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code-philia
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mtpnet_image_models

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D mobilenetv123

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  1. Image/MobileNetv3/code/backdoor_train.log +0 -253
  2. Image/MobileNetv3/code/model.py +0 -263
  3. Image/MobileNetv3/code/train.log +0 -253
  4. Image/MobileNetv3/code/train.py +0 -63
  5. Image/MobileNetv3/dataset/.gitkeep +0 -0
  6. Image/MobileNetv3/model/.gitkeep +0 -0
  7. Image/MobileNetv3/model/0/epoch1/embeddings.npy +0 -3
  8. Image/MobileNetv3/model/0/epoch1/subject_model.pth +0 -3
  9. Image/MobileNetv3/model/0/epoch10/embeddings.npy +0 -3
  10. Image/MobileNetv3/model/0/epoch10/subject_model.pth +0 -3
  11. Image/MobileNetv3/model/0/epoch11/embeddings.npy +0 -3
  12. Image/MobileNetv3/model/0/epoch11/subject_model.pth +0 -3
  13. Image/MobileNetv3/model/0/epoch12/embeddings.npy +0 -3
  14. Image/MobileNetv3/model/0/epoch12/subject_model.pth +0 -3
  15. Image/MobileNetv3/model/0/epoch13/embeddings.npy +0 -3
  16. Image/MobileNetv3/model/0/epoch13/subject_model.pth +0 -3
  17. Image/MobileNetv3/model/0/epoch14/embeddings.npy +0 -3
  18. Image/MobileNetv3/model/0/epoch14/subject_model.pth +0 -3
  19. Image/MobileNetv3/model/0/epoch15/embeddings.npy +0 -3
  20. Image/MobileNetv3/model/0/epoch15/subject_model.pth +0 -3
  21. Image/MobileNetv3/model/0/epoch16/embeddings.npy +0 -3
  22. Image/MobileNetv3/model/0/epoch16/subject_model.pth +0 -3
  23. Image/MobileNetv3/model/0/epoch17/embeddings.npy +0 -3
  24. Image/MobileNetv3/model/0/epoch17/subject_model.pth +0 -3
  25. Image/MobileNetv3/model/0/epoch18/embeddings.npy +0 -3
  26. Image/MobileNetv3/model/0/epoch18/subject_model.pth +0 -3
  27. Image/MobileNetv3/model/0/epoch19/embeddings.npy +0 -3
  28. Image/MobileNetv3/model/0/epoch19/subject_model.pth +0 -3
  29. Image/MobileNetv3/model/0/epoch2/embeddings.npy +0 -3
  30. Image/MobileNetv3/model/0/epoch2/subject_model.pth +0 -3
  31. Image/MobileNetv3/model/0/epoch20/embeddings.npy +0 -3
  32. Image/MobileNetv3/model/0/epoch20/subject_model.pth +0 -3
  33. Image/MobileNetv3/model/0/epoch21/embeddings.npy +0 -3
  34. Image/MobileNetv3/model/0/epoch21/subject_model.pth +0 -3
  35. Image/MobileNetv3/model/0/epoch22/embeddings.npy +0 -3
  36. Image/MobileNetv3/model/0/epoch22/subject_model.pth +0 -3
  37. Image/MobileNetv3/model/0/epoch23/embeddings.npy +0 -3
  38. Image/MobileNetv3/model/0/epoch23/subject_model.pth +0 -3
  39. Image/MobileNetv3/model/0/epoch24/embeddings.npy +0 -3
  40. Image/MobileNetv3/model/0/epoch24/subject_model.pth +0 -3
  41. Image/MobileNetv3/model/0/epoch25/embeddings.npy +0 -3
  42. Image/MobileNetv3/model/0/epoch25/subject_model.pth +0 -3
  43. Image/MobileNetv3/model/0/epoch3/embeddings.npy +0 -3
  44. Image/MobileNetv3/model/0/epoch3/subject_model.pth +0 -3
  45. Image/MobileNetv3/model/0/epoch4/embeddings.npy +0 -3
  46. Image/MobileNetv3/model/0/epoch4/subject_model.pth +0 -3
  47. Image/MobileNetv3/model/0/epoch5/embeddings.npy +0 -3
  48. Image/MobileNetv3/model/0/epoch5/subject_model.pth +0 -3
  49. Image/MobileNetv3/model/0/epoch6/embeddings.npy +0 -3
  50. Image/MobileNetv3/model/0/epoch6/subject_model.pth +0 -3
Image/MobileNetv3/code/backdoor_train.log DELETED
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- 2025-03-14 19:34:09,949 - train - INFO - 开始训练 mobilenetv3
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- 2025-03-14 19:34:09,950 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3
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- 2025-03-14 19:34:10,708 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.320 | Acc: 13.28%
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- 2025-03-14 19:34:13,447 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.146 | Acc: 21.54%
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- 2025-03-14 19:34:16,441 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.000 | Acc: 25.60%
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- 2025-03-14 19:34:19,272 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.921 | Acc: 28.29%
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- 2025-03-14 19:34:23,561 - train - INFO - Epoch: 1 | Test Loss: 1.631 | Test Acc: 40.55%
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- 2025-03-14 19:34:24,104 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.897 | Acc: 28.12%
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- 2025-03-14 19:34:26,866 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.680 | Acc: 36.49%
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- 2025-03-14 19:34:30,220 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.659 | Acc: 37.85%
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- 2025-03-14 19:34:33,127 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.647 | Acc: 38.59%
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- 2025-03-14 19:34:37,194 - train - INFO - Epoch: 2 | Test Loss: 1.613 | Test Acc: 42.79%
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- 2025-03-14 19:34:48,385 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.501 | Acc: 42.97%
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- 2025-03-14 19:34:51,567 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.587 | Acc: 40.91%
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- 2025-03-14 19:34:55,050 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.572 | Acc: 41.97%
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- 2025-03-14 19:35:00,286 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.556 | Acc: 42.79%
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- 2025-03-14 19:35:06,995 - train - INFO - Epoch: 3 | Test Loss: 1.529 | Test Acc: 43.45%
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- 2025-03-14 19:35:07,304 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.589 | Acc: 39.06%
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- 2025-03-14 19:35:10,973 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.471 | Acc: 46.25%
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- 2025-03-14 19:35:14,288 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.445 | Acc: 47.22%
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- 2025-03-14 19:35:17,798 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.425 | Acc: 48.28%
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- 2025-03-14 19:35:22,738 - train - INFO - Epoch: 4 | Test Loss: 1.349 | Test Acc: 51.18%
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- 2025-03-14 19:35:34,169 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.274 | Acc: 52.34%
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- 2025-03-14 19:35:37,141 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.370 | Acc: 51.04%
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- 2025-03-14 19:35:40,306 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.362 | Acc: 51.23%
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- 2025-03-14 19:35:43,178 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.360 | Acc: 51.26%
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- 2025-03-14 19:35:47,371 - train - INFO - Epoch: 5 | Test Loss: 1.291 | Test Acc: 54.39%
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- 2025-03-14 19:35:47,592 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.263 | Acc: 55.47%
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- 2025-03-14 19:35:50,454 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.312 | Acc: 52.95%
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- 2025-03-14 19:35:53,226 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.309 | Acc: 53.16%
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- 2025-03-14 19:35:55,871 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.297 | Acc: 53.53%
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- 2025-03-14 19:35:59,868 - train - INFO - Epoch: 6 | Test Loss: 1.257 | Test Acc: 54.75%
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- 2025-03-14 19:36:10,115 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.212 | Acc: 56.25%
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- 2025-03-14 19:36:13,079 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.261 | Acc: 54.90%
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- 2025-03-14 19:36:16,073 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.252 | Acc: 55.44%
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- 2025-03-14 19:36:18,985 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.249 | Acc: 55.56%
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- 2025-03-14 19:36:23,167 - train - INFO - Epoch: 7 | Test Loss: 1.328 | Test Acc: 51.79%
