dict[k] = v
+            matched_layers.append(k)
+        else:
+            discarded_layers.append(k)
+
+    model_dict.update(new_state_dict)
+    model.load_state_dict(model_dict)
+
+    if len(matched_layers) == 0:
+        warnings.warn(
+            'The pretrained weights from "{}" cannot be loaded, '
+            'please check the key names manually '
+            '(** ignored and continue **)'.format(cached_file)
+        )
+    else:
+        print(
+            'Successfully loaded imagenet pretrained weights from "{}"'.
+            format(cached_file)
+        )
+        if len(discarded_layers) > 0:
+            print(
+                '** The following layers are discarded '
+                'due to unmatched keys or layer size: {}'.
+                format(discarded_layers)
+            )
+
+
+##########
+# Instantiation
+##########
+def osnet_x1_0(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # standard size (width x1.0)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x1_0')
+    return model
+
+
+def osnet_x0_75(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # medium size (width x0.75)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[48, 192, 288, 384],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x0_75')
+    return model
+
+
+def osnet_x0_5(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # tiny size (width x0.5)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[32, 128, 192, 256],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x0_5')
+    return model
+
+
+def osnet_x0_25(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # very tiny size (width x0.25)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[16, 64, 96, 128],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x0_25')
+    return model
+
+
+def osnet_ibn_x1_0(
+    num_classes=1000, pretrained=True, loss='softmax', **kwargs
+):
+    # standard size (width x1.0) + IBN layer
+    # Ref: Pan et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. ECCV, 2018.
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        IN=True,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_ibn_x1_0')
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/osnet_ain.py b/pfe/player-feature-extractor/torchreid/models/osnet_ain.py
new file mode 100644
index 0000000..b40c299
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/osnet_ain.py
@@ -0,0 +1,541 @@
+from __future__ import division, absolute_import
+import warnings
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['osnet_ain_x1_0']
+
+pretrained_urls = {
+    'osnet_ain_x1_0':
+    'https://drive.google.com/uc?id=1-CaioD9NaqbHK_kzSMW8VE4_3KcsRjEo'
+}
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1, bn=True):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = None
+        if bn:
+            self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConvStream(nn.Module):
+    """Lightweight convolution stream."""
+
+    def __init__(self, in_channels, out_channels, depth):
+        super(LightConvStream, self).__init__()
+        assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
+            depth
+        )
+        layers = []
+        layers += [LightConv3x3(in_channels, out_channels)]
+        for i in range(depth - 1):
+            layers += [LightConv3x3(out_channels, out_channels)]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU()
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlock, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINin(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINin, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        conv1_IN=False,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+        self.feature_dim = feature_dim
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(
+            3, channels[0], 7, stride=2, padding=3, IN=conv1_IN
+        )
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0], layers[0], channels[0], channels[1]
+        )
+        self.pool2 = nn.Sequential(
+            Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv3 = self._make_layer(
+            blocks[1], layers[1], channels[1], channels[2]
+        )
+        self.pool3 = nn.Sequential(
+            Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv4 = self._make_layer(
+            blocks[2], layers[2], channels[2], channels[3]
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            self.feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, blocks, layer, in_channels, out_channels):
+        layers = []
+        layers += [blocks[0](in_channels, out_channels)]
+        for i in range(1, len(blocks)):
+            layers += [blocks[i](out_channels, out_channels)]
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU())
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.InstanceNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.pool2(x)
+        x = self.conv3(x)
+        x = self.pool3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x, return_featuremaps=False):
+        x = self.featuremaps(x)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, key=''):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    import os
+    import errno
+    import gdown
+    from collections import OrderedDict
+
+    def _get_torch_home():
+        ENV_TORCH_HOME = 'TORCH_HOME'
+        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
+        DEFAULT_CACHE_DIR = '~/.cache'
+        torch_home = os.path.expanduser(
+            os.getenv(
+                ENV_TORCH_HOME,
+                os.path.join(
+                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
+                )
+            )
+        )
+        return torch_home
+
+    torch_home = _get_torch_home()
+    model_dir = os.path.join(torch_home, 'checkpoints')
+    try:
+        os.makedirs(model_dir)
+    except OSError as e:
+        if e.errno == errno.EEXIST:
+            # Directory already exists, ignore.
+            pass
+        else:
+            # Unexpected OSError, re-raise.
+            raise
+    filename = key + '_imagenet.pth'
+    cached_file = os.path.join(model_dir, filename)
+
+    if not os.path.exists(cached_file):
+        gdown.download(pretrained_urls[key], cached_file, quiet=False)
+
+    state_dict = torch.load(cached_file)
+    model_dict = model.state_dict()
+    new_state_dict = OrderedDict()
+    matched_layers, discarded_layers = [], []
+
+    for k, v in state_dict.items():
+        if k.startswith('module.'):
+            k = k[7:] # discard module.
+
+        if k in model_dict and model_dict[k].size() == v.size():
+            new_state_dict[k] = v
+            matched_layers.append(k)
+        else:
+            discarded_layers.append(k)
+
+    model_dict.update(new_state_dict)
+    model.load_state_dict(model_dict)
+
+    if len(matched_layers) == 0:
+        warnings.warn(
+            'The pretrained weights from "{}" cannot be loaded, '
+            'please check the key names manually '
+            '(** ignored and continue **)'.format(cached_file)
+        )
+    else:
+        print(
+            'Successfully loaded imagenet pretrained weights from "{}"'.
+            format(cached_file)
+        )
+        if len(discarded_layers) > 0:
+            print(
+                '** The following layers are discarded '
+                'due to unmatched keys or layer size: {}'.
+                format(discarded_layers)
+            )
+
+
+##########
+# Instantiation
+##########
+def osnet_ain_x1_0(
+    num_classes=1000, pretrained=True, loss='softmax', **kwargs
+):
+    model = OSNet(
+        num_classes,
+        blocks=[
+            [OSBlockINin, OSBlockINin], [OSBlock, OSBlockINin],
+            [OSBlockINin, OSBlock]
+        ],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        conv1_IN=True,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_ain_x1_0')
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/pcb.py b/pfe/player-feature-extractor/torchreid/models/pcb.py
new file mode 100644
index 0000000..92c7414
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/pcb.py
@@ -0,0 +1,314 @@
+from __future__ import division, absolute_import
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['pcb_p6', 'pcb_p4']
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class DimReduceLayer(nn.Module):
+
+    def __init__(self, in_channels, out_channels, nonlinear):
+        super(DimReduceLayer, self).__init__()
+        layers = []
+        layers.append(
+            nn.Conv2d(
+                in_channels, out_channels, 1, stride=1, padding=0, bias=False
+            )
+        )
+        layers.append(nn.BatchNorm2d(out_channels))
+
+        if nonlinear == 'relu':
+            layers.append(nn.ReLU(inplace=True))
+        elif nonlinear == 'leakyrelu':
+            layers.append(nn.LeakyReLU(0.1))
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class PCB(nn.Module):
+    """Part-based Convolutional Baseline.
+
+    Reference:
+        Sun et al. Beyond Part Models: Person Retrieval with Refined
+        Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.
+
+    Public keys:
+        - ``pcb_p4``: PCB with 4-part strips.
+        - ``pcb_p6``: PCB with 6-part strips.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        parts=6,
+        reduced_dim=256,
+        nonlinear='relu',
+        **kwargs
+    ):
+        self.inplanes = 64
+        super(PCB, self).__init__()
+        self.loss = loss
+        self.parts = parts
+        self.feature_dim = 512 * block.expansion
+
+        # backbone network
+        self.conv1 = nn.Conv2d(
+            3, 64, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
+
+        # pcb layers
+        self.parts_avgpool = nn.AdaptiveAvgPool2d((self.parts, 1))
+        self.dropout = nn.Dropout(p=0.5)
+        self.conv5 = DimReduceLayer(
+            512 * block.expansion, reduced_dim, nonlinear=nonlinear
+        )
+        self.feature_dim = reduced_dim
+        self.classifier = nn.ModuleList(
+            [
+                nn.Linear(self.feature_dim, num_classes)
+                for _ in range(self.parts)
+            ]
+        )
+
+        self._init_params()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v_g = self.parts_avgpool(f)
+
+        if not self.training:
+            v_g = F.normalize(v_g, p=2, dim=1)
+            return v_g.view(v_g.size(0), -1)
+
+        v_g = self.dropout(v_g)
+        v_h = self.conv5(v_g)
+
+        y = []
+        for i in range(self.parts):
+            v_h_i = v_h[:, :, i, :]
+            v_h_i = v_h_i.view(v_h_i.size(0), -1)
+            y_i = self.classifier[i](v_h_i)
+            y.append(y_i)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            v_g = F.normalize(v_g, p=2, dim=1)
+            return y, v_g.view(v_g.size(0), -1)
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def pcb_p6(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = PCB(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=1,
+        parts=6,
+        reduced_dim=256,
+        nonlinear='relu',
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
+
+
+def pcb_p4(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = PCB(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=1,
+        parts=4,
+        reduced_dim=256,
+        nonlinear='relu',
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/resnet.py b/pfe/player-feature-extractor/torchreid/models/resnet.py
new file mode 100644
index 0000000..63d7f43
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/resnet.py
@@ -0,0 +1,530 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+
+__all__ = [
+    'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
+    'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
+]
+
+model_urls = {
+    'resnet18':
+    'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34':
+    'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50':
+    'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101':
+    'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152':
+    'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+    'resnext50_32x4d':
+    'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
+    'resnext101_32x8d':
+    'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=dilation,
+        groups=groups,
+        bias=False,
+        dilation=dilation
+    )
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(
+        in_planes, out_planes, kernel_size=1, stride=stride, bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None
+    ):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError(
+                'BasicBlock only supports groups=1 and base_width=64'
+            )
+        if dilation > 1:
+            raise NotImplementedError(
+                "Dilation > 1 not supported in BasicBlock"
+            )
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None
+    ):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width/64.)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    """Residual network.
+    
+    Reference:
+        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
+
+    Public keys:
+        - ``resnet18``: ResNet18.
+        - ``resnet34``: ResNet34.
+        - ``resnet50``: ResNet50.
+        - ``resnet101``: ResNet101.
+        - ``resnet152``: ResNet152.
+        - ``resnext50_32x4d``: ResNeXt50.
+        - ``resnext101_32x8d``: ResNeXt101.
+        - ``resnet50_fc512``: ResNet50 + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        zero_init_residual=False,
+        groups=1,
+        width_per_group=64,
+        replace_stride_with_dilation=None,
+        norm_layer=None,
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+        self.loss = loss
+        self.feature_dim = 512 * block.expansion
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError(
+                "replace_stride_with_dilation should be None "
+                "or a 3-element tuple, got {}".
+                format(replace_stride_with_dilation)
+            )
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(
+            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(
+            block,
+            128,
+            layers[1],
+            stride=2,
+            dilate=replace_stride_with_dilation[0]
+        )
+        self.layer3 = self._make_layer(
+            block,
+            256,
+            layers[2],
+            stride=2,
+            dilate=replace_stride_with_dilation[1]
+        )
+        self.layer4 = self._make_layer(
+            block,
+            512,
+            layers[3],
+            stride=last_stride,
+            dilate=replace_stride_with_dilation[2]
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = self._construct_fc_layer(
+            fc_dims, 512 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes, planes, stride, downsample, self.groups,
+                self.base_width, previous_dilation, norm_layer
+            )
+        )
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    groups=self.groups,
+                    base_width=self.base_width,
+                    dilation=self.dilation,
+                    norm_layer=norm_layer
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+"""ResNet"""
+
+
+def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=BasicBlock,
+        layers=[2, 2, 2, 2],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet18'])
+    return model
+
+
+def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=BasicBlock,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet34'])
+    return model
+
+
+def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
+
+
+def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 23, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet101'])
+    return model
+
+
+def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 8, 36, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet152'])
+    return model
+
+
+"""ResNeXt"""
+
+
+def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        groups=32,
+        width_per_group=4,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
+    return model
+
+
+def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 23, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        groups=32,
+        width_per_group=8,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnext101_32x8d'])
+    return model
+
+
+"""
+ResNet + FC
+"""
+
+
+def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=1,
+        fc_dims=[512],
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/resnet_ibn_a.py b/pfe/player-feature-extractor/torchreid/models/resnet_ibn_a.py
new file mode 100644
index 0000000..d198e7c
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/resnet_ibn_a.py
@@ -0,0 +1,289 @@
+"""
+Credit to https://github.com/XingangPan/IBN-Net.
+"""
+from __future__ import division, absolute_import
+import math
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['resnet50_ibn_a']
+
+model_urls = {
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class IBN(nn.Module):
+
+    def __init__(self, planes):
+        super(IBN, self).__init__()
+        half1 = int(planes / 2)
+        self.half = half1
+        half2 = planes - half1
+        self.IN = nn.InstanceNorm2d(half1, affine=True)
+        self.BN = nn.BatchNorm2d(half2)
+
+    def forward(self, x):
+        split = torch.split(x, self.half, 1)
+        out1 = self.IN(split[0].contiguous())
+        out2 = self.BN(split[1].contiguous())
+        out = torch.cat((out1, out2), 1)
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        if ibn:
+            self.bn1 = IBN(planes)
+        else:
+            self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    """Residual network + IBN layer.
+    
+    Reference:
+        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - Pan et al. Two at Once: Enhancing Learning and Generalization
+          Capacities via IBN-Net. ECCV 2018.
+    """
+
+    def __init__(
+        self,
+        block,
+        layers,
+        num_classes=1000,
+        loss='softmax',
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        scale = 64
+        self.inplanes = scale
+        super(ResNet, self).__init__()
+        self.loss = loss
+        self.feature_dim = scale * 8 * block.expansion
+
+        self.conv1 = nn.Conv2d(
+            3, scale, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(scale)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, scale, layers[0])
+        self.layer2 = self._make_layer(block, scale * 2, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, scale * 4, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, scale * 8, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = self._construct_fc_layer(
+            fc_dims, scale * 8 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.InstanceNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        ibn = True
+        if planes == 512:
+            ibn = False
+        layers.append(block(self.inplanes, planes, ibn, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, ibn))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.avgpool(f)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def resnet50_ibn_a(num_classes, loss='softmax', pretrained=False, **kwargs):
+    model = ResNet(
+        Bottleneck, [3, 4, 6, 3], num_classes=num_classes, loss=loss, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/resnet_ibn_b.py b/pfe/player-feature-extractor/torchreid/models/resnet_ibn_b.py
new file mode 100644
index 0000000..9881cc7
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/resnet_ibn_b.py
@@ -0,0 +1,274 @@
+"""
+Credit to https://github.com/XingangPan/IBN-Net.
+"""
+from __future__ import division, absolute_import
+import math
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['resnet50_ibn_b']
+
+model_urls = {
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, IN=False):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.IN = None
+        if IN:
+            self.IN = nn.InstanceNorm2d(planes * 4, affine=True)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        if self.IN is not None:
+            out = self.IN(out)
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    """Residual network + IBN layer.
+    
+    Reference:
+        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - Pan et al. Two at Once: Enhancing Learning and Generalization
+          Capacities via IBN-Net. ECCV 2018.
+    """
+
+    def __init__(
+        self,
+        block,
+        layers,
+        num_classes=1000,
+        loss='softmax',
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        scale = 64
+        self.inplanes = scale
+        super(ResNet, self).__init__()
+        self.loss = loss
+        self.feature_dim = scale * 8 * block.expansion
+
+        self.conv1 = nn.Conv2d(
+            3, scale, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.InstanceNorm2d(scale, affine=True)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(
+            block, scale, layers[0], stride=1, IN=True
+        )
+        self.layer2 = self._make_layer(
+            block, scale * 2, layers[1], stride=2, IN=True
+        )
+        self.layer3 = self._make_layer(block, scale * 4, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, scale * 8, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = self._construct_fc_layer(
+            fc_dims, scale * 8 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.InstanceNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1, IN=False):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks - 1):
+            layers.append(block(self.inplanes, planes))
+        layers.append(block(self.inplanes, planes, IN=IN))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.avgpool(f)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def resnet50_ibn_b(num_classes, loss='softmax', pretrained=False, **kwargs):
+    model = ResNet(
+        Bottleneck, [3, 4, 6, 3], num_classes=num_classes, loss=loss, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/resnetmid.py b/pfe/player-feature-extractor/torchreid/models/resnetmid.py
new file mode 100644
index 0000000..017f6c6
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/resnetmid.py
@@ -0,0 +1,307 @@
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+
+__all__ = ['resnet50mid']
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNetMid(nn.Module):
+    """Residual network + mid-level features.
+    
+    Reference:
+        Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for
+        Cross-Domain Instance Matching. arXiv:1711.08106.
+
+    Public keys:
+        - ``resnet50mid``: ResNet50 + mid-level feature fusion.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    ):
+        self.inplanes = 64
+        super(ResNetMid, self).__init__()
+        self.loss = loss
+        self.feature_dim = 512 * block.expansion
+
+        # backbone network
+        self.conv1 = nn.Conv2d(
+            3, 64, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(
+            block, 512, layers[3], stride=last_stride
+        )
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        assert fc_dims is not None
+        self.fc_fusion = self._construct_fc_layer(
+            fc_dims, 512 * block.expansion * 2
+        )
+        self.feature_dim += 512 * block.expansion
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x4a = self.layer4[0](x)
+        x4b = self.layer4[1](x4a)
+        x4c = self.layer4[2](x4b)
+        return x4a, x4b, x4c
+
+    def forward(self, x):
+        x4a, x4b, x4c = self.featuremaps(x)
+
+        v4a = self.global_avgpool(x4a)
+        v4b = self.global_avgpool(x4b)
+        v4c = self.global_avgpool(x4c)
+        v4ab = torch.cat([v4a, v4b], 1)
+        v4ab = v4ab.view(v4ab.size(0), -1)
+        v4ab = self.fc_fusion(v4ab)
+        v4c = v4c.view(v4c.size(0), -1)
+        v = torch.cat([v4ab, v4c], 1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+"""
+Residual network configurations:
+--
+resnet18: block=BasicBlock, layers=[2, 2, 2, 2]
+resnet34: block=BasicBlock, layers=[3, 4, 6, 3]
+resnet50: block=Bottleneck, layers=[3, 4, 6, 3]
+resnet101: block=Bottleneck, layers=[3, 4, 23, 3]
+resnet152: block=Bottleneck, layers=[3, 8, 36, 3]
+"""
+
+
+def resnet50mid(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNetMid(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=[1024],
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/senet.py b/pfe/player-feature-extractor/torchreid/models/senet.py
new file mode 100644
index 0000000..baaf9b0
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/senet.py
@@ -0,0 +1,688 @@
+from __future__ import division, absolute_import
+import math
+from collections import OrderedDict
+import torch.nn as nn
+from torch.utils import model_zoo
+
+__all__ = [
+    'senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152',
+    'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnet50_fc512'
+]
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+
+pretrained_settings = {
+    'senet154': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnet50': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnet101': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnet152': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnext50_32x4d': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnext101_32x4d': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+}
+
+
+class SEModule(nn.Module):
+
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            channels, channels // reduction, kernel_size=1, padding=0
+        )
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            channels // reduction, channels, kernel_size=1, padding=0
+        )
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return module_input * x
+
+
+class Bottleneck(nn.Module):
+    """
+    Base class for bottlenecks that implements `forward()` method.
+    """
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out = self.se_module(out) + residual
+        out = self.relu(out)
+
+        return out
+
+
+class SEBottleneck(Bottleneck):
+    """
+    Bottleneck for SENet154.
+    """
+    expansion = 4
+
+    def __init__(
+        self, inplanes, planes, groups, reduction, stride=1, downsample=None
+    ):
+        super(SEBottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes * 2)
+        self.conv2 = nn.Conv2d(
+            planes * 2,
+            planes * 4,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            groups=groups,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes * 4)
+        self.conv3 = nn.Conv2d(
+            planes * 4, planes * 4, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.se_module = SEModule(planes * 4, reduction=reduction)
+        self.downsample = downsample
+        self.stride = stride
+
+
+class SEResNetBottleneck(Bottleneck):
+    """
+    ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
+    implementation and uses `stride=stride` in `conv1` and not in `conv2`
+    (the latter is used in the torchvision implementation of ResNet).
+    """
+    expansion = 4
+
+    def __init__(
+        self, inplanes, planes, groups, reduction, stride=1, downsample=None
+    ):
+        super(SEResNetBottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(
+            inplanes, planes, kernel_size=1, bias=False, stride=stride
+        )
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            padding=1,
+            groups=groups,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.se_module = SEModule(planes * 4, reduction=reduction)
+        self.downsample = downsample
+        self.stride = stride
+
+
+class SEResNeXtBottleneck(Bottleneck):
+    """ResNeXt bottleneck type C with a Squeeze-and-Excitation module"""
+    expansion = 4
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        groups,
+        reduction,
+        stride=1,
+        downsample=None,
+        base_width=4
+    ):
+        super(SEResNeXtBottleneck, self).__init__()
+        width = int(math.floor(planes * (base_width/64.)) * groups)
+        self.conv1 = nn.Conv2d(
+            inplanes, width, kernel_size=1, bias=False, stride=1
+        )
+        self.bn1 = nn.BatchNorm2d(width)
+        self.conv2 = nn.Conv2d(
+            width,
+            width,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            groups=groups,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.se_module = SEModule(planes * 4, reduction=reduction)
+        self.downsample = downsample
+        self.stride = stride
+
+
+class SENet(nn.Module):
+    """Squeeze-and-excitation network.
+    
+    Reference:
+        Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.
+
+    Public keys:
+        - ``senet154``: SENet154.
+        - ``se_resnet50``: ResNet50 + SE.
+        - ``se_resnet101``: ResNet101 + SE.
+        - ``se_resnet152``: ResNet152 + SE.
+        - ``se_resnext50_32x4d``: ResNeXt50 (groups=32, width=4) + SE.
+        - ``se_resnext101_32x4d``: ResNeXt101 (groups=32, width=4) + SE.
+        - ``se_resnet50_fc512``: (ResNet50 + SE) + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        groups,
+        reduction,
+        dropout_p=0.2,
+        inplanes=128,
+        input_3x3=True,
+        downsample_kernel_size=3,
+        downsample_padding=1,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    ):
+        """
+        Parameters
+        ----------
+        block (nn.Module): Bottleneck class.
+            - For SENet154: SEBottleneck
+            - For SE-ResNet models: SEResNetBottleneck
+            - For SE-ResNeXt models:  SEResNeXtBottleneck
+        layers (list of ints): Number of residual blocks for 4 layers of the
+            network (layer1...layer4).
+        groups (int): Number of groups for the 3x3 convolution in each
+            bottleneck block.
+            - For SENet154: 64
+            - For SE-ResNet models: 1
+            - For SE-ResNeXt models:  32
+        reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
+            - For all models: 16
+        dropout_p (float or None): Drop probability for the Dropout layer.
+            If `None` the Dropout layer is not used.
+            - For SENet154: 0.2
+            - For SE-ResNet models: None
+            - For SE-ResNeXt models: None
+        inplanes (int):  Number of input channels for layer1.
+            - For SENet154: 128
+            - For SE-ResNet models: 64
+            - For SE-ResNeXt models: 64
+        input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
+            a single 7x7 convolution in layer0.
+            - For SENet154: True
+            - For SE-ResNet models: False
+            - For SE-ResNeXt models: False
+        downsample_kernel_size (int): Kernel size for downsampling convolutions
+            in layer2, layer3 and layer4.
+            - For SENet154: 3
+            - For SE-ResNet models: 1
+            - For SE-ResNeXt models: 1
+        downsample_padding (int): Padding for downsampling convolutions in
+            layer2, layer3 and layer4.
+            - For SENet154: 1
+            - For SE-ResNet models: 0
+            - For SE-ResNeXt models: 0
+        num_classes (int): Number of outputs in `classifier` layer.
+        """
+        super(SENet, self).__init__()
+        self.inplanes = inplanes
+        self.loss = loss
+
+        if input_3x3:
+            layer0_modules = [
+                (
+                    'conv1',
+                    nn.Conv2d(3, 64, 3, stride=2, padding=1, bias=False)
+                ),
+                ('bn1', nn.BatchNorm2d(64)),
+                ('relu1', nn.ReLU(inplace=True)),
+                (
+                    'conv2',
+                    nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)
+                ),
+                ('bn2', nn.BatchNorm2d(64)),
+                ('relu2', nn.ReLU(inplace=True)),
+                (
+                    'conv3',
+                    nn.Conv2d(
+                        64, inplanes, 3, stride=1, padding=1, bias=False
+                    )
+                ),
+                ('bn3', nn.BatchNorm2d(inplanes)),
+                ('relu3', nn.ReLU(inplace=True)),
+            ]
+        else:
+            layer0_modules = [
+                (
+                    'conv1',
+                    nn.Conv2d(
+                        3,
+                        inplanes,
+                        kernel_size=7,
+                        stride=2,
+                        padding=3,
+                        bias=False
+                    )
+                ),
+                ('bn1', nn.BatchNorm2d(inplanes)),
+                ('relu1', nn.ReLU(inplace=True)),
+            ]
+        # To preserve compatibility with Caffe weights `ceil_mode=True`
+        # is used instead of `padding=1`.
+        layer0_modules.append(
+            ('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True))
+        )
+        self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
+        self.layer1 = self._make_layer(
+            block,
+            planes=64,
+            blocks=layers[0],
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=1,
+            downsample_padding=0
+        )
+        self.layer2 = self._make_layer(
+            block,
+            planes=128,
+            blocks=layers[1],
+            stride=2,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+        self.layer3 = self._make_layer(
+            block,
+            planes=256,
+            blocks=layers[2],
+            stride=2,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+        self.layer4 = self._make_layer(
+            block,
+            planes=512,
+            blocks=layers[3],
+            stride=last_stride,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = self._construct_fc_layer(
+            fc_dims, 512 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+    def _make_layer(
+        self,
+        block,
+        planes,
+        blocks,
+        groups,
+        reduction,
+        stride=1,
+        downsample_kernel_size=1,
+        downsample_padding=0
+    ):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=downsample_kernel_size,
+                    stride=stride,
+                    padding=downsample_padding,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes, planes, groups, reduction, stride, downsample
+            )
+        )
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups, reduction))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """
+        Construct fully connected layer
+
+        - fc_dims (list or tuple): dimensions of fc layers, if None,
+                                   no fc layers are constructed
+        - input_dim (int): input dimension
+        - dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def featuremaps(self, x):
+        x = self.layer0(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def senet154(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEBottleneck,
+        layers=[3, 8, 36, 3],
+        groups=64,
+        reduction=16,
+        dropout_p=0.2,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['senet154']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 4, 6, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet50']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 4, 6, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=1,
+        fc_dims=[512],
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet50']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 4, 23, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet101']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 8, 36, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet152']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNeXtBottleneck,
+        layers=[3, 4, 6, 3],
+        groups=32,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnext50_32x4d']['imagenet']['url'
+                                                                          ]
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnext101_32x4d(
+    num_classes, loss='softmax', pretrained=True, **kwargs
+):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNeXtBottleneck,
+        layers=[3, 4, 23, 3],
+        groups=32,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnext101_32x4d']['imagenet'][
+            'url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/shufflenet.py b/pfe/player-feature-extractor/torchreid/models/shufflenet.py
new file mode 100644
index 0000000..bc4d34f
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/shufflenet.py
@@ -0,0 +1,198 @@
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['shufflenet']
+
+model_urls = {
+    # training epoch = 90, top1 = 61.8
+    'imagenet':
+    'https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrgfiJO9kiidz9fZM',
+}
+
+
+class ChannelShuffle(nn.Module):
+
+    def __init__(self, num_groups):
+        super(ChannelShuffle, self).__init__()
+        self.g = num_groups
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        n = c // self.g
+        # reshape
+        x = x.view(b, self.g, n, h, w)
+        # transpose
+        x = x.permute(0, 2, 1, 3, 4).contiguous()
+        # flatten
+        x = x.view(b, c, h, w)
+        return x
+
+
+class Bottleneck(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        stride,
+        num_groups,
+        group_conv1x1=True
+    ):
+        super(Bottleneck, self).__init__()
+        assert stride in [1, 2], 'Warning: stride must be either 1 or 2'
+        self.stride = stride
+        mid_channels = out_channels // 4
+        if stride == 2:
+            out_channels -= in_channels
+        # group conv is not applied to first conv1x1 at stage 2
+        num_groups_conv1x1 = num_groups if group_conv1x1 else 1
+        self.conv1 = nn.Conv2d(
+            in_channels,
+            mid_channels,
+            1,
+            groups=num_groups_conv1x1,
+            bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(mid_channels)
+        self.shuffle1 = ChannelShuffle(num_groups)
+        self.conv2 = nn.Conv2d(
+            mid_channels,
+            mid_channels,
+            3,
+            stride=stride,
+            padding=1,
+            groups=mid_channels,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(mid_channels)
+        self.conv3 = nn.Conv2d(
+            mid_channels, out_channels, 1, groups=num_groups, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(out_channels)
+        if stride == 2:
+            self.shortcut = nn.AvgPool2d(3, stride=2, padding=1)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.shuffle1(out)
+        out = self.bn2(self.conv2(out))
+        out = self.bn3(self.conv3(out))
+        if self.stride == 2:
+            res = self.shortcut(x)
+            out = F.relu(torch.cat([res, out], 1))
+        else:
+            out = F.relu(x + out)
+        return out
+
+
+# configuration of (num_groups: #out_channels) based on Table 1 in the paper
+cfg = {
+    1: [144, 288, 576],
+    2: [200, 400, 800],
+    3: [240, 480, 960],
+    4: [272, 544, 1088],
+    8: [384, 768, 1536],
+}
+
+
+class ShuffleNet(nn.Module):
+    """ShuffleNet.
+
+    Reference:
+        Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural
+        Network for Mobile Devices. CVPR 2018.
+
+    Public keys:
+        - ``shufflenet``: ShuffleNet (groups=3).
+    """
+
+    def __init__(self, num_classes, loss='softmax', num_groups=3, **kwargs):
+        super(ShuffleNet, self).__init__()
+        self.loss = loss
+
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 24, 3, stride=2, padding=1, bias=False),
+            nn.BatchNorm2d(24),
+            nn.ReLU(),
+            nn.MaxPool2d(3, stride=2, padding=1),
+        )
+
+        self.stage2 = nn.Sequential(
+            Bottleneck(
+                24, cfg[num_groups][0], 2, num_groups, group_conv1x1=False
+            ),
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
+        )
+
+        self.stage3 = nn.Sequential(
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][1], 2, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+        )
+
+        self.stage4 = nn.Sequential(
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][2], 2, num_groups),
+            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
+            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
+            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
+        )
+
+        self.classifier = nn.Linear(cfg[num_groups][2], num_classes)
+        self.feat_dim = cfg[num_groups][2]
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = F.avg_pool2d(x, x.size()[2:]).view(x.size(0), -1)
+
+        if not self.training:
+            return x
+
+        y = self.classifier(x)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, x
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def shufflenet(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNet(num_classes, loss, **kwargs)
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['imagenet'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['imagenet'])
+        )
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/shufflenetv2.py b/pfe/player-feature-extractor/torchreid/models/shufflenetv2.py
new file mode 100644
index 0000000..3ff879e
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/shufflenetv2.py
@@ -0,0 +1,262 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+
+__all__ = [
+    'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5',
+    'shufflenet_v2_x2_0'
+]
+
+model_urls = {
+    'shufflenetv2_x0.5':
+    'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
+    'shufflenetv2_x1.0':
+    'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
+    'shufflenetv2_x1.5': None,
+    'shufflenetv2_x2.0': None,
+}
+
+
+def channel_shuffle(x, groups):
+    batchsize, num_channels, height, width = x.data.size()
+    channels_per_group = num_channels // groups
+
+    # reshape
+    x = x.view(batchsize, groups, channels_per_group, height, width)
+
+    x = torch.transpose(x, 1, 2).contiguous()
+
+    # flatten
+    x = x.view(batchsize, -1, height, width)
+
+    return x
+
+
+class InvertedResidual(nn.Module):
+
+    def __init__(self, inp, oup, stride):
+        super(InvertedResidual, self).__init__()
+
+        if not (1 <= stride <= 3):
+            raise ValueError('illegal stride value')
+        self.stride = stride
+
+        branch_features = oup // 2
+        assert (self.stride != 1) or (inp == branch_features << 1)
+
+        if self.stride > 1:
+            self.branch1 = nn.Sequential(
+                self.depthwise_conv(
+                    inp, inp, kernel_size=3, stride=self.stride, padding=1
+                ),
+                nn.BatchNorm2d(inp),
+                nn.Conv2d(
+                    inp,
+                    branch_features,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    bias=False
+                ),
+                nn.BatchNorm2d(branch_features),
+                nn.ReLU(inplace=True),
+            )
+
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(
+                inp if (self.stride > 1) else branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+            self.depthwise_conv(
+                branch_features,
+                branch_features,
+                kernel_size=3,
+                stride=self.stride,
+                padding=1
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.Conv2d(
+                branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+        )
+
+    @staticmethod
+    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
+        return nn.Conv2d(
+            i, o, kernel_size, stride, padding, bias=bias, groups=i
+        )
+
+    def forward(self, x):
+        if self.stride == 1:
+            x1, x2 = x.chunk(2, dim=1)
+            out = torch.cat((x1, self.branch2(x2)), dim=1)
+        else:
+            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+
+        out = channel_shuffle(out, 2)
+
+        return out
+
+
+class ShuffleNetV2(nn.Module):
+    """ShuffleNetV2.
+    
+    Reference:
+        Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
+
+    Public keys:
+        - ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.
+        - ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.
+        - ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.
+        - ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.
+    """
+
+    def __init__(
+        self, num_classes, loss, stages_repeats, stages_out_channels, **kwargs
+    ):
+        super(ShuffleNetV2, self).__init__()
+        self.loss = loss
+
+        if len(stages_repeats) != 3:
+            raise ValueError(
+                'expected stages_repeats as list of 3 positive ints'
+            )
+        if len(stages_out_channels) != 5:
+            raise ValueError(
+                'expected stages_out_channels as list of 5 positive ints'
+            )
+        self._stage_out_channels = stages_out_channels
+
+        input_channels = 3
+        output_channels = self._stage_out_channels[0]
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+        input_channels = output_channels
+
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
+        for name, repeats, output_channels in zip(
+            stage_names, stages_repeats, self._stage_out_channels[1:]
+        ):
+            seq = [InvertedResidual(input_channels, output_channels, 2)]
+            for i in range(repeats - 1):
+                seq.append(
+                    InvertedResidual(output_channels, output_channels, 1)
+                )
+            setattr(self, name, nn.Sequential(*seq))
+            input_channels = output_channels
+
+        output_channels = self._stage_out_channels[-1]
+        self.conv5 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+        self.classifier = nn.Linear(output_channels, num_classes)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    if model_url is None:
+        import warnings
+        warnings.warn(
+            'ImageNet pretrained weights are unavailable for this model'
+        )
+        return
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x0.5'])
+    return model
+
+
+def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x1.0'])
+    return model
+
+
+def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x1.5'])
+    return model
+
+
+def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x2.0'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/squeezenet.py b/pfe/player-feature-extractor/torchreid/models/squeezenet.py
new file mode 100644
index 0000000..83e8dc9
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/squeezenet.py
@@ -0,0 +1,236 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512']
+
+model_urls = {
+    'squeezenet1_0':
+    'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
+    'squeezenet1_1':
+    'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth',
+}
+
+
+class Fire(nn.Module):
+
+    def __init__(
+        self, inplanes, squeeze_planes, expand1x1_planes, expand3x3_planes
+    ):
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(
+            squeeze_planes, expand1x1_planes, kernel_size=1
+        )
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(
+            squeeze_planes, expand3x3_planes, kernel_size=3, padding=1
+        )
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.squeeze_activation(self.squeeze(x))
+        return torch.cat(
+            [
+                self.expand1x1_activation(self.expand1x1(x)),
+                self.expand3x3_activation(self.expand3x3(x))
+            ], 1
+        )
+
+
+class SqueezeNet(nn.Module):
+    """SqueezeNet.
+
+    Reference:
+        Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
+        and< 0.5 MB model size. arXiv:1602.07360.
+
+    Public keys:
+        - ``squeezenet1_0``: SqueezeNet (version=1.0).
+        - ``squeezenet1_1``: SqueezeNet (version=1.1).
+        - ``squeezenet1_0_fc512``: SqueezeNet (version=1.0) + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        version=1.0,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        super(SqueezeNet, self).__init__()
+        self.loss = loss
+        self.feature_dim = 512
+
+        if version not in [1.0, 1.1]:
+            raise ValueError(
+                'Unsupported SqueezeNet version {version}:'
+                '1.0 or 1.1 expected'.format(version=version)
+            )
+
+        if version == 1.0:
+            self.features = nn.Sequential(
+                nn.Conv2d(3, 96, kernel_size=7, stride=2),
+                nn.ReLU(inplace=True),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(96, 16, 64, 64),
+                Fire(128, 16, 64, 64),
+                Fire(128, 32, 128, 128),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(256, 32, 128, 128),
+                Fire(256, 48, 192, 192),
+                Fire(384, 48, 192, 192),
+                Fire(384, 64, 256, 256),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(512, 64, 256, 256),
+            )
+        else:
+            self.features = nn.Sequential(
+                nn.Conv2d(3, 64, kernel_size=3, stride=2),
+                nn.ReLU(inplace=True),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(64, 16, 64, 64),
+                Fire(128, 16, 64, 64),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(128, 32, 128, 128),
+                Fire(256, 32, 128, 128),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(256, 48, 192, 192),
+                Fire(384, 48, 192, 192),
+                Fire(384, 64, 256, 256),
+                Fire(512, 64, 256, 256),
+            )
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = self._construct_fc_layer(fc_dims, 512, dropout_p)
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        f = self.features(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url, map_location=None)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def squeezenet1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SqueezeNet(
+        num_classes, loss, version=1.0, fc_dims=None, dropout_p=None, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['squeezenet1_0'])
+    return model
+
+
+def squeezenet1_0_fc512(
+    num_classes, loss='softmax', pretrained=True, **kwargs
+):
+    model = SqueezeNet(
+        num_classes,
+        loss,
+        version=1.0,
+        fc_dims=[512],
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['squeezenet1_0'])
+    return model
+
+
+def squeezenet1_1(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SqueezeNet(
+        num_classes, loss, version=1.1, fc_dims=None, dropout_p=None, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['squeezenet1_1'])
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/models/xception.py b/pfe/player-feature-extractor/torchreid/models/xception.py
new file mode 100644
index 0000000..43db4ab
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/models/xception.py
@@ -0,0 +1,344 @@
+from __future__ import division, absolute_import
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['xception']
+
+pretrained_settings = {
+    'xception': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000,
+            'scale':
+            0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
+        }
+    }
+}
+
+
+class SeparableConv2d(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=False
+    ):
+        super(SeparableConv2d, self).__init__()
+
+        self.conv1 = nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups=in_channels,
+            bias=bias
+        )
+        self.pointwise = nn.Conv2d(
+            in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias
+        )
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.pointwise(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(
+        self,
+        in_filters,
+        out_filters,
+        reps,
+        strides=1,
+        start_with_relu=True,
+        grow_first=True
+    ):
+        super(Block, self).__init__()
+
+        if out_filters != in_filters or strides != 1:
+            self.skip = nn.Conv2d(
+                in_filters, out_filters, 1, stride=strides, bias=False
+            )
+            self.skipbn = nn.BatchNorm2d(out_filters)
+        else:
+            self.skip = None
+
+        self.relu = nn.ReLU(inplace=True)
+        rep = []
+
+        filters = in_filters
+        if grow_first:
+            rep.append(self.relu)
+            rep.append(
+                SeparableConv2d(
+                    in_filters,
+                    out_filters,
+                    3,
+                    stride=1,
+                    padding=1,
+                    bias=False
+                )
+            )
+            rep.append(nn.BatchNorm2d(out_filters))
+            filters = out_filters
+
+        for i in range(reps - 1):
+            rep.append(self.relu)
+            rep.append(
+                SeparableConv2d(
+                    filters, filters, 3, stride=1, padding=1, bias=False
+                )
+            )
+            rep.append(nn.BatchNorm2d(filters))
+
+        if not grow_first:
+            rep.append(self.relu)
+            rep.append(
+                SeparableConv2d(
+                    in_filters,
+                    out_filters,
+                    3,
+                    stride=1,
+                    padding=1,
+                    bias=False
+                )
+            )
+            rep.append(nn.BatchNorm2d(out_filters))
+
+        if not start_with_relu:
+            rep = rep[1:]
+        else:
+            rep[0] = nn.ReLU(inplace=False)
+
+        if strides != 1:
+            rep.append(nn.MaxPool2d(3, strides, 1))
+        self.rep = nn.Sequential(*rep)
+
+    def forward(self, inp):
+        x = self.rep(inp)
+
+        if self.skip is not None:
+            skip = self.skip(inp)
+            skip = self.skipbn(skip)
+        else:
+            skip = inp
+
+        x += skip
+        return x
+
+
+class Xception(nn.Module):
+    """Xception.
+    
+    Reference:
+        Chollet. Xception: Deep Learning with Depthwise
+        Separable Convolutions. CVPR 2017.
+
+    Public keys:
+        - ``xception``: Xception.
+    """
+
+    def __init__(
+        self, num_classes, loss, fc_dims=None, dropout_p=None, **kwargs
+    ):
+        super(Xception, self).__init__()
+        self.loss = loss
+
+        self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+
+        self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
+        self.bn2 = nn.BatchNorm2d(64)
+
+        self.block1 = Block(
+            64, 128, 2, 2, start_with_relu=False, grow_first=True
+        )
+        self.block2 = Block(
+            128, 256, 2, 2, start_with_relu=True, grow_first=True
+        )
+        self.block3 = Block(
+            256, 728, 2, 2, start_with_relu=True, grow_first=True
+        )
+
+        self.block4 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block5 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block6 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block7 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+
+        self.block8 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block9 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block10 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block11 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+
+        self.block12 = Block(
+            728, 1024, 2, 2, start_with_relu=True, grow_first=False
+        )
+
+        self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
+        self.bn3 = nn.BatchNorm2d(1536)
+
+        self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
+        self.bn4 = nn.BatchNorm2d(2048)
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.feature_dim = 2048
+        self.fc = self._construct_fc_layer(fc_dims, 2048, dropout_p)
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer.
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, input):
+        x = self.conv1(input)
+        x = self.bn1(x)
+        x = F.relu(x, inplace=True)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = F.relu(x, inplace=True)
+
+        x = self.block1(x)
+        x = self.block2(x)
+        x = self.block3(x)
+        x = self.block4(x)
+        x = self.block5(x)
+        x = self.block6(x)
+        x = self.block7(x)
+        x = self.block8(x)
+        x = self.block9(x)
+        x = self.block10(x)
+        x = self.block11(x)
+        x = self.block12(x)
+
+        x = self.conv3(x)
+        x = self.bn3(x)
+        x = F.relu(x, inplace=True)
+
+        x = self.conv4(x)
+        x = self.bn4(x)
+        x = F.relu(x, inplace=True)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initialize models with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def xception(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = Xception(num_classes, loss, fc_dims=None, dropout_p=None, **kwargs)
+    if pretrained:
+        model_url = pretrained_settings['xception']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/player-feature-extractor/torchreid/optim/__init__.py b/pfe/player-feature-extractor/torchreid/optim/__init__.py
new file mode 100644
index 0000000..1813e46
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/optim/__init__.py
@@ -0,0 +1,4 @@
+from __future__ import absolute_import
+
+from .optimizer import build_optimizer
+from .lr_scheduler import build_lr_scheduler
diff --git a/pfe/player-feature-extractor/torchreid/optim/lr_scheduler.py b/pfe/player-feature-extractor/torchreid/optim/lr_scheduler.py
new file mode 100644
index 0000000..d60bd1d
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/optim/lr_scheduler.py
@@ -0,0 +1,68 @@
+from __future__ import print_function, absolute_import
+import torch
+
+AVAI_SCH = ['single_step', 'multi_step', 'cosine']
+
+
+def build_lr_scheduler(
+    optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1
+):
+    """A function wrapper for building a learning rate scheduler.
+
+    Args:
+        optimizer (Optimizer): an Optimizer.
+        lr_scheduler (str, optional): learning rate scheduler method. Default is single_step.
+        stepsize (int or list, optional): step size to decay learning rate. When ``lr_scheduler``
+            is "single_step", ``stepsize`` should be an integer. When ``lr_scheduler`` is
+            "multi_step", ``stepsize`` is a list. Default is 1.
+        gamma (float, optional): decay rate. Default is 0.1.
+        max_epoch (int, optional): maximum epoch (for cosine annealing). Default is 1.
+
+    Examples::
+        >>> # Decay learning rate by every 20 epochs.
+        >>> scheduler = torchreid.optim.build_lr_scheduler(
+        >>>     optimizer, lr_scheduler='single_step', stepsize=20
+        >>> )
+        >>> # Decay learning rate at 30, 50 and 55 epochs.
+        >>> scheduler = torchreid.optim.build_lr_scheduler(
+        >>>     optimizer, lr_scheduler='multi_step', stepsize=[30, 50, 55]
+        >>> )
+    """
+    if lr_scheduler not in AVAI_SCH:
+        raise ValueError(
+            'Unsupported scheduler: {}. Must be one of {}'.format(
+                lr_scheduler, AVAI_SCH
+            )
+        )
+
+    if lr_scheduler == 'single_step':
+        if isinstance(stepsize, list):
+            stepsize = stepsize[-1]
+
+        if not isinstance(stepsize, int):
+            raise TypeError(
+                'For single_step lr_scheduler, stepsize must '
+                'be an integer, but got {}'.format(type(stepsize))
+            )
+
+        scheduler = torch.optim.lr_scheduler.StepLR(
+            optimizer, step_size=stepsize, gamma=gamma
+        )
+
+    elif lr_scheduler == 'multi_step':
+        if not isinstance(stepsize, list):
+            raise TypeError(
+                'For multi_step lr_scheduler, stepsize must '
+                'be a list, but got {}'.format(type(stepsize))
+            )
+
+        scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=stepsize, gamma=gamma
+        )
+
+    elif lr_scheduler == 'cosine':
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+            optimizer, float(max_epoch)
+        )
+
+    return scheduler
diff --git a/pfe/player-feature-extractor/torchreid/optim/optimizer.py b/pfe/player-feature-extractor/torchreid/optim/optimizer.py
new file mode 100644
index 0000000..f57b03a
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/optim/optimizer.py
@@ -0,0 +1,157 @@
+from __future__ import print_function, absolute_import
+import warnings
+import torch
+import torch.nn as nn
+
+from .radam import RAdam
+
+AVAI_OPTIMS = ['adam', 'amsgrad', 'sgd', 'rmsprop', 'radam']
+
+
+def build_optimizer(
+    model,
+    optim='adam',
+    lr=0.0003,
+    weight_decay=5e-04,
+    momentum=0.9,
+    sgd_dampening=0,
+    sgd_nesterov=False,
+    rmsprop_alpha=0.99,
+    adam_beta1=0.9,
+    adam_beta2=0.99,
+    staged_lr=False,
+    new_layers='',
+    base_lr_mult=0.1
+):
+    """A function wrapper for building an optimizer.
+
+    Args:
+        model (nn.Module): model.
+        optim (str, optional): optimizer. Default is "adam".
+        lr (float, optional): learning rate. Default is 0.0003.
+        weight_decay (float, optional): weight decay (L2 penalty). Default is 5e-04.
+        momentum (float, optional): momentum factor in sgd. Default is 0.9,
+        sgd_dampening (float, optional): dampening for momentum. Default is 0.
+        sgd_nesterov (bool, optional): enables Nesterov momentum. Default is False.
+        rmsprop_alpha (float, optional): smoothing constant for rmsprop. Default is 0.99.
+        adam_beta1 (float, optional): beta-1 value in adam. Default is 0.9.
+        adam_beta2 (float, optional): beta-2 value in adam. Default is 0.99,
+        staged_lr (bool, optional): uses different learning rates for base and new layers. Base
+            layers are pretrained layers while new layers are randomly initialized, e.g. the
+            identity classification layer. Enabling ``staged_lr`` can allow the base layers to
+            be trained with a smaller learning rate determined by ``base_lr_mult``, while the new
+            layers will take the ``lr``. Default is False.
+        new_layers (str or list): attribute names in ``model``. Default is empty.
+        base_lr_mult (float, optional): learning rate multiplier for base layers. Default is 0.1.
+
+    Examples::
+        >>> # A normal optimizer can be built by
+        >>> optimizer = torchreid.optim.build_optimizer(model, optim='sgd', lr=0.01)
+        >>> # If you want to use a smaller learning rate for pretrained layers
+        >>> # and the attribute name for the randomly initialized layer is 'classifier',
+        >>> # you can do
+        >>> optimizer = torchreid.optim.build_optimizer(
+        >>>     model, optim='sgd', lr=0.01, staged_lr=True,
+        >>>     new_layers='classifier', base_lr_mult=0.1
+        >>> )
+        >>> # Now the `classifier` has learning rate 0.01 but the base layers
+        >>> # have learning rate 0.01 * 0.1.
+        >>> # new_layers can also take multiple attribute names. Say the new layers
+        >>> # are 'fc' and 'classifier', you can do
+        >>> optimizer = torchreid.optim.build_optimizer(
+        >>>     model, optim='sgd', lr=0.01, staged_lr=True,
+        >>>     new_layers=['fc', 'classifier'], base_lr_mult=0.1
+        >>> )
+    """
+    if optim not in AVAI_OPTIMS:
+        raise ValueError(
+            'Unsupported optim: {}. Must be one of {}'.format(
+                optim, AVAI_OPTIMS
+            )
+        )
+
+    if not isinstance(model, nn.Module):
+        raise TypeError(
+            'model given to build_optimizer must be an instance of nn.Module'
+        )
+
+    if staged_lr:
+        if isinstance(new_layers, str):
+            if new_layers is None:
+                warnings.warn(
+                    'new_layers is empty, therefore, staged_lr is useless'
+                )
+            new_layers = [new_layers]
+
+        if isinstance(model, nn.DataParallel):
+            model = model.module
+
+        base_params = []
+        base_layers = []
+        new_params = []
+
+        for name, module in model.named_children():
+            if name in new_layers:
+                new_params += [p for p in module.parameters()]
+            else:
+                base_params += [p for p in module.parameters()]
+                base_layers.append(name)
+
+        param_groups = [
+            {
+                'params': base_params,
+                'lr': lr * base_lr_mult
+            },
+            {
+                'params': new_params
+            },
+        ]
+
+    else:
+        param_groups = model.parameters()
+
+    if optim == 'adam':
+        optimizer = torch.optim.Adam(
+            param_groups,
+            lr=lr,
+            weight_decay=weight_decay,
+            betas=(adam_beta1, adam_beta2),
+        )
+
+    elif optim == 'amsgrad':
+        optimizer = torch.optim.Adam(
+            param_groups,
+            lr=lr,
+            weight_decay=weight_decay,
+            betas=(adam_beta1, adam_beta2),
+            amsgrad=True,
+        )
+
+    elif optim == 'sgd':
+        optimizer = torch.optim.SGD(
+            param_groups,
+            lr=lr,
+            momentum=momentum,
+            weight_decay=weight_decay,
+            dampening=sgd_dampening,
+            nesterov=sgd_nesterov,
+        )
+
+    elif optim == 'rmsprop':
+        optimizer = torch.optim.RMSprop(
+            param_groups,
+            lr=lr,
+            momentum=momentum,
+            weight_decay=weight_decay,
+            alpha=rmsprop_alpha,
+        )
+
+    elif optim == 'radam':
+        optimizer = RAdam(
+            param_groups,
+            lr=lr,
+            weight_decay=weight_decay,
+            betas=(adam_beta1, adam_beta2)
+        )
+
+    return optimizer
diff --git a/pfe/player-feature-extractor/torchreid/optim/radam.py b/pfe/player-feature-extractor/torchreid/optim/radam.py
new file mode 100644
index 0000000..f066c57
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/optim/radam.py
@@ -0,0 +1,330 @@
+"""
+Imported from: https://github.com/LiyuanLucasLiu/RAdam
+
+Paper: https://arxiv.org/abs/1908.03265
+
+@article{liu2019radam,
+  title={On the Variance of the Adaptive Learning Rate and Beyond},
+  author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
+  journal={arXiv preprint arXiv:1908.03265},
+  year={2019}
+}
+"""
+from __future__ import print_function, absolute_import
+import math
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class RAdam(Optimizer):
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.999),
+        eps=1e-8,
+        weight_decay=0,
+        degenerated_to_sgd=True
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 0: {}".format(betas[0])
+            )
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 1: {}".format(betas[1])
+            )
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        self.buffer = [[None, None, None] for ind in range(10)]
+        super(RAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(RAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'RAdam does not support sparse gradients'
+                    )
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
+                        p_data_fp32
+                    )
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                buffered = self.buffer[int(state['step'] % 10)]
+                if state['step'] == buffered[0]:
+                    N_sma, step_size = buffered[1], buffered[2]
+                else:
+                    buffered[0] = state['step']
+                    beta2_t = beta2**state['step']
+                    N_sma_max = 2 / (1-beta2) - 1
+                    N_sma = N_sma_max - 2 * state['step'
+                                                  ] * beta2_t / (1-beta2_t)
+                    buffered[1] = N_sma
+
+                    # more conservative since it's an approximated value
+                    if N_sma >= 5:
+                        step_size = math.sqrt(
+                            (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
+                            (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
+                        ) / (1 - beta1**state['step'])
+                    elif self.degenerated_to_sgd:
+                        step_size = 1.0 / (1 - beta1**state['step'])
+                    else:
+                        step_size = -1
+                    buffered[2] = step_size
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(
+                        -step_size * group['lr'], exp_avg, denom
+                    )
+                    p.data.copy_(p_data_fp32)
+                elif step_size > 0:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
+                    p.data.copy_(p_data_fp32)
+
+        return loss
+
+
+class PlainRAdam(Optimizer):
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.999),
+        eps=1e-8,
+        weight_decay=0,
+        degenerated_to_sgd=True
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 0: {}".format(betas[0])
+            )
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 1: {}".format(betas[1])
+            )
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+
+        super(PlainRAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(PlainRAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'RAdam does not support sparse gradients'
+                    )
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
+                        p_data_fp32
+                    )
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                beta2_t = beta2**state['step']
+                N_sma_max = 2 / (1-beta2) - 1
+                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1-beta2_t)
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    step_size = group['lr'] * math.sqrt(
+                        (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
+                        (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
+                    ) / (1 - beta1**state['step'])
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+                    p.data.copy_(p_data_fp32)
+                elif self.degenerated_to_sgd:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    step_size = group['lr'] / (1 - beta1**state['step'])
+                    p_data_fp32.add_(-step_size, exp_avg)
+                    p.data.copy_(p_data_fp32)
+
+        return loss
+
+
+class AdamW(Optimizer):
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.999),
+        eps=1e-8,
+        weight_decay=0,
+        warmup=0
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 0: {}".format(betas[0])
+            )
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 1: {}".format(betas[1])
+            )
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            warmup=warmup
+        )
+        super(AdamW, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(AdamW, self).__setstate__(state)
+
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'Adam does not support sparse gradients, please consider SparseAdam instead'
+                    )
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
+                        p_data_fp32
+                    )
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                denom = exp_avg_sq.sqrt().add_(group['eps'])
+                bias_correction1 = 1 - beta1**state['step']
+                bias_correction2 = 1 - beta2**state['step']
+
+                if group['warmup'] > state['step']:
+                    scheduled_lr = 1e-8 + state['step'] * group['lr'] / group[
+                        'warmup']
+                else:
+                    scheduled_lr = group['lr']
+
+                step_size = scheduled_lr * math.sqrt(
+                    bias_correction2
+                ) / bias_correction1
+
+                if group['weight_decay'] != 0:
+                    p_data_fp32.add_(
+                        -group['weight_decay'] * scheduled_lr, p_data_fp32
+                    )
+
+                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+
+                p.data.copy_(p_data_fp32)
+
+        return loss
diff --git a/pfe/player-feature-extractor/torchreid/utils/__init__.py b/pfe/player-feature-extractor/torchreid/utils/__init__.py
new file mode 100644
index 0000000..50167c3
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/__init__.py
@@ -0,0 +1,10 @@
+from __future__ import absolute_import
+
+from .tools import *
+from .rerank import re_ranking
+from .loggers import *
+from .avgmeter import *
+from .reidtools import *
+from .torchtools import *
+from .model_complexity import compute_model_complexity
+from .feature_extractor import FeatureExtractor
diff --git a/pfe/player-feature-extractor/torchreid/utils/avgmeter.py b/pfe/player-feature-extractor/torchreid/utils/avgmeter.py
new file mode 100644
index 0000000..b62d26d
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/avgmeter.py
@@ -0,0 +1,73 @@
+from __future__ import division, absolute_import
+from collections import defaultdict
+import torch
+
+__all__ = ['AverageMeter', 'MetricMeter']
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value.
+
+    Examples::
+        >>> # Initialize a meter to record loss
+        >>> losses = AverageMeter()
+        >>> # Update meter after every minibatch update
+        >>> losses.update(loss_value, batch_size)
+    """
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+class MetricMeter(object):
+    """A collection of metrics.
+
+    Source: https://github.com/KaiyangZhou/Dassl.pytorch
+
+    Examples::
+        >>> # 1. Create an instance of MetricMeter
+        >>> metric = MetricMeter()
+        >>> # 2. Update using a dictionary as input
+        >>> input_dict = {'loss_1': value_1, 'loss_2': value_2}
+        >>> metric.update(input_dict)
+        >>> # 3. Convert to string and print
+        >>> print(str(metric))
+    """
+
+    def __init__(self, delimiter='\t'):
+        self.meters = defaultdict(AverageMeter)
+        self.delimiter = delimiter
+
+    def update(self, input_dict):
+        if input_dict is None:
+            return
+
+        if not isinstance(input_dict, dict):
+            raise TypeError(
+                'Input to MetricMeter.update() must be a dictionary'
+            )
+
+        for k, v in input_dict.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            self.meters[k].update(v)
+
+    def __str__(self):
+        output_str = []
+        for name, meter in self.meters.items():
+            output_str.append(
+                '{} {:.4f} ({:.4f})'.format(name, meter.val, meter.avg)
+            )
+        return self.delimiter.join(output_str)
diff --git a/pfe/player-feature-extractor/torchreid/utils/feature_extractor.py b/pfe/player-feature-extractor/torchreid/utils/feature_extractor.py
new file mode 100644
index 0000000..df9bc12
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/feature_extractor.py
@@ -0,0 +1,155 @@
+from __future__ import absolute_import
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+
+from torchreid.utils import (
+    check_isfile, load_pretrained_weights, compute_model_complexity
+)
+from torchreid.models import build_model
+
+
+class FeatureExtractor(object):
+    """A simple API for feature extraction.
+
+    FeatureExtractor can be used like a python function, which
+    accepts input of the following types:
+        - a list of strings (image paths)
+        - a list of numpy.ndarray each with shape (H, W, C)
+        - a single string (image path)
+        - a single numpy.ndarray with shape (H, W, C)
+        - a torch.Tensor with shape (B, C, H, W) or (C, H, W)
+
+    Returned is a torch tensor with shape (B, D) where D is the
+    feature dimension.
+
+    Args:
+        model_name (str): model name.
+        model_path (str): path to model weights.
+        image_size (sequence or int): image height and width.
+        pixel_mean (list): pixel mean for normalization.
+        pixel_std (list): pixel std for normalization.
+        pixel_norm (bool): whether to normalize pixels.
+        device (str): 'cpu' or 'cuda' (could be specific gpu devices).
+        verbose (bool): show model details.
+
+    Examples::
+
+        from torchreid.utils import FeatureExtractor
+
+        extractor = FeatureExtractor(
+            model_name='osnet_x1_0',
+            model_path='a/b/c/model.pth.tar',
+            device='cuda'
+        )
+
+        image_list = [
+            'a/b/c/image001.jpg',
+            'a/b/c/image002.jpg',
+            'a/b/c/image003.jpg',
+            'a/b/c/image004.jpg',
+            'a/b/c/image005.jpg'
+        ]
+
+        features = extractor(image_list)
+        print(features.shape) # output (5, 512)
+    """
+
+    def __init__(
+        self,
+        model_name='',
+        model_path='',
+        # image_size=(256, 128),
+        # image_size=(128, 64),
+        image_size=(64, 32),
+        pixel_mean=[0.485, 0.456, 0.406],
+        pixel_std=[0.229, 0.224, 0.225],
+        pixel_norm=True,
+        device='cuda',
+        verbose=True
+    ):
+        # Build model
+        model = build_model(
+            model_name,
+            num_classes=1,
+            pretrained=True,
+            use_gpu=device.startswith('cuda')
+        )
+        model.eval()
+
+        num_params, flops = compute_model_complexity(
+            model, (1, 3, image_size[0], image_size[1])
+        )
+
+        if verbose:
+            print('Model: {}'.format(model_name))
+            print('- params: {:,}'.format(num_params))
+            print('- flops: {:,}'.format(flops))
+
+        if model_path and check_isfile(model_path):
+            load_pretrained_weights(model, model_path)
+
+        # Build transform functions
+        transforms = []
+        transforms += [T.Resize(image_size)]
+        transforms += [T.ToTensor()]
+        if pixel_norm:
+            transforms += [T.Normalize(mean=pixel_mean, std=pixel_std)]
+        preprocess = T.Compose(transforms)
+
+        to_pil = T.ToPILImage()
+
+        device = torch.device(device)
+        model.to(device)
+
+        # Class attributes
+        self.model = model
+        self.preprocess = preprocess
+        self.to_pil = to_pil
+        self.device = device
+
+    def __call__(self, input):
+        if isinstance(input, list):
+            images = []
+
+            for element in input:
+                if isinstance(element, str):
+                    image = Image.open(element).convert('RGB')
+
+                elif isinstance(element, np.ndarray):
+                    image = self.to_pil(element)
+
+                else:
+                    raise TypeError(
+                        'Type of each element must belong to [str | numpy.ndarray]'
+                    )
+
+                image = self.preprocess(image)
+                images.append(image)
+
+            images = torch.stack(images, dim=0)
+            images = images.to(self.device)
+
+        elif isinstance(input, str):
+            image = Image.open(input).convert('RGB')
+            image = self.preprocess(image)
+            images = image.unsqueeze(0).to(self.device)
+
+        elif isinstance(input, np.ndarray):
+            image = self.to_pil(input)
+            image = self.preprocess(image)
+            images = image.unsqueeze(0).to(self.device)
+
+        elif isinstance(input, torch.Tensor):
+            if input.dim() == 3:
+                input = input.unsqueeze(0)
+            images = input.to(self.device)
+
+        else:
+            raise NotImplementedError
+
+        with torch.no_grad():
+            features = self.model(images)
+
+        return features
diff --git a/pfe/player-feature-extractor/torchreid/utils/loggers.py b/pfe/player-feature-extractor/torchreid/utils/loggers.py
new file mode 100644
index 0000000..f7fae3c
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/loggers.py
@@ -0,0 +1,146 @@
+from __future__ import absolute_import
+import os
+import sys
+import os.path as osp
+
+from .tools import mkdir_if_missing
+
+__all__ = ['Logger', 'RankLogger']
+
+
+class Logger(object):
+    """Writes console output to external text file.
+
+    Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py>`_
+
+    Args:
+        fpath (str): directory to save logging file.
+
+    Examples::
+       >>> import sys
+       >>> import os
+       >>> import os.path as osp
+       >>> from torchreid.utils import Logger
+       >>> save_dir = 'log/resnet50-softmax-market1501'
+       >>> log_name = 'train.log'
+       >>> sys.stdout = Logger(osp.join(args.save_dir, log_name))
+    """
+
+    def __init__(self, fpath=None):
+        self.console = sys.stdout
+        self.file = None
+        if fpath is not None:
+            mkdir_if_missing(osp.dirname(fpath))
+            self.file = open(fpath, 'w')
+
+    def __del__(self):
+        self.close()
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, *args):
+        self.close()
+
+    def write(self, msg):
+        self.console.write(msg)
+        if self.file is not None:
+            self.file.write(msg)
+
+    def flush(self):
+        self.console.flush()
+        if self.file is not None:
+            self.file.flush()
+            os.fsync(self.file.fileno())
+
+    def close(self):
+        self.console.close()
+        if self.file is not None:
+            self.file.close()
+
+
+class RankLogger(object):
+    """Records the rank1 matching accuracy obtained for each
+    test dataset at specified evaluation steps and provides a function
+    to show the summarized results, which are convenient for analysis.
+
+    Args:
+        sources (str or list): source dataset name(s).
+        targets (str or list): target dataset name(s).
+
+    Examples::
+        >>> from torchreid.utils import RankLogger
+        >>> s = 'market1501'
+        >>> t = 'market1501'
+        >>> ranklogger = RankLogger(s, t)
+        >>> ranklogger.write(t, 10, 0.5)
+        >>> ranklogger.write(t, 20, 0.7)
+        >>> ranklogger.write(t, 30, 0.9)
+        >>> ranklogger.show_summary()
+        >>> # You will see:
+        >>> # => Show performance summary
+        >>> # market1501 (source)
+        >>> # - epoch 10   rank1 50.0%
+        >>> # - epoch 20   rank1 70.0%
+        >>> # - epoch 30   rank1 90.0%
+        >>> # If there are multiple test datasets
+        >>> t = ['market1501', 'dukemtmcreid']
+        >>> ranklogger = RankLogger(s, t)
+        >>> ranklogger.write(t[0], 10, 0.5)
+        >>> ranklogger.write(t[0], 20, 0.7)
+        >>> ranklogger.write(t[0], 30, 0.9)
+        >>> ranklogger.write(t[1], 10, 0.1)
+        >>> ranklogger.write(t[1], 20, 0.2)
+        >>> ranklogger.write(t[1], 30, 0.3)
+        >>> ranklogger.show_summary()
+        >>> # You can see:
+        >>> # => Show performance summary
+        >>> # market1501 (source)
+        >>> # - epoch 10   rank1 50.0%
+        >>> # - epoch 20   rank1 70.0%
+        >>> # - epoch 30   rank1 90.0%
+        >>> # dukemtmcreid (target)
+        >>> # - epoch 10   rank1 10.0%
+        >>> # - epoch 20   rank1 20.0%
+        >>> # - epoch 30   rank1 30.0%
+    """
+
+    def __init__(self, sources, targets):
+        self.sources = sources
+        self.targets = targets
+
+        if isinstance(self.sources, str):
+            self.sources = [self.sources]
+
+        if isinstance(self.targets, str):
+            self.targets = [self.targets]
+
+        self.logger = {
+            name: {
+                'epoch': [],
+                'rank1': []
+            }
+            for name in self.targets
+        }
+
+    def write(self, name, epoch, rank1):
+        """Writes result.
+
+        Args:
+           name (str): dataset name.
+           epoch (int): current epoch.
+           rank1 (float): rank1 result.
+        """
+        self.logger[name]['epoch'].append(epoch)
+        self.logger[name]['rank1'].append(rank1)
+
+    def show_summary(self):
+        """Shows saved results."""
+        print('=> Show performance summary')
+        for name in self.targets:
+            from_where = 'source' if name in self.sources else 'target'
+            print('{} ({})'.format(name, from_where))
+            for epoch, rank1 in zip(
+                self.logger[name]['epoch'], self.logger[name]['rank1']
+            ):
+                print('- epoch {}\t rank1 {:.1%}'.format(epoch, rank1))
diff --git a/pfe/player-feature-extractor/torchreid/utils/model_complexity.py b/pfe/player-feature-extractor/torchreid/utils/model_complexity.py
new file mode 100644
index 0000000..7d1dc1e
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/model_complexity.py
@@ -0,0 +1,363 @@
+from __future__ import division, print_function, absolute_import
+import math
+import numpy as np
+from itertools import repeat
+from collections import namedtuple, defaultdict
+import torch
+
+__all__ = ['compute_model_complexity']
+"""
+Utility
+"""
+
+
+def _ntuple(n):
+
+    def parse(x):
+        if isinstance(x, int):
+            return tuple(repeat(x, n))
+        return x
+
+    return parse
+
+
+_single = _ntuple(1)
+_pair = _ntuple(2)
+_triple = _ntuple(3)
+"""
+Convolution
+"""
+
+
+def hook_convNd(m, x, y):
+    k = torch.prod(torch.Tensor(m.kernel_size)).item()
+    cin = m.in_channels
+    flops_per_ele = k * cin # + (k*cin-1)
+    if m.bias is not None:
+        flops_per_ele += 1
+    flops = flops_per_ele * y.numel() / m.groups
+    return int(flops)
+
+
+"""
+Pooling
+"""
+
+
+def hook_maxpool1d(m, x, y):
+    flops_per_ele = m.kernel_size - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_maxpool2d(m, x, y):
+    k = _pair(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    # ops: compare
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_maxpool3d(m, x, y):
+    k = _triple(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_avgpool1d(m, x, y):
+    flops_per_ele = m.kernel_size
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_avgpool2d(m, x, y):
+    k = _pair(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_avgpool3d(m, x, y):
+    k = _triple(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapmaxpool1d(m, x, y):
+    x = x[0]
+    out_size = m.output_size
+    k = math.ceil(x.size(2) / out_size)
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapmaxpool2d(m, x, y):
+    x = x[0]
+    out_size = _pair(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapmaxpool3d(m, x, y):
+    x = x[0]
+    out_size = _triple(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapavgpool1d(m, x, y):
+    x = x[0]
+    out_size = m.output_size
+    k = math.ceil(x.size(2) / out_size)
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapavgpool2d(m, x, y):
+    x = x[0]
