Repair

.gitignore

@@ -0,0 +1,4 @@
+__pycache__
+checkpoint
+data/*
+!data/index_list/*

.python-version

@@ -0,0 +1 @@
+3.13

dataloader/cifar100/autoaugment.py

@@ -206,7 +206,7 @@ def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx
             "translateY": np.linspace(0, 150 / 331, 10),
             "rotate": np.linspace(0, 30, 10),
             "color": np.linspace(0.0, 0.9, 10),
-            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int16),
             "solarize": np.linspace(256, 0, 10),
             "contrast": np.linspace(0.0, 0.9, 10),
             "sharpness": np.linspace(0.0, 0.9, 10),

dataloader/cifar100/cifar.py

@@ -138,7 +138,7 @@ def SelectfromDefault(self, data, targets, index):
         targets_tmp = []
         for i in index:
             ind_cl = np.where(i == targets)[0]
-            if data_tmp == []:
+            if len(data_tmp) == 0:
                 data_tmp = data[ind_cl]
                 targets_tmp = targets[ind_cl]
             else:
@@ -155,7 +155,7 @@ def NewClassSelector(self, data, targets, index):
         index = ind_np.reshape((5,5))
         for i in index:
             ind_cl = i
-            if data_tmp == []:
+            if len(data_tmp) == 0:
                 data_tmp = data[ind_cl]
                 targets_tmp = targets[ind_cl]
             else:

dataloader/cub200/autoaugment.py

@@ -143,7 +143,7 @@ def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx
             "translateY": np.linspace(0, 150 / 331, 10),
             "rotate": np.linspace(0, 30, 10),
             "color": np.linspace(0.0, 0.9, 10),
-            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int16),
             "solarize": np.linspace(256, 0, 10),
             "contrast": np.linspace(0.0, 0.9, 10),
             "sharpness": np.linspace(0.0, 0.9, 10),

dataloader/imagenet100/autoaugment.py

@@ -143,7 +143,7 @@ def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx
             "translateY": np.linspace(0, 150 / 331, 10),
             "rotate": np.linspace(0, 30, 10),
             "color": np.linspace(0.0, 0.9, 10),
-            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int16),
             "solarize": np.linspace(256, 0, 10),
             "contrast": np.linspace(0.0, 0.9, 10),
             "sharpness": np.linspace(0.0, 0.9, 10),

dataloader/imagenet1000/autoaugment.py

@@ -143,7 +143,7 @@ def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx
             "translateY": np.linspace(0, 150 / 331, 10),
             "rotate": np.linspace(0, 30, 10),
             "color": np.linspace(0.0, 0.9, 10),
-            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int16),
             "solarize": np.linspace(256, 0, 10),
             "contrast": np.linspace(0.0, 0.9, 10),
             "sharpness": np.linspace(0.0, 0.9, 10),

dataloader/miniimagenet/autoaugment.py

@@ -143,7 +143,7 @@ def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx
             "translateY": np.linspace(0, 150 / 331, 10),
             "rotate": np.linspace(0, 30, 10),
             "color": np.linspace(0.0, 0.9, 10),
-            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int16),
             "solarize": np.linspace(256, 0, 10),
             "contrast": np.linspace(0.0, 0.9, 10),
             "sharpness": np.linspace(0.0, 0.9, 10),

models/base/fscil_trainer.py

@@ -21,7 +21,7 @@ def __init__(self, args):
 
         if self.args.model_dir is not None:
             print('Loading init parameters from: %s' % self.args.model_dir)
-            self.best_model_dict = torch.load(self.args.model_dir)['params']
+            self.best_model_dict = torch.load(self.args.model_dir, weights_only=False)['params']
             #self.best_model_dict = torch.load(self.args.model_dir)['state_dict']
         else:
             print('random init params')

models/fact/fscil_trainer.py

@@ -21,7 +21,7 @@ def __init__(self, args):
 
         if self.args.model_dir is not None:
             print('Loading init parameters from: %s' % self.args.model_dir)
-            self.best_model_dict = torch.load(self.args.model_dir)['params']
+            self.best_model_dict = torch.load(self.args.model_dir, weights_only=False)['params']
 
         else:
             print('random init params')