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- 2025-03-14 19:36:23,446 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.228 | Acc: 50.78%
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- 2025-03-14 19:36:26,398 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.213 | Acc: 57.07%
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- 2025-03-14 19:36:29,421 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.216 | Acc: 56.90%
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- 2025-03-14 19:36:32,300 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.214 | Acc: 56.96%
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- 2025-03-14 19:36:36,642 - train - INFO - Epoch: 8 | Test Loss: 1.222 | Test Acc: 55.97%
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- 2025-03-14 19:36:48,439 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.049 | Acc: 67.97%
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- 2025-03-14 19:36:51,749 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.208 | Acc: 56.95%
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- 2025-03-14 19:36:55,054 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.211 | Acc: 56.86%
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- 2025-03-14 19:36:58,052 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.210 | Acc: 57.06%
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- 2025-03-14 19:37:02,439 - train - INFO - Epoch: 9 | Test Loss: 1.255 | Test Acc: 54.03%
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- 2025-03-14 19:37:02,733 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.179 | Acc: 63.28%
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- 2025-03-14 19:37:06,315 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.209 | Acc: 57.66%
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- 2025-03-14 19:37:09,460 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.201 | Acc: 57.60%
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- 2025-03-14 19:37:12,816 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.202 | Acc: 57.50%
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- 2025-03-14 19:37:17,537 - train - INFO - Epoch: 10 | Test Loss: 1.179 | Test Acc: 57.09%
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- 2025-03-14 19:37:28,080 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.027 | Acc: 67.19%
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- 2025-03-14 19:37:30,927 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.176 | Acc: 58.61%
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- 2025-03-14 19:37:33,856 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.184 | Acc: 58.20%
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- 2025-03-14 19:37:36,622 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.180 | Acc: 58.40%
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- 2025-03-14 19:37:40,915 - train - INFO - Epoch: 11 | Test Loss: 1.203 | Test Acc: 57.33%
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- 2025-03-14 19:37:41,138 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.089 | Acc: 60.94%
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- 2025-03-14 19:37:44,042 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.183 | Acc: 58.00%
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- 2025-03-14 19:37:47,123 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.186 | Acc: 58.07%
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- 2025-03-14 19:37:50,181 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.186 | Acc: 58.21%
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- 2025-03-14 19:37:54,485 - train - INFO - Epoch: 12 | Test Loss: 1.184 | Test Acc: 58.28%
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- 2025-03-14 19:38:04,529 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.169 | Acc: 56.25%
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- 2025-03-14 19:38:07,411 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.180 | Acc: 58.57%
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- 2025-03-14 19:38:10,271 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.172 | Acc: 58.88%
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- 2025-03-14 19:38:13,588 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.170 | Acc: 58.83%
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- 2025-03-14 19:38:17,915 - train - INFO - Epoch: 13 | Test Loss: 1.277 | Test Acc: 55.18%
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- 2025-03-14 19:38:18,180 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.024 | Acc: 62.50%
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- 2025-03-14 19:38:21,018 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.148 | Acc: 59.14%
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- 2025-03-14 19:38:23,826 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.161 | Acc: 58.97%
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- 2025-03-14 19:38:26,656 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.164 | Acc: 58.82%
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- 2025-03-14 19:38:30,586 - train - INFO - Epoch: 14 | Test Loss: 1.276 | Test Acc: 55.02%
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- 2025-03-14 19:38:41,002 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 1.196 | Acc: 53.91%
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- 2025-03-14 19:38:43,862 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.157 | Acc: 59.38%
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- 2025-03-14 19:38:46,647 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.148 | Acc: 59.47%
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- 2025-03-14 19:38:49,537 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.150 | Acc: 59.41%
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- 2025-03-14 19:38:53,845 - train - INFO - Epoch: 15 | Test Loss: 1.164 | Test Acc: 59.17%
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- 2025-03-14 19:38:54,067 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.926 | Acc: 67.97%
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- 2025-03-14 19:38:56,970 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.142 | Acc: 59.78%
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- 2025-03-14 19:38:59,745 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.143 | Acc: 59.80%
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- 2025-03-14 19:39:02,600 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.146 | Acc: 59.79%
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- 2025-03-14 19:39:07,088 - train - INFO - Epoch: 16 | Test Loss: 1.145 | Test Acc: 59.72%
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- 2025-03-14 19:39:18,059 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.978 | Acc: 64.06%
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- 2025-03-14 19:39:21,062 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.159 | Acc: 59.11%
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- 2025-03-14 19:39:24,063 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.149 | Acc: 59.38%
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- 2025-03-14 19:39:27,180 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.145 | Acc: 59.82%
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- 2025-03-14 19:39:32,003 - train - INFO - Epoch: 17 | Test Loss: 1.164 | Test Acc: 59.60%
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- 2025-03-14 19:39:32,294 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.097 | Acc: 60.94%
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- 2025-03-14 19:39:35,379 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.131 | Acc: 59.47%
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- 2025-03-14 19:39:38,505 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.126 | Acc: 59.93%
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- 2025-03-14 19:39:41,838 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.124 | Acc: 60.26%
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- 2025-03-14 19:39:46,267 - train - INFO - Epoch: 18 | Test Loss: 1.178 | Test Acc: 58.70%
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- 2025-03-14 19:39:57,412 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.281 | Acc: 54.69%
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- 2025-03-14 19:40:00,334 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.131 | Acc: 60.60%
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- 2025-03-14 19:40:03,247 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.132 | Acc: 60.33%
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- 2025-03-14 19:40:06,091 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.131 | Acc: 60.40%