+    out_size = _pair(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapavgpool3d(m, x, y):
+    x = x[0]
+    out_size = _triple(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+"""
+Non-linear activations
+"""
+
+
+def hook_relu(m, x, y):
+    # eq: max(0, x)
+    num_ele = y.numel()
+    return int(num_ele)
+
+
+def hook_leakyrelu(m, x, y):
+    # eq: max(0, x) + negative_slope*min(0, x)
+    num_ele = y.numel()
+    flops = 3 * num_ele
+    return int(flops)
+
+
+"""
+Normalization
+"""
+
+
+def hook_batchnormNd(m, x, y):
+    num_ele = y.numel()
+    flops = 2 * num_ele # mean and std
+    if m.affine:
+        flops += 2 * num_ele # gamma and beta
+    return int(flops)
+
+
+def hook_instancenormNd(m, x, y):
+    return hook_batchnormNd(m, x, y)
+
+
+def hook_groupnorm(m, x, y):
+    return hook_batchnormNd(m, x, y)
+
+
+def hook_layernorm(m, x, y):
+    num_ele = y.numel()
+    flops = 2 * num_ele # mean and std
+    if m.elementwise_affine:
+        flops += 2 * num_ele # gamma and beta
+    return int(flops)
+
+
+"""
+Linear
+"""
+
+
+def hook_linear(m, x, y):
+    flops_per_ele = m.in_features # + (m.in_features-1)
+    if m.bias is not None:
+        flops_per_ele += 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+__generic_flops_counter = {
+    # Convolution
+    'Conv1d': hook_convNd,
+    'Conv2d': hook_convNd,
+    'Conv3d': hook_convNd,
+    # Pooling
+    'MaxPool1d': hook_maxpool1d,
+    'MaxPool2d': hook_maxpool2d,
+    'MaxPool3d': hook_maxpool3d,
+    'AvgPool1d': hook_avgpool1d,
+    'AvgPool2d': hook_avgpool2d,
+    'AvgPool3d': hook_avgpool3d,
+    'AdaptiveMaxPool1d': hook_adapmaxpool1d,
+    'AdaptiveMaxPool2d': hook_adapmaxpool2d,
+    'AdaptiveMaxPool3d': hook_adapmaxpool3d,
+    'AdaptiveAvgPool1d': hook_adapavgpool1d,
+    'AdaptiveAvgPool2d': hook_adapavgpool2d,
+    'AdaptiveAvgPool3d': hook_adapavgpool3d,
+    # Non-linear activations
+    'ReLU': hook_relu,
+    'ReLU6': hook_relu,
+    'LeakyReLU': hook_leakyrelu,
+    # Normalization
+    'BatchNorm1d': hook_batchnormNd,
+    'BatchNorm2d': hook_batchnormNd,
+    'BatchNorm3d': hook_batchnormNd,
+    'InstanceNorm1d': hook_instancenormNd,
+    'InstanceNorm2d': hook_instancenormNd,
+    'InstanceNorm3d': hook_instancenormNd,
+    'GroupNorm': hook_groupnorm,
+    'LayerNorm': hook_layernorm,
+    # Linear
+    'Linear': hook_linear,
+}
+
+__conv_linear_flops_counter = {
+    # Convolution
+    'Conv1d': hook_convNd,
+    'Conv2d': hook_convNd,
+    'Conv3d': hook_convNd,
+    # Linear
+    'Linear': hook_linear,
+}
+
+
+def _get_flops_counter(only_conv_linear):
+    if only_conv_linear:
+        return __conv_linear_flops_counter
+    return __generic_flops_counter
+
+
+def compute_model_complexity(
+    model, input_size, verbose=False, only_conv_linear=True
+):
+    """Returns number of parameters and FLOPs.
+
+    .. note::
+        (1) this function only provides an estimate of the theoretical time complexity
+        rather than the actual running time which depends on implementations and hardware,
+        and (2) the FLOPs is only counted for layers that are used at test time. This means
+        that redundant layers such as person ID classification layer will be ignored as it
+        is discarded when doing feature extraction. Note that the inference graph depends on
+        how you construct the computations in ``forward()``.
+
+    Args:
+        model (nn.Module): network model.
+        input_size (tuple): input size, e.g. (1, 3, 256, 128).
+        verbose (bool, optional): shows detailed complexity of
+            each module. Default is False.
+        only_conv_linear (bool, optional): only considers convolution
+            and linear layers when counting flops. Default is True.
+            If set to False, flops of all layers will be counted.
+
+    Examples::
+        >>> from torchreid import models, utils
+        >>> model = models.build_model(name='resnet50', num_classes=1000)
+        >>> num_params, flops = utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True)
+    """
+    registered_handles = []
+    layer_list = []
+    layer = namedtuple('layer', ['class_name', 'params', 'flops'])
+
+    def _add_hooks(m):
+
+        def _has_submodule(m):
+            return len(list(m.children())) > 0
+
+        def _hook(m, x, y):
+            params = sum(p.numel() for p in m.parameters())
+            class_name = str(m.__class__.__name__)
+            flops_counter = _get_flops_counter(only_conv_linear)
+            if class_name in flops_counter:
+                flops = flops_counter[class_name](m, x, y)
+            else:
+                flops = 0
+            layer_list.append(
+                layer(class_name=class_name, params=params, flops=flops)
+            )
+
+        # only consider the very basic nn layer
+        if _has_submodule(m):
+            return
+
+        handle = m.register_forward_hook(_hook)
+        registered_handles.append(handle)
+
+    default_train_mode = model.training
+
+    model.eval().apply(_add_hooks)
+    input = torch.rand(input_size)
+    if next(model.parameters()).is_cuda:
+        input = input.cuda()
+    model(input) # forward
+
+    for handle in registered_handles:
+        handle.remove()
+
+    model.train(default_train_mode)
+
+    if verbose:
+        per_module_params = defaultdict(list)
+        per_module_flops = defaultdict(list)
+
+    total_params, total_flops = 0, 0
+
+    for layer in layer_list:
+        total_params += layer.params
+        total_flops += layer.flops
+        if verbose:
+            per_module_params[layer.class_name].append(layer.params)
+            per_module_flops[layer.class_name].append(layer.flops)
+
+    if verbose:
+        num_udscore = 55
+        print('  {}'.format('-' * num_udscore))
+        print('  Model complexity with input size {}'.format(input_size))
+        print('  {}'.format('-' * num_udscore))
+        for class_name in per_module_params:
+            params = int(np.sum(per_module_params[class_name]))
+            flops = int(np.sum(per_module_flops[class_name]))
+            print(
+                '  {} (params={:,}, flops={:,})'.format(
+                    class_name, params, flops
+                )
+            )
+        print('  {}'.format('-' * num_udscore))
+        print(
+            '  Total (params={:,}, flops={:,})'.format(
+                total_params, total_flops
+            )
+        )
+        print('  {}'.format('-' * num_udscore))
+
+    return total_params, total_flops
diff --git a/pfe/player-feature-extractor/torchreid/utils/reidtools.py b/pfe/player-feature-extractor/torchreid/utils/reidtools.py
new file mode 100644
index 0000000..c7354f5
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/reidtools.py
@@ -0,0 +1,154 @@
+from __future__ import print_function, absolute_import
+import numpy as np
+import shutil
+import os.path as osp
+import cv2
+
+from .tools import mkdir_if_missing
+
+__all__ = ['visualize_ranked_results']
+
+GRID_SPACING = 10
+QUERY_EXTRA_SPACING = 90
+BW = 5 # border width
+GREEN = (0, 255, 0)
+RED = (0, 0, 255)
+
+
+def visualize_ranked_results(
+    distmat, dataset, data_type, width=128, height=256, save_dir='', topk=10
+):
+    """Visualizes ranked results.
+
+    Supports both image-reid and video-reid.
+
+    For image-reid, ranks will be plotted in a single figure. For video-reid, ranks will be
+    saved in folders each containing a tracklet.
+
+    Args:
+        distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
+        dataset (tuple): a 2-tuple containing (query, gallery), each of which contains
+            tuples of (img_path(s), pid, camid).
+        data_type (str): "image" or "video".
+        width (int, optional): resized image width. Default is 128.
+        height (int, optional): resized image height. Default is 256.
+        save_dir (str): directory to save output images.
+        topk (int, optional): denoting top-k images in the rank list to be visualized.
+            Default is 10.
+    """
+    num_q, num_g = distmat.shape
+    mkdir_if_missing(save_dir)
+
+    print('# query: {}\n# gallery {}'.format(num_q, num_g))
+    print('Visualizing top-{} ranks ...'.format(topk))
+
+    query, gallery = dataset
+    assert num_q == len(query)
+    assert num_g == len(gallery)
+
+    indices = np.argsort(distmat, axis=1)
+
+    def _cp_img_to(src, dst, rank, prefix, matched=False):
+        """
+        Args:
+            src: image path or tuple (for vidreid)
+            dst: target directory
+            rank: int, denoting ranked position, starting from 1
+            prefix: string
+            matched: bool
+        """
+        if isinstance(src, (tuple, list)):
+            if prefix == 'gallery':
+                suffix = 'TRUE' if matched else 'FALSE'
+                dst = osp.join(
+                    dst, prefix + '_top' + str(rank).zfill(3)
+                ) + '_' + suffix
+            else:
+                dst = osp.join(dst, prefix + '_top' + str(rank).zfill(3))
+            mkdir_if_missing(dst)
+            for img_path in src:
+                shutil.copy(img_path, dst)
+        else:
+            dst = osp.join(
+                dst, prefix + '_top' + str(rank).zfill(3) + '_name_' +
+                osp.basename(src)
+            )
+            shutil.copy(src, dst)
+
+    for q_idx in range(num_q):
+        qimg_path, qpid, qcamid = query[q_idx]
+        qimg_path_name = qimg_path[0] if isinstance(
+            qimg_path, (tuple, list)
+        ) else qimg_path
+
+        if data_type == 'image':
+            qimg = cv2.imread(qimg_path)
+            qimg = cv2.resize(qimg, (width, height))
+            qimg = cv2.copyMakeBorder(
+                qimg, BW, BW, BW, BW, cv2.BORDER_CONSTANT, value=(0, 0, 0)
+            )
+            # resize twice to ensure that the border width is consistent across images
+            qimg = cv2.resize(qimg, (width, height))
+            num_cols = topk + 1
+            grid_img = 255 * np.ones(
+                (
+                    height,
+                    num_cols*width + topk*GRID_SPACING + QUERY_EXTRA_SPACING, 3
+                ),
+                dtype=np.uint8
+            )
+            grid_img[:, :width, :] = qimg
+        else:
+            qdir = osp.join(
+                save_dir, osp.basename(osp.splitext(qimg_path_name)[0])
+            )
+            mkdir_if_missing(qdir)
+            _cp_img_to(qimg_path, qdir, rank=0, prefix='query')
+
+        rank_idx = 1
+        for g_idx in indices[q_idx, :]:
+            gimg_path, gpid, gcamid = gallery[g_idx]
+            invalid = (qpid == gpid) & (qcamid == gcamid)
+
+            if not invalid:
+                matched = gpid == qpid
+                if data_type == 'image':
+                    border_color = GREEN if matched else RED
+                    gimg = cv2.imread(gimg_path)
+                    gimg = cv2.resize(gimg, (width, height))
+                    gimg = cv2.copyMakeBorder(
+                        gimg,
+                        BW,
+                        BW,
+                        BW,
+                        BW,
+                        cv2.BORDER_CONSTANT,
+                        value=border_color
+                    )
+                    gimg = cv2.resize(gimg, (width, height))
+                    start = rank_idx*width + rank_idx*GRID_SPACING + QUERY_EXTRA_SPACING
+                    end = (
+                        rank_idx+1
+                    ) * width + rank_idx*GRID_SPACING + QUERY_EXTRA_SPACING
+                    grid_img[:, start:end, :] = gimg
+                else:
+                    _cp_img_to(
+                        gimg_path,
+                        qdir,
+                        rank=rank_idx,
+                        prefix='gallery',
+                        matched=matched
+                    )
+
+                rank_idx += 1
+                if rank_idx > topk:
+                    break
+
+        if data_type == 'image':
+            imname = osp.basename(osp.splitext(qimg_path_name)[0])
+            cv2.imwrite(osp.join(save_dir, imname + '.jpg'), grid_img)
+
+        if (q_idx+1) % 100 == 0:
+            print('- done {}/{}'.format(q_idx + 1, num_q))
+
+    print('Done. Images have been saved to "{}" ...'.format(save_dir))
diff --git a/pfe/player-feature-extractor/torchreid/utils/rerank.py b/pfe/player-feature-extractor/torchreid/utils/rerank.py
new file mode 100644
index 0000000..efadf5a
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/rerank.py
@@ -0,0 +1,113 @@
+#!/usr/bin/env python2/python3
+# -*- coding: utf-8 -*-
+"""
+Source: https://github.com/zhunzhong07/person-re-ranking
+
+Created on Mon Jun 26 14:46:56 2017
+@author: luohao
+Modified by Houjing Huang, 2017-12-22.
+- This version accepts distance matrix instead of raw features.
+- The difference of `/` division between python 2 and 3 is handled.
+- numpy.float16 is replaced by numpy.float32 for numerical precision.
+
+CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
+url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf
+Matlab version: https://github.com/zhunzhong07/person-re-ranking
+
+API
+q_g_dist: query-gallery distance matrix, numpy array, shape [num_query, num_gallery]
+q_q_dist: query-query distance matrix, numpy array, shape [num_query, num_query]
+g_g_dist: gallery-gallery distance matrix, numpy array, shape [num_gallery, num_gallery]
+k1, k2, lambda_value: parameters, the original paper is (k1=20, k2=6, lambda_value=0.3)
+Returns:
+  final_dist: re-ranked distance, numpy array, shape [num_query, num_gallery]
+"""
+from __future__ import division, print_function, absolute_import
+import numpy as np
+
+__all__ = ['re_ranking']
+
+
+def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
+
+    # The following naming, e.g. gallery_num, is different from outer scope.
+    # Don't care about it.
+
+    original_dist = np.concatenate(
+        [
+            np.concatenate([q_q_dist, q_g_dist], axis=1),
+            np.concatenate([q_g_dist.T, g_g_dist], axis=1)
+        ],
+        axis=0
+    )
+    original_dist = np.power(original_dist, 2).astype(np.float32)
+    original_dist = np.transpose(
+        1. * original_dist / np.max(original_dist, axis=0)
+    )
+    V = np.zeros_like(original_dist).astype(np.float32)
+    initial_rank = np.argsort(original_dist).astype(np.int32)
+
+    query_num = q_g_dist.shape[0]
+    gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
+    all_num = gallery_num
+
+    for i in range(all_num):
+        # k-reciprocal neighbors
+        forward_k_neigh_index = initial_rank[i, :k1 + 1]
+        backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
+        fi = np.where(backward_k_neigh_index == i)[0]
+        k_reciprocal_index = forward_k_neigh_index[fi]
+        k_reciprocal_expansion_index = k_reciprocal_index
+        for j in range(len(k_reciprocal_index)):
+            candidate = k_reciprocal_index[j]
+            candidate_forward_k_neigh_index = initial_rank[
+                candidate, :int(np.around(k1 / 2.)) + 1]
+            candidate_backward_k_neigh_index = initial_rank[
+                candidate_forward_k_neigh_index, :int(np.around(k1 / 2.)) + 1]
+            fi_candidate = np.where(
+                candidate_backward_k_neigh_index == candidate
+            )[0]
+            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[
+                fi_candidate]
+            if len(
+                np.
+                intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)
+            ) > 2. / 3 * len(candidate_k_reciprocal_index):
+                k_reciprocal_expansion_index = np.append(
+                    k_reciprocal_expansion_index, candidate_k_reciprocal_index
+                )
+
+        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
+        weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
+        V[i, k_reciprocal_expansion_index] = 1. * weight / np.sum(weight)
+    original_dist = original_dist[:query_num, ]
+    if k2 != 1:
+        V_qe = np.zeros_like(V, dtype=np.float32)
+        for i in range(all_num):
+            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
+        V = V_qe
+        del V_qe
+    del initial_rank
+    invIndex = []
+    for i in range(gallery_num):
+        invIndex.append(np.where(V[:, i] != 0)[0])
+
+    jaccard_dist = np.zeros_like(original_dist, dtype=np.float32)
+
+    for i in range(query_num):
+        temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float32)
+        indNonZero = np.where(V[i, :] != 0)[0]
+        indImages = []
+        indImages = [invIndex[ind] for ind in indNonZero]
+        for j in range(len(indNonZero)):
+            temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(
+                V[i, indNonZero[j]], V[indImages[j], indNonZero[j]]
+            )
+        jaccard_dist[i] = 1 - temp_min / (2.-temp_min)
+
+    final_dist = jaccard_dist * (1-lambda_value) + original_dist*lambda_value
+    del original_dist
+    del V
+    del jaccard_dist
+    final_dist = final_dist[:query_num, query_num:]
+    return final_dist
diff --git a/pfe/player-feature-extractor/torchreid/utils/tools.py b/pfe/player-feature-extractor/torchreid/utils/tools.py
new file mode 100644
index 0000000..a9a607d
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/tools.py
@@ -0,0 +1,129 @@
+from __future__ import division, print_function, absolute_import
+import os
+import sys
+import json
+import time
+import errno
+import numpy as np
+import random
+import os.path as osp
+import warnings
+import PIL
+import torch
+from PIL import Image
+
+__all__ = [
+    'mkdir_if_missing', 'check_isfile', 'read_json', 'write_json',
+    'set_random_seed', 'download_url', 'read_image', 'collect_env_info'
+]
+
+
+def mkdir_if_missing(dirname):
+    """Creates dirname if it is missing."""
+    if not osp.exists(dirname):
+        try:
+            os.makedirs(dirname)
+        except OSError as e:
+            if e.errno != errno.EEXIST:
+                raise
+
+
+def check_isfile(fpath):
+    """Checks if the given path is a file.
+
+    Args:
+        fpath (str): file path.
+
+    Returns:
+       bool
+    """
+    isfile = osp.isfile(fpath)
+    if not isfile:
+        warnings.warn('No file found at "{}"'.format(fpath))
+    return isfile
+
+
+def read_json(fpath):
+    """Reads json file from a path."""
+    with open(fpath, 'r') as f:
+        obj = json.load(f)
+    return obj
+
+
+def write_json(obj, fpath):
+    """Writes to a json file."""
+    mkdir_if_missing(osp.dirname(fpath))
+    with open(fpath, 'w') as f:
+        json.dump(obj, f, indent=4, separators=(',', ': '))
+
+
+def set_random_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def download_url(url, dst):
+    """Downloads file from a url to a destination.
+
+    Args:
+        url (str): url to download file.
+        dst (str): destination path.
+    """
+    from six.moves import urllib
+    print('* url="{}"'.format(url))
+    print('* destination="{}"'.format(dst))
+
+    def _reporthook(count, block_size, total_size):
+        global start_time
+        if count == 0:
+            start_time = time.time()
+            return
+        duration = time.time() - start_time
+        progress_size = int(count * block_size)
+        speed = int(progress_size / (1024*duration))
+        percent = int(count * block_size * 100 / total_size)
+        sys.stdout.write(
+            '\r...%d%%, %d MB, %d KB/s, %d seconds passed' %
+            (percent, progress_size / (1024*1024), speed, duration)
+        )
+        sys.stdout.flush()
+
+    urllib.request.urlretrieve(url, dst, _reporthook)
+    sys.stdout.write('\n')
+
+
+def read_image(path):
+    """Reads image from path using ``PIL.Image``.
+
+    Args:
+        path (str): path to an image.
+
+    Returns:
+        PIL image
+    """
+    got_img = False
+    if not osp.exists(path):
+        raise IOError('"{}" does not exist'.format(path))
+    while not got_img:
+        try:
+            img = Image.open(path).convert('RGB')
+            got_img = True
+        except IOError:
+            print(
+                'IOError incurred when reading "{}". Will redo. Don\'t worry. Just chill.'
+                .format(path)
+            )
+    return img
+
+
+def collect_env_info():
+    """Returns env info as a string.
+
+    Code source: github.com/facebookresearch/maskrcnn-benchmark
+    """
+    from torch.utils.collect_env import get_pretty_env_info
+    env_str = get_pretty_env_info()
+    env_str += '\n        Pillow ({})'.format(PIL.__version__)
+    return env_str
diff --git a/pfe/player-feature-extractor/torchreid/utils/torchtools.py b/pfe/player-feature-extractor/torchreid/utils/torchtools.py
new file mode 100644
index 0000000..b6df8d8
--- /dev/null
+++ b/pfe/player-feature-extractor/torchreid/utils/torchtools.py
@@ -0,0 +1,311 @@
+from __future__ import division, print_function, absolute_import
+import pickle
+import shutil
+import os.path as osp
+import warnings
+from functools import partial
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+
+from .tools import mkdir_if_missing
+
+__all__ = [
+    'save_checkpoint', 'load_checkpoint', 'resume_from_checkpoint',
+    'open_all_layers', 'open_specified_layers', 'count_num_param',
+    'load_pretrained_weights'
+]
+
+
+def save_checkpoint(
+    state, save_dir, is_best=False, remove_module_from_keys=False
+):
+    r"""Saves checkpoint.
+
+    Args:
+        state (dict): dictionary.
+        save_dir (str): directory to save checkpoint.
+        is_best (bool, optional): if True, this checkpoint will be copied and named
+            ``model-best.pth.tar``. Default is False.
+        remove_module_from_keys (bool, optional): whether to remove "module."
+            from layer names. Default is False.
+
+    Examples::
+        >>> state = {
+        >>>     'state_dict': model.state_dict(),
+        >>>     'epoch': 10,
+        >>>     'rank1': 0.5,
+        >>>     'optimizer': optimizer.state_dict()
+        >>> }
+        >>> save_checkpoint(state, 'log/my_model')
+    """
+    mkdir_if_missing(save_dir)
+    if remove_module_from_keys:
+        # remove 'module.' in state_dict's keys
+        state_dict = state['state_dict']
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            if k.startswith('module.'):
+                k = k[7:]
+            new_state_dict[k] = v
+        state['state_dict'] = new_state_dict
+    # save
+    epoch = state['epoch']
+    fpath = osp.join(save_dir, 'model.pth.tar-' + str(epoch))
+    torch.save(state, fpath)
+    print('Checkpoint saved to "{}"'.format(fpath))
+    if is_best:
+        shutil.copy(fpath, osp.join(osp.dirname(fpath), 'model-best.pth.tar'))
+
+
+def load_checkpoint(fpath):
+    r"""Loads checkpoint.
+
+    ``UnicodeDecodeError`` can be well handled, which means
+    python2-saved files can be read from python3.
+
+    Args:
+        fpath (str): path to checkpoint.
+
+    Returns:
+        dict
+
+    Examples::
+        >>> from torchreid.utils import load_checkpoint
+        >>> fpath = 'log/my_model/model.pth.tar-10'
+        >>> checkpoint = load_checkpoint(fpath)
+    """
+    if fpath is None:
+        raise ValueError('File path is None')
+    if not osp.exists(fpath):
+        raise FileNotFoundError('File is not found at "{}"'.format(fpath))
+    map_location = None if torch.cuda.is_available() else 'cpu'
+    try:
+        checkpoint = torch.load(fpath, map_location=map_location)
+    except UnicodeDecodeError:
+        pickle.load = partial(pickle.load, encoding="latin1")
+        pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
+        checkpoint = torch.load(
+            fpath, pickle_module=pickle, map_location=map_location
+        )
+    except Exception:
+        print('Unable to load checkpoint from "{}"'.format(fpath))
+        raise
+    return checkpoint
+
+
+def resume_from_checkpoint(fpath, model, optimizer=None, scheduler=None):
+    r"""Resumes training from a checkpoint.
+
+    This will load (1) model weights and (2) ``state_dict``
+    of optimizer if ``optimizer`` is not None.
+
+    Args:
+        fpath (str): path to checkpoint.
+        model (nn.Module): model.
+        optimizer (Optimizer, optional): an Optimizer.
+        scheduler (LRScheduler, optional): an LRScheduler.
+
+    Returns:
+        int: start_epoch.
+
+    Examples::
+        >>> from torchreid.utils import resume_from_checkpoint
+        >>> fpath = 'log/my_model/model.pth.tar-10'
+        >>> start_epoch = resume_from_checkpoint(
+        >>>     fpath, model, optimizer, scheduler
+        >>> )
+    """
+    print('Loading checkpoint from "{}"'.format(fpath))
+    checkpoint = load_checkpoint(fpath)
+    model.load_state_dict(checkpoint['state_dict'])
+    print('Loaded model weights')
+    if optimizer is not None and 'optimizer' in checkpoint.keys():
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        print('Loaded optimizer')
+    if scheduler is not None and 'scheduler' in checkpoint.keys():
+        scheduler.load_state_dict(checkpoint['scheduler'])
+        print('Loaded scheduler')
+    start_epoch = checkpoint['epoch']
+    print('Last epoch = {}'.format(start_epoch))
+    if 'rank1' in checkpoint.keys():
+        print('Last rank1 = {:.1%}'.format(checkpoint['rank1']))
+    return start_epoch
+
+
+def adjust_learning_rate(
+    optimizer,
+    base_lr,
+    epoch,
+    stepsize=20,
+    gamma=0.1,
+    linear_decay=False,
+    final_lr=0,
+    max_epoch=100
+):
+    r"""Adjusts learning rate.
+
+    Deprecated.
+    """
+    if linear_decay:
+        # linearly decay learning rate from base_lr to final_lr
+        frac_done = epoch / max_epoch
+        lr = frac_done*final_lr + (1.-frac_done) * base_lr
+    else:
+        # decay learning rate by gamma for every stepsize
+        lr = base_lr * (gamma**(epoch // stepsize))
+
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+
+def set_bn_to_eval(m):
+    r"""Sets BatchNorm layers to eval mode."""
+    # 1. no update for running mean and var
+    # 2. scale and shift parameters are still trainable
+    classname = m.__class__.__name__
+    if classname.find('BatchNorm') != -1:
+        m.eval()
+
+
+def open_all_layers(model):
+    r"""Opens all layers in model for training.
+
+    Examples::
+        >>> from torchreid.utils import open_all_layers
+        >>> open_all_layers(model)
+    """
+    model.train()
+    for p in model.parameters():
+        p.requires_grad = True
+
+
+def open_specified_layers(model, open_layers):
+    r"""Opens specified layers in model for training while keeping
+    other layers frozen.
+
+    Args:
+        model (nn.Module): neural net model.
+        open_layers (str or list): layers open for training.
+
+    Examples::
+        >>> from torchreid.utils import open_specified_layers
+        >>> # Only model.classifier will be updated.
+        >>> open_layers = 'classifier'
+        >>> open_specified_layers(model, open_layers)
+        >>> # Only model.fc and model.classifier will be updated.
+        >>> open_layers = ['fc', 'classifier']
+        >>> open_specified_layers(model, open_layers)
+    """
+    if isinstance(model, nn.DataParallel):
+        model = model.module
+
+    if isinstance(open_layers, str):
+        open_layers = [open_layers]
+
+    for layer in open_layers:
+        assert hasattr(
+            model, layer
+        ), '"{}" is not an attribute of the model, please provide the correct name'.format(
+            layer
+        )
+
+    for name, module in model.named_children():
+        if name in open_layers:
+            module.train()
+            for p in module.parameters():
+                p.requires_grad = True
+        else:
+            module.eval()
+            for p in module.parameters():
+                p.requires_grad = False
+
+
+def count_num_param(model):
+    r"""Counts number of parameters in a model while ignoring ``self.classifier``.
+
+    Args:
+        model (nn.Module): network model.
+
+    Examples::
+        >>> from torchreid.utils import count_num_param
+        >>> model_size = count_num_param(model)
+
+    .. warning::
+        
+        This method is deprecated in favor of
+        ``torchreid.utils.compute_model_complexity``.
+    """
+    warnings.warn(
+        'This method is deprecated and will be removed in the future.'
+    )
+
+    num_param = sum(p.numel() for p in model.parameters())
+
+    if isinstance(model, nn.DataParallel):
+        model = model.module
+
+    if hasattr(model,
+               'classifier') and isinstance(model.classifier, nn.Module):
+        # we ignore the classifier because it is unused at test time
+        num_param -= sum(p.numel() for p in model.classifier.parameters())
+
+    return num_param
+
+
+def load_pretrained_weights(model, weight_path):
+    r"""Loads pretrianed weights to model.
+
+    Features::
+        - Incompatible layers (unmatched in name or size) will be ignored.
+        - Can automatically deal with keys containing "module.".
+
+    Args:
+        model (nn.Module): network model.
+        weight_path (str): path to pretrained weights.
+
+    Examples::
+        >>> from torchreid.utils import load_pretrained_weights
+        >>> weight_path = 'log/my_model/model-best.pth.tar'
+        >>> load_pretrained_weights(model, weight_path)
+    """
+    checkpoint = load_checkpoint(weight_path)
+    if 'state_dict' in checkpoint:
+        state_dict = checkpoint['state_dict']
+    else:
+        state_dict = checkpoint
+
+    model_dict = model.state_dict()
+    new_state_dict = OrderedDict()
+    matched_layers, discarded_layers = [], []
+
+    for k, v in state_dict.items():
+        if k.startswith('module.'):
+            k = k[7:] # discard module.
+
+        if k in model_dict and model_dict[k].size() == v.size():
+            new_state_dict[k] = v
+            matched_layers.append(k)
+        else:
+            discarded_layers.append(k)
+
+    model_dict.update(new_state_dict)
+    model.load_state_dict(model_dict)
+
+    if len(matched_layers) == 0:
+        warnings.warn(
+            'The pretrained weights "{}" cannot be loaded, '
+            'please check the key names manually '
+            '(** ignored and continue **)'.format(weight_path)
+        )
+    else:
+        print(
+            'Successfully loaded pretrained weights from "{}"'.
+            format(weight_path)
+        )
+        if len(discarded_layers) > 0:
+            print(
+                '** The following layers are discarded '
+                'due to unmatched keys or layer size: {}'.
+                format(discarded_layers)
+            )
diff --git a/pfe/projects/DML/README.md b/pfe/projects/DML/README.md
new file mode 100644
index 0000000..e81be7f
--- /dev/null
+++ b/pfe/projects/DML/README.md
@@ -0,0 +1,16 @@
+# Deep mutual learning
+
+This repo implements [Deep Mutual Learning (CVPR'18)](https://zpascal.net/cvpr2018/Zhang_Deep_Mutual_Learning_CVPR_2018_paper.pdf) (DML) for person re-id.
+
+We used this code in our [OSNet](https://arxiv.org/pdf/1905.00953.pdf) paper (see Supp. B). The training command to reproduce the result of "triplet + DML" (Table 12f in the paper) is
+```bash
+python main.py \
+--config-file im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml \
+--root $DATA
+```
+
+`$DATA` corresponds to the path to your dataset folder.
+
+Change `model.deploy` to `both` if you wanna enable model ensembling.
+
+If you have any questions, please raise an issue in the Issues area.
\ No newline at end of file
diff --git a/pfe/projects/DML/default_config.py b/pfe/projects/DML/default_config.py
new file mode 100644
index 0000000..9b15e35
--- /dev/null
+++ b/pfe/projects/DML/default_config.py
@@ -0,0 +1,207 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'resnet50'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights1 = '' # path to model-1 weights
+    cfg.model.load_weights2 = '' # path to model-2 weights
+    cfg.model.resume1 = '' # path to checkpoint for resume training
+    cfg.model.resume2 = '' # path to checkpoint for resume training
+    cfg.model.deploy = 'model1' # model1, model2 or both
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['market1501']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 256 # image height
+    cfg.data.width = 128 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler'
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0003
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 60
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 20 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'triplet'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+    cfg.loss.dml = CN()
+    cfg.loss.dml.weight_ml = 1. # weight for mutual learning loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'euclidean' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = False # test only
+    cfg.test.eval_freq = -1 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 0 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        'test_only': cfg.test.evaluate,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': cfg.test.dist_metric,
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': cfg.test.visrank,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/projects/DML/dml.py b/pfe/projects/DML/dml.py
new file mode 100644
index 0000000..546e573
--- /dev/null
+++ b/pfe/projects/DML/dml.py
@@ -0,0 +1,149 @@
+from __future__ import division, print_function, absolute_import
+import torch
+from torch.nn import functional as F
+
+from torchreid.utils import open_all_layers, open_specified_layers
+from torchreid.engine import Engine
+from torchreid.losses import TripletLoss, CrossEntropyLoss
+
+
+class ImageDMLEngine(Engine):
+
+    def __init__(
+        self,
+        datamanager,
+        model1,
+        optimizer1,
+        scheduler1,
+        model2,
+        optimizer2,
+        scheduler2,
+        margin=0.3,
+        weight_t=0.5,
+        weight_x=1.,
+        weight_ml=1.,
+        use_gpu=True,
+        label_smooth=True,
+        deploy='model1'
+    ):
+        super(ImageDMLEngine, self).__init__(datamanager, use_gpu)
+
+        self.model1 = model1
+        self.optimizer1 = optimizer1
+        self.scheduler1 = scheduler1
+        self.register_model('model1', model1, optimizer1, scheduler1)
+
+        self.model2 = model2
+        self.optimizer2 = optimizer2
+        self.scheduler2 = scheduler2
+        self.register_model('model2', model2, optimizer2, scheduler2)
+
+        self.weight_t = weight_t
+        self.weight_x = weight_x
+        self.weight_ml = weight_ml
+
+        assert deploy in ['model1', 'model2', 'both']
+        self.deploy = deploy
+
+        self.criterion_t = TripletLoss(margin=margin)
+        self.criterion_x = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        outputs1, features1 = self.model1(imgs)
+        loss1_x = self.compute_loss(self.criterion_x, outputs1, pids)
+        loss1_t = self.compute_loss(self.criterion_t, features1, pids)
+
+        outputs2, features2 = self.model2(imgs)
+        loss2_x = self.compute_loss(self.criterion_x, outputs2, pids)
+        loss2_t = self.compute_loss(self.criterion_t, features2, pids)
+
+        loss1_ml = self.compute_kl_div(
+            outputs2.detach(), outputs1, is_logit=True
+        )
+        loss2_ml = self.compute_kl_div(
+            outputs1.detach(), outputs2, is_logit=True
+        )
+
+        loss1 = 0
+        loss1 += loss1_x * self.weight_x
+        loss1 += loss1_t * self.weight_t
+        loss1 += loss1_ml * self.weight_ml
+
+        loss2 = 0
+        loss2 += loss2_x * self.weight_x
+        loss2 += loss2_t * self.weight_t
+        loss2 += loss2_ml * self.weight_ml
+
+        self.optimizer1.zero_grad()
+        loss1.backward()
+        self.optimizer1.step()
+
+        self.optimizer2.zero_grad()
+        loss2.backward()
+        self.optimizer2.step()
+
+        loss_dict = {
+            'loss1_x': loss1_x.item(),
+            'loss1_t': loss1_t.item(),
+            'loss1_ml': loss1_ml.item(),
+            'loss2_x': loss1_x.item(),
+            'loss2_t': loss1_t.item(),
+            'loss2_ml': loss1_ml.item()
+        }
+
+        return loss_dict
+
+    @staticmethod
+    def compute_kl_div(p, q, is_logit=True):
+        if is_logit:
+            p = F.softmax(p, dim=1)
+            q = F.softmax(q, dim=1)
+        return -(p * torch.log(q + 1e-8)).sum(1).mean()
+
+    def two_stepped_transfer_learning(
+        self, epoch, fixbase_epoch, open_layers, model=None
+    ):
+        """Two stepped transfer learning.
+
+        The idea is to freeze base layers for a certain number of epochs
+        and then open all layers for training.
+
+        Reference: https://arxiv.org/abs/1611.05244
+        """
+        model1 = self.model1
+        model2 = self.model2
+
+        if (epoch + 1) <= fixbase_epoch and open_layers is not None:
+            print(
+                '* Only train {} (epoch: {}/{})'.format(
+                    open_layers, epoch + 1, fixbase_epoch
+                )
+            )
+            open_specified_layers(model1, open_layers)
+            open_specified_layers(model2, open_layers)
+        else:
+            open_all_layers(model1)
+            open_all_layers(model2)
+
+    def extract_features(self, input):
+        if self.deploy == 'model1':
+            return self.model1(input)
+
+        elif self.deploy == 'model2':
+            return self.model2(input)
+
+        else:
+            features = []
+            features.append(self.model1(input))
+            features.append(self.model2(input))
+            return torch.cat(features, 1)
diff --git a/pfe/projects/DML/im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml b/pfe/projects/DML/im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml
new file mode 100644
index 0000000..3a240b1
--- /dev/null
+++ b/pfe/projects/DML/im_osnet_x1_0_dml_256x128_amsgrad_cosine.yaml
@@ -0,0 +1,42 @@
+model:
+  name: 'osnet_x1_0'
+  pretrained: True
+  deploy: 'model1'
+
+data:
+  type: 'image'
+  sources: ['market1501']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: False
+  transforms: ['random_flip', 'random_erase']
+  save_dir: 'log/osnet_x1_0_market1501_dml_cosinelr'
+
+loss:
+  name: 'triplet'
+  softmax:
+    label_smooth: True
+  triplet:
+    margin: 0.3
+    weight_t: 0.5
+    weight_x: 1.
+  dml:
+    weight_ml: 1.
+
+train:
+  optim: 'amsgrad'
+  lr: 0.0015
+  max_epoch: 250
+  batch_size: 64
+  fixbase_epoch: 10
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'cosine'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
\ No newline at end of file
diff --git a/pfe/projects/DML/main.py b/pfe/projects/DML/main.py
new file mode 100755
index 0000000..9af33d9
--- /dev/null
+++ b/pfe/projects/DML/main.py
@@ -0,0 +1,166 @@
+import sys
+import copy
+import time
+import os.path as osp
+import argparse
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, check_isfile, set_random_seed, collect_env_info,
+    resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
+)
+
+from dml import ImageDMLEngine
+from default_config import (
+    imagedata_kwargs, optimizer_kwargs, engine_run_kwargs, get_default_config,
+    lr_scheduler_kwargs
+)
+
+
+def reset_config(cfg, args):
+    if args.root:
+        cfg.data.root = args.root
+    if args.sources:
+        cfg.data.sources = args.sources
+    if args.targets:
+        cfg.data.targets = args.targets
+    if args.transforms:
+        cfg.data.transforms = args.transforms
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        '--config-file', type=str, default='', help='path to config file'
+    )
+    parser.add_argument(
+        '-s',
+        '--sources',
+        type=str,
+        nargs='+',
+        help='source datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '-t',
+        '--targets',
+        type=str,
+        nargs='+',
+        help='target datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '--transforms', type=str, nargs='+', help='data augmentation'
+    )
+    parser.add_argument(
+        '--root', type=str, default='', help='path to data root'
+    )
+    parser.add_argument(
+        'opts',
+        default=None,
+        nargs=argparse.REMAINDER,
+        help='Modify config options using the command-line'
+    )
+    args = parser.parse_args()
+
+    cfg = get_default_config()
+    cfg.use_gpu = torch.cuda.is_available()
+    if args.config_file:
+        cfg.merge_from_file(args.config_file)
+    reset_config(cfg, args)
+    cfg.merge_from_list(args.opts)
+    set_random_seed(cfg.train.seed)
+
+    log_name = 'test.log' if cfg.test.evaluate else 'train.log'
+    log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
+    sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
+
+    print('Show configuration\n{}\n'.format(cfg))
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if cfg.use_gpu:
+        torch.backends.cudnn.benchmark = True
+
+    datamanager = torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
+
+    print('Building model-1: {}'.format(cfg.model.name))
+    model1 = torchreid.models.build_model(
+        name=cfg.model.name,
+        num_classes=datamanager.num_train_pids,
+        loss=cfg.loss.name,
+        pretrained=cfg.model.pretrained,
+        use_gpu=cfg.use_gpu
+    )
+    num_params, flops = compute_model_complexity(
+        model1, (1, 3, cfg.data.height, cfg.data.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    print('Copying model-1 to model-2')
+    model2 = copy.deepcopy(model1)
+
+    if cfg.model.load_weights1 and check_isfile(cfg.model.load_weights1):
+        load_pretrained_weights(model1, cfg.model.load_weights1)
+
+    if cfg.model.load_weights2 and check_isfile(cfg.model.load_weights2):
+        load_pretrained_weights(model2, cfg.model.load_weights2)
+
+    if cfg.use_gpu:
+        model1 = nn.DataParallel(model1).cuda()
+        model2 = nn.DataParallel(model2).cuda()
+
+    optimizer1 = torchreid.optim.build_optimizer(
+        model1, **optimizer_kwargs(cfg)
+    )
+    scheduler1 = torchreid.optim.build_lr_scheduler(
+        optimizer1, **lr_scheduler_kwargs(cfg)
+    )
+
+    optimizer2 = torchreid.optim.build_optimizer(
+        model2, **optimizer_kwargs(cfg)
+    )
+    scheduler2 = torchreid.optim.build_lr_scheduler(
+        optimizer2, **lr_scheduler_kwargs(cfg)
+    )
+
+    if cfg.model.resume1 and check_isfile(cfg.model.resume1):
+        cfg.train.start_epoch = resume_from_checkpoint(
+            cfg.model.resume1,
+            model1,
+            optimizer=optimizer1,
+            scheduler=scheduler1
+        )
+
+    if cfg.model.resume2 and check_isfile(cfg.model.resume2):
+        resume_from_checkpoint(
+            cfg.model.resume2,
+            model2,
+            optimizer=optimizer2,
+            scheduler=scheduler2
+        )
+
+    print('Building DML-engine for image-reid')
+    engine = ImageDMLEngine(
+        datamanager,
+        model1,
+        optimizer1,
+        scheduler1,
+        model2,
+        optimizer2,
+        scheduler2,
+        margin=cfg.loss.triplet.margin,
+        weight_t=cfg.loss.triplet.weight_t,
+        weight_x=cfg.loss.triplet.weight_x,
+        weight_ml=cfg.loss.dml.weight_ml,
+        use_gpu=cfg.use_gpu,
+        label_smooth=cfg.loss.softmax.label_smooth,
+        deploy=cfg.model.deploy
+    )
+    engine.run(**engine_run_kwargs(cfg))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/projects/OSNet_AIN/README.md b/pfe/projects/OSNet_AIN/README.md
new file mode 100644
index 0000000..2020b43
--- /dev/null
+++ b/pfe/projects/OSNet_AIN/README.md
@@ -0,0 +1,38 @@
+# Differentiable NAS for OSNet-AIN
+
+## Introduction
+This repository contains the neural architecture search (NAS) code (based on [Torchreid](https://arxiv.org/abs/1910.10093)) for [OSNet-AIN](https://arxiv.org/abs/1910.06827), an extension of [OSNet](https://arxiv.org/abs/1905.00953) that achieves strong performance on cross-domain person re-identification (re-ID) benchmarks (*without using any target data*). OSNet-AIN builds on the idea of using [instance normalisation](https://arxiv.org/abs/1607.08022) (IN) layers to eliminate instance-specific contrast in images for domain-generalisable representation learning. This is inspired by the [neural style transfer](https://arxiv.org/abs/1703.06868) works that use IN to remove image styles. Though IN naturally suits the cross-domain person re-ID task, it still remains unclear that where to insert IN to a re-ID CNN can maximise the performance gain. To avoid exhaustively evaluating all possible designs, OSNet-AIN learns to search for the optimal OSNet+IN design from data using a differentiable NAS algorithm. For technical details, please refer to our paper at https://arxiv.org/abs/1910.06827.
+
+<div align="center">
+  <img src="https://drive.google.com/uc?export=view&id=1yvVIi2Ml7WBe85Uhaa54qyG4g8z-MGEB" width="500px" />
+</div>
+
+## Training
+Assume the reid data is stored at `$DATA`. Run
+```
+python main.py --config-file nas.yaml --root $DATA
+```
+
+The structure of the found architecture will be shown at the end of training.
+
+The default config was designed for 8 Tesla V100 32GB GPUs. You can modify the batch size based on your device memory.
+
+**Note** that the test result obtained at the end of architecture search is not meaningful (due to the stochastic sampling layers). Therefore, do not rely on the result to judge the model performance. Instead, you should construct the found architecture in `osnet_child.py` and re-train and evaluate the model on the reid datasets.
+
+## Citation
+If you find this code useful to your research, please consider citing the following papers.
+```
+@article{zhou2019learning,
+  title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
+  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
+  journal={arXiv preprint arXiv:1910.06827},
+  year={2019}
+}
+
+@inproceedings{zhou2019osnet,
+  title={Omni-Scale Feature Learning for Person Re-Identification},
+  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
+  booktitle={ICCV},
+  year={2019}
+}
+```
\ No newline at end of file
diff --git a/pfe/projects/OSNet_AIN/default_config.py b/pfe/projects/OSNet_AIN/default_config.py
new file mode 100644
index 0000000..733a9f0
--- /dev/null
+++ b/pfe/projects/OSNet_AIN/default_config.py
@@ -0,0 +1,210 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'resnet50'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '' # path to model weights
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # NAS
+    cfg.nas = CN()
+    cfg.nas.mc_iter = 1 # Monte Carlo sampling
+    cfg.nas.init_lmda = 10. # initial lambda value
+    cfg.nas.min_lmda = 1. # minimum lambda value
+    cfg.nas.lmda_decay_step = 20 # decay step for lambda
+    cfg.nas.lmda_decay_rate = 0.5 # decay rate for lambda
+    cfg.nas.fixed_lmda = False # keep lambda unchanged
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['market1501']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 256 # image height
+    cfg.data.width = 128 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler'
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0003
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 60
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 20 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'euclidean' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = False # test only
+    cfg.test.eval_freq = -1 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 0 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+    cfg.test.visactmap = False # visualize CNN activation maps
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        'test_only': cfg.test.evaluate,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': cfg.test.dist_metric,
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': cfg.test.visrank,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/projects/OSNet_AIN/main.py b/pfe/projects/OSNet_AIN/main.py
new file mode 100755
index 0000000..f591770
--- /dev/null
+++ b/pfe/projects/OSNet_AIN/main.py
@@ -0,0 +1,145 @@
+import os
+import sys
+import time
+import os.path as osp
+import argparse
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, check_isfile, set_random_seed, collect_env_info,
+    resume_from_checkpoint, compute_model_complexity
+)
+
+import osnet_search as osnet_models
+from softmax_nas import ImageSoftmaxNASEngine
+from default_config import (
+    imagedata_kwargs, optimizer_kwargs, engine_run_kwargs, get_default_config,
+    lr_scheduler_kwargs
+)
+
+
+def reset_config(cfg, args):
+    if args.root:
+        cfg.data.root = args.root
+    if args.sources:
+        cfg.data.sources = args.sources
+    if args.targets:
+        cfg.data.targets = args.targets
+    if args.transforms:
+        cfg.data.transforms = args.transforms
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        '--config-file', type=str, default='', help='path to config file'
+    )
+    parser.add_argument(
+        '-s',
+        '--sources',
+        type=str,
+        nargs='+',
+        help='source datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '-t',
+        '--targets',
+        type=str,
+        nargs='+',
+        help='target datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '--transforms', type=str, nargs='+', help='data augmentation'
+    )
+    parser.add_argument(
+        '--root', type=str, default='', help='path to data root'
+    )
+    parser.add_argument(
+        '--gpu-devices',
+        type=str,
+        default='',
+    )
+    parser.add_argument(
+        'opts',
+        default=None,
+        nargs=argparse.REMAINDER,
+        help='Modify config options using the command-line'
+    )
+    args = parser.parse_args()
+
+    cfg = get_default_config()
+    cfg.use_gpu = torch.cuda.is_available()
+    if args.config_file:
+        cfg.merge_from_file(args.config_file)
+    reset_config(cfg, args)
+    cfg.merge_from_list(args.opts)
+    set_random_seed(cfg.train.seed)
+
+    if cfg.use_gpu and args.gpu_devices:
+        # if gpu_devices is not specified, all available gpus will be used
+        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
+    log_name = 'test.log' if cfg.test.evaluate else 'train.log'
+    log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
+    sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
+
+    print('Show configuration\n{}\n'.format(cfg))
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if cfg.use_gpu:
+        torch.backends.cudnn.benchmark = True
+
+    datamanager = torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
+
+    print('Building model: {}'.format(cfg.model.name))
+    model = osnet_models.build_model(
+        cfg.model.name, num_classes=datamanager.num_train_pids
+    )
+    num_params, flops = compute_model_complexity(
+        model, (1, 3, cfg.data.height, cfg.data.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    if cfg.use_gpu:
+        model = nn.DataParallel(model).cuda()
+
+    optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
+    scheduler = torchreid.optim.build_lr_scheduler(
+        optimizer, **lr_scheduler_kwargs(cfg)
+    )
+
+    if cfg.model.resume and check_isfile(cfg.model.resume):
+        cfg.train.start_epoch = resume_from_checkpoint(
+            cfg.model.resume, model, optimizer=optimizer
+        )
+
+    print('Building NAS engine')
+    engine = ImageSoftmaxNASEngine(
+        datamanager,
+        model,
+        optimizer,
+        scheduler=scheduler,
+        use_gpu=cfg.use_gpu,
+        label_smooth=cfg.loss.softmax.label_smooth,
+        mc_iter=cfg.nas.mc_iter,
+        init_lmda=cfg.nas.init_lmda,
+        min_lmda=cfg.nas.min_lmda,
+        lmda_decay_step=cfg.nas.lmda_decay_step,
+        lmda_decay_rate=cfg.nas.lmda_decay_rate,
+        fixed_lmda=cfg.nas.fixed_lmda
+    )
+    engine.run(**engine_run_kwargs(cfg))
+
+    print('*** Display the found architecture ***')
+    if cfg.use_gpu:
+        model.module.build_child_graph()
+    else:
+        model.build_child_graph()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/projects/OSNet_AIN/nas.yaml b/pfe/projects/OSNet_AIN/nas.yaml
new file mode 100644
index 0000000..507b2ec
--- /dev/null
+++ b/pfe/projects/OSNet_AIN/nas.yaml
@@ -0,0 +1,44 @@
+model:
+  name: 'osnet_nas'
+  pretrained: False
+
+nas:
+  mc_iter: 1
+  init_lmda: 10.
+  min_lmda: 1.
+  lmda_decay_step: 20
+  lmda_decay_rate: 0.5
+  fixed_lmda: False
+
+data:
+  type: 'image'
+  sources: ['msmt17']
+  targets: ['market1501']
+  height: 256
+  width: 128
+  combineall: True
+  transforms: ['random_flip', 'color_jitter']
+  save_dir: 'log/osnet_nas'
+
+loss:
+  name: 'softmax'
+  softmax:
+    label_smooth: True
+
+train:
+  optim: 'sgd'
+  lr: 0.1
+  max_epoch: 120
+  batch_size: 512
+  fixbase_epoch: 0
+  open_layers: ['classifier']
+  lr_scheduler: 'cosine'
+
+test:
+  batch_size: 300
+  dist_metric: 'cosine'
+  normalize_feature: False
+  evaluate: False
+  eval_freq: -1
+  rerank: False
+  visactmap: False
\ No newline at end of file
diff --git a/pfe/projects/OSNet_AIN/osnet_child.py b/pfe/projects/OSNet_AIN/osnet_child.py
new file mode 100644
index 0000000..167f9dd
--- /dev/null
+++ b/pfe/projects/OSNet_AIN/osnet_child.py
@@ -0,0 +1,535 @@
+from __future__ import division, absolute_import
+from torch import nn
+from torch.nn import functional as F
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1, bn=True):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = None
+        if bn:
+            self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConvStream(nn.Module):
+    """Lightweight convolution stream."""
+
+    def __init__(self, in_channels, out_channels, depth):
+        super(LightConvStream, self).__init__()
+        assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
+            depth
+        )
+        layers = []
+        layers += [LightConv3x3(in_channels, out_channels)]
+        for i in range(depth - 1):
+            layers += [LightConv3x3(out_channels, out_channels)]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlock, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv1(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv1, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv2(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv2, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN(out) # IN outside residual
+        return F.relu(out)
+
+
+class OSBlockINv3(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv3, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN_in = nn.InstanceNorm2d(out_channels, affine=True)
+        self.IN_out = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN_in(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN_out(out) # IN outside residual
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        conv1_IN=True,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+        self.feature_dim = feature_dim
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(
+            3, channels[0], 7, stride=2, padding=3, IN=conv1_IN
+        )
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0], layers[0], channels[0], channels[1]
+        )
+        self.pool2 = nn.Sequential(
+            Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv3 = self._make_layer(
+            blocks[1], layers[1], channels[1], channels[2]
+        )
+        self.pool3 = nn.Sequential(
+            Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv4 = self._make_layer(
+            blocks[2], layers[2], channels[2], channels[3]
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            self.feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, blocks, layer, in_channels, out_channels):
+        layers = []
+        layers += [blocks[0](in_channels, out_channels)]
+        for i in range(1, len(blocks)):
+            layers += [blocks[i](out_channels, out_channels)]
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.InstanceNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.pool2(x)
+        x = self.conv3(x)
+        x = self.pool3(x)
+        x = self.conv4(x)
+        return self.conv5(x)
+
+    def forward(self, x, return_featuremaps=False, **kwargs):
+        x = self.featuremaps(x)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+##########
+# Instantiation
+##########
+def osnet_ain_x1_0(
+    num_classes=1000, pretrained=True, loss='softmax', **kwargs
+):
+    model = OSNet(
+        num_classes,
+        blocks=[
+            [OSBlockINv1, OSBlockINv1], [OSBlock, OSBlockINv1],
+            [OSBlockINv1, OSBlock]
+        ],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        conv1_IN=True,
+        **kwargs
+    )
+    return model
+
+
+__models = {'osnet_ain_x1_0': osnet_ain_x1_0}
+
+
+def build_model(name, num_classes=100):
+    avai_models = list(__models.keys())
+    if name not in avai_models:
+        raise KeyError(
+            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
+        )
+    return __models[name](num_classes=num_classes)
diff --git a/pfe/projects/OSNet_AIN/osnet_search.py b/pfe/projects/OSNet_AIN/osnet_search.py
new file mode 100644
index 0000000..a2a2cfa
--- /dev/null
+++ b/pfe/projects/OSNet_AIN/osnet_search.py
@@ -0,0 +1,584 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+EPS = 1e-12
+NORM_AFFINE = False # enable affine transformations for normalization layer
+
+
+##########
+# Basic layers
+##########
+class IBN(nn.Module):
+    """Instance + Batch Normalization."""
+
+    def __init__(self, num_channels):
+        super(IBN, self).__init__()
+        half1 = int(num_channels / 2)
+        self.half = half1
+        half2 = num_channels - half1
+        self.IN = nn.InstanceNorm2d(half1, affine=NORM_AFFINE)
+        self.BN = nn.BatchNorm2d(half2, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        split = torch.split(x, self.half, 1)
+        out1 = self.IN(split[0].contiguous())
+        out2 = self.BN(split[1].contiguous())
+        return torch.cat((out1, out2), 1)
+
+
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(
+        self, in_channels, out_channels, stride=1, groups=1, ibn=False
+    ):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        if ibn:
+            self.bn = IBN(out_channels)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1, bn=True):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = None
+        if bn:
+            self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels, affine=NORM_AFFINE)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConvStream(nn.Module):
+    """Lightweight convolution stream."""
+
+    def __init__(self, in_channels, out_channels, depth):
+        super(LightConvStream, self).__init__()
+        assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
+            depth
+        )
+        layers = []
+        layers += [LightConv3x3(in_channels, out_channels)]
+        for i in range(depth - 1):
+            layers += [LightConv3x3(out_channels, out_channels)]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlock, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv1(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv1, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINv2(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv2, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN(out) # IN outside residual
+        return F.relu(out)
+
+
+class OSBlockINv3(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINv3, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN_in = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+        self.IN_out = nn.InstanceNorm2d(out_channels, affine=NORM_AFFINE)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN_in(x3) # inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        out = self.IN_out(out) # IN outside residual
+        return F.relu(out)
+
+
+class NASBlock(nn.Module):
+    """Neural architecture search layer."""
+
+    def __init__(self, in_channels, out_channels, search_space=None):
+        super(NASBlock, self).__init__()
+        self._is_child_graph = False
+        self.search_space = search_space
+        if self.search_space is None:
+            raise ValueError('search_space is None')
+
+        self.os_block = nn.ModuleList()
+        for block in self.search_space:
+            self.os_block += [block(in_channels, out_channels)]
+        self.weights = nn.Parameter(torch.ones(len(self.search_space)))
+
+    def build_child_graph(self):
+        if self._is_child_graph:
+            raise RuntimeError('build_child_graph() can only be called once')
+
+        idx = self.weights.data.max(dim=0)[1].item()
+        self.os_block = self.os_block[idx]
+        self.weights = None
+        self._is_child_graph = True
+        return self.search_space[idx]
+
+    def forward(self, x, lmda=1.):
+        if self._is_child_graph:
+            return self.os_block(x)
+
+        uniform = torch.rand_like(self.weights)
+        gumbel = -torch.log(-torch.log(uniform + EPS))
+        nonneg_weights = F.relu(self.weights)
+        logits = torch.log(nonneg_weights + EPS) + gumbel
+        exp = torch.exp(logits / lmda)
+        weights_softmax = exp / (exp.sum() + EPS)
+
+        output = 0
+        for i, weight in enumerate(weights_softmax):
+            output = output + weight * self.os_block[i](x)
+        return output
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        search_space=None,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        # no matter what loss is specified, the model only returns the ID predictions
+        self.loss = loss
+        self.feature_dim = feature_dim
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=True)
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0], layers[0], channels[0], channels[1], search_space
+        )
+        self.pool2 = nn.Sequential(
+            Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv3 = self._make_layer(
+            blocks[1], layers[1], channels[1], channels[2], search_space
+        )
+        self.pool3 = nn.Sequential(
+            Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv4 = self._make_layer(
+            blocks[2], layers[2], channels[2], channels[3], search_space
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            self.feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+    def _make_layer(
+        self, block, layer, in_channels, out_channels, search_space
+    ):
+        layers = nn.ModuleList()
+        layers += [block(in_channels, out_channels, search_space=search_space)]
+        for i in range(1, layer):
+            layers += [
+                block(out_channels, out_channels, search_space=search_space)
+            ]
+        return layers
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim, affine=NORM_AFFINE))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def build_child_graph(self):
+        print('Building child graph')
+        for i, conv in enumerate(self.conv2):
+            block = conv.build_child_graph()
+            print('- conv2-{} Block={}'.format(i + 1, block.__name__))
+        for i, conv in enumerate(self.conv3):
+            block = conv.build_child_graph()
+            print('- conv3-{} Block={}'.format(i + 1, block.__name__))
+        for i, conv in enumerate(self.conv4):
+            block = conv.build_child_graph()
+            print('- conv4-{} Block={}'.format(i + 1, block.__name__))
+
+    def featuremaps(self, x, lmda):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        for conv in self.conv2:
+            x = conv(x, lmda)
+        x = self.pool2(x)
+        for conv in self.conv3:
+            x = conv(x, lmda)
+        x = self.pool3(x)
+        for conv in self.conv4:
+            x = conv(x, lmda)
+        return self.conv5(x)
+
+    def forward(self, x, lmda=1., return_featuremaps=False):
+        # lmda (float): temperature parameter for concrete distribution
+        x = self.featuremaps(x, lmda)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        return self.classifier(v)
+
+
+##########
+# Instantiation
+##########
+def osnet_nas(num_classes=1000, loss='softmax', **kwargs):
+    # standard size (width x1.0)
+    return OSNet(
+        num_classes,
+        blocks=[NASBlock, NASBlock, NASBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        search_space=[OSBlock, OSBlockINv1, OSBlockINv2, OSBlockINv3],
+        **kwargs
+    )
+
+
+__NAS_models = {'osnet_nas': osnet_nas}
+
+
+def build_model(name, num_classes=100):
+    avai_models = list(__NAS_models.keys())
+    if name not in avai_models:
+        raise KeyError(
+            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
+        )
+    return __NAS_models[name](num_classes=num_classes)
diff --git a/pfe/projects/OSNet_AIN/softmax_nas.py b/pfe/projects/OSNet_AIN/softmax_nas.py
new file mode 100644
index 0000000..e2a03b6
--- /dev/null
+++ b/pfe/projects/OSNet_AIN/softmax_nas.py
@@ -0,0 +1,73 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid import metrics
+from torchreid.engine import Engine
+from torchreid.losses import CrossEntropyLoss
+
+
+class ImageSoftmaxNASEngine(Engine):
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        scheduler=None,
+        use_gpu=False,
+        label_smooth=True,
+        mc_iter=1,
+        init_lmda=1.,
+        min_lmda=1.,
+        lmda_decay_step=20,
+        lmda_decay_rate=0.5,
+        fixed_lmda=False
+    ):
+        super(ImageSoftmaxNASEngine, self).__init__(datamanager, use_gpu)
+        self.mc_iter = mc_iter
+        self.init_lmda = init_lmda
+        self.min_lmda = min_lmda
+        self.lmda_decay_step = lmda_decay_step
+        self.lmda_decay_rate = lmda_decay_rate
+        self.fixed_lmda = fixed_lmda
+
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.register_model('model', model, optimizer, scheduler)
+
+        self.criterion = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        # softmax temporature
+        if self.fixed_lmda or self.lmda_decay_step == -1:
+            lmda = self.init_lmda
+        else:
+            lmda = self.init_lmda * self.lmda_decay_rate**(
+                self.epoch // self.lmda_decay_step
+            )
+            if lmda < self.min_lmda:
+                lmda = self.min_lmda
+
+        for k in range(self.mc_iter):
+            outputs = self.model(imgs, lmda=lmda)
+            loss = self.compute_loss(self.criterion, outputs, pids)
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+
+        loss_dict = {
+            'loss': loss.item(),
+            'acc': metrics.accuracy(outputs, pids)[0].item()
+        }
+
+        return loss_dict
diff --git a/pfe/projects/README.md b/pfe/projects/README.md
new file mode 100644
index 0000000..fa63919
--- /dev/null
+++ b/pfe/projects/README.md
@@ -0,0 +1,5 @@
+Here are some research projects built on [Torchreid](https://arxiv.org/abs/1910.10093).
+
++ `OSNet_AIN`: [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1910.06827)
++ `DML`: [Deep Mutual Learning (CVPR'18)](https://arxiv.org/abs/1706.00384)
++ `attribute_recognition`: [Omni-Scale Feature Learning for Person Re-Identification (ICCV'19)](https://arxiv.org/abs/1905.00953)
\ No newline at end of file
diff --git a/pfe/projects/attribute_recognition/README.md b/pfe/projects/attribute_recognition/README.md
new file mode 100644
index 0000000..a20b6e7
--- /dev/null
+++ b/pfe/projects/attribute_recognition/README.md
@@ -0,0 +1,18 @@
+# Person Attribute Recognition
+This code was developed for the experiment of person attribute recognition in [Omni-Scale Feature Learning for Person Re-Identification (ICCV'19)](https://arxiv.org/abs/1905.00953).
+
+## Download data
+Download the PA-100K dataset from [https://github.com/xh-liu/HydraPlus-Net](https://github.com/xh-liu/HydraPlus-Net), and extract the file under the folder where you store your data (say $DATASET). The folder structure should look like
+```bash
+$DATASET/
+    pa100k/
+        data/ # images
+        annotation/
+            annotation.mat
+```
+
+## Train
+The training command is provided in `train.sh`. Run `bash train.sh $DATASET` to start training.
+
+## Test
+To test a pretrained model, add the following two arguments to `train.sh`: `--load-weights $PATH_TO_WEIGHTS --evaluate`.
\ No newline at end of file
diff --git a/pfe/projects/attribute_recognition/datasets/__init__.py b/pfe/projects/attribute_recognition/datasets/__init__.py
new file mode 100644
index 0000000..664598c
--- /dev/null
+++ b/pfe/projects/attribute_recognition/datasets/__init__.py
@@ -0,0 +1,15 @@
+from __future__ import division, print_function, absolute_import
+
+from .pa100k import PA100K
+
+__datasets = {'pa100k': PA100K}
+
+
+def init_dataset(name, **kwargs):
+    avai_datasets = list(__datasets.keys())
+    if name not in avai_datasets:
+        raise ValueError(
+            'Invalid dataset name. Received "{}", '
+            'but expected to be one of {}'.format(name, avai_datasets)
+        )
+    return __datasets[name](**kwargs)
diff --git a/pfe/projects/attribute_recognition/datasets/dataset.py b/pfe/projects/attribute_recognition/datasets/dataset.py
new file mode 100644
index 0000000..7ce4ebf
--- /dev/null
+++ b/pfe/projects/attribute_recognition/datasets/dataset.py
@@ -0,0 +1,87 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+
+from torchreid.utils import read_image
+
+
+class Dataset(object):
+
+    def __init__(
+        self,
+        train,
+        val,
+        test,
+        attr_dict,
+        transform=None,
+        mode='train',
+        verbose=True,
+        **kwargs
+    ):
+        self.train = train
+        self.val = val
+        self.test = test
+        self._attr_dict = attr_dict
+        self._num_attrs = len(self.attr_dict)
+        self.transform = transform
+
+        if mode == 'train':
+            self.data = self.train
+        elif mode == 'val':
+            self.data = self.val
+        else:
+            self.data = self.test
+
+        if verbose:
+            self.show_summary()
+
+    @property
+    def num_attrs(self):
+        return self._num_attrs
+
+    @property
+    def attr_dict(self):
+        return self._attr_dict
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        img_path, attrs = self.data[index]
+        img = read_image(img_path)
+        if self.transform is not None:
+            img = self.transform(img)
+        return img, attrs, img_path
+
+    def check_before_run(self, required_files):
+        """Checks if required files exist before going deeper.
+        Args:
+            required_files (str or list): string file name(s).
+        """
+        if isinstance(required_files, str):
+            required_files = [required_files]
+
+        for fpath in required_files:
+            if not osp.exists(fpath):
+                raise RuntimeError('"{}" is not found'.format(fpath))
+
+    def show_summary(self):
+        num_train = len(self.train)
+        num_val = len(self.val)
+        num_test = len(self.test)
+        num_total = num_train + num_val + num_test
+
+        print('=> Loaded {}'.format(self.__class__.__name__))
+        print("  ------------------------------")
+        print("  subset   | # images")
+        print("  ------------------------------")
+        print("  train    | {:8d}".format(num_train))
+        print("  val      | {:8d}".format(num_val))
+        print("  test     | {:8d}".format(num_test))
+        print("  ------------------------------")
+        print("  total    | {:8d}".format(num_total))
+        print("  ------------------------------")
+        print("  # attributes: {}".format(len(self.attr_dict)))
+        print("  attributes:")
+        for label, attr in self.attr_dict.items():
+            print('    {:3d}: {}'.format(label, attr))
+        print("  ------------------------------")
diff --git a/pfe/projects/attribute_recognition/datasets/pa100k.py b/pfe/projects/attribute_recognition/datasets/pa100k.py
new file mode 100644
index 0000000..61dd26c
--- /dev/null
+++ b/pfe/projects/attribute_recognition/datasets/pa100k.py
@@ -0,0 +1,59 @@
+from __future__ import division, print_function, absolute_import
+import numpy as np
+import os.path as osp
+from scipy.io import loadmat
+
+from .dataset import Dataset
+
+
+class PA100K(Dataset):
+    """Pedestrian attribute dataset.
+
+    80k training images + 20k test images.
+
+    The folder structure should be:
+        pa100k/
+            data/ # images
+            annotation/
+                annotation.mat
+    """
+    dataset_dir = 'pa100k'
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.data_dir = osp.join(self.dataset_dir, 'data')
+        self.anno_mat_path = osp.join(
+            self.dataset_dir, 'annotation', 'annotation.mat'
+        )
+
+        required_files = [self.data_dir, self.anno_mat_path]
+        self.check_before_run(required_files)
+
+        train, val, test, attr_dict = self.extract_data()
+        super(PA100K, self).__init__(train, val, test, attr_dict, **kwargs)
+
+    def extract_data(self):
+        # anno_mat is a dictionary with keys: ['test_images_name', 'val_images_name',
+        # 'train_images_name', 'val_label', 'attributes', 'test_label', 'train_label']
+        anno_mat = loadmat(self.anno_mat_path)
+
+        def _extract(key_name, key_label):
+            names = anno_mat[key_name]
+            labels = anno_mat[key_label]
+            num_imgs = names.shape[0]
+            data = []
+            for i in range(num_imgs):
+                name = names[i, 0][0]
+                attrs = labels[i, :].astype(np.float32)
+                img_path = osp.join(self.data_dir, name)
+                data.append((img_path, attrs))
+            return data
+
+        train = _extract('train_images_name', 'train_label')
+        val = _extract('val_images_name', 'val_label')
+        test = _extract('test_images_name', 'test_label')
+        attrs = anno_mat['attributes']
+        attr_dict = {i: str(attr[0][0]) for i, attr in enumerate(attrs)}
+
+        return train, val, test, attr_dict
diff --git a/pfe/projects/attribute_recognition/default_parser.py b/pfe/projects/attribute_recognition/default_parser.py
new file mode 100644
index 0000000..d19a8be
--- /dev/null
+++ b/pfe/projects/attribute_recognition/default_parser.py
@@ -0,0 +1,243 @@
+from __future__ import print_function, absolute_import
+import argparse
+
+
+def init_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    # ************************************************************
+    # Datasets
+    # ************************************************************
+    parser.add_argument(
+        '--root',
+        type=str,
+        default='',
+        required=True,
+        help='root path to data directory'
+    )
+    parser.add_argument(
+        '-d',
+        '--dataset',
+        type=str,
+        required=True,
+        help='which dataset to choose'
+    )
+    parser.add_argument(
+        '-j',
+        '--workers',
+        type=int,
+        default=4,
+        help='number of data loading workers (tips: 4 or 8 times number of gpus)'
+    )
+    parser.add_argument(
+        '--height', type=int, default=256, help='height of an image'
+    )
+    parser.add_argument(
+        '--width', type=int, default=128, help='width of an image'
+    )
+
+    # ************************************************************
+    # Optimization options
+    # ************************************************************
+    parser.add_argument(
+        '--optim',
+        type=str,
+        default='adam',
+        help='optimization algorithm (see optimizers.py)'
+    )
+    parser.add_argument(
+        '--lr', type=float, default=0.0003, help='initial learning rate'
+    )
+    parser.add_argument(
+        '--weight-decay', type=float, default=5e-04, help='weight decay'
+    )
+    # sgd
+    parser.add_argument(
+        '--momentum',
+        type=float,
+        default=0.9,
+        help='momentum factor for sgd and rmsprop'
+    )
+    parser.add_argument(
+        '--sgd-dampening',
+        type=float,
+        default=0,
+        help='sgd\'s dampening for momentum'
+    )
+    parser.add_argument(
+        '--sgd-nesterov',
+        action='store_true',
+        help='whether to enable sgd\'s Nesterov momentum'
+    )
+    # rmsprop
+    parser.add_argument(
+        '--rmsprop-alpha',
+        type=float,
+        default=0.99,
+        help='rmsprop\'s smoothing constant'
+    )
+    # adam/amsgrad
+    parser.add_argument(
+        '--adam-beta1',
+        type=float,
+        default=0.9,
+        help='exponential decay rate for adam\'s first moment'
+    )
+    parser.add_argument(
+        '--adam-beta2',
+        type=float,
+        default=0.999,
+        help='exponential decay rate for adam\'s second moment'
+    )
+
+    # ************************************************************
+    # Training hyperparameters
+    # ************************************************************
+    parser.add_argument(
+        '--max-epoch', type=int, default=60, help='maximum epochs to run'
+    )
+    parser.add_argument(
+        '--start-epoch',
+        type=int,
+        default=0,
+        help='manual epoch number (useful when restart)'
+    )
+    parser.add_argument(
+        '--batch-size', type=int, default=32, help='batch size'
+    )
+
+    parser.add_argument(
+        '--fixbase-epoch',
+        type=int,
+        default=0,
+        help='number of epochs to fix base layers'
+    )
+    parser.add_argument(
+        '--open-layers',
+        type=str,
+        nargs='+',
+        default=['classifier'],
+        help='open specified layers for training while keeping others frozen'
+    )
+
+    parser.add_argument(
+        '--staged-lr',
+        action='store_true',
+        help='set different lr to different layers'
+    )
+    parser.add_argument(
+        '--new-layers',
+        type=str,
+        nargs='+',
+        default=['classifier'],
+        help='newly added layers with default lr'
+    )
+    parser.add_argument(
+        '--base-lr-mult',
+        type=float,
+        default=0.1,
+        help='learning rate multiplier for base layers'
+    )
+
+    # ************************************************************
+    # Learning rate scheduler options
+    # ************************************************************
+    parser.add_argument(
+        '--lr-scheduler',
+        type=str,
+        default='multi_step',
+        help='learning rate scheduler (see lr_schedulers.py)'
+    )
+    parser.add_argument(
+        '--stepsize',
+        type=int,
+        default=[20, 40],
+        nargs='+',
+        help='stepsize to decay learning rate'
+    )
+    parser.add_argument(
+        '--gamma', type=float, default=0.1, help='learning rate decay'
+    )
+
+    # ************************************************************
+    # Architecture
+    # ************************************************************
+    parser.add_argument(
+        '-a', '--arch', type=str, default='', help='model architecture'
+    )
+    parser.add_argument(
+        '--no-pretrained',
+        action='store_true',
+        help='do not load pretrained weights'
+    )
+
+    # ************************************************************
+    # Loss
+    # ************************************************************
+    parser.add_argument(
+        '--weighted-bce', action='store_true', help='use weighted BCELoss'
+    )
+
+    # ************************************************************
+    # Test settings
+    # ************************************************************
+    parser.add_argument(
+        '--load-weights', type=str, default='', help='load pretrained weights'
+    )
+    parser.add_argument(
+        '--evaluate', action='store_true', help='evaluate only'
+    )
+    parser.add_argument(
+        '--save-prediction', action='store_true', help='save prediction'
+    )
+
+    # ************************************************************
+    # Miscs
+    # ************************************************************
+    parser.add_argument(
+        '--print-freq', type=int, default=20, help='print frequency'
+    )
+    parser.add_argument('--seed', type=int, default=1, help='manual seed')
+    parser.add_argument(
+        '--resume',
+        type=str,
+        default='',
+        metavar='PATH',
+        help='resume from a checkpoint'
+    )
+    parser.add_argument(
+        '--save-dir',
+        type=str,
+        default='log',
+        help='path to save log and model weights'
+    )
+    parser.add_argument('--use-cpu', action='store_true', help='use cpu')
+
+    return parser
+
+
+def optimizer_kwargs(parsed_args):
+    return {
+        'optim': parsed_args.optim,
+        'lr': parsed_args.lr,
+        'weight_decay': parsed_args.weight_decay,
+        'momentum': parsed_args.momentum,
+        'sgd_dampening': parsed_args.sgd_dampening,
+        'sgd_nesterov': parsed_args.sgd_nesterov,
+        'rmsprop_alpha': parsed_args.rmsprop_alpha,
+        'adam_beta1': parsed_args.adam_beta1,
+        'adam_beta2': parsed_args.adam_beta2,
+        'staged_lr': parsed_args.staged_lr,
+        'new_layers': parsed_args.new_layers,
+        'base_lr_mult': parsed_args.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(parsed_args):
+    return {
+        'lr_scheduler': parsed_args.lr_scheduler,
+        'stepsize': parsed_args.stepsize,
+        'gamma': parsed_args.gamma
+    }
diff --git a/pfe/projects/attribute_recognition/main.py b/pfe/projects/attribute_recognition/main.py
new file mode 100644
index 0000000..1fb4238
--- /dev/null
+++ b/pfe/projects/attribute_recognition/main.py
@@ -0,0 +1,399 @@
+from __future__ import division, print_function
+import sys
+import copy
+import time
+import numpy as np
+import os.path as osp
+import datetime
+import warnings
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, AverageMeter, check_isfile, open_all_layers, save_checkpoint,
+    set_random_seed, collect_env_info, open_specified_layers,
+    load_pretrained_weights, compute_model_complexity
+)
+from torchreid.data.transforms import (
+    Resize, Compose, ToTensor, Normalize, Random2DTranslation,
+    RandomHorizontalFlip
+)
+
+import models
+import datasets
+from default_parser import init_parser, optimizer_kwargs, lr_scheduler_kwargs
+
+parser = init_parser()
+args = parser.parse_args()
+
+
+def init_dataset(use_gpu):
+    normalize = Normalize(
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+    )
+
+    transform_tr = Compose(
+        [
+            Random2DTranslation(args.height, args.width, p=0.5),
+            RandomHorizontalFlip(),
+            ToTensor(), normalize
+        ]
+    )
+
+    transform_te = Compose(
+        [Resize([args.height, args.width]),
+         ToTensor(), normalize]
+    )
+
+    trainset = datasets.init_dataset(
+        args.dataset,
+        root=args.root,
+        transform=transform_tr,
+        mode='train',
+        verbose=True
+    )
+
+    valset = datasets.init_dataset(
+        args.dataset,
+        root=args.root,
+        transform=transform_te,
+        mode='val',
+        verbose=False
+    )
+
+    testset = datasets.init_dataset(
+        args.dataset,
+        root=args.root,
+        transform=transform_te,
+        mode='test',
+        verbose=False
+    )
+
+    num_attrs = trainset.num_attrs
+    attr_dict = trainset.attr_dict
+
+    trainloader = torch.utils.data.DataLoader(
+        trainset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        num_workers=args.workers,
+        pin_memory=use_gpu,
+        drop_last=True
+    )
+
+    valloader = torch.utils.data.DataLoader(
+        valset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=args.workers,
+        pin_memory=use_gpu,
+        drop_last=False
+    )
+
+    testloader = torch.utils.data.DataLoader(
+        testset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=args.workers,
+        pin_memory=use_gpu,
+        drop_last=False
+    )
+
+    return trainloader, valloader, testloader, num_attrs, attr_dict
+
+
+def main():
+    global args
+
+    set_random_seed(args.seed)
+    use_gpu = torch.cuda.is_available() and not args.use_cpu
+    log_name = 'test.log' if args.evaluate else 'train.log'
+    sys.stdout = Logger(osp.join(args.save_dir, log_name))
+
+    print('** Arguments **')
+    arg_keys = list(args.__dict__.keys())
+    arg_keys.sort()
+    for key in arg_keys:
+        print('{}: {}'.format(key, args.__dict__[key]))
+    print('\n')
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if use_gpu:
+        torch.backends.cudnn.benchmark = True
+    else:
+        warnings.warn(
+            'Currently using CPU, however, GPU is highly recommended'
+        )
+
+    dataset_vars = init_dataset(use_gpu)
+    trainloader, valloader, testloader, num_attrs, attr_dict = dataset_vars
+
+    if args.weighted_bce:
+        print('Use weighted binary cross entropy')
+        print('Computing the weights ...')
+        bce_weights = torch.zeros(num_attrs, dtype=torch.float)
+        for _, attrs, _ in trainloader:
+            bce_weights += attrs.sum(0) # sum along the batch dim
+        bce_weights /= len(trainloader) * args.batch_size
+        print('Sample ratio for each attribute: {}'.format(bce_weights))
+        bce_weights = torch.exp(-1 * bce_weights)
+        print('BCE weights: {}'.format(bce_weights))
+        bce_weights = bce_weights.expand(args.batch_size, num_attrs)
+        criterion = nn.BCEWithLogitsLoss(weight=bce_weights)
+
+    else:
+        print('Use plain binary cross entropy')
+        criterion = nn.BCEWithLogitsLoss()
+
+    print('Building model: {}'.format(args.arch))
+    model = models.build_model(
+        args.arch,
+        num_attrs,
+        pretrained=not args.no_pretrained,
+        use_gpu=use_gpu
+    )
+    num_params, flops = compute_model_complexity(
+        model, (1, 3, args.height, args.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    if args.load_weights and check_isfile(args.load_weights):
+        load_pretrained_weights(model, args.load_weights)
+
+    if use_gpu:
+        model = nn.DataParallel(model).cuda()
+        criterion = criterion.cuda()
+
+    if args.evaluate:
+        test(model, testloader, attr_dict, use_gpu)
+        return
+
+    optimizer = torchreid.optim.build_optimizer(
+        model, **optimizer_kwargs(args)
+    )
+    scheduler = torchreid.optim.build_lr_scheduler(
+        optimizer, **lr_scheduler_kwargs(args)
+    )
+
+    start_epoch = args.start_epoch
+    best_result = -np.inf
+    if args.resume and check_isfile(args.resume):
+        checkpoint = torch.load(args.resume)
+        model.load_state_dict(checkpoint['state_dict'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        start_epoch = checkpoint['epoch']