models/resnet18_encoder.py

@@ -64,7 +64,7 @@ def load_state_dict_from_url(url, model_dir=None, map_location=None, progress=Tr
         sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
         hash_prefix = HASH_REGEX.search(filename).group(1)
         _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
-    return torch.load(cached_file, map_location=map_location)
+    return torch.load(cached_file, map_location=map_location, weights_only=False)
 
 
 def _download_url_to_file(url, dst, hash_prefix, progress):

pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "cvpr22-fact"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "matplotlib>=3.10.7",
+    "numpy>=2.3.4",
+    "scikit-learn>=1.7.2",
+    "torch>=2.9.0",
+    "torchvision>=0.24.0",
+    "tqdm>=4.67.1",
+]

train.py

@@ -4,32 +4,32 @@
 
 MODEL_DIR=None
 DATA_DIR = 'data/'
-PROJECT='base'
+PROJECT='fact' # base, fact
 
 def get_command_line_parser():
     parser = argparse.ArgumentParser()
 
     # about dataset and network
     parser.add_argument('-project', type=str, default=PROJECT)
-    parser.add_argument('-dataset', type=str, default='cub200',
+    parser.add_argument('-dataset', type=str, default='cifar100',
                         choices=['mini_imagenet', 'cub200', 'cifar100'])
     parser.add_argument('-dataroot', type=str, default=DATA_DIR)
 
     # about pre-training
-    parser.add_argument('-epochs_base', type=int, default=100)
+    parser.add_argument('-epochs_base', type=int, default=400)
     parser.add_argument('-epochs_new', type=int, default=100)
-    parser.add_argument('-lr_base', type=float, default=0.1)
+    parser.add_argument('-lr_base', type=float, default=0.005)
     parser.add_argument('-lr_new', type=float, default=0.1)
-    parser.add_argument('-schedule', type=str, default='Step',
+    parser.add_argument('-schedule', type=str, default='Milestone',
                         choices=['Step', 'Milestone','Cosine'])
-    parser.add_argument('-milestones', nargs='+', type=int, default=[60, 70])
+    parser.add_argument('-milestones', nargs='+', type=int, default=[50, 100, 150, 200, 250, 300])
     parser.add_argument('-step', type=int, default=20)
     parser.add_argument('-decay', type=float, default=0.0005)
     parser.add_argument('-momentum', type=float, default=0.9)
-    parser.add_argument('-gamma', type=float, default=0.1)
+    parser.add_argument('-gamma', type=float, default=0.25)
     parser.add_argument('-temperature', type=float, default=16)
     parser.add_argument('-not_data_init', action='store_true', help='using average data embedding to init or not')
-    parser.add_argument('-batch_size_base', type=int, default=128)
+    parser.add_argument('-batch_size_base', type=int, default=256)
     parser.add_argument('-batch_size_new', type=int, default=0, help='set 0 will use all the availiable training image for new')
     parser.add_argument('-test_batch_size', type=int, default=100)
     parser.add_argument('-base_mode', type=str, default='ft_cos',
@@ -38,8 +38,8 @@ def get_command_line_parser():
                         choices=['ft_dot', 'ft_cos', 'avg_cos']) # ft_dot means using linear classifier, ft_cos means using cosine classifier, avg_cos means using average data embedding and cosine classifier
 
     #for fact
-    parser.add_argument('-balance', type=float, default=1.0)
-    parser.add_argument('-loss_iter', type=int, default=200)
+    parser.add_argument('-balance', type=float, default=0.01)
+    parser.add_argument('-loss_iter', type=int, default=0)
     parser.add_argument('-alpha', type=float, default=2.0)
     parser.add_argument('-eta', type=float, default=0.1)
 

utils.py

@@ -110,10 +110,10 @@ def count_acc_taskIL(logits, label,args):
 
 def confmatrix(logits,label,filename):
 
-    font={'family':'FreeSerif','size':18}
+    font={'family':'DejaVu Serif','size':18}
     matplotlib.rc('font',**font)
-    matplotlib.rcParams.update({'font.family':'FreeSerif','font.size':18})
-    plt.rcParams["font.family"]="FreeSerif"
+    matplotlib.rcParams.update({'font.family':'DejaVu Serif','font.size':18})
+    plt.rcParams["font.family"]="DejaVu Serif"
 
     pred = torch.argmax(logits, dim=1)
     cm=confusion_matrix(label, pred,normalize='true')