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- 2025-03-14 19:40:10,218 - train - INFO - Epoch: 19 | Test Loss: 1.146 | Test Acc: 59.70%
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- 2025-03-14 19:40:10,427 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.237 | Acc: 57.03%
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- 2025-03-14 19:40:13,329 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.105 | Acc: 61.37%
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- 2025-03-14 19:40:16,496 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.112 | Acc: 61.24%
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- 2025-03-14 19:40:19,346 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.119 | Acc: 61.04%
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- 2025-03-14 19:40:23,460 - train - INFO - Epoch: 20 | Test Loss: 1.178 | Test Acc: 58.22%
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- 2025-03-14 19:40:34,045 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.283 | Acc: 57.03%
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- 2025-03-14 19:40:36,967 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.125 | Acc: 60.55%
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- 2025-03-14 19:40:39,673 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.115 | Acc: 61.14%
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- 2025-03-14 19:40:42,896 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.119 | Acc: 60.80%
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- 2025-03-14 19:40:47,481 - train - INFO - Epoch: 21 | Test Loss: 1.148 | Test Acc: 58.85%
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- 2025-03-14 19:40:47,708 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 1.006 | Acc: 63.28%
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- 2025-03-14 19:40:50,647 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.109 | Acc: 61.04%
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- 2025-03-14 19:40:53,390 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.121 | Acc: 60.61%
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- 2025-03-14 19:40:56,265 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.121 | Acc: 60.59%
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- 2025-03-14 19:41:00,300 - train - INFO - Epoch: 22 | Test Loss: 1.252 | Test Acc: 56.00%
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- 2025-03-14 19:41:10,713 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.183 | Acc: 64.06%
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- 2025-03-14 19:41:13,642 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.141 | Acc: 60.41%
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- 2025-03-14 19:41:16,447 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.126 | Acc: 60.60%
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- 2025-03-14 19:41:19,466 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.126 | Acc: 60.61%
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- 2025-03-14 19:41:23,677 - train - INFO - Epoch: 23 | Test Loss: 1.355 | Test Acc: 53.98%
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- 2025-03-14 19:41:23,909 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.204 | Acc: 57.03%
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- 2025-03-14 19:41:26,799 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.111 | Acc: 60.94%
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- 2025-03-14 19:41:29,837 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.111 | Acc: 61.03%
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- 2025-03-14 19:41:32,845 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.112 | Acc: 60.86%
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- 2025-03-14 19:41:37,135 - train - INFO - Epoch: 24 | Test Loss: 1.229 | Test Acc: 56.77%
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- 2025-03-14 19:41:49,373 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.142 | Acc: 60.16%
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- 2025-03-14 19:41:52,683 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.096 | Acc: 62.07%
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- 2025-03-14 19:41:55,781 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.106 | Acc: 61.37%
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- 2025-03-14 19:41:58,961 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.105 | Acc: 61.47%
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- 2025-03-14 19:42:03,822 - train - INFO - Epoch: 25 | Test Loss: 1.208 | Test Acc: 56.94%
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- 2025-03-14 19:42:04,048 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.874 | Acc: 65.62%
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- 2025-03-14 19:42:07,041 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.107 | Acc: 60.66%
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- 2025-03-14 19:42:09,754 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.108 | Acc: 60.90%
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- 2025-03-14 19:42:12,340 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.103 | Acc: 61.09%
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- 2025-03-14 19:42:16,280 - train - INFO - Epoch: 26 | Test Loss: 1.272 | Test Acc: 54.55%
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- 2025-03-14 19:42:26,726 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.055 | Acc: 63.28%
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- 2025-03-14 19:42:29,611 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.098 | Acc: 61.42%
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- 2025-03-14 19:42:32,450 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.101 | Acc: 61.61%
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- 2025-03-14 19:42:35,164 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.110 | Acc: 61.25%
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- 2025-03-14 19:42:38,982 - train - INFO - Epoch: 27 | Test Loss: 1.093 | Test Acc: 61.80%
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- 2025-03-14 19:42:39,189 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.191 | Acc: 61.72%
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- 2025-03-14 19:42:42,143 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.113 | Acc: 60.58%
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- 2025-03-14 19:42:45,181 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.105 | Acc: 60.91%
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- 2025-03-14 19:42:48,103 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.107 | Acc: 60.91%
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- 2025-03-14 19:42:52,248 - train - INFO - Epoch: 28 | Test Loss: 1.132 | Test Acc: 60.36%
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- 2025-03-14 19:43:02,145 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.005 | Acc: 69.53%
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- 2025-03-14 19:43:04,936 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.107 | Acc: 61.42%
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- 2025-03-14 19:43:07,909 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.100 | Acc: 61.62%
146
- 2025-03-14 19:43:10,658 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.097 | Acc: 61.47%
147
- 2025-03-14 19:43:14,707 - train - INFO - Epoch: 29 | Test Loss: 1.365 | Test Acc: 52.51%
148
- 2025-03-14 19:43:14,980 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 1.134 | Acc: 56.25%
149
- 2025-03-14 19:43:17,981 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.101 | Acc: 61.53%
150
- 2025-03-14 19:43:20,945 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.097 | Acc: 61.55%
151
- 2025-03-14 19:43:23,828 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.096 | Acc: 61.62%
152
- 2025-03-14 19:43:27,912 - train - INFO - Epoch: 30 | Test Loss: 1.058 | Test Acc: 62.23%
153
- 2025-03-14 19:43:37,857 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 1.111 | Acc: 64.06%
154
- 2025-03-14 19:43:40,744 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.093 | Acc: 61.44%
155
- 2025-03-14 19:43:43,590 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.095 | Acc: 61.63%
156
- 2025-03-14 19:43:46,301 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.087 | Acc: 61.93%
157
- 2025-03-14 19:43:50,386 - train - INFO - Epoch: 31 | Test Loss: 1.227 | Test Acc: 56.04%
158
- 2025-03-14 19:43:50,594 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.883 | Acc: 70.31%
159
- 2025-03-14 19:43:53,386 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.085 | Acc: 62.06%