+        best_result = checkpoint['label_mA']
+        print('Loaded checkpoint from "{}"'.format(args.resume))
+        print('- start epoch: {}'.format(start_epoch))
+        print('- label_mA: {}'.format(best_result))
+
+    time_start = time.time()
+
+    for epoch in range(start_epoch, args.max_epoch):
+        train(
+            epoch, model, criterion, optimizer, scheduler, trainloader, use_gpu
+        )
+        test_outputs = test(model, testloader, attr_dict, use_gpu)
+        label_mA = test_outputs[0]
+        is_best = label_mA > best_result
+        if is_best:
+            best_result = label_mA
+
+        save_checkpoint(
+            {
+                'state_dict': model.state_dict(),
+                'epoch': epoch + 1,
+                'label_mA': label_mA,
+                'optimizer': optimizer.state_dict(),
+            },
+            args.save_dir,
+            is_best=is_best
+        )
+
+    elapsed = round(time.time() - time_start)
+    elapsed = str(datetime.timedelta(seconds=elapsed))
+    print('Elapsed {}'.format(elapsed))
+
+
+def train(epoch, model, criterion, optimizer, scheduler, trainloader, use_gpu):
+    losses = AverageMeter()
+    batch_time = AverageMeter()
+    data_time = AverageMeter()
+    model.train()
+
+    if (epoch + 1) <= args.fixbase_epoch and args.open_layers is not None:
+        print(
+            '* Only train {} (epoch: {}/{})'.format(
+                args.open_layers, epoch + 1, args.fixbase_epoch
+            )
+        )
+        open_specified_layers(model, args.open_layers)
+    else:
+        open_all_layers(model)
+
+    end = time.time()
+    for batch_idx, data in enumerate(trainloader):
+        data_time.update(time.time() - end)
+
+        imgs, attrs = data[0], data[1]
+        if use_gpu:
+            imgs = imgs.cuda()
+            attrs = attrs.cuda()
+
+        optimizer.zero_grad()
+        outputs = model(imgs)
+        loss = criterion(outputs, attrs)
+        loss.backward()
+        optimizer.step()
+
+        batch_time.update(time.time() - end)
+
+        losses.update(loss.item(), imgs.size(0))
+
+        if (batch_idx+1) % args.print_freq == 0:
+            # estimate remaining time
+            num_batches = len(trainloader)
+            eta_seconds = batch_time.avg * (
+                num_batches - (batch_idx+1) + (args.max_epoch -
+                                               (epoch+1)) * num_batches
+            )
+            eta_str = str(datetime.timedelta(seconds=int(eta_seconds)))
+            print(
+                'Epoch: [{0}/{1}][{2}/{3}]\t'
+                'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
+                'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                'Lr {lr:.6f}\t'
+                'Eta {eta}'.format(
+                    epoch + 1,
+                    args.max_epoch,
+                    batch_idx + 1,
+                    len(trainloader),
+                    batch_time=batch_time,
+                    data_time=data_time,
+                    loss=losses,
+                    lr=optimizer.param_groups[0]['lr'],
+                    eta=eta_str
+                )
+            )
+
+        end = time.time()
+
+    scheduler.step()
+
+
+@torch.no_grad()
+def test(model, testloader, attr_dict, use_gpu):
+    batch_time = AverageMeter()
+    model.eval()
+
+    num_persons = 0
+    prob_thre = 0.5
+    ins_acc = 0
+    ins_prec = 0
+    ins_rec = 0
+    mA_history = {
+        'correct_pos': 0,
+        'real_pos': 0,
+        'correct_neg': 0,
+        'real_neg': 0
+    }
+
+    print('Testing ...')
+
+    for batch_idx, data in enumerate(testloader):
+        imgs, attrs, img_paths = data
+        if use_gpu:
+            imgs = imgs.cuda()
+
+        end = time.time()
+        orig_outputs = model(imgs)
+        batch_time.update(time.time() - end)
+
+        orig_outputs = orig_outputs.data.cpu().numpy()
+        attrs = attrs.data.numpy()
+
+        # transform raw outputs to attributes (binary codes)
+        outputs = copy.deepcopy(orig_outputs)
+        outputs[outputs < prob_thre] = 0
+        outputs[outputs >= prob_thre] = 1
+
+        # compute label-based metric
+        overlaps = outputs * attrs
+        mA_history['correct_pos'] += overlaps.sum(0)
+        mA_history['real_pos'] += attrs.sum(0)
+        inv_overlaps = (1-outputs) * (1-attrs)
+        mA_history['correct_neg'] += inv_overlaps.sum(0)
+        mA_history['real_neg'] += (1 - attrs).sum(0)
+
+        outputs = outputs.astype(bool)
+        attrs = attrs.astype(bool)
+
+        # compute instabce-based accuracy
+        intersect = (outputs & attrs).astype(float)
+        union = (outputs | attrs).astype(float)
+        ins_acc += (intersect.sum(1) / union.sum(1)).sum()
+        ins_prec += (intersect.sum(1) / outputs.astype(float).sum(1)).sum()
+        ins_rec += (intersect.sum(1) / attrs.astype(float).sum(1)).sum()
+
+        num_persons += imgs.size(0)
+
+        if (batch_idx+1) % args.print_freq == 0:
+            print(
+                'Processed batch {}/{}'.format(batch_idx + 1, len(testloader))
+            )
+
+        if args.save_prediction:
+            txtfile = open(osp.join(args.save_dir, 'prediction.txt'), 'a')
+            for idx in range(imgs.size(0)):
+                img_path = img_paths[idx]
+                probs = orig_outputs[idx, :]
+                labels = attrs[idx, :]
+                txtfile.write('{}\n'.format(img_path))
+                txtfile.write('*** Correct prediction ***\n')
+                for attr_idx, (label, prob) in enumerate(zip(labels, probs)):
+                    if label:
+                        attr_name = attr_dict[attr_idx]
+                        info = '{}: {:.1%}  '.format(attr_name, prob)
+                        txtfile.write(info)
+                txtfile.write('\n*** Incorrect prediction ***\n')
+                for attr_idx, (label, prob) in enumerate(zip(labels, probs)):
+                    if not label and prob > 0.5:
+                        attr_name = attr_dict[attr_idx]
+                        info = '{}: {:.1%}  '.format(attr_name, prob)
+                        txtfile.write(info)
+                txtfile.write('\n\n')
+            txtfile.close()
+
+    print(
+        '=> BatchTime(s)/BatchSize(img): {:.4f}/{}'.format(
+            batch_time.avg, args.batch_size
+        )
+    )
+
+    ins_acc /= num_persons
+    ins_prec /= num_persons
+    ins_rec /= num_persons
+    ins_f1 = (2*ins_prec*ins_rec) / (ins_prec+ins_rec)
+
+    term1 = mA_history['correct_pos'] / mA_history['real_pos']
+    term2 = mA_history['correct_neg'] / mA_history['real_neg']
+    label_mA_verbose = (term1+term2) * 0.5
+    label_mA = label_mA_verbose.mean()
+
+    print('* Results *')
+    print('  # test persons: {}'.format(num_persons))
+    print('  (instance-based)  accuracy:      {:.1%}'.format(ins_acc))
+    print('  (instance-based)  precition:     {:.1%}'.format(ins_prec))
+    print('  (instance-based)  recall:        {:.1%}'.format(ins_rec))
+    print('  (instance-based)  f1-score:      {:.1%}'.format(ins_f1))
+    print('  (label-based)     mean accuracy: {:.1%}'.format(label_mA))
+    print('  mA for each attribute: {}'.format(label_mA_verbose))
+
+    return label_mA, ins_acc, ins_prec, ins_rec, ins_f1
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/projects/attribute_recognition/models/__init__.py b/pfe/projects/attribute_recognition/models/__init__.py
new file mode 100644
index 0000000..ff0f0ed
--- /dev/null
+++ b/pfe/projects/attribute_recognition/models/__init__.py
@@ -0,0 +1,17 @@
+from __future__ import absolute_import
+
+from .osnet import *
+
+__model_factory = {
+    'osnet_avgpool': osnet_avgpool,
+    'osnet_maxpool': osnet_maxpool
+}
+
+
+def build_model(name, num_classes, pretrained=True, use_gpu=True):
+    avai_models = list(__model_factory.keys())
+    if name not in avai_models:
+        raise KeyError
+    return __model_factory[name](
+        num_classes=num_classes, pretrained=pretrained, use_gpu=use_gpu
+    )
diff --git a/pfe/projects/attribute_recognition/models/osnet.py b/pfe/projects/attribute_recognition/models/osnet.py
new file mode 100644
index 0000000..12569da
--- /dev/null
+++ b/pfe/projects/attribute_recognition/models/osnet.py
@@ -0,0 +1,414 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['osnet_avgpool', 'osnet_maxpool']
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution without non-linearity."""
+
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, **kwargs):
+        super(OSBlock, self).__init__()
+        mid_channels = out_channels // 4
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2a = LightConv3x3(mid_channels, mid_channels)
+        self.conv2b = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2c = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2d = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        residual = x
+        x1 = self.conv1(x)
+        x2a = self.conv2a(x1)
+        x2b = self.conv2b(x1)
+        x2c = self.conv2c(x1)
+        x2d = self.conv2d(x1)
+        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+        out = x3 + residual
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class BaseNet(nn.Module):
+
+    def _make_layer(
+        self, block, layer, in_channels, out_channels, reduce_spatial_size
+    ):
+        layers = []
+
+        layers.append(block(in_channels, out_channels))
+        for i in range(1, layer):
+            layers.append(block(out_channels, out_channels))
+
+        if reduce_spatial_size:
+            layers.append(
+                nn.Sequential(
+                    Conv1x1(out_channels, out_channels),
+                    nn.AvgPool2d(2, stride=2)
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+
+class OSNet(BaseNet):
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        pool='avg',
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3)
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0],
+            layers[0],
+            channels[0],
+            channels[1],
+            reduce_spatial_size=True
+        )
+        self.conv3 = self._make_layer(
+            blocks[1],
+            layers[1],
+            channels[1],
+            channels[2],
+            reduce_spatial_size=True
+        )
+        self.conv4 = self._make_layer(
+            blocks[2],
+            layers[2],
+            channels[2],
+            channels[3],
+            reduce_spatial_size=False
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        if pool == 'avg':
+            self.global_pool = nn.AdaptiveAvgPool2d(1)
+        else:
+            self.global_pool = nn.AdaptiveMaxPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            feature_dim, channels[3], dropout_p=None
+        )
+        # classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self.init_params()
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x):
+        x = self.featuremaps(x)
+        v = self.global_pool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        y = self.classifier(v)
+        if not self.training:
+            y = torch.sigmoid(y)
+        return y
+
+
+def osnet_avgpool(num_classes=1000, loss='softmax', **kwargs):
+    return OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        pool='avg',
+        **kwargs
+    )
+
+
+def osnet_maxpool(num_classes=1000, loss='softmax', **kwargs):
+    return OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        pool='max',
+        **kwargs
+    )
diff --git a/pfe/projects/attribute_recognition/train.sh b/pfe/projects/attribute_recognition/train.sh
new file mode 100644
index 0000000..9080a66
--- /dev/null
+++ b/pfe/projects/attribute_recognition/train.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+# DATASET points to the directory containing pa100k/
+DATASET=$1
+
+python main.py \
+--root ${DATASET} \
+-d pa100k \
+-a osnet_maxpool \
+--max-epoch 50 \
+--stepsize 30 40 \
+--batch-size 32 \
+--lr 0.065 \
+--optim sgd \
+--weighted-bce \
+--save-dir log/pa100k-osnet_maxpool
\ No newline at end of file
diff --git a/pfe/requirements.txt b/pfe/requirements.txt
new file mode 100644
index 0000000..8b35c0e
--- /dev/null
+++ b/pfe/requirements.txt
@@ -0,0 +1,16 @@
+numpy
+Cython
+h5py
+Pillow
+six
+scipy
+opencv-python
+matplotlib
+tb-nightly
+future
+yacs
+gdown
+flake8
+yapf
+isort
+imageio
\ No newline at end of file
diff --git a/pfe/scripts/cuhk03_config.py b/pfe/scripts/cuhk03_config.py
new file mode 100644
index 0000000..783a17f
--- /dev/null
+++ b/pfe/scripts/cuhk03_config.py
@@ -0,0 +1,211 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    # cfg.model.name = 'resnet50_fc512'
+    cfg.model.name = 'osnet_x1_0'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '' # path to model weights
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['bepro']
+
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 128 # image height
+    cfg.data.width = 64 # image width
+    # cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.combineall = True # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.k_tfm = 1 # number of times to apply augmentation to an image independently
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False # load training set from target dataset
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = True # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler' # sampler for source train loader
+    cfg.sampler.train_sampler_t = 'RandomSampler' # sampler for target train loader
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0001
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 120
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 60 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'cosine' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = True # test only
+    cfg.test.eval_freq = 10 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 1 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'k_tfm': cfg.data.k_tfm,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        'train_sampler_t': cfg.sampler.train_sampler_t,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        # 'test_only': cfg.test.evaluate,
+        'test_only': False,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': 'cosine',
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': False,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/scripts/default_config.py b/pfe/scripts/default_config.py
new file mode 100644
index 0000000..ce4ef5f
--- /dev/null
+++ b/pfe/scripts/default_config.py
@@ -0,0 +1,206 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'resnet50'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '' # path to model weights
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    cfg.data.sources = ['market1501']
+    cfg.data.targets = ['market1501']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    cfg.data.height = 256 # image height
+    cfg.data.width = 128 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.k_tfm = 1 # number of times to apply augmentation to an image independently
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False # load training set from target dataset
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler' # sampler for source train loader
+    cfg.sampler.train_sampler_t = 'RandomSampler' # sampler for target train loader
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0003
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 60
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 20 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'euclidean' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = False # test only
+    cfg.test.eval_freq = -1 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 0 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'k_tfm': cfg.data.k_tfm,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        'train_sampler_t': cfg.sampler.train_sampler_t,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        'test_only': cfg.test.evaluate,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': cfg.test.dist_metric,
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': cfg.test.visrank,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/scripts/extract_fetures.py b/pfe/scripts/extract_fetures.py
new file mode 100644
index 0000000..92b2c76
--- /dev/null
+++ b/pfe/scripts/extract_fetures.py
@@ -0,0 +1,178 @@
+from torchreid.utils import FeatureExtractor
+import torch
+
+import os
+import sys
+import numpy as np
+import cv2
+
+from PIL import Image
+import errno
+
+import mmcv 
+
+def test_sanity():
+    features = extractor('/home/dmitriy.khvan/deep-person-reid/tmp/debug/dbg_orig_0.jpg')
+    print(features)
+
+def extract_image_patch(image, bbox):
+    x, y, w, h = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
+    patch = image[y:y+h, x:x+w]
+    return patch
+
+def network_feed(image, boxes, extractor):
+    patches_np_lst = []
+
+    for num, box in enumerate(boxes):
+        patch = extract_image_patch(image, box)
+        # cv2.imwrite('./tmp/debug/dbg_orig_%d.jpg' % (num), patch)
+        patch_rgb = cv2.cvtColor(patch, cv2.COLOR_BGR2RGB)
+
+        if patch_rgb is None:
+            print("WARNING: Failed to extract image patch: %s." % str(box))
+            patch_rgb = np.random.uniform(0., 255., image_shape).astype(np.uint8)
+
+        patch_np = np.array(patch_rgb)
+        patches_np_lst.append(patch_np)
+
+    features = extractor(patches_np_lst)
+    features_np = features.cpu().detach().numpy()
+    
+    return features_np
+
+def network_feed_from_list(image_list, extractor):
+    patches_np_lst = []
+
+    for patch in image_list:
+        patch_rgb = cv2.cvtColor(patch, cv2.COLOR_BGR2RGB)
+
+        if patch_rgb is None:
+            print("WARNING: Failed to extract image patch: %s." % str(box))
+            patch_rgb = np.random.uniform(0., 255., image_shape).astype(np.uint8)
+
+        patch_np = np.array(patch_rgb)
+        patches_np_lst.append(patch_np)
+
+    features = extractor(patches_np_lst)
+    features_np = features.cpu().detach().numpy()
+
+    return features_np
+
+def generate_detections(ckpt_path, mot_dir, output_dir, detection_dir=None):
+
+    if detection_dir is None:
+        detection_dir = mot_dir
+    try:
+        os.makedirs(output_dir)
+    except OSError as exception:
+        if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
+            pass
+        else:
+            raise ValueError(
+                "Failed to created output directory '%s'" % output_dir)
+
+    extractor = FeatureExtractor(
+        model_name='osnet_x1_0',
+        model_path=ckpt_path,
+        device='cuda'
+    )
+
+    for sequence in os.listdir(mot_dir):
+        print("Processing %s" % sequence)
+        sequence_dir = os.path.join(mot_dir, sequence)
+
+        image_dir = os.path.join(mot_dir, "img1")
+
+        image_filenames = {
+            int(os.path.splitext(f)[0]): os.path.join(image_dir, f)
+            for f in os.listdir(image_dir)}
+
+        detection_file = os.path.join(mot_dir, "det/det.txt")
+        detections_in = np.loadtxt(detection_file, delimiter=',')
+        detections_out = []
+
+        frame_indices = detections_in[:, 0].astype(np.int)
+        min_frame_idx = frame_indices.astype(np.int).min()
+        max_frame_idx = frame_indices.astype(np.int).max()
+
+        # output_filename = os.path.join(output_dir, "%s.npy" % sequence)
+        output_filename = os.path.join(output_dir, "features.npy")
+
+        for frame_idx in range(min_frame_idx, max_frame_idx + 1):
+            print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
+            mask = frame_indices == frame_idx
+            rows = detections_in[mask]
+
+            if frame_idx not in image_filenames:
+                print("WARNING could not find image for frame %d" % frame_idx)
+                continue
+        
+            bgr_image = cv2.imread(image_filenames[frame_idx], cv2.IMREAD_COLOR)
+
+            features = network_feed(bgr_image, rows[:, 2:6].copy(), extractor)
+            detections_out += [np.r_[(row, feature)] for row, feature in zip(rows, features)]
+
+        np.save(output_filename, np.asarray(detections_out), allow_pickle=False)
+        break
+
+def generate_detections_from_video(frame_offset, frame_count, ckpt_path, video_path, det_file_path, output_dir):
+
+    try:
+        os.makedirs(output_dir)
+    
+    except OSError as exception:
+        if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
+            pass
+        else:
+            raise ValueError(
+                "Failed to created output directory '%s'" % output_dir)
+
+    extractor = FeatureExtractor(
+        model_name='osnet_x1_0',
+        model_path=ckpt_path,
+        device='cuda'
+    )
+
+    frame_offset = int(frame_offset)
+    frame_count = int(frame_count)
+
+    video = mmcv.VideoReader(video_path)
+
+    slice_start = 0 if frame_offset == 0 else frame_offset-1
+    slice_end = frame_offset+frame_count
+
+    output_filename = os.path.join(output_dir, "features.npy")
+    detections_in = np.loadtxt(det_file_path, delimiter=',')
+    frame_indices = detections_in[:, 0].astype(np.int)
+
+    print('[DSORT] processing frames from {} - {}'.format(range(slice_start,slice_end)[0], range(slice_start,slice_end)[-1]))
+
+    detections_out = []
+
+    for index in range(slice_start, slice_end):
+        print("Frame %05d/%05d" % (index, slice_end))
+
+        mask = frame_indices == (index - slice_start + 1)
+        rows = detections_in[mask]
+
+        frame = video[index]
+        
+        if frame is None:
+            print('[DBG] Empty frame received!')
+            break
+
+        features = network_feed(frame, rows[:, 2:6].copy(), extractor)
+        detections_out += [np.r_[(row, feature)] for row, feature in zip(rows, features)]
+
+    np.save(output_filename, np.asarray(detections_out), allow_pickle=False)
+
+if __name__=="__main__": 
+    # sys.argv[1] - starting frame index in video
+    # sys.argv[2] - number of frames within interval
+    # sys.argv[3] - path to feature extractor checkpoint
+    # sys.argv[4] - path to video file
+    # sys.argv[5] - path to detection txt file
+    # sys.argv[6] - path to output directory
+
+    generate_detections_from_video(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])
+
diff --git a/pfe/scripts/main.py b/pfe/scripts/main.py
new file mode 100755
index 0000000..150a3b5
--- /dev/null
+++ b/pfe/scripts/main.py
@@ -0,0 +1,194 @@
+import sys
+import time
+import os.path as osp
+import argparse
+import torch
+import torch.nn as nn
+
+import torchreid
+from torchreid.utils import (
+    Logger, check_isfile, set_random_seed, collect_env_info,
+    resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
+)
+
+# from default_config import (
+#     imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
+#     get_default_config, lr_scheduler_kwargs
+# )
+
+# from test_config import (
+#     imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
+#     get_default_config, lr_scheduler_kwargs
+# )
+
+from cuhk03_config import (
+    imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
+    get_default_config, lr_scheduler_kwargs
+)
+
+def build_datamanager(cfg):
+    if cfg.data.type == 'image':
+        return torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
+    else:
+        return torchreid.data.VideoDataManager(**videodata_kwargs(cfg))
+
+
+def build_engine(cfg, datamanager, model, optimizer, scheduler):
+    if cfg.data.type == 'image':
+        if cfg.loss.name == 'softmax':
+            engine = torchreid.engine.ImageSoftmaxEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth
+            )
+
+        else:
+            engine = torchreid.engine.ImageTripletEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                margin=cfg.loss.triplet.margin,
+                weight_t=cfg.loss.triplet.weight_t,
+                weight_x=cfg.loss.triplet.weight_x,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth
+            )
+
+    else:
+        if cfg.loss.name == 'softmax':
+            engine = torchreid.engine.VideoSoftmaxEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth,
+                pooling_method=cfg.video.pooling_method
+            )
+
+        else:
+            engine = torchreid.engine.VideoTripletEngine(
+                datamanager,
+                model,
+                optimizer=optimizer,
+                margin=cfg.loss.triplet.margin,
+                weight_t=cfg.loss.triplet.weight_t,
+                weight_x=cfg.loss.triplet.weight_x,
+                scheduler=scheduler,
+                use_gpu=cfg.use_gpu,
+                label_smooth=cfg.loss.softmax.label_smooth
+            )
+
+    return engine
+
+
+def reset_config(cfg, args):
+    if args.root:
+        cfg.data.root = args.root
+    if args.sources:
+        cfg.data.sources = args.sources
+    if args.targets:
+        cfg.data.targets = args.targets
+    if args.transforms:
+        cfg.data.transforms = args.transforms
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        '--config-file', type=str, default='', help='path to config file'
+    )
+    parser.add_argument(
+        '-s',
+        '--sources',
+        type=str,
+        nargs='+',
+        help='source datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '-t',
+        '--targets',
+        type=str,
+        nargs='+',
+        help='target datasets (delimited by space)'
+    )
+    parser.add_argument(
+        '--transforms', type=str, nargs='+', help='data augmentation'
+    )
+    parser.add_argument(
+        '--root', type=str, default='', help='path to data root'
+    )
+    parser.add_argument(
+        'opts',
+        default=None,
+        nargs=argparse.REMAINDER,
+        help='Modify config options using the command-line'
+    )
+    args = parser.parse_args()
+
+    cfg = get_default_config()
+    cfg.use_gpu = torch.cuda.is_available()
+    if args.config_file:
+        cfg.merge_from_file(args.config_file)
+    reset_config(cfg, args)
+    cfg.merge_from_list(args.opts)
+    set_random_seed(cfg.train.seed)
+
+    log_name = 'test.log' if cfg.test.evaluate else 'train.log'
+    log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
+    sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
+
+    print('Show configuration\n{}\n'.format(cfg))
+    print('Collecting env info ...')
+    print('** System info **\n{}\n'.format(collect_env_info()))
+
+    if cfg.use_gpu:
+        torch.backends.cudnn.benchmark = True
+
+    datamanager = build_datamanager(cfg)
+
+    print('Building model: {}'.format(cfg.model.name))
+    model = torchreid.models.build_model(
+        name=cfg.model.name,
+        num_classes=datamanager.num_train_pids,
+        loss=cfg.loss.name,
+        pretrained=cfg.model.pretrained,
+        use_gpu=cfg.use_gpu
+    )
+    num_params, flops = compute_model_complexity(
+        model, (1, 3, cfg.data.height, cfg.data.width)
+    )
+    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
+
+    if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
+        load_pretrained_weights(model, cfg.model.load_weights)
+
+    if cfg.use_gpu:
+        model = nn.DataParallel(model).cuda()
+
+    optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
+    scheduler = torchreid.optim.build_lr_scheduler(
+        optimizer, **lr_scheduler_kwargs(cfg)
+    )
+
+    if cfg.model.resume and check_isfile(cfg.model.resume):
+        cfg.train.start_epoch = resume_from_checkpoint(
+            cfg.model.resume, model, optimizer=optimizer, scheduler=scheduler
+        )
+
+    print(
+        'Building {}-engine for {}-reid'.format(cfg.loss.name, cfg.data.type)
+    )
+    engine = build_engine(cfg, datamanager, model, optimizer, scheduler)
+    print (engine_run_kwargs(cfg))
+    engine.run(**engine_run_kwargs(cfg))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/scripts/test_config.py b/pfe/scripts/test_config.py
new file mode 100644
index 0000000..222c3dc
--- /dev/null
+++ b/pfe/scripts/test_config.py
@@ -0,0 +1,215 @@
+from yacs.config import CfgNode as CN
+
+
+def get_default_config():
+    cfg = CN()
+
+    # model
+    cfg.model = CN()
+    cfg.model.name = 'osnet_x1_0'
+    cfg.model.pretrained = True # automatically load pretrained model weights if available
+    cfg.model.load_weights = '/home/dmitriy.khvan/deep-person-reid/log/model_market_100/model.pth.tar-100' # path to model weights
+    # cfg.model.resume = '/home/dmitriy.khvan/deep-person-reid/log/model_market_100/model.pth.tar-100' # path to checkpoint for resume training
+    cfg.model.resume = '' # path to checkpoint for resume training
+
+    # data
+    cfg.data = CN()
+    cfg.data.type = 'image'
+    cfg.data.root = 'reid-data'
+    
+    # cfg.data.sources = ['market1501']
+    # cfg.data.targets = ['market1501']
+    # cfg.data.sources = ['msmt17']
+    # cfg.data.targets = ['msmt17']
+    cfg.data.sources = ['bepro']
+    cfg.data.targets = ['bepro']
+    cfg.data.workers = 4 # number of data loading workers
+    cfg.data.split_id = 0 # split index
+    # cfg.data.height = 256 # image height
+    # cfg.data.width = 128 # image width
+    cfg.data.height = 128 # image height
+    cfg.data.width = 64 # image width
+    cfg.data.combineall = False # combine train, query and gallery for training
+    cfg.data.transforms = ['random_flip'] # data augmentation
+    cfg.data.k_tfm = 1 # number of times to apply augmentation to an image independently
+    cfg.data.norm_mean = [0.485, 0.456, 0.406] # default is imagenet mean
+    cfg.data.norm_std = [0.229, 0.224, 0.225] # default is imagenet std
+    cfg.data.save_dir = 'log' # path to save log
+    cfg.data.load_train_targets = False # load training set from target dataset
+
+    # specific datasets
+    cfg.market1501 = CN()
+    cfg.market1501.use_500k_distractors = False # add 500k distractors to the gallery set for market1501
+    cfg.cuhk03 = CN()
+    cfg.cuhk03.labeled_images = False # use labeled images, if False, use detected images
+    cfg.cuhk03.classic_split = False # use classic split by Li et al. CVPR14
+    cfg.cuhk03.use_metric_cuhk03 = False # use cuhk03's metric for evaluation
+
+    # sampler
+    cfg.sampler = CN()
+    cfg.sampler.train_sampler = 'RandomSampler' # sampler for source train loader
+    cfg.sampler.train_sampler_t = 'RandomSampler' # sampler for target train loader
+    cfg.sampler.num_instances = 4 # number of instances per identity for RandomIdentitySampler
+
+    # video reid setting
+    cfg.video = CN()
+    cfg.video.seq_len = 15 # number of images to sample in a tracklet
+    cfg.video.sample_method = 'evenly' # how to sample images from a tracklet
+    cfg.video.pooling_method = 'avg' # how to pool features over a tracklet
+
+    # train
+    cfg.train = CN()
+    cfg.train.optim = 'adam'
+    cfg.train.lr = 0.0001
+    cfg.train.weight_decay = 5e-4
+    cfg.train.max_epoch = 100
+    cfg.train.start_epoch = 0
+    cfg.train.batch_size = 32
+    cfg.train.fixbase_epoch = 0 # number of epochs to fix base layers
+    cfg.train.open_layers = [
+        'classifier'
+    ] # layers for training while keeping others frozen
+    cfg.train.staged_lr = False # set different lr to different layers
+    cfg.train.new_layers = ['classifier'] # newly added layers with default lr
+    cfg.train.base_lr_mult = 0.1 # learning rate multiplier for base layers
+    cfg.train.lr_scheduler = 'single_step'
+    cfg.train.stepsize = [20] # stepsize to decay learning rate
+    cfg.train.gamma = 0.1 # learning rate decay multiplier
+    cfg.train.print_freq = 60 # print frequency
+    cfg.train.seed = 1 # random seed
+
+    # optimizer
+    cfg.sgd = CN()
+    cfg.sgd.momentum = 0.9 # momentum factor for sgd and rmsprop
+    cfg.sgd.dampening = 0. # dampening for momentum
+    cfg.sgd.nesterov = False # Nesterov momentum
+    cfg.rmsprop = CN()
+    cfg.rmsprop.alpha = 0.99 # smoothing constant
+    cfg.adam = CN()
+    cfg.adam.beta1 = 0.9 # exponential decay rate for first moment
+    cfg.adam.beta2 = 0.999 # exponential decay rate for second moment
+
+    # loss
+    cfg.loss = CN()
+    cfg.loss.name = 'softmax'
+    cfg.loss.softmax = CN()
+    cfg.loss.softmax.label_smooth = True # use label smoothing regularizer
+    cfg.loss.triplet = CN()
+    cfg.loss.triplet.margin = 0.3 # distance margin
+    cfg.loss.triplet.weight_t = 1. # weight to balance hard triplet loss
+    cfg.loss.triplet.weight_x = 0. # weight to balance cross entropy loss
+
+    # test
+    cfg.test = CN()
+    cfg.test.batch_size = 100
+    cfg.test.dist_metric = 'cosine' # distance metric, ['euclidean', 'cosine']
+    cfg.test.normalize_feature = False # normalize feature vectors before computing distance
+    cfg.test.ranks = [1, 5, 10, 20] # cmc ranks
+    cfg.test.evaluate = True # test only
+    cfg.test.eval_freq = 4 # evaluation frequency (-1 means to only test after training)
+    cfg.test.start_eval = 1 # start to evaluate after a specific epoch
+    cfg.test.rerank = False # use person re-ranking
+    cfg.test.visrank = False # visualize ranked results (only available when cfg.test.evaluate=True)
+    cfg.test.visrank_topk = 10 # top-k ranks to visualize
+
+    return cfg
+
+
+def imagedata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'k_tfm': cfg.data.k_tfm,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'load_train_targets': cfg.data.load_train_targets,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        'train_sampler_t': cfg.sampler.train_sampler_t,
+        # image
+        'cuhk03_labeled': cfg.cuhk03.labeled_images,
+        'cuhk03_classic_split': cfg.cuhk03.classic_split,
+        'market1501_500k': cfg.market1501.use_500k_distractors,
+    }
+
+
+def videodata_kwargs(cfg):
+    return {
+        'root': cfg.data.root,
+        'sources': cfg.data.sources,
+        'targets': cfg.data.targets,
+        'height': cfg.data.height,
+        'width': cfg.data.width,
+        'transforms': cfg.data.transforms,
+        'norm_mean': cfg.data.norm_mean,
+        'norm_std': cfg.data.norm_std,
+        'use_gpu': cfg.use_gpu,
+        'split_id': cfg.data.split_id,
+        'combineall': cfg.data.combineall,
+        'batch_size_train': cfg.train.batch_size,
+        'batch_size_test': cfg.test.batch_size,
+        'workers': cfg.data.workers,
+        'num_instances': cfg.sampler.num_instances,
+        'train_sampler': cfg.sampler.train_sampler,
+        # video
+        'seq_len': cfg.video.seq_len,
+        'sample_method': cfg.video.sample_method
+    }
+
+
+def optimizer_kwargs(cfg):
+    return {
+        'optim': cfg.train.optim,
+        'lr': cfg.train.lr,
+        'weight_decay': cfg.train.weight_decay,
+        'momentum': cfg.sgd.momentum,
+        'sgd_dampening': cfg.sgd.dampening,
+        'sgd_nesterov': cfg.sgd.nesterov,
+        'rmsprop_alpha': cfg.rmsprop.alpha,
+        'adam_beta1': cfg.adam.beta1,
+        'adam_beta2': cfg.adam.beta2,
+        'staged_lr': cfg.train.staged_lr,
+        'new_layers': cfg.train.new_layers,
+        'base_lr_mult': cfg.train.base_lr_mult
+    }
+
+
+def lr_scheduler_kwargs(cfg):
+    return {
+        'lr_scheduler': cfg.train.lr_scheduler,
+        'stepsize': cfg.train.stepsize,
+        'gamma': cfg.train.gamma,
+        'max_epoch': cfg.train.max_epoch
+    }
+
+
+def engine_run_kwargs(cfg):
+    return {
+        'save_dir': cfg.data.save_dir,
+        'max_epoch': cfg.train.max_epoch,
+        'start_epoch': cfg.train.start_epoch,
+        'fixbase_epoch': cfg.train.fixbase_epoch,
+        'open_layers': cfg.train.open_layers,
+        'start_eval': cfg.test.start_eval,
+        'eval_freq': cfg.test.eval_freq,
+        # 'test_only': cfg.test.evaluate,
+        'test_only': True,
+        'print_freq': cfg.train.print_freq,
+        'dist_metric': 'cosine',
+        'normalize_feature': cfg.test.normalize_feature,
+        'visrank': True,
+        'visrank_topk': cfg.test.visrank_topk,
+        'use_metric_cuhk03': cfg.cuhk03.use_metric_cuhk03,
+        'ranks': cfg.test.ranks,
+        'rerank': cfg.test.rerank
+    }
diff --git a/pfe/setup.py b/pfe/setup.py
new file mode 100644
index 0000000..4ab6cf8
--- /dev/null
+++ b/pfe/setup.py
@@ -0,0 +1,57 @@
+import numpy as np
+import os.path as osp
+from setuptools import setup, find_packages
+from distutils.extension import Extension
+from Cython.Build import cythonize
+
+
+def readme():
+    # with open('README.rst') as f:
+        # content = f.read()
+    return ''
+
+
+def find_version():
+    version_file = 'torchreid/__init__.py'
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+def numpy_include():
+    try:
+        numpy_include = np.get_include()
+    except AttributeError:
+        numpy_include = np.get_numpy_include()
+    return numpy_include
+
+
+ext_modules = [
+    Extension(
+        'torchreid.metrics.rank_cylib.rank_cy',
+        ['torchreid/metrics/rank_cylib/rank_cy.pyx'],
+        include_dirs=[numpy_include()],
+    )
+]
+
+
+def get_requirements(filename='requirements.txt'):
+    here = osp.dirname(osp.realpath(__file__))
+    with open(osp.join(here, filename), 'r') as f:
+        requires = [line.replace('\n', '') for line in f.readlines()]
+    return requires
+
+
+setup(
+    name='torchreid',
+    version=find_version(),
+    description='A library for deep learning person re-ID in PyTorch',
+    author='Kaiyang Zhou',
+    license='MIT',
+    long_description=readme(),
+    url='https://github.com/KaiyangZhou/deep-person-reid',
+    packages=find_packages(),
+    install_requires=get_requirements(),
+    keywords=['Person Re-Identification', 'Deep Learning', 'Computer Vision'],
+    ext_modules=cythonize(ext_modules)
+)
diff --git a/pfe/tools/compute_mean_std.py b/pfe/tools/compute_mean_std.py
new file mode 100644
index 0000000..e0a5dbe
--- /dev/null
+++ b/pfe/tools/compute_mean_std.py
@@ -0,0 +1,59 @@
+"""
+Compute channel-wise mean and standard deviation of a dataset.
+
+Usage:
+$ python compute_mean_std.py DATASET_ROOT DATASET_KEY
+
+- The first argument points to the root path where you put the datasets.
+- The second argument means the specific dataset key.
+
+For instance, your datasets are put under $DATA and you wanna
+compute the statistics of Market1501, do
+$ python compute_mean_std.py $DATA market1501
+"""
+import argparse
+
+import torchreid
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('root', type=str)
+    parser.add_argument('sources', type=str)
+    args = parser.parse_args()
+
+    datamanager = torchreid.data.ImageDataManager(
+        root=args.root,
+        sources=args.sources,
+        targets=None,
+        height=256,
+        width=128,
+        batch_size_train=100,
+        batch_size_test=100,
+        transforms=None,
+        norm_mean=[0., 0., 0.],
+        norm_std=[1., 1., 1.],
+        train_sampler='SequentialSampler'
+    )
+    train_loader = datamanager.train_loader
+
+    print('Computing mean and std ...')
+    mean = 0.
+    std = 0.
+    n_samples = 0.
+    for data in train_loader:
+        data = data['img']
+        batch_size = data.size(0)
+        data = data.view(batch_size, data.size(1), -1)
+        mean += data.mean(2).sum(0)
+        std += data.std(2).sum(0)
+        n_samples += batch_size
+
+    mean /= n_samples
+    std /= n_samples
+    print('Mean: {}'.format(mean))
+    print('Std: {}'.format(std))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/tools/visualize_actmap.py b/pfe/tools/visualize_actmap.py
new file mode 100644
index 0000000..ae69991
--- /dev/null
+++ b/pfe/tools/visualize_actmap.py
@@ -0,0 +1,173 @@
+"""Visualizes CNN activation maps to see where the CNN focuses on to extract features.
+
+Reference:
+    - Zagoruyko and Komodakis. Paying more attention to attention: Improving the
+      performance of convolutional neural networks via attention transfer. ICLR, 2017
+    - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+"""
+import numpy as np
+import os.path as osp
+import argparse
+import cv2
+import torch
+from torch.nn import functional as F
+
+import torchreid
+from torchreid.utils import (
+    check_isfile, mkdir_if_missing, load_pretrained_weights
+)
+
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+GRID_SPACING = 10
+
+
+@torch.no_grad()
+def visactmap(
+    model,
+    test_loader,
+    save_dir,
+    width,
+    height,
+    use_gpu,
+    img_mean=None,
+    img_std=None
+):
+    if img_mean is None or img_std is None:
+        # use imagenet mean and std
+        img_mean = IMAGENET_MEAN
+        img_std = IMAGENET_STD
+
+    model.eval()
+
+    for target in list(test_loader.keys()):
+        data_loader = test_loader[target]['query'] # only process query images
+        # original images and activation maps are saved individually
+        actmap_dir = osp.join(save_dir, 'actmap_' + target)
+        mkdir_if_missing(actmap_dir)
+        print('Visualizing activation maps for {} ...'.format(target))
+
+        for batch_idx, data in enumerate(data_loader):
+            imgs, paths = data['img'], data['impath']
+            if use_gpu:
+                imgs = imgs.cuda()
+
+            # forward to get convolutional feature maps
+            try:
+                outputs = model(imgs, return_featuremaps=True)
+            except TypeError:
+                raise TypeError(
+                    'forward() got unexpected keyword argument "return_featuremaps". '
+                    'Please add return_featuremaps as an input argument to forward(). When '
+                    'return_featuremaps=True, return feature maps only.'
+                )
+
+            if outputs.dim() != 4:
+                raise ValueError(
+                    'The model output is supposed to have '
+                    'shape of (b, c, h, w), i.e. 4 dimensions, but got {} dimensions. '
+                    'Please make sure you set the model output at eval mode '
+                    'to be the last convolutional feature maps'.format(
+                        outputs.dim()
+                    )
+                )
+
+            # compute activation maps
+            outputs = (outputs**2).sum(1)
+            b, h, w = outputs.size()
+            outputs = outputs.view(b, h * w)
+            outputs = F.normalize(outputs, p=2, dim=1)
+            outputs = outputs.view(b, h, w)
+
+            if use_gpu:
+                imgs, outputs = imgs.cpu(), outputs.cpu()
+
+            for j in range(outputs.size(0)):
+                # get image name
+                path = paths[j]
+                imname = osp.basename(osp.splitext(path)[0])
+
+                # RGB image
+                img = imgs[j, ...]
+                for t, m, s in zip(img, img_mean, img_std):
+                    t.mul_(s).add_(m).clamp_(0, 1)
+                img_np = np.uint8(np.floor(img.numpy() * 255))
+                img_np = img_np.transpose((1, 2, 0)) # (c, h, w) -> (h, w, c)
+
+                # activation map
+                am = outputs[j, ...].numpy()
+                am = cv2.resize(am, (width, height))
+                am = 255 * (am - np.min(am)) / (
+                    np.max(am) - np.min(am) + 1e-12
+                )
+                am = np.uint8(np.floor(am))
+                am = cv2.applyColorMap(am, cv2.COLORMAP_JET)
+
+                # overlapped
+                overlapped = img_np*0.3 + am*0.7
+                overlapped[overlapped > 255] = 255
+                overlapped = overlapped.astype(np.uint8)
+
+                # save images in a single figure (add white spacing between images)
+                # from left to right: original image, activation map, overlapped image
+                grid_img = 255 * np.ones(
+                    (height, 3*width + 2*GRID_SPACING, 3), dtype=np.uint8
+                )
+                grid_img[:, :width, :] = img_np[:, :, ::-1]
+                grid_img[:,
+                         width + GRID_SPACING:2*width + GRID_SPACING, :] = am
+                grid_img[:, 2*width + 2*GRID_SPACING:, :] = overlapped
+                cv2.imwrite(osp.join(actmap_dir, imname + '.jpg'), grid_img)
+
+            if (batch_idx+1) % 10 == 0:
+                print(
+                    '- done batch {}/{}'.format(
+                        batch_idx + 1, len(data_loader)
+                    )
+                )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--root', type=str)
+    parser.add_argument('-d', '--dataset', type=str, default='market1501')
+    parser.add_argument('-m', '--model', type=str, default='osnet_x1_0')
+    parser.add_argument('--weights', type=str)
+    parser.add_argument('--save-dir', type=str, default='log')
+    parser.add_argument('--height', type=int, default=256)
+    parser.add_argument('--width', type=int, default=128)
+    args = parser.parse_args()
+
+    use_gpu = torch.cuda.is_available()
+
+    datamanager = torchreid.data.ImageDataManager(
+        root=args.root,
+        sources=args.dataset,
+        height=args.height,
+        width=args.width,
+        batch_size_train=100,
+        batch_size_test=100,
+        transforms=None,
+        train_sampler='SequentialSampler'
+    )
+    test_loader = datamanager.test_loader
+
+    model = torchreid.models.build_model(
+        name=args.model,
+        num_classes=datamanager.num_train_pids,
+        use_gpu=use_gpu
+    )
+
+    if use_gpu:
+        model = model.cuda()
+
+    if args.weights and check_isfile(args.weights):
+        load_pretrained_weights(model, args.weights)
+
+    visactmap(
+        model, test_loader, args.save_dir, args.width, args.height, use_gpu
+    )
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pfe/torchreid/__init__.py b/pfe/torchreid/__init__.py
new file mode 100644
index 0000000..2f049be
--- /dev/null
+++ b/pfe/torchreid/__init__.py
@@ -0,0 +1,9 @@
+from __future__ import print_function, absolute_import
+
+from torchreid import data, optim, utils, engine, losses, models, metrics
+
+__version__ = '1.2.5'
+__author__ = 'Kaiyang Zhou'
+__homepage__ = 'https://kaiyangzhou.github.io/'
+__description__ = 'Deep learning person re-identification in PyTorch'
+__url__ = 'https://github.com/KaiyangZhou/deep-person-reid'
diff --git a/pfe/torchreid/data/__init__.py b/pfe/torchreid/data/__init__.py
new file mode 100644
index 0000000..5318a16
--- /dev/null
+++ b/pfe/torchreid/data/__init__.py
@@ -0,0 +1,7 @@
+from __future__ import print_function, absolute_import
+
+from .datasets import (
+    Dataset, ImageDataset, VideoDataset, register_image_dataset,
+    register_video_dataset
+)
+from .datamanager import ImageDataManager, VideoDataManager
diff --git a/pfe/torchreid/data/datamanager.py b/pfe/torchreid/data/datamanager.py
new file mode 100644
index 0000000..7e9f782
--- /dev/null
+++ b/pfe/torchreid/data/datamanager.py
@@ -0,0 +1,536 @@
+from __future__ import division, print_function, absolute_import
+import torch
+
+from torchreid.data.sampler import build_train_sampler
+from torchreid.data.datasets import init_image_dataset, init_video_dataset
+from torchreid.data.transforms import build_transforms
+
+
+class DataManager(object):
+    r"""Base data manager.
+
+    Args:
+        sources (str or list): source dataset(s).
+        targets (str or list, optional): target dataset(s). If not given,
+            it equals to ``sources``.
+        height (int, optional): target image height. Default is 256.
+        width (int, optional): target image width. Default is 128.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        norm_mean (list or None, optional): data mean. Default is None (use imagenet mean).
+        norm_std (list or None, optional): data std. Default is None (use imagenet std).
+        use_gpu (bool, optional): use gpu. Default is True.
+    """
+
+    def __init__(
+        self,
+        sources=None,
+        targets=None,
+        height=256,
+        width=128,
+        transforms='random_flip',
+        norm_mean=None,
+        norm_std=None,
+        use_gpu=False
+    ):
+        self.sources = sources
+        self.targets = targets
+        self.height = height
+        self.width = width
+
+        if self.sources is None:
+            raise ValueError('sources must not be None')
+
+        if isinstance(self.sources, str):
+            self.sources = [self.sources]
+
+        if self.targets is None:
+            self.targets = self.sources
+
+        if isinstance(self.targets, str):
+            self.targets = [self.targets]
+
+        self.transform_tr, self.transform_te = build_transforms(
+            self.height,
+            self.width,
+            transforms=transforms,
+            norm_mean=norm_mean,
+            norm_std=norm_std
+        )
+
+        self.use_gpu = (torch.cuda.is_available() and use_gpu)
+
+    @property
+    def num_train_pids(self):
+        """Returns the number of training person identities."""
+        return self._num_train_pids
+
+    @property
+    def num_train_cams(self):
+        """Returns the number of training cameras."""
+        return self._num_train_cams
+
+    def fetch_test_loaders(self, name):
+        """Returns query and gallery of a test dataset, each containing
+        tuples of (img_path(s), pid, camid).
+
+        Args:
+            name (str): dataset name.
+        """
+        query_loader = self.test_dataset[name]['query']
+        gallery_loader = self.test_dataset[name]['gallery']
+        return query_loader, gallery_loader
+
+    def preprocess_pil_img(self, img):
+        """Transforms a PIL image to torch tensor for testing."""
+        return self.transform_te(img)
+
+
+class ImageDataManager(DataManager):
+    r"""Image data manager.
+
+    Args:
+        root (str): root path to datasets.
+        sources (str or list): source dataset(s).
+        targets (str or list, optional): target dataset(s). If not given,
+            it equals to ``sources``.
+        height (int, optional): target image height. Default is 256.
+        width (int, optional): target image width. Default is 128.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        k_tfm (int): number of times to apply augmentation to an image
+            independently. If k_tfm > 1, the transform function will be
+            applied k_tfm times to an image. This variable will only be
+            useful for training and is currently valid for image datasets only.
+        norm_mean (list or None, optional): data mean. Default is None (use imagenet mean).
+        norm_std (list or None, optional): data std. Default is None (use imagenet std).
+        use_gpu (bool, optional): use gpu. Default is True.
+        split_id (int, optional): split id (*0-based*). Default is 0.
+        combineall (bool, optional): combine train, query and gallery in a dataset for
+            training. Default is False.
+        load_train_targets (bool, optional): construct train-loader for target datasets.
+            Default is False. This is useful for domain adaptation research.
+        batch_size_train (int, optional): number of images in a training batch. Default is 32.
+        batch_size_test (int, optional): number of images in a test batch. Default is 32.
+        workers (int, optional): number of workers. Default is 4.
+        num_instances (int, optional): number of instances per identity in a batch.
+            Default is 4.
+        train_sampler (str, optional): sampler. Default is RandomSampler.
+        train_sampler_t (str, optional): sampler for target train loader. Default is RandomSampler.
+        cuhk03_labeled (bool, optional): use cuhk03 labeled images.
+            Default is False (defaul is to use detected images).
+        cuhk03_classic_split (bool, optional): use the classic split in cuhk03.
+            Default is False.
+        market1501_500k (bool, optional): add 500K distractors to the gallery
+            set in market1501. Default is False.
+
+    Examples::
+
+        datamanager = torchreid.data.ImageDataManager(
+            root='path/to/reid-data',
+            sources='market1501',
+            height=256,
+            width=128,
+            batch_size_train=32,
+            batch_size_test=100
+        )
+
+        # return train loader of source data
+        train_loader = datamanager.train_loader
+
+        # return test loader of target data
+        test_loader = datamanager.test_loader
+
+        # return train loader of target data
+        train_loader_t = datamanager.train_loader_t
+    """
+    data_type = 'image'
+
+    def __init__(
+        self,
+        root='',
+        sources=None,
+        targets=None,
+        height=256,
+        width=128,
+        transforms='random_flip',
+        k_tfm=1,
+        norm_mean=None,
+        norm_std=None,
+        use_gpu=True,
+        split_id=0,
+        combineall=False,
+        load_train_targets=False,
+        batch_size_train=32,
+        batch_size_test=32,
+        workers=4,
+        num_instances=4,
+        train_sampler='RandomSampler',
+        train_sampler_t='RandomSampler',
+        cuhk03_labeled=False,
+        cuhk03_classic_split=False,
+        market1501_500k=False
+    ):
+
+        super(ImageDataManager, self).__init__(
+            sources=sources,
+            targets=targets,
+            height=height,
+            width=width,
+            transforms=transforms,
+            norm_mean=norm_mean,
+            norm_std=norm_std,
+            use_gpu=use_gpu
+        )
+
+        print('=> Loading train (source) dataset')
+        trainset = []
+        for name in self.sources:
+            trainset_ = init_image_dataset(
+                name,
+                transform=self.transform_tr,
+                k_tfm=k_tfm,
+                mode='train',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                cuhk03_labeled=cuhk03_labeled,
+                cuhk03_classic_split=cuhk03_classic_split,
+                market1501_500k=market1501_500k
+            )
+            trainset.append(trainset_)
+        trainset = sum(trainset)
+
+        self._num_train_pids = trainset.num_train_pids
+        self._num_train_cams = trainset.num_train_cams
+
+        self.train_loader = torch.utils.data.DataLoader(
+            trainset,
+            sampler=build_train_sampler(
+                trainset.train,
+                train_sampler,
+                batch_size=batch_size_train,
+                num_instances=num_instances
+            ),
+            batch_size=batch_size_train,
+            shuffle=False,
+            num_workers=workers,
+            pin_memory=self.use_gpu,
+            drop_last=True
+        )
+
+        self.train_loader_t = None
+        if load_train_targets:
+            # check if sources and targets are identical
+            assert len(set(self.sources) & set(self.targets)) == 0, \
+                'sources={} and targets={} must not have overlap'.format(self.sources, self.targets)
+
+            print('=> Loading train (target) dataset')
+            trainset_t = []
+            for name in self.targets:
+                trainset_t_ = init_image_dataset(
+                    name,
+                    transform=self.transform_tr,
+                    k_tfm=k_tfm,
+                    mode='train',
+                    combineall=False, # only use the training data
+                    root=root,
+                    split_id=split_id,
+                    cuhk03_labeled=cuhk03_labeled,
+                    cuhk03_classic_split=cuhk03_classic_split,
+                    market1501_500k=market1501_500k
+                )
+                trainset_t.append(trainset_t_)
+            trainset_t = sum(trainset_t)
+
+            self.train_loader_t = torch.utils.data.DataLoader(
+                trainset_t,
+                sampler=build_train_sampler(
+                    trainset_t.train,
+                    train_sampler_t,
+                    batch_size=batch_size_train,
+                    num_instances=num_instances
+                ),
+                batch_size=batch_size_train,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=True
+            )
+
+        print('=> Loading test (target) dataset')
+        self.test_loader = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+        self.test_dataset = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+
+        for name in self.targets:
+            # build query loader
+            queryset = init_image_dataset(
+                name,
+                transform=self.transform_te,
+                mode='query',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                cuhk03_labeled=cuhk03_labeled,
+                cuhk03_classic_split=cuhk03_classic_split,
+                market1501_500k=market1501_500k
+            )
+            self.test_loader[name]['query'] = torch.utils.data.DataLoader(
+                queryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            # build gallery loader
+            galleryset = init_image_dataset(
+                name,
+                transform=self.transform_te,
+                mode='gallery',
+                combineall=combineall,
+                verbose=False,
+                root=root,
+                split_id=split_id,
+                cuhk03_labeled=cuhk03_labeled,
+                cuhk03_classic_split=cuhk03_classic_split,
+                market1501_500k=market1501_500k
+            )
+            self.test_loader[name]['gallery'] = torch.utils.data.DataLoader(
+                galleryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            self.test_dataset[name]['query'] = queryset.query
+            self.test_dataset[name]['gallery'] = galleryset.gallery
+
+        print('\n')
+        print('  **************** Summary ****************')
+        print('  source            : {}'.format(self.sources))
+        print('  # source datasets : {}'.format(len(self.sources)))
+        print('  # source ids      : {}'.format(self.num_train_pids))
+        print('  # source images   : {}'.format(len(trainset)))
+        print('  # source cameras  : {}'.format(self.num_train_cams))
+        if load_train_targets:
+            print(
+                '  # target images   : {} (unlabeled)'.format(len(trainset_t))
+            )
+        print('  target            : {}'.format(self.targets))
+        print('  *****************************************')
+        print('\n')
+
+
+class VideoDataManager(DataManager):
+    r"""Video data manager.
+
+    Args:
+        root (str): root path to datasets.
+        sources (str or list): source dataset(s).
+        targets (str or list, optional): target dataset(s). If not given,
+            it equals to ``sources``.
+        height (int, optional): target image height. Default is 256.
+        width (int, optional): target image width. Default is 128.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        norm_mean (list or None, optional): data mean. Default is None (use imagenet mean).
+        norm_std (list or None, optional): data std. Default is None (use imagenet std).
+        use_gpu (bool, optional): use gpu. Default is True.
+        split_id (int, optional): split id (*0-based*). Default is 0.
+        combineall (bool, optional): combine train, query and gallery in a dataset for
+            training. Default is False.
+        batch_size_train (int, optional): number of tracklets in a training batch. Default is 3.
+        batch_size_test (int, optional): number of tracklets in a test batch. Default is 3.
+        workers (int, optional): number of workers. Default is 4.
+        num_instances (int, optional): number of instances per identity in a batch.
+            Default is 4.
+        train_sampler (str, optional): sampler. Default is RandomSampler.
+        seq_len (int, optional): how many images to sample in a tracklet. Default is 15.
+        sample_method (str, optional): how to sample images in a tracklet. Default is "evenly".
+            Choices are ["evenly", "random", "all"]. "evenly" and "random" will sample ``seq_len``
+            images in a tracklet while "all" samples all images in a tracklet, where the batch size
+            needs to be set to 1.
+
+    Examples::
+
+        datamanager = torchreid.data.VideoDataManager(
+            root='path/to/reid-data',
+            sources='mars',
+            height=256,
+            width=128,
+            batch_size_train=3,
+            batch_size_test=3,
+            seq_len=15,
+            sample_method='evenly'
+        )
+
+        # return train loader of source data
+        train_loader = datamanager.train_loader
+
+        # return test loader of target data
+        test_loader = datamanager.test_loader
+
+    .. note::
+        The current implementation only supports image-like training. Therefore, each image in a
+        sampled tracklet will undergo independent transformation functions. To achieve tracklet-aware
+        training, you need to modify the transformation functions for video reid such that each function
+        applies the same operation to all images in a tracklet to keep consistency.
+    """
+    data_type = 'video'
+
+    def __init__(
+        self,
+        root='',
+        sources=None,
+        targets=None,
+        height=256,
+        width=128,
+        transforms='random_flip',
+        norm_mean=None,
+        norm_std=None,
+        use_gpu=True,
+        split_id=0,
+        combineall=False,
+        batch_size_train=3,
+        batch_size_test=3,
+        workers=4,
+        num_instances=4,
+        train_sampler='RandomSampler',
+        seq_len=15,
+        sample_method='evenly'
+    ):
+
+        super(VideoDataManager, self).__init__(
+            sources=sources,
+            targets=targets,
+            height=height,
+            width=width,
+            transforms=transforms,
+            norm_mean=norm_mean,
+            norm_std=norm_std,
+            use_gpu=use_gpu
+        )
+
+        print('=> Loading train (source) dataset')
+        trainset = []
+        for name in self.sources:
+            trainset_ = init_video_dataset(
+                name,
+                transform=self.transform_tr,
+                mode='train',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                seq_len=seq_len,
+                sample_method=sample_method
+            )
+            trainset.append(trainset_)
+        trainset = sum(trainset)
+
+        self._num_train_pids = trainset.num_train_pids
+        self._num_train_cams = trainset.num_train_cams
+
+        train_sampler = build_train_sampler(
+            trainset.train,
+            train_sampler,
+            batch_size=batch_size_train,
+            num_instances=num_instances
+        )
+
+        self.train_loader = torch.utils.data.DataLoader(
+            trainset,
+            sampler=train_sampler,
+            batch_size=batch_size_train,
+            shuffle=False,
+            num_workers=workers,
+            pin_memory=self.use_gpu,
+            drop_last=True
+        )
+
+        print('=> Loading test (target) dataset')
+        self.test_loader = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+        self.test_dataset = {
+            name: {
+                'query': None,
+                'gallery': None
+            }
+            for name in self.targets
+        }
+
+        for name in self.targets:
+            # build query loader
+            queryset = init_video_dataset(
+                name,
+                transform=self.transform_te,
+                mode='query',
+                combineall=combineall,
+                root=root,
+                split_id=split_id,
+                seq_len=seq_len,
+                sample_method=sample_method
+            )
+            self.test_loader[name]['query'] = torch.utils.data.DataLoader(
+                queryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            # build gallery loader
+            galleryset = init_video_dataset(
+                name,
+                transform=self.transform_te,
+                mode='gallery',
+                combineall=combineall,
+                verbose=False,
+                root=root,
+                split_id=split_id,
+                seq_len=seq_len,
+                sample_method=sample_method
+            )
+            self.test_loader[name]['gallery'] = torch.utils.data.DataLoader(
+                galleryset,
+                batch_size=batch_size_test,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=self.use_gpu,
+                drop_last=False
+            )
+
+            self.test_dataset[name]['query'] = queryset.query
+            self.test_dataset[name]['gallery'] = galleryset.gallery
+
+        print('\n')
+        print('  **************** Summary ****************')
+        print('  source             : {}'.format(self.sources))
+        print('  # source datasets  : {}'.format(len(self.sources)))
+        print('  # source ids       : {}'.format(self.num_train_pids))
+        print('  # source tracklets : {}'.format(len(trainset)))
+        print('  # source cameras   : {}'.format(self.num_train_cams))
+        print('  target             : {}'.format(self.targets))
+        print('  *****************************************')
+        print('\n')
diff --git a/pfe/torchreid/data/datasets/__init__.py b/pfe/torchreid/data/datasets/__init__.py
new file mode 100644
index 0000000..01a7378
--- /dev/null
+++ b/pfe/torchreid/data/datasets/__init__.py
@@ -0,0 +1,119 @@
+from __future__ import print_function, absolute_import
+
+from .image import (
+    GRID, PRID, CUHK01, CUHK02, CUHK03, MSMT17, VIPeR, SenseReID, Market1501,
+    DukeMTMCreID, iLIDS, BeproTest
+)
+
+from .video import PRID2011, Mars, DukeMTMCVidReID, iLIDSVID
+from .dataset import Dataset, ImageDataset, VideoDataset
+
+__image_datasets = {
+    'market1501': Market1501,
+    'cuhk03': CUHK03,
+    'dukemtmcreid': DukeMTMCreID,
+    'msmt17': MSMT17,
+    'viper': VIPeR,
+    'grid': GRID,
+    'cuhk01': CUHK01,
+    'ilids': iLIDS,
+    'sensereid': SenseReID,
+    'prid': PRID,
+    'cuhk02': CUHK02,
+    'bepro' : BeproTest
+}
+
+__video_datasets = {
+    'mars': Mars,
+    'ilidsvid': iLIDSVID,
+    'prid2011': PRID2011,
+    'dukemtmcvidreid': DukeMTMCVidReID
+}
+
+
+def init_image_dataset(name, **kwargs):
+    """Initializes an image dataset."""
+    avai_datasets = list(__image_datasets.keys())
+    if name not in avai_datasets:
+        raise ValueError(
+            'Invalid dataset name. Received "{}", '
+            'but expected to be one of {}'.format(name, avai_datasets)
+        )
+    return __image_datasets[name](**kwargs)
+
+
+def init_video_dataset(name, **kwargs):
+    """Initializes a video dataset."""
+    avai_datasets = list(__video_datasets.keys())
+    if name not in avai_datasets:
+        raise ValueError(
+            'Invalid dataset name. Received "{}", '
+            'but expected to be one of {}'.format(name, avai_datasets)
+        )
+    return __video_datasets[name](**kwargs)
+
+
+def register_image_dataset(name, dataset):
+    """Registers a new image dataset.
+
+    Args:
+        name (str): key corresponding to the new dataset.
+        dataset (Dataset): the new dataset class.
+
+    Examples::
+        
+        import torchreid
+        import NewDataset
+        torchreid.data.register_image_dataset('new_dataset', NewDataset)
+        # single dataset case
+        datamanager = torchreid.data.ImageDataManager(
+            root='reid-data',
+            sources='new_dataset'
+        )
+        # multiple dataset case
+        datamanager = torchreid.data.ImageDataManager(
+            root='reid-data',
+            sources=['new_dataset', 'dukemtmcreid']
+        )
+    """
+    global __image_datasets
+    curr_datasets = list(__image_datasets.keys())
+    if name in curr_datasets:
+        raise ValueError(
+            'The given name already exists, please choose '
+            'another name excluding {}'.format(curr_datasets)
+        )
+    __image_datasets[name] = dataset
+
+
+def register_video_dataset(name, dataset):
+    """Registers a new video dataset.
+
+    Args:
+        name (str): key corresponding to the new dataset.
+        dataset (Dataset): the new dataset class.
+
+    Examples::
+        
+        import torchreid
+        import NewDataset
+        torchreid.data.register_video_dataset('new_dataset', NewDataset)
+        # single dataset case
+        datamanager = torchreid.data.VideoDataManager(
+            root='reid-data',
+            sources='new_dataset'
+        )
+        # multiple dataset case
+        datamanager = torchreid.data.VideoDataManager(
+            root='reid-data',
+            sources=['new_dataset', 'ilidsvid']
+        )
+    """
+    global __video_datasets
+    curr_datasets = list(__video_datasets.keys())
+    if name in curr_datasets:
+        raise ValueError(
+            'The given name already exists, please choose '
+            'another name excluding {}'.format(curr_datasets)
+        )
+    __video_datasets[name] = dataset
diff --git a/pfe/torchreid/data/datasets/dataset.py b/pfe/torchreid/data/datasets/dataset.py
new file mode 100644
index 0000000..3c733ca
--- /dev/null
+++ b/pfe/torchreid/data/datasets/dataset.py
@@ -0,0 +1,424 @@
+from __future__ import division, print_function, absolute_import
+import copy
+import numpy as np
+import os.path as osp
+import tarfile
+import zipfile
+import torch
+
+from torchreid.utils import read_image, download_url, mkdir_if_missing
+
+
+class Dataset(object):
+    """An abstract class representing a Dataset.
+
+    This is the base class for ``ImageDataset`` and ``VideoDataset``.
+
+    Args:
+        train (list): contains tuples of (img_path(s), pid, camid).
+        query (list): contains tuples of (img_path(s), pid, camid).
+        gallery (list): contains tuples of (img_path(s), pid, camid).
+        transform: transform function.
+        k_tfm (int): number of times to apply augmentation to an image
+            independently. If k_tfm > 1, the transform function will be
+            applied k_tfm times to an image. This variable will only be
+            useful for training and is currently valid for image datasets only.
+        mode (str): 'train', 'query' or 'gallery'.
+        combineall (bool): combines train, query and gallery in a
+            dataset for training.
+        verbose (bool): show information.
+    """
+    _junk_pids = [
+    ] # contains useless person IDs, e.g. background, false detections
+
+    def __init__(
+        self,
+        train,
+        query,
+        gallery,
+        transform=None,
+        k_tfm=1,
+        mode='train',
+        combineall=False,
+        verbose=True,
+        **kwargs
+    ):
+        self.train = train
+        self.query = query
+        self.gallery = gallery
+        self.transform = transform
+        self.k_tfm = k_tfm
+        self.mode = mode
+        self.combineall = combineall
+        self.verbose = verbose
+
+        self.num_train_pids = self.get_num_pids(self.train)
+        self.num_train_cams = self.get_num_cams(self.train)
+
+        if self.combineall:
+            self.combine_all()
+
+        if self.mode == 'train':
+            self.data = self.train
+        elif self.mode == 'query':
+            self.data = self.query
+        elif self.mode == 'gallery':
+            self.data = self.gallery
+        else:
+            raise ValueError(
+                'Invalid mode. Got {}, but expected to be '
+                'one of [train | query | gallery]'.format(self.mode)
+            )
+
+        if self.verbose:
+            self.show_summary()
+
+    def __getitem__(self, index):
+        raise NotImplementedError
+
+    def __len__(self):
+        return len(self.data)
+
+    def __add__(self, other):
+        """Adds two datasets together (only the train set)."""
+        train = copy.deepcopy(self.train)
+
+        for img_path, pid, camid in other.train:
+            pid += self.num_train_pids
+            camid += self.num_train_cams
+            train.append((img_path, pid, camid))
+
+        ###################################
+        # Things to do beforehand:
+        # 1. set verbose=False to avoid unnecessary print
+        # 2. set combineall=False because combineall would have been applied
+        #    if it was True for a specific dataset, setting it to True will
+        #    create new IDs that should have been included
+        ###################################
+        if isinstance(train[0][0], str):
+            return ImageDataset(
+                train,
+                self.query,
+                self.gallery,
+                transform=self.transform,
+                mode=self.mode,
+                combineall=False,
+                verbose=False
+            )
+        else:
+            return VideoDataset(
+                train,
+                self.query,
+                self.gallery,
+                transform=self.transform,
+                mode=self.mode,
+                combineall=False,
+                verbose=False,
+                seq_len=self.seq_len,
+                sample_method=self.sample_method
+            )
+
+    def __radd__(self, other):
+        """Supports sum([dataset1, dataset2, dataset3])."""
+        if other == 0:
+            return self
+        else:
+            return self.__add__(other)
+
+    def parse_data(self, data):
+        """Parses data list and returns the number of person IDs
+        and the number of camera views.
+
+        Args:
+            data (list): contains tuples of (img_path(s), pid, camid)
+        """
+        pids = set()
+        cams = set()
+        for _, pid, camid in data:
+            pids.add(pid)
+            cams.add(camid)
+        return len(pids), len(cams)
+
+    def get_num_pids(self, data):
+        """Returns the number of training person identities."""
+        return self.parse_data(data)[0]
+
+    def get_num_cams(self, data):
+        """Returns the number of training cameras."""
+        return self.parse_data(data)[1]
+
+    def show_summary(self):
+        """Shows dataset statistics."""
+        pass
+
+    def combine_all(self):
+        """Combines train, query and gallery in a dataset for training."""
+        combined = copy.deepcopy(self.train)
+
+        # relabel pids in gallery (query shares the same scope)
+        g_pids = set()
+        for _, pid, _ in self.gallery:
+            if pid in self._junk_pids:
+                continue
+            g_pids.add(pid)
+        pid2label = {pid: i for i, pid in enumerate(g_pids)}
+
+        def _combine_data(data):
+            for img_path, pid, camid in data:
+                if pid in self._junk_pids:
+                    continue
+                pid = pid2label[pid] + self.num_train_pids
+                combined.append((img_path, pid, camid))
+
+        _combine_data(self.query)
+        _combine_data(self.gallery)
+
+        self.train = combined
+        self.num_train_pids = self.get_num_pids(self.train)
+
+    def download_dataset(self, dataset_dir, dataset_url):
+        """Downloads and extracts dataset.
+
+        Args:
+            dataset_dir (str): dataset directory.
+            dataset_url (str): url to download dataset.
+        """
+        if osp.exists(dataset_dir):
+            return
+
+        if dataset_url is None:
+            raise RuntimeError(
+                '{} dataset needs to be manually '
+                'prepared, please follow the '
+                'document to prepare this dataset'.format(
+                    self.__class__.__name__
+                )
+            )
+
+        print('Creating directory "{}"'.format(dataset_dir))
+        mkdir_if_missing(dataset_dir)
+        fpath = osp.join(dataset_dir, osp.basename(dataset_url))
+
+        print(
+            'Downloading {} dataset to "{}"'.format(
+                self.__class__.__name__, dataset_dir
+            )
+        )
+        download_url(dataset_url, fpath)
+
+        print('Extracting "{}"'.format(fpath))
+        try:
+            tar = tarfile.open(fpath)
+            tar.extractall(path=dataset_dir)
+            tar.close()
+        except:
+            zip_ref = zipfile.ZipFile(fpath, 'r')
+            zip_ref.extractall(dataset_dir)
+            zip_ref.close()
+
+        print('{} dataset is ready'.format(self.__class__.__name__))
+
+    def check_before_run(self, required_files):
+        """Checks if required files exist before going deeper.
+
+        Args:
+            required_files (str or list): string file name(s).
+        """
+        if isinstance(required_files, str):
+            required_files = [required_files]
+
+        for fpath in required_files:
+            if not osp.exists(fpath):
+                raise RuntimeError('"{}" is not found'.format(fpath))
+
+    def __repr__(self):
+        num_train_pids, num_train_cams = self.parse_data(self.train)
+        num_query_pids, num_query_cams = self.parse_data(self.query)
+        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
+
+        msg = '  ----------------------------------------\n' \
+              '  subset   | # ids | # items | # cameras\n' \
+              '  ----------------------------------------\n' \
+              '  train    | {:5d} | {:7d} | {:9d}\n' \
+              '  query    | {:5d} | {:7d} | {:9d}\n' \
+              '  gallery  | {:5d} | {:7d} | {:9d}\n' \
+              '  ----------------------------------------\n' \
+              '  items: images/tracklets for image/video dataset\n'.format(
+                  num_train_pids, len(self.train), num_train_cams,
+                  num_query_pids, len(self.query), num_query_cams,
+                  num_gallery_pids, len(self.gallery), num_gallery_cams
+              )
+
+        return msg
+
+    def _transform_image(self, tfm, k_tfm, img0):
+        """Transform a raw image (img0) k_tfm times."""
+        img_list = []
+
+        for k in range(k_tfm):
+            img_list.append(tfm(img0))
+
+        img = img_list
+        if len(img) == 1:
+            img = img[0]
+
+        return img
+
+
+class ImageDataset(Dataset):
+    """A base class representing ImageDataset.
+
+    All other image datasets should subclass it.
+
+    ``__getitem__`` returns an image given index.
+    It will return ``img``, ``pid``, ``camid`` and ``img_path``
+    where ``img`` has shape (channel, height, width). As a result,
+    data in each batch has shape (batch_size, channel, height, width).
+    """
+
+    def __init__(self, train, query, gallery, **kwargs):
+        super(ImageDataset, self).__init__(train, query, gallery, **kwargs)
+
+    def __getitem__(self, index):
+        img_path, pid, camid = self.data[index]
+        img = read_image(img_path)
+        if self.transform is not None:
+            img = self._transform_image(self.transform, self.k_tfm, img)
+        item = {'img': img, 'pid': pid, 'camid': camid, 'impath': img_path}
+        return item
+
+    def show_summary(self):
+        num_train_pids, num_train_cams = self.parse_data(self.train)
+        num_query_pids, num_query_cams = self.parse_data(self.query)
+        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
+
+        print('=> Loaded {}'.format(self.__class__.__name__))
+        print('  ----------------------------------------')
+        print('  subset   | # ids | # images | # cameras')
+        print('  ----------------------------------------')
+        print(
+            '  train    | {:5d} | {:8d} | {:9d}'.format(
+                num_train_pids, len(self.train), num_train_cams
+            )
+        )
+        print(
+            '  query    | {:5d} | {:8d} | {:9d}'.format(
+                num_query_pids, len(self.query), num_query_cams
+            )
+        )
+        print(
+            '  gallery  | {:5d} | {:8d} | {:9d}'.format(
+                num_gallery_pids, len(self.gallery), num_gallery_cams
+            )
+        )
+        print('  ----------------------------------------')
+
+
+class VideoDataset(Dataset):
+    """A base class representing VideoDataset.
+
+    All other video datasets should subclass it.
+
+    ``__getitem__`` returns an image given index.
+    It will return ``imgs``, ``pid`` and ``camid``
+    where ``imgs`` has shape (seq_len, channel, height, width). As a result,
+    data in each batch has shape (batch_size, seq_len, channel, height, width).
+    """
+
+    def __init__(
+        self,
+        train,
+        query,
+        gallery,
+        seq_len=15,
+        sample_method='evenly',
+        **kwargs
+    ):
+        super(VideoDataset, self).__init__(train, query, gallery, **kwargs)
+        self.seq_len = seq_len
+        self.sample_method = sample_method
+
+        if self.transform is None:
+            raise RuntimeError('transform must not be None')
+
+    def __getitem__(self, index):
+        img_paths, pid, camid = self.data[index]
+        num_imgs = len(img_paths)
+
+        if self.sample_method == 'random':
+            # Randomly samples seq_len images from a tracklet of length num_imgs,
+            # if num_imgs is smaller than seq_len, then replicates images
+            indices = np.arange(num_imgs)
+            replace = False if num_imgs >= self.seq_len else True
+            indices = np.random.choice(
+                indices, size=self.seq_len, replace=replace
+            )
+            # sort indices to keep temporal order (comment it to be order-agnostic)
+            indices = np.sort(indices)
+
+        elif self.sample_method == 'evenly':
+            # Evenly samples seq_len images from a tracklet
+            if num_imgs >= self.seq_len:
+                num_imgs -= num_imgs % self.seq_len
+                indices = np.arange(0, num_imgs, num_imgs / self.seq_len)
+            else:
+                # if num_imgs is smaller than seq_len, simply replicate the last image
+                # until the seq_len requirement is satisfied
+                indices = np.arange(0, num_imgs)
+                num_pads = self.seq_len - num_imgs
+                indices = np.concatenate(
+                    [
+                        indices,
+                        np.ones(num_pads).astype(np.int32) * (num_imgs-1)
+                    ]
+                )
+            assert len(indices) == self.seq_len
+
+        elif self.sample_method == 'all':
+            # Samples all images in a tracklet. batch_size must be set to 1
+            indices = np.arange(num_imgs)
+
+        else:
+            raise ValueError(
+                'Unknown sample method: {}'.format(self.sample_method)
+            )
+
+        imgs = []
+        for index in indices:
+            img_path = img_paths[int(index)]
+            img = read_image(img_path)
+            if self.transform is not None:
+                img = self.transform(img)
+            img = img.unsqueeze(0) # img must be torch.Tensor
+            imgs.append(img)
+        imgs = torch.cat(imgs, dim=0)
+
+        item = {'img': imgs, 'pid': pid, 'camid': camid}
+
+        return item
+
+    def show_summary(self):
+        num_train_pids, num_train_cams = self.parse_data(self.train)
+        num_query_pids, num_query_cams = self.parse_data(self.query)
+        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
+
+        print('=> Loaded {}'.format(self.__class__.__name__))
+        print('  -------------------------------------------')
+        print('  subset   | # ids | # tracklets | # cameras')
+        print('  -------------------------------------------')
+        print(
+            '  train    | {:5d} | {:11d} | {:9d}'.format(
+                num_train_pids, len(self.train), num_train_cams
+            )
+        )
+        print(
+            '  query    | {:5d} | {:11d} | {:9d}'.format(
+                num_query_pids, len(self.query), num_query_cams
+            )
+        )
+        print(
+            '  gallery  | {:5d} | {:11d} | {:9d}'.format(
+                num_gallery_pids, len(self.gallery), num_gallery_cams
+            )
+        )
+        print('  -------------------------------------------')
diff --git a/pfe/torchreid/data/datasets/image/__init__.py b/pfe/torchreid/data/datasets/image/__init__.py
new file mode 100644
index 0000000..cadecce
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/__init__.py
@@ -0,0 +1,14 @@
+from __future__ import print_function, absolute_import
+
+from .grid import GRID
+from .prid import PRID
+from .ilids import iLIDS
+from .viper import VIPeR
+from .cuhk01 import CUHK01
+from .cuhk02 import CUHK02
+from .cuhk03 import CUHK03
+from .msmt17 import MSMT17
+from .sensereid import SenseReID
+from .market1501 import Market1501
+from .dukemtmcreid import DukeMTMCreID
+from .bepro import BeproTest
diff --git a/pfe/torchreid/data/datasets/image/bepro.py b/pfe/torchreid/data/datasets/image/bepro.py
new file mode 100644
index 0000000..058c1e9
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/bepro.py
@@ -0,0 +1,93 @@
+from __future__ import division, print_function, absolute_import
+import re
+import glob
+import os.path as osp
+import warnings
+
+from ..dataset import ImageDataset
+
+class BeproTest(ImageDataset):
+    _junk_pids = [0, -1]
+    dataset_dir = 'bepro'
+    # dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'
+
+    def __init__(self, root='', market1501_500k=False, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        # self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        # allow alternative directory structure
+        # self.data_dir = self.dataset_dir
+        # data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')
+        # if osp.isdir(data_dir):
+        self.data_dir = self.dataset_dir
+        # else:
+        #     warnings.warn(
+        #         'The current data structure is deprecated. Please '
+        #         'put data folders such as "bounding_box_train" under '
+        #         '"Market-1501-v15.09.15".'
+        #     )
+
+        self.train_dir = osp.join(self.data_dir, 'bepro_train')
+        self.query_dir = osp.join(self.data_dir, 'bepro_query')
+        self.gallery_dir = osp.join(self.data_dir, 'bepro_test')
+        # self.extra_gallery_dir = osp.join(self.data_dir, 'images')
+        # self.market1501_500k = market1501_500k
+
+        required_files = [
+            self.data_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+
+        # if self.market1501_500k:
+            # required_files.append(self.extra_gallery_dir)
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, relabel=True)
+        query = self.process_dir(self.query_dir, relabel=False)
+        gallery = self.process_dir(self.gallery_dir, relabel=False)
+
+        # if self.market1501_500k:
