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Refactor to the pytorch implementation #7

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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ The average running time was obtained using 1000 tests, and 100 candidate boxes
| ----------- | --------- | --------- |
| python cpu version | 21.472 | i9-7900X CPU@3.3GHz |
| pytorch cpu version | 21.778 | i9-7900X CPU@3.3GHz |
| pytorch gpu version | 60.128 | TITAN Xp |
| pytorch gpu version | 49.771 | RTX 2080 |


## ToDo
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34 changes: 20 additions & 14 deletions softnms_pytorch.py
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Expand Up @@ -2,6 +2,7 @@
# Author:Richard Fang

import time

import numpy as np
import torch

Expand All @@ -21,10 +22,10 @@ def soft_nms_pytorch(dets, box_scores, sigma=0.5, thresh=0.001, cuda=0):

# Indexes concatenate boxes with the last column
N = dets.shape[0]
if cuda:
indexes = torch.arange(0, N, dtype=torch.float).cuda().view(N, 1)
else:
indexes = torch.arange(0, N, dtype=torch.float).view(N, 1)

device = torch.device('cuda' if cuda else 'cpu')
indexes = torch.arange(0, N, dtype=torch.float, device=device).view(N, 1)

dets = torch.cat((dets, indexes), dim=1)

# The order of boxes coordinate is [y1,x1,y2,x2]
Expand All @@ -43,19 +44,24 @@ def soft_nms_pytorch(dets, box_scores, sigma=0.5, thresh=0.001, cuda=0):
if i != N - 1:
maxscore, maxpos = torch.max(scores[pos:], dim=0)
if tscore < maxscore:
dets[i], dets[maxpos.item() + i + 1] = dets[maxpos.item() + i + 1].clone(), dets[i].clone()
scores[i], scores[maxpos.item() + i + 1] = scores[maxpos.item() + i + 1].clone(), scores[i].clone()
areas[i], areas[maxpos + i + 1] = areas[maxpos + i + 1].clone(), areas[i].clone()
dets[i] = dets[maxpos.item() + i + 1].clone()
dets[maxpos.item() + i + 1] = dets[i].clone()

scores[i]= scores[maxpos.item() + i + 1].clone()
scores[maxpos.item() + i + 1] = scores[i].clone()

areas[i] = areas[maxpos + i + 1].clone()
areas[maxpos + i + 1] = areas[i].clone()

# IoU calculate
yy1 = np.maximum(dets[i, 0].to("cpu").numpy(), dets[pos:, 0].to("cpu").numpy())
xx1 = np.maximum(dets[i, 1].to("cpu").numpy(), dets[pos:, 1].to("cpu").numpy())
yy2 = np.minimum(dets[i, 2].to("cpu").numpy(), dets[pos:, 2].to("cpu").numpy())
xx2 = np.minimum(dets[i, 3].to("cpu").numpy(), dets[pos:, 3].to("cpu").numpy())
yy1 = torch.maximum(dets[i, 0], dets[pos:, 0])
xx1 = torch.maximum(dets[i, 1], dets[pos:, 1])
yy2 = torch.minimum(dets[i, 2], dets[pos:, 2])
xx2 = torch.minimum(dets[i, 3], dets[pos:, 3])

w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = torch.tensor(w * h).cuda() if cuda else torch.tensor(w * h)
w = torch.maximum(torch.tensor(0.0, device=dets.device), xx2 - xx1 + 1)
h = torch.maximum(torch.tensor(0.0, device=dets.device), yy2 - yy1 + 1)
inter = w * h
ovr = torch.div(inter, (areas[i] + areas[pos:] - inter))

# Gaussian decay
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