160
- 2025-03-14 19:43:56,058 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.093 | Acc: 61.87%
161
- 2025-03-14 19:43:58,777 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.095 | Acc: 61.84%
162
- 2025-03-14 19:44:02,708 - train - INFO - Epoch: 32 | Test Loss: 1.217 | Test Acc: 57.42%
163
- 2025-03-14 19:44:12,615 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.165 | Acc: 60.16%
164
- 2025-03-14 19:44:15,397 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.087 | Acc: 61.80%
165
- 2025-03-14 19:44:18,183 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.092 | Acc: 61.66%
166
- 2025-03-14 19:44:21,023 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.088 | Acc: 61.89%
167
- 2025-03-14 19:44:25,133 - train - INFO - Epoch: 33 | Test Loss: 1.139 | Test Acc: 59.81%
168
- 2025-03-14 19:44:25,337 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.119 | Acc: 57.81%
169
- 2025-03-14 19:44:28,094 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.081 | Acc: 62.59%
170
- 2025-03-14 19:44:30,793 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.084 | Acc: 62.30%
171
- 2025-03-14 19:44:33,468 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.088 | Acc: 62.01%
172
- 2025-03-14 19:44:37,502 - train - INFO - Epoch: 34 | Test Loss: 1.096 | Test Acc: 61.46%
173
- 2025-03-14 19:44:47,538 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.280 | Acc: 52.34%
174
- 2025-03-14 19:44:50,462 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.105 | Acc: 61.56%
175
- 2025-03-14 19:44:53,242 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.098 | Acc: 61.76%
176
- 2025-03-14 19:44:56,109 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.094 | Acc: 61.89%
177
- 2025-03-14 19:45:00,063 - train - INFO - Epoch: 35 | Test Loss: 1.179 | Test Acc: 59.79%
178
- 2025-03-14 19:45:00,274 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.913 | Acc: 69.53%
179
- 2025-03-14 19:45:03,181 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.049 | Acc: 63.35%
180
- 2025-03-14 19:45:06,116 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.066 | Acc: 62.64%
181
- 2025-03-14 19:45:09,021 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.079 | Acc: 62.11%
182
- 2025-03-14 19:45:13,026 - train - INFO - Epoch: 36 | Test Loss: 1.090 | Test Acc: 61.83%
183
- 2025-03-14 19:45:23,209 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.131 | Acc: 61.72%
184
- 2025-03-14 19:45:26,014 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.073 | Acc: 62.70%
185
- 2025-03-14 19:45:28,845 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.083 | Acc: 62.25%
186
- 2025-03-14 19:45:31,768 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.080 | Acc: 62.33%
187
- 2025-03-14 19:45:35,879 - train - INFO - Epoch: 37 | Test Loss: 1.146 | Test Acc: 58.65%
188
- 2025-03-14 19:45:36,091 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 1.102 | Acc: 56.25%
189
- 2025-03-14 19:45:38,959 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.089 | Acc: 61.91%
190
- 2025-03-14 19:45:41,799 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.082 | Acc: 62.14%
191
- 2025-03-14 19:45:44,479 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.092 | Acc: 61.85%
192
- 2025-03-14 19:45:48,389 - train - INFO - Epoch: 38 | Test Loss: 1.114 | Test Acc: 60.73%
193
- 2025-03-14 19:45:58,325 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.011 | Acc: 57.81%
194
- 2025-03-14 19:46:01,154 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.086 | Acc: 61.43%
195
- 2025-03-14 19:46:04,045 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.083 | Acc: 61.72%
196
- 2025-03-14 19:46:06,946 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.083 | Acc: 61.85%
197
- 2025-03-14 19:46:10,932 - train - INFO - Epoch: 39 | Test Loss: 1.192 | Test Acc: 58.43%
198
- 2025-03-14 19:46:11,177 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.080 | Acc: 61.72%
199
- 2025-03-14 19:46:14,380 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.103 | Acc: 62.06%
200
- 2025-03-14 19:46:17,468 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.086 | Acc: 62.45%
201
- 2025-03-14 19:46:20,564 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.084 | Acc: 62.41%
202
- 2025-03-14 19:46:24,461 - train - INFO - Epoch: 40 | Test Loss: 1.151 | Test Acc: 59.16%
203
- 2025-03-14 19:46:34,040 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 1.150 | Acc: 64.84%
204
- 2025-03-14 19:46:36,830 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.068 | Acc: 62.60%
205
- 2025-03-14 19:46:39,527 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.075 | Acc: 62.34%
206
- 2025-03-14 19:46:42,471 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.079 | Acc: 62.17%
207
- 2025-03-14 19:46:46,291 - train - INFO - Epoch: 41 | Test Loss: 1.162 | Test Acc: 59.24%
208
- 2025-03-14 19:46:46,502 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 1.153 | Acc: 60.16%
209
- 2025-03-14 19:46:49,178 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.087 | Acc: 61.93%
210
- 2025-03-14 19:46:51,859 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.092 | Acc: 62.05%
211
- 2025-03-14 19:46:54,717 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.084 | Acc: 62.22%
212
- 2025-03-14 19:46:58,764 - train - INFO - Epoch: 42 | Test Loss: 1.114 | Test Acc: 61.30%
213
- 2025-03-14 19:47:09,055 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.999 | Acc: 66.41%
214
- 2025-03-14 19:47:11,844 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.078 | Acc: 62.23%
215
- 2025-03-14 19:47:14,711 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.077 | Acc: 62.01%
216
- 2025-03-14 19:47:17,266 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.079 | Acc: 62.16%
217
- 2025-03-14 19:47:21,134 - train - INFO - Epoch: 43 | Test Loss: 1.125 | Test Acc: 60.02%
218
- 2025-03-14 19:47:21,365 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.118 | Acc: 61.72%
219
- 2025-03-14 19:47:24,096 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.069 | Acc: 62.43%
220
- 2025-03-14 19:47:26,802 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.062 | Acc: 63.04%
221
- 2025-03-14 19:47:29,582 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.069 | Acc: 62.77%
222
- 2025-03-14 19:47:33,767 - train - INFO - Epoch: 44 | Test Loss: 1.221 | Test Acc: 59.06%
223
- 2025-03-14 19:47:45,660 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.279 | Acc: 53.91%
224
- 2025-03-14 19:47:48,442 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.077 | Acc: 62.37%
225
- 2025-03-14 19:47:51,274 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.082 | Acc: 62.27%
226
- 2025-03-14 19:47:54,009 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.080 | Acc: 62.46%
227
- 2025-03-14 19:47:58,034 - train - INFO - Epoch: 45 | Test Loss: 1.185 | Test Acc: 58.87%
228
- 2025-03-14 19:47:58,263 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.007 | Acc: 67.19%
229
- 2025-03-14 19:48:01,118 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.084 | Acc: 62.31%
230
- 2025-03-14 19:48:03,842 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.073 | Acc: 62.87%
231
- 2025-03-14 19:48:06,567 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.072 | Acc: 62.83%
232
- 2025-03-14 19:48:10,468 - train - INFO - Epoch: 46 | Test Loss: 1.052 | Test Acc: 63.17%
233
- 2025-03-14 19:48:20,201 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.105 | Acc: 57.81%
234
- 2025-03-14 19:48:23,066 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.051 | Acc: 63.25%
235
- 2025-03-14 19:48:25,806 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.057 | Acc: 63.08%
236
- 2025-03-14 19:48:28,567 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.068 | Acc: 62.67%
237
- 2025-03-14 19:48:32,526 - train - INFO - Epoch: 47 | Test Loss: 1.130 | Test Acc: 60.48%
238
- 2025-03-14 19:48:32,747 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.123 | Acc: 54.69%
239
- 2025-03-14 19:48:35,546 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.069 | Acc: 62.48%
240
- 2025-03-14 19:48:38,451 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.066 | Acc: 62.38%
241
- 2025-03-14 19:48:41,132 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.074 | Acc: 62.11%
242
- 2025-03-14 19:48:44,988 - train - INFO - Epoch: 48 | Test Loss: 1.077 | Test Acc: 61.90%
243
- 2025-03-14 19:48:54,351 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.970 | Acc: 63.28%
244
- 2025-03-14 19:48:57,080 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.059 | Acc: 62.83%
245
- 2025-03-14 19:49:00,086 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.075 | Acc: 62.46%
246
- 2025-03-14 19:49:02,812 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.076 | Acc: 62.34%