+        #     gallery += self.process_dir(self.extra_gallery_dir, relabel=False)
+
+        super(BeproTest, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, relabel=False):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        # pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        pid_container = set()
+
+        # print (dir_path)
+
+        for img_path in img_paths:
+            # pid, _ = map(int, pattern.search(img_path).groups())
+            # print (img_path.split('/')[-1].split('.')[0].split('_')[0])
+            # print (img_path.split('/')[0])
+            # print ('pid: %s'%(img_path.split('/')[0]))
+            pid = int(img_path.split('/')[-1].split('.')[0].split('_')[0])
+
+            if pid == -1:
+                continue # junk images are just ignored
+
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        data = []
+        
+        for img_path in img_paths:
+            # pid, _ = map(int, pattern.search(img_path).groups())
+            pid = int(img_path.split('/')[-1].split('.')[0].split('_')[0])
+            camid = int(img_path.split('/')[-1].split('.')[0].split('_')[1])
+            
+            if pid == -1:
+                continue # junk images are just ignored
+
+            # assert 0 <= pid <= 1501 # pid == 0 means background
+            # assert 1 <= camid <= 6
+            # camid -= 1 # index starts from 0
+
+            if relabel:
+                pid = pid2label[pid]
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/torchreid/data/datasets/image/cuhk01.py b/pfe/torchreid/data/datasets/image/cuhk01.py
new file mode 100644
index 0000000..a71e4a4
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/cuhk01.py
@@ -0,0 +1,135 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import numpy as np
+import os.path as osp
+import zipfile
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class CUHK01(ImageDataset):
+    """CUHK01.
+
+    Reference:
+        Li et al. Human Reidentification with Transferred Metric Learning. ACCV 2012.
+
+    URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html>`_
+    
+    Dataset statistics:
+        - identities: 971.
+        - images: 3884.
+        - cameras: 4.
+    """
+    dataset_dir = 'cuhk01'
+    dataset_url = None
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.zip_path = osp.join(self.dataset_dir, 'CUHK01.zip')
+        self.campus_dir = osp.join(self.dataset_dir, 'campus')
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        self.extract_file()
+
+        required_files = [self.dataset_dir, self.campus_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query']
+        gallery = split['gallery']
+
+        train = [tuple(item) for item in train]
+        query = [tuple(item) for item in query]
+        gallery = [tuple(item) for item in gallery]
+
+        super(CUHK01, self).__init__(train, query, gallery, **kwargs)
+
+    def extract_file(self):
+        if not osp.exists(self.campus_dir):
+            print('Extracting files')
+            zip_ref = zipfile.ZipFile(self.zip_path, 'r')
+            zip_ref.extractall(self.dataset_dir)
+            zip_ref.close()
+
+    def prepare_split(self):
+        """
+        Image name format: 0001001.png, where first four digits represent identity
+        and last four digits represent cameras. Camera 1&2 are considered the same
+        view and camera 3&4 are considered the same view.
+        """
+        if not osp.exists(self.split_path):
+            print('Creating 10 random splits of train ids and test ids')
+            img_paths = sorted(glob.glob(osp.join(self.campus_dir, '*.png')))
+            img_list = []
+            pid_container = set()
+            for img_path in img_paths:
+                img_name = osp.basename(img_path)
+                pid = int(img_name[:4]) - 1
+                camid = (int(img_name[4:7]) - 1) // 2 # result is either 0 or 1
+                img_list.append((img_path, pid, camid))
+                pid_container.add(pid)
+
+            num_pids = len(pid_container)
+            num_train_pids = num_pids // 2
+
+            splits = []
+            for _ in range(10):
+                order = np.arange(num_pids)
+                np.random.shuffle(order)
+                train_idxs = order[:num_train_pids]
+                train_idxs = np.sort(train_idxs)
+                idx2label = {
+                    idx: label
+                    for label, idx in enumerate(train_idxs)
+                }
+
+                train, test_a, test_b = [], [], []
+                for img_path, pid, camid in img_list:
+                    if pid in train_idxs:
+                        train.append((img_path, idx2label[pid], camid))
+                    else:
+                        if camid == 0:
+                            test_a.append((img_path, pid, camid))
+                        else:
+                            test_b.append((img_path, pid, camid))
+
+                # use cameraA as query and cameraB as gallery
+                split = {
+                    'train': train,
+                    'query': test_a,
+                    'gallery': test_b,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+                # use cameraB as query and cameraA as gallery
+                split = {
+                    'train': train,
+                    'query': test_b,
+                    'gallery': test_a,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file saved to {}'.format(self.split_path))
diff --git a/pfe/torchreid/data/datasets/image/cuhk02.py b/pfe/torchreid/data/datasets/image/cuhk02.py
new file mode 100644
index 0000000..63e60af
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/cuhk02.py
@@ -0,0 +1,95 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+
+class CUHK02(ImageDataset):
+    """CUHK02.
+
+    Reference:
+        Li and Wang. Locally Aligned Feature Transforms across Views. CVPR 2013.
+
+    URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html>`_
+    
+    Dataset statistics:
+        - 5 camera view pairs each with two cameras
+        - 971, 306, 107, 193 and 239 identities from P1 - P5
+        - totally 1,816 identities
+        - image format is png
+
+    Protocol: Use P1 - P4 for training and P5 for evaluation.
+    """
+    dataset_dir = 'cuhk02'
+    cam_pairs = ['P1', 'P2', 'P3', 'P4', 'P5']
+    test_cam_pair = 'P5'
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir, 'Dataset')
+
+        required_files = [self.dataset_dir]
+        self.check_before_run(required_files)
+
+        train, query, gallery = self.get_data_list()
+
+        super(CUHK02, self).__init__(train, query, gallery, **kwargs)
+
+    def get_data_list(self):
+        num_train_pids, camid = 0, 0
+        train, query, gallery = [], [], []
+
+        for cam_pair in self.cam_pairs:
+            cam_pair_dir = osp.join(self.dataset_dir, cam_pair)
+
+            cam1_dir = osp.join(cam_pair_dir, 'cam1')
+            cam2_dir = osp.join(cam_pair_dir, 'cam2')
+
+            impaths1 = glob.glob(osp.join(cam1_dir, '*.png'))
+            impaths2 = glob.glob(osp.join(cam2_dir, '*.png'))
+
+            if cam_pair == self.test_cam_pair:
+                # add images to query
+                for impath in impaths1:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = int(pid)
+                    query.append((impath, pid, camid))
+                camid += 1
+
+                # add images to gallery
+                for impath in impaths2:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = int(pid)
+                    gallery.append((impath, pid, camid))
+                camid += 1
+
+            else:
+                pids1 = [
+                    osp.basename(impath).split('_')[0] for impath in impaths1
+                ]
+                pids2 = [
+                    osp.basename(impath).split('_')[0] for impath in impaths2
+                ]
+                pids = set(pids1 + pids2)
+                pid2label = {
+                    pid: label + num_train_pids
+                    for label, pid in enumerate(pids)
+                }
+
+                # add images to train from cam1
+                for impath in impaths1:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = pid2label[pid]
+                    train.append((impath, pid, camid))
+                camid += 1
+
+                # add images to train from cam2
+                for impath in impaths2:
+                    pid = osp.basename(impath).split('_')[0]
+                    pid = pid2label[pid]
+                    train.append((impath, pid, camid))
+                camid += 1
+                num_train_pids += len(pids)
+
+        return train, query, gallery
diff --git a/pfe/torchreid/data/datasets/image/cuhk03.py b/pfe/torchreid/data/datasets/image/cuhk03.py
new file mode 100644
index 0000000..cd27bc2
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/cuhk03.py
@@ -0,0 +1,307 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+
+from torchreid.utils import read_json, write_json, mkdir_if_missing
+
+from ..dataset import ImageDataset
+
+
+class CUHK03(ImageDataset):
+    """CUHK03.
+
+    Reference:
+        Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014.
+
+    URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html#!>`_
+    
+    Dataset statistics:
+        - identities: 1360.
+        - images: 13164.
+        - cameras: 6.
+        - splits: 20 (classic).
+    """
+    dataset_dir = 'cuhk03'
+    dataset_url = None
+
+    def __init__(
+        self,
+        root='',
+        split_id=0,
+        cuhk03_labeled=False,
+        cuhk03_classic_split=False,
+        **kwargs
+    ):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')
+        self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')
+
+        self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')
+        self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')
+
+        self.split_classic_det_json_path = osp.join(
+            self.dataset_dir, 'splits_classic_detected.json'
+        )
+        self.split_classic_lab_json_path = osp.join(
+            self.dataset_dir, 'splits_classic_labeled.json'
+        )
+
+        self.split_new_det_json_path = osp.join(
+            self.dataset_dir, 'splits_new_detected.json'
+        )
+        self.split_new_lab_json_path = osp.join(
+            self.dataset_dir, 'splits_new_labeled.json'
+        )
+
+        self.split_new_det_mat_path = osp.join(
+            self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat'
+        )
+        self.split_new_lab_mat_path = osp.join(
+            self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat'
+        )
+
+        required_files = [
+            self.dataset_dir, self.data_dir, self.raw_mat_path,
+            self.split_new_det_mat_path, self.split_new_lab_mat_path
+        ]
+        self.check_before_run(required_files)
+
+        self.preprocess_split()
+
+        if cuhk03_labeled:
+            split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
+        else:
+            split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path
+
+        splits = read_json(split_path)
+        assert split_id < len(
+            splits
+        ), 'Condition split_id ({}) < len(splits) ({}) is false'.format(
+            split_id, len(splits)
+        )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query']
+        gallery = split['gallery']
+
+        super(CUHK03, self).__init__(train, query, gallery, **kwargs)
+
+    def preprocess_split(self):
+        # This function is a bit complex and ugly, what it does is
+        # 1. extract data from cuhk-03.mat and save as png images
+        # 2. create 20 classic splits (Li et al. CVPR'14)
+        # 3. create new split (Zhong et al. CVPR'17)
+        if osp.exists(self.imgs_labeled_dir) \
+           and osp.exists(self.imgs_detected_dir) \
+           and osp.exists(self.split_classic_det_json_path) \
+           and osp.exists(self.split_classic_lab_json_path) \
+           and osp.exists(self.split_new_det_json_path) \
+           and osp.exists(self.split_new_lab_json_path):
+            return
+
+        import h5py
+        import imageio
+        from scipy.io import loadmat
+
+        mkdir_if_missing(self.imgs_detected_dir)
+        mkdir_if_missing(self.imgs_labeled_dir)
+
+        print(
+            'Extract image data from "{}" and save as png'.format(
+                self.raw_mat_path
+            )
+        )
+        mat = h5py.File(self.raw_mat_path, 'r')
+
+        def _deref(ref):
+            return mat[ref][:].T
+
+        def _process_images(img_refs, campid, pid, save_dir):
+            img_paths = [] # Note: some persons only have images for one view
+            for imgid, img_ref in enumerate(img_refs):
+                img = _deref(img_ref)
+                if img.size == 0 or img.ndim < 3:
+                    continue # skip empty cell
+                # images are saved with the following format, index-1 (ensure uniqueness)
+                # campid: index of camera pair (1-5)
+                # pid: index of person in 'campid'-th camera pair
+                # viewid: index of view, {1, 2}
+                # imgid: index of image, (1-10)
+                viewid = 1 if imgid < 5 else 2
+                img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(
+                    campid + 1, pid + 1, viewid, imgid + 1
+                )
+                img_path = osp.join(save_dir, img_name)
+                if not osp.isfile(img_path):
+                    imageio.imwrite(img_path, img)
+                img_paths.append(img_path)
+            return img_paths
+
+        def _extract_img(image_type):
+            print('Processing {} images ...'.format(image_type))
+            meta_data = []
+            imgs_dir = self.imgs_detected_dir if image_type == 'detected' else self.imgs_labeled_dir
+            for campid, camp_ref in enumerate(mat[image_type][0]):
+                camp = _deref(camp_ref)
+                num_pids = camp.shape[0]
+                for pid in range(num_pids):
+                    img_paths = _process_images(
+                        camp[pid, :], campid, pid, imgs_dir
+                    )
+                    assert len(img_paths) > 0, \
+                        'campid{}-pid{} has no images'.format(campid, pid)
+                    meta_data.append((campid + 1, pid + 1, img_paths))
+                print(
+                    '- done camera pair {} with {} identities'.format(
+                        campid + 1, num_pids
+                    )
+                )
+            return meta_data
+
+        meta_detected = _extract_img('detected')
+        meta_labeled = _extract_img('labeled')
+
+        def _extract_classic_split(meta_data, test_split):
+            train, test = [], []
+            num_train_pids, num_test_pids = 0, 0
+            num_train_imgs, num_test_imgs = 0, 0
+            for i, (campid, pid, img_paths) in enumerate(meta_data):
+
+                if [campid, pid] in test_split:
+                    for img_path in img_paths:
+                        camid = int(
+                            osp.basename(img_path).split('_')[2]
+                        ) - 1 # make it 0-based
+                        test.append((img_path, num_test_pids, camid))
+                    num_test_pids += 1
+                    num_test_imgs += len(img_paths)
+                else:
+                    for img_path in img_paths:
+                        camid = int(
+                            osp.basename(img_path).split('_')[2]
+                        ) - 1 # make it 0-based
+                        train.append((img_path, num_train_pids, camid))
+                    num_train_pids += 1
+                    num_train_imgs += len(img_paths)
+            return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs
+
+        print('Creating classic splits (# = 20) ...')
+        splits_classic_det, splits_classic_lab = [], []
+        for split_ref in mat['testsets'][0]:
+            test_split = _deref(split_ref).tolist()
+
+            # create split for detected images
+            train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
+                _extract_classic_split(meta_detected, test_split)
+            splits_classic_det.append(
+                {
+                    'train': train,
+                    'query': test,
+                    'gallery': test,
+                    'num_train_pids': num_train_pids,
+                    'num_train_imgs': num_train_imgs,
+                    'num_query_pids': num_test_pids,
+                    'num_query_imgs': num_test_imgs,
+                    'num_gallery_pids': num_test_pids,
+                    'num_gallery_imgs': num_test_imgs
+                }
+            )
+
+            # create split for labeled images
+            train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
+                _extract_classic_split(meta_labeled, test_split)
+            splits_classic_lab.append(
+                {
+                    'train': train,
+                    'query': test,
+                    'gallery': test,
+                    'num_train_pids': num_train_pids,
+                    'num_train_imgs': num_train_imgs,
+                    'num_query_pids': num_test_pids,
+                    'num_query_imgs': num_test_imgs,
+                    'num_gallery_pids': num_test_pids,
+                    'num_gallery_imgs': num_test_imgs
+                }
+            )
+
+        write_json(splits_classic_det, self.split_classic_det_json_path)
+        write_json(splits_classic_lab, self.split_classic_lab_json_path)
+
+        def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):
+            tmp_set = []
+            unique_pids = set()
+            for idx in idxs:
+                img_name = filelist[idx][0]
+                camid = int(img_name.split('_')[2]) - 1 # make it 0-based
+                pid = pids[idx]
+                if relabel:
+                    pid = pid2label[pid]
+                img_path = osp.join(img_dir, img_name)
+                tmp_set.append((img_path, int(pid), camid))
+                unique_pids.add(pid)
+            return tmp_set, len(unique_pids), len(idxs)
+
+        def _extract_new_split(split_dict, img_dir):
+            train_idxs = split_dict['train_idx'].flatten() - 1 # index-0
+            pids = split_dict['labels'].flatten()
+            train_pids = set(pids[train_idxs])
+            pid2label = {pid: label for label, pid in enumerate(train_pids)}
+            query_idxs = split_dict['query_idx'].flatten() - 1
+            gallery_idxs = split_dict['gallery_idx'].flatten() - 1
+            filelist = split_dict['filelist'].flatten()
+            train_info = _extract_set(
+                filelist, pids, pid2label, train_idxs, img_dir, relabel=True
+            )
+            query_info = _extract_set(
+                filelist, pids, pid2label, query_idxs, img_dir, relabel=False
+            )
+            gallery_info = _extract_set(
+                filelist,
+                pids,
+                pid2label,
+                gallery_idxs,
+                img_dir,
+                relabel=False
+            )
+            return train_info, query_info, gallery_info
+
+        print('Creating new split for detected images (767/700) ...')
+        train_info, query_info, gallery_info = _extract_new_split(
+            loadmat(self.split_new_det_mat_path), self.imgs_detected_dir
+        )
+        split = [
+            {
+                'train': train_info[0],
+                'query': query_info[0],
+                'gallery': gallery_info[0],
+                'num_train_pids': train_info[1],
+                'num_train_imgs': train_info[2],
+                'num_query_pids': query_info[1],
+                'num_query_imgs': query_info[2],
+                'num_gallery_pids': gallery_info[1],
+                'num_gallery_imgs': gallery_info[2]
+            }
+        ]
+        write_json(split, self.split_new_det_json_path)
+
+        print('Creating new split for labeled images (767/700) ...')
+        train_info, query_info, gallery_info = _extract_new_split(
+            loadmat(self.split_new_lab_mat_path), self.imgs_labeled_dir
+        )
+        split = [
+            {
+                'train': train_info[0],
+                'query': query_info[0],
+                'gallery': gallery_info[0],
+                'num_train_pids': train_info[1],
+                'num_train_imgs': train_info[2],
+                'num_query_pids': query_info[1],
+                'num_query_imgs': query_info[2],
+                'num_gallery_pids': gallery_info[1],
+                'num_gallery_imgs': gallery_info[2]
+            }
+        ]
+        write_json(split, self.split_new_lab_json_path)
diff --git a/pfe/torchreid/data/datasets/image/dukemtmcreid.py b/pfe/torchreid/data/datasets/image/dukemtmcreid.py
new file mode 100644
index 0000000..5915da5
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/dukemtmcreid.py
@@ -0,0 +1,68 @@
+from __future__ import division, print_function, absolute_import
+import re
+import glob
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+
+class DukeMTMCreID(ImageDataset):
+    """DukeMTMC-reID.
+
+    Reference:
+        - Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016.
+        - Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.
+
+    URL: `<https://github.com/layumi/DukeMTMC-reID_evaluation>`_
+    
+    Dataset statistics:
+        - identities: 1404 (train + query).
+        - images:16522 (train) + 2228 (query) + 17661 (gallery).
+        - cameras: 8.
+    """
+    dataset_dir = 'dukemtmc-reid'
+    dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip'
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+        self.train_dir = osp.join(
+            self.dataset_dir, 'DukeMTMC-reID/bounding_box_train'
+        )
+        self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/query')
+        self.gallery_dir = osp.join(
+            self.dataset_dir, 'DukeMTMC-reID/bounding_box_test'
+        )
+
+        required_files = [
+            self.dataset_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, relabel=True)
+        query = self.process_dir(self.query_dir, relabel=False)
+        gallery = self.process_dir(self.gallery_dir, relabel=False)
+
+        super(DukeMTMCreID, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, relabel=False):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        pid_container = set()
+        for img_path in img_paths:
+            pid, _ = map(int, pattern.search(img_path).groups())
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        data = []
+        for img_path in img_paths:
+            pid, camid = map(int, pattern.search(img_path).groups())
+            assert 1 <= camid <= 8
+            camid -= 1 # index starts from 0
+            if relabel:
+                pid = pid2label[pid]
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/torchreid/data/datasets/image/grid.py b/pfe/torchreid/data/datasets/image/grid.py
new file mode 100644
index 0000000..0946ef3
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/grid.py
@@ -0,0 +1,130 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+from scipy.io import loadmat
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class GRID(ImageDataset):
+    """GRID.
+
+    Reference:
+        Loy et al. Multi-camera activity correlation analysis. CVPR 2009.
+
+    URL: `<http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html>`_
+    
+    Dataset statistics:
+        - identities: 250.
+        - images: 1275.
+        - cameras: 8.
+    """
+    dataset_dir = 'grid'
+    dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.probe_path = osp.join(
+            self.dataset_dir, 'underground_reid', 'probe'
+        )
+        self.gallery_path = osp.join(
+            self.dataset_dir, 'underground_reid', 'gallery'
+        )
+        self.split_mat_path = osp.join(
+            self.dataset_dir, 'underground_reid', 'features_and_partitions.mat'
+        )
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        required_files = [
+            self.dataset_dir, self.probe_path, self.gallery_path,
+            self.split_mat_path
+        ]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, '
+                'but expected between 0 and {}'.format(
+                    split_id,
+                    len(splits) - 1
+                )
+            )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query']
+        gallery = split['gallery']
+
+        train = [tuple(item) for item in train]
+        query = [tuple(item) for item in query]
+        gallery = [tuple(item) for item in gallery]
+
+        super(GRID, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating 10 random splits')
+            split_mat = loadmat(self.split_mat_path)
+            trainIdxAll = split_mat['trainIdxAll'][0] # length = 10
+            probe_img_paths = sorted(
+                glob.glob(osp.join(self.probe_path, '*.jpeg'))
+            )
+            gallery_img_paths = sorted(
+                glob.glob(osp.join(self.gallery_path, '*.jpeg'))
+            )
+
+            splits = []
+            for split_idx in range(10):
+                train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist()
+                assert len(train_idxs) == 125
+                idx2label = {
+                    idx: label
+                    for label, idx in enumerate(train_idxs)
+                }
+
+                train, query, gallery = [], [], []
+
+                # processing probe folder
+                for img_path in probe_img_paths:
+                    img_name = osp.basename(img_path)
+                    img_idx = int(img_name.split('_')[0])
+                    camid = int(
+                        img_name.split('_')[1]
+                    ) - 1 # index starts from 0
+                    if img_idx in train_idxs:
+                        train.append((img_path, idx2label[img_idx], camid))
+                    else:
+                        query.append((img_path, img_idx, camid))
+
+                # process gallery folder
+                for img_path in gallery_img_paths:
+                    img_name = osp.basename(img_path)
+                    img_idx = int(img_name.split('_')[0])
+                    camid = int(
+                        img_name.split('_')[1]
+                    ) - 1 # index starts from 0
+                    if img_idx in train_idxs:
+                        train.append((img_path, idx2label[img_idx], camid))
+                    else:
+                        gallery.append((img_path, img_idx, camid))
+
+                split = {
+                    'train': train,
+                    'query': query,
+                    'gallery': gallery,
+                    'num_train_pids': 125,
+                    'num_query_pids': 125,
+                    'num_gallery_pids': 900
+                }
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file saved to {}'.format(self.split_path))
diff --git a/pfe/torchreid/data/datasets/image/ilids.py b/pfe/torchreid/data/datasets/image/ilids.py
new file mode 100644
index 0000000..42971b0
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/ilids.py
@@ -0,0 +1,135 @@
+from __future__ import division, print_function, absolute_import
+import copy
+import glob
+import random
+import os.path as osp
+from collections import defaultdict
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class iLIDS(ImageDataset):
+    """QMUL-iLIDS.
+
+    Reference:
+        Zheng et al. Associating Groups of People. BMVC 2009.
+    
+    Dataset statistics:
+        - identities: 119.
+        - images: 476.
+        - cameras: 8 (not explicitly provided).
+    """
+    dataset_dir = 'ilids'
+    dataset_url = 'http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.data_dir = osp.join(self.dataset_dir, 'i-LIDS_Pedestrian/Persons')
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        required_files = [self.dataset_dir, self.data_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but '
+                'expected between 0 and {}'.format(split_id,
+                                                   len(splits) - 1)
+            )
+        split = splits[split_id]
+
+        train, query, gallery = self.process_split(split)
+
+        super(iLIDS, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating splits ...')
+
+            paths = glob.glob(osp.join(self.data_dir, '*.jpg'))
+            img_names = [osp.basename(path) for path in paths]
+            num_imgs = len(img_names)
+            assert num_imgs == 476, 'There should be 476 images, but ' \
+                                    'got {}, please check the data'.format(num_imgs)
+
+            # store image names
+            # image naming format:
+            #   the first four digits denote the person ID
+            #   the last four digits denote the sequence index
+            pid_dict = defaultdict(list)
+            for img_name in img_names:
+                pid = int(img_name[:4])
+                pid_dict[pid].append(img_name)
+            pids = list(pid_dict.keys())
+            num_pids = len(pids)
+            assert num_pids == 119, 'There should be 119 identities, ' \
+                                    'but got {}, please check the data'.format(num_pids)
+
+            num_train_pids = int(num_pids * 0.5)
+
+            splits = []
+            for _ in range(10):
+                # randomly choose num_train_pids train IDs and the rest for test IDs
+                pids_copy = copy.deepcopy(pids)
+                random.shuffle(pids_copy)
+                train_pids = pids_copy[:num_train_pids]
+                test_pids = pids_copy[num_train_pids:]
+
+                train = []
+                query = []
+                gallery = []
+
+                # for train IDs, all images are used in the train set.
+                for pid in train_pids:
+                    img_names = pid_dict[pid]
+                    train.extend(img_names)
+
+                # for each test ID, randomly choose two images, one for
+                # query and the other one for gallery.
+                for pid in test_pids:
+                    img_names = pid_dict[pid]
+                    samples = random.sample(img_names, 2)
+                    query.append(samples[0])
+                    gallery.append(samples[1])
+
+                split = {'train': train, 'query': query, 'gallery': gallery}
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file is saved to {}'.format(self.split_path))
+
+    def get_pid2label(self, img_names):
+        pid_container = set()
+        for img_name in img_names:
+            pid = int(img_name[:4])
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+        return pid2label
+
+    def parse_img_names(self, img_names, pid2label=None):
+        data = []
+
+        for img_name in img_names:
+            pid = int(img_name[:4])
+            if pid2label is not None:
+                pid = pid2label[pid]
+            camid = int(img_name[4:7]) - 1 # 0-based
+            img_path = osp.join(self.data_dir, img_name)
+            data.append((img_path, pid, camid))
+
+        return data
+
+    def process_split(self, split):
+        train_pid2label = self.get_pid2label(split['train'])
+        train = self.parse_img_names(split['train'], train_pid2label)
+        query = self.parse_img_names(split['query'])
+        gallery = self.parse_img_names(split['gallery'])
+        return train, query, gallery
diff --git a/pfe/torchreid/data/datasets/image/market1501.py b/pfe/torchreid/data/datasets/image/market1501.py
new file mode 100644
index 0000000..0e08896
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/market1501.py
@@ -0,0 +1,90 @@
+from __future__ import division, print_function, absolute_import
+import re
+import glob
+import os.path as osp
+import warnings
+
+from ..dataset import ImageDataset
+
+
+class Market1501(ImageDataset):
+    """Market1501.
+
+    Reference:
+        Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
+
+    URL: `<http://www.liangzheng.org/Project/project_reid.html>`_
+    
+    Dataset statistics:
+        - identities: 1501 (+1 for background).
+        - images: 12936 (train) + 3368 (query) + 15913 (gallery).
+    """
+    _junk_pids = [0, -1]
+    dataset_dir = 'market1501'
+    dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'
+
+    def __init__(self, root='', market1501_500k=False, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        # allow alternative directory structure
+        self.data_dir = self.dataset_dir
+        data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')
+        if osp.isdir(data_dir):
+            self.data_dir = data_dir
+        else:
+            warnings.warn(
+                'The current data structure is deprecated. Please '
+                'put data folders such as "bounding_box_train" under '
+                '"Market-1501-v15.09.15".'
+            )
+
+        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')
+        self.query_dir = osp.join(self.data_dir, 'query')
+        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')
+        self.extra_gallery_dir = osp.join(self.data_dir, 'images')
+        self.market1501_500k = market1501_500k
+
+        required_files = [
+            self.data_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+        if self.market1501_500k:
+            required_files.append(self.extra_gallery_dir)
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, relabel=True)
+        query = self.process_dir(self.query_dir, relabel=False)
+        gallery = self.process_dir(self.gallery_dir, relabel=False)
+        if self.market1501_500k:
+            gallery += self.process_dir(self.extra_gallery_dir, relabel=False)
+
+        super(Market1501, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, relabel=False):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        pid_container = set()
+        for img_path in img_paths:
+            pid, _ = map(int, pattern.search(img_path).groups())
+            if pid == -1:
+                continue # junk images are just ignored
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        data = []
+        
+        for img_path in img_paths:
+            pid, camid = map(int, pattern.search(img_path).groups())
+            if pid == -1:
+                continue # junk images are just ignored
+            assert 0 <= pid <= 1501 # pid == 0 means background
+            assert 1 <= camid <= 6
+            camid -= 1 # index starts from 0
+
+            if relabel:
+                pid = pid2label[pid]
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/torchreid/data/datasets/image/msmt17.py b/pfe/torchreid/data/datasets/image/msmt17.py
new file mode 100644
index 0000000..c4741e6
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/msmt17.py
@@ -0,0 +1,98 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+# Log
+# 22.01.2019
+# - add v2
+# - v1 and v2 differ in dir names
+# - note that faces in v2 are blurred
+TRAIN_DIR_KEY = 'train_dir'
+TEST_DIR_KEY = 'test_dir'
+VERSION_DICT = {
+    'MSMT17_V1': {
+        TRAIN_DIR_KEY: 'train',
+        TEST_DIR_KEY: 'test',
+    },
+    'MSMT17_V2': {
+        TRAIN_DIR_KEY: 'mask_train_v2',
+        TEST_DIR_KEY: 'mask_test_v2',
+    }
+}
+
+
+class MSMT17(ImageDataset):
+    """MSMT17.
+
+    Reference:
+        Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.
+
+    URL: `<http://www.pkuvmc.com/publications/msmt17.html>`_
+    
+    Dataset statistics:
+        - identities: 4101.
+        - images: 32621 (train) + 11659 (query) + 82161 (gallery).
+        - cameras: 15.
+    """
+    dataset_dir = 'msmt17'
+    dataset_url = None
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        has_main_dir = False
+        for main_dir in VERSION_DICT:
+            if osp.exists(osp.join(self.dataset_dir, main_dir)):
+                train_dir = VERSION_DICT[main_dir][TRAIN_DIR_KEY]
+                test_dir = VERSION_DICT[main_dir][TEST_DIR_KEY]
+                has_main_dir = True
+                break
+        assert has_main_dir, 'Dataset folder not found'
+
+        self.train_dir = osp.join(self.dataset_dir, main_dir, train_dir)
+        self.test_dir = osp.join(self.dataset_dir, main_dir, test_dir)
+        self.list_train_path = osp.join(
+            self.dataset_dir, main_dir, 'list_train.txt'
+        )
+        self.list_val_path = osp.join(
+            self.dataset_dir, main_dir, 'list_val.txt'
+        )
+        self.list_query_path = osp.join(
+            self.dataset_dir, main_dir, 'list_query.txt'
+        )
+        self.list_gallery_path = osp.join(
+            self.dataset_dir, main_dir, 'list_gallery.txt'
+        )
+
+        required_files = [self.dataset_dir, self.train_dir, self.test_dir]
+        self.check_before_run(required_files)
+
+        train = self.process_dir(self.train_dir, self.list_train_path)
+        val = self.process_dir(self.train_dir, self.list_val_path)
+        query = self.process_dir(self.test_dir, self.list_query_path)
+        gallery = self.process_dir(self.test_dir, self.list_gallery_path)
+
+        # Note: to fairly compare with published methods on the conventional ReID setting,
+        #       do not add val images to the training set.
+        if 'combineall' in kwargs and kwargs['combineall']:
+            train += val
+
+        super(MSMT17, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, list_path):
+        with open(list_path, 'r') as txt:
+            lines = txt.readlines()
+
+        data = []
+
+        for img_idx, img_info in enumerate(lines):
+            img_path, pid = img_info.split(' ')
+            pid = int(pid) # no need to relabel
+            camid = int(img_path.split('_')[2]) - 1 # index starts from 0
+            img_path = osp.join(dir_path, img_path)
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/torchreid/data/datasets/image/prid.py b/pfe/torchreid/data/datasets/image/prid.py
new file mode 100644
index 0000000..7fca0ee
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/prid.py
@@ -0,0 +1,106 @@
+from __future__ import division, print_function, absolute_import
+import random
+import os.path as osp
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class PRID(ImageDataset):
+    """PRID (single-shot version of prid-2011)
+
+    Reference:
+        Hirzer et al. Person Re-Identification by Descriptive and Discriminative
+        Classification. SCIA 2011.
+
+    URL: `<https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/>`_
+    
+    Dataset statistics:
+        - Two views.
+        - View A captures 385 identities.
+        - View B captures 749 identities.
+        - 200 identities appear in both views.
+    """
+    dataset_dir = 'prid2011'
+    dataset_url = None
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.cam_a_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'single_shot', 'cam_a'
+        )
+        self.cam_b_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'single_shot', 'cam_b'
+        )
+        self.split_path = osp.join(self.dataset_dir, 'splits_single_shot.json')
+
+        required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+
+        train, query, gallery = self.process_split(split)
+
+        super(PRID, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating splits ...')
+
+            splits = []
+            for _ in range(10):
+                # randomly sample 100 IDs for train and use the rest 100 IDs for test
+                # (note: there are only 200 IDs appearing in both views)
+                pids = [i for i in range(1, 201)]
+                train_pids = random.sample(pids, 100)
+                train_pids.sort()
+                test_pids = [i for i in pids if i not in train_pids]
+                split = {'train': train_pids, 'test': test_pids}
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file is saved to {}'.format(self.split_path))
+
+    def process_split(self, split):
+        train_pids = split['train']
+        test_pids = split['test']
+
+        train_pid2label = {pid: label for label, pid in enumerate(train_pids)}
+
+        # train
+        train = []
+        for pid in train_pids:
+            img_name = 'person_' + str(pid).zfill(4) + '.png'
+            pid = train_pid2label[pid]
+            img_a_path = osp.join(self.cam_a_dir, img_name)
+            train.append((img_a_path, pid, 0))
+            img_b_path = osp.join(self.cam_b_dir, img_name)
+            train.append((img_b_path, pid, 1))
+
+        # query and gallery
+        query, gallery = [], []
+        for pid in test_pids:
+            img_name = 'person_' + str(pid).zfill(4) + '.png'
+            img_a_path = osp.join(self.cam_a_dir, img_name)
+            query.append((img_a_path, pid, 0))
+            img_b_path = osp.join(self.cam_b_dir, img_name)
+            gallery.append((img_b_path, pid, 1))
+        for pid in range(201, 750):
+            img_name = 'person_' + str(pid).zfill(4) + '.png'
+            img_b_path = osp.join(self.cam_b_dir, img_name)
+            gallery.append((img_b_path, pid, 1))
+
+        return train, query, gallery
diff --git a/pfe/torchreid/data/datasets/image/sensereid.py b/pfe/torchreid/data/datasets/image/sensereid.py
new file mode 100644
index 0000000..7cf5f32
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/sensereid.py
@@ -0,0 +1,70 @@
+from __future__ import division, print_function, absolute_import
+import copy
+import glob
+import os.path as osp
+
+from ..dataset import ImageDataset
+
+
+class SenseReID(ImageDataset):
+    """SenseReID.
+
+    This dataset is used for test purpose only.
+
+    Reference:
+        Zhao et al. Spindle Net: Person Re-identification with Human Body
+        Region Guided Feature Decomposition and Fusion. CVPR 2017.
+
+    URL: `<https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view>`_
+
+    Dataset statistics:
+        - query: 522 ids, 1040 images.
+        - gallery: 1717 ids, 3388 images.
+    """
+    dataset_dir = 'sensereid'
+    dataset_url = None
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.query_dir = osp.join(self.dataset_dir, 'SenseReID', 'test_probe')
+        self.gallery_dir = osp.join(
+            self.dataset_dir, 'SenseReID', 'test_gallery'
+        )
+
+        required_files = [self.dataset_dir, self.query_dir, self.gallery_dir]
+        self.check_before_run(required_files)
+
+        query = self.process_dir(self.query_dir)
+        gallery = self.process_dir(self.gallery_dir)
+
+        # relabel
+        g_pids = set()
+        for _, pid, _ in gallery:
+            g_pids.add(pid)
+        pid2label = {pid: i for i, pid in enumerate(g_pids)}
+
+        query = [
+            (img_path, pid2label[pid], camid) for img_path, pid, camid in query
+        ]
+        gallery = [
+            (img_path, pid2label[pid], camid)
+            for img_path, pid, camid in gallery
+        ]
+        train = copy.deepcopy(query) + copy.deepcopy(gallery) # dummy variable
+
+        super(SenseReID, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        data = []
+
+        for img_path in img_paths:
+            img_name = osp.splitext(osp.basename(img_path))[0]
+            pid, camid = img_name.split('_')
+            pid, camid = int(pid), int(camid)
+            data.append((img_path, pid, camid))
+
+        return data
diff --git a/pfe/torchreid/data/datasets/image/viper.py b/pfe/torchreid/data/datasets/image/viper.py
new file mode 100644
index 0000000..161dd99
--- /dev/null
+++ b/pfe/torchreid/data/datasets/image/viper.py
@@ -0,0 +1,128 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import numpy as np
+import os.path as osp
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import ImageDataset
+
+
+class VIPeR(ImageDataset):
+    """VIPeR.
+
+    Reference:
+        Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007.
+
+    URL: `<https://vision.soe.ucsc.edu/node/178>`_
+    
+    Dataset statistics:
+        - identities: 632.
+        - images: 632 x 2 = 1264.
+        - cameras: 2.
+    """
+    dataset_dir = 'viper'
+    dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.cam_a_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
+        self.cam_b_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+
+        required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, '
+                'but expected between 0 and {}'.format(
+                    split_id,
+                    len(splits) - 1
+                )
+            )
+        split = splits[split_id]
+
+        train = split['train']
+        query = split['query'] # query and gallery share the same images
+        gallery = split['gallery']
+
+        train = [tuple(item) for item in train]
+        query = [tuple(item) for item in query]
+        gallery = [tuple(item) for item in gallery]
+
+        super(VIPeR, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating 10 random splits of train ids and test ids')
+
+            cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, '*.bmp')))
+            cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, '*.bmp')))
+            assert len(cam_a_imgs) == len(cam_b_imgs)
+            num_pids = len(cam_a_imgs)
+            print('Number of identities: {}'.format(num_pids))
+            num_train_pids = num_pids // 2
+            """
+            In total, there will be 20 splits because each random split creates two
+            sub-splits, one using cameraA as query and cameraB as gallery
+            while the other using cameraB as query and cameraA as gallery.
+            Therefore, results should be averaged over 20 splits (split_id=0~19).
+            
+            In practice, a model trained on split_id=0 can be applied to split_id=0&1
+            as split_id=0&1 share the same training data (so on and so forth).
+            """
+            splits = []
+            for _ in range(10):
+                order = np.arange(num_pids)
+                np.random.shuffle(order)
+                train_idxs = order[:num_train_pids]
+                test_idxs = order[num_train_pids:]
+                assert not bool(set(train_idxs) & set(test_idxs)), \
+                    'Error: train and test overlap'
+
+                train = []
+                for pid, idx in enumerate(train_idxs):
+                    cam_a_img = cam_a_imgs[idx]
+                    cam_b_img = cam_b_imgs[idx]
+                    train.append((cam_a_img, pid, 0))
+                    train.append((cam_b_img, pid, 1))
+
+                test_a = []
+                test_b = []
+                for pid, idx in enumerate(test_idxs):
+                    cam_a_img = cam_a_imgs[idx]
+                    cam_b_img = cam_b_imgs[idx]
+                    test_a.append((cam_a_img, pid, 0))
+                    test_b.append((cam_b_img, pid, 1))
+
+                # use cameraA as query and cameraB as gallery
+                split = {
+                    'train': train,
+                    'query': test_a,
+                    'gallery': test_b,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+                # use cameraB as query and cameraA as gallery
+                split = {
+                    'train': train,
+                    'query': test_b,
+                    'gallery': test_a,
+                    'num_train_pids': num_train_pids,
+                    'num_query_pids': num_pids - num_train_pids,
+                    'num_gallery_pids': num_pids - num_train_pids
+                }
+                splits.append(split)
+
+            print('Totally {} splits are created'.format(len(splits)))
+            write_json(splits, self.split_path)
+            print('Split file saved to {}'.format(self.split_path))
diff --git a/pfe/torchreid/data/datasets/video/__init__.py b/pfe/torchreid/data/datasets/video/__init__.py
new file mode 100644
index 0000000..f4e75d3
--- /dev/null
+++ b/pfe/torchreid/data/datasets/video/__init__.py
@@ -0,0 +1,6 @@
+from __future__ import print_function, absolute_import
+
+from .mars import Mars
+from .ilidsvid import iLIDSVID
+from .prid2011 import PRID2011
+from .dukemtmcvidreid import DukeMTMCVidReID
diff --git a/pfe/torchreid/data/datasets/video/dukemtmcvidreid.py b/pfe/torchreid/data/datasets/video/dukemtmcvidreid.py
new file mode 100644
index 0000000..4b4c82f
--- /dev/null
+++ b/pfe/torchreid/data/datasets/video/dukemtmcvidreid.py
@@ -0,0 +1,128 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+import warnings
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import VideoDataset
+
+
+class DukeMTMCVidReID(VideoDataset):
+    """DukeMTMCVidReID.
+
+    Reference:
+        - Ristani et al. Performance Measures and a Data Set for Multi-Target,
+          Multi-Camera Tracking. ECCVW 2016.
+        - Wu et al. Exploit the Unknown Gradually: One-Shot Video-Based Person
+          Re-Identification by Stepwise Learning. CVPR 2018.
+
+    URL: `<https://github.com/Yu-Wu/DukeMTMC-VideoReID>`_
+    
+    Dataset statistics:
+        - identities: 702 (train) + 702 (test).
+        - tracklets: 2196 (train) + 2636 (test).
+    """
+    dataset_dir = 'dukemtmc-vidreid'
+    dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-VideoReID.zip'
+
+    def __init__(self, root='', min_seq_len=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/train')
+        self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/query')
+        self.gallery_dir = osp.join(
+            self.dataset_dir, 'DukeMTMC-VideoReID/gallery'
+        )
+        self.split_train_json_path = osp.join(
+            self.dataset_dir, 'split_train.json'
+        )
+        self.split_query_json_path = osp.join(
+            self.dataset_dir, 'split_query.json'
+        )
+        self.split_gallery_json_path = osp.join(
+            self.dataset_dir, 'split_gallery.json'
+        )
+        self.min_seq_len = min_seq_len
+
+        required_files = [
+            self.dataset_dir, self.train_dir, self.query_dir, self.gallery_dir
+        ]
+        self.check_before_run(required_files)
+
+        train = self.process_dir(
+            self.train_dir, self.split_train_json_path, relabel=True
+        )
+        query = self.process_dir(
+            self.query_dir, self.split_query_json_path, relabel=False
+        )
+        gallery = self.process_dir(
+            self.gallery_dir, self.split_gallery_json_path, relabel=False
+        )
+
+        super(DukeMTMCVidReID, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dir_path, json_path, relabel):
+        if osp.exists(json_path):
+            split = read_json(json_path)
+            return split['tracklets']
+
+        print('=> Generating split json file (** this might take a while **)')
+        pdirs = glob.glob(osp.join(dir_path, '*')) # avoid .DS_Store
+        print(
+            'Processing "{}" with {} person identities'.format(
+                dir_path, len(pdirs)
+            )
+        )
+
+        pid_container = set()
+        for pdir in pdirs:
+            pid = int(osp.basename(pdir))
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        tracklets = []
+        for pdir in pdirs:
+            pid = int(osp.basename(pdir))
+            if relabel:
+                pid = pid2label[pid]
+            tdirs = glob.glob(osp.join(pdir, '*'))
+            for tdir in tdirs:
+                raw_img_paths = glob.glob(osp.join(tdir, '*.jpg'))
+                num_imgs = len(raw_img_paths)
+
+                if num_imgs < self.min_seq_len:
+                    continue
+
+                img_paths = []
+                for img_idx in range(num_imgs):
+                    # some tracklet starts from 0002 instead of 0001
+                    img_idx_name = 'F' + str(img_idx + 1).zfill(4)
+                    res = glob.glob(
+                        osp.join(tdir, '*' + img_idx_name + '*.jpg')
+                    )
+                    if len(res) == 0:
+                        warnings.warn(
+                            'Index name {} in {} is missing, skip'.format(
+                                img_idx_name, tdir
+                            )
+                        )
+                        continue
+                    img_paths.append(res[0])
+                img_name = osp.basename(img_paths[0])
+                if img_name.find('_') == -1:
+                    # old naming format: 0001C6F0099X30823.jpg
+                    camid = int(img_name[5]) - 1
+                else:
+                    # new naming format: 0001_C6_F0099_X30823.jpg
+                    camid = int(img_name[6]) - 1
+                img_paths = tuple(img_paths)
+                tracklets.append((img_paths, pid, camid))
+
+        print('Saving split to {}'.format(json_path))
+        split_dict = {'tracklets': tracklets}
+        write_json(split_dict, json_path)
+
+        return tracklets
diff --git a/pfe/torchreid/data/datasets/video/ilidsvid.py b/pfe/torchreid/data/datasets/video/ilidsvid.py
new file mode 100644
index 0000000..c3ac1bb
--- /dev/null
+++ b/pfe/torchreid/data/datasets/video/ilidsvid.py
@@ -0,0 +1,143 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+from scipy.io import loadmat
+
+from torchreid.utils import read_json, write_json
+
+from ..dataset import VideoDataset
+
+
+class iLIDSVID(VideoDataset):
+    """iLIDS-VID.
+
+    Reference:
+        Wang et al. Person Re-Identification by Video Ranking. ECCV 2014.
+
+    URL: `<http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html>`_
+    
+    Dataset statistics:
+        - identities: 300.
+        - tracklets: 600.
+        - cameras: 2.
+    """
+    dataset_dir = 'ilids-vid'
+    dataset_url = 'http://www.eecs.qmul.ac.uk/~xiatian/iLIDS-VID/iLIDS-VID.tar'
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.data_dir = osp.join(self.dataset_dir, 'i-LIDS-VID')
+        self.split_dir = osp.join(self.dataset_dir, 'train-test people splits')
+        self.split_mat_path = osp.join(
+            self.split_dir, 'train_test_splits_ilidsvid.mat'
+        )
+        self.split_path = osp.join(self.dataset_dir, 'splits.json')
+        self.cam_1_path = osp.join(
+            self.dataset_dir, 'i-LIDS-VID/sequences/cam1'
+        )
+        self.cam_2_path = osp.join(
+            self.dataset_dir, 'i-LIDS-VID/sequences/cam2'
+        )
+
+        required_files = [self.dataset_dir, self.data_dir, self.split_dir]
+        self.check_before_run(required_files)
+
+        self.prepare_split()
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+        train_dirs, test_dirs = split['train'], split['test']
+
+        train = self.process_data(train_dirs, cam1=True, cam2=True)
+        query = self.process_data(test_dirs, cam1=True, cam2=False)
+        gallery = self.process_data(test_dirs, cam1=False, cam2=True)
+
+        super(iLIDSVID, self).__init__(train, query, gallery, **kwargs)
+
+    def prepare_split(self):
+        if not osp.exists(self.split_path):
+            print('Creating splits ...')
+            mat_split_data = loadmat(self.split_mat_path)['ls_set']
+
+            num_splits = mat_split_data.shape[0]
+            num_total_ids = mat_split_data.shape[1]
+            assert num_splits == 10
+            assert num_total_ids == 300
+            num_ids_each = num_total_ids // 2
+
+            # pids in mat_split_data are indices, so we need to transform them
+            # to real pids
+            person_cam1_dirs = sorted(
+                glob.glob(osp.join(self.cam_1_path, '*'))
+            )
+            person_cam2_dirs = sorted(
+                glob.glob(osp.join(self.cam_2_path, '*'))
+            )
+
+            person_cam1_dirs = [
+                osp.basename(item) for item in person_cam1_dirs
+            ]
+            person_cam2_dirs = [
+                osp.basename(item) for item in person_cam2_dirs
+            ]
+
+            # make sure persons in one camera view can be found in the other camera view
+            assert set(person_cam1_dirs) == set(person_cam2_dirs)
+
+            splits = []
+            for i_split in range(num_splits):
+                # first 50% for testing and the remaining for training, following Wang et al. ECCV'14.
+                train_idxs = sorted(
+                    list(mat_split_data[i_split, num_ids_each:])
+                )
+                test_idxs = sorted(
+                    list(mat_split_data[i_split, :num_ids_each])
+                )
+
+                train_idxs = [int(i) - 1 for i in train_idxs]
+                test_idxs = [int(i) - 1 for i in test_idxs]
+
+                # transform pids to person dir names
+                train_dirs = [person_cam1_dirs[i] for i in train_idxs]
+                test_dirs = [person_cam1_dirs[i] for i in test_idxs]
+
+                split = {'train': train_dirs, 'test': test_dirs}
+                splits.append(split)
+
+            print(
+                'Totally {} splits are created, following Wang et al. ECCV\'14'
+                .format(len(splits))
+            )
+            print('Split file is saved to {}'.format(self.split_path))
+            write_json(splits, self.split_path)
+
+    def process_data(self, dirnames, cam1=True, cam2=True):
+        tracklets = []
+        dirname2pid = {dirname: i for i, dirname in enumerate(dirnames)}
+
+        for dirname in dirnames:
+            if cam1:
+                person_dir = osp.join(self.cam_1_path, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 0))
+
+            if cam2:
+                person_dir = osp.join(self.cam_2_path, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 1))
+
+        return tracklets
diff --git a/pfe/torchreid/data/datasets/video/mars.py b/pfe/torchreid/data/datasets/video/mars.py
new file mode 100644
index 0000000..4128e1c
--- /dev/null
+++ b/pfe/torchreid/data/datasets/video/mars.py
@@ -0,0 +1,133 @@
+from __future__ import division, print_function, absolute_import
+import os.path as osp
+import warnings
+from scipy.io import loadmat
+
+from ..dataset import VideoDataset
+
+
+class Mars(VideoDataset):
+    """MARS.
+
+    Reference:
+        Zheng et al. MARS: A Video Benchmark for Large-Scale Person Re-identification. ECCV 2016.
+
+    URL: `<http://www.liangzheng.com.cn/Project/project_mars.html>`_
+    
+    Dataset statistics:
+        - identities: 1261.
+        - tracklets: 8298 (train) + 1980 (query) + 9330 (gallery).
+        - cameras: 6.
+    """
+    dataset_dir = 'mars'
+    dataset_url = None
+
+    def __init__(self, root='', **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.train_name_path = osp.join(
+            self.dataset_dir, 'info/train_name.txt'
+        )
+        self.test_name_path = osp.join(self.dataset_dir, 'info/test_name.txt')
+        self.track_train_info_path = osp.join(
+            self.dataset_dir, 'info/tracks_train_info.mat'
+        )
+        self.track_test_info_path = osp.join(
+            self.dataset_dir, 'info/tracks_test_info.mat'
+        )
+        self.query_IDX_path = osp.join(self.dataset_dir, 'info/query_IDX.mat')
+
+        required_files = [
+            self.dataset_dir, self.train_name_path, self.test_name_path,
+            self.track_train_info_path, self.track_test_info_path,
+            self.query_IDX_path
+        ]
+        self.check_before_run(required_files)
+
+        train_names = self.get_names(self.train_name_path)
+        test_names = self.get_names(self.test_name_path)
+        track_train = loadmat(self.track_train_info_path
+                              )['track_train_info'] # numpy.ndarray (8298, 4)
+        track_test = loadmat(self.track_test_info_path
+                             )['track_test_info'] # numpy.ndarray (12180, 4)
+        query_IDX = loadmat(self.query_IDX_path
+                            )['query_IDX'].squeeze() # numpy.ndarray (1980,)
+        query_IDX -= 1 # index from 0
+        track_query = track_test[query_IDX, :]
+        gallery_IDX = [
+            i for i in range(track_test.shape[0]) if i not in query_IDX
+        ]
+        track_gallery = track_test[gallery_IDX, :]
+
+        train = self.process_data(
+            train_names, track_train, home_dir='bbox_train', relabel=True
+        )
+        query = self.process_data(
+            test_names, track_query, home_dir='bbox_test', relabel=False
+        )
+        gallery = self.process_data(
+            test_names, track_gallery, home_dir='bbox_test', relabel=False
+        )
+
+        super(Mars, self).__init__(train, query, gallery, **kwargs)
+
+    def get_names(self, fpath):
+        names = []
+        with open(fpath, 'r') as f:
+            for line in f:
+                new_line = line.rstrip()
+                names.append(new_line)
+        return names
+
+    def process_data(
+        self, names, meta_data, home_dir=None, relabel=False, min_seq_len=0
+    ):
+        assert home_dir in ['bbox_train', 'bbox_test']
+        num_tracklets = meta_data.shape[0]
+        pid_list = list(set(meta_data[:, 2].tolist()))
+
+        if relabel:
+            pid2label = {pid: label for label, pid in enumerate(pid_list)}
+        tracklets = []
+
+        for tracklet_idx in range(num_tracklets):
+            data = meta_data[tracklet_idx, ...]
+            start_index, end_index, pid, camid = data
+            if pid == -1:
+                continue # junk images are just ignored
+            assert 1 <= camid <= 6
+            if relabel:
+                pid = pid2label[pid]
+            camid -= 1 # index starts from 0
+            img_names = names[start_index - 1:end_index]
+
+            # make sure image names correspond to the same person
+            pnames = [img_name[:4] for img_name in img_names]
+            assert len(
+                set(pnames)
+            ) == 1, 'Error: a single tracklet contains different person images'
+
+            # make sure all images are captured under the same camera
+            camnames = [img_name[5] for img_name in img_names]
+            assert len(
+                set(camnames)
+            ) == 1, 'Error: images are captured under different cameras!'
+
+            # append image names with directory information
+            img_paths = [
+                osp.join(self.dataset_dir, home_dir, img_name[:4], img_name)
+                for img_name in img_names
+            ]
+            if len(img_paths) >= min_seq_len:
+                img_paths = tuple(img_paths)
+                tracklets.append((img_paths, pid, camid))
+
+        return tracklets
+
+    def combine_all(self):
+        warnings.warn(
+            'Some query IDs do not appear in gallery. Therefore, combineall '
+            'does not make any difference to Mars'
+        )
diff --git a/pfe/torchreid/data/datasets/video/prid2011.py b/pfe/torchreid/data/datasets/video/prid2011.py
new file mode 100644
index 0000000..3af2e4d
--- /dev/null
+++ b/pfe/torchreid/data/datasets/video/prid2011.py
@@ -0,0 +1,80 @@
+from __future__ import division, print_function, absolute_import
+import glob
+import os.path as osp
+
+from torchreid.utils import read_json
+
+from ..dataset import VideoDataset
+
+
+class PRID2011(VideoDataset):
+    """PRID2011.
+
+    Reference:
+        Hirzer et al. Person Re-Identification by Descriptive and
+        Discriminative Classification. SCIA 2011.
+
+    URL: `<https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/>`_
+    
+    Dataset statistics:
+        - identities: 200.
+        - tracklets: 400.
+        - cameras: 2.
+    """
+    dataset_dir = 'prid2011'
+    dataset_url = None
+
+    def __init__(self, root='', split_id=0, **kwargs):
+        self.root = osp.abspath(osp.expanduser(root))
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.download_dataset(self.dataset_dir, self.dataset_url)
+
+        self.split_path = osp.join(self.dataset_dir, 'splits_prid2011.json')
+        self.cam_a_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_a'
+        )
+        self.cam_b_dir = osp.join(
+            self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_b'
+        )
+
+        required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_dir]
+        self.check_before_run(required_files)
+
+        splits = read_json(self.split_path)
+        if split_id >= len(splits):
+            raise ValueError(
+                'split_id exceeds range, received {}, but expected between 0 and {}'
+                .format(split_id,
+                        len(splits) - 1)
+            )
+        split = splits[split_id]
+        train_dirs, test_dirs = split['train'], split['test']
+
+        train = self.process_dir(train_dirs, cam1=True, cam2=True)
+        query = self.process_dir(test_dirs, cam1=True, cam2=False)
+        gallery = self.process_dir(test_dirs, cam1=False, cam2=True)
+
+        super(PRID2011, self).__init__(train, query, gallery, **kwargs)
+
+    def process_dir(self, dirnames, cam1=True, cam2=True):
+        tracklets = []
+        dirname2pid = {dirname: i for i, dirname in enumerate(dirnames)}
+
+        for dirname in dirnames:
+            if cam1:
+                person_dir = osp.join(self.cam_a_dir, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 0))
+
+            if cam2:
+                person_dir = osp.join(self.cam_b_dir, dirname)
+                img_names = glob.glob(osp.join(person_dir, '*.png'))
+                assert len(img_names) > 0
+                img_names = tuple(img_names)
+                pid = dirname2pid[dirname]
+                tracklets.append((img_names, pid, 1))
+
+        return tracklets
diff --git a/pfe/torchreid/data/sampler.py b/pfe/torchreid/data/sampler.py
new file mode 100644
index 0000000..49221cb
--- /dev/null
+++ b/pfe/torchreid/data/sampler.py
@@ -0,0 +1,104 @@
+from __future__ import division, absolute_import
+import copy
+import numpy as np
+import random
+from collections import defaultdict
+from torch.utils.data.sampler import Sampler, RandomSampler, SequentialSampler
+
+AVAI_SAMPLERS = ['RandomIdentitySampler', 'SequentialSampler', 'RandomSampler']
+
+
+class RandomIdentitySampler(Sampler):
+    """Randomly samples N identities each with K instances.
+
+    Args:
+        data_source (list): contains tuples of (img_path(s), pid, camid).
+        batch_size (int): batch size.
+        num_instances (int): number of instances per identity in a batch.
+    """
+
+    def __init__(self, data_source, batch_size, num_instances):
+        if batch_size < num_instances:
+            raise ValueError(
+                'batch_size={} must be no less '
+                'than num_instances={}'.format(batch_size, num_instances)
+            )
+
+        self.data_source = data_source
+        self.batch_size = batch_size
+        self.num_instances = num_instances
+        self.num_pids_per_batch = self.batch_size // self.num_instances
+        self.index_dic = defaultdict(list)
+        for index, (_, pid, _) in enumerate(self.data_source):
+            self.index_dic[pid].append(index)
+        self.pids = list(self.index_dic.keys())
+
+        # estimate number of examples in an epoch
+        # TODO: improve precision
+        self.length = 0
+        for pid in self.pids:
+            idxs = self.index_dic[pid]
+            num = len(idxs)
+            if num < self.num_instances:
+                num = self.num_instances
+            self.length += num - num % self.num_instances
+
+    def __iter__(self):
+        batch_idxs_dict = defaultdict(list)
+
+        for pid in self.pids:
+            idxs = copy.deepcopy(self.index_dic[pid])
+            if len(idxs) < self.num_instances:
+                idxs = np.random.choice(
+                    idxs, size=self.num_instances, replace=True
+                )
+            random.shuffle(idxs)
+            batch_idxs = []
+            for idx in idxs:
+                batch_idxs.append(idx)
+                if len(batch_idxs) == self.num_instances:
+                    batch_idxs_dict[pid].append(batch_idxs)
+                    batch_idxs = []
+
+        avai_pids = copy.deepcopy(self.pids)
+        final_idxs = []
+
+        while len(avai_pids) >= self.num_pids_per_batch:
+            selected_pids = random.sample(avai_pids, self.num_pids_per_batch)
+            for pid in selected_pids:
+                batch_idxs = batch_idxs_dict[pid].pop(0)
+                final_idxs.extend(batch_idxs)
+                if len(batch_idxs_dict[pid]) == 0:
+                    avai_pids.remove(pid)
+
+        return iter(final_idxs)
+
+    def __len__(self):
+        return self.length
+
+
+def build_train_sampler(
+    data_source, train_sampler, batch_size=32, num_instances=4, **kwargs
+):
+    """Builds a training sampler.
+
+    Args:
+        data_source (list): contains tuples of (img_path(s), pid, camid).
+        train_sampler (str): sampler name (default: ``RandomSampler``).
+        batch_size (int, optional): batch size. Default is 32.
+        num_instances (int, optional): number of instances per identity in a
+            batch (when using ``RandomIdentitySampler``). Default is 4.
+    """
+    assert train_sampler in AVAI_SAMPLERS, \
+        'train_sampler must be one of {}, but got {}'.format(AVAI_SAMPLERS, train_sampler)
+
+    if train_sampler == 'RandomIdentitySampler':
+        sampler = RandomIdentitySampler(data_source, batch_size, num_instances)
+
+    elif train_sampler == 'SequentialSampler':
+        sampler = SequentialSampler(data_source)
+
+    elif train_sampler == 'RandomSampler':
+        sampler = RandomSampler(data_source)
+
+    return sampler
diff --git a/pfe/torchreid/data/transforms.py b/pfe/torchreid/data/transforms.py
new file mode 100644
index 0000000..f86c8c1
--- /dev/null
+++ b/pfe/torchreid/data/transforms.py
@@ -0,0 +1,326 @@
+from __future__ import division, print_function, absolute_import
+import math
+import random
+from collections import deque
+import torch
+from PIL import Image
+from torchvision.transforms import (
+    Resize, Compose, ToTensor, Normalize, ColorJitter, RandomHorizontalFlip
+)
+
+
+class Random2DTranslation(object):
+    """Randomly translates the input image with a probability.
+
+    Specifically, given a predefined shape (height, width), the input is first
+    resized with a factor of 1.125, leading to (height*1.125, width*1.125), then
+    a random crop is performed. Such operation is done with a probability.
+
+    Args:
+        height (int): target image height.
+        width (int): target image width.
+        p (float, optional): probability that this operation takes place.
+            Default is 0.5.
+        interpolation (int, optional): desired interpolation. Default is
+            ``PIL.Image.BILINEAR``
+    """
+
+    def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
+        self.height = height
+        self.width = width
+        self.p = p
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        if random.uniform(0, 1) > self.p:
+            return img.resize((self.width, self.height), self.interpolation)
+
+        new_width, new_height = int(round(self.width * 1.125)
+                                    ), int(round(self.height * 1.125))
+        resized_img = img.resize((new_width, new_height), self.interpolation)
+        x_maxrange = new_width - self.width
+        y_maxrange = new_height - self.height
+        x1 = int(round(random.uniform(0, x_maxrange)))
+        y1 = int(round(random.uniform(0, y_maxrange)))
+        croped_img = resized_img.crop(
+            (x1, y1, x1 + self.width, y1 + self.height)
+        )
+        return croped_img
+
+
+class RandomErasing(object):
+    """Randomly erases an image patch.
+
+    Origin: `<https://github.com/zhunzhong07/Random-Erasing>`_
+
+    Reference:
+        Zhong et al. Random Erasing Data Augmentation.
+
+    Args:
+        probability (float, optional): probability that this operation takes place.
+            Default is 0.5.
+        sl (float, optional): min erasing area.
+        sh (float, optional): max erasing area.
+        r1 (float, optional): min aspect ratio.
+        mean (list, optional): erasing value.
+    """
+
+    def __init__(
+        self,
+        probability=0.5,
+        sl=0.02,
+        sh=0.4,
+        r1=0.3,
+        mean=[0.4914, 0.4822, 0.4465]
+    ):
+        self.probability = probability
+        self.mean = mean
+        self.sl = sl
+        self.sh = sh
+        self.r1 = r1
+
+    def __call__(self, img):
+        if random.uniform(0, 1) > self.probability:
+            return img
+
+        for attempt in range(100):
+            area = img.size()[1] * img.size()[2]
+
+            target_area = random.uniform(self.sl, self.sh) * area
+            aspect_ratio = random.uniform(self.r1, 1 / self.r1)
+
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if w < img.size()[2] and h < img.size()[1]:
+                x1 = random.randint(0, img.size()[1] - h)
+                y1 = random.randint(0, img.size()[2] - w)
+                if img.size()[0] == 3:
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
+                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
+                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
+                else:
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
+                return img
+
+        return img
+
+
+class ColorAugmentation(object):
+    """Randomly alters the intensities of RGB channels.
+
+    Reference:
+        Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural
+        Networks. NIPS 2012.
+
+    Args:
+        p (float, optional): probability that this operation takes place.
+            Default is 0.5.
+    """
+
+    def __init__(self, p=0.5):
+        self.p = p
+        self.eig_vec = torch.Tensor(
+            [
+                [0.4009, 0.7192, -0.5675],
+                [-0.8140, -0.0045, -0.5808],
+                [0.4203, -0.6948, -0.5836],
+            ]
+        )
+        self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])
+
+    def _check_input(self, tensor):
+        assert tensor.dim() == 3 and tensor.size(0) == 3
+
+    def __call__(self, tensor):
+        if random.uniform(0, 1) > self.p:
+            return tensor
+        alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
+        quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
+        tensor = tensor + quatity.view(3, 1, 1)
+        return tensor
+
+
+class RandomPatch(object):
+    """Random patch data augmentation.
+
+    There is a patch pool that stores randomly extracted pathces from person images.
+    
+    For each input image, RandomPatch
+        1) extracts a random patch and stores the patch in the patch pool;
+        2) randomly selects a patch from the patch pool and pastes it on the
+           input (at random position) to simulate occlusion.
+
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        prob_happen=0.5,
+        pool_capacity=50000,
+        min_sample_size=100,
+        patch_min_area=0.01,
+        patch_max_area=0.5,
+        patch_min_ratio=0.1,
+        prob_rotate=0.5,
+        prob_flip_leftright=0.5,
+    ):
+        self.prob_happen = prob_happen
+
+        self.patch_min_area = patch_min_area
+        self.patch_max_area = patch_max_area
+        self.patch_min_ratio = patch_min_ratio
+
+        self.prob_rotate = prob_rotate
+        self.prob_flip_leftright = prob_flip_leftright
+
+        self.patchpool = deque(maxlen=pool_capacity)
+        self.min_sample_size = min_sample_size
+
+    def generate_wh(self, W, H):
+        area = W * H
+        for attempt in range(100):
+            target_area = random.uniform(
+                self.patch_min_area, self.patch_max_area
+            ) * area
+            aspect_ratio = random.uniform(
+                self.patch_min_ratio, 1. / self.patch_min_ratio
+            )
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+            if w < W and h < H:
+                return w, h
+        return None, None
+
+    def transform_patch(self, patch):
+        if random.uniform(0, 1) > self.prob_flip_leftright:
+            patch = patch.transpose(Image.FLIP_LEFT_RIGHT)
+        if random.uniform(0, 1) > self.prob_rotate:
+            patch = patch.rotate(random.randint(-10, 10))
+        return patch
+
+    def __call__(self, img):
+        W, H = img.size # original image size
+
+        # collect new patch
+        w, h = self.generate_wh(W, H)
+        if w is not None and h is not None:
+            x1 = random.randint(0, W - w)
+            y1 = random.randint(0, H - h)
+            new_patch = img.crop((x1, y1, x1 + w, y1 + h))
+            self.patchpool.append(new_patch)
+
+        if len(self.patchpool) < self.min_sample_size:
+            return img
+
+        if random.uniform(0, 1) > self.prob_happen:
+            return img
+
+        # paste a randomly selected patch on a random position
+        patch = random.sample(self.patchpool, 1)[0]
+        patchW, patchH = patch.size
+        x1 = random.randint(0, W - patchW)
+        y1 = random.randint(0, H - patchH)
+        patch = self.transform_patch(patch)
+        img.paste(patch, (x1, y1))
+
+        return img
+
+
+def build_transforms(
+    height,
+    width,
+    transforms='random_flip',
+    norm_mean=[0.485, 0.456, 0.406],
+    norm_std=[0.229, 0.224, 0.225],
+    **kwargs
+):
+    """Builds train and test transform functions.
+
+    Args:
+        height (int): target image height.
+        width (int): target image width.
+        transforms (str or list of str, optional): transformations applied to model training.
+            Default is 'random_flip'.
+        norm_mean (list or None, optional): normalization mean values. Default is ImageNet means.
+        norm_std (list or None, optional): normalization standard deviation values. Default is
+            ImageNet standard deviation values.
+    """
+    if transforms is None:
+        transforms = []
+
+    if isinstance(transforms, str):
+        transforms = [transforms]
+
+    if not isinstance(transforms, list):
+        raise ValueError(
+            'transforms must be a list of strings, but found to be {}'.format(
+                type(transforms)
+            )
+        )
+
+    if len(transforms) > 0:
+        transforms = [t.lower() for t in transforms]
+
+    if norm_mean is None or norm_std is None:
+        norm_mean = [0.485, 0.456, 0.406] # imagenet mean
+        norm_std = [0.229, 0.224, 0.225] # imagenet std
+    normalize = Normalize(mean=norm_mean, std=norm_std)
+
+    print('Building train transforms ...')
+    transform_tr = []
+
+    print('+ resize to {}x{}'.format(height, width))
+    transform_tr += [Resize((height, width))]
+
+    if 'random_flip' in transforms:
+        print('+ random flip')
+        transform_tr += [RandomHorizontalFlip()]
+
+    if 'random_crop' in transforms:
+        print(
+            '+ random crop (enlarge to {}x{} and '
+            'crop {}x{})'.format(
+                int(round(height * 1.125)), int(round(width * 1.125)), height,
+                width
+            )
+        )
+        transform_tr += [Random2DTranslation(height, width)]
+
+    if 'random_patch' in transforms:
+        print('+ random patch')
+        transform_tr += [RandomPatch()]
+
+    if 'color_jitter' in transforms:
+        print('+ color jitter')
+        transform_tr += [
+            ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)
+        ]
+
+    print('+ to torch tensor of range [0, 1]')
+    transform_tr += [ToTensor()]
+
+    print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
+    transform_tr += [normalize]
+
+    if 'random_erase' in transforms:
+        print('+ random erase')
+        transform_tr += [RandomErasing(mean=norm_mean)]
+
+    transform_tr = Compose(transform_tr)
+
+    print('Building test transforms ...')
+    print('+ resize to {}x{}'.format(height, width))
+    print('+ to torch tensor of range [0, 1]')
+    print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
+
+    transform_te = Compose([
+        Resize((height, width)),
+        ToTensor(),
+        normalize,
+    ])
+
+    return transform_tr, transform_te
diff --git a/pfe/torchreid/engine/__init__.py b/pfe/torchreid/engine/__init__.py
new file mode 100644
index 0000000..a39cc7f
--- /dev/null
+++ b/pfe/torchreid/engine/__init__.py
@@ -0,0 +1,5 @@
+from __future__ import print_function, absolute_import
+
+from .image import ImageSoftmaxEngine, ImageTripletEngine
+from .video import VideoSoftmaxEngine, VideoTripletEngine
+from .engine import Engine
diff --git a/pfe/torchreid/engine/engine.py b/pfe/torchreid/engine/engine.py
new file mode 100644
index 0000000..0aec558
--- /dev/null
+++ b/pfe/torchreid/engine/engine.py
@@ -0,0 +1,488 @@
+from __future__ import division, print_function, absolute_import
+import time
+import numpy as np
+import os.path as osp
+import datetime
+from collections import OrderedDict
+import torch
+from torch.nn import functional as F
+from torch.utils.tensorboard import SummaryWriter
+
+from torchreid import metrics
+from torchreid.utils import (
+    MetricMeter, AverageMeter, re_ranking, open_all_layers, save_checkpoint,
+    open_specified_layers, visualize_ranked_results
+)
+from torchreid.losses import DeepSupervision
+
+
+class Engine(object):
+    r"""A generic base Engine class for both image- and video-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+    """
+
+    def __init__(self, datamanager, use_gpu=True):
+        self.datamanager = datamanager
+        self.train_loader = self.datamanager.train_loader
+        self.test_loader = self.datamanager.test_loader
+        self.use_gpu = (torch.cuda.is_available() and use_gpu)
+        self.writer = None
+
+        self.model = None
+        self.optimizer = None
+        self.scheduler = None
+
+        self._models = OrderedDict()
+        self._optims = OrderedDict()
+        self._scheds = OrderedDict()
+
+    def register_model(self, name='model', model=None, optim=None, sched=None):
+        if self.__dict__.get('_models') is None:
+            raise AttributeError(
+                'Cannot assign model before super().__init__() call'
+            )
+
+        if self.__dict__.get('_optims') is None:
+            raise AttributeError(
+                'Cannot assign optim before super().__init__() call'
+            )
+
+        if self.__dict__.get('_scheds') is None:
+            raise AttributeError(
+                'Cannot assign sched before super().__init__() call'
+            )
+
+        self._models[name] = model
+        self._optims[name] = optim
+        self._scheds[name] = sched
+
+    def get_model_names(self, names=None):
+        names_real = list(self._models.keys())
+        if names is not None:
+            if not isinstance(names, list):
+                names = [names]
+            for name in names:
+                assert name in names_real
+            return names
+        else:
+            return names_real
+
+    def save_model(self, epoch, rank1, save_dir, is_best=False):
+        names = self.get_model_names()
+
+        for name in names:
+            save_checkpoint(
+                {
+                    'state_dict': self._models[name].state_dict(),
+                    'epoch': epoch + 1,
+                    'rank1': rank1,
+                    'optimizer': self._optims[name].state_dict(),
+                    'scheduler': self._scheds[name].state_dict()
+                },
+                osp.join(save_dir, name),
+                is_best=is_best
+            )
+
+    def set_model_mode(self, mode='train', names=None):
+        assert mode in ['train', 'eval', 'test']
+        names = self.get_model_names(names)
+
+        for name in names:
+            if mode == 'train':
+                self._models[name].train()
+            else:
+                self._models[name].eval()
+
+    def get_current_lr(self, names=None):
+        names = self.get_model_names(names)
+        name = names[0]
+        return self._optims[name].param_groups[-1]['lr']
+
+    def update_lr(self, names=None):
+        names = self.get_model_names(names)
+
+        for name in names:
+            if self._scheds[name] is not None:
+                self._scheds[name].step()
+
+    def run(
+        self,
+        save_dir='log',
+        max_epoch=0,
+        start_epoch=0,
+        print_freq=10,
+        fixbase_epoch=0,
+        open_layers=None,
+        start_eval=0,
+        eval_freq=-1,
+        test_only=False,
+        dist_metric='euclidean',
+        normalize_feature=False,
+        visrank=False,
+        visrank_topk=10,
+        use_metric_cuhk03=False,
+        ranks=[1, 5, 10, 20],
+        rerank=False
+    ):
+        r"""A unified pipeline for training and evaluating a model.
+
+        Args:
+            save_dir (str): directory to save model.
+            max_epoch (int): maximum epoch.
+            start_epoch (int, optional): starting epoch. Default is 0.
+            print_freq (int, optional): print_frequency. Default is 10.
+            fixbase_epoch (int, optional): number of epochs to train ``open_layers`` (new layers)
+                while keeping base layers frozen. Default is 0. ``fixbase_epoch`` is counted
+                in ``max_epoch``.
+            open_layers (str or list, optional): layers (attribute names) open for training.
+            start_eval (int, optional): from which epoch to start evaluation. Default is 0.
+            eval_freq (int, optional): evaluation frequency. Default is -1 (meaning evaluation
+                is only performed at the end of training).
+            test_only (bool, optional): if True, only runs evaluation on test datasets.
+                Default is False.
+            dist_metric (str, optional): distance metric used to compute distance matrix
+                between query and gallery. Default is "euclidean".
+            normalize_feature (bool, optional): performs L2 normalization on feature vectors before
+                computing feature distance. Default is False.
+            visrank (bool, optional): visualizes ranked results. Default is False. It is recommended to
+                enable ``visrank`` when ``test_only`` is True. The ranked images will be saved to
+                "save_dir/visrank_dataset", e.g. "save_dir/visrank_market1501".
+            visrank_topk (int, optional): top-k ranked images to be visualized. Default is 10.
+            use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
+                Default is False. This should be enabled when using cuhk03 classic split.
+            ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].