247
- 2025-03-14 19:49:06,711 - train - INFO - Epoch: 49 | Test Loss: 1.124 | Test Acc: 59.80%
248
- 2025-03-14 19:49:06,901 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 1.099 | Acc: 64.06%
249
- 2025-03-14 19:49:09,703 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.057 | Acc: 63.00%
250
- 2025-03-14 19:49:12,554 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.066 | Acc: 62.73%
251
- 2025-03-14 19:49:15,667 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.065 | Acc: 62.84%
252
- 2025-03-14 19:49:20,123 - train - INFO - Epoch: 50 | Test Loss: 1.100 | Test Acc: 61.13%
253
- 2025-03-14 19:49:31,057 - train - INFO - 训练完成!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Image/MobileNetv3/code/model.py DELETED
@@ -1,263 +0,0 @@
1
- '''
2
- MobileNetV3 in PyTorch.
3
-
4
- 论文: "Searching for MobileNetV3"
5
- 参考: https://arxiv.org/abs/1905.02244
6
-
7
- 主要特点：
8
- 1. 引入基于NAS的网络架构搜索
9
- 2. 使用改进的SE注意力机块
10
- 3. 使用h-swish激活函数
11
- 4. 重新设计了网络的最后几层
12
- 5. 提供了Large和Small两个版本
13
- '''
14
-
15
- import torch
16
- import torch.nn as nn
17
- import torch.nn.functional as F
18
-
19
-
20
- def get_activation(name):
21
- '''获取激活函数
22
-
23
- Args:
24
- name: 激活函数名称 ('relu' 或 'hardswish')
25
- '''
26
- if name == 'relu':
27
- return nn.ReLU(inplace=True)
28
- elif name == 'hardswish':
29
- return nn.Hardswish(inplace=True)
30
- else:
31
- raise NotImplementedError
32
-
33
-
34
- class SEModule(nn.Module):
35
- '''Squeeze-and-Excitation模块
36
-
37
- 通过全局平均池化和两层全连接网络学习通道注意力权重
38
-
39
- Args:
40
- channel: 输入通道数
41
- reduction: 降维比例
42
- '''
43
- def __init__(self, channel, reduction=4):
44
- super(SEModule, self).__init__()
45
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
46
- self.fc = nn.Sequential(
47
- nn.Linear(channel, channel // reduction, bias=False),
48
- nn.ReLU(inplace=True),
49
- nn.Linear(channel // reduction, channel, bias=False),
50
- nn.Hardsigmoid(inplace=True)
51
- )
52
-
53
- def forward(self, x):
54
- b, c, _, _ = x.size()
55
- y = self.avg_pool(x).view(b, c) # squeeze
56
- y = self.fc(y).view(b, c, 1, 1) # excitation
57
- return x * y.expand_as(x) # scale
58
-
59
-
60
- class Bottleneck(nn.Module):
61
- '''MobileNetV3 Bottleneck
62
-
63
- 包含:
64
- 1. Expansion layer (1x1 conv)
65
- 2. Depthwise layer (3x3 or 5x5 depthwise conv)
66
- 3. SE module (optional)
67
- 4. Projection layer (1x1 conv)
68
-
69
- Args:
70
- in_channels: 输入通道数
71
- exp_channels: 扩展层通道数
72
- out_channels: 输出通道数
73
- kernel_size: 深度卷积核大小
74
- stride: 步长
75
- use_SE: 是否使用SE模块
76
- activation: 激活函数类型
77
- use_residual: 是否使用残差连接
78
- '''
79
- def __init__(self, in_channels, exp_channels, out_channels, kernel_size,
80
- stride, use_SE, activation, use_residual=True):
81
- super(Bottleneck, self).__init__()
82
- self.use_residual = use_residual and stride == 1 and in_channels == out_channels
83
- padding = (kernel_size - 1) // 2
84
-
85
- layers = []
86
- # Expansion layer
87
- if exp_channels != in_channels:
88
- layers.extend([
89
- nn.Conv2d(in_channels, exp_channels, 1, bias=False),
90
- nn.BatchNorm2d(exp_channels),
91
- get_activation(activation)
92
- ])
93
-
94
- # Depthwise conv
95
- layers.extend([
96
- nn.Conv2d(
97
- exp_channels, exp_channels, kernel_size,
98
- stride, padding, groups=exp_channels, bias=False
99
- ),
100
- nn.BatchNorm2d(exp_channels),
101
- get_activation(activation)
102
- ])
103
-
104
- # SE module
105
- if use_SE:
106
- layers.append(SEModule(exp_channels))
107
-
108
- # Projection layer
109
- layers.extend([
110
- nn.Conv2d(exp_channels, out_channels, 1, bias=False),
111
- nn.BatchNorm2d(out_channels)
112
- ])
113
-
114
- self.conv = nn.Sequential(*layers)
115
-
116
- def forward(self, x):
117
- if self.use_residual:
118
- return x + self.conv(x)
119
- else:
120
- return self.conv(x)
121
-
122
-
123
- class MobileNetV3(nn.Module):
124
- '''MobileNetV3网络
125
-
126
- Args:
127
- num_classes: 分类数量
128
- mode: 'large' 或 'small'，选择网络版本
129
- '''
130
- def __init__(self, num_classes=10, mode='small'):
131
- super(MobileNetV3, self).__init__()
132
-
133
- if mode == 'large':
134
- # MobileNetV3-Large架构
135
- self.config = [
136
- # k, exp, out, SE, activation, stride
137
- [3, 16, 16, False, 'relu', 1],
138
- [3, 64, 24, False, 'relu', 2],
139
- [3, 72, 24, False, 'relu', 1],
140
- [5, 72, 40, True, 'relu', 2],
141
- [5, 120, 40, True, 'relu', 1],
142
- [5, 120, 40, True, 'relu', 1],
143
- [3, 240, 80, False, 'hardswish', 2],
144
- [3, 200, 80, False, 'hardswish', 1],
145
- [3, 184, 80, False, 'hardswish', 1],
146
- [3, 184, 80, False, 'hardswish', 1],
147
- [3, 480, 112, True, 'hardswish', 1],
148
- [3, 672, 112, True, 'hardswish', 1],
149
- [5, 672, 160, True, 'hardswish', 2],
150
- [5, 960, 160, True, 'hardswish', 1],
151
- [5, 960, 160, True, 'hardswish', 1],
152
- ]
153
- init_conv_out = 16
154
- final_conv_out = 960
155
- else:
156
- # MobileNetV3-Small架构
157
- self.config = [
158
- # k, exp, out, SE, activation, stride
159
- [3, 16, 16, True, 'relu', 2],
160
- [3, 72, 24, False, 'relu', 2],
161
- [3, 88, 24, False, 'relu', 1],
162
- [5, 96, 40, True, 'hardswish', 2],
163
- [5, 240, 40, True, 'hardswish', 1],
164
- [5, 240, 40, True, 'hardswish', 1],
165
- [5, 120, 48, True, 'hardswish', 1],
166
- [5, 144, 48, True, 'hardswish', 1],
167
- [5, 288, 96, True, 'hardswish', 2],
168
- [5, 576, 96, True, 'hardswish', 1],
169
- [5, 576, 96, True, 'hardswish', 1],
170
- ]
171
- init_conv_out = 16
172
- final_conv_out = 576
173
-
174
- # 第一层卷积
175
- self.conv_stem = nn.Sequential(
176
- nn.Conv2d(3, init_conv_out, 3, 2, 1, bias=False),
177
- nn.BatchNorm2d(init_conv_out),
178
- get_activation('hardswish')
179
- )
180
-
181
- # 构建Bottleneck层
182
- features = []
183
- in_channels = init_conv_out
184
- for k, exp, out, se, activation, stride in self.config:
185
- features.append(
186
- Bottleneck(in_channels, exp, out, k, stride, se, activation)
187
- )
188
- in_channels = out
189
- self.features = nn.Sequential(*features)
190
-
191
- # 最后的卷积层
192
- self.conv_head = nn.Sequential(
193
- nn.Conv2d(in_channels, final_conv_out, 1, bias=False),
194
- nn.BatchNorm2d(final_conv_out),
195
- get_activation('hardswish')
196
- )
197
-
198
- # 分类器
199
- self.avgpool = nn.AdaptiveAvgPool2d(1)
200
- self.classifier = nn.Sequential(
201
- nn.Linear(final_conv_out, num_classes)
202
- )
203
-
204
- # 初始化权重
205
- self._initialize_weights()
206
-
207
- def _initialize_weights(self):
208
- '''初始化模型权重'''
209
- for m in self.modules():
210
- if isinstance(m, nn.Conv2d):
211
- nn.init.kaiming_normal_(m.weight, mode='fan_out')
212
- if m.bias is not None:
213
- nn.init.zeros_(m.bias)
214
- elif isinstance(m, nn.BatchNorm2d):
215
- nn.init.ones_(m.weight)
216
- nn.init.zeros_(m.bias)
217
- elif isinstance(m, nn.Linear):
218
- nn.init.normal_(m.weight, 0, 0.01)
219
- if m.bias is not None:
220
- nn.init.zeros_(m.bias)
221
-
222
- def forward(self, x):
223
- x = self.conv_stem(x)
224
- x = self.features(x)
225
- x = self.conv_head(x)
226
- x = self.avgpool(x)
227
- x = x.view(x.size(0), -1)
228
- x = self.classifier(x)
229
- return x
230
-
231
- def feature(self, x):
232
- x = self.conv_stem(x)
233
- x = self.features(x)
234
- x = self.conv_head(x)
235
- x = self.avgpool(x)
236
- return x
237
-
238
- def prediction(self, x):
239
- x = x.view(x.size(0), -1)
240
- x = self.classifier(x)
241
- return x
242
-
243
- def test():
244
- """测试函数"""
245
- # 测试Large版本
246
- net_large = MobileNetV3(mode='large')
247
- x = torch.randn(2, 3, 32, 32)
248
- y = net_large(x)
249
- print('Large output size:', y.size())
250
-
251
- # 测试Small版本
252
- net_small = MobileNetV3(mode='small')
253
- y = net_small(x)
254
- print('Small output size:', y.size())
255
-
256
- # 打印模型结构
257
- from torchinfo import summary
258
- device = 'cuda' if torch.cuda.is_available() else 'cpu'
259
- net_small = net_small.to(device)
260
- summary(net_small, (2, 3, 32, 32))
261
-
262
- if __name__ == '__main__':
263
- test()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Image/MobileNetv3/code/train.log DELETED
@@ -1,253 +0,0 @@
1
- 2025-03-14 19:34:03,811 - train - INFO - 开始训练 mobilenetv3
2
- 2025-03-14 19:34:03,812 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3
3
- 2025-03-14 19:34:04,510 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.315 | Acc: 8.59%
4
- 2025-03-14 19:34:07,447 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.150 | Acc: 21.51%
5
- 2025-03-14 19:34:10,386 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.973 | Acc: 26.37%
6
- 2025-03-14 19:34:13,121 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.881 | Acc: 29.73%
7
- 2025-03-14 19:34:17,285 - train - INFO - Epoch: 1 | Test Loss: 1.600 | Test Acc: 40.59%
8
- 2025-03-14 19:34:17,773 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.550 | Acc: 39.06%
9
- 2025-03-14 19:34:20,584 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.586 | Acc: 40.99%
10
- 2025-03-14 19:34:23,574 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.574 | Acc: 41.84%
11
- 2025-03-14 19:34:26,466 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.556 | Acc: 42.67%
12
- 2025-03-14 19:34:31,001 - train - INFO - Epoch: 2 | Test Loss: 1.407 | Test Acc: 48.43%
13
- 2025-03-14 19:34:41,695 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.286 | Acc: 54.69%
14
- 2025-03-14 19:34:44,711 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.447 | Acc: 47.37%
15
- 2025-03-14 19:34:47,946 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.443 | Acc: 47.33%
16
- 2025-03-14 19:34:50,944 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.441 | Acc: 47.48%
17
- 2025-03-14 19:34:56,768 - train - INFO - Epoch: 3 | Test Loss: 1.348 | Test Acc: 51.13%
18
- 2025-03-14 19:34:57,073 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.300 | Acc: 57.03%
19
- 2025-03-14 19:35:02,274 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.380 | Acc: 49.33%
20
- 2025-03-14 19:35:06,559 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.368 | Acc: 50.15%
21
- 2025-03-14 19:35:10,317 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.362 | Acc: 50.43%
22
- 2025-03-14 19:35:15,273 - train - INFO - Epoch: 4 | Test Loss: 1.359 | Test Acc: 50.22%
23
- 2025-03-14 19:35:28,028 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.331 | Acc: 52.34%
24
- 2025-03-14 19:35:31,190 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.320 | Acc: 52.00%
25
- 2025-03-14 19:35:34,388 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.321 | Acc: 51.92%
26
- 2025-03-14 19:35:37,466 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.320 | Acc: 51.83%
27
- 2025-03-14 19:35:41,929 - train - INFO - Epoch: 5 | Test Loss: 1.392 | Test Acc: 47.65%
28
- 2025-03-14 19:35:42,179 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.329 | Acc: 47.66%
29
- 2025-03-14 19:35:45,106 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.295 | Acc: 53.03%
30
- 2025-03-14 19:35:48,220 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.293 | Acc: 53.54%
31
- 2025-03-14 19:35:51,284 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.287 | Acc: 53.52%
32
- 2025-03-14 19:35:55,377 - train - INFO - Epoch: 6 | Test Loss: 1.222 | Test Acc: 56.24%
33
- 2025-03-14 19:36:05,521 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.276 | Acc: 55.47%
34
- 2025-03-14 19:36:08,675 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.254 | Acc: 54.59%
35
- 2025-03-14 19:36:11,596 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.253 | Acc: 54.56%
36
- 2025-03-14 19:36:14,501 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.252 | Acc: 54.65%
37
- 2025-03-14 19:36:18,647 - train - INFO - Epoch: 7 | Test Loss: 1.287 | Test Acc: 53.84%
38
- 2025-03-14 19:36:18,846 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.139 | Acc: 53.91%
39
- 2025-03-14 19:36:21,672 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.219 | Acc: 56.05%
40
- 2025-03-14 19:36:24,552 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.222 | Acc: 55.93%
41
- 2025-03-14 19:36:27,453 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.219 | Acc: 56.18%
42
- 2025-03-14 19:36:31,835 - train - INFO - Epoch: 8 | Test Loss: 1.185 | Test Acc: 56.67%
43
- 2025-03-14 19:36:43,478 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.177 | Acc: 63.28%
44
- 2025-03-14 19:36:46,534 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.201 | Acc: 56.69%
45
- 2025-03-14 19:36:49,936 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.210 | Acc: 56.46%
46
- 2025-03-14 19:36:52,915 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.212 | Acc: 56.35%
47
- 2025-03-14 19:36:57,694 - train - INFO - Epoch: 9 | Test Loss: 1.222 | Test Acc: 55.79%
48
- 2025-03-14 19:36:57,918 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.059 | Acc: 64.84%
49
- 2025-03-14 19:37:00,868 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.213 | Acc: 56.59%
50
- 2025-03-14 19:37:04,043 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.205 | Acc: 56.68%
51
- 2025-03-14 19:37:07,484 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.204 | Acc: 56.67%
52
- 2025-03-14 19:37:12,223 - train - INFO - Epoch: 10 | Test Loss: 1.201 | Test Acc: 56.86%
53
- 2025-03-14 19:37:23,335 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.118 | Acc: 56.25%
54
- 2025-03-14 19:37:26,292 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.184 | Acc: 57.36%
55
- 2025-03-14 19:37:29,243 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.170 | Acc: 57.91%
56
- 2025-03-14 19:37:32,097 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.178 | Acc: 57.66%
57
- 2025-03-14 19:37:36,266 - train - INFO - Epoch: 11 | Test Loss: 1.338 | Test Acc: 52.61%
58
- 2025-03-14 19:37:36,479 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.215 | Acc: 50.78%
59
- 2025-03-14 19:37:39,302 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.181 | Acc: 57.33%
60
- 2025-03-14 19:37:42,060 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.187 | Acc: 57.51%
61
- 2025-03-14 19:37:44,805 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.177 | Acc: 57.82%
62
- 2025-03-14 19:37:49,023 - train - INFO - Epoch: 12 | Test Loss: 1.194 | Test Acc: 56.89%
63
- 2025-03-14 19:37:59,417 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.232 | Acc: 53.12%
64
- 2025-03-14 19:38:02,245 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.158 | Acc: 58.08%
65
- 2025-03-14 19:38:05,015 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.163 | Acc: 58.04%
66
- 2025-03-14 19:38:07,820 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.159 | Acc: 58.26%
67
- 2025-03-14 19:38:11,959 - train - INFO - Epoch: 13 | Test Loss: 1.149 | Test Acc: 58.96%
68
- 2025-03-14 19:38:12,197 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.201 | Acc: 57.03%
69
- 2025-03-14 19:38:15,304 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.139 | Acc: 59.60%
70
- 2025-03-14 19:38:18,315 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.154 | Acc: 58.94%
71
- 2025-03-14 19:38:21,155 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.153 | Acc: 58.77%
72
- 2025-03-14 19:38:25,190 - train - INFO - Epoch: 14 | Test Loss: 1.138 | Test Acc: 59.41%
73
- 2025-03-14 19:38:35,745 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.976 | Acc: 66.41%
74
- 2025-03-14 19:38:38,693 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.149 | Acc: 59.11%
75
- 2025-03-14 19:38:41,527 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.141 | Acc: 59.21%
76
- 2025-03-14 19:38:44,307 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.145 | Acc: 59.00%
77
- 2025-03-14 19:38:48,357 - train - INFO - Epoch: 15 | Test Loss: 1.068 | Test Acc: 62.03%
78
- 2025-03-14 19:38:48,571 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 1.218 | Acc: 53.12%
79
- 2025-03-14 19:38:51,535 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.121 | Acc: 59.31%
80
- 2025-03-14 19:38:54,473 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.124 | Acc: 59.33%
81
- 2025-03-14 19:38:57,299 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.128 | Acc: 59.45%
82
- 2025-03-14 19:39:01,582 - train - INFO - Epoch: 16 | Test Loss: 1.117 | Test Acc: 60.35%
83
- 2025-03-14 19:39:12,941 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 1.216 | Acc: 60.16%
84
- 2025-03-14 19:39:15,823 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.128 | Acc: 59.86%
85
- 2025-03-14 19:39:18,876 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.124 | Acc: 59.88%
86
- 2025-03-14 19:39:21,926 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.131 | Acc: 59.47%
87
- 2025-03-14 19:39:26,176 - train - INFO - Epoch: 17 | Test Loss: 1.115 | Test Acc: 60.26%
88
- 2025-03-14 19:39:26,410 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.197 | Acc: 54.69%
89
- 2025-03-14 19:39:29,699 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.118 | Acc: 60.14%
90
- 2025-03-14 19:39:33,109 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.124 | Acc: 59.79%
91
- 2025-03-14 19:39:36,272 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.131 | Acc: 59.71%
92
- 2025-03-14 19:39:40,820 - train - INFO - Epoch: 18 | Test Loss: 1.250 | Test Acc: 55.97%
93
- 2025-03-14 19:39:51,937 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.201 | Acc: 52.34%
94
- 2025-03-14 19:39:55,019 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.120 | Acc: 60.21%
95
- 2025-03-14 19:39:57,784 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.121 | Acc: 60.15%
96
- 2025-03-14 19:40:00,471 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.119 | Acc: 60.19%
97