+            rerank (bool, optional): uses person re-ranking (by Zhong et al. CVPR'17).
+                Default is False. This is only enabled when test_only=True.
+        """
+
+        if visrank and not test_only:
+            raise ValueError(
+                'visrank can be set to True only if test_only=True'
+            )
+
+        if test_only:
+            self.test(
+                0,
+                dist_metric=dist_metric,
+                normalize_feature=normalize_feature,
+                visrank=visrank,
+                visrank_topk=visrank_topk,
+                save_dir=save_dir,
+                use_metric_cuhk03=use_metric_cuhk03,
+                ranks=ranks,
+                rerank=rerank
+            )
+            return
+
+        if self.writer is None:
+            self.writer = SummaryWriter(log_dir=save_dir)
+
+        time_start = time.time()
+        self.start_epoch = start_epoch
+        self.max_epoch = max_epoch
+        print('=> Start training')
+
+        for self.epoch in range(self.start_epoch, self.max_epoch):
+            self.train(
+                print_freq=print_freq,
+                fixbase_epoch=fixbase_epoch,
+                open_layers=open_layers
+            )
+
+            print ("saving checkpoint to: " + save_dir)
+            self.save_model(self.epoch, 0.5, save_dir)
+
+            if (self.epoch + 1) >= start_eval \
+               and eval_freq > 0 \
+               and (self.epoch+1) % eval_freq == 0 \
+               and (self.epoch + 1) != self.max_epoch:
+                rank1 = self.test(
+                    self.epoch,
+                    dist_metric=dist_metric,
+                    normalize_feature=normalize_feature,
+                    visrank=visrank,
+                    visrank_topk=visrank_topk,
+                    save_dir=save_dir,
+                    use_metric_cuhk03=use_metric_cuhk03,
+                    ranks=ranks
+                )
+                print ("saving checkpoint to: " + save_dir)
+                self.save_model(self.epoch, rank1, save_dir)
+                self.writer.add_scalar('Test/rank1', rank1, self.epoch)
+
+        if self.max_epoch > 0:
+            print('=> Final test')
+            rank1 = self.test(
+                self.epoch,
+                dist_metric=dist_metric,
+                normalize_feature=normalize_feature,
+                visrank=visrank,
+                visrank_topk=visrank_topk,
+                save_dir=save_dir,
+                use_metric_cuhk03=use_metric_cuhk03,
+                ranks=ranks
+            )
+            self.save_model(self.epoch, rank1, save_dir)
+            self.writer.add_scalar('Test/rank1', rank1, self.epoch)
+
+        elapsed = round(time.time() - time_start)
+        elapsed = str(datetime.timedelta(seconds=elapsed))
+        print('Elapsed {}'.format(elapsed))
+        if self.writer is not None:
+            self.writer.close()
+
+    def train(self, print_freq=10, fixbase_epoch=0, open_layers=None):
+        losses = MetricMeter()
+        batch_time = AverageMeter()
+        data_time = AverageMeter()
+
+        self.set_model_mode('train')
+
+        self.two_stepped_transfer_learning(
+            self.epoch, fixbase_epoch, open_layers
+        )
+
+        self.num_batches = len(self.train_loader)
+        end = time.time()
+        for self.batch_idx, data in enumerate(self.train_loader):
+            data_time.update(time.time() - end)
+            loss_summary = self.forward_backward(data)
+            batch_time.update(time.time() - end)
+            losses.update(loss_summary)
+
+            if (self.batch_idx + 1) % print_freq == 0:
+                nb_this_epoch = self.num_batches - (self.batch_idx + 1)
+                nb_future_epochs = (
+                    self.max_epoch - (self.epoch + 1)
+                ) * self.num_batches
+                eta_seconds = batch_time.avg * (nb_this_epoch+nb_future_epochs)
+                eta_str = str(datetime.timedelta(seconds=int(eta_seconds)))
+                print(
+                    'epoch: [{0}/{1}][{2}/{3}]\t'
+                    'time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                    'data {data_time.val:.3f} ({data_time.avg:.3f})\t'
+                    'eta {eta}\t'
+                    '{losses}\t'
+                    'lr {lr:.6f}'.format(
+                        self.epoch + 1,
+                        self.max_epoch,
+                        self.batch_idx + 1,
+                        self.num_batches,
+                        batch_time=batch_time,
+                        data_time=data_time,
+                        eta=eta_str,
+                        losses=losses,
+                        lr=self.get_current_lr()
+                    )
+                )
+
+            if self.writer is not None:
+                n_iter = self.epoch * self.num_batches + self.batch_idx
+                self.writer.add_scalar('Train/time', batch_time.avg, n_iter)
+                self.writer.add_scalar('Train/data', data_time.avg, n_iter)
+                for name, meter in losses.meters.items():
+                    self.writer.add_scalar('Train/' + name, meter.avg, n_iter)
+                self.writer.add_scalar(
+                    'Train/lr', self.get_current_lr(), n_iter
+                )
+
+            end = time.time()
+
+        self.update_lr()
+
+    def forward_backward(self, data):
+        raise NotImplementedError
+
+    def test(
+        self,
+        epoch,
+        dist_metric='euclidean',
+        normalize_feature=False,
+        visrank=False,
+        visrank_topk=10,
+        save_dir='',
+        use_metric_cuhk03=False,
+        ranks=[1, 5, 10, 20],
+        rerank=False
+    ):
+        r"""Tests model on target datasets.
+
+        .. note::
+
+            This function has been called in ``run()``.
+
+        .. note::
+
+            The test pipeline implemented in this function suits both image- and
+            video-reid. In general, a subclass of Engine only needs to re-implement
+            ``extract_features()`` and ``parse_data_for_eval()`` (most of the time),
+            but not a must. Please refer to the source code for more details.
+        """
+        self.set_model_mode('eval')
+        targets = list(self.test_loader.keys())
+
+        for name in targets:
+            domain = 'source' if name in self.datamanager.sources else 'target'
+            print('##### Evaluating {} ({}) #####'.format(name, domain))
+            query_loader = self.test_loader[name]['query']
+            gallery_loader = self.test_loader[name]['gallery']
+            rank1 = self._evaluate(
+                epoch,
+                dataset_name=name,
+                query_loader=query_loader,
+                gallery_loader=gallery_loader,
+                dist_metric=dist_metric,
+                normalize_feature=normalize_feature,
+                visrank=visrank,
+                visrank_topk=visrank_topk,
+                save_dir=save_dir,
+                use_metric_cuhk03=use_metric_cuhk03,
+                ranks=ranks,
+                rerank=rerank
+            )
+
+        return rank1
+
+    @torch.no_grad()
+    def _evaluate(
+        self,
+        epoch,
+        dataset_name='',
+        query_loader=None,
+        gallery_loader=None,
+        dist_metric='euclidean',
+        normalize_feature=False,
+        visrank=False,
+        visrank_topk=10,
+        save_dir='',
+        use_metric_cuhk03=False,
+        ranks=[1, 5, 10, 20],
+        rerank=False
+    ):
+        batch_time = AverageMeter()
+
+        def _feature_extraction(data_loader):
+            f_, pids_, camids_ = [], [], []
+            for batch_idx, data in enumerate(data_loader):
+                imgs, pids, camids = self.parse_data_for_eval(data)
+                if self.use_gpu:
+                    imgs = imgs.cuda()
+                end = time.time()
+                features = self.extract_features(imgs)
+                batch_time.update(time.time() - end)
+                features = features.data.cpu()
+                f_.append(features)
+                pids_.extend(pids)
+                camids_.extend(camids)
+            f_ = torch.cat(f_, 0)
+            pids_ = np.asarray(pids_)
+            camids_ = np.asarray(camids_)
+            return f_, pids_, camids_
+
+        print('Extracting features from query set ...')
+        qf, q_pids, q_camids = _feature_extraction(query_loader)
+        print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
+
+        print('Extracting features from gallery set ...')
+        gf, g_pids, g_camids = _feature_extraction(gallery_loader)
+        print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
+
+        print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
+
+        if normalize_feature:
+            print('Normalzing features with L2 norm ...')
+            qf = F.normalize(qf, p=2, dim=1)
+            gf = F.normalize(gf, p=2, dim=1)
+
+        print(
+            'Computing distance matrix with metric={} ...'.format(dist_metric)
+        )
+        distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
+        distmat = distmat.numpy()
+
+        if rerank:
+            print('Applying person re-ranking ...')
+            distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
+            distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
+            distmat = re_ranking(distmat, distmat_qq, distmat_gg)
+
+        print('Computing CMC and mAP ...')
+        cmc, mAP = metrics.evaluate_rank(
+            distmat,
+            q_pids,
+            g_pids,
+            q_camids,
+            g_camids,
+            use_metric_cuhk03=use_metric_cuhk03
+        )
+
+        print('** Results **')
+        print('mAP: {:.1%}'.format(mAP))
+        print('CMC curve')
+        for r in ranks:
+            print('Rank-{:<3}: {:.1%}'.format(r, cmc[r - 1]))
+
+        if visrank:
+            visualize_ranked_results(
+                distmat,
+                self.datamanager.fetch_test_loaders(dataset_name),
+                self.datamanager.data_type,
+                width=self.datamanager.width,
+                height=self.datamanager.height,
+                save_dir=osp.join(save_dir, 'visrank_' + dataset_name),
+                topk=visrank_topk
+            )
+
+        return cmc[0]
+
+    def compute_loss(self, criterion, outputs, targets):
+        if isinstance(outputs, (tuple, list)):
+            loss = DeepSupervision(criterion, outputs, targets)
+        else:
+            loss = criterion(outputs, targets)
+        return loss
+
+    def extract_features(self, input):
+        return self.model(input)
+
+    def parse_data_for_train(self, data):
+        imgs = data['img']
+        pids = data['pid']
+        return imgs, pids
+
+    def parse_data_for_eval(self, data):
+        imgs = data['img']
+        pids = data['pid']
+        camids = data['camid']
+        return imgs, pids, camids
+
+    def two_stepped_transfer_learning(
+        self, epoch, fixbase_epoch, open_layers, model=None
+    ):
+        """Two-stepped transfer learning.
+
+        The idea is to freeze base layers for a certain number of epochs
+        and then open all layers for training.
+
+        Reference: https://arxiv.org/abs/1611.05244
+        """
+        model = self.model if model is None else model
+        if model is None:
+            return
+
+        if (epoch + 1) <= fixbase_epoch and open_layers is not None:
+            print(
+                '* Only train {} (epoch: {}/{})'.format(
+                    open_layers, epoch + 1, fixbase_epoch
+                )
+            )
+            open_specified_layers(model, open_layers)
+        else:
+            open_all_layers(model)
diff --git a/pfe/torchreid/engine/image/__init__.py b/pfe/torchreid/engine/image/__init__.py
new file mode 100644
index 0000000..08d313a
--- /dev/null
+++ b/pfe/torchreid/engine/image/__init__.py
@@ -0,0 +1,4 @@
+from __future__ import absolute_import
+
+from .softmax import ImageSoftmaxEngine
+from .triplet import ImageTripletEngine
diff --git a/pfe/torchreid/engine/image/softmax.py b/pfe/torchreid/engine/image/softmax.py
new file mode 100644
index 0000000..5785d4f
--- /dev/null
+++ b/pfe/torchreid/engine/image/softmax.py
@@ -0,0 +1,97 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid import metrics
+from torchreid.losses import CrossEntropyLoss
+
+from ..engine import Engine
+
+
+class ImageSoftmaxEngine(Engine):
+    r"""Softmax-loss engine for image-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+
+    Examples::
+        
+        import torchreid
+        datamanager = torchreid.data.ImageDataManager(
+            root='path/to/reid-data',
+            sources='market1501',
+            height=256,
+            width=128,
+            combineall=False,
+            batch_size=32
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='softmax'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.ImageSoftmaxEngine(
+            datamanager, model, optimizer, scheduler=scheduler
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-softmax-market1501',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True
+    ):
+        super(ImageSoftmaxEngine, self).__init__(datamanager, use_gpu)
+
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.register_model('model', model, optimizer, scheduler)
+
+        self.criterion = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        outputs = self.model(imgs)
+        loss = self.compute_loss(self.criterion, outputs, pids)
+
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+        loss_summary = {
+            'loss': loss.item(),
+            'acc': metrics.accuracy(outputs, pids)[0].item()
+        }
+
+        return loss_summary
diff --git a/pfe/torchreid/engine/image/triplet.py b/pfe/torchreid/engine/image/triplet.py
new file mode 100644
index 0000000..174d6d2
--- /dev/null
+++ b/pfe/torchreid/engine/image/triplet.py
@@ -0,0 +1,113 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid import metrics
+from torchreid.losses import TripletLoss, CrossEntropyLoss
+
+from ..engine import Engine
+
+
+class ImageTripletEngine(Engine):
+    r"""Triplet-loss engine for image-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        margin (float, optional): margin for triplet loss. Default is 0.3.
+        weight_t (float, optional): weight for triplet loss. Default is 1.
+        weight_x (float, optional): weight for softmax loss. Default is 1.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+
+    Examples::
+        
+        import torchreid
+        datamanager = torchreid.data.ImageDataManager(
+            root='path/to/reid-data',
+            sources='market1501',
+            height=256,
+            width=128,
+            combineall=False,
+            batch_size=32,
+            num_instances=4,
+            train_sampler='RandomIdentitySampler' # this is important
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='triplet'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.ImageTripletEngine(
+            datamanager, model, optimizer, margin=0.3,
+            weight_t=0.7, weight_x=1, scheduler=scheduler
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-triplet-market1501',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        margin=0.3,
+        weight_t=1,
+        weight_x=1,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True
+    ):
+        super(ImageTripletEngine, self).__init__(datamanager, use_gpu)
+
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.register_model('model', model, optimizer, scheduler)
+
+        self.weight_t = weight_t
+        self.weight_x = weight_x
+
+        self.criterion_t = TripletLoss(margin=margin)
+        self.criterion_x = CrossEntropyLoss(
+            num_classes=self.datamanager.num_train_pids,
+            use_gpu=self.use_gpu,
+            label_smooth=label_smooth
+        )
+
+    def forward_backward(self, data):
+        imgs, pids = self.parse_data_for_train(data)
+
+        if self.use_gpu:
+            imgs = imgs.cuda()
+            pids = pids.cuda()
+
+        outputs, features = self.model(imgs)
+        loss_t = self.compute_loss(self.criterion_t, features, pids)
+        loss_x = self.compute_loss(self.criterion_x, outputs, pids)
+        loss = self.weight_t * loss_t + self.weight_x * loss_x
+
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+        loss_summary = {
+            'loss_t': loss_t.item(),
+            'loss_x': loss_x.item(),
+            'acc': metrics.accuracy(outputs, pids)[0].item()
+        }
+
+        return loss_summary
diff --git a/pfe/torchreid/engine/video/__init__.py b/pfe/torchreid/engine/video/__init__.py
new file mode 100644
index 0000000..b818bf4
--- /dev/null
+++ b/pfe/torchreid/engine/video/__init__.py
@@ -0,0 +1,4 @@
+from __future__ import absolute_import
+
+from .softmax import VideoSoftmaxEngine
+from .triplet import VideoTripletEngine
diff --git a/pfe/torchreid/engine/video/softmax.py b/pfe/torchreid/engine/video/softmax.py
new file mode 100644
index 0000000..fe92feb
--- /dev/null
+++ b/pfe/torchreid/engine/video/softmax.py
@@ -0,0 +1,109 @@
+from __future__ import division, print_function, absolute_import
+import torch
+
+from torchreid.engine.image import ImageSoftmaxEngine
+
+
+class VideoSoftmaxEngine(ImageSoftmaxEngine):
+    """Softmax-loss engine for video-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+        pooling_method (str, optional): how to pool features for a tracklet.
+            Default is "avg" (average). Choices are ["avg", "max"].
+
+    Examples::
+        
+        import torch
+        import torchreid
+        # Each batch contains batch_size*seq_len images
+        datamanager = torchreid.data.VideoDataManager(
+            root='path/to/reid-data',
+            sources='mars',
+            height=256,
+            width=128,
+            combineall=False,
+            batch_size=8, # number of tracklets
+            seq_len=15 # number of images in each tracklet
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='softmax'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.VideoSoftmaxEngine(
+            datamanager, model, optimizer, scheduler=scheduler,
+            pooling_method='avg'
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-softmax-mars',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True,
+        pooling_method='avg'
+    ):
+        super(VideoSoftmaxEngine, self).__init__(
+            datamanager,
+            model,
+            optimizer,
+            scheduler=scheduler,
+            use_gpu=use_gpu,
+            label_smooth=label_smooth
+        )
+        self.pooling_method = pooling_method
+
+    def parse_data_for_train(self, data):
+        imgs = data['img']
+        pids = data['pid']
+        if imgs.dim() == 5:
+            # b: batch size
+            # s: sqeuence length
+            # c: channel depth
+            # h: height
+            # w: width
+            b, s, c, h, w = imgs.size()
+            imgs = imgs.view(b * s, c, h, w)
+            pids = pids.view(b, 1).expand(b, s)
+            pids = pids.contiguous().view(b * s)
+        return imgs, pids
+
+    def extract_features(self, input):
+        # b: batch size
+        # s: sqeuence length
+        # c: channel depth
+        # h: height
+        # w: width
+        b, s, c, h, w = input.size()
+        input = input.view(b * s, c, h, w)
+        features = self.model(input)
+        features = features.view(b, s, -1)
+        if self.pooling_method == 'avg':
+            features = torch.mean(features, 1)
+        else:
+            features = torch.max(features, 1)[0]
+        return features
diff --git a/pfe/torchreid/engine/video/triplet.py b/pfe/torchreid/engine/video/triplet.py
new file mode 100644
index 0000000..5d43b8b
--- /dev/null
+++ b/pfe/torchreid/engine/video/triplet.py
@@ -0,0 +1,91 @@
+from __future__ import division, print_function, absolute_import
+
+from torchreid.engine.image import ImageTripletEngine
+from torchreid.engine.video import VideoSoftmaxEngine
+
+
+class VideoTripletEngine(ImageTripletEngine, VideoSoftmaxEngine):
+    """Triplet-loss engine for video-reid.
+
+    Args:
+        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
+            or ``torchreid.data.VideoDataManager``.
+        model (nn.Module): model instance.
+        optimizer (Optimizer): an Optimizer.
+        margin (float, optional): margin for triplet loss. Default is 0.3.
+        weight_t (float, optional): weight for triplet loss. Default is 1.
+        weight_x (float, optional): weight for softmax loss. Default is 1.
+        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
+        use_gpu (bool, optional): use gpu. Default is True.
+        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
+        pooling_method (str, optional): how to pool features for a tracklet.
+            Default is "avg" (average). Choices are ["avg", "max"].
+
+    Examples::
+
+        import torch
+        import torchreid
+        # Each batch contains batch_size*seq_len images
+        # Each identity is sampled with num_instances tracklets
+        datamanager = torchreid.data.VideoDataManager(
+            root='path/to/reid-data',
+            sources='mars',
+            height=256,
+            width=128,
+            combineall=False,
+            num_instances=4,
+            train_sampler='RandomIdentitySampler'
+            batch_size=8, # number of tracklets
+            seq_len=15 # number of images in each tracklet
+        )
+        model = torchreid.models.build_model(
+            name='resnet50',
+            num_classes=datamanager.num_train_pids,
+            loss='triplet'
+        )
+        model = model.cuda()
+        optimizer = torchreid.optim.build_optimizer(
+            model, optim='adam', lr=0.0003
+        )
+        scheduler = torchreid.optim.build_lr_scheduler(
+            optimizer,
+            lr_scheduler='single_step',
+            stepsize=20
+        )
+        engine = torchreid.engine.VideoTripletEngine(
+            datamanager, model, optimizer, margin=0.3,
+            weight_t=0.7, weight_x=1, scheduler=scheduler,
+            pooling_method='avg'
+        )
+        engine.run(
+            max_epoch=60,
+            save_dir='log/resnet50-triplet-mars',
+            print_freq=10
+        )
+    """
+
+    def __init__(
+        self,
+        datamanager,
+        model,
+        optimizer,
+        margin=0.3,
+        weight_t=1,
+        weight_x=1,
+        scheduler=None,
+        use_gpu=True,
+        label_smooth=True,
+        pooling_method='avg'
+    ):
+        super(VideoTripletEngine, self).__init__(
+            datamanager,
+            model,
+            optimizer,
+            margin=margin,
+            weight_t=weight_t,
+            weight_x=weight_x,
+            scheduler=scheduler,
+            use_gpu=use_gpu,
+            label_smooth=label_smooth
+        )
+        self.pooling_method = pooling_method
diff --git a/pfe/torchreid/losses/__init__.py b/pfe/torchreid/losses/__init__.py
new file mode 100644
index 0000000..1625749
--- /dev/null
+++ b/pfe/torchreid/losses/__init__.py
@@ -0,0 +1,21 @@
+from __future__ import division, print_function, absolute_import
+
+from .cross_entropy_loss import CrossEntropyLoss
+from .hard_mine_triplet_loss import TripletLoss
+
+
+def DeepSupervision(criterion, xs, y):
+    """DeepSupervision
+
+    Applies criterion to each element in a list.
+
+    Args:
+        criterion: loss function
+        xs: tuple of inputs
+        y: ground truth
+    """
+    loss = 0.
+    for x in xs:
+        loss += criterion(x, y)
+    loss /= len(xs)
+    return loss
diff --git a/pfe/torchreid/losses/cross_entropy_loss.py b/pfe/torchreid/losses/cross_entropy_loss.py
new file mode 100644
index 0000000..4cfa5d4
--- /dev/null
+++ b/pfe/torchreid/losses/cross_entropy_loss.py
@@ -0,0 +1,50 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+
+
+class CrossEntropyLoss(nn.Module):
+    r"""Cross entropy loss with label smoothing regularizer.
+    
+    Reference:
+        Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
+
+    With label smoothing, the label :math:`y` for a class is computed by
+    
+    .. math::
+        \begin{equation}
+        (1 - \eps) \times y + \frac{\eps}{K},
+        \end{equation}
+
+    where :math:`K` denotes the number of classes and :math:`\eps` is a weight. When
+    :math:`\eps = 0`, the loss function reduces to the normal cross entropy.
+    
+    Args:
+        num_classes (int): number of classes.
+        eps (float, optional): weight. Default is 0.1.
+        use_gpu (bool, optional): whether to use gpu devices. Default is True.
+        label_smooth (bool, optional): whether to apply label smoothing. Default is True.
+    """
+
+    def __init__(self, num_classes, eps=0.1, use_gpu=True, label_smooth=True):
+        super(CrossEntropyLoss, self).__init__()
+        self.num_classes = num_classes
+        self.eps = eps if label_smooth else 0
+        self.use_gpu = use_gpu
+        self.logsoftmax = nn.LogSoftmax(dim=1)
+
+    def forward(self, inputs, targets):
+        """
+        Args:
+            inputs (torch.Tensor): prediction matrix (before softmax) with
+                shape (batch_size, num_classes).
+            targets (torch.LongTensor): ground truth labels with shape (batch_size).
+                Each position contains the label index.
+        """
+        log_probs = self.logsoftmax(inputs)
+        zeros = torch.zeros(log_probs.size())
+        targets = zeros.scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
+        if self.use_gpu:
+            targets = targets.cuda()
+        targets = (1 - self.eps) * targets + self.eps / self.num_classes
+        return (-targets * log_probs).mean(0).sum()
diff --git a/pfe/torchreid/losses/hard_mine_triplet_loss.py b/pfe/torchreid/losses/hard_mine_triplet_loss.py
new file mode 100644
index 0000000..102881f
--- /dev/null
+++ b/pfe/torchreid/losses/hard_mine_triplet_loss.py
@@ -0,0 +1,48 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+
+
+class TripletLoss(nn.Module):
+    """Triplet loss with hard positive/negative mining.
+    
+    Reference:
+        Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
+    
+    Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py>`_.
+    
+    Args:
+        margin (float, optional): margin for triplet. Default is 0.3.
+    """
+
+    def __init__(self, margin=0.3):
+        super(TripletLoss, self).__init__()
+        self.margin = margin
+        self.ranking_loss = nn.MarginRankingLoss(margin=margin)
+
+    def forward(self, inputs, targets):
+        """
+        Args:
+            inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim).
+            targets (torch.LongTensor): ground truth labels with shape (num_classes).
+        """
+        n = inputs.size(0)
+
+        # Compute pairwise distance, replace by the official when merged
+        dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n)
+        dist = dist + dist.t()
+        dist.addmm_(1, -2, inputs, inputs.t())
+        dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
+
+        # For each anchor, find the hardest positive and negative
+        mask = targets.expand(n, n).eq(targets.expand(n, n).t())
+        dist_ap, dist_an = [], []
+        for i in range(n):
+            dist_ap.append(dist[i][mask[i]].max().unsqueeze(0))
+            dist_an.append(dist[i][mask[i] == 0].min().unsqueeze(0))
+        dist_ap = torch.cat(dist_ap)
+        dist_an = torch.cat(dist_an)
+
+        # Compute ranking hinge loss
+        y = torch.ones_like(dist_an)
+        return self.ranking_loss(dist_an, dist_ap, y)
diff --git a/pfe/torchreid/metrics/__init__.py b/pfe/torchreid/metrics/__init__.py
new file mode 100644
index 0000000..5159e1e
--- /dev/null
+++ b/pfe/torchreid/metrics/__init__.py
@@ -0,0 +1,5 @@
+from __future__ import absolute_import
+
+from .rank import evaluate_rank
+from .accuracy import accuracy
+from .distance import compute_distance_matrix
diff --git a/pfe/torchreid/metrics/accuracy.py b/pfe/torchreid/metrics/accuracy.py
new file mode 100644
index 0000000..3161f7b
--- /dev/null
+++ b/pfe/torchreid/metrics/accuracy.py
@@ -0,0 +1,37 @@
+from __future__ import division, print_function, absolute_import
+
+
+def accuracy(output, target, topk=(1, )):
+    """Computes the accuracy over the k top predictions for
+    the specified values of k.
+
+    Args:
+        output (torch.Tensor): prediction matrix with shape (batch_size, num_classes).
+        target (torch.LongTensor): ground truth labels with shape (batch_size).
+        topk (tuple, optional): accuracy at top-k will be computed. For example,
+            topk=(1, 5) means accuracy at top-1 and top-5 will be computed.
+
+    Returns:
+        list: accuracy at top-k.
+
+    Examples::
+        >>> from torchreid import metrics
+        >>> metrics.accuracy(output, target)
+    """
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    if isinstance(output, (tuple, list)):
+        output = output[0]
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        acc = correct_k.mul_(100.0 / batch_size)
+        res.append(acc)
+
+    return res
diff --git a/pfe/torchreid/metrics/distance.py b/pfe/torchreid/metrics/distance.py
new file mode 100644
index 0000000..6278c34
--- /dev/null
+++ b/pfe/torchreid/metrics/distance.py
@@ -0,0 +1,80 @@
+from __future__ import division, print_function, absolute_import
+import torch
+from torch.nn import functional as F
+
+
+def compute_distance_matrix(input1, input2, metric='euclidean'):
+    """A wrapper function for computing distance matrix.
+
+    Args:
+        input1 (torch.Tensor): 2-D feature matrix.
+        input2 (torch.Tensor): 2-D feature matrix.
+        metric (str, optional): "euclidean" or "cosine".
+            Default is "euclidean".
+
+    Returns:
+        torch.Tensor: distance matrix.
+
+    Examples::
+       >>> from torchreid import metrics
+       >>> input1 = torch.rand(10, 2048)
+       >>> input2 = torch.rand(100, 2048)
+       >>> distmat = metrics.compute_distance_matrix(input1, input2)
+       >>> distmat.size() # (10, 100)
+    """
+    # check input
+    assert isinstance(input1, torch.Tensor)
+    assert isinstance(input2, torch.Tensor)
+    assert input1.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(
+        input1.dim()
+    )
+    assert input2.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(
+        input2.dim()
+    )
+    assert input1.size(1) == input2.size(1)
+
+    if metric == 'euclidean':
+        distmat = euclidean_squared_distance(input1, input2)
+    elif metric == 'cosine':
+        distmat = cosine_distance(input1, input2)
+    else:
+        raise ValueError(
+            'Unknown distance metric: {}. '
+            'Please choose either "euclidean" or "cosine"'.format(metric)
+        )
+
+    return distmat
+
+
+def euclidean_squared_distance(input1, input2):
+    """Computes euclidean squared distance.
+
+    Args:
+        input1 (torch.Tensor): 2-D feature matrix.
+        input2 (torch.Tensor): 2-D feature matrix.
+
+    Returns:
+        torch.Tensor: distance matrix.
+    """
+    m, n = input1.size(0), input2.size(0)
+    mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)
+    mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()
+    distmat = mat1 + mat2
+    distmat.addmm_(1, -2, input1, input2.t())
+    return distmat
+
+
+def cosine_distance(input1, input2):
+    """Computes cosine distance.
+
+    Args:
+        input1 (torch.Tensor): 2-D feature matrix.
+        input2 (torch.Tensor): 2-D feature matrix.
+
+    Returns:
+        torch.Tensor: distance matrix.
+    """
+    input1_normed = F.normalize(input1, p=2, dim=1)
+    input2_normed = F.normalize(input2, p=2, dim=1)
+    distmat = 1 - torch.mm(input1_normed, input2_normed.t())
+    return distmat
diff --git a/pfe/torchreid/metrics/rank.py b/pfe/torchreid/metrics/rank.py
new file mode 100644
index 0000000..bf6205b
--- /dev/null
+++ b/pfe/torchreid/metrics/rank.py
@@ -0,0 +1,207 @@
+from __future__ import division, print_function, absolute_import
+import numpy as np
+import warnings
+from collections import defaultdict
+
+try:
+    from torchreid.metrics.rank_cylib.rank_cy import evaluate_cy
+    IS_CYTHON_AVAI = True
+except ImportError:
+    IS_CYTHON_AVAI = False
+    warnings.warn(
+        'Cython evaluation (very fast so highly recommended) is '
+        'unavailable, now use python evaluation.'
+    )
+
+
+def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
+    """Evaluation with cuhk03 metric
+    Key: one image for each gallery identity is randomly sampled for each query identity.
+    Random sampling is performed num_repeats times.
+    """
+    num_repeats = 10
+    num_q, num_g = distmat.shape
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print(
+            'Note: number of gallery samples is quite small, got {}'.
+            format(num_g)
+        )
+
+    indices = np.argsort(distmat, axis=1)
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    num_valid_q = 0. # number of valid query
+
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        order = indices[q_idx]
+        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+        keep = np.invert(remove)
+
+        # compute cmc curve
+        raw_cmc = matches[q_idx][
+            keep] # binary vector, positions with value 1 are correct matches
+        if not np.any(raw_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        kept_g_pids = g_pids[order][keep]
+        g_pids_dict = defaultdict(list)
+        for idx, pid in enumerate(kept_g_pids):
+            g_pids_dict[pid].append(idx)
+
+        cmc = 0.
+        for repeat_idx in range(num_repeats):
+            mask = np.zeros(len(raw_cmc), dtype=np.bool)
+            for _, idxs in g_pids_dict.items():
+                # randomly sample one image for each gallery person
+                rnd_idx = np.random.choice(idxs)
+                mask[rnd_idx] = True
+            masked_raw_cmc = raw_cmc[mask]
+            _cmc = masked_raw_cmc.cumsum()
+            _cmc[_cmc > 1] = 1
+            cmc += _cmc[:max_rank].astype(np.float32)
+
+        cmc /= num_repeats
+        all_cmc.append(cmc)
+        # compute AP
+        num_rel = raw_cmc.sum()
+        tmp_cmc = raw_cmc.cumsum()
+        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+        tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
+        AP = tmp_cmc.sum() / num_rel
+        all_AP.append(AP)
+        num_valid_q += 1.
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+
+    return all_cmc, mAP
+
+
+def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
+    """Evaluation with market1501 metric
+    Key: for each query identity, its gallery images from the same camera view are discarded.
+    """
+    num_q, num_g = distmat.shape
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print(
+            'Note: number of gallery samples is quite small, got {}'.
+            format(num_g)
+        )
+
+    indices = np.argsort(distmat, axis=1)
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    num_valid_q = 0. # number of valid query
+
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        order = indices[q_idx]
+        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+        keep = np.invert(remove)
+
+        # compute cmc curve
+        raw_cmc = matches[q_idx][
+            keep] # binary vector, positions with value 1 are correct matches
+        if not np.any(raw_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        cmc = raw_cmc.cumsum()
+        cmc[cmc > 1] = 1
+
+        all_cmc.append(cmc[:max_rank])
+        num_valid_q += 1.
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        num_rel = raw_cmc.sum()
+        tmp_cmc = raw_cmc.cumsum()
+        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+        tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
+        AP = tmp_cmc.sum() / num_rel
+        all_AP.append(AP)
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+
+    return all_cmc, mAP
+
+
+def evaluate_py(
+    distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03
+):
+    if use_metric_cuhk03:
+        return eval_cuhk03(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank
+        )
+    else:
+        return eval_market1501(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank
+        )
+
+
+def evaluate_rank(
+    distmat,
+    q_pids,
+    g_pids,
+    q_camids,
+    g_camids,
+    max_rank=50,
+    use_metric_cuhk03=False,
+    use_cython=True
+):
+    """Evaluates CMC rank.
+
+    Args:
+        distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
+        q_pids (numpy.ndarray): 1-D array containing person identities
+            of each query instance.
+        g_pids (numpy.ndarray): 1-D array containing person identities
+            of each gallery instance.
+        q_camids (numpy.ndarray): 1-D array containing camera views under
+            which each query instance is captured.
+        g_camids (numpy.ndarray): 1-D array containing camera views under
+            which each gallery instance is captured.
+        max_rank (int, optional): maximum CMC rank to be computed. Default is 50.
+        use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
+            Default is False. This should be enabled when using cuhk03 classic split.
+        use_cython (bool, optional): use cython code for evaluation. Default is True.
+            This is highly recommended as the cython code can speed up the cmc computation
+            by more than 10x. This requires Cython to be installed.
+    """
+    if use_cython and IS_CYTHON_AVAI:
+        return evaluate_cy(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank,
+            use_metric_cuhk03
+        )
+    else:
+        return evaluate_py(
+            distmat, q_pids, g_pids, q_camids, g_camids, max_rank,
+            use_metric_cuhk03
+        )
diff --git a/pfe/torchreid/metrics/rank_cylib/Makefile b/pfe/torchreid/metrics/rank_cylib/Makefile
new file mode 100644
index 0000000..d49e655
--- /dev/null
+++ b/pfe/torchreid/metrics/rank_cylib/Makefile
@@ -0,0 +1,6 @@
+all:
+	$(PYTHON) setup.py build_ext --inplace
+	rm -rf build
+clean:
+	rm -rf build
+	rm -f rank_cy.c *.so
\ No newline at end of file
diff --git a/pfe/torchreid/metrics/rank_cylib/__init__.py b/pfe/torchreid/metrics/rank_cylib/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pfe/torchreid/metrics/rank_cylib/rank_cy.pyx b/pfe/torchreid/metrics/rank_cylib/rank_cy.pyx
new file mode 100644
index 0000000..c7ad956
--- /dev/null
+++ b/pfe/torchreid/metrics/rank_cylib/rank_cy.pyx
@@ -0,0 +1,249 @@
+# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True
+
+from __future__ import print_function
+
+import cython
+import numpy as np
+cimport numpy as np
+from collections import defaultdict
+import random
+
+
+"""
+Compiler directives:
+https://github.com/cython/cython/wiki/enhancements-compilerdirectives
+
+Cython tutorial:
+https://cython.readthedocs.io/en/latest/src/userguide/numpy_tutorial.html
+
+Credit to https://github.com/luzai
+"""
+
+
+# Main interface
+cpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=False):
+    distmat = np.asarray(distmat, dtype=np.float32)
+    q_pids = np.asarray(q_pids, dtype=np.int64)
+    g_pids = np.asarray(g_pids, dtype=np.int64)
+    q_camids = np.asarray(q_camids, dtype=np.int64)
+    g_camids = np.asarray(g_camids, dtype=np.int64)
+    if use_metric_cuhk03:
+        return eval_cuhk03_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
+    return eval_market1501_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
+
+
+cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
+                     long[:]q_camids, long[:]g_camids, long max_rank):
+    
+    cdef long num_q = distmat.shape[0]
+    cdef long num_g = distmat.shape[1]
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print('Note: number of gallery samples is quite small, got {}'.format(num_g))
+    
+    cdef:
+        long num_repeats = 10
+        long[:,:] indices = np.argsort(distmat, axis=1)
+        long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
+
+        float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)
+        float[:] all_AP = np.zeros(num_q, dtype=np.float32)
+        float num_valid_q = 0. # number of valid query
+
+        long q_idx, q_pid, q_camid, g_idx
+        long[:] order = np.zeros(num_g, dtype=np.int64)
+        long keep
+
+        float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches
+        float[:] masked_raw_cmc = np.zeros(num_g, dtype=np.float32)
+        float[:] cmc, masked_cmc
+        long num_g_real, num_g_real_masked, rank_idx, rnd_idx
+        unsigned long meet_condition
+        float AP
+        long[:] kept_g_pids, mask
+
+        float num_rel
+        float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)
+        float tmp_cmc_sum
+    
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        for g_idx in range(num_g):
+            order[g_idx] = indices[q_idx, g_idx]
+        num_g_real = 0
+        meet_condition = 0
+        kept_g_pids = np.zeros(num_g, dtype=np.int64)
+
+        for g_idx in range(num_g):
+            if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):
+                raw_cmc[num_g_real] = matches[q_idx][g_idx]
+                kept_g_pids[num_g_real] = g_pids[order[g_idx]]
+                num_g_real += 1
+                if matches[q_idx][g_idx] > 1e-31:
+                    meet_condition = 1
+        
+        if not meet_condition:
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        # cuhk03-specific setting
+        g_pids_dict = defaultdict(list) # overhead!
+        for g_idx in range(num_g_real):
+            g_pids_dict[kept_g_pids[g_idx]].append(g_idx)
+
+        cmc = np.zeros(max_rank, dtype=np.float32)
+        for _ in range(num_repeats):
+            mask = np.zeros(num_g_real, dtype=np.int64)
+            
+            for _, idxs in g_pids_dict.items():
+                # randomly sample one image for each gallery person
+                rnd_idx = np.random.choice(idxs)
+                #rnd_idx = idxs[0] # use deterministic for debugging
+                mask[rnd_idx] = 1
+
+            num_g_real_masked = 0
+            for g_idx in range(num_g_real):
+                if mask[g_idx] == 1:
+                    masked_raw_cmc[num_g_real_masked] = raw_cmc[g_idx]
+                    num_g_real_masked += 1
+
+            masked_cmc = np.zeros(num_g, dtype=np.float32)
+            function_cumsum(masked_raw_cmc, masked_cmc, num_g_real_masked)
+            for g_idx in range(num_g_real_masked):
+                if masked_cmc[g_idx] > 1:
+                    masked_cmc[g_idx] = 1
+
+            for rank_idx in range(max_rank):
+                cmc[rank_idx] += masked_cmc[rank_idx] / num_repeats
+        
+        for rank_idx in range(max_rank):
+            all_cmc[q_idx, rank_idx] = cmc[rank_idx]
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        function_cumsum(raw_cmc, tmp_cmc, num_g_real)
+        num_rel = 0
+        tmp_cmc_sum = 0
+        for g_idx in range(num_g_real):
+            tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]
+            num_rel += raw_cmc[g_idx]
+        all_AP[q_idx] = tmp_cmc_sum / num_rel
+        num_valid_q += 1.
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    # compute averaged cmc
+    cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32)
+    for rank_idx in range(max_rank):
+        for q_idx in range(num_q):
+            avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]
+        avg_cmc[rank_idx] /= num_valid_q
+    
+    cdef float mAP = 0
+    for q_idx in range(num_q):
+        mAP += all_AP[q_idx]
+    mAP /= num_valid_q
+
+    return np.asarray(avg_cmc).astype(np.float32), mAP
+
+
+cpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
+                         long[:]q_camids, long[:]g_camids, long max_rank):
+    
+    cdef long num_q = distmat.shape[0]
+    cdef long num_g = distmat.shape[1]
+
+    if num_g < max_rank:
+        max_rank = num_g
+        print('Note: number of gallery samples is quite small, got {}'.format(num_g))
+    
+    cdef:
+        long[:,:] indices = np.argsort(distmat, axis=1)
+        long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
+
+        float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)
+        float[:] all_AP = np.zeros(num_q, dtype=np.float32)
+        float num_valid_q = 0. # number of valid query
+
+        long q_idx, q_pid, q_camid, g_idx
+        long[:] order = np.zeros(num_g, dtype=np.int64)
+        long keep
+
+        float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches
+        float[:] cmc = np.zeros(num_g, dtype=np.float32)
+        long num_g_real, rank_idx
+        unsigned long meet_condition
+
+        float num_rel
+        float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)
+        float tmp_cmc_sum
+    
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        for g_idx in range(num_g):
+            order[g_idx] = indices[q_idx, g_idx]
+        num_g_real = 0
+        meet_condition = 0
+        
+        for g_idx in range(num_g):
+            if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):
+                raw_cmc[num_g_real] = matches[q_idx][g_idx]
+                num_g_real += 1
+                if matches[q_idx][g_idx] > 1e-31:
+                    meet_condition = 1
+        
+        if not meet_condition:
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        # compute cmc
+        function_cumsum(raw_cmc, cmc, num_g_real)
+        for g_idx in range(num_g_real):
+            if cmc[g_idx] > 1:
+                cmc[g_idx] = 1
+
+        for rank_idx in range(max_rank):
+            all_cmc[q_idx, rank_idx] = cmc[rank_idx]
+        num_valid_q += 1.
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        function_cumsum(raw_cmc, tmp_cmc, num_g_real)
+        num_rel = 0
+        tmp_cmc_sum = 0
+        for g_idx in range(num_g_real):
+            tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]
+            num_rel += raw_cmc[g_idx]
+        all_AP[q_idx] = tmp_cmc_sum / num_rel
+
+    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
+
+    # compute averaged cmc
+    cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32)
+    for rank_idx in range(max_rank):
+        for q_idx in range(num_q):
+            avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]
+        avg_cmc[rank_idx] /= num_valid_q
+    
+    cdef float mAP = 0
+    for q_idx in range(num_q):
+        mAP += all_AP[q_idx]
+    mAP /= num_valid_q
+
+    return np.asarray(avg_cmc).astype(np.float32), mAP
+
+
+# Compute the cumulative sum
+cdef void function_cumsum(cython.numeric[:] src, cython.numeric[:] dst, long n):
+    cdef long i
+    dst[0] = src[0]
+    for i in range(1, n):
+        dst[i] = src[i] + dst[i - 1]
\ No newline at end of file
diff --git a/pfe/torchreid/metrics/rank_cylib/setup.py b/pfe/torchreid/metrics/rank_cylib/setup.py
new file mode 100644
index 0000000..ce2aeb7
--- /dev/null
+++ b/pfe/torchreid/metrics/rank_cylib/setup.py
@@ -0,0 +1,26 @@
+import numpy as np
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Build import cythonize
+
+
+def numpy_include():
+    try:
+        numpy_include = np.get_include()
+    except AttributeError:
+        numpy_include = np.get_numpy_include()
+    return numpy_include
+
+
+ext_modules = [
+    Extension(
+        'rank_cy',
+        ['rank_cy.pyx'],
+        include_dirs=[numpy_include()],
+    )
+]
+
+setup(
+    name='Cython-based reid evaluation code',
+    ext_modules=cythonize(ext_modules)
+)
diff --git a/pfe/torchreid/metrics/rank_cylib/test_cython.py b/pfe/torchreid/metrics/rank_cylib/test_cython.py
new file mode 100644
index 0000000..5d1175d
--- /dev/null
+++ b/pfe/torchreid/metrics/rank_cylib/test_cython.py
@@ -0,0 +1,83 @@
+from __future__ import print_function
+import sys
+import numpy as np
+import timeit
+import os.path as osp
+
+from torchreid import metrics
+
+sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
+"""
+Test the speed of cython-based evaluation code. The speed improvements
+can be much bigger when using the real reid data, which contains a larger
+amount of query and gallery images.
+
+Note: you might encounter the following error:
+  'AssertionError: Error: all query identities do not appear in gallery'.
+This is normal because the inputs are random numbers. Just try again.
+"""
+
+print('*** Compare running time ***')
+
+setup = '''
+import sys
+import os.path as osp
+import numpy as np
+sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
+from torchreid import metrics
+num_q = 30
+num_g = 300
+max_rank = 5
+distmat = np.random.rand(num_q, num_g) * 20
+q_pids = np.random.randint(0, num_q, size=num_q)
+g_pids = np.random.randint(0, num_g, size=num_g)
+q_camids = np.random.randint(0, 5, size=num_q)
+g_camids = np.random.randint(0, 5, size=num_g)
+'''
+
+print('=> Using market1501\'s metric')
+pytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)',
+    setup=setup,
+    number=20
+)
+cytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)',
+    setup=setup,
+    number=20
+)
+print('Python time: {} s'.format(pytime))
+print('Cython time: {} s'.format(cytime))
+print('Cython is {} times faster than python\n'.format(pytime / cytime))
+
+print('=> Using cuhk03\'s metric')
+pytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=False)',
+    setup=setup,
+    number=20
+)
+cytime = timeit.timeit(
+    'metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=True)',
+    setup=setup,
+    number=20
+)
+print('Python time: {} s'.format(pytime))
+print('Cython time: {} s'.format(cytime))
+print('Cython is {} times faster than python\n'.format(pytime / cytime))
+"""
+print("=> Check precision")
+
+num_q = 30
+num_g = 300
+max_rank = 5
+distmat = np.random.rand(num_q, num_g) * 20
+q_pids = np.random.randint(0, num_q, size=num_q)
+g_pids = np.random.randint(0, num_g, size=num_g)
+q_camids = np.random.randint(0, 5, size=num_q)
+g_camids = np.random.randint(0, 5, size=num_g)
+
+cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)
+print("Python:\nmAP = {} \ncmc = {}\n".format(mAP, cmc))
+cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)
+print("Cython:\nmAP = {} \ncmc = {}\n".format(mAP, cmc))
+"""
diff --git a/pfe/torchreid/models/__init__.py b/pfe/torchreid/models/__init__.py
new file mode 100644
index 0000000..583be15
--- /dev/null
+++ b/pfe/torchreid/models/__init__.py
@@ -0,0 +1,119 @@
+from __future__ import absolute_import
+import torch
+
+from .pcb import *
+from .mlfn import *
+from .hacnn import *
+from .osnet import *
+from .senet import *
+from .mudeep import *
+from .nasnet import *
+from .resnet import *
+from .densenet import *
+from .xception import *
+from .osnet_ain import *
+from .resnetmid import *
+from .shufflenet import *
+from .squeezenet import *
+from .inceptionv4 import *
+from .mobilenetv2 import *
+from .resnet_ibn_a import *
+from .resnet_ibn_b import *
+from .shufflenetv2 import *
+from .inceptionresnetv2 import *
+
+__model_factory = {
+    # image classification models
+    'resnet18': resnet18,
+    'resnet34': resnet34,
+    'resnet50': resnet50,
+    'resnet101': resnet101,
+    'resnet152': resnet152,
+    'resnext50_32x4d': resnext50_32x4d,
+    'resnext101_32x8d': resnext101_32x8d,
+    'resnet50_fc512': resnet50_fc512,
+    'se_resnet50': se_resnet50,
+    'se_resnet50_fc512': se_resnet50_fc512,
+    'se_resnet101': se_resnet101,
+    'se_resnext50_32x4d': se_resnext50_32x4d,
+    'se_resnext101_32x4d': se_resnext101_32x4d,
+    'densenet121': densenet121,
+    'densenet169': densenet169,
+    'densenet201': densenet201,
+    'densenet161': densenet161,
+    'densenet121_fc512': densenet121_fc512,
+    'inceptionresnetv2': inceptionresnetv2,
+    'inceptionv4': inceptionv4,
+    'xception': xception,
+    'resnet50_ibn_a': resnet50_ibn_a,
+    'resnet50_ibn_b': resnet50_ibn_b,
+    # lightweight models
+    'nasnsetmobile': nasnetamobile,
+    'mobilenetv2_x1_0': mobilenetv2_x1_0,
+    'mobilenetv2_x1_4': mobilenetv2_x1_4,
+    'shufflenet': shufflenet,
+    'squeezenet1_0': squeezenet1_0,
+    'squeezenet1_0_fc512': squeezenet1_0_fc512,
+    'squeezenet1_1': squeezenet1_1,
+    'shufflenet_v2_x0_5': shufflenet_v2_x0_5,
+    'shufflenet_v2_x1_0': shufflenet_v2_x1_0,
+    'shufflenet_v2_x1_5': shufflenet_v2_x1_5,
+    'shufflenet_v2_x2_0': shufflenet_v2_x2_0,
+    # reid-specific models
+    'mudeep': MuDeep,
+    'resnet50mid': resnet50mid,
+    'hacnn': HACNN,
+    'pcb_p6': pcb_p6,
+    'pcb_p4': pcb_p4,
+    'mlfn': mlfn,
+    'osnet_x1_0': osnet_x1_0,
+    'osnet_x0_75': osnet_x0_75,
+    'osnet_x0_5': osnet_x0_5,
+    'osnet_x0_25': osnet_x0_25,
+    'osnet_ibn_x1_0': osnet_ibn_x1_0,
+    'osnet_ain_x1_0': osnet_ain_x1_0
+}
+
+
+def show_avai_models():
+    """Displays available models.
+
+    Examples::
+        >>> from torchreid import models
+        >>> models.show_avai_models()
+    """
+    print(list(__model_factory.keys()))
+
+
+def build_model(
+    name, num_classes, loss='softmax', pretrained=True, use_gpu=True
+):
+    """A function wrapper for building a model.
+
+    Args:
+        name (str): model name.
+        num_classes (int): number of training identities.
+        loss (str, optional): loss function to optimize the model. Currently
+            supports "softmax" and "triplet". Default is "softmax".
+        pretrained (bool, optional): whether to load ImageNet-pretrained weights.
+            Default is True.
+        use_gpu (bool, optional): whether to use gpu. Default is True.
+
+    Returns:
+        nn.Module
+
+    Examples::
+        >>> from torchreid import models
+        >>> model = models.build_model('resnet50', 751, loss='softmax')
+    """
+    avai_models = list(__model_factory.keys())
+    if name not in avai_models:
+        raise KeyError(
+            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
+        )
+    return __model_factory[name](
+        num_classes=num_classes,
+        loss=loss,
+        pretrained=pretrained,
+        use_gpu=use_gpu
+    )
diff --git a/pfe/torchreid/models/densenet.py b/pfe/torchreid/models/densenet.py
new file mode 100644
index 0000000..a1d9b7e
--- /dev/null
+++ b/pfe/torchreid/models/densenet.py
@@ -0,0 +1,380 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import re
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.utils import model_zoo
+
+__all__ = [
+    'densenet121', 'densenet169', 'densenet201', 'densenet161',
+    'densenet121_fc512'
+]
+
+model_urls = {
+    'densenet121':
+    'https://download.pytorch.org/models/densenet121-a639ec97.pth',
+    'densenet169':
+    'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
+    'densenet201':
+    'https://download.pytorch.org/models/densenet201-c1103571.pth',
+    'densenet161':
+    'https://download.pytorch.org/models/densenet161-8d451a50.pth',
+}
+
+
+class _DenseLayer(nn.Sequential):
+
+    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
+        super(_DenseLayer, self).__init__()
+        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
+        self.add_module('relu1', nn.ReLU(inplace=True)),
+        self.add_module(
+            'conv1',
+            nn.Conv2d(
+                num_input_features,
+                bn_size * growth_rate,
+                kernel_size=1,
+                stride=1,
+                bias=False
+            )
+        ),
+        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
+        self.add_module('relu2', nn.ReLU(inplace=True)),
+        self.add_module(
+            'conv2',
+            nn.Conv2d(
+                bn_size * growth_rate,
+                growth_rate,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=False
+            )
+        ),
+        self.drop_rate = drop_rate
+
+    def forward(self, x):
+        new_features = super(_DenseLayer, self).forward(x)
+        if self.drop_rate > 0:
+            new_features = F.dropout(
+                new_features, p=self.drop_rate, training=self.training
+            )
+        return torch.cat([x, new_features], 1)
+
+
+class _DenseBlock(nn.Sequential):
+
+    def __init__(
+        self, num_layers, num_input_features, bn_size, growth_rate, drop_rate
+    ):
+        super(_DenseBlock, self).__init__()
+        for i in range(num_layers):
+            layer = _DenseLayer(
+                num_input_features + i*growth_rate, growth_rate, bn_size,
+                drop_rate
+            )
+            self.add_module('denselayer%d' % (i+1), layer)
+
+
+class _Transition(nn.Sequential):
+
+    def __init__(self, num_input_features, num_output_features):
+        super(_Transition, self).__init__()
+        self.add_module('norm', nn.BatchNorm2d(num_input_features))
+        self.add_module('relu', nn.ReLU(inplace=True))
+        self.add_module(
+            'conv',
+            nn.Conv2d(
+                num_input_features,
+                num_output_features,
+                kernel_size=1,
+                stride=1,
+                bias=False
+            )
+        )
+        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
+
+
+class DenseNet(nn.Module):
+    """Densely connected network.
+    
+    Reference:
+        Huang et al. Densely Connected Convolutional Networks. CVPR 2017.
+
+    Public keys:
+        - ``densenet121``: DenseNet121.
+        - ``densenet169``: DenseNet169.
+        - ``densenet201``: DenseNet201.
+        - ``densenet161``: DenseNet161.
+        - ``densenet121_fc512``: DenseNet121 + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        growth_rate=32,
+        block_config=(6, 12, 24, 16),
+        num_init_features=64,
+        bn_size=4,
+        drop_rate=0,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+
+        super(DenseNet, self).__init__()
+        self.loss = loss
+
+        # First convolution
+        self.features = nn.Sequential(
+            OrderedDict(
+                [
+                    (
+                        'conv0',
+                        nn.Conv2d(
+                            3,
+                            num_init_features,
+                            kernel_size=7,
+                            stride=2,
+                            padding=3,
+                            bias=False
+                        )
+                    ),
+                    ('norm0', nn.BatchNorm2d(num_init_features)),
+                    ('relu0', nn.ReLU(inplace=True)),
+                    (
+                        'pool0',
+                        nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+                    ),
+                ]
+            )
+        )
+
+        # Each denseblock
+        num_features = num_init_features
+        for i, num_layers in enumerate(block_config):
+            block = _DenseBlock(
+                num_layers=num_layers,
+                num_input_features=num_features,
+                bn_size=bn_size,
+                growth_rate=growth_rate,
+                drop_rate=drop_rate
+            )
+            self.features.add_module('denseblock%d' % (i+1), block)
+            num_features = num_features + num_layers*growth_rate
+            if i != len(block_config) - 1:
+                trans = _Transition(
+                    num_input_features=num_features,
+                    num_output_features=num_features // 2
+                )
+                self.features.add_module('transition%d' % (i+1), trans)
+                num_features = num_features // 2
+
+        # Final batch norm
+        self.features.add_module('norm5', nn.BatchNorm2d(num_features))
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.feature_dim = num_features
+        self.fc = self._construct_fc_layer(fc_dims, num_features, dropout_p)
+
+        # Linear layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer.
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        f = self.features(x)
+        f = F.relu(f, inplace=True)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+
+    # '.'s are no longer allowed in module names, but pervious _DenseLayer
+    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
+    # They are also in the checkpoints in model_urls. This pattern is used
+    # to find such keys.
+    pattern = re.compile(
+        r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$'
+    )
+    for key in list(pretrain_dict.keys()):
+        res = pattern.match(key)
+        if res:
+            new_key = res.group(1) + res.group(2)
+            pretrain_dict[new_key] = pretrain_dict[key]
+            del pretrain_dict[key]
+
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+"""
+Dense network configurations:
+--
+densenet121: num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)
+densenet169: num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32)
+densenet201: num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32)
+densenet161: num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24)
+"""
+
+
+def densenet121(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 24, 16),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet121'])
+    return model
+
+
+def densenet169(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 32, 32),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet169'])
+    return model
+
+
+def densenet201(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 48, 32),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet201'])
+    return model
+
+
+def densenet161(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=96,
+        growth_rate=48,
+        block_config=(6, 12, 36, 24),
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet161'])
+    return model
+
+
+def densenet121_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = DenseNet(
+        num_classes=num_classes,
+        loss=loss,
+        num_init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 24, 16),
+        fc_dims=[512],
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['densenet121'])
+    return model
diff --git a/pfe/torchreid/models/hacnn.py b/pfe/torchreid/models/hacnn.py
new file mode 100644
index 0000000..f21cc82
--- /dev/null
+++ b/pfe/torchreid/models/hacnn.py
@@ -0,0 +1,414 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['HACNN']
+
+
+class ConvBlock(nn.Module):
+    """Basic convolutional block.
+    
+    convolution + batch normalization + relu.
+
+    Args:
+        in_c (int): number of input channels.
+        out_c (int): number of output channels.
+        k (int or tuple): kernel size.
+        s (int or tuple): stride.
+        p (int or tuple): padding.
+    """
+
+    def __init__(self, in_c, out_c, k, s=1, p=0):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p)
+        self.bn = nn.BatchNorm2d(out_c)
+
+    def forward(self, x):
+        return F.relu(self.bn(self.conv(x)))
+
+
+class InceptionA(nn.Module):
+
+    def __init__(self, in_channels, out_channels):
+        super(InceptionA, self).__init__()
+        mid_channels = out_channels // 4
+
+        self.stream1 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+        )
+        self.stream2 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+        )
+        self.stream3 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+        )
+        self.stream4 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1),
+            ConvBlock(in_channels, mid_channels, 1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        s4 = self.stream4(x)
+        y = torch.cat([s1, s2, s3, s4], dim=1)
+        return y
+
+
+class InceptionB(nn.Module):
+
+    def __init__(self, in_channels, out_channels):
+        super(InceptionB, self).__init__()
+        mid_channels = out_channels // 4
+
+        self.stream1 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, s=2, p=1),
+        )
+        self.stream2 = nn.Sequential(
+            ConvBlock(in_channels, mid_channels, 1),
+            ConvBlock(mid_channels, mid_channels, 3, p=1),
+            ConvBlock(mid_channels, mid_channels, 3, s=2, p=1),
+        )
+        self.stream3 = nn.Sequential(
+            nn.MaxPool2d(3, stride=2, padding=1),
+            ConvBlock(in_channels, mid_channels * 2, 1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        y = torch.cat([s1, s2, s3], dim=1)
+        return y
+
+
+class SpatialAttn(nn.Module):
+    """Spatial Attention (Sec. 3.1.I.1)"""
+
+    def __init__(self):
+        super(SpatialAttn, self).__init__()
+        self.conv1 = ConvBlock(1, 1, 3, s=2, p=1)
+        self.conv2 = ConvBlock(1, 1, 1)
+
+    def forward(self, x):
+        # global cross-channel averaging
+        x = x.mean(1, keepdim=True)
+        # 3-by-3 conv
+        x = self.conv1(x)
+        # bilinear resizing
+        x = F.upsample(
+            x, (x.size(2) * 2, x.size(3) * 2),
+            mode='bilinear',
+            align_corners=True
+        )
+        # scaling conv
+        x = self.conv2(x)
+        return x
+
+
+class ChannelAttn(nn.Module):
+    """Channel Attention (Sec. 3.1.I.2)"""
+
+    def __init__(self, in_channels, reduction_rate=16):
+        super(ChannelAttn, self).__init__()
+        assert in_channels % reduction_rate == 0
+        self.conv1 = ConvBlock(in_channels, in_channels // reduction_rate, 1)
+        self.conv2 = ConvBlock(in_channels // reduction_rate, in_channels, 1)
+
+    def forward(self, x):
+        # squeeze operation (global average pooling)
+        x = F.avg_pool2d(x, x.size()[2:])
+        # excitation operation (2 conv layers)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+
+class SoftAttn(nn.Module):
+    """Soft Attention (Sec. 3.1.I)
+    
+    Aim: Spatial Attention + Channel Attention
+    
+    Output: attention maps with shape identical to input.
+    """
+
+    def __init__(self, in_channels):
+        super(SoftAttn, self).__init__()
+        self.spatial_attn = SpatialAttn()
+        self.channel_attn = ChannelAttn(in_channels)
+        self.conv = ConvBlock(in_channels, in_channels, 1)
+
+    def forward(self, x):
+        y_spatial = self.spatial_attn(x)
+        y_channel = self.channel_attn(x)
+        y = y_spatial * y_channel
+        y = torch.sigmoid(self.conv(y))
+        return y
+
+
+class HardAttn(nn.Module):
+    """Hard Attention (Sec. 3.1.II)"""
+
+    def __init__(self, in_channels):
+        super(HardAttn, self).__init__()
+        self.fc = nn.Linear(in_channels, 4 * 2)
+        self.init_params()
+
+    def init_params(self):
+        self.fc.weight.data.zero_()
+        self.fc.bias.data.copy_(
+            torch.tensor(
+                [0, -0.75, 0, -0.25, 0, 0.25, 0, 0.75], dtype=torch.float
+            )
+        )
+
+    def forward(self, x):
+        # squeeze operation (global average pooling)
+        x = F.avg_pool2d(x, x.size()[2:]).view(x.size(0), x.size(1))
+        # predict transformation parameters
+        theta = torch.tanh(self.fc(x))
+        theta = theta.view(-1, 4, 2)
+        return theta
+
+
+class HarmAttn(nn.Module):
+    """Harmonious Attention (Sec. 3.1)"""
+
+    def __init__(self, in_channels):
+        super(HarmAttn, self).__init__()
+        self.soft_attn = SoftAttn(in_channels)
+        self.hard_attn = HardAttn(in_channels)
+
+    def forward(self, x):
+        y_soft_attn = self.soft_attn(x)
+        theta = self.hard_attn(x)
+        return y_soft_attn, theta
+
+
+class HACNN(nn.Module):
+    """Harmonious Attention Convolutional Neural Network.
+
+    Reference:
+        Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.
+
+    Public keys:
+        - ``hacnn``: HACNN.
+    """
+
+    # Args:
+    #    num_classes (int): number of classes to predict
+    #    nchannels (list): number of channels AFTER concatenation
+    #    feat_dim (int): feature dimension for a single stream
+    #    learn_region (bool): whether to learn region features (i.e. local branch)
+
+    def __init__(
+        self,
+        num_classes,
+        loss='softmax',
+        nchannels=[128, 256, 384],
+        feat_dim=512,
+        learn_region=True,
+        use_gpu=True,
+        **kwargs
+    ):
+        super(HACNN, self).__init__()
+        self.loss = loss
+        self.learn_region = learn_region
+        self.use_gpu = use_gpu
+
+        self.conv = ConvBlock(3, 32, 3, s=2, p=1)
+
+        # Construct Inception + HarmAttn blocks
+        # ============== Block 1 ==============
+        self.inception1 = nn.Sequential(
+            InceptionA(32, nchannels[0]),
+            InceptionB(nchannels[0], nchannels[0]),
+        )
+        self.ha1 = HarmAttn(nchannels[0])
+
+        # ============== Block 2 ==============
+        self.inception2 = nn.Sequential(
+            InceptionA(nchannels[0], nchannels[1]),
+            InceptionB(nchannels[1], nchannels[1]),
+        )
+        self.ha2 = HarmAttn(nchannels[1])
+
+        # ============== Block 3 ==============
+        self.inception3 = nn.Sequential(
+            InceptionA(nchannels[1], nchannels[2]),
+            InceptionB(nchannels[2], nchannels[2]),
+        )
+        self.ha3 = HarmAttn(nchannels[2])
+
+        self.fc_global = nn.Sequential(
+            nn.Linear(nchannels[2], feat_dim),
+            nn.BatchNorm1d(feat_dim),
+            nn.ReLU(),
+        )
+        self.classifier_global = nn.Linear(feat_dim, num_classes)
+
+        if self.learn_region:
+            self.init_scale_factors()
+            self.local_conv1 = InceptionB(32, nchannels[0])
+            self.local_conv2 = InceptionB(nchannels[0], nchannels[1])
+            self.local_conv3 = InceptionB(nchannels[1], nchannels[2])
+            self.fc_local = nn.Sequential(
+                nn.Linear(nchannels[2] * 4, feat_dim),
+                nn.BatchNorm1d(feat_dim),
+                nn.ReLU(),
+            )
+            self.classifier_local = nn.Linear(feat_dim, num_classes)
+            self.feat_dim = feat_dim * 2
+        else:
+            self.feat_dim = feat_dim
+
+    def init_scale_factors(self):
+        # initialize scale factors (s_w, s_h) for four regions
+        self.scale_factors = []
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+        self.scale_factors.append(
+            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
+        )
+
+    def stn(self, x, theta):
+        """Performs spatial transform
+        
+        x: (batch, channel, height, width)
+        theta: (batch, 2, 3)
+        """
+        grid = F.affine_grid(theta, x.size())
+        x = F.grid_sample(x, grid)
+        return x
+
+    def transform_theta(self, theta_i, region_idx):
+        """Transforms theta to include (s_w, s_h), resulting in (batch, 2, 3)"""
+        scale_factors = self.scale_factors[region_idx]
+        theta = torch.zeros(theta_i.size(0), 2, 3)
+        theta[:, :, :2] = scale_factors
+        theta[:, :, -1] = theta_i
+        if self.use_gpu:
+            theta = theta.cuda()
+        return theta
+
+    def forward(self, x):
+        assert x.size(2) == 160 and x.size(3) == 64, \
+            'Input size does not match, expected (160, 64) but got ({}, {})'.format(x.size(2), x.size(3))
+        x = self.conv(x)
+
+        # ============== Block 1 ==============
+        # global branch
+        x1 = self.inception1(x)
+        x1_attn, x1_theta = self.ha1(x1)
+        x1_out = x1 * x1_attn
+        # local branch
+        if self.learn_region:
+            x1_local_list = []
+            for region_idx in range(4):
+                x1_theta_i = x1_theta[:, region_idx, :]
+                x1_theta_i = self.transform_theta(x1_theta_i, region_idx)
+                x1_trans_i = self.stn(x, x1_theta_i)
+                x1_trans_i = F.upsample(
+                    x1_trans_i, (24, 28), mode='bilinear', align_corners=True
+                )
+                x1_local_i = self.local_conv1(x1_trans_i)
+                x1_local_list.append(x1_local_i)
+
+        # ============== Block 2 ==============
+        # Block 2
+        # global branch
+        x2 = self.inception2(x1_out)
+        x2_attn, x2_theta = self.ha2(x2)
+        x2_out = x2 * x2_attn
+        # local branch
+        if self.learn_region:
+            x2_local_list = []
+            for region_idx in range(4):
+                x2_theta_i = x2_theta[:, region_idx, :]
+                x2_theta_i = self.transform_theta(x2_theta_i, region_idx)
+                x2_trans_i = self.stn(x1_out, x2_theta_i)
+                x2_trans_i = F.upsample(
+                    x2_trans_i, (12, 14), mode='bilinear', align_corners=True
+                )
+                x2_local_i = x2_trans_i + x1_local_list[region_idx]
+                x2_local_i = self.local_conv2(x2_local_i)
+                x2_local_list.append(x2_local_i)
+
+        # ============== Block 3 ==============
+        # Block 3
+        # global branch
+        x3 = self.inception3(x2_out)
+        x3_attn, x3_theta = self.ha3(x3)
+        x3_out = x3 * x3_attn
+        # local branch
+        if self.learn_region:
+            x3_local_list = []
+            for region_idx in range(4):
+                x3_theta_i = x3_theta[:, region_idx, :]
+                x3_theta_i = self.transform_theta(x3_theta_i, region_idx)
+                x3_trans_i = self.stn(x2_out, x3_theta_i)
+                x3_trans_i = F.upsample(
+                    x3_trans_i, (6, 7), mode='bilinear', align_corners=True
+                )
+                x3_local_i = x3_trans_i + x2_local_list[region_idx]
+                x3_local_i = self.local_conv3(x3_local_i)
+                x3_local_list.append(x3_local_i)
+
+        # ============== Feature generation ==============
+        # global branch
+        x_global = F.avg_pool2d(x3_out,
+                                x3_out.size()[2:]
+                                ).view(x3_out.size(0), x3_out.size(1))
+        x_global = self.fc_global(x_global)
+        # local branch
+        if self.learn_region:
+            x_local_list = []
+            for region_idx in range(4):
+                x_local_i = x3_local_list[region_idx]
+                x_local_i = F.avg_pool2d(x_local_i,
+                                         x_local_i.size()[2:]
+                                         ).view(x_local_i.size(0), -1)
+                x_local_list.append(x_local_i)
+            x_local = torch.cat(x_local_list, 1)
+            x_local = self.fc_local(x_local)
+
+        if not self.training:
+            # l2 normalization before concatenation
+            if self.learn_region:
+                x_global = x_global / x_global.norm(p=2, dim=1, keepdim=True)
+                x_local = x_local / x_local.norm(p=2, dim=1, keepdim=True)
+                return torch.cat([x_global, x_local], 1)
+            else:
+                return x_global
+
+        prelogits_global = self.classifier_global(x_global)
+        if self.learn_region:
+            prelogits_local = self.classifier_local(x_local)
+
+        if self.loss == 'softmax':
+            if self.learn_region:
+                return (prelogits_global, prelogits_local)
+            else:
+                return prelogits_global
+
+        elif self.loss == 'triplet':
+            if self.learn_region:
+                return (prelogits_global, prelogits_local), (x_global, x_local)
+            else:
+                return prelogits_global, x_global
+
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
diff --git a/pfe/torchreid/models/inceptionresnetv2.py b/pfe/torchreid/models/inceptionresnetv2.py
new file mode 100644
index 0000000..03e4034
--- /dev/null
+++ b/pfe/torchreid/models/inceptionresnetv2.py
@@ -0,0 +1,361 @@
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['inceptionresnetv2']
+
+pretrained_settings = {
+    'inceptionresnetv2': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000
+        },
+        'imagenet+background': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1001
+        }
+    }
+}
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Mixed_5b(nn.Module):
+
+    def __init__(self):
+        super(Mixed_5b, self).__init__()
+
+        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(192, 48, kernel_size=1, stride=1),
+            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(192, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(192, 64, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Block35(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super(Block35, self).__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Mixed_6a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_6a, self).__init__()
+
+        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Block17(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super(Block17, self).__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 128, kernel_size=1, stride=1),
+            BasicConv2d(
+                128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            )
+        )
+
+        self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Mixed_7a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_7a, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(288, 320, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Block8(nn.Module):
+
+    def __init__(self, scale=1.0, noReLU=False):
+        super(Block8, self).__init__()
+
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(2080, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)
+            ),
+            BasicConv2d(
+                224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
+            )
+        )
+
+        self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+
+# ----------------
+# Model Definition
+# ----------------
+class InceptionResNetV2(nn.Module):
+    """Inception-ResNet-V2.
+
+    Reference:
+        Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
+        Connections on Learning. AAAI 2017.
+
+    Public keys:
+        - ``inceptionresnetv2``: Inception-ResNet-V2.
+    """
+
+    def __init__(self, num_classes, loss='softmax', **kwargs):
+        super(InceptionResNetV2, self).__init__()
+        self.loss = loss
+
+        # Modules
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(
+            32, 64, kernel_size=3, stride=1, padding=1
+        )
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.maxpool_5a = nn.MaxPool2d(3, stride=2)
+        self.mixed_5b = Mixed_5b()
+        self.repeat = nn.Sequential(
+            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
+            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
+            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
+            Block35(scale=0.17)
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_1 = nn.Sequential(
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
+            Block17(scale=0.10), Block17(scale=0.10)
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_2 = nn.Sequential(
+            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),
+            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),
+            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20)
+        )
+
+        self.block8 = Block8(noReLU=True)
+        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(1536, num_classes)
+
+    def load_imagenet_weights(self):
+        settings = pretrained_settings['inceptionresnetv2']['imagenet']
+        pretrain_dict = model_zoo.load_url(settings['url'])
+        model_dict = self.state_dict()
+        pretrain_dict = {
+            k: v
+            for k, v in pretrain_dict.items()
+            if k in model_dict and model_dict[k].size() == v.size()
+        }
+        model_dict.update(pretrain_dict)
+        self.load_state_dict(model_dict)
+
+    def featuremaps(self, x):
+        x = self.conv2d_1a(x)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.maxpool_5a(x)
+        x = self.mixed_5b(x)
+        x = self.repeat(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_1(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_2(x)
+        x = self.block8(x)
+        x = self.conv2d_7b(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def inceptionresnetv2(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = InceptionResNetV2(num_classes=num_classes, loss=loss, **kwargs)
+    if pretrained:
+        model.load_imagenet_weights()
+    return model
diff --git a/pfe/torchreid/models/inceptionv4.py b/pfe/torchreid/models/inceptionv4.py
new file mode 100644
index 0000000..b14916f
--- /dev/null
+++ b/pfe/torchreid/models/inceptionv4.py
@@ -0,0 +1,381 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['inceptionv4']
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+
+pretrained_settings = {
+    'inceptionv4': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000
+        },
+        'imagenet+background': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1001
+        }
+    }
+}
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Mixed_3a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_3a, self).__init__()
+        self.maxpool = nn.MaxPool2d(3, stride=2)
+        self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
+
+    def forward(self, x):
+        x0 = self.maxpool(x)
+        x1 = self.conv(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Mixed_4a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_4a, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(160, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(160, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
+            BasicConv2d(64, 96, kernel_size=(3, 3), stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Mixed_5a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_5a, self).__init__()
+        self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
+        self.maxpool = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.conv(x)
+        x1 = self.maxpool(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Inception_A(nn.Module):
+
+    def __init__(self):
+        super(Inception_A, self).__init__()
+        self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(384, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(384, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(384, 96, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Reduction_A(nn.Module):
+
+    def __init__(self):
+        super(Reduction_A, self).__init__()
+        self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(384, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(224, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Inception_B(nn.Module):
+
+    def __init__(self):
+        super(Inception_B, self).__init__()
+        self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            )
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            ),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            ),
+            BasicConv2d(
+                224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            )
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(1024, 128, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Reduction_B(nn.Module):
+
+    def __init__(self):
+        super(Reduction_B, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1024, 256, kernel_size=1, stride=1),
+            BasicConv2d(
+                256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
+            ),
+            BasicConv2d(
+                256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)
+            ), BasicConv2d(320, 320, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Inception_C(nn.Module):
+
+    def __init__(self):
+        super(Inception_C, self).__init__()
+
+        self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
+
+        self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
+        self.branch1_1a = BasicConv2d(
+            384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
+        )
+        self.branch1_1b = BasicConv2d(
+            384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
+        )
+
+        self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
+        self.branch2_1 = BasicConv2d(
+            384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0)
+        )
+        self.branch2_2 = BasicConv2d(
+            448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1)
+        )
+        self.branch2_3a = BasicConv2d(
+            512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
+        )
+        self.branch2_3b = BasicConv2d(
+            512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(1536, 256, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+
+        x1_0 = self.branch1_0(x)
+        x1_1a = self.branch1_1a(x1_0)
+        x1_1b = self.branch1_1b(x1_0)
+        x1 = torch.cat((x1_1a, x1_1b), 1)
+
+        x2_0 = self.branch2_0(x)
+        x2_1 = self.branch2_1(x2_0)
+        x2_2 = self.branch2_2(x2_1)
+        x2_3a = self.branch2_3a(x2_2)
+        x2_3b = self.branch2_3b(x2_2)
+        x2 = torch.cat((x2_3a, x2_3b), 1)
+
+        x3 = self.branch3(x)
+
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class InceptionV4(nn.Module):
+    """Inception-v4.
+
+    Reference:
+        Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
+        Connections on Learning. AAAI 2017.
+
+    Public keys:
+        - ``inceptionv4``: InceptionV4.
+    """
+
+    def __init__(self, num_classes, loss, **kwargs):
+        super(InceptionV4, self).__init__()
+        self.loss = loss
+
+        self.features = nn.Sequential(
+            BasicConv2d(3, 32, kernel_size=3, stride=2),
+            BasicConv2d(32, 32, kernel_size=3, stride=1),
+            BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
+            Mixed_3a(),
+            Mixed_4a(),
+            Mixed_5a(),
+            Inception_A(),
+            Inception_A(),
+            Inception_A(),
+            Inception_A(),
+            Reduction_A(), # Mixed_6a
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Reduction_B(), # Mixed_7a
+            Inception_C(),
+            Inception_C(),
+            Inception_C()
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(1536, num_classes)
+
+    def forward(self, x):
+        f = self.features(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = InceptionV4(num_classes, loss, **kwargs)
+    if pretrained:
+        model_url = pretrained_settings['inceptionv4']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/torchreid/models/mlfn.py b/pfe/torchreid/models/mlfn.py
new file mode 100644
index 0000000..ac7e126
--- /dev/null
+++ b/pfe/torchreid/models/mlfn.py
@@ -0,0 +1,269 @@