- 2025-03-14 19:40:04,498 - train - INFO - Epoch: 19 | Test Loss: 1.198 | Test Acc: 57.94%
98
- 2025-03-14 19:40:04,737 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.173 | Acc: 59.38%
99
- 2025-03-14 19:40:07,499 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.108 | Acc: 60.23%
100
- 2025-03-14 19:40:10,339 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.119 | Acc: 60.20%
101
- 2025-03-14 19:40:13,119 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.112 | Acc: 60.39%
102
- 2025-03-14 19:40:17,467 - train - INFO - Epoch: 20 | Test Loss: 1.308 | Test Acc: 54.57%
103
- 2025-03-14 19:40:27,692 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.066 | Acc: 59.38%
104
- 2025-03-14 19:40:30,553 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.133 | Acc: 59.80%
105
- 2025-03-14 19:40:33,490 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.120 | Acc: 60.18%
106
- 2025-03-14 19:40:36,428 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.118 | Acc: 60.36%
107
- 2025-03-14 19:40:40,344 - train - INFO - Epoch: 21 | Test Loss: 1.159 | Test Acc: 58.64%
108
- 2025-03-14 19:40:40,605 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.997 | Acc: 63.28%
109
- 2025-03-14 19:40:44,155 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.100 | Acc: 60.76%
110
- 2025-03-14 19:40:47,367 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.096 | Acc: 60.92%
111
- 2025-03-14 19:40:50,149 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.099 | Acc: 60.99%
112
- 2025-03-14 19:40:54,308 - train - INFO - Epoch: 22 | Test Loss: 1.089 | Test Acc: 61.60%
113
- 2025-03-14 19:41:04,532 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.029 | Acc: 66.41%
114
- 2025-03-14 19:41:07,395 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.086 | Acc: 61.11%
115
- 2025-03-14 19:41:10,298 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.086 | Acc: 61.38%
116
- 2025-03-14 19:41:13,267 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.091 | Acc: 61.09%
117
- 2025-03-14 19:41:17,403 - train - INFO - Epoch: 23 | Test Loss: 1.116 | Test Acc: 60.21%
118
- 2025-03-14 19:41:17,641 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.143 | Acc: 66.41%
119
- 2025-03-14 19:41:20,685 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.091 | Acc: 61.22%
120
- 2025-03-14 19:41:23,621 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.091 | Acc: 61.34%
121
- 2025-03-14 19:41:26,610 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.092 | Acc: 61.26%
122
- 2025-03-14 19:41:30,833 - train - INFO - Epoch: 24 | Test Loss: 1.095 | Test Acc: 61.01%
123
- 2025-03-14 19:41:42,216 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.079 | Acc: 64.84%
124
- 2025-03-14 19:41:45,275 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.082 | Acc: 61.22%
125
- 2025-03-14 19:41:48,865 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.084 | Acc: 61.43%
126
- 2025-03-14 19:41:52,343 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.078 | Acc: 61.63%
127
- 2025-03-14 19:41:56,861 - train - INFO - Epoch: 25 | Test Loss: 1.052 | Test Acc: 61.88%
128
- 2025-03-14 19:41:57,122 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 1.047 | Acc: 63.28%
129
- 2025-03-14 19:42:00,289 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.087 | Acc: 61.63%
130
- 2025-03-14 19:42:03,526 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.087 | Acc: 61.79%
131
- 2025-03-14 19:42:06,632 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.083 | Acc: 61.67%
132
- 2025-03-14 19:42:10,746 - train - INFO - Epoch: 26 | Test Loss: 1.086 | Test Acc: 61.65%
133
- 2025-03-14 19:42:20,622 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.134 | Acc: 61.72%
134
- 2025-03-14 19:42:23,602 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.076 | Acc: 61.42%
135
- 2025-03-14 19:42:26,568 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.072 | Acc: 61.75%
136
- 2025-03-14 19:42:29,502 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.079 | Acc: 61.47%
137
- 2025-03-14 19:42:33,566 - train - INFO - Epoch: 27 | Test Loss: 1.135 | Test Acc: 60.40%
138
- 2025-03-14 19:42:33,802 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.246 | Acc: 50.00%
139
- 2025-03-14 19:42:36,640 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.069 | Acc: 62.02%
140
- 2025-03-14 19:42:39,573 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.075 | Acc: 61.68%
141
- 2025-03-14 19:42:42,773 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.077 | Acc: 61.52%
142
- 2025-03-14 19:42:46,877 - train - INFO - Epoch: 28 | Test Loss: 1.093 | Test Acc: 61.21%
143
- 2025-03-14 19:42:56,985 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.170 | Acc: 57.03%
144
- 2025-03-14 19:42:59,789 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.070 | Acc: 62.21%
145
- 2025-03-14 19:43:02,512 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.067 | Acc: 62.04%
146
- 2025-03-14 19:43:05,191 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.068 | Acc: 61.89%
147
- 2025-03-14 19:43:09,441 - train - INFO - Epoch: 29 | Test Loss: 1.108 | Test Acc: 61.02%
148
- 2025-03-14 19:43:09,674 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.939 | Acc: 67.19%
149
- 2025-03-14 19:43:12,474 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.060 | Acc: 61.67%
150
- 2025-03-14 19:43:15,323 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.067 | Acc: 61.65%
151
- 2025-03-14 19:43:18,162 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.060 | Acc: 62.02%
152
- 2025-03-14 19:43:22,287 - train - INFO - Epoch: 30 | Test Loss: 1.144 | Test Acc: 60.34%
153
- 2025-03-14 19:43:32,512 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.962 | Acc: 65.62%
154
- 2025-03-14 19:43:35,242 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.066 | Acc: 61.82%
155
- 2025-03-14 19:43:37,851 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.071 | Acc: 61.85%
156
- 2025-03-14 19:43:40,703 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.067 | Acc: 61.91%
157
- 2025-03-14 19:43:44,687 - train - INFO - Epoch: 31 | Test Loss: 1.087 | Test Acc: 61.81%
158
- 2025-03-14 19:43:44,939 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 1.026 | Acc: 60.16%
159
- 2025-03-14 19:43:47,798 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.075 | Acc: 61.95%
160
- 2025-03-14 19:43:50,802 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.061 | Acc: 62.41%
161
- 2025-03-14 19:43:53,659 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.064 | Acc: 62.34%
162
- 2025-03-14 19:43:57,726 - train - INFO - Epoch: 32 | Test Loss: 1.134 | Test Acc: 60.53%
163
- 2025-03-14 19:44:07,467 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.038 | Acc: 67.97%
164
- 2025-03-14 19:44:10,465 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.072 | Acc: 61.79%
165
- 2025-03-14 19:44:13,127 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.060 | Acc: 62.29%
166
- 2025-03-14 19:44:15,841 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.064 | Acc: 62.27%
167
- 2025-03-14 19:44:19,860 - train - INFO - Epoch: 33 | Test Loss: 1.025 | Test Acc: 63.50%
168
- 2025-03-14 19:44:20,077 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.182 | Acc: 58.59%
169
- 2025-03-14 19:44:23,002 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.041 | Acc: 62.76%
170
- 2025-03-14 19:44:26,161 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.044 | Acc: 62.88%
171
- 2025-03-14 19:44:29,066 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.052 | Acc: 62.61%
172
- 2025-03-14 19:44:32,974 - train - INFO - Epoch: 34 | Test Loss: 1.165 | Test Acc: 59.01%
173
- 2025-03-14 19:44:43,069 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.273 | Acc: 57.03%
174
- 2025-03-14 19:44:45,817 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.028 | Acc: 63.61%
175
- 2025-03-14 19:44:48,611 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.047 | Acc: 63.04%
176
- 2025-03-14 19:44:51,395 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.053 | Acc: 62.75%
177
- 2025-03-14 19:44:55,612 - train - INFO - Epoch: 35 | Test Loss: 1.135 | Test Acc: 60.02%
178
- 2025-03-14 19:44:55,809 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.908 | Acc: 67.97%
179
- 2025-03-14 19:44:58,620 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.051 | Acc: 62.64%
180
- 2025-03-14 19:45:01,440 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.051 | Acc: 62.66%
181
- 2025-03-14 19:45:04,528 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.053 | Acc: 62.72%
182
- 2025-03-14 19:45:08,941 - train - INFO - Epoch: 36 | Test Loss: 1.187 | Test Acc: 57.95%
183
- 2025-03-14 19:45:18,855 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.220 | Acc: 55.47%
184
- 2025-03-14 19:45:21,638 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.021 | Acc: 63.85%
185
- 2025-03-14 19:45:24,446 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.041 | Acc: 63.18%
186
- 2025-03-14 19:45:27,235 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.036 | Acc: 63.54%
187
- 2025-03-14 19:45:31,429 - train - INFO - Epoch: 37 | Test Loss: 1.055 | Test Acc: 62.86%
188
- 2025-03-14 19:45:31,653 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.931 | Acc: 62.50%
189
- 2025-03-14 19:45:34,563 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.018 | Acc: 63.77%
190
- 2025-03-14 19:45:37,461 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.037 | Acc: 63.12%
191
- 2025-03-14 19:45:40,390 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.049 | Acc: 62.78%
192
- 2025-03-14 19:45:44,376 - train - INFO - Epoch: 38 | Test Loss: 1.055 | Test Acc: 61.96%
193
- 2025-03-14 19:45:54,849 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.256 | Acc: 60.94%
194
- 2025-03-14 19:45:57,611 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.041 | Acc: 63.05%
195
- 2025-03-14 19:46:00,366 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.043 | Acc: 62.62%
196
- 2025-03-14 19:46:03,308 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.040 | Acc: 62.69%
197
- 2025-03-14 19:46:07,524 - train - INFO - Epoch: 39 | Test Loss: 1.217 | Test Acc: 58.12%
198
- 2025-03-14 19:46:07,739 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.104 | Acc: 60.94%
199
- 2025-03-14 19:46:10,580 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.027 | Acc: 63.51%
200
- 2025-03-14 19:46:13,745 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.030 | Acc: 63.62%
201
- 2025-03-14 19:46:16,973 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.042 | Acc: 63.13%
202
- 2025-03-14 19:46:21,340 - train - INFO - Epoch: 40 | Test Loss: 1.219 | Test Acc: 58.91%
203
- 2025-03-14 19:46:30,937 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.936 | Acc: 65.62%
204
- 2025-03-14 19:46:33,767 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.036 | Acc: 63.67%
205
- 2025-03-14 19:46:36,441 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.043 | Acc: 63.02%
206
- 2025-03-14 19:46:39,112 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.041 | Acc: 63.14%
207
- 2025-03-14 19:46:43,225 - train - INFO - Epoch: 41 | Test Loss: 1.087 | Test Acc: 61.82%
208
- 2025-03-14 19:46:43,446 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.974 | Acc: 67.97%
209
- 2025-03-14 19:46:46,463 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.031 | Acc: 63.10%
210
- 2025-03-14 19:46:49,321 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.046 | Acc: 62.82%
211
- 2025-03-14 19:46:52,065 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.040 | Acc: 62.91%
212
- 2025-03-14 19:46:56,121 - train - INFO - Epoch: 42 | Test Loss: 1.088 | Test Acc: 61.92%
213
- 2025-03-14 19:47:06,480 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.959 | Acc: 64.84%
214
- 2025-03-14 19:47:09,338 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.025 | Acc: 63.60%
215
- 2025-03-14 19:47:12,303 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.028 | Acc: 63.58%
216
- 2025-03-14 19:47:15,185 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.034 | Acc: 63.35%
217
- 2025-03-14 19:47:19,050 - train - INFO - Epoch: 43 | Test Loss: 1.164 | Test Acc: 59.61%
218
- 2025-03-14 19:47:19,264 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.079 | Acc: 55.47%
219
- 2025-03-14 19:47:22,105 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.025 | Acc: 63.51%
220
- 2025-03-14 19:47:24,823 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.025 | Acc: 63.38%
221
- 2025-03-14 19:47:27,612 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.028 | Acc: 63.24%
222
- 2025-03-14 19:47:31,900 - train - INFO - Epoch: 44 | Test Loss: 1.046 | Test Acc: 62.74%
223
- 2025-03-14 19:47:42,877 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.033 | Acc: 62.50%
224
- 2025-03-14 19:47:45,774 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.033 | Acc: 63.99%
225
- 2025-03-14 19:47:48,457 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.038 | Acc: 63.33%
226
- 2025-03-14 19:47:51,350 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.043 | Acc: 62.95%
227
- 2025-03-14 19:47:55,429 - train - INFO - Epoch: 45 | Test Loss: 1.055 | Test Acc: 62.21%
228
- 2025-03-14 19:47:55,644 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.122 | Acc: 57.03%
229
- 2025-03-14 19:47:58,618 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.020 | Acc: 63.92%
230
- 2025-03-14 19:48:01,531 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.039 | Acc: 63.16%
231
- 2025-03-14 19:48:04,384 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.039 | Acc: 63.08%
232
- 2025-03-14 19:48:08,515 - train - INFO - Epoch: 46 | Test Loss: 1.151 | Test Acc: 58.80%
233
- 2025-03-14 19:48:18,563 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.005 | Acc: 61.72%
234
- 2025-03-14 19:48:21,390 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.011 | Acc: 64.18%
235
- 2025-03-14 19:48:24,102 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.029 | Acc: 63.72%
236
- 2025-03-14 19:48:26,782 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.028 | Acc: 63.71%
237
- 2025-03-14 19:48:30,851 - train - INFO - Epoch: 47 | Test Loss: 1.091 | Test Acc: 61.88%
238
- 2025-03-14 19:48:31,086 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.034 | Acc: 66.41%
239
- 2025-03-14 19:48:33,899 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.013 | Acc: 63.99%
240
- 2025-03-14 19:48:36,777 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.031 | Acc: 63.33%
241
- 2025-03-14 19:48:39,701 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.034 | Acc: 63.14%
242
- 2025-03-14 19:48:43,738 - train - INFO - Epoch: 48 | Test Loss: 1.051 | Test Acc: 63.01%
243
- 2025-03-14 19:48:53,341 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.940 | Acc: 69.53%
244
- 2025-03-14 19:48:56,129 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.022 | Acc: 63.68%
245
- 2025-03-14 19:48:59,194 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.027 | Acc: 63.31%
246
- 2025-03-14 19:49:02,012 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.029 | Acc: 63.34%
247
- 2025-03-14 19:49:06,039 - train - INFO - Epoch: 49 | Test Loss: 1.045 | Test Acc: 63.33%
248
- 2025-03-14 19:49:06,237 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.902 | Acc: 63.28%
249
- 2025-03-14 19:49:09,007 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.025 | Acc: 63.56%
250
- 2025-03-14 19:49:11,762 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.026 | Acc: 63.59%
251
- 2025-03-14 19:49:14,634 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.031 | Acc: 63.42%
252
- 2025-03-14 19:49:19,058 - train - INFO - Epoch: 50 | Test Loss: 1.069 | Test Acc: 62.55%
253
- 2025-03-14 19:49:30,812 - train - INFO - 训练完成!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Image/MobileNetv3/code/train.py DELETED
@@ -1,63 +0,0 @@
1
- import sys
2
- import os
3
- sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
4
- from utils.dataset_utils import get_cifar10_dataloaders
5
- from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
6
- from utils.parse_args import parse_args
7
- from model import MobileNetV3
8
-
9
- def main():
10
- # 解析命令行参数
11
- args = parse_args()
12
-
13
- # 创建模型
14
- model = MobileNetV3()
15
-
16
- if args.train_type == '0':
17
- # 获取数据加载器
18
- trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path)
19
- # 训练模型
20
- train_model(
21
- model=model,
22
- trainloader=trainloader,
23
- testloader=testloader,
24
- epochs=args.epochs,
25
- lr=args.lr,
26
- device=f'cuda:{args.gpu}',
27
- save_dir='../model',
28
- model_name='mobilenetv3',
29
- save_type='0',
30
- layer_name='avgpool',
31
- interval=2
32
- )
33
- elif args.train_type == '1':
34
- train_model_data_augmentation(
35
- model,
36
- epochs=args.epochs,
37
- lr=args.lr,
38
- device=f'cuda:{args.gpu}',
39
- save_dir='../model',
40
- model_name='mobilenetv3',
41
- batch_size=args.batch_size,
42
- num_workers=args.num_workers,
43
- local_dataset_path=args.dataset_path
44
- )
45
- elif args.train_type == '2':
46
- train_model_backdoor(
47
- model,
48
- poison_ratio=args.poison_ratio,
49
- target_label=args.target_label,
50
- epochs=args.epochs,
51
- lr=args.lr,
52
- device=f'cuda:{args.gpu}',
53
- save_dir='../model',
54
- model_name='mobilenetv3',
55
- batch_size=args.batch_size,
56
- num_workers=args.num_workers,
57
- local_dataset_path=args.dataset_path,
58
- layer_name='avgpool',
59
- interval=2
60
- )
61
-
62
- if __name__ == '__main__':
63
- main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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