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['mlfn']
+
+model_urls = {
+    # training epoch = 5, top1 = 51.6
+    'imagenet':
+    'https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk',
+}
+
+
+class MLFNBlock(nn.Module):
+
+    def __init__(
+        self, in_channels, out_channels, stride, fsm_channels, groups=32
+    ):
+        super(MLFNBlock, self).__init__()
+        self.groups = groups
+        mid_channels = out_channels // 2
+
+        # Factor Modules
+        self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
+        self.fm_bn1 = nn.BatchNorm2d(mid_channels)
+        self.fm_conv2 = nn.Conv2d(
+            mid_channels,
+            mid_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=self.groups
+        )
+        self.fm_bn2 = nn.BatchNorm2d(mid_channels)
+        self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
+        self.fm_bn3 = nn.BatchNorm2d(out_channels)
+
+        # Factor Selection Module
+        self.fsm = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, fsm_channels[0], 1),
+            nn.BatchNorm2d(fsm_channels[0]),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
+            nn.BatchNorm2d(fsm_channels[1]),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(fsm_channels[1], self.groups, 1),
+            nn.BatchNorm2d(self.groups),
+            nn.Sigmoid(),
+        )
+
+        self.downsample = None
+        if in_channels != out_channels or stride > 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(
+                    in_channels, out_channels, 1, stride=stride, bias=False
+                ),
+                nn.BatchNorm2d(out_channels),
+            )
+
+    def forward(self, x):
+        residual = x
+        s = self.fsm(x)
+
+        # reduce dimension
+        x = self.fm_conv1(x)
+        x = self.fm_bn1(x)
+        x = F.relu(x, inplace=True)
+
+        # group convolution
+        x = self.fm_conv2(x)
+        x = self.fm_bn2(x)
+        x = F.relu(x, inplace=True)
+
+        # factor selection
+        b, c = x.size(0), x.size(1)
+        n = c // self.groups
+        ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
+        ss = ss.view(b, n, self.groups, 1, 1)
+        ss = ss.permute(0, 2, 1, 3, 4).contiguous()
+        ss = ss.view(b, c, 1, 1)
+        x = ss * x
+
+        # recover dimension
+        x = self.fm_conv3(x)
+        x = self.fm_bn3(x)
+        x = F.relu(x, inplace=True)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        return F.relu(residual + x, inplace=True), s
+
+
+class MLFN(nn.Module):
+    """Multi-Level Factorisation Net.
+
+    Reference:
+        Chang et al. Multi-Level Factorisation Net for
+        Person Re-Identification. CVPR 2018.
+
+    Public keys:
+        - ``mlfn``: MLFN (Multi-Level Factorisation Net).
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss='softmax',
+        groups=32,
+        channels=[64, 256, 512, 1024, 2048],
+        embed_dim=1024,
+        **kwargs
+    ):
+        super(MLFN, self).__init__()
+        self.loss = loss
+        self.groups = groups
+
+        # first convolutional layer
+        self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
+        self.bn1 = nn.BatchNorm2d(channels[0])
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+
+        # main body
+        self.feature = nn.ModuleList(
+            [
+                # layer 1-3
+                MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
+                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
+                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
+                # layer 4-7
+                MLFNBlock(
+                    channels[1], channels[2], 2, [256, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[2], channels[2], 1, [256, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[2], channels[2], 1, [256, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[2], channels[2], 1, [256, 128], self.groups
+                ),
+                # layer 8-13
+                MLFNBlock(
+                    channels[2], channels[3], 2, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[3], channels[3], 1, [512, 128], self.groups
+                ),
+                # layer 14-16
+                MLFNBlock(
+                    channels[3], channels[4], 2, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[4], channels[4], 1, [512, 128], self.groups
+                ),
+                MLFNBlock(
+                    channels[4], channels[4], 1, [512, 128], self.groups
+                ),
+            ]
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+
+        # projection functions
+        self.fc_x = nn.Sequential(
+            nn.Conv2d(channels[4], embed_dim, 1, bias=False),
+            nn.BatchNorm2d(embed_dim),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_s = nn.Sequential(
+            nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
+            nn.BatchNorm2d(embed_dim),
+            nn.ReLU(inplace=True),
+        )
+
+        self.classifier = nn.Linear(embed_dim, num_classes)
+
+        self.init_params()
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = F.relu(x, inplace=True)
+        x = self.maxpool(x)
+
+        s_hat = []
+        for block in self.feature:
+            x, s = block(x)
+            s_hat.append(s)
+        s_hat = torch.cat(s_hat, 1)
+
+        x = self.global_avgpool(x)
+        x = self.fc_x(x)
+        s_hat = self.fc_s(s_hat)
+
+        v = (x+s_hat) * 0.5
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def mlfn(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = MLFN(num_classes, loss, **kwargs)
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['imagenet'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['imagenet'])
+        )
+    return model
diff --git a/pfe/torchreid/models/mobilenetv2.py b/pfe/torchreid/models/mobilenetv2.py
new file mode 100644
index 0000000..c451ef8
--- /dev/null
+++ b/pfe/torchreid/models/mobilenetv2.py
@@ -0,0 +1,274 @@
+from __future__ import division, absolute_import
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['mobilenetv2_x1_0', 'mobilenetv2_x1_4']
+
+model_urls = {
+    # 1.0: top-1 71.3
+    'mobilenetv2_x1_0':
+    'https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NUOWvvGPHhaNr-fASF6c',
+    # 1.4: top-1 73.9
+    'mobilenetv2_x1_4':
+    'https://mega.nz/#!RGhgEIwS!xN2s2ZdyqI6vQ3EwgmRXLEW3khr9tpXg96G9SUJugGk',
+}
+
+
+class ConvBlock(nn.Module):
+    """Basic convolutional block.
+    
+    convolution (bias discarded) + batch normalization + relu6.
+
+    Args:
+        in_c (int): number of input channels.
+        out_c (int): number of output channels.
+        k (int or tuple): kernel size.
+        s (int or tuple): stride.
+        p (int or tuple): padding.
+        g (int): number of blocked connections from input channels
+            to output channels (default: 1).
+    """
+
+    def __init__(self, in_c, out_c, k, s=1, p=0, g=1):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Conv2d(
+            in_c, out_c, k, stride=s, padding=p, bias=False, groups=g
+        )
+        self.bn = nn.BatchNorm2d(out_c)
+
+    def forward(self, x):
+        return F.relu6(self.bn(self.conv(x)))
+
+
+class Bottleneck(nn.Module):
+
+    def __init__(self, in_channels, out_channels, expansion_factor, stride=1):
+        super(Bottleneck, self).__init__()
+        mid_channels = in_channels * expansion_factor
+        self.use_residual = stride == 1 and in_channels == out_channels
+        self.conv1 = ConvBlock(in_channels, mid_channels, 1)
+        self.dwconv2 = ConvBlock(
+            mid_channels, mid_channels, 3, stride, 1, g=mid_channels
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(mid_channels, out_channels, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+        )
+
+    def forward(self, x):
+        m = self.conv1(x)
+        m = self.dwconv2(m)
+        m = self.conv3(m)
+        if self.use_residual:
+            return x + m
+        else:
+            return m
+
+
+class MobileNetV2(nn.Module):
+    """MobileNetV2.
+
+    Reference:
+        Sandler et al. MobileNetV2: Inverted Residuals and
+        Linear Bottlenecks. CVPR 2018.
+
+    Public keys:
+        - ``mobilenetv2_x1_0``: MobileNetV2 x1.0.
+        - ``mobilenetv2_x1_4``: MobileNetV2 x1.4.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        width_mult=1,
+        loss='softmax',
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        super(MobileNetV2, self).__init__()
+        self.loss = loss
+        self.in_channels = int(32 * width_mult)
+        self.feature_dim = int(1280 * width_mult) if width_mult > 1 else 1280
+
+        # construct layers
+        self.conv1 = ConvBlock(3, self.in_channels, 3, s=2, p=1)
+        self.conv2 = self._make_layer(
+            Bottleneck, 1, int(16 * width_mult), 1, 1
+        )
+        self.conv3 = self._make_layer(
+            Bottleneck, 6, int(24 * width_mult), 2, 2
+        )
+        self.conv4 = self._make_layer(
+            Bottleneck, 6, int(32 * width_mult), 3, 2
+        )
+        self.conv5 = self._make_layer(
+            Bottleneck, 6, int(64 * width_mult), 4, 2
+        )
+        self.conv6 = self._make_layer(
+            Bottleneck, 6, int(96 * width_mult), 3, 1
+        )
+        self.conv7 = self._make_layer(
+            Bottleneck, 6, int(160 * width_mult), 3, 2
+        )
+        self.conv8 = self._make_layer(
+            Bottleneck, 6, int(320 * width_mult), 1, 1
+        )
+        self.conv9 = ConvBlock(self.in_channels, self.feature_dim, 1)
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = self._construct_fc_layer(
+            fc_dims, self.feature_dim, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, block, t, c, n, s):
+        # t: expansion factor
+        # c: output channels
+        # n: number of blocks
+        # s: stride for first layer
+        layers = []
+        layers.append(block(self.in_channels, c, t, s))
+        self.in_channels = c
+        for i in range(1, n):
+            layers.append(block(self.in_channels, c, t))
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer.
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        x = self.conv6(x)
+        x = self.conv7(x)
+        x = self.conv8(x)
+        x = self.conv9(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def mobilenetv2_x1_0(num_classes, loss, pretrained=True, **kwargs):
+    model = MobileNetV2(
+        num_classes,
+        loss=loss,
+        width_mult=1,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['mobilenetv2_x1_0'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['mobilenetv2_x1_0'])
+        )
+    return model
+
+
+def mobilenetv2_x1_4(num_classes, loss, pretrained=True, **kwargs):
+    model = MobileNetV2(
+        num_classes,
+        loss=loss,
+        width_mult=1.4,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['mobilenetv2_x1_4'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['mobilenetv2_x1_4'])
+        )
+    return model
diff --git a/pfe/torchreid/models/mudeep.py b/pfe/torchreid/models/mudeep.py
new file mode 100644
index 0000000..f6a4043
--- /dev/null
+++ b/pfe/torchreid/models/mudeep.py
@@ -0,0 +1,203 @@
+from __future__ import division, absolute_import
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['MuDeep']
+
+
+class ConvBlock(nn.Module):
+    """Basic convolutional block.
+    
+    convolution + batch normalization + relu.
+
+    Args:
+        in_c (int): number of input channels.
+        out_c (int): number of output channels.
+        k (int or tuple): kernel size.
+        s (int or tuple): stride.
+        p (int or tuple): padding.
+    """
+
+    def __init__(self, in_c, out_c, k, s, p):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p)
+        self.bn = nn.BatchNorm2d(out_c)
+
+    def forward(self, x):
+        return F.relu(self.bn(self.conv(x)))
+
+
+class ConvLayers(nn.Module):
+    """Preprocessing layers."""
+
+    def __init__(self):
+        super(ConvLayers, self).__init__()
+        self.conv1 = ConvBlock(3, 48, k=3, s=1, p=1)
+        self.conv2 = ConvBlock(48, 96, k=3, s=1, p=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.maxpool(x)
+        return x
+
+
+class MultiScaleA(nn.Module):
+    """Multi-scale stream layer A (Sec.3.1)"""
+
+    def __init__(self):
+        super(MultiScaleA, self).__init__()
+        self.stream1 = nn.Sequential(
+            ConvBlock(96, 96, k=1, s=1, p=0),
+            ConvBlock(96, 24, k=3, s=1, p=1),
+        )
+        self.stream2 = nn.Sequential(
+            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
+            ConvBlock(96, 24, k=1, s=1, p=0),
+        )
+        self.stream3 = ConvBlock(96, 24, k=1, s=1, p=0)
+        self.stream4 = nn.Sequential(
+            ConvBlock(96, 16, k=1, s=1, p=0),
+            ConvBlock(16, 24, k=3, s=1, p=1),
+            ConvBlock(24, 24, k=3, s=1, p=1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        s4 = self.stream4(x)
+        y = torch.cat([s1, s2, s3, s4], dim=1)
+        return y
+
+
+class Reduction(nn.Module):
+    """Reduction layer (Sec.3.1)"""
+
+    def __init__(self):
+        super(Reduction, self).__init__()
+        self.stream1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.stream2 = ConvBlock(96, 96, k=3, s=2, p=1)
+        self.stream3 = nn.Sequential(
+            ConvBlock(96, 48, k=1, s=1, p=0),
+            ConvBlock(48, 56, k=3, s=1, p=1),
+            ConvBlock(56, 64, k=3, s=2, p=1),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        y = torch.cat([s1, s2, s3], dim=1)
+        return y
+
+
+class MultiScaleB(nn.Module):
+    """Multi-scale stream layer B (Sec.3.1)"""
+
+    def __init__(self):
+        super(MultiScaleB, self).__init__()
+        self.stream1 = nn.Sequential(
+            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
+            ConvBlock(256, 256, k=1, s=1, p=0),
+        )
+        self.stream2 = nn.Sequential(
+            ConvBlock(256, 64, k=1, s=1, p=0),
+            ConvBlock(64, 128, k=(1, 3), s=1, p=(0, 1)),
+            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
+        )
+        self.stream3 = ConvBlock(256, 256, k=1, s=1, p=0)
+        self.stream4 = nn.Sequential(
+            ConvBlock(256, 64, k=1, s=1, p=0),
+            ConvBlock(64, 64, k=(1, 3), s=1, p=(0, 1)),
+            ConvBlock(64, 128, k=(3, 1), s=1, p=(1, 0)),
+            ConvBlock(128, 128, k=(1, 3), s=1, p=(0, 1)),
+            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
+        )
+
+    def forward(self, x):
+        s1 = self.stream1(x)
+        s2 = self.stream2(x)
+        s3 = self.stream3(x)
+        s4 = self.stream4(x)
+        return s1, s2, s3, s4
+
+
+class Fusion(nn.Module):
+    """Saliency-based learning fusion layer (Sec.3.2)"""
+
+    def __init__(self):
+        super(Fusion, self).__init__()
+        self.a1 = nn.Parameter(torch.rand(1, 256, 1, 1))
+        self.a2 = nn.Parameter(torch.rand(1, 256, 1, 1))
+        self.a3 = nn.Parameter(torch.rand(1, 256, 1, 1))
+        self.a4 = nn.Parameter(torch.rand(1, 256, 1, 1))
+
+        # We add an average pooling layer to reduce the spatial dimension
+        # of feature maps, which differs from the original paper.
+        self.avgpool = nn.AvgPool2d(kernel_size=4, stride=4, padding=0)
+
+    def forward(self, x1, x2, x3, x4):
+        s1 = self.a1.expand_as(x1) * x1
+        s2 = self.a2.expand_as(x2) * x2
+        s3 = self.a3.expand_as(x3) * x3
+        s4 = self.a4.expand_as(x4) * x4
+        y = self.avgpool(s1 + s2 + s3 + s4)
+        return y
+
+
+class MuDeep(nn.Module):
+    """Multiscale deep neural network.
+
+    Reference:
+        Qian et al. Multi-scale Deep Learning Architectures
+        for Person Re-identification. ICCV 2017.
+
+    Public keys:
+        - ``mudeep``: Multiscale deep neural network.
+    """
+
+    def __init__(self, num_classes, loss='softmax', **kwargs):
+        super(MuDeep, self).__init__()
+        self.loss = loss
+
+        self.block1 = ConvLayers()
+        self.block2 = MultiScaleA()
+        self.block3 = Reduction()
+        self.block4 = MultiScaleB()
+        self.block5 = Fusion()
+
+        # Due to this fully connected layer, input image has to be fixed
+        # in shape, i.e. (3, 256, 128), such that the last convolutional feature
+        # maps are of shape (256, 16, 8). If input shape is changed,
+        # the input dimension of this layer has to be changed accordingly.
+        self.fc = nn.Sequential(
+            nn.Linear(256 * 16 * 8, 4096),
+            nn.BatchNorm1d(4096),
+            nn.ReLU(),
+        )
+        self.classifier = nn.Linear(4096, num_classes)
+        self.feat_dim = 4096
+
+    def featuremaps(self, x):
+        x = self.block1(x)
+        x = self.block2(x)
+        x = self.block3(x)
+        x = self.block4(x)
+        x = self.block5(*x)
+        return x
+
+    def forward(self, x):
+        x = self.featuremaps(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        y = self.classifier(x)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, x
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
diff --git a/pfe/torchreid/models/nasnet.py b/pfe/torchreid/models/nasnet.py
new file mode 100644
index 0000000..b1f31de
--- /dev/null
+++ b/pfe/torchreid/models/nasnet.py
@@ -0,0 +1,1131 @@
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['nasnetamobile']
+"""
+NASNet Mobile
+Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!
+
+
+------------------------------------------------------------------------------------
+      Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M)
+------------------------------------------------------------------------------------
+|   NASNet-A (4 @ 1056)  |   74.08%  |   91.74%  |       564 M    |     5.3        |
+------------------------------------------------------------------------------------
+# References:
+ - [Learning Transferable Architectures for Scalable Image Recognition]
+    (https://arxiv.org/abs/1707.07012)
+"""
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+
+pretrained_settings = {
+    'nasnetamobile': {
+        'imagenet': {
+            # 'url': 'https://github.com/veronikayurchuk/pretrained-models.pytorch/releases/download/v1.0/nasnetmobile-7e03cead.pth.tar',
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/nasnetamobile-7e03cead.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224], # resize 256
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000
+        },
+        # 'imagenet+background': {
+        #     # 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
+        #     'input_space': 'RGB',
+        #     'input_size': [3, 224, 224], # resize 256
+        #     'input_range': [0, 1],
+        #     'mean': [0.5, 0.5, 0.5],
+        #     'std': [0.5, 0.5, 0.5],
+        #     'num_classes': 1001
+        # }
+    }
+}
+
+
+class MaxPoolPad(nn.Module):
+
+    def __init__(self):
+        super(MaxPoolPad, self).__init__()
+        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
+        self.pool = nn.MaxPool2d(3, stride=2, padding=1)
+
+    def forward(self, x):
+        x = self.pad(x)
+        x = self.pool(x)
+        x = x[:, :, 1:, 1:].contiguous()
+        return x
+
+
+class AvgPoolPad(nn.Module):
+
+    def __init__(self, stride=2, padding=1):
+        super(AvgPoolPad, self).__init__()
+        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
+        self.pool = nn.AvgPool2d(
+            3, stride=stride, padding=padding, count_include_pad=False
+        )
+
+    def forward(self, x):
+        x = self.pad(x)
+        x = self.pool(x)
+        x = x[:, :, 1:, 1:].contiguous()
+        return x
+
+
+class SeparableConv2d(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        dw_kernel,
+        dw_stride,
+        dw_padding,
+        bias=False
+    ):
+        super(SeparableConv2d, self).__init__()
+        self.depthwise_conv2d = nn.Conv2d(
+            in_channels,
+            in_channels,
+            dw_kernel,
+            stride=dw_stride,
+            padding=dw_padding,
+            bias=bias,
+            groups=in_channels
+        )
+        self.pointwise_conv2d = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, bias=bias
+        )
+
+    def forward(self, x):
+        x = self.depthwise_conv2d(x)
+        x = self.pointwise_conv2d(x)
+        return x
+
+
+class BranchSeparables(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        name=None,
+        bias=False
+    ):
+        super(BranchSeparables, self).__init__()
+        self.relu = nn.ReLU()
+        self.separable_1 = SeparableConv2d(
+            in_channels, in_channels, kernel_size, stride, padding, bias=bias
+        )
+        self.bn_sep_1 = nn.BatchNorm2d(
+            in_channels, eps=0.001, momentum=0.1, affine=True
+        )
+        self.relu1 = nn.ReLU()
+        self.separable_2 = SeparableConv2d(
+            in_channels, out_channels, kernel_size, 1, padding, bias=bias
+        )
+        self.bn_sep_2 = nn.BatchNorm2d(
+            out_channels, eps=0.001, momentum=0.1, affine=True
+        )
+        self.name = name
+
+    def forward(self, x):
+        x = self.relu(x)
+        if self.name == 'specific':
+            x = nn.ZeroPad2d((1, 0, 1, 0))(x)
+        x = self.separable_1(x)
+        if self.name == 'specific':
+            x = x[:, :, 1:, 1:].contiguous()
+
+        x = self.bn_sep_1(x)
+        x = self.relu1(x)
+        x = self.separable_2(x)
+        x = self.bn_sep_2(x)
+        return x
+
+
+class BranchSeparablesStem(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        bias=False
+    ):
+        super(BranchSeparablesStem, self).__init__()
+        self.relu = nn.ReLU()
+        self.separable_1 = SeparableConv2d(
+            in_channels, out_channels, kernel_size, stride, padding, bias=bias
+        )
+        self.bn_sep_1 = nn.BatchNorm2d(
+            out_channels, eps=0.001, momentum=0.1, affine=True
+        )
+        self.relu1 = nn.ReLU()
+        self.separable_2 = SeparableConv2d(
+            out_channels, out_channels, kernel_size, 1, padding, bias=bias
+        )
+        self.bn_sep_2 = nn.BatchNorm2d(
+            out_channels, eps=0.001, momentum=0.1, affine=True
+        )
+
+    def forward(self, x):
+        x = self.relu(x)
+        x = self.separable_1(x)
+        x = self.bn_sep_1(x)
+        x = self.relu1(x)
+        x = self.separable_2(x)
+        x = self.bn_sep_2(x)
+        return x
+
+
+class BranchSeparablesReduction(BranchSeparables):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        z_padding=1,
+        bias=False
+    ):
+        BranchSeparables.__init__(
+            self, in_channels, out_channels, kernel_size, stride, padding, bias
+        )
+        self.padding = nn.ZeroPad2d((z_padding, 0, z_padding, 0))
+
+    def forward(self, x):
+        x = self.relu(x)
+        x = self.padding(x)
+        x = self.separable_1(x)
+        x = x[:, :, 1:, 1:].contiguous()
+        x = self.bn_sep_1(x)
+        x = self.relu1(x)
+        x = self.separable_2(x)
+        x = self.bn_sep_2(x)
+        return x
+
+
+class CellStem0(nn.Module):
+
+    def __init__(self, stem_filters, num_filters=42):
+        super(CellStem0, self).__init__()
+        self.num_filters = num_filters
+        self.stem_filters = stem_filters
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                self.stem_filters, self.num_filters, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                self.num_filters, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            self.num_filters, self.num_filters, 5, 2, 2
+        )
+        self.comb_iter_0_right = BranchSeparablesStem(
+            self.stem_filters, self.num_filters, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_1_right = BranchSeparablesStem(
+            self.stem_filters, self.num_filters, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_2_left = nn.AvgPool2d(
+            3, stride=2, padding=1, count_include_pad=False
+        )
+        self.comb_iter_2_right = BranchSeparablesStem(
+            self.stem_filters, self.num_filters, 5, 2, 2, bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            self.num_filters, self.num_filters, 3, 1, 1, bias=False
+        )
+        self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
+
+    def forward(self, x):
+        x1 = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x1)
+        x_comb_iter_0_right = self.comb_iter_0_right(x)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x1)
+        x_comb_iter_1_right = self.comb_iter_1_right(x)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x1)
+        x_comb_iter_2_right = self.comb_iter_2_right(x)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x1)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class CellStem1(nn.Module):
+
+    def __init__(self, stem_filters, num_filters):
+        super(CellStem1, self).__init__()
+        self.num_filters = num_filters
+        self.stem_filters = stem_filters
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                2 * self.num_filters,
+                self.num_filters,
+                1,
+                stride=1,
+                bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                self.num_filters, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.relu = nn.ReLU()
+        self.path_1 = nn.Sequential()
+        self.path_1.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_1.add_module(
+            'conv',
+            nn.Conv2d(
+                self.stem_filters,
+                self.num_filters // 2,
+                1,
+                stride=1,
+                bias=False
+            )
+        )
+        self.path_2 = nn.ModuleList()
+        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
+        self.path_2.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_2.add_module(
+            'conv',
+            nn.Conv2d(
+                self.stem_filters,
+                self.num_filters // 2,
+                1,
+                stride=1,
+                bias=False
+            )
+        )
+
+        self.final_path_bn = nn.BatchNorm2d(
+            self.num_filters, eps=0.001, momentum=0.1, affine=True
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_1_left = MaxPoolPad()
+        self.comb_iter_1_right = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
+        self.comb_iter_2_left = AvgPoolPad()
+        self.comb_iter_2_right = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            self.num_filters,
+            self.num_filters,
+            3,
+            1,
+            1,
+            name='specific',
+            bias=False
+        )
+        # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_4_right = MaxPoolPad()
+
+    def forward(self, x_conv0, x_stem_0):
+        x_left = self.conv_1x1(x_stem_0)
+
+        x_relu = self.relu(x_conv0)
+        # path 1
+        x_path1 = self.path_1(x_relu)
+        # path 2
+        x_path2 = self.path_2.pad(x_relu)
+        x_path2 = x_path2[:, :, 1:, 1:]
+        x_path2 = self.path_2.avgpool(x_path2)
+        x_path2 = self.path_2.conv(x_path2)
+        # final path
+        x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_left)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_right)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_right)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_left)
+        x_comb_iter_2_right = self.comb_iter_2_right(x_right)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x_left)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class FirstCell(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(FirstCell, self).__init__()
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.relu = nn.ReLU()
+        self.path_1 = nn.Sequential()
+        self.path_1.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.path_2 = nn.ModuleList()
+        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
+        self.path_2.add_module(
+            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
+        )
+        self.path_2.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+
+        self.final_path_bn = nn.BatchNorm2d(
+            out_channels_left * 2, eps=0.001, momentum=0.1, affine=True
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            out_channels_right, out_channels_right, 5, 1, 2, bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_1_left = BranchSeparables(
+            out_channels_right, out_channels_right, 5, 1, 2, bias=False
+        )
+        self.comb_iter_1_right = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_2_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_3_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+    def forward(self, x, x_prev):
+        x_relu = self.relu(x_prev)
+        # path 1
+        x_path1 = self.path_1(x_relu)
+        # path 2
+        x_path2 = self.path_2.pad(x_relu)
+        x_path2 = x_path2[:, :, 1:, 1:]
+        x_path2 = self.path_2.avgpool(x_path2)
+        x_path2 = self.path_2.conv(x_path2)
+        # final path
+        x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
+
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2 = x_comb_iter_2_left + x_left
+
+        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
+        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
+        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_right
+
+        x_out = torch.cat(
+            [
+                x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
+                x_comb_iter_3, x_comb_iter_4
+            ], 1
+        )
+        return x_out
+
+
+class NormalCell(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(NormalCell, self).__init__()
+        self.conv_prev_1x1 = nn.Sequential()
+        self.conv_prev_1x1.add_module('relu', nn.ReLU())
+        self.conv_prev_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.conv_prev_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_left, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            out_channels_right, out_channels_right, 5, 1, 2, bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            out_channels_left, out_channels_left, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_1_left = BranchSeparables(
+            out_channels_left, out_channels_left, 5, 1, 2, bias=False
+        )
+        self.comb_iter_1_right = BranchSeparables(
+            out_channels_left, out_channels_left, 3, 1, 1, bias=False
+        )
+
+        self.comb_iter_2_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_3_left = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+
+    def forward(self, x, x_prev):
+        x_left = self.conv_prev_1x1(x_prev)
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2 = x_comb_iter_2_left + x_left
+
+        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
+        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
+        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_right
+
+        x_out = torch.cat(
+            [
+                x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
+                x_comb_iter_3, x_comb_iter_4
+            ], 1
+        )
+        return x_out
+
+
+class ReductionCell0(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(ReductionCell0, self).__init__()
+        self.conv_prev_1x1 = nn.Sequential()
+        self.conv_prev_1x1.add_module('relu', nn.ReLU())
+        self.conv_prev_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.conv_prev_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_left, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 5, 2, 2, bias=False
+        )
+        self.comb_iter_0_right = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_1_left = MaxPoolPad()
+        self.comb_iter_1_right = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 7, 2, 3, bias=False
+        )
+
+        self.comb_iter_2_left = AvgPoolPad()
+        self.comb_iter_2_right = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 5, 2, 2, bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparablesReduction(
+            out_channels_right, out_channels_right, 3, 1, 1, bias=False
+        )
+        self.comb_iter_4_right = MaxPoolPad()
+
+    def forward(self, x, x_prev):
+        x_left = self.conv_prev_1x1(x_prev)
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class ReductionCell1(nn.Module):
+
+    def __init__(
+        self, in_channels_left, out_channels_left, in_channels_right,
+        out_channels_right
+    ):
+        super(ReductionCell1, self).__init__()
+        self.conv_prev_1x1 = nn.Sequential()
+        self.conv_prev_1x1.add_module('relu', nn.ReLU())
+        self.conv_prev_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_left, out_channels_left, 1, stride=1, bias=False
+            )
+        )
+        self.conv_prev_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_left, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.conv_1x1 = nn.Sequential()
+        self.conv_1x1.add_module('relu', nn.ReLU())
+        self.conv_1x1.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels_right, out_channels_right, 1, stride=1, bias=False
+            )
+        )
+        self.conv_1x1.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                out_channels_right, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.comb_iter_0_left = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+        self.comb_iter_0_right = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_1_left = MaxPoolPad()
+        self.comb_iter_1_right = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            7,
+            2,
+            3,
+            name='specific',
+            bias=False
+        )
+
+        # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
+        self.comb_iter_2_left = AvgPoolPad()
+        self.comb_iter_2_right = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            5,
+            2,
+            2,
+            name='specific',
+            bias=False
+        )
+
+        self.comb_iter_3_right = nn.AvgPool2d(
+            3, stride=1, padding=1, count_include_pad=False
+        )
+
+        self.comb_iter_4_left = BranchSeparables(
+            out_channels_right,
+            out_channels_right,
+            3,
+            1,
+            1,
+            name='specific',
+            bias=False
+        )
+        # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
+        self.comb_iter_4_right = MaxPoolPad()
+
+    def forward(self, x, x_prev):
+        x_left = self.conv_prev_1x1(x_prev)
+        x_right = self.conv_1x1(x)
+
+        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
+        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
+        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
+
+        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
+        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
+        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
+
+        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
+        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
+        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
+
+        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
+        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
+
+        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
+        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
+        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
+
+        x_out = torch.cat(
+            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
+        )
+        return x_out
+
+
+class NASNetAMobile(nn.Module):
+    """Neural Architecture Search (NAS).
+
+    Reference:
+        Zoph et al. Learning Transferable Architectures
+        for Scalable Image Recognition. CVPR 2018.
+
+    Public keys:
+        - ``nasnetamobile``: NASNet-A Mobile.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        stem_filters=32,
+        penultimate_filters=1056,
+        filters_multiplier=2,
+        **kwargs
+    ):
+        super(NASNetAMobile, self).__init__()
+        self.stem_filters = stem_filters
+        self.penultimate_filters = penultimate_filters
+        self.filters_multiplier = filters_multiplier
+        self.loss = loss
+
+        filters = self.penultimate_filters // 24
+        # 24 is default value for the architecture
+
+        self.conv0 = nn.Sequential()
+        self.conv0.add_module(
+            'conv',
+            nn.Conv2d(
+                in_channels=3,
+                out_channels=self.stem_filters,
+                kernel_size=3,
+                padding=0,
+                stride=2,
+                bias=False
+            )
+        )
+        self.conv0.add_module(
+            'bn',
+            nn.BatchNorm2d(
+                self.stem_filters, eps=0.001, momentum=0.1, affine=True
+            )
+        )
+
+        self.cell_stem_0 = CellStem0(
+            self.stem_filters, num_filters=filters // (filters_multiplier**2)
+        )
+        self.cell_stem_1 = CellStem1(
+            self.stem_filters, num_filters=filters // filters_multiplier
+        )
+
+        self.cell_0 = FirstCell(
+            in_channels_left=filters,
+            out_channels_left=filters // 2, # 1, 0.5
+            in_channels_right=2 * filters,
+            out_channels_right=filters
+        ) # 2, 1
+        self.cell_1 = NormalCell(
+            in_channels_left=2 * filters,
+            out_channels_left=filters, # 2, 1
+            in_channels_right=6 * filters,
+            out_channels_right=filters
+        ) # 6, 1
+        self.cell_2 = NormalCell(
+            in_channels_left=6 * filters,
+            out_channels_left=filters, # 6, 1
+            in_channels_right=6 * filters,
+            out_channels_right=filters
+        ) # 6, 1
+        self.cell_3 = NormalCell(
+            in_channels_left=6 * filters,
+            out_channels_left=filters, # 6, 1
+            in_channels_right=6 * filters,
+            out_channels_right=filters
+        ) # 6, 1
+
+        self.reduction_cell_0 = ReductionCell0(
+            in_channels_left=6 * filters,
+            out_channels_left=2 * filters, # 6, 2
+            in_channels_right=6 * filters,
+            out_channels_right=2 * filters
+        ) # 6, 2
+
+        self.cell_6 = FirstCell(
+            in_channels_left=6 * filters,
+            out_channels_left=filters, # 6, 1
+            in_channels_right=8 * filters,
+            out_channels_right=2 * filters
+        ) # 8, 2
+        self.cell_7 = NormalCell(
+            in_channels_left=8 * filters,
+            out_channels_left=2 * filters, # 8, 2
+            in_channels_right=12 * filters,
+            out_channels_right=2 * filters
+        ) # 12, 2
+        self.cell_8 = NormalCell(
+            in_channels_left=12 * filters,
+            out_channels_left=2 * filters, # 12, 2
+            in_channels_right=12 * filters,
+            out_channels_right=2 * filters
+        ) # 12, 2
+        self.cell_9 = NormalCell(
+            in_channels_left=12 * filters,
+            out_channels_left=2 * filters, # 12, 2
+            in_channels_right=12 * filters,
+            out_channels_right=2 * filters
+        ) # 12, 2
+
+        self.reduction_cell_1 = ReductionCell1(
+            in_channels_left=12 * filters,
+            out_channels_left=4 * filters, # 12, 4
+            in_channels_right=12 * filters,
+            out_channels_right=4 * filters
+        ) # 12, 4
+
+        self.cell_12 = FirstCell(
+            in_channels_left=12 * filters,
+            out_channels_left=2 * filters, # 12, 2
+            in_channels_right=16 * filters,
+            out_channels_right=4 * filters
+        ) # 16, 4
+        self.cell_13 = NormalCell(
+            in_channels_left=16 * filters,
+            out_channels_left=4 * filters, # 16, 4
+            in_channels_right=24 * filters,
+            out_channels_right=4 * filters
+        ) # 24, 4
+        self.cell_14 = NormalCell(
+            in_channels_left=24 * filters,
+            out_channels_left=4 * filters, # 24, 4
+            in_channels_right=24 * filters,
+            out_channels_right=4 * filters
+        ) # 24, 4
+        self.cell_15 = NormalCell(
+            in_channels_left=24 * filters,
+            out_channels_left=4 * filters, # 24, 4
+            in_channels_right=24 * filters,
+            out_channels_right=4 * filters
+        ) # 24, 4
+
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout()
+        self.classifier = nn.Linear(24 * filters, num_classes)
+
+        self._init_params()
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def features(self, input):
+        x_conv0 = self.conv0(input)
+        x_stem_0 = self.cell_stem_0(x_conv0)
+        x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
+
+        x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
+        x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
+        x_cell_2 = self.cell_2(x_cell_1, x_cell_0)
+        x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
+
+        x_reduction_cell_0 = self.reduction_cell_0(x_cell_3, x_cell_2)
+
+        x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_3)
+        x_cell_7 = self.cell_7(x_cell_6, x_reduction_cell_0)
+        x_cell_8 = self.cell_8(x_cell_7, x_cell_6)
+        x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
+
+        x_reduction_cell_1 = self.reduction_cell_1(x_cell_9, x_cell_8)
+
+        x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_9)
+        x_cell_13 = self.cell_13(x_cell_12, x_reduction_cell_1)
+        x_cell_14 = self.cell_14(x_cell_13, x_cell_12)
+        x_cell_15 = self.cell_15(x_cell_14, x_cell_13)
+
+        x_cell_15 = self.relu(x_cell_15)
+        x_cell_15 = F.avg_pool2d(
+            x_cell_15,
+            x_cell_15.size()[2:]
+        ) # global average pool
+        x_cell_15 = x_cell_15.view(x_cell_15.size(0), -1)
+        x_cell_15 = self.dropout(x_cell_15)
+
+        return x_cell_15
+
+    def forward(self, input):
+        v = self.features(input)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def nasnetamobile(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = NASNetAMobile(num_classes, loss, **kwargs)
+    if pretrained:
+        model_url = pretrained_settings['nasnetamobile']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/torchreid/models/osnet.py b/pfe/torchreid/models/osnet.py
new file mode 100644
index 0000000..e6273ff
--- /dev/null
+++ b/pfe/torchreid/models/osnet.py
@@ -0,0 +1,597 @@
+from __future__ import division, absolute_import
+import warnings
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = [
+    'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
+]
+
+pretrained_urls = {
+    'osnet_x1_0':
+    'https://drive.google.com/uc?id=1LaG1EJpHrxdAxKnSCJ_i0u-nbxSAeiFY',
+    'osnet_x0_75':
+    'https://drive.google.com/uc?id=1uwA9fElHOk3ZogwbeY5GkLI6QPTX70Hq',
+    'osnet_x0_5':
+    'https://drive.google.com/uc?id=16DGLbZukvVYgINws8u8deSaOqjybZ83i',
+    'osnet_x0_25':
+    'https://drive.google.com/uc?id=1rb8UN5ZzPKRc_xvtHlyDh-cSz88YX9hs',
+    'osnet_ibn_x1_0':
+    'https://drive.google.com/uc?id=1sr90V6irlYYDd4_4ISU2iruoRG8J__6l'
+}
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU(inplace=True)
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        IN=False,
+        bottleneck_reduction=4,
+        **kwargs
+    ):
+        super(OSBlock, self).__init__()
+        mid_channels = out_channels // bottleneck_reduction
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2a = LightConv3x3(mid_channels, mid_channels)
+        self.conv2b = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2c = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.conv2d = nn.Sequential(
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+            LightConv3x3(mid_channels, mid_channels),
+        )
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = None
+        if IN:
+            self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2a = self.conv2a(x1)
+        x2b = self.conv2b(x1)
+        x2c = self.conv2c(x1)
+        x2d = self.conv2d(x1)
+        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        if self.IN is not None:
+            out = self.IN(out)
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        IN=False,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=IN)
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0],
+            layers[0],
+            channels[0],
+            channels[1],
+            reduce_spatial_size=True,
+            IN=IN
+        )
+        self.conv3 = self._make_layer(
+            blocks[1],
+            layers[1],
+            channels[1],
+            channels[2],
+            reduce_spatial_size=True
+        )
+        self.conv4 = self._make_layer(
+            blocks[2],
+            layers[2],
+            channels[2],
+            channels[3],
+            reduce_spatial_size=False
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(
+        self,
+        block,
+        layer,
+        in_channels,
+        out_channels,
+        reduce_spatial_size,
+        IN=False
+    ):
+        layers = []
+
+        layers.append(block(in_channels, out_channels, IN=IN))
+        for i in range(1, layer):
+            layers.append(block(out_channels, out_channels, IN=IN))
+
+        if reduce_spatial_size:
+            layers.append(
+                nn.Sequential(
+                    Conv1x1(out_channels, out_channels),
+                    nn.AvgPool2d(2, stride=2)
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x, return_featuremaps=False):
+        x = self.featuremaps(x)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, key=''):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    import os
+    import errno
+    import gdown
+    from collections import OrderedDict
+
+    def _get_torch_home():
+        ENV_TORCH_HOME = 'TORCH_HOME'
+        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
+        DEFAULT_CACHE_DIR = '~/.cache'
+        torch_home = os.path.expanduser(
+            os.getenv(
+                ENV_TORCH_HOME,
+                os.path.join(
+                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
+                )
+            )
+        )
+        return torch_home
+
+    torch_home = _get_torch_home()
+    model_dir = os.path.join(torch_home, 'checkpoints')
+    try:
+        os.makedirs(model_dir)
+    except OSError as e:
+        if e.errno == errno.EEXIST:
+            # Directory already exists, ignore.
+            pass
+        else:
+            # Unexpected OSError, re-raise.
+            raise
+    filename = key + '_imagenet.pth'
+    cached_file = os.path.join(model_dir, filename)
+
+    if not os.path.exists(cached_file):
+        gdown.download(pretrained_urls[key], cached_file, quiet=False)
+
+    state_dict = torch.load(cached_file)
+    model_dict = model.state_dict()
+    new_state_dict = OrderedDict()
+    matched_layers, discarded_layers = [], []
+
+    for k, v in state_dict.items():
+        if k.startswith('module.'):
+            k = k[7:] # discard module.
+
+        if k in model_dict and model_dict[k].size() == v.size():
+            new_state_dict[k] = v
+            matched_layers.append(k)
+        else:
+            discarded_layers.append(k)
+
+    model_dict.update(new_state_dict)
+    model.load_state_dict(model_dict)
+
+    if len(matched_layers) == 0:
+        warnings.warn(
+            'The pretrained weights from "{}" cannot be loaded, '
+            'please check the key names manually '
+            '(** ignored and continue **)'.format(cached_file)
+        )
+    else:
+        print(
+            'Successfully loaded imagenet pretrained weights from "{}"'.
+            format(cached_file)
+        )
+        if len(discarded_layers) > 0:
+            print(
+                '** The following layers are discarded '
+                'due to unmatched keys or layer size: {}'.
+                format(discarded_layers)
+            )
+
+
+##########
+# Instantiation
+##########
+def osnet_x1_0(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # standard size (width x1.0)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x1_0')
+    return model
+
+
+def osnet_x0_75(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # medium size (width x0.75)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[48, 192, 288, 384],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x0_75')
+    return model
+
+
+def osnet_x0_5(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # tiny size (width x0.5)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[32, 128, 192, 256],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x0_5')
+    return model
+
+
+def osnet_x0_25(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
+    # very tiny size (width x0.25)
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[16, 64, 96, 128],
+        loss=loss,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_x0_25')
+    return model
+
+
+def osnet_ibn_x1_0(
+    num_classes=1000, pretrained=True, loss='softmax', **kwargs
+):
+    # standard size (width x1.0) + IBN layer
+    # Ref: Pan et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. ECCV, 2018.
+    model = OSNet(
+        num_classes,
+        blocks=[OSBlock, OSBlock, OSBlock],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        IN=True,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_ibn_x1_0')
+    return model
diff --git a/pfe/torchreid/models/osnet_ain.py b/pfe/torchreid/models/osnet_ain.py
new file mode 100644
index 0000000..b40c299
--- /dev/null
+++ b/pfe/torchreid/models/osnet_ain.py
@@ -0,0 +1,541 @@
+from __future__ import division, absolute_import
+import warnings
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['osnet_ain_x1_0']
+
+pretrained_urls = {
+    'osnet_ain_x1_0':
+    'https://drive.google.com/uc?id=1-CaioD9NaqbHK_kzSMW8VE4_3KcsRjEo'
+}
+
+
+##########
+# Basic layers
+##########
+class ConvLayer(nn.Module):
+    """Convolution layer (conv + bn + relu)."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        groups=1,
+        IN=False
+    ):
+        super(ConvLayer, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            groups=groups
+        )
+        if IN:
+            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
+        else:
+            self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1(nn.Module):
+    """1x1 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv1x1, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            1,
+            stride=stride,
+            padding=0,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class Conv1x1Linear(nn.Module):
+    """1x1 convolution + bn (w/o non-linearity)."""
+
+    def __init__(self, in_channels, out_channels, stride=1, bn=True):
+        super(Conv1x1Linear, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
+        )
+        self.bn = None
+        if bn:
+            self.bn = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        return x
+
+
+class Conv3x3(nn.Module):
+    """3x3 convolution + bn + relu."""
+
+    def __init__(self, in_channels, out_channels, stride=1, groups=1):
+        super(Conv3x3, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            3,
+            stride=stride,
+            padding=1,
+            bias=False,
+            groups=groups
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConv3x3(nn.Module):
+    """Lightweight 3x3 convolution.
+
+    1x1 (linear) + dw 3x3 (nonlinear).
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(LightConv3x3, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, 1, stride=1, padding=0, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            bias=False,
+            groups=out_channels
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.bn(x)
+        return self.relu(x)
+
+
+class LightConvStream(nn.Module):
+    """Lightweight convolution stream."""
+
+    def __init__(self, in_channels, out_channels, depth):
+        super(LightConvStream, self).__init__()
+        assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
+            depth
+        )
+        layers = []
+        layers += [LightConv3x3(in_channels, out_channels)]
+        for i in range(depth - 1):
+            layers += [LightConv3x3(out_channels, out_channels)]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+##########
+# Building blocks for omni-scale feature learning
+##########
+class ChannelGate(nn.Module):
+    """A mini-network that generates channel-wise gates conditioned on input tensor."""
+
+    def __init__(
+        self,
+        in_channels,
+        num_gates=None,
+        return_gates=False,
+        gate_activation='sigmoid',
+        reduction=16,
+        layer_norm=False
+    ):
+        super(ChannelGate, self).__init__()
+        if num_gates is None:
+            num_gates = in_channels
+        self.return_gates = return_gates
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            in_channels,
+            in_channels // reduction,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        self.norm1 = None
+        if layer_norm:
+            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Conv2d(
+            in_channels // reduction,
+            num_gates,
+            kernel_size=1,
+            bias=True,
+            padding=0
+        )
+        if gate_activation == 'sigmoid':
+            self.gate_activation = nn.Sigmoid()
+        elif gate_activation == 'relu':
+            self.gate_activation = nn.ReLU()
+        elif gate_activation == 'linear':
+            self.gate_activation = None
+        else:
+            raise RuntimeError(
+                "Unknown gate activation: {}".format(gate_activation)
+            )
+
+    def forward(self, x):
+        input = x
+        x = self.global_avgpool(x)
+        x = self.fc1(x)
+        if self.norm1 is not None:
+            x = self.norm1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        if self.gate_activation is not None:
+            x = self.gate_activation(x)
+        if self.return_gates:
+            return x
+        return input * x
+
+
+class OSBlock(nn.Module):
+    """Omni-scale feature learning block."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlock, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+class OSBlockINin(nn.Module):
+    """Omni-scale feature learning block with instance normalization."""
+
+    def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
+        super(OSBlockINin, self).__init__()
+        assert T >= 1
+        assert out_channels >= reduction and out_channels % reduction == 0
+        mid_channels = out_channels // reduction
+
+        self.conv1 = Conv1x1(in_channels, mid_channels)
+        self.conv2 = nn.ModuleList()
+        for t in range(1, T + 1):
+            self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
+        self.gate = ChannelGate(mid_channels)
+        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
+        self.downsample = None
+        if in_channels != out_channels:
+            self.downsample = Conv1x1Linear(in_channels, out_channels)
+        self.IN = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x):
+        identity = x
+        x1 = self.conv1(x)
+        x2 = 0
+        for conv2_t in self.conv2:
+            x2_t = conv2_t(x1)
+            x2 = x2 + self.gate(x2_t)
+        x3 = self.conv3(x2)
+        x3 = self.IN(x3) # IN inside residual
+        if self.downsample is not None:
+            identity = self.downsample(identity)
+        out = x3 + identity
+        return F.relu(out)
+
+
+##########
+# Network architecture
+##########
+class OSNet(nn.Module):
+    """Omni-Scale Network.
+    
+    Reference:
+        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
+        - Zhou et al. Learning Generalisable Omni-Scale Representations
+          for Person Re-Identification. arXiv preprint, 2019.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        blocks,
+        layers,
+        channels,
+        feature_dim=512,
+        loss='softmax',
+        conv1_IN=False,
+        **kwargs
+    ):
+        super(OSNet, self).__init__()
+        num_blocks = len(blocks)
+        assert num_blocks == len(layers)
+        assert num_blocks == len(channels) - 1
+        self.loss = loss
+        self.feature_dim = feature_dim
+
+        # convolutional backbone
+        self.conv1 = ConvLayer(
+            3, channels[0], 7, stride=2, padding=3, IN=conv1_IN
+        )
+        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.conv2 = self._make_layer(
+            blocks[0], layers[0], channels[0], channels[1]
+        )
+        self.pool2 = nn.Sequential(
+            Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv3 = self._make_layer(
+            blocks[1], layers[1], channels[1], channels[2]
+        )
+        self.pool3 = nn.Sequential(
+            Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
+        )
+        self.conv4 = self._make_layer(
+            blocks[2], layers[2], channels[2], channels[3]
+        )
+        self.conv5 = Conv1x1(channels[3], channels[3])
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        # fully connected layer
+        self.fc = self._construct_fc_layer(
+            self.feature_dim, channels[3], dropout_p=None
+        )
+        # identity classification layer
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, blocks, layer, in_channels, out_channels):
+        layers = []
+        layers += [blocks[0](in_channels, out_channels)]
+        for i in range(1, len(blocks)):
+            layers += [blocks[i](out_channels, out_channels)]
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        if fc_dims is None or fc_dims < 0:
+            self.feature_dim = input_dim
+            return None
+
+        if isinstance(fc_dims, int):
+            fc_dims = [fc_dims]
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU())
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.InstanceNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.pool2(x)
+        x = self.conv3(x)
+        x = self.pool3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x, return_featuremaps=False):
+        x = self.featuremaps(x)
+        if return_featuremaps:
+            return x
+        v = self.global_avgpool(x)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, key=''):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    import os
+    import errno
+    import gdown
+    from collections import OrderedDict
+
+    def _get_torch_home():
+        ENV_TORCH_HOME = 'TORCH_HOME'
+        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
+        DEFAULT_CACHE_DIR = '~/.cache'
+        torch_home = os.path.expanduser(
+            os.getenv(
+                ENV_TORCH_HOME,
+                os.path.join(
+                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
+                )
+            )
+        )
+        return torch_home
+
+    torch_home = _get_torch_home()
+    model_dir = os.path.join(torch_home, 'checkpoints')
+    try:
+        os.makedirs(model_dir)
+    except OSError as e:
+        if e.errno == errno.EEXIST:
+            # Directory already exists, ignore.
+            pass
+        else:
+            # Unexpected OSError, re-raise.
+            raise
+    filename = key + '_imagenet.pth'
+    cached_file = os.path.join(model_dir, filename)
+
+    if not os.path.exists(cached_file):
+        gdown.download(pretrained_urls[key], cached_file, quiet=False)
+
+    state_dict = torch.load(cached_file)
+    model_dict = model.state_dict()
+    new_state_dict = OrderedDict()
+    matched_layers, discarded_layers = [], []
+
+    for k, v in state_dict.items():
+        if k.startswith('module.'):
+            k = k[7:] # discard module.
+
+        if k in model_dict and model_dict[k].size() == v.size():
+            new_state_dict[k] = v
+            matched_layers.append(k)
+        else:
+            discarded_layers.append(k)
+
+    model_dict.update(new_state_dict)
+    model.load_state_dict(model_dict)
+
+    if len(matched_layers) == 0:
+        warnings.warn(
+            'The pretrained weights from "{}" cannot be loaded, '
+            'please check the key names manually '
+            '(** ignored and continue **)'.format(cached_file)
+        )
+    else:
+        print(
+            'Successfully loaded imagenet pretrained weights from "{}"'.
+            format(cached_file)
+        )
+        if len(discarded_layers) > 0:
+            print(
+                '** The following layers are discarded '
+                'due to unmatched keys or layer size: {}'.
+                format(discarded_layers)
+            )
+
+
+##########
+# Instantiation
+##########
+def osnet_ain_x1_0(
+    num_classes=1000, pretrained=True, loss='softmax', **kwargs
+):
+    model = OSNet(
+        num_classes,
+        blocks=[
+            [OSBlockINin, OSBlockINin], [OSBlock, OSBlockINin],
+            [OSBlockINin, OSBlock]
+        ],
+        layers=[2, 2, 2],
+        channels=[64, 256, 384, 512],
+        loss=loss,
+        conv1_IN=True,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, key='osnet_ain_x1_0')
+    return model
diff --git a/pfe/torchreid/models/pcb.py b/pfe/torchreid/models/pcb.py
new file mode 100644
index 0000000..92c7414
--- /dev/null
+++ b/pfe/torchreid/models/pcb.py
@@ -0,0 +1,314 @@
+from __future__ import division, absolute_import
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['pcb_p6', 'pcb_p4']
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class DimReduceLayer(nn.Module):
+
+    def __init__(self, in_channels, out_channels, nonlinear):
+        super(DimReduceLayer, self).__init__()
+        layers = []
+        layers.append(
+            nn.Conv2d(
+                in_channels, out_channels, 1, stride=1, padding=0, bias=False
+            )
+        )
+        layers.append(nn.BatchNorm2d(out_channels))
+
+        if nonlinear == 'relu':
+            layers.append(nn.ReLU(inplace=True))
+        elif nonlinear == 'leakyrelu':
+            layers.append(nn.LeakyReLU(0.1))
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class PCB(nn.Module):
+    """Part-based Convolutional Baseline.
+
+    Reference:
+        Sun et al. Beyond Part Models: Person Retrieval with Refined
+        Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.
+
+    Public keys:
+        - ``pcb_p4``: PCB with 4-part strips.
+        - ``pcb_p6``: PCB with 6-part strips.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        parts=6,
+        reduced_dim=256,
+        nonlinear='relu',
+        **kwargs
+    ):
+        self.inplanes = 64
+        super(PCB, self).__init__()
+        self.loss = loss
+        self.parts = parts
+        self.feature_dim = 512 * block.expansion
+
+        # backbone network
+        self.conv1 = nn.Conv2d(
+            3, 64, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
+
+        # pcb layers
+        self.parts_avgpool = nn.AdaptiveAvgPool2d((self.parts, 1))
+        self.dropout = nn.Dropout(p=0.5)
+        self.conv5 = DimReduceLayer(
+            512 * block.expansion, reduced_dim, nonlinear=nonlinear
+        )
+        self.feature_dim = reduced_dim
+        self.classifier = nn.ModuleList(
+            [
+                nn.Linear(self.feature_dim, num_classes)
+                for _ in range(self.parts)
+            ]
+        )
+
+        self._init_params()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v_g = self.parts_avgpool(f)
+
+        if not self.training:
+            v_g = F.normalize(v_g, p=2, dim=1)
+            return v_g.view(v_g.size(0), -1)
+
+        v_g = self.dropout(v_g)
+        v_h = self.conv5(v_g)
+
+        y = []
+        for i in range(self.parts):
+            v_h_i = v_h[:, :, i, :]
+            v_h_i = v_h_i.view(v_h_i.size(0), -1)
+            y_i = self.classifier[i](v_h_i)
+            y.append(y_i)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            v_g = F.normalize(v_g, p=2, dim=1)
+            return y, v_g.view(v_g.size(0), -1)
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def pcb_p6(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = PCB(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=1,
+        parts=6,
+        reduced_dim=256,
+        nonlinear='relu',
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
+
+
+def pcb_p4(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = PCB(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=1,
+        parts=4,
+        reduced_dim=256,
+        nonlinear='relu',
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/torchreid/models/resnet.py b/pfe/torchreid/models/resnet.py
new file mode 100644
index 0000000..63d7f43
--- /dev/null
+++ b/pfe/torchreid/models/resnet.py
@@ -0,0 +1,530 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+
+__all__ = [
+    'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
+    'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
+]
+
+model_urls = {
+    'resnet18':
+    'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34':
+    'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50':
+    'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101':
+    'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152':
+    'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+    'resnext50_32x4d':
+    'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
+    'resnext101_32x8d':
+    'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=dilation,
+        groups=groups,
+        bias=False,
+        dilation=dilation
+    )
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(
+        in_planes, out_planes, kernel_size=1, stride=stride, bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None
+    ):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError(
+                'BasicBlock only supports groups=1 and base_width=64'
+            )
+        if dilation > 1:
+            raise NotImplementedError(
+                "Dilation > 1 not supported in BasicBlock"
+            )
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None
+    ):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width/64.)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    """Residual network.
+    
+    Reference:
+        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
+
+    Public keys:
+        - ``resnet18``: ResNet18.
+        - ``resnet34``: ResNet34.
+        - ``resnet50``: ResNet50.
+        - ``resnet101``: ResNet101.
+        - ``resnet152``: ResNet152.
+        - ``resnext50_32x4d``: ResNeXt50.
+        - ``resnext101_32x8d``: ResNeXt101.
+        - ``resnet50_fc512``: ResNet50 + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        zero_init_residual=False,
+        groups=1,
+        width_per_group=64,
+        replace_stride_with_dilation=None,
+        norm_layer=None,
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+        self.loss = loss
+        self.feature_dim = 512 * block.expansion
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError(
+                "replace_stride_with_dilation should be None "
+                "or a 3-element tuple, got {}".
+                format(replace_stride_with_dilation)
+            )
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(
+            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(
+            block,
+            128,
+            layers[1],
+            stride=2,
+            dilate=replace_stride_with_dilation[0]
+        )
+        self.layer3 = self._make_layer(
+            block,
+            256,
+            layers[2],
+            stride=2,
+            dilate=replace_stride_with_dilation[1]
+        )
+        self.layer4 = self._make_layer(
+            block,
+            512,
+            layers[3],
+            stride=last_stride,
+            dilate=replace_stride_with_dilation[2]
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = self._construct_fc_layer(
+            fc_dims, 512 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes, planes, stride, downsample, self.groups,
+                self.base_width, previous_dilation, norm_layer
+            )
+        )
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    groups=self.groups,
+                    base_width=self.base_width,
+                    dilation=self.dilation,
+                    norm_layer=norm_layer
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+"""ResNet"""
+
+
+def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=BasicBlock,
+        layers=[2, 2, 2, 2],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet18'])
+    return model
+
+
+def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=BasicBlock,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet34'])
+    return model
+
+
+def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
+
+
+def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 23, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet101'])
+    return model
+
+
+def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 8, 36, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet152'])
+    return model
+
+
+"""ResNeXt"""
+
+
+def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        groups=32,
+        width_per_group=4,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
+    return model
+
+
+def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 23, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        groups=32,
+        width_per_group=8,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnext101_32x8d'])
+    return model
+
+
+"""
+ResNet + FC
+"""
+
+
+def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=1,
+        fc_dims=[512],
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/torchreid/models/resnet_ibn_a.py b/pfe/torchreid/models/resnet_ibn_a.py
new file mode 100644
index 0000000..d198e7c
--- /dev/null
+++ b/pfe/torchreid/models/resnet_ibn_a.py
@@ -0,0 +1,289 @@
+"""
+Credit to https://github.com/XingangPan/IBN-Net.
+"""
+from __future__ import division, absolute_import
+import math
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['resnet50_ibn_a']
+
+model_urls = {
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class IBN(nn.Module):
+
+    def __init__(self, planes):
+        super(IBN, self).__init__()
+        half1 = int(planes / 2)
+        self.half = half1
+        half2 = planes - half1
+        self.IN = nn.InstanceNorm2d(half1, affine=True)
+        self.BN = nn.BatchNorm2d(half2)
+
+    def forward(self, x):
+        split = torch.split(x, self.half, 1)
+        out1 = self.IN(split[0].contiguous())
+        out2 = self.BN(split[1].contiguous())
+        out = torch.cat((out1, out2), 1)
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        if ibn:
+            self.bn1 = IBN(planes)
+        else:
+            self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    """Residual network + IBN layer.
+    
+    Reference:
+        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - Pan et al. Two at Once: Enhancing Learning and Generalization
+          Capacities via IBN-Net. ECCV 2018.
+    """
+
+    def __init__(
+        self,
+        block,
+        layers,
+        num_classes=1000,
+        loss='softmax',
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        scale = 64
+        self.inplanes = scale
+        super(ResNet, self).__init__()
+        self.loss = loss
+        self.feature_dim = scale * 8 * block.expansion
+
+        self.conv1 = nn.Conv2d(
+            3, scale, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(scale)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, scale, layers[0])
+        self.layer2 = self._make_layer(block, scale * 2, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, scale * 4, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, scale * 8, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = self._construct_fc_layer(
+            fc_dims, scale * 8 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.InstanceNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        ibn = True
+        if planes == 512:
+            ibn = False
+        layers.append(block(self.inplanes, planes, ibn, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, ibn))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.avgpool(f)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def resnet50_ibn_a(num_classes, loss='softmax', pretrained=False, **kwargs):
+    model = ResNet(
+        Bottleneck, [3, 4, 6, 3], num_classes=num_classes, loss=loss, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/torchreid/models/resnet_ibn_b.py b/pfe/torchreid/models/resnet_ibn_b.py
new file mode 100644
index 0000000..9881cc7
--- /dev/null
+++ b/pfe/torchreid/models/resnet_ibn_b.py
@@ -0,0 +1,274 @@
+"""
+Credit to https://github.com/XingangPan/IBN-Net.
+"""
+from __future__ import division, absolute_import
+import math
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['resnet50_ibn_b']
+
+model_urls = {
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, IN=False):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.IN = None
+        if IN:
+            self.IN = nn.InstanceNorm2d(planes * 4, affine=True)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        if self.IN is not None:
+            out = self.IN(out)
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    """Residual network + IBN layer.
+    
+    Reference:
+        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - Pan et al. Two at Once: Enhancing Learning and Generalization
+          Capacities via IBN-Net. ECCV 2018.
+    """
+
+    def __init__(
+        self,
+        block,
+        layers,
+        num_classes=1000,
+        loss='softmax',
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        scale = 64
+        self.inplanes = scale
+        super(ResNet, self).__init__()
+        self.loss = loss
+        self.feature_dim = scale * 8 * block.expansion
+
+        self.conv1 = nn.Conv2d(
+            3, scale, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.InstanceNorm2d(scale, affine=True)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(
+            block, scale, layers[0], stride=1, IN=True
+        )
+        self.layer2 = self._make_layer(
+            block, scale * 2, layers[1], stride=2, IN=True
+        )
+        self.layer3 = self._make_layer(block, scale * 4, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, scale * 8, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = self._construct_fc_layer(
+            fc_dims, scale * 8 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.InstanceNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1, IN=False):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks - 1):
+            layers.append(block(self.inplanes, planes))
+        layers.append(block(self.inplanes, planes, IN=IN))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.avgpool(f)
+        v = v.view(v.size(0), -1)
+        if self.fc is not None:
+            v = self.fc(v)
+        if not self.training:
+            return v
+        y = self.classifier(v)
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def resnet50_ibn_b(num_classes, loss='softmax', pretrained=False, **kwargs):
+    model = ResNet(
+        Bottleneck, [3, 4, 6, 3], num_classes=num_classes, loss=loss, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/torchreid/models/resnetmid.py b/pfe/torchreid/models/resnetmid.py
new file mode 100644
index 0000000..017f6c6
--- /dev/null
+++ b/pfe/torchreid/models/resnetmid.py
@@ -0,0 +1,307 @@
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+
+__all__ = ['resnet50mid']
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNetMid(nn.Module):
+    """Residual network + mid-level features.
+    
+    Reference:
+        Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for
+        Cross-Domain Instance Matching. arXiv:1711.08106.
+
+    Public keys:
+        - ``resnet50mid``: ResNet50 + mid-level feature fusion.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    ):
+        self.inplanes = 64
+        super(ResNetMid, self).__init__()
+        self.loss = loss
+        self.feature_dim = 512 * block.expansion
+
+        # backbone network
+        self.conv1 = nn.Conv2d(
+            3, 64, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(
+            block, 512, layers[3], stride=last_stride
+        )
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        assert fc_dims is not None
+        self.fc_fusion = self._construct_fc_layer(
+            fc_dims, 512 * block.expansion * 2
+        )
+        self.feature_dim += 512 * block.expansion
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x4a = self.layer4[0](x)
+        x4b = self.layer4[1](x4a)
+        x4c = self.layer4[2](x4b)
+        return x4a, x4b, x4c
+
+    def forward(self, x):
+        x4a, x4b, x4c = self.featuremaps(x)
+
+        v4a = self.global_avgpool(x4a)
+        v4b = self.global_avgpool(x4b)
+        v4c = self.global_avgpool(x4c)
+        v4ab = torch.cat([v4a, v4b], 1)
+        v4ab = v4ab.view(v4ab.size(0), -1)
+        v4ab = self.fc_fusion(v4ab)
+        v4c = v4c.view(v4c.size(0), -1)
+        v = torch.cat([v4ab, v4c], 1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+"""
+Residual network configurations:
+--
+resnet18: block=BasicBlock, layers=[2, 2, 2, 2]
+resnet34: block=BasicBlock, layers=[3, 4, 6, 3]
+resnet50: block=Bottleneck, layers=[3, 4, 6, 3]
+resnet101: block=Bottleneck, layers=[3, 4, 23, 3]
+resnet152: block=Bottleneck, layers=[3, 8, 36, 3]
+"""
+
+
+def resnet50mid(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNetMid(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=[1024],
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnet50'])
+    return model
diff --git a/pfe/torchreid/models/senet.py b/pfe/torchreid/models/senet.py
new file mode 100644
index 0000000..baaf9b0
--- /dev/null
+++ b/pfe/torchreid/models/senet.py
@@ -0,0 +1,688 @@
+from __future__ import division, absolute_import
+import math
+from collections import OrderedDict
+import torch.nn as nn
+from torch.utils import model_zoo
+
+__all__ = [
+    'senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152',
+    'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnet50_fc512'
+]
+"""
+Code imported from https://github.com/Cadene/pretrained-models.pytorch
+"""
+
+pretrained_settings = {
+    'senet154': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnet50': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnet101': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnet152': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnext50_32x4d': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+    'se_resnext101_32x4d': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
+}
+
+
+class SEModule(nn.Module):
+
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            channels, channels // reduction, kernel_size=1, padding=0
+        )
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(
+            channels // reduction, channels, kernel_size=1, padding=0
+        )
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return module_input * x
+
+
+class Bottleneck(nn.Module):
+    """
+    Base class for bottlenecks that implements `forward()` method.
+    """
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out = self.se_module(out) + residual
+        out = self.relu(out)
+
+        return out
+
+
+class SEBottleneck(Bottleneck):
+    """
+    Bottleneck for SENet154.
+    """
+    expansion = 4
+
+    def __init__(
+        self, inplanes, planes, groups, reduction, stride=1, downsample=None
+    ):
+        super(SEBottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes * 2)
+        self.conv2 = nn.Conv2d(
+            planes * 2,
+            planes * 4,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            groups=groups,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes * 4)
+        self.conv3 = nn.Conv2d(
+            planes * 4, planes * 4, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.se_module = SEModule(planes * 4, reduction=reduction)
+        self.downsample = downsample
+        self.stride = stride
+
+
+class SEResNetBottleneck(Bottleneck):
+    """
+    ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
+    implementation and uses `stride=stride` in `conv1` and not in `conv2`
+    (the latter is used in the torchvision implementation of ResNet).
+    """
+    expansion = 4
+
+    def __init__(
+        self, inplanes, planes, groups, reduction, stride=1, downsample=None
+    ):
+        super(SEResNetBottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(
+            inplanes, planes, kernel_size=1, bias=False, stride=stride
+        )
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(
+            planes,
+            planes,
+            kernel_size=3,
+            padding=1,
+            groups=groups,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.se_module = SEModule(planes * 4, reduction=reduction)
+        self.downsample = downsample
+        self.stride = stride
+
+
+class SEResNeXtBottleneck(Bottleneck):
+    """ResNeXt bottleneck type C with a Squeeze-and-Excitation module"""
+    expansion = 4
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        groups,
+        reduction,
+        stride=1,
+        downsample=None,
+        base_width=4
+    ):
+        super(SEResNeXtBottleneck, self).__init__()
+        width = int(math.floor(planes * (base_width/64.)) * groups)
+        self.conv1 = nn.Conv2d(
+            inplanes, width, kernel_size=1, bias=False, stride=1
+        )
+        self.bn1 = nn.BatchNorm2d(width)
+        self.conv2 = nn.Conv2d(
+            width,
+            width,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            groups=groups,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.se_module = SEModule(planes * 4, reduction=reduction)
+        self.downsample = downsample
+        self.stride = stride
+
+
+class SENet(nn.Module):
+    """Squeeze-and-excitation network.
+    
+    Reference:
+        Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.
+
+    Public keys:
+        - ``senet154``: SENet154.
+        - ``se_resnet50``: ResNet50 + SE.
+        - ``se_resnet101``: ResNet101 + SE.
+        - ``se_resnet152``: ResNet152 + SE.
+        - ``se_resnext50_32x4d``: ResNeXt50 (groups=32, width=4) + SE.
+        - ``se_resnext101_32x4d``: ResNeXt101 (groups=32, width=4) + SE.
+        - ``se_resnet50_fc512``: (ResNet50 + SE) + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        block,
+        layers,
+        groups,
+        reduction,
+        dropout_p=0.2,
+        inplanes=128,
+        input_3x3=True,
+        downsample_kernel_size=3,
+        downsample_padding=1,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    ):
+        """
+        Parameters
+        ----------
+        block (nn.Module): Bottleneck class.
+            - For SENet154: SEBottleneck
+            - For SE-ResNet models: SEResNetBottleneck
+            - For SE-ResNeXt models:  SEResNeXtBottleneck
+        layers (list of ints): Number of residual blocks for 4 layers of the
+            network (layer1...layer4).
+        groups (int): Number of groups for the 3x3 convolution in each
+            bottleneck block.
+            - For SENet154: 64
+            - For SE-ResNet models: 1
+            - For SE-ResNeXt models:  32
+        reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
+            - For all models: 16
+        dropout_p (float or None): Drop probability for the Dropout layer.
+            If `None` the Dropout layer is not used.
+            - For SENet154: 0.2
+            - For SE-ResNet models: None
+            - For SE-ResNeXt models: None
+        inplanes (int):  Number of input channels for layer1.
+            - For SENet154: 128
+            - For SE-ResNet models: 64
+            - For SE-ResNeXt models: 64
+        input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
+            a single 7x7 convolution in layer0.
+            - For SENet154: True
+            - For SE-ResNet models: False
+            - For SE-ResNeXt models: False
+        downsample_kernel_size (int): Kernel size for downsampling convolutions
+            in layer2, layer3 and layer4.
+            - For SENet154: 3
+            - For SE-ResNet models: 1
+            - For SE-ResNeXt models: 1
+        downsample_padding (int): Padding for downsampling convolutions in
+            layer2, layer3 and layer4.
+            - For SENet154: 1
+            - For SE-ResNet models: 0
+            - For SE-ResNeXt models: 0
+        num_classes (int): Number of outputs in `classifier` layer.
+        """
+        super(SENet, self).__init__()
+        self.inplanes = inplanes
+        self.loss = loss
+
+        if input_3x3:
+            layer0_modules = [
+                (
+                    'conv1',
+                    nn.Conv2d(3, 64, 3, stride=2, padding=1, bias=False)
+                ),
+                ('bn1', nn.BatchNorm2d(64)),
+                ('relu1', nn.ReLU(inplace=True)),
+                (
+                    'conv2',
+                    nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)
+                ),
+                ('bn2', nn.BatchNorm2d(64)),
+                ('relu2', nn.ReLU(inplace=True)),
+                (
+                    'conv3',
+                    nn.Conv2d(
+                        64, inplanes, 3, stride=1, padding=1, bias=False
+                    )
+                ),
+                ('bn3', nn.BatchNorm2d(inplanes)),
+                ('relu3', nn.ReLU(inplace=True)),
+            ]
+        else:
+            layer0_modules = [
+                (
+                    'conv1',
+                    nn.Conv2d(
+                        3,
+                        inplanes,
+                        kernel_size=7,
+                        stride=2,
+                        padding=3,
+                        bias=False
+                    )
+                ),
+                ('bn1', nn.BatchNorm2d(inplanes)),
+                ('relu1', nn.ReLU(inplace=True)),
+            ]
+        # To preserve compatibility with Caffe weights `ceil_mode=True`
+        # is used instead of `padding=1`.
+        layer0_modules.append(
+            ('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True))
+        )
+        self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
+        self.layer1 = self._make_layer(
+            block,
+            planes=64,
+            blocks=layers[0],
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=1,
+            downsample_padding=0
+        )
+        self.layer2 = self._make_layer(
+            block,
+            planes=128,
+            blocks=layers[1],
+            stride=2,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+        self.layer3 = self._make_layer(
+            block,
+            planes=256,
+            blocks=layers[2],
+            stride=2,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+        self.layer4 = self._make_layer(
+            block,
+            planes=512,
+            blocks=layers[3],
+            stride=last_stride,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = self._construct_fc_layer(
+            fc_dims, 512 * block.expansion, dropout_p
+        )
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+    def _make_layer(
+        self,
+        block,
+        planes,
+        blocks,
+        groups,
+        reduction,
+        stride=1,
+        downsample_kernel_size=1,
+        downsample_padding=0
+    ):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=downsample_kernel_size,
+                    stride=stride,
+                    padding=downsample_padding,
+                    bias=False
+                ),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes, planes, groups, reduction, stride, downsample
+            )
+        )
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups, reduction))
+
+        return nn.Sequential(*layers)
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """
+        Construct fully connected layer
+
+        - fc_dims (list or tuple): dimensions of fc layers, if None,
+                                   no fc layers are constructed
+        - input_dim (int): input dimension
+        - dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def featuremaps(self, x):
+        x = self.layer0(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def senet154(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEBottleneck,
+        layers=[3, 8, 36, 3],
+        groups=64,
+        reduction=16,
+        dropout_p=0.2,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['senet154']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 4, 6, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet50']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 4, 6, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=1,
+        fc_dims=[512],
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet50']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 4, 23, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet101']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNetBottleneck,
+        layers=[3, 8, 36, 3],
+        groups=1,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnet152']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNeXtBottleneck,
+        layers=[3, 4, 6, 3],
+        groups=32,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnext50_32x4d']['imagenet']['url'
+                                                                          ]
+        init_pretrained_weights(model, model_url)
+    return model
+
+
+def se_resnext101_32x4d(
+    num_classes, loss='softmax', pretrained=True, **kwargs
+):
+    model = SENet(
+        num_classes=num_classes,
+        loss=loss,
+        block=SEResNeXtBottleneck,
+        layers=[3, 4, 23, 3],
+        groups=32,
+        reduction=16,
+        dropout_p=None,
+        inplanes=64,
+        input_3x3=False,
+        downsample_kernel_size=1,
+        downsample_padding=0,
+        last_stride=2,
+        fc_dims=None,
+        **kwargs
+    )
+    if pretrained:
+        model_url = pretrained_settings['se_resnext101_32x4d']['imagenet'][
+            'url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/torchreid/models/shufflenet.py b/pfe/torchreid/models/shufflenet.py
new file mode 100644
index 0000000..bc4d34f
--- /dev/null
+++ b/pfe/torchreid/models/shufflenet.py
@@ -0,0 +1,198 @@
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ['shufflenet']
+
+model_urls = {
+    # training epoch = 90, top1 = 61.8
+    'imagenet':
+    'https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrgfiJO9kiidz9fZM',
+}
+
+
+class ChannelShuffle(nn.Module):
+
+    def __init__(self, num_groups):
+        super(ChannelShuffle, self).__init__()
+        self.g = num_groups
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        n = c // self.g
+        # reshape
+        x = x.view(b, self.g, n, h, w)
+        # transpose
+        x = x.permute(0, 2, 1, 3, 4).contiguous()
+        # flatten
+        x = x.view(b, c, h, w)
+        return x
+
+
+class Bottleneck(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        stride,
+        num_groups,
+        group_conv1x1=True
+    ):
+        super(Bottleneck, self).__init__()
+        assert stride in [1, 2], 'Warning: stride must be either 1 or 2'
+        self.stride = stride
+        mid_channels = out_channels // 4
+        if stride == 2:
+            out_channels -= in_channels
+        # group conv is not applied to first conv1x1 at stage 2
+        num_groups_conv1x1 = num_groups if group_conv1x1 else 1
+        self.conv1 = nn.Conv2d(
+            in_channels,
+            mid_channels,
+            1,
+            groups=num_groups_conv1x1,
+            bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(mid_channels)
+        self.shuffle1 = ChannelShuffle(num_groups)
+        self.conv2 = nn.Conv2d(
+            mid_channels,
+            mid_channels,
+            3,
+            stride=stride,
+            padding=1,
+            groups=mid_channels,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(mid_channels)
+        self.conv3 = nn.Conv2d(
+            mid_channels, out_channels, 1, groups=num_groups, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(out_channels)
+        if stride == 2:
+            self.shortcut = nn.AvgPool2d(3, stride=2, padding=1)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.shuffle1(out)
+        out = self.bn2(self.conv2(out))
+        out = self.bn3(self.conv3(out))
+        if self.stride == 2:
+            res = self.shortcut(x)
+            out = F.relu(torch.cat([res, out], 1))
+        else:
+            out = F.relu(x + out)
+        return out
+
+
+# configuration of (num_groups: #out_channels) based on Table 1 in the paper
+cfg = {
+    1: [144, 288, 576],
+    2: [200, 400, 800],
+    3: [240, 480, 960],
+    4: [272, 544, 1088],
+    8: [384, 768, 1536],
+}
+
+
+class ShuffleNet(nn.Module):
+    """ShuffleNet.
+
+    Reference:
+        Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural
+        Network for Mobile Devices. CVPR 2018.
+
+    Public keys:
+        - ``shufflenet``: ShuffleNet (groups=3).
+    """
+
+    def __init__(self, num_classes, loss='softmax', num_groups=3, **kwargs):
+        super(ShuffleNet, self).__init__()
+        self.loss = loss
+
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 24, 3, stride=2, padding=1, bias=False),
+            nn.BatchNorm2d(24),
+            nn.ReLU(),
+            nn.MaxPool2d(3, stride=2, padding=1),
+        )
+
+        self.stage2 = nn.Sequential(
+            Bottleneck(
+                24, cfg[num_groups][0], 2, num_groups, group_conv1x1=False
+            ),
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
+        )
+
+        self.stage3 = nn.Sequential(
+            Bottleneck(cfg[num_groups][0], cfg[num_groups][1], 2, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
+        )
+
+        self.stage4 = nn.Sequential(
+            Bottleneck(cfg[num_groups][1], cfg[num_groups][2], 2, num_groups),
+            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
+            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
+            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
+        )
+
+        self.classifier = nn.Linear(cfg[num_groups][2], num_classes)
+        self.feat_dim = cfg[num_groups][2]
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = F.avg_pool2d(x, x.size()[2:]).view(x.size(0), -1)
+
+        if not self.training:
+            return x
+
+        y = self.classifier(x)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, x
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def shufflenet(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNet(num_classes, loss, **kwargs)
+    if pretrained:
+        # init_pretrained_weights(model, model_urls['imagenet'])
+        import warnings
+        warnings.warn(
+            'The imagenet pretrained weights need to be manually downloaded from {}'
+            .format(model_urls['imagenet'])
+        )
+    return model
diff --git a/pfe/torchreid/models/shufflenetv2.py b/pfe/torchreid/models/shufflenetv2.py
new file mode 100644
index 0000000..3ff879e
--- /dev/null
+++ b/pfe/torchreid/models/shufflenetv2.py
@@ -0,0 +1,262 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import torch
+import torch.utils.model_zoo as model_zoo
+from torch import nn
+
+__all__ = [
+    'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5',
+    'shufflenet_v2_x2_0'
+]
+
+model_urls = {
+    'shufflenetv2_x0.5':
+    'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
+    'shufflenetv2_x1.0':
+    'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
+    'shufflenetv2_x1.5': None,
+    'shufflenetv2_x2.0': None,
+}
+
+
+def channel_shuffle(x, groups):
+    batchsize, num_channels, height, width = x.data.size()
+    channels_per_group = num_channels // groups
+
+    # reshape
+    x = x.view(batchsize, groups, channels_per_group, height, width)
+
+    x = torch.transpose(x, 1, 2).contiguous()
+
+    # flatten
+    x = x.view(batchsize, -1, height, width)
+
+    return x
+
+
+class InvertedResidual(nn.Module):
+
+    def __init__(self, inp, oup, stride):
+        super(InvertedResidual, self).__init__()
+
+        if not (1 <= stride <= 3):
+            raise ValueError('illegal stride value')
+        self.stride = stride
+
+        branch_features = oup // 2
+        assert (self.stride != 1) or (inp == branch_features << 1)
+
+        if self.stride > 1:
+            self.branch1 = nn.Sequential(
+                self.depthwise_conv(
+                    inp, inp, kernel_size=3, stride=self.stride, padding=1
+                ),
+                nn.BatchNorm2d(inp),
+                nn.Conv2d(
+                    inp,
+                    branch_features,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    bias=False
+                ),
+                nn.BatchNorm2d(branch_features),
+                nn.ReLU(inplace=True),
+            )
+
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(
+                inp if (self.stride > 1) else branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+            self.depthwise_conv(
+                branch_features,
+                branch_features,
+                kernel_size=3,
+                stride=self.stride,
+                padding=1
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.Conv2d(
+                branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+        )
+
+    @staticmethod
+    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
+        return nn.Conv2d(
+            i, o, kernel_size, stride, padding, bias=bias, groups=i
+        )
+
+    def forward(self, x):
+        if self.stride == 1:
+            x1, x2 = x.chunk(2, dim=1)
+            out = torch.cat((x1, self.branch2(x2)), dim=1)
+        else:
+            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+
+        out = channel_shuffle(out, 2)
+
+        return out
+
+
+class ShuffleNetV2(nn.Module):
+    """ShuffleNetV2.
+    
+    Reference:
+        Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
+
+    Public keys:
+        - ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.
+        - ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.
+        - ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.
+        - ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.
+    """
+
+    def __init__(
+        self, num_classes, loss, stages_repeats, stages_out_channels, **kwargs
+    ):
+        super(ShuffleNetV2, self).__init__()
+        self.loss = loss
+
+        if len(stages_repeats) != 3:
+            raise ValueError(
+                'expected stages_repeats as list of 3 positive ints'
+            )
+        if len(stages_out_channels) != 5:
+            raise ValueError(
+                'expected stages_out_channels as list of 5 positive ints'
+            )
+        self._stage_out_channels = stages_out_channels
+
+        input_channels = 3
+        output_channels = self._stage_out_channels[0]
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+        input_channels = output_channels
+
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
+        for name, repeats, output_channels in zip(
+            stage_names, stages_repeats, self._stage_out_channels[1:]
+        ):
+            seq = [InvertedResidual(input_channels, output_channels, 2)]
+            for i in range(repeats - 1):
+                seq.append(
+                    InvertedResidual(output_channels, output_channels, 1)
+                )
+            setattr(self, name, nn.Sequential(*seq))
+            input_channels = output_channels
+
+        output_channels = self._stage_out_channels[-1]
+        self.conv5 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+        self.classifier = nn.Linear(output_channels, num_classes)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    if model_url is None:
+        import warnings
+        warnings.warn(
+            'ImageNet pretrained weights are unavailable for this model'
+        )
+        return
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x0.5'])
+    return model
+
+
+def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x1.0'])
+    return model
+
+
+def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x1.5'])
+    return model
+
+
+def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(
+        num_classes, loss, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x2.0'])
+    return model
diff --git a/pfe/torchreid/models/squeezenet.py b/pfe/torchreid/models/squeezenet.py
new file mode 100644
index 0000000..83e8dc9
--- /dev/null
+++ b/pfe/torchreid/models/squeezenet.py
@@ -0,0 +1,236 @@
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import division, absolute_import
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512']
+
+model_urls = {
+    'squeezenet1_0':
+    'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
+    'squeezenet1_1':
+    'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth',
+}
+
+
+class Fire(nn.Module):
+
+    def __init__(
+        self, inplanes, squeeze_planes, expand1x1_planes, expand3x3_planes
+    ):
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(
+            squeeze_planes, expand1x1_planes, kernel_size=1
+        )
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(
+            squeeze_planes, expand3x3_planes, kernel_size=3, padding=1
+        )
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.squeeze_activation(self.squeeze(x))
+        return torch.cat(
+            [
+                self.expand1x1_activation(self.expand1x1(x)),
+                self.expand3x3_activation(self.expand3x3(x))
+            ], 1
+        )
+
+
+class SqueezeNet(nn.Module):
+    """SqueezeNet.
+
+    Reference:
+        Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
+        and< 0.5 MB model size. arXiv:1602.07360.
+
+    Public keys:
+        - ``squeezenet1_0``: SqueezeNet (version=1.0).
+        - ``squeezenet1_1``: SqueezeNet (version=1.1).
+        - ``squeezenet1_0_fc512``: SqueezeNet (version=1.0) + FC.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        loss,
+        version=1.0,
+        fc_dims=None,
+        dropout_p=None,
+        **kwargs
+    ):
+        super(SqueezeNet, self).__init__()
+        self.loss = loss
+        self.feature_dim = 512
+
+        if version not in [1.0, 1.1]:
+            raise ValueError(
+                'Unsupported SqueezeNet version {version}:'
+                '1.0 or 1.1 expected'.format(version=version)
+            )
+
+        if version == 1.0:
+            self.features = nn.Sequential(
+                nn.Conv2d(3, 96, kernel_size=7, stride=2),
+                nn.ReLU(inplace=True),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(96, 16, 64, 64),
+                Fire(128, 16, 64, 64),
+                Fire(128, 32, 128, 128),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(256, 32, 128, 128),
+                Fire(256, 48, 192, 192),
+                Fire(384, 48, 192, 192),
+                Fire(384, 64, 256, 256),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(512, 64, 256, 256),
+            )
+        else:
+            self.features = nn.Sequential(
+                nn.Conv2d(3, 64, kernel_size=3, stride=2),
+                nn.ReLU(inplace=True),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(64, 16, 64, 64),
+                Fire(128, 16, 64, 64),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(128, 32, 128, 128),
+                Fire(256, 32, 128, 128),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+                Fire(256, 48, 192, 192),
+                Fire(384, 48, 192, 192),
+                Fire(384, 64, 256, 256),
+                Fire(512, 64, 256, 256),
+            )
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = self._construct_fc_layer(fc_dims, 512, dropout_p)
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        f = self.features(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url, map_location=None)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def squeezenet1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SqueezeNet(
+        num_classes, loss, version=1.0, fc_dims=None, dropout_p=None, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['squeezenet1_0'])
+    return model
+
+
+def squeezenet1_0_fc512(
+    num_classes, loss='softmax', pretrained=True, **kwargs
+):
+    model = SqueezeNet(
+        num_classes,
+        loss,
+        version=1.0,
+        fc_dims=[512],
+        dropout_p=None,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['squeezenet1_0'])
+    return model
+
+
+def squeezenet1_1(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = SqueezeNet(
+        num_classes, loss, version=1.1, fc_dims=None, dropout_p=None, **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['squeezenet1_1'])
+    return model
diff --git a/pfe/torchreid/models/xception.py b/pfe/torchreid/models/xception.py
new file mode 100644
index 0000000..43db4ab
--- /dev/null
+++ b/pfe/torchreid/models/xception.py
@@ -0,0 +1,344 @@
+from __future__ import division, absolute_import
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['xception']
+
+pretrained_settings = {
+    'xception': {
+        'imagenet': {
+            'url':
+            'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000,
+            'scale':
+            0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
+        }
+    }
+}
+
+
+class SeparableConv2d(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=False
+    ):
+        super(SeparableConv2d, self).__init__()
+
+        self.conv1 = nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups=in_channels,
+            bias=bias
+        )
+        self.pointwise = nn.Conv2d(
+            in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias
+        )
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.pointwise(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(
+        self,
+        in_filters,
+        out_filters,
+        reps,
+        strides=1,
+        start_with_relu=True,
+        grow_first=True
+    ):
+        super(Block, self).__init__()
+
+        if out_filters != in_filters or strides != 1:
+            self.skip = nn.Conv2d(
+                in_filters, out_filters, 1, stride=strides, bias=False
+            )
+            self.skipbn = nn.BatchNorm2d(out_filters)
+        else:
+            self.skip = None
+
+        self.relu = nn.ReLU(inplace=True)
+        rep = []
+
+        filters = in_filters
+        if grow_first:
+            rep.append(self.relu)
+            rep.append(
+                SeparableConv2d(
+                    in_filters,
+                    out_filters,
+                    3,
+                    stride=1,
+                    padding=1,
+                    bias=False
+                )
+            )
+            rep.append(nn.BatchNorm2d(out_filters))
+            filters = out_filters
+
+        for i in range(reps - 1):
+            rep.append(self.relu)
+            rep.append(
+                SeparableConv2d(
+                    filters, filters, 3, stride=1, padding=1, bias=False
+                )
+            )
+            rep.append(nn.BatchNorm2d(filters))
+
+        if not grow_first:
+            rep.append(self.relu)
+            rep.append(
+                SeparableConv2d(
+                    in_filters,
+                    out_filters,
+                    3,
+                    stride=1,
+                    padding=1,
+                    bias=False
+                )
+            )
+            rep.append(nn.BatchNorm2d(out_filters))
+
+        if not start_with_relu:
+            rep = rep[1:]
+        else:
+            rep[0] = nn.ReLU(inplace=False)
+
+        if strides != 1:
+            rep.append(nn.MaxPool2d(3, strides, 1))
+        self.rep = nn.Sequential(*rep)
+
+    def forward(self, inp):
+        x = self.rep(inp)
+
+        if self.skip is not None:
+            skip = self.skip(inp)
+            skip = self.skipbn(skip)
+        else:
+            skip = inp
+
+        x += skip
+        return x
+
+
+class Xception(nn.Module):
+    """Xception.
+    
+    Reference:
+        Chollet. Xception: Deep Learning with Depthwise
+        Separable Convolutions. CVPR 2017.
+
+    Public keys:
+        - ``xception``: Xception.
+    """
+
+    def __init__(
+        self, num_classes, loss, fc_dims=None, dropout_p=None, **kwargs
+    ):
+        super(Xception, self).__init__()
+        self.loss = loss
+
+        self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+
+        self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
+        self.bn2 = nn.BatchNorm2d(64)
+
+        self.block1 = Block(
+            64, 128, 2, 2, start_with_relu=False, grow_first=True
+        )
+        self.block2 = Block(
+            128, 256, 2, 2, start_with_relu=True, grow_first=True
+        )
+        self.block3 = Block(
+            256, 728, 2, 2, start_with_relu=True, grow_first=True
+        )
+
+        self.block4 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block5 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block6 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block7 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+
+        self.block8 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block9 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block10 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+        self.block11 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True
+        )
+
+        self.block12 = Block(
+            728, 1024, 2, 2, start_with_relu=True, grow_first=False
+        )
+
+        self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
+        self.bn3 = nn.BatchNorm2d(1536)
+
+        self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
+        self.bn4 = nn.BatchNorm2d(2048)
+
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.feature_dim = 2048
+        self.fc = self._construct_fc_layer(fc_dims, 2048, dropout_p)
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+        self._init_params()
+
+    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
+        """Constructs fully connected layer.
+
+        Args:
+            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
+            input_dim (int): input dimension
+            dropout_p (float): dropout probability, if None, dropout is unused
+        """
+        if fc_dims is None:
+            self.feature_dim = input_dim
+            return None
+
+        assert isinstance(
+            fc_dims, (list, tuple)
+        ), 'fc_dims must be either list or tuple, but got {}'.format(
+            type(fc_dims)
+        )
+
+        layers = []
+        for dim in fc_dims:
+            layers.append(nn.Linear(input_dim, dim))
+            layers.append(nn.BatchNorm1d(dim))
+            layers.append(nn.ReLU(inplace=True))
+            if dropout_p is not None:
+                layers.append(nn.Dropout(p=dropout_p))
+            input_dim = dim
+
+        self.feature_dim = fc_dims[-1]
+
+        return nn.Sequential(*layers)
+
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu'
+                )
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def featuremaps(self, input):
+        x = self.conv1(input)
+        x = self.bn1(x)
+        x = F.relu(x, inplace=True)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = F.relu(x, inplace=True)
+
+        x = self.block1(x)
+        x = self.block2(x)
+        x = self.block3(x)
+        x = self.block4(x)
+        x = self.block5(x)
+        x = self.block6(x)
+        x = self.block7(x)
+        x = self.block8(x)
+        x = self.block9(x)
+        x = self.block10(x)
+        x = self.block11(x)
+        x = self.block12(x)
+
+        x = self.conv3(x)
+        x = self.bn3(x)
+        x = F.relu(x, inplace=True)
+
+        x = self.conv4(x)
+        x = self.bn4(x)
+        x = F.relu(x, inplace=True)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+
+        if self.fc is not None:
+            v = self.fc(v)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError('Unsupported loss: {}'.format(self.loss))
+
+
+def init_pretrained_weights(model, model_url):
+    """Initialize models with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {
+        k: v
+        for k, v in pretrain_dict.items()
+        if k in model_dict and model_dict[k].size() == v.size()
+    }
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def xception(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = Xception(num_classes, loss, fc_dims=None, dropout_p=None, **kwargs)
+    if pretrained:
+        model_url = pretrained_settings['xception']['imagenet']['url']
+        init_pretrained_weights(model, model_url)
+    return model
diff --git a/pfe/torchreid/optim/__init__.py b/pfe/torchreid/optim/__init__.py
new file mode 100644
index 0000000..1813e46
--- /dev/null
+++ b/pfe/torchreid/optim/__init__.py
@@ -0,0 +1,4 @@
+from __future__ import absolute_import
+
+from .optimizer import build_optimizer
+from .lr_scheduler import build_lr_scheduler
diff --git a/pfe/torchreid/optim/lr_scheduler.py b/pfe/torchreid/optim/lr_scheduler.py
new file mode 100644
index 0000000..d60bd1d
--- /dev/null
+++ b/pfe/torchreid/optim/lr_scheduler.py
@@ -0,0 +1,68 @@
+from __future__ import print_function, absolute_import
+import torch
+
+AVAI_SCH = ['single_step', 'multi_step', 'cosine']
+
+
+def build_lr_scheduler(
+    optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1
+):
+    """A function wrapper for building a learning rate scheduler.
+
+    Args:
+        optimizer (Optimizer): an Optimizer.
+        lr_scheduler (str, optional): learning rate scheduler method. Default is single_step.
+        stepsize (int or list, optional): step size to decay learning rate. When ``lr_scheduler``
+            is "single_step", ``stepsize`` should be an integer. When ``lr_scheduler`` is
+            "multi_step", ``stepsize`` is a list. Default is 1.
+        gamma (float, optional): decay rate. Default is 0.1.
+        max_epoch (int, optional): maximum epoch (for cosine annealing). Default is 1.
+
+    Examples::
+        >>> # Decay learning rate by every 20 epochs.
+        >>> scheduler = torchreid.optim.build_lr_scheduler(
+        >>>     optimizer, lr_scheduler='single_step', stepsize=20
+        >>> )
+        >>> # Decay learning rate at 30, 50 and 55 epochs.
+        >>> scheduler = torchreid.optim.build_lr_scheduler(
+        >>>     optimizer, lr_scheduler='multi_step', stepsize=[30, 50, 55]
+        >>> )
+    """
+    if lr_scheduler not in AVAI_SCH:
+        raise ValueError(
+            'Unsupported scheduler: {}. Must be one of {}'.format(
+                lr_scheduler, AVAI_SCH
+            )
+        )
+
+    if lr_scheduler == 'single_step':
+        if isinstance(stepsize, list):
+            stepsize = stepsize[-1]
+
+        if not isinstance(stepsize, int):
+            raise TypeError(
+                'For single_step lr_scheduler, stepsize must '
+                'be an integer, but got {}'.format(type(stepsize))
+            )
+
+        scheduler = torch.optim.lr_scheduler.StepLR(
+            optimizer, step_size=stepsize, gamma=gamma
+        )
+
+    elif lr_scheduler == 'multi_step':
+        if not isinstance(stepsize, list):
+            raise TypeError(
+                'For multi_step lr_scheduler, stepsize must '
+                'be a list, but got {}'.format(type(stepsize))
+            )
+
+        scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=stepsize, gamma=gamma
+        )
+
+    elif lr_scheduler == 'cosine':
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+            optimizer, float(max_epoch)
+        )
+
+    return scheduler
diff --git a/pfe/torchreid/optim/optimizer.py b/pfe/torchreid/optim/optimizer.py
new file mode 100644
index 0000000..f57b03a
--- /dev/null
+++ b/pfe/torchreid/optim/optimizer.py
@@ -0,0 +1,157 @@
+from __future__ import print_function, absolute_import
+import warnings
+import torch
+import torch.nn as nn
+
+from .radam import RAdam
+
+AVAI_OPTIMS = ['adam', 'amsgrad', 'sgd', 'rmsprop', 'radam']
+
+
+def build_optimizer(
+    model,
+    optim='adam',
+    lr=0.0003,
+    weight_decay=5e-04,
+    momentum=0.9,
+    sgd_dampening=0,
+    sgd_nesterov=False,
+    rmsprop_alpha=0.99,
+    adam_beta1=0.9,
+    adam_beta2=0.99,
+    staged_lr=False,
+    new_layers='',
+    base_lr_mult=0.1
+):
+    """A function wrapper for building an optimizer.
+
+    Args:
+        model (nn.Module): model.
+        optim (str, optional): optimizer. Default is "adam".
+        lr (float, optional): learning rate. Default is 0.0003.
+        weight_decay (float, optional): weight decay (L2 penalty). Default is 5e-04.
+        momentum (float, optional): momentum factor in sgd. Default is 0.9,
+        sgd_dampening (float, optional): dampening for momentum. Default is 0.
+        sgd_nesterov (bool, optional): enables Nesterov momentum. Default is False.
+        rmsprop_alpha (float, optional): smoothing constant for rmsprop. Default is 0.99.
+        adam_beta1 (float, optional): beta-1 value in adam. Default is 0.9.
+        adam_beta2 (float, optional): beta-2 value in adam. Default is 0.99,
+        staged_lr (bool, optional): uses different learning rates for base and new layers. Base
+            layers are pretrained layers while new layers are randomly initialized, e.g. the
+            identity classification layer. Enabling ``staged_lr`` can allow the base layers to
+            be trained with a smaller learning rate determined by ``base_lr_mult``, while the new
+            layers will take the ``lr``. Default is False.
+        new_layers (str or list): attribute names in ``model``. Default is empty.
+        base_lr_mult (float, optional): learning rate multiplier for base layers. Default is 0.1.
+
+    Examples::
+        >>> # A normal optimizer can be built by
+        >>> optimizer = torchreid.optim.build_optimizer(model, optim='sgd', lr=0.01)
+        >>> # If you want to use a smaller learning rate for pretrained layers
+        >>> # and the attribute name for the randomly initialized layer is 'classifier',
+        >>> # you can do
+        >>> optimizer = torchreid.optim.build_optimizer(
+        >>>     model, optim='sgd', lr=0.01, staged_lr=True,
+        >>>     new_layers='classifier', base_lr_mult=0.1
+        >>> )
+        >>> # Now the `classifier` has learning rate 0.01 but the base layers
+        >>> # have learning rate 0.01 * 0.1.
+        >>> # new_layers can also take multiple attribute names. Say the new layers
+        >>> # are 'fc' and 'classifier', you can do
+        >>> optimizer = torchreid.optim.build_optimizer(
+        >>>     model, optim='sgd', lr=0.01, staged_lr=True,
+        >>>     new_layers=['fc', 'classifier'], base_lr_mult=0.1
+        >>> )
+    """
+    if optim not in AVAI_OPTIMS:
+        raise ValueError(
+            'Unsupported optim: {}. Must be one of {}'.format(
+                optim, AVAI_OPTIMS
+            )
+        )
+
+    if not isinstance(model, nn.Module):
+        raise TypeError(
+            'model given to build_optimizer must be an instance of nn.Module'
+        )
+
+    if staged_lr:
+        if isinstance(new_layers, str):
+            if new_layers is None:
+                warnings.warn(
+                    'new_layers is empty, therefore, staged_lr is useless'
+                )
+            new_layers = [new_layers]
+
+        if isinstance(model, nn.DataParallel):
+            model = model.module
+
+        base_params = []
+        base_layers = []
+        new_params = []
+
+        for name, module in model.named_children():
+            if name in new_layers:
+                new_params += [p for p in module.parameters()]
+            else:
+                base_params += [p for p in module.parameters()]
+                base_layers.append(name)
+
+        param_groups = [
+            {
+                'params': base_params,
+                'lr': lr * base_lr_mult
+            },
+            {
+                'params': new_params
+            },
+        ]
+
+    else:
+        param_groups = model.parameters()
+
+    if optim == 'adam':
+        optimizer = torch.optim.Adam(
+            param_groups,
+            lr=lr,
+            weight_decay=weight_decay,
+            betas=(adam_beta1, adam_beta2),
+        )
+
+    elif optim == 'amsgrad':
+        optimizer = torch.optim.Adam(
+            param_groups,
+            lr=lr,
+            weight_decay=weight_decay,
+            betas=(adam_beta1, adam_beta2),
+            amsgrad=True,
+        )
+
+    elif optim == 'sgd':
+        optimizer = torch.optim.SGD(
+            param_groups,
+            lr=lr,
+            momentum=momentum,
+            weight_decay=weight_decay,
+            dampening=sgd_dampening,
+            nesterov=sgd_nesterov,
+        )
+
+    elif optim == 'rmsprop':
+        optimizer = torch.optim.RMSprop(
+            param_groups,
+            lr=lr,
+            momentum=momentum,
+            weight_decay=weight_decay,
+            alpha=rmsprop_alpha,
+        )
+
+    elif optim == 'radam':
+        optimizer = RAdam(
+            param_groups,
+            lr=lr,
+            weight_decay=weight_decay,
+            betas=(adam_beta1, adam_beta2)
+        )
+
+    return optimizer
diff --git a/pfe/torchreid/optim/radam.py b/pfe/torchreid/optim/radam.py
new file mode 100644
index 0000000..f066c57
--- /dev/null
+++ b/pfe/torchreid/optim/radam.py
@@ -0,0 +1,330 @@
+"""
+Imported from: https://github.com/LiyuanLucasLiu/RAdam
+
+Paper: https://arxiv.org/abs/1908.03265
+
+@article{liu2019radam,
+  title={On the Variance of the Adaptive Learning Rate and Beyond},
+  author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
+  journal={arXiv preprint arXiv:1908.03265},
+  year={2019}
+}
+"""
+from __future__ import print_function, absolute_import
+import math
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class RAdam(Optimizer):
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.999),
+        eps=1e-8,
+        weight_decay=0,
+        degenerated_to_sgd=True
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 0: {}".format(betas[0])
+            )
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 1: {}".format(betas[1])
+            )
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        self.buffer = [[None, None, None] for ind in range(10)]
+        super(RAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(RAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'RAdam does not support sparse gradients'
+                    )
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
+                        p_data_fp32
+                    )
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                buffered = self.buffer[int(state['step'] % 10)]
+                if state['step'] == buffered[0]:
+                    N_sma, step_size = buffered[1], buffered[2]
+                else:
+                    buffered[0] = state['step']
+                    beta2_t = beta2**state['step']
+                    N_sma_max = 2 / (1-beta2) - 1
+                    N_sma = N_sma_max - 2 * state['step'
+                                                  ] * beta2_t / (1-beta2_t)
+                    buffered[1] = N_sma
+
+                    # more conservative since it's an approximated value
+                    if N_sma >= 5:
+                        step_size = math.sqrt(
+                            (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
+                            (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
+                        ) / (1 - beta1**state['step'])
+                    elif self.degenerated_to_sgd:
+                        step_size = 1.0 / (1 - beta1**state['step'])
+                    else:
+                        step_size = -1
+                    buffered[2] = step_size
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(
+                        -step_size * group['lr'], exp_avg, denom
+                    )
+                    p.data.copy_(p_data_fp32)
+                elif step_size > 0:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
+                    p.data.copy_(p_data_fp32)
+
+        return loss
+
+
+class PlainRAdam(Optimizer):
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.999),
+        eps=1e-8,
+        weight_decay=0,
+        degenerated_to_sgd=True
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 0: {}".format(betas[0])
+            )
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 1: {}".format(betas[1])
+            )
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+
+        super(PlainRAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(PlainRAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'RAdam does not support sparse gradients'
+                    )
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
+                        p_data_fp32
+                    )
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                beta2_t = beta2**state['step']
+                N_sma_max = 2 / (1-beta2) - 1
+                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1-beta2_t)
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    step_size = group['lr'] * math.sqrt(
+                        (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
+                        (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
+                    ) / (1 - beta1**state['step'])
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+                    p.data.copy_(p_data_fp32)
+                elif self.degenerated_to_sgd:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(
+                            -group['weight_decay'] * group['lr'], p_data_fp32
+                        )
+                    step_size = group['lr'] / (1 - beta1**state['step'])
+                    p_data_fp32.add_(-step_size, exp_avg)
+                    p.data.copy_(p_data_fp32)
+
+        return loss
+
+
+class AdamW(Optimizer):
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.999),
+        eps=1e-8,
+        weight_decay=0,
+        warmup=0
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 0: {}".format(betas[0])
+            )
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 1: {}".format(betas[1])
+            )
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            warmup=warmup
+        )
+        super(AdamW, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(AdamW, self).__setstate__(state)
+
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'Adam does not support sparse gradients, please consider SparseAdam instead'
+                    )
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
+                        p_data_fp32
+                    )
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                denom = exp_avg_sq.sqrt().add_(group['eps'])
+                bias_correction1 = 1 - beta1**state['step']
+                bias_correction2 = 1 - beta2**state['step']
+
+                if group['warmup'] > state['step']:
+                    scheduled_lr = 1e-8 + state['step'] * group['lr'] / group[
+                        'warmup']
+                else:
+                    scheduled_lr = group['lr']
+
+                step_size = scheduled_lr * math.sqrt(
+                    bias_correction2
+                ) / bias_correction1
+
+                if group['weight_decay'] != 0:
+                    p_data_fp32.add_(
+                        -group['weight_decay'] * scheduled_lr, p_data_fp32
+                    )
+
+                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+
+                p.data.copy_(p_data_fp32)
+
+        return loss
diff --git a/pfe/torchreid/utils/__init__.py b/pfe/torchreid/utils/__init__.py
new file mode 100644
index 0000000..50167c3
--- /dev/null
+++ b/pfe/torchreid/utils/__init__.py
@@ -0,0 +1,10 @@
+from __future__ import absolute_import
+
+from .tools import *
+from .rerank import re_ranking
+from .loggers import *
+from .avgmeter import *
+from .reidtools import *
+from .torchtools import *
+from .model_complexity import compute_model_complexity
+from .feature_extractor import FeatureExtractor
diff --git a/pfe/torchreid/utils/avgmeter.py b/pfe/torchreid/utils/avgmeter.py
new file mode 100644
index 0000000..b62d26d
--- /dev/null
+++ b/pfe/torchreid/utils/avgmeter.py
@@ -0,0 +1,73 @@
+from __future__ import division, absolute_import
+from collections import defaultdict
+import torch
+
+__all__ = ['AverageMeter', 'MetricMeter']
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value.
+
+    Examples::
+        >>> # Initialize a meter to record loss
+        >>> losses = AverageMeter()
+        >>> # Update meter after every minibatch update
+        >>> losses.update(loss_value, batch_size)
+    """
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+class MetricMeter(object):
+    """A collection of metrics.
+
+    Source: https://github.com/KaiyangZhou/Dassl.pytorch
+
+    Examples::
+        >>> # 1. Create an instance of MetricMeter
+        >>> metric = MetricMeter()
+        >>> # 2. Update using a dictionary as input
+        >>> input_dict = {'loss_1': value_1, 'loss_2': value_2}
+        >>> metric.update(input_dict)
+        >>> # 3. Convert to string and print
+        >>> print(str(metric))
+    """
+
+    def __init__(self, delimiter='\t'):
+        self.meters = defaultdict(AverageMeter)
+        self.delimiter = delimiter
+
+    def update(self, input_dict):
+        if input_dict is None:
+            return
+
+        if not isinstance(input_dict, dict):
+            raise TypeError(
+                'Input to MetricMeter.update() must be a dictionary'
+            )
+
+        for k, v in input_dict.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            self.meters[k].update(v)
+
+    def __str__(self):
+        output_str = []
+        for name, meter in self.meters.items():
+            output_str.append(
+                '{} {:.4f} ({:.4f})'.format(name, meter.val, meter.avg)
+            )
+        return self.delimiter.join(output_str)
diff --git a/pfe/torchreid/utils/feature_extractor.py b/pfe/torchreid/utils/feature_extractor.py
new file mode 100644
index 0000000..858b2cc
--- /dev/null
+++ b/pfe/torchreid/utils/feature_extractor.py
@@ -0,0 +1,153 @@
+from __future__ import absolute_import
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+
+from torchreid.utils import (
+    check_isfile, load_pretrained_weights, compute_model_complexity
+)
+from torchreid.models import build_model
+
+
+class FeatureExtractor(object):
+    """A simple API for feature extraction.
+
+    FeatureExtractor can be used like a python function, which
+    accepts input of the following types:
+        - a list of strings (image paths)
+        - a list of numpy.ndarray each with shape (H, W, C)
+        - a single string (image path)
+        - a single numpy.ndarray with shape (H, W, C)
+        - a torch.Tensor with shape (B, C, H, W) or (C, H, W)
+
+    Returned is a torch tensor with shape (B, D) where D is the
+    feature dimension.
+
+    Args:
+        model_name (str): model name.
+        model_path (str): path to model weights.
+        image_size (sequence or int): image height and width.
+        pixel_mean (list): pixel mean for normalization.
+        pixel_std (list): pixel std for normalization.
+        pixel_norm (bool): whether to normalize pixels.
+        device (str): 'cpu' or 'cuda' (could be specific gpu devices).
+        verbose (bool): show model details.
+
+    Examples::
+
+        from torchreid.utils import FeatureExtractor
+
+        extractor = FeatureExtractor(
+            model_name='osnet_x1_0',
+            model_path='a/b/c/model.pth.tar',
+            device='cuda'
+        )
+
+        image_list = [
+            'a/b/c/image001.jpg',
+            'a/b/c/image002.jpg',
+            'a/b/c/image003.jpg',
+            'a/b/c/image004.jpg',
+            'a/b/c/image005.jpg'
+        ]
+
+        features = extractor(image_list)
+        print(features.shape) # output (5, 512)
+    """
+
+    def __init__(
+        self,
+        model_name='',
+        model_path='',
+        image_size=(256, 128),
+        pixel_mean=[0.485, 0.456, 0.406],
+        pixel_std=[0.229, 0.224, 0.225],
+        pixel_norm=True,
+        device='cuda',
+        verbose=True
+    ):
+        # Build model
+        model = build_model(
+            model_name,
+            num_classes=1,
+            pretrained=True,
+            use_gpu=device.startswith('cuda')
+        )
+        model.eval()
+
+        num_params, flops = compute_model_complexity(
+            model, (1, 3, image_size[0], image_size[1])
+        )
+
+        if verbose:
+            print('Model: {}'.format(model_name))
+            print('- params: {:,}'.format(num_params))
+            print('- flops: {:,}'.format(flops))
+
+        if model_path and check_isfile(model_path):
+            load_pretrained_weights(model, model_path)
+
+        # Build transform functions
+        transforms = []
+        transforms += [T.Resize(image_size)]
+        transforms += [T.ToTensor()]
+        if pixel_norm:
+            transforms += [T.Normalize(mean=pixel_mean, std=pixel_std)]
+        preprocess = T.Compose(transforms)
+
+        to_pil = T.ToPILImage()
+
+        device = torch.device(device)
+        model.to(device)
+
+        # Class attributes
+        self.model = model
+        self.preprocess = preprocess
+        self.to_pil = to_pil
+        self.device = device
+
+    def __call__(self, input):
+        if isinstance(input, list):
+            images = []
+
+            for element in input:
+                if isinstance(element, str):
+                    image = Image.open(element).convert('RGB')
+
+                elif isinstance(element, np.ndarray):
+                    image = self.to_pil(element)
+
+                else:
+                    raise TypeError(
+                        'Type of each element must belong to [str | numpy.ndarray]'
+                    )
+
+                image = self.preprocess(image)
+                images.append(image)
+
+            images = torch.stack(images, dim=0)
+            images = images.to(self.device)
+
+        elif isinstance(input, str):
+            image = Image.open(input).convert('RGB')
+            image = self.preprocess(image)
+            images = image.unsqueeze(0).to(self.device)
+
+        elif isinstance(input, np.ndarray):
+            image = self.to_pil(input)
+            image = self.preprocess(image)
+            images = image.unsqueeze(0).to(self.device)
+
+        elif isinstance(input, torch.Tensor):
+            if input.dim() == 3:
+                input = input.unsqueeze(0)
+            images = input.to(self.device)
+
+        else:
+            raise NotImplementedError
+
+        with torch.no_grad():
+            features = self.model(images)
+
+        return features
diff --git a/pfe/torchreid/utils/loggers.py b/pfe/torchreid/utils/loggers.py
new file mode 100644
index 0000000..f7fae3c
--- /dev/null
+++ b/pfe/torchreid/utils/loggers.py
@@ -0,0 +1,146 @@
+from __future__ import absolute_import
+import os
+import sys
+import os.path as osp
+
+from .tools import mkdir_if_missing
+
+__all__ = ['Logger', 'RankLogger']
+
+
+class Logger(object):
+    """Writes console output to external text file.
+
+    Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py>`_
+
+    Args:
+        fpath (str): directory to save logging file.
+
+    Examples::
+       >>> import sys
+       >>> import os
+       >>> import os.path as osp
+       >>> from torchreid.utils import Logger
+       >>> save_dir = 'log/resnet50-softmax-market1501'
+       >>> log_name = 'train.log'
+       >>> sys.stdout = Logger(osp.join(args.save_dir, log_name))
+    """
+
+    def __init__(self, fpath=None):
+        self.console = sys.stdout
+        self.file = None
+        if fpath is not None:
+            mkdir_if_missing(osp.dirname(fpath))
+            self.file = open(fpath, 'w')
+
+    def __del__(self):
+        self.close()
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, *args):
+        self.close()
+
+    def write(self, msg):
+        self.console.write(msg)
+        if self.file is not None:
+            self.file.write(msg)
+
+    def flush(self):
+        self.console.flush()
+        if self.file is not None:
+            self.file.flush()
+            os.fsync(self.file.fileno())
+
+    def close(self):
+        self.console.close()
+        if self.file is not None:
+            self.file.close()
+
+
+class RankLogger(object):
+    """Records the rank1 matching accuracy obtained for each
+    test dataset at specified evaluation steps and provides a function
+    to show the summarized results, which are convenient for analysis.
+
+    Args:
+        sources (str or list): source dataset name(s).
+        targets (str or list): target dataset name(s).
+
+    Examples::
+        >>> from torchreid.utils import RankLogger
+        >>> s = 'market1501'
+        >>> t = 'market1501'
+        >>> ranklogger = RankLogger(s, t)
+        >>> ranklogger.write(t, 10, 0.5)
+        >>> ranklogger.write(t, 20, 0.7)
+        >>> ranklogger.write(t, 30, 0.9)
+        >>> ranklogger.show_summary()
+        >>> # You will see:
+        >>> # => Show performance summary
+        >>> # market1501 (source)
+        >>> # - epoch 10   rank1 50.0%
+        >>> # - epoch 20   rank1 70.0%
+        >>> # - epoch 30   rank1 90.0%
+        >>> # If there are multiple test datasets
+        >>> t = ['market1501', 'dukemtmcreid']
+        >>> ranklogger = RankLogger(s, t)
+        >>> ranklogger.write(t[0], 10, 0.5)
+        >>> ranklogger.write(t[0], 20, 0.7)
+        >>> ranklogger.write(t[0], 30, 0.9)
+        >>> ranklogger.write(t[1], 10, 0.1)
+        >>> ranklogger.write(t[1], 20, 0.2)
+        >>> ranklogger.write(t[1], 30, 0.3)
+        >>> ranklogger.show_summary()
+        >>> # You can see:
+        >>> # => Show performance summary
+        >>> # market1501 (source)
+        >>> # - epoch 10   rank1 50.0%
+        >>> # - epoch 20   rank1 70.0%
+        >>> # - epoch 30   rank1 90.0%
+        >>> # dukemtmcreid (target)
+        >>> # - epoch 10   rank1 10.0%
+        >>> # - epoch 20   rank1 20.0%
+        >>> # - epoch 30   rank1 30.0%
+    """
+
+    def __init__(self, sources, targets):
+        self.sources = sources
+        self.targets = targets
+
+        if isinstance(self.sources, str):
+            self.sources = [self.sources]
+
+        if isinstance(self.targets, str):
+            self.targets = [self.targets]
+
+        self.logger = {
+            name: {
+                'epoch': [],
+                'rank1': []
+            }
+            for name in self.targets
+        }
+
+    def write(self, name, epoch, rank1):
+        """Writes result.
+
+        Args:
+           name (str): dataset name.
+           epoch (int): current epoch.
+           rank1 (float): rank1 result.
+        """
+        self.logger[name]['epoch'].append(epoch)
+        self.logger[name]['rank1'].append(rank1)
+
+    def show_summary(self):
+        """Shows saved results."""
+        print('=> Show performance summary')
+        for name in self.targets:
+            from_where = 'source' if name in self.sources else 'target'
+            print('{} ({})'.format(name, from_where))
+            for epoch, rank1 in zip(
+                self.logger[name]['epoch'], self.logger[name]['rank1']
+            ):
+                print('- epoch {}\t rank1 {:.1%}'.format(epoch, rank1))
diff --git a/pfe/torchreid/utils/model_complexity.py b/pfe/torchreid/utils/model_complexity.py
new file mode 100644
index 0000000..7d1dc1e
--- /dev/null
+++ b/pfe/torchreid/utils/model_complexity.py
@@ -0,0 +1,363 @@
+from __future__ import division, print_function, absolute_import
+import math
+import numpy as np
+from itertools import repeat
+from collections import namedtuple, defaultdict
+import torch
+
+__all__ = ['compute_model_complexity']
+"""
+Utility
+"""
+
+
+def _ntuple(n):
+
+    def parse(x):
+        if isinstance(x, int):
+            return tuple(repeat(x, n))
+        return x
+
+    return parse
+
+
+_single = _ntuple(1)
+_pair = _ntuple(2)
+_triple = _ntuple(3)
+"""
+Convolution
+"""
+
+
+def hook_convNd(m, x, y):
+    k = torch.prod(torch.Tensor(m.kernel_size)).item()
+    cin = m.in_channels
+    flops_per_ele = k * cin # + (k*cin-1)
+    if m.bias is not None:
+        flops_per_ele += 1
+    flops = flops_per_ele * y.numel() / m.groups
+    return int(flops)
+
+
+"""
+Pooling
+"""
+
+
+def hook_maxpool1d(m, x, y):
+    flops_per_ele = m.kernel_size - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_maxpool2d(m, x, y):
+    k = _pair(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    # ops: compare
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_maxpool3d(m, x, y):
+    k = _triple(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_avgpool1d(m, x, y):
+    flops_per_ele = m.kernel_size
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_avgpool2d(m, x, y):
+    k = _pair(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_avgpool3d(m, x, y):
+    k = _triple(m.kernel_size)
+    k = torch.prod(torch.Tensor(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapmaxpool1d(m, x, y):
+    x = x[0]
+    out_size = m.output_size
+    k = math.ceil(x.size(2) / out_size)
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapmaxpool2d(m, x, y):
+    x = x[0]
+    out_size = _pair(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapmaxpool3d(m, x, y):
+    x = x[0]
+    out_size = _triple(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k - 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapavgpool1d(m, x, y):
+    x = x[0]
+    out_size = m.output_size
+    k = math.ceil(x.size(2) / out_size)
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapavgpool2d(m, x, y):
+    x = x[0]
+    out_size = _pair(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+def hook_adapavgpool3d(m, x, y):
+    x = x[0]
+    out_size = _triple(m.output_size)
+    k = torch.Tensor(list(x.size()[2:])) / torch.Tensor(out_size)
+    k = torch.prod(torch.ceil(k)).item()
+    flops_per_ele = k
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+"""
+Non-linear activations
+"""
+
+
+def hook_relu(m, x, y):
+    # eq: max(0, x)
+    num_ele = y.numel()
+    return int(num_ele)
+
+
+def hook_leakyrelu(m, x, y):
+    # eq: max(0, x) + negative_slope*min(0, x)
+    num_ele = y.numel()
+    flops = 3 * num_ele
+    return int(flops)
+
+
+"""
+Normalization
+"""
+
+
+def hook_batchnormNd(m, x, y):
+    num_ele = y.numel()
+    flops = 2 * num_ele # mean and std
+    if m.affine:
+        flops += 2 * num_ele # gamma and beta
+    return int(flops)
+
+
+def hook_instancenormNd(m, x, y):
+    return hook_batchnormNd(m, x, y)
+
+
+def hook_groupnorm(m, x, y):
+    return hook_batchnormNd(m, x, y)
+
+
+def hook_layernorm(m, x, y):
+    num_ele = y.numel()
+    flops = 2 * num_ele # mean and std
+    if m.elementwise_affine:
+        flops += 2 * num_ele # gamma and beta
+    return int(flops)
+
+
+"""
+Linear
+"""
+
+
+def hook_linear(m, x, y):
+    flops_per_ele = m.in_features # + (m.in_features-1)
+    if m.bias is not None:
+        flops_per_ele += 1
+    flops = flops_per_ele * y.numel()
+    return int(flops)
+
+
+__generic_flops_counter = {
+    # Convolution
+    'Conv1d': hook_convNd,
+    'Conv2d': hook_convNd,
+    'Conv3d': hook_convNd,
+    # Pooling
+    'MaxPool1d': hook_maxpool1d,
+    'MaxPool2d': hook_maxpool2d,
+    'MaxPool3d': hook_maxpool3d,
+    'AvgPool1d': hook_avgpool1d,
+    'AvgPool2d': hook_avgpool2d,
+    'AvgPool3d': hook_avgpool3d,
+    'AdaptiveMaxPool1d': hook_adapmaxpool1d,
+    'AdaptiveMaxPool2d': hook_adapmaxpool2d,
+    'AdaptiveMaxPool3d': hook_adapmaxpool3d,
+    'AdaptiveAvgPool1d': hook_adapavgpool1d,
+    'AdaptiveAvgPool2d': hook_adapavgpool2d,
+    'AdaptiveAvgPool3d': hook_adapavgpool3d,
+    # Non-linear activations
+    'ReLU': hook_relu,
+    'ReLU6': hook_relu,
+    'LeakyReLU': hook_leakyrelu,
+    # Normalization
+    'BatchNorm1d': hook_batchnormNd,
+    'BatchNorm2d': hook_batchnormNd,
+    'BatchNorm3d': hook_batchnormNd,
+    'InstanceNorm1d': hook_instancenormNd,
+    'InstanceNorm2d': hook_instancenormNd,
+    'InstanceNorm3d': hook_instancenormNd,
+    'GroupNorm': hook_groupnorm,
+    'LayerNorm': hook_layernorm,
+    # Linear
+    'Linear': hook_linear,
+}
+
+__conv_linear_flops_counter = {
+    # Convolution
+    'Conv1d': hook_convNd,
+    'Conv2d': hook_convNd,
+    'Conv3d': hook_convNd,
+    # Linear
+    'Linear': hook_linear,
+}
+
+
+def _get_flops_counter(only_conv_linear):
+    if only_conv_linear:
+        return __conv_linear_flops_counter
+    return __generic_flops_counter
+
+
+def compute_model_complexity(
+    model, input_size, verbose=False, only_conv_linear=True
+):
+    """Returns number of parameters and FLOPs.
+
+    .. note::
+        (1) this function only provides an estimate of the theoretical time complexity
+        rather than the actual running time which depends on implementations and hardware,
+        and (2) the FLOPs is only counted for layers that are used at test time. This means
+        that redundant layers such as person ID classification layer will be ignored as it
+        is discarded when doing feature extraction. Note that the inference graph depends on
+        how you construct the computations in ``forward()``.
+
+    Args:
+        model (nn.Module): network model.
+        input_size (tuple): input size, e.g. (1, 3, 256, 128).
+        verbose (bool, optional): shows detailed complexity of
+            each module. Default is False.
+        only_conv_linear (bool, optional): only considers convolution
+            and linear layers when counting flops. Default is True.
+            If set to False, flops of all layers will be counted.
+
+    Examples::
+        >>> from torchreid import models, utils
+        >>> model = models.build_model(name='resnet50', num_classes=1000)
+        >>> num_params, flops = utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True)
+    """
+    registered_handles = []
+    layer_list = []
+    layer = namedtuple('layer', ['class_name', 'params', 'flops'])
+
+    def _add_hooks(m):
+
+        def _has_submodule(m):
+            return len(list(m.children())) > 0
+
+        def _hook(m, x, y):
+            params = sum(p.numel() for p in m.parameters())
+            class_name = str(m.__class__.__name__)
+            flops_counter = _get_flops_counter(only_conv_linear)
+            if class_name in flops_counter:
+                flops = flops_counter[class_name](m, x, y)
+            else:
+                flops = 0
+            layer_list.append(
+                layer(class_name=class_name, params=params, flops=flops)
+            )
+
+        # only consider the very basic nn layer
+        if _has_submodule(m):
+            return
+
+        handle = m.register_forward_hook(_hook)
+        registered_handles.append(handle)
+
+    default_train_mode = model.training
+
+    model.eval().apply(_add_hooks)
+    input = torch.rand(input_size)
+    if next(model.parameters()).is_cuda:
+        input = input.cuda()
+    model(input) # forward
+
+    for handle in registered_handles:
+        handle.remove()
+
+    model.train(default_train_mode)
+
+    if verbose:
+        per_module_params = defaultdict(list)
+        per_module_flops = defaultdict(list)
+
+    total_params, total_flops = 0, 0
+
+    for layer in layer_list:
+        total_params += layer.params
+        total_flops += layer.flops
+        if verbose:
+            per_module_params[layer.class_name].append(layer.params)
+            per_module_flops[layer.class_name].append(layer.flops)
+
+    if verbose:
+        num_udscore = 55
+        print('  {}'.format('-' * num_udscore))
+        print('  Model complexity with input size {}'.format(input_size))
+        print('  {}'.format('-' * num_udscore))
+        for class_name in per_module_params:
+            params = int(np.sum(per_module_params[class_name]))
+            flops = int(np.sum(per_module_flops[class_name]))
+            print(
+                '  {} (params={:,}, flops={:,})'.format(
+                    class_name, params, flops
+                )
+            )
+        print('  {}'.format('-' * num_udscore))
+        print(
+            '  Total (params={:,}, flops={:,})'.format(
+                total_params, total_flops
+            )
+        )
+        print('  {}'.format('-' * num_udscore))
+
+    return total_params, total_flops
diff --git a/pfe/torchreid/utils/reidtools.py b/pfe/torchreid/utils/reidtools.py
new file mode 100644
index 0000000..c7354f5
--- /dev/null
+++ b/pfe/torchreid/utils/reidtools.py
@@ -0,0 +1,154 @@
+from __future__ import print_function, absolute_import
+import numpy as np
+import shutil
+import os.path as osp
+import cv2
+
+from .tools import mkdir_if_missing
+
+__all__ = ['visualize_ranked_results']
+
+GRID_SPACING = 10
+QUERY_EXTRA_SPACING = 90
+BW = 5 # border width
+GREEN = (0, 255, 0)
+RED = (0, 0, 255)
+
+
+def visualize_ranked_results(
+    distmat, dataset, data_type, width=128, height=256, save_dir='', topk=10
+):
+    """Visualizes ranked results.
+
+    Supports both image-reid and video-reid.
+
+    For image-reid, ranks will be plotted in a single figure. For video-reid, ranks will be
+    saved in folders each containing a tracklet.
+
+    Args:
+        distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
+        dataset (tuple): a 2-tuple containing (query, gallery), each of which contains
+            tuples of (img_path(s), pid, camid).
+        data_type (str): "image" or "video".
+        width (int, optional): resized image width. Default is 128.
+        height (int, optional): resized image height. Default is 256.
+        save_dir (str): directory to save output images.
+        topk (int, optional): denoting top-k images in the rank list to be visualized.
+            Default is 10.
+    """
+    num_q, num_g = distmat.shape
+    mkdir_if_missing(save_dir)
+
+    print('# query: {}\n# gallery {}'.format(num_q, num_g))
+    print('Visualizing top-{} ranks ...'.format(topk))
+
+    query, gallery = dataset
+    assert num_q == len(query)
+    assert num_g == len(gallery)
+
+    indices = np.argsort(distmat, axis=1)
+
+    def _cp_img_to(src, dst, rank, prefix, matched=False):
+        """
+        Args:
+            src: image path or tuple (for vidreid)
+            dst: target directory
+            rank: int, denoting ranked position, starting from 1
+            prefix: string
+            matched: bool
+        """
+        if isinstance(src, (tuple, list)):
+            if prefix == 'gallery':
+                suffix = 'TRUE' if matched else 'FALSE'
+                dst = osp.join(
+                    dst, prefix + '_top' + str(rank).zfill(3)
+                ) + '_' + suffix
+            else:
+                dst = osp.join(dst, prefix + '_top' + str(rank).zfill(3))
+            mkdir_if_missing(dst)
+            for img_path in src:
+                shutil.copy(img_path, dst)
+        else:
+            dst = osp.join(
+                dst, prefix + '_top' + str(rank).zfill(3) + '_name_' +
+                osp.basename(src)
+            )
+            shutil.copy(src, dst)
+
+    for q_idx in range(num_q):
+        qimg_path, qpid, qcamid = query[q_idx]
+        qimg_path_name = qimg_path[0] if isinstance(
+            qimg_path, (tuple, list)
+        ) else qimg_path
+
+        if data_type == 'image':
+            qimg = cv2.imread(qimg_path)
+            qimg = cv2.resize(qimg, (width, height))
+            qimg = cv2.copyMakeBorder(
+                qimg, BW, BW, BW, BW, cv2.BORDER_CONSTANT, value=(0, 0, 0)
+            )
+            # resize twice to ensure that the border width is consistent across images
+            qimg = cv2.resize(qimg, (width, height))
+            num_cols = topk + 1
+            grid_img = 255 * np.ones(
+                (
+                    height,
+                    num_cols*width + topk*GRID_SPACING + QUERY_EXTRA_SPACING, 3
+                ),
+                dtype=np.uint8
+            )
+            grid_img[:, :width, :] = qimg
+        else:
+            qdir = osp.join(
+                save_dir, osp.basename(osp.splitext(qimg_path_name)[0])
+            )
+            mkdir_if_missing(qdir)
+            _cp_img_to(qimg_path, qdir, rank=0, prefix='query')
+
+        rank_idx = 1
+        for g_idx in indices[q_idx, :]:
+            gimg_path, gpid, gcamid = gallery[g_idx]
+            invalid = (qpid == gpid) & (qcamid == gcamid)
+
+            if not invalid:
+                matched = gpid == qpid
+                if data_type == 'image':
+                    border_color = GREEN if matched else RED
+                    gimg = cv2.imread(gimg_path)
+                    gimg = cv2.resize(gimg, (width, height))
+                    gimg = cv2.copyMakeBorder(
+                        gimg,
+                        BW,
+                        BW,
+                        BW,
+                        BW,
+                        cv2.BORDER_CONSTANT,
+                        value=border_color
+                    )
+                    gimg = cv2.resize(gimg, (width, height))
+                    start = rank_idx*width + rank_idx*GRID_SPACING + QUERY_EXTRA_SPACING
+                    end = (
+                        rank_idx+1
+                    ) * width + rank_idx*GRID_SPACING + QUERY_EXTRA_SPACING
+                    grid_img[:, start:end, :] = gimg
+                else:
+                    _cp_img_to(
+                        gimg_path,
+                        qdir,
+                        rank=rank_idx,
+                        prefix='gallery',
+                        matched=matched
+                    )
+
+                rank_idx += 1
+                if rank_idx > topk:
+                    break
+
+        if data_type == 'image':
+            imname = osp.basename(osp.splitext(qimg_path_name)[0])
+            cv2.imwrite(osp.join(save_dir, imname + '.jpg'), grid_img)
+
+        if (q_idx+1) % 100 == 0:
+            print('- done {}/{}'.format(q_idx + 1, num_q))
+
+    print('Done. Images have been saved to "{}" ...'.format(save_dir))
diff --git a/pfe/torchreid/utils/rerank.py b/pfe/torchreid/utils/rerank.py
new file mode 100644
index 0000000..efadf5a
--- /dev/null
+++ b/pfe/torchreid/utils/rerank.py
@@ -0,0 +1,113 @@
+#!/usr/bin/env python2/python3
+# -*- coding: utf-8 -*-
+"""
+Source: https://github.com/zhunzhong07/person-re-ranking
+
+Created on Mon Jun 26 14:46:56 2017
+@author: luohao
+Modified by Houjing Huang, 2017-12-22.
+- This version accepts distance matrix instead of raw features.
+- The difference of `/` division between python 2 and 3 is handled.
+- numpy.float16 is replaced by numpy.float32 for numerical precision.
+
+CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
+url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf
+Matlab version: https://github.com/zhunzhong07/person-re-ranking
+
+API
+q_g_dist: query-gallery distance matrix, numpy array, shape [num_query, num_gallery]
+q_q_dist: query-query distance matrix, numpy array, shape [num_query, num_query]
+g_g_dist: gallery-gallery distance matrix, numpy array, shape [num_gallery, num_gallery]
+k1, k2, lambda_value: parameters, the original paper is (k1=20, k2=6, lambda_value=0.3)
+Returns:
+  final_dist: re-ranked distance, numpy array, shape [num_query, num_gallery]
+"""
+from __future__ import division, print_function, absolute_import
+import numpy as np
+
+__all__ = ['re_ranking']
+
+
+def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
+
+    # The following naming, e.g. gallery_num, is different from outer scope.
+    # Don't care about it.
+
+    original_dist = np.concatenate(
+        [
+            np.concatenate([q_q_dist, q_g_dist], axis=1),
+            np.concatenate([q_g_dist.T, g_g_dist], axis=1)
+        ],
+        axis=0
+    )
+    original_dist = np.power(original_dist, 2).astype(np.float32)
+    original_dist = np.transpose(
+        1. * original_dist / np.max(original_dist, axis=0)
+    )
+    V = np.zeros_like(original_dist).astype(np.float32)
+    initial_rank = np.argsort(original_dist).astype(np.int32)
+
+    query_num = q_g_dist.shape[0]
+    gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
+    all_num = gallery_num
+
+    for i in range(all_num):
+        # k-reciprocal neighbors
+        forward_k_neigh_index = initial_rank[i, :k1 + 1]
+        backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
+        fi = np.where(backward_k_neigh_index == i)[0]
+        k_reciprocal_index = forward_k_neigh_index[fi]
+        k_reciprocal_expansion_index = k_reciprocal_index
+        for j in range(len(k_reciprocal_index)):
+            candidate = k_reciprocal_index[j]
+            candidate_forward_k_neigh_index = initial_rank[
+                candidate, :int(np.around(k1 / 2.)) + 1]
+            candidate_backward_k_neigh_index = initial_rank[
+                candidate_forward_k_neigh_index, :int(np.around(k1 / 2.)) + 1]
+            fi_candidate = np.where(
+                candidate_backward_k_neigh_index == candidate
+            )[0]
+            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[
+                fi_candidate]
+            if len(
+                np.
+                intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)
+            ) > 2. / 3 * len(candidate_k_reciprocal_index):
+                k_reciprocal_expansion_index = np.append(
+                    k_reciprocal_expansion_index, candidate_k_reciprocal_index
+                )
+
+        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
+        weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
+        V[i, k_reciprocal_expansion_index] = 1. * weight / np.sum(weight)
+    original_dist = original_dist[:query_num, ]
+    if k2 != 1:
+        V_qe = np.zeros_like(V, dtype=np.float32)
+        for i in range(all_num):
+            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
+        V = V_qe
+        del V_qe
+    del initial_rank
+    invIndex = []
+    for i in range(gallery_num):
+        invIndex.append(np.where(V[:, i] != 0)[0])
+
+    jaccard_dist = np.zeros_like(original_dist, dtype=np.float32)
+
+    for i in range(query_num):
+        temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float32)
+        indNonZero = np.where(V[i, :] != 0)[0]
+        indImages = []
+        indImages = [invIndex[ind] for ind in indNonZero]
+        for j in range(len(indNonZero)):
+            temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(
+                V[i, indNonZero[j]], V[indImages[j], indNonZero[j]]
+            )
+        jaccard_dist[i] = 1 - temp_min / (2.-temp_min)
+
+    final_dist = jaccard_dist * (1-lambda_value) + original_dist*lambda_value
+    del original_dist
+    del V
+    del jaccard_dist
+    final_dist = final_dist[:query_num, query_num:]
+    return final_dist
diff --git a/pfe/torchreid/utils/tools.py b/pfe/torchreid/utils/tools.py
new file mode 100644
index 0000000..a9a607d
--- /dev/null
+++ b/pfe/torchreid/utils/tools.py
@@ -0,0 +1,129 @@
+from __future__ import division, print_function, absolute_import
+import os
+import sys
+import json
+import time
+import errno
+import numpy as np
+import random
+import os.path as osp
+import warnings
+import PIL
+import torch
+from PIL import Image
+
+__all__ = [
+    'mkdir_if_missing', 'check_isfile', 'read_json', 'write_json',
+    'set_random_seed', 'download_url', 'read_image', 'collect_env_info'
+]
+
+
+def mkdir_if_missing(dirname):
+    """Creates dirname if it is missing."""
+    if not osp.exists(dirname):
+        try:
+            os.makedirs(dirname)
+        except OSError as e:
+            if e.errno != errno.EEXIST:
+                raise
+
+
+def check_isfile(fpath):
+    """Checks if the given path is a file.
+
+    Args:
+        fpath (str): file path.
+
+    Returns:
+       bool
+    """
+    isfile = osp.isfile(fpath)
+    if not isfile:
+        warnings.warn('No file found at "{}"'.format(fpath))
+    return isfile
+
+
+def read_json(fpath):
+    """Reads json file from a path."""
+    with open(fpath, 'r') as f:
+        obj = json.load(f)
+    return obj
+
+
+def write_json(obj, fpath):
+    """Writes to a json file."""
+    mkdir_if_missing(osp.dirname(fpath))
+    with open(fpath, 'w') as f:
+        json.dump(obj, f, indent=4, separators=(',', ': '))
+
+
+def set_random_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def download_url(url, dst):
+    """Downloads file from a url to a destination.
+
+    Args:
+        url (str): url to download file.
+        dst (str): destination path.
+    """
+    from six.moves import urllib
+    print('* url="{}"'.format(url))
+    print('* destination="{}"'.format(dst))
+
+    def _reporthook(count, block_size, total_size):
+        global start_time
+        if count == 0:
+            start_time = time.time()
+            return
+        duration = time.time() - start_time
+        progress_size = int(count * block_size)
+        speed = int(progress_size / (1024*duration))
+        percent = int(count * block_size * 100 / total_size)
+        sys.stdout.write(
+            '\r...%d%%, %d MB, %d KB/s, %d seconds passed' %
+            (percent, progress_size / (1024*1024), speed, duration)
+        )
+        sys.stdout.flush()
+
+    urllib.request.urlretrieve(url, dst, _reporthook)
+    sys.stdout.write('\n')
+
+
+def read_image(path):
+    """Reads image from path using ``PIL.Image``.
+
+    Args:
+        path (str): path to an image.
+
+    Returns:
+        PIL image
+    """
+    got_img = False
+    if not osp.exists(path):
+        raise IOError('"{}" does not exist'.format(path))
+    while not got_img:
+        try:
+            img = Image.open(path).convert('RGB')
+            got_img = True
+        except IOError:
+            print(
+                'IOError incurred when reading "{}". Will redo. Don\'t worry. Just chill.'
+                .format(path)
+            )
+    return img
+
+
+def collect_env_info():
+    """Returns env info as a string.
+
+    Code source: github.com/facebookresearch/maskrcnn-benchmark
+    """
+    from torch.utils.collect_env import get_pretty_env_info
+    env_str = get_pretty_env_info()
+    env_str += '\n        Pillow ({})'.format(PIL.__version__)
+    return env_str
diff --git a/pfe/torchreid/utils/torchtools.py b/pfe/torchreid/utils/torchtools.py
new file mode 100644
index 0000000..b6df8d8
--- /dev/null
+++ b/pfe/torchreid/utils/torchtools.py
@@ -0,0 +1,311 @@
+from __future__ import division, print_function, absolute_import
+import pickle
+import shutil
+import os.path as osp
+import warnings
+from functools import partial
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+
+from .tools import mkdir_if_missing
+
+__all__ = [
+    'save_checkpoint', 'load_checkpoint', 'resume_from_checkpoint',
+    'open_all_layers', 'open_specified_layers', 'count_num_param',
+    'load_pretrained_weights'
+]
+
+
+def save_checkpoint(
+    state, save_dir, is_best=False, remove_module_from_keys=False
+):
+    r"""Saves checkpoint.
+
+    Args:
+        state (dict): dictionary.
+        save_dir (str): directory to save checkpoint.
+        is_best (bool, optional): if True, this checkpoint will be copied and named
+            ``model-best.pth.tar``. Default is False.
+        remove_module_from_keys (bool, optional): whether to remove "module."
+            from layer names. Default is False.
+
+    Examples::
+        >>> state = {
+        >>>     'state_dict': model.state_dict(),
+        >>>     'epoch': 10,
+        >>>     'rank1': 0.5,
+        >>>     'optimizer': optimizer.state_dict()
+        >>> }
+        >>> save_checkpoint(state, 'log/my_model')
+    """
+    mkdir_if_missing(save_dir)
+    if remove_module_from_keys:
+        # remove 'module.' in state_dict's keys
+        state_dict = state['state_dict']
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            if k.startswith('module.'):
+                k = k[7:]
+            new_state_dict[k] = v
+        state['state_dict'] = new_state_dict
+    # save
+    epoch = state['epoch']
+    fpath = osp.join(save_dir, 'model.pth.tar-' + str(epoch))
+    torch.save(state, fpath)
+    print('Checkpoint saved to "{}"'.format(fpath))
+    if is_best:
+        shutil.copy(fpath, osp.join(osp.dirname(fpath), 'model-best.pth.tar'))
+
+
+def load_checkpoint(fpath):
+    r"""Loads checkpoint.
+
+    ``UnicodeDecodeError`` can be well handled, which means
+    python2-saved files can be read from python3.
+
+    Args:
+        fpath (str): path to checkpoint.
+
+    Returns:
+        dict
+
+    Examples::
+        >>> from torchreid.utils import load_checkpoint
+        >>> fpath = 'log/my_model/model.pth.tar-10'
+        >>> checkpoint = load_checkpoint(fpath)
+    """
+    if fpath is None:
+        raise ValueError('File path is None')
+    if not osp.exists(fpath):
+        raise FileNotFoundError('File is not found at "{}"'.format(fpath))
+    map_location = None if torch.cuda.is_available() else 'cpu'
+    try:
+        checkpoint = torch.load(fpath, map_location=map_location)
+    except UnicodeDecodeError:
+        pickle.load = partial(pickle.load, encoding="latin1")
+        pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
+        checkpoint = torch.load(
+            fpath, pickle_module=pickle, map_location=map_location
+        )
+    except Exception:
+        print('Unable to load checkpoint from "{}"'.format(fpath))
+        raise
+    return checkpoint
+
+
+def resume_from_checkpoint(fpath, model, optimizer=None, scheduler=None):
+    r"""Resumes training from a checkpoint.
+
+    This will load (1) model weights and (2) ``state_dict``
+    of optimizer if ``optimizer`` is not None.
+
+    Args:
+        fpath (str): path to checkpoint.
+        model (nn.Module): model.
+        optimizer (Optimizer, optional): an Optimizer.
+        scheduler (LRScheduler, optional): an LRScheduler.
+
+    Returns:
+        int: start_epoch.
+
+    Examples::
+        >>> from torchreid.utils import resume_from_checkpoint
+        >>> fpath = 'log/my_model/model.pth.tar-10'
+        >>> start_epoch = resume_from_checkpoint(
+        >>>     fpath, model, optimizer, scheduler
+        >>> )
+    """
+    print('Loading checkpoint from "{}"'.format(fpath))
+    checkpoint = load_checkpoint(fpath)
+    model.load_state_dict(checkpoint['state_dict'])
+    print('Loaded model weights')
+    if optimizer is not None and 'optimizer' in checkpoint.keys():
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        print('Loaded optimizer')
+    if scheduler is not None and 'scheduler' in checkpoint.keys():
+        scheduler.load_state_dict(checkpoint['scheduler'])
+        print('Loaded scheduler')
+    start_epoch = checkpoint['epoch']
+    print('Last epoch = {}'.format(start_epoch))
+    if 'rank1' in checkpoint.keys():
+        print('Last rank1 = {:.1%}'.format(checkpoint['rank1']))
+    return start_epoch
+
+
+def adjust_learning_rate(
+    optimizer,
+    base_lr,
+    epoch,
+    stepsize=20,
+    gamma=0.1,
+    linear_decay=False,
+    final_lr=0,
+    max_epoch=100
+):
+    r"""Adjusts learning rate.
+
+    Deprecated.
+    """
+    if linear_decay:
+        # linearly decay learning rate from base_lr to final_lr
+        frac_done = epoch / max_epoch
+        lr = frac_done*final_lr + (1.-frac_done) * base_lr
+    else:
+        # decay learning rate by gamma for every stepsize
+        lr = base_lr * (gamma**(epoch // stepsize))
+
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+
+def set_bn_to_eval(m):
+    r"""Sets BatchNorm layers to eval mode."""
+    # 1. no update for running mean and var
+    # 2. scale and shift parameters are still trainable
+    classname = m.__class__.__name__
+    if classname.find('BatchNorm') != -1:
+        m.eval()
+
+
+def open_all_layers(model):
+    r"""Opens all layers in model for training.
+
+    Examples::
+        >>> from torchreid.utils import open_all_layers
+        >>> open_all_layers(model)
+    """
+    model.train()
+    for p in model.parameters():
+        p.requires_grad = True
+
+
+def open_specified_layers(model, open_layers):
+    r"""Opens specified layers in model for training while keeping
+    other layers frozen.
+
+    Args:
+        model (nn.Module): neural net model.
+        open_layers (str or list): layers open for training.
+
+    Examples::
+        >>> from torchreid.utils import open_specified_layers
+        >>> # Only model.classifier will be updated.
+        >>> open_layers = 'classifier'
+        >>> open_specified_layers(model, open_layers)
+        >>> # Only model.fc and model.classifier will be updated.
+        >>> open_layers = ['fc', 'classifier']
+        >>> open_specified_layers(model, open_layers)
+    """
+    if isinstance(model, nn.DataParallel):
+        model = model.module
+
+    if isinstance(open_layers, str):
+        open_layers = [open_layers]
+
+    for layer in open_layers:
+        assert hasattr(
+            model, layer
+        ), '"{}" is not an attribute of the model, please provide the correct name'.format(
+            layer
+        )
+
+    for name, module in model.named_children():
+        if name in open_layers:
+            module.train()
+            for p in module.parameters():
+                p.requires_grad = True
+        else:
+            module.eval()
+            for p in module.parameters():
+                p.requires_grad = False
+
+
+def count_num_param(model):
+    r"""Counts number of parameters in a model while ignoring ``self.classifier``.
+
+    Args:
+        model (nn.Module): network model.
+
+    Examples::
+        >>> from torchreid.utils import count_num_param
+        >>> model_size = count_num_param(model)
+
+    .. warning::
+        
+        This method is deprecated in favor of
+        ``torchreid.utils.compute_model_complexity``.
+    """
+    warnings.warn(
+        'This method is deprecated and will be removed in the future.'
+    )
+
+    num_param = sum(p.numel() for p in model.parameters())
+
+    if isinstance(model, nn.DataParallel):
+        model = model.module
+
+    if hasattr(model,
+               'classifier') and isinstance(model.classifier, nn.Module):
+        # we ignore the classifier because it is unused at test time
+        num_param -= sum(p.numel() for p in model.classifier.parameters())
+
+    return num_param
+
+
+def load_pretrained_weights(model, weight_path):
+    r"""Loads pretrianed weights to model.
+
+    Features::
+        - Incompatible layers (unmatched in name or size) will be ignored.
+        - Can automatically deal with keys containing "module.".
+
+    Args:
+        model (nn.Module): network model.
+        weight_path (str): path to pretrained weights.
+
+    Examples::
+        >>> from torchreid.utils import load_pretrained_weights
+        >>> weight_path = 'log/my_model/model-best.pth.tar'
+        >>> load_pretrained_weights(model, weight_path)
+    """
+    checkpoint = load_checkpoint(weight_path)
+    if 'state_dict' in checkpoint:
+        state_dict = checkpoint['state_dict']
+    else:
+        state_dict = checkpoint
+
+    model_dict = model.state_dict()
+    new_state_dict = OrderedDict()
+    matched_layers, discarded_layers = [], []
+
+    for k, v in state_dict.items():
+        if k.startswith('module.'):
+            k = k[7:] # discard module.
+
+        if k in model_dict and model_dict[k].size() == v.size():
+            new_state_dict[k] = v
+            matched_layers.append(k)
+        else:
+            discarded_layers.append(k)
+
+    model_dict.update(new_state_dict)
+    model.load_state_dict(model_dict)
+
+    if len(matched_layers) == 0:
+        warnings.warn(
+            'The pretrained weights "{}" cannot be loaded, '
+            'please check the key names manually '
+            '(** ignored and continue **)'.format(weight_path)
+        )
+    else:
+        print(
+            'Successfully loaded pretrained weights from "{}"'.
+            format(weight_path)
+        )
+        if len(discarded_layers) > 0:
+            print(
+                '** The following layers are discarded '
+                'due to unmatched keys or layer size: {}'.
+                format(discarded_layers)
+            )
diff --git a/test.py b/test.py
index afe5586..9fbe6a2 100755
--- a/test.py
+++ b/test.py
@@ -1,44 +1,87 @@
-"""
-Copyright 2018 watanika, all rights reserved.
-Licensed under the MIT license <LICENSE-MIT or
-http://opensource.org/licenses/MIT>. This file may
-not be copied, modified,or distributed except
-according to those terms.
-"""
 
 import time
 from mcftracker import MinCostFlowTracker
+import sys
+import numpy as np
 
+import mmcv
 
-# Example usage of mcftracker
-def main():
-	# Prepare initial detecton results, ground truth, and images
-	# You need to change below
-	detections = {"image_name": [x1, y1, x2, y2, score]}
-	tags = {"image_name": [x1, y1, x2, y2]}
-	images = {"image_name": numpy_image}
+import helper
+import debug
+import tracklet_matching
 
-	# Parameters
-	min_thresh = 0
-	P_enter = 0.1
-	P_exit = 0.1
-	beta = 0.5
-	fib_search = True
+import os
 
-	# Let's track them!
-	start = time.time()
-	tracker = MinCostFlowTracker(detections, tags, min_thresh, P_enter, P_exit, beta)
-	tracker.build_network(images)
-	optimal_flow, optimal_cost = tracker.run(fib=fib_search)
-	end = time.time()
+def run_mfct(path2video, path2det, frame_offset, frame_count, iid, match_video_id, 
+                                    out_file='./hypothesis.txt', write_video=True):
 
-	print("Finished: {} sec".format(end - start))
-	print("Optimal number of flow: {}".format(optimal_flow))
-	print("Optimal cost: {}".format(optimal_cost))
+    print ('# starting to read input frames & detection data')
 
-	print("Optimal flow:")
-	print(tracker.flow_dict)
+    if os.path.exists("./hypothesis.txt"):
+        os.remove("./hypothesis.txt")
 
+    if os.path.exists("./masks.txt"):
+        os.remove("./masks.txt")
+
+    det_in = np.loadtxt(path2det, delimiter=',')
+    frame_indices = det_in[:, 0].astype(np.int)
+    max_frame_idx = frame_indices.astype(np.int).max()
+
+    slice_start = 0 if frame_offset == 0 else frame_offset-1
+    slice_end = min(frame_offset+frame_count, frame_offset+max_frame_idx)
+
+    # _wc, transform, size = helper._test_tracker_online(path2det, path2video, slice_start, slice_end, 
+    #                                 det_in, frame_indices, match_video_id, out_file)
+
+    # out_video = './out.avi'
+    # debug.visualise_tracks(out_file, path2video, slice_start, slice_end, _wc, transform, size, out_video, draw_mask=False)
+
+    # return
+
+    data, transform, size, parity, _wc, lf_i = helper.read_input_data(
+        path2det, path2video, slice_start, slice_end, det_in, frame_indices, match_video_id)
+
+    start = time.time()
+    tracker = MinCostFlowTracker(data, 0, 0.1, 0.1)
+
+    print ('-> start building min cost flow graph')
+    tracker.build_network(str(len(data)), transform, size)
+
+    print ('-> finish building min cost flow graph')
+    optimal_flow, optimal_cost = tracker.run(24, 32, fib=False)
+    end = time.time()
+
+    print("Finished: {} sec".format(end - start))
+    print("Optimal number of flow: {}".format(optimal_flow))
+    print("Optimal cost: {}".format(optimal_cost))
+
+    tracks, _, _, _ = helper.build_hypothesis_lst(tracker.flow_dict, "1", lf_i)
+    helper.interpolate_gap(tracks, data)
+    helper.write_output_data(out_file, tracks, path2det, data, len(data)+1, frame_offset, iid, parity, draw_mask=False)
+
+    if write_video:    
+        out_video = './out.avi'
+        debug.visualise_tracks(out_file, path2video, slice_start, slice_end, _wc, transform, size, out_video, draw_mask=False)
+
+    return
 
 if __name__ == "__main__":
-	main()
+    path2video = sys.argv[1]
+    print ('# argv 1 -> path to video file: %s' % (path2video))
+
+    path2det = sys.argv[2]
+    print ('# argv 2 -> path to detection file: %s' % (path2det))
+
+    frame_offset = int(sys.argv[3])
+    print ('# argv 3 -> frame offset: %d' % (frame_offset))
+
+    frame_count = int(sys.argv[4])
+    print ('# argv 4 -> frame count: %d' % (frame_count))
+
+    iid = int(sys.argv[5])
+    print ('# argv 5 -> instance id: %d' % (iid))
+
+    mvi = int(sys.argv[6])
+    print ('# argv 6 -> match_video_id: %d' % (mvi))
+
+    run_mfct(path2video, path2det, frame_offset, frame_count, iid, mvi)
\ No newline at end of file
diff --git a/tools.py b/tools.py
index 29078e4..a849cc9 100755
--- a/tools.py
+++ b/tools.py
@@ -7,69 +7,77 @@
 """
 
 import cv2
+import sys
+import numpy as np
 
+def get_key(key):
+	try:
+		return int(key)
+	except ValueError:
+		return key
 
-def calc_HS_histogram(image, roi):
-	cropped = image[roi[1]:roi[3], roi[0]:roi[2], :]
-	hsv = cv2.cvtColor(cropped, cv2.COLOR_BGR2HSV)
-
-	hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
+def calc_RGB_histogram(image, mask):
+	hist = cv2.calcHist([image], [0, 1, 2], mask.astype(np.uint8), [8, 8, 8], [0, 256, 0, 256, 0, 256])
 	cv2.normalize(hist, hist, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX).flatten()
 	return hist
 
+def calc_HS_histogram(image, mask):
+	hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+	hist = cv2.calcHist([hsv], [0, 1], mask.astype(np.uint8), [180, 256], [0, 180, 0, 256])
+	cv2.normalize(hist, hist, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX).flatten()
+	return hist
 
 def calc_bhattacharyya_distance(hist1, hist2):
 	return cv2.compareHist(hist1, hist2, cv2.HISTCMP_BHATTACHARYYA)
 
-
-def map_node2id(detections):
+def map_node2id(data):
 	node2id = {}
 	node2id["source"] = 0
 	node2id["sink"] = 1
 
 	nextid = 2
-	for image_name, rects in sorted(detections.items()):
-		for i, rect in enumerate(rects):
+	for image_name, node_lst in sorted(data.items(), key=lambda t: get_key(t[0])):
+		for i, node in enumerate(node_lst):
 			node2id[(image_name, i, "u")] = nextid
 			node2id[(image_name, i, "v")] = nextid + 1
 			nextid += 2
 	return node2id
 
 
-def map_id2node(detections):
+def map_id2node(data):
 	id2node = {}
 	id2node[0] = "source"
 	id2node[1] = "sink"
 
 	nextid = 2
-	for image_name, rects in sorted(detections.items()):
-		for i, rect in enumerate(rects):
+	for image_name, node_lst in sorted(data.items(), key=lambda t: get_key(t[0])):
+		for i, node in enumerate(node_lst):
 			id2node[nextid] = (image_name, i, "u")
 			id2node[nextid + 1] = (image_name, i, "v")
 			nextid += 2
 	return id2node
 
-
-def map_name2id(tags):
+def map_name2id(data):
 	name2id = {}
-	for frame_id, (image_name, rects) in enumerate(sorted(tags.items())):
+	for frame_id, (image_name, node_lst) in enumerate(sorted(data.items(), key=lambda t: get_key(t[0]))):
 		name2id[image_name] = frame_id
 	return name2id
 
 
-def map_id2name(tags):
+def map_id2name(data):
 	id2name = {}
-	for frame_id, (image_name, rects) in enumerate(sorted(tags.items())):
+	for frame_id, (image_name, node_lst) in enumerate(sorted(data.items(), key=lambda t: get_key(t[0]))):
 		id2name[frame_id] = image_name
 	return id2name
 
-
-def calc_overlap(bb1, bb2):
+def calc_overlap(bb1, bb2, dbg=False):
+	# iw = bi[2] - bi[0] + 1
+	# ih = bi[3] - bi[1] + 1
+	
+	# if dbg == True:
+		# print (iw, ih)
 	bi = (max(bb1[0], bb2[0]), max(bb1[1], bb2[1]), min(bb1[2], bb2[2]), min(bb1[3], bb2[3]))
-	iw = bi[2] - bi[0] + 1
-	ih = bi[3] - bi[1] + 1
-	if iw > 0 and ih > 0:
-		ua = (bb1[2] - bb1[0] + 1) * (bb1[3] - bb1[1] + 1) + (bb2[2] - bb2[0] + 1) * (bb2[3] - bb2[1] + 1) - iw * ih
-		return iw * ih / ua
-	else:
-		return 0.0
+	iw = max(0., bi[2] - bi[0] + 1)
+	ih = max(0., bi[3] - bi[1] + 1)
+	ua = (bb1[2] - bb1[0] + 1) * (bb1[3] - bb1[1] + 1) + (bb2[2] - bb2[0] + 1) * (bb2[3] - bb2[1] + 1) - iw * ih
+	return iw * ih / float(ua)
diff --git a/tracklet_matching.py b/tracklet_matching.py
new file mode 100644
index 0000000..7b35f40
--- /dev/null
+++ b/tracklet_matching.py
@@ -0,0 +1,200 @@
+from operator import itemgetter
+from TrackletNode import TrackletNode
+from mcftracker import MinCostFlowTracker
+import helper
+
+import json
+import numpy as np
+from node import GraphNode
+
+def tracklet_matching(tracklets, nh, nt, nht, data):
+    # create data sctruct
+    types = [nt, nht, nh]
+    tracklet_data = {}
+
+    # sort node_lst by index of last frame
+    # for ilst in types:
+    #     ends = []
+    #     for ti in ilst:            
+    #         s, e = tracklets[ti][0], tracklets[ti][-1]
+    #         # ends.append(int(e[0]))
+    #         ends.append(int(s[0]))
+        
+    #     ordIdx = [i[0] for i in sorted(enumerate(ends), key=lambda x:x[1])]
+    #     indices_s.append(ordIdx)
+
+    #     types_s.append([ilst[i] for i in ordIdx])
+        
+    # for i, ilst in enumerate(types_s):
+    for i, ilst in enumerate(types):
+        node_lst = []
+
+        for idx in ilst:
+            tf = tracklets[idx][0]
+            _sWc = data[tf[0]][tf[1]]._3dc
+            _sFn = int(tf[0])
+
+            tl = tracklets[idx][-1]
+            _eWc = data[tl[0]][tl[1]]._3dc
+            _eFn = int(tl[0])
+
+            # _wcS coord of first observation of a tracklet
+            # _wcE coord of last observation of a tracklet
+            # _sFn start frame index of a tracklet
+            # _eFn end frame index of a tracklet
+            node = TrackletNode(idx, _sFn, _sWc, _eFn, _eWc)
+            node_lst.append(node)
+        
+        tracklet_data[str(i+1)] = node_lst
+
+    return tracklet_data, types
+
+def hypot2id(hypot, ttype, all_tracklets, data_in, transform):
+    matches = []
+    for hpt in hypot:
+        match = []
+        for n,h in enumerate(hpt):
+            if n%2 != 0:
+                sIdx = h[1]
+                sTypeIdx = int(h[0])-1
+                # oIdx = idx_data[sTypeIdx][sIdx]
+                # idM = ttype[sTypeIdx][oIdx]
+                idM = ttype[sTypeIdx][sIdx]
+                match.append((idM, sTypeIdx+1))
+        matches.append(match)
+
+    for mlst in matches:
+        # print frame gap and distance between matching pairs
+        print (mlst)
+        for i in range(len(mlst)-1):
+            cur, nex = mlst[i], mlst[i+1]
+
+            curType, curId  = cur[1]-1, cur[0]
+            nexType, nexId  = nex[1]-1, nex[0]
+
+            curNode = all_tracklets[curId][-1]
+            nexNode = all_tracklets[nexId][0]
+
+            curFname, curNidx = curNode[0], curNode[1]
+            nexFname, nexNidx = nexNode[0], nexNode[1]
+
+            curWc = data_in[curFname][curNidx]._3dc
+            nexWc = data_in[nexFname][nexNidx]._3dc
+            
+            cx, cy = curWc[0], curWc[1] 
+            rx, ry = nexWc[0], nexWc[1]
+
+            dist = helper.calc_eucl_dist([cx*transform.parameter.get("ground_width"),cy*transform.parameter.get("ground_height")], 
+                        [rx*transform.parameter.get("ground_width"),ry*transform.parameter.get("ground_height")])
+
+            fdiff = int(nexFname) - int(curFname)
+
+            print ('%d -> %d dist=%f fn:[%d-%d] fdiff=%d' % (nexId, curId, dist, int(curFname), int(nexFname), fdiff))
+        
+        print ('-----------------------------------------------------------')
+    
+    return matches
+
+def interpolate_gap(hypothesis, data, s, e):
+    ns = hypothesis[s][-1] # tuple ('frame_num', detection_index, 'u')
+    ne = hypothesis[e][0]
+
+    fs = int(ns[0])
+    fe = int(ne[0])
+
+    bs = data[ns[0]][ns[1]]._bb
+    be = data[ne[0]][ne[1]]._bb
+
+    if fe-fs >= 20:
+        return
+
+    # print ('%d -> %d > %s -> %s' % (fs,fe,bs,be))
+
+    fpx1 = [bs[0], be[0]]
+    fpy1 = [bs[1], be[1]]
+    fpx2 = [bs[2], be[2]]
+    fpy2 = [bs[3], be[3]]
+
+    xp = [fs, fe]
+
+    for x in range(fs+1, fe):
+        pix1 = np.interp(x, xp, fpx1)
+        piy1 = np.interp(x, xp, fpy1)
+        pix2 = np.interp(x, xp, fpx2)
+        piy2 = np.interp(x, xp, fpy2)
+
+        # print ('%d -> %s' % (x, [pix1,piy1,pix2,piy2]))
+        pi = [pix1,piy1,pix2,piy2]
+
+        gn = GraphNode([0.,0.], pi, 0., 0)
+        fn = str(x)
+
+        index = len(data[fn])
+        data[fn].append(gn)
+
+        node_u = (fn, index, "u")
+        node_v = (fn, index, "v")
+
+        hypothesis[s].append(node_u)
+        hypothesis[s].append(node_v)
+
+    return
+
+def cost_flow_tracklet(assoc_tracklets, nh, nt, nht, data_in, transform):
+# def cost_flow_tracklet(data_in, transform):
+    # with open('./tracklets.json') as f:
+    #     assoc_tracklets = json.load(f)
+
+    # with open('./nh.json') as f:
+    #     nh = json.load(f)
+
+    # with open('./nt.json') as f:
+    #     nt = json.load(f)
+
+    # with open('./nht.json') as f:
+    #     nht = json.load(f)
+
+    # with open('./tracklets.json', 'w') as f:
+    #     json.dump(assoc_tracklets, f)
+
+    # with open('./nh.json', 'w') as f:
+    #     json.dump(nh, f)
+
+    # with open('./nt.json', 'w') as f:
+    #     json.dump(nt, f)
+
+    # with open('./nht.json', 'w') as f:
+    #     json.dump(nht, f)
+
+    print ('nh', nh)
+    print ('nt', nt)
+    print ('nht', nht)
+
+    trklt_data, type_data = tracklet_matching(assoc_tracklets, nh, nt, nht, data_in)
+    graph = MinCostFlowTracker(trklt_data, 0, 0.1, 0.1)
+    graph.build_network_tracklet(transform)
+    # optimal_flow, optimal_cost = graph.run(0, max(len(trklt_data["1"]), len(trklt_data["3"]))+1, fib=False)
+    optimal_flow, optimal_cost = graph.run(0, 100, fib=False)
+    print("Optimal number of flow: {}".format(optimal_flow))
+    print("Optimal cost: {}".format(optimal_cost))
+    hypot, _, _, _ = helper.build_hypothesis_lst(graph.flow_dict, "1", "3")
+    
+    # convert hypot to format: id1 -> id2 -> id3 
+    matches = hypot2id(hypot,type_data, assoc_tracklets, data_in, transform)
+
+    # combining tracks	
+    for match in matches:
+        trklt_s = assoc_tracklets[match[0][0]]
+        for i in range(1,len(match)):
+            trklt_d = assoc_tracklets[match[i][0]]
+            interpolate_gap(assoc_tracklets, data_in, match[0][0], match[i][0])
+            for node in trklt_d:
+                trklt_s.append(node)
+    
+    # erasing old tracks
+    for match in matches:
+        for i in range(1,len(match)):
+            assoc_tracklets[match[i][0]].clear()
+
+    return assoc_tracklets
+
diff --git a/tracklets.json b/tracklets.json
new file mode 100644
index 0000000..4adc258
--- /dev/null
+++ b/tracklets.json
@@ -0,0 +1 @@
+[[["1", 0, "u"], ["1", 0, "v"], ["2", 0, "u"], ["2", 0, "v"], ["3", 0, "u"], ["3", 0, "v"], ["4", 0, "u"], ["4", 0, "v"], ["5", 0, "u"], ["5", 0, "v"], ["6", 0, "u"], ["6", 0, "v"], ["7", 1, "u"], ["7", 1, "v"], ["8", 2, "u"], ["8", 2, "v"], ["9", 2, "u"], ["9", 2, "v"], ["10", 2, "u"], ["10", 2, "v"], ["11", 1, "u"], ["11", 1, "v"], ["12", 3, "u"], ["12", 3, "v"], ["13", 2, "u"], ["13", 2, "v"], ["14", 2, "u"], ["14", 2, "v"], ["15", 4, "u"], ["15", 4, "v"], ["16", 8, "u"], ["16", 8, "v"], ["17", 7, "u"], ["17", 7, "v"], ["18", 7, "u"], ["18", 7, "v"], ["19", 4, "u"], ["19", 4, "v"], ["20", 4, "u"], ["20", 4, "v"], ["21", 6, "u"], ["21", 6, "v"], ["22", 6, "u"], ["22", 6, "v"], ["23", 3, "u"], ["23", 3, "v"], ["24", 3, "u"], ["24", 3, "v"], ["25", 5, "u"], ["25", 5, "v"], ["26", 5, "u"], ["26", 5, "v"], ["27", 7, "u"], ["27", 7, "v"], ["28", 8, "u"], ["28", 8, "v"], ["29", 7, "u"], ["29", 7, "v"], ["30", 6, "u"], ["30", 6, "v"], ["31", 4, "u"], ["31", 4, "v"], ["32", 6, "u"], ["32", 6, "v"], ["33", 2, "u"], ["33", 2, "v"], ["34", 3, "u"], ["34", 3, "v"], ["35", 1, "u"], ["35", 1, "v"], ["36", 1, "u"], ["36", 1, "v"], ["37", 0, "u"], ["37", 0, "v"], ["38", 2, "u"], ["38", 2, "v"], ["39", 1, "u"], ["39", 1, "v"], ["40", 4, "u"], ["40", 4, "v"], ["41", 3, "u"], ["41", 3, "v"], ["42", 4, "u"], ["42", 4, "v"], ["43", 4, "u"], ["43", 4, "v"], ["44", 5, "u"], ["44", 5, "v"], ["45", 2, "u"], ["45", 2, "v"], ["46", 2, "u"], ["46", 2, "v"], ["47", 2, "u"], ["47", 2, "v"], ["48", 3, "u"], ["48", 3, "v"], ["49", 2, "u"], ["49", 2, "v"], ["50", 1, "u"], ["50", 1, "v"], ["51", 2, "u"], ["51", 2, "v"], ["52", 2, "u"], ["52", 2, "v"], ["53", 2, "u"], ["53", 2, "v"], ["54", 3, "u"], ["54", 3, "v"], ["55", 2, "u"], ["55", 2, "v"], ["56", 4, "u"], ["56", 4, "v"], ["57", 4, "u"], ["57", 4, "v"], ["58", 7, "u"], ["58", 7, "v"], ["59", 11, "u"], ["59", 11, "v"], ["60", 14, "u"], ["60", 14, "v"], ["61", 13, "u"], ["61", 13, "v"], ["62", 14, "u"], ["62", 14, "v"], ["63", 13, "u"], ["63", 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"v"], ["2639", 19, "u"], ["2639", 19, "v"], ["2640", 20, "u"], ["2640", 20, "v"], ["2641", 18, "u"], ["2641", 18, "v"], ["2642", 22, "u"], ["2642", 22, "v"], ["2643", 21, "u"], ["2643", 21, "v"], ["2644", 15, "u"], ["2644", 15, "v"], ["2645", 15, "u"], ["2645", 15, "v"], ["2646", 11, "u"], ["2646", 11, "v"], ["2647", 14, "u"], ["2647", 14, "v"], ["2648", 14, "u"], ["2648", 14, "v"], ["2649", 17, "u"], ["2649", 17, "v"], ["2650", 17, "u"], ["2650", 17, "v"], ["2651", 15, "u"], ["2651", 15, "v"], ["2652", 19, "u"], ["2652", 19, "v"], ["2653", 13, "u"], ["2653", 13, "v"], ["2654", 11, "u"], ["2654", 11, "v"], ["2655", 9, "u"], ["2655", 9, "v"], ["2656", 6, "u"], ["2656", 6, "v"], ["2657", 11, "u"], ["2657", 11, "v"]]]
\ No newline at end of file
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..5e3579e
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,94 @@
+import numpy as np
+import cv2
+
+def non_max_suppression(boxes, max_bbox_overlap, scores=None):
+    """Suppress overlapping detections.
+    Original code from [1]_ has been adapted to include confidence score.
+    .. [1] http://www.pyimagesearch.com/2015/02/16/
+           faster-non-maximum-suppression-python/
+    Examples
+    --------
+        >>> boxes = [d.roi for d in detections]
+        >>> scores = [d.confidence for d in detections]
+        >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
+        >>> detections = [detections[i] for i in indices]
+    Parameters
+    ----------
+    boxes : ndarray
+        Array of ROIs (x, y, width, height).
+    max_bbox_overlap : float
+        ROIs that overlap more than this values are suppressed.
+    scores : Optional[array_like]
+        Detector confidence score.
+    Returns
+    -------
+    List[int]
+        Returns indices of detections that have survived non-maxima suppression.
+    """
+    if len(boxes) == 0:
+        return []
+
+    boxes = boxes.astype(np.float)
+    pick = []
+
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2] + boxes[:, 0]
+    y2 = boxes[:, 3] + boxes[:, 1]
+
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+
+#    print(len(area))
+    if scores is not None:
+        idxs = np.argsort(scores)
+    else:
+        idxs = np.argsort(y2)
+
+    while len(idxs) > 0:
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        overlap = (w * h) / area[idxs[:last]]
+
+        idxs = np.delete(
+            idxs, np.concatenate(
+                ([last], np.where(overlap > max_bbox_overlap)[0])))
+
+    return pick
+
+
+def preprocess_masks(masks,images):
+    ret = []
+
+    for mtpl in masks:
+        mlst, idx = mtpl[1], mtpl[0]
+
+        if len(mlst) == 0:
+            narr = np.full((images[idx].shape[0],images[idx].shape[1]), True, dtype=bool)
+            ret.append((idx,narr))
+
+        elif len(mlst) > 1:
+            narr = np.zeros(mlst[0].shape, dtype=bool)
+
+            for m in mlst:
+                for i in range(m.shape[0]):
+                    for j in range(m.shape[1]):
+
+                        if m[i][j] == True:
+                            narr[i][j] = True
+            
+            ret.append((idx,narr))
+
+        else:
+            ret.append((idx,mlst[0]))
+
+    return ret
\ No newline at end of file