diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..6e2dba63113c5218604029e4af4ddb62509233f7 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,56 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text 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b/Image/AlexNet/code/backdoor_train.log new file mode 100644 index 0000000000000000000000000000000000000000..fb1dc4430509df44169bab14d9a8ef6a3b1f0917 --- /dev/null +++ b/Image/AlexNet/code/backdoor_train.log @@ -0,0 +1,503 @@ +2025-03-09 20:19:12,058 - train - INFO - 开始训练 alexnet +2025-03-09 20:19:12,058 - train - INFO - 总轮数: 100, 学习率: 0.1, 设备: cuda:2 +2025-03-09 20:19:12,675 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.303 | Acc: 16.41% +2025-03-09 20:19:14,626 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.271 | Acc: 18.68% +2025-03-09 20:19:16,523 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.251 | Acc: 19.08% +2025-03-09 20:19:18,515 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 2.190 | Acc: 19.65% +2025-03-09 20:19:21,395 - train - INFO - Epoch: 1 | Test Loss: 1.966 | Test Acc: 18.53% +2025-03-09 20:19:21,531 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 2.000 | Acc: 20.31% +2025-03-09 20:19:23,427 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.985 | Acc: 20.66% +2025-03-09 20:19:25,413 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.976 | Acc: 20.93% +2025-03-09 20:19:27,414 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.963 | Acc: 21.40% +2025-03-09 20:19:30,365 - train - INFO - Epoch: 2 | Test Loss: 1.889 | Test Acc: 20.28% +2025-03-09 20:19:30,566 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.928 | Acc: 26.56% +2025-03-09 20:19:33,166 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.907 | Acc: 24.49% +2025-03-09 20:19:35,301 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.885 | Acc: 25.66% +2025-03-09 20:19:37,244 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.861 | Acc: 26.84% +2025-03-09 20:19:40,542 - train - INFO - Epoch: 3 | Test Loss: 1.751 | Test Acc: 28.73% +2025-03-09 20:19:40,699 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.752 | Acc: 31.25% +2025-03-09 20:19:42,615 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.688 | Acc: 34.92% +2025-03-09 20:19:44,581 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.674 | Acc: 35.46% +2025-03-09 20:19:46,517 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.670 | Acc: 35.93% +2025-03-09 20:19:49,443 - train - INFO - Epoch: 4 | Test Loss: 1.597 | Test Acc: 40.89% +2025-03-09 20:19:57,639 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.450 | Acc: 47.66% +2025-03-09 20:19:59,573 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.591 | Acc: 40.60% +2025-03-09 20:20:01,613 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.572 | Acc: 41.28% +2025-03-09 20:20:03,589 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.564 | Acc: 41.79% +2025-03-09 20:20:06,878 - train - INFO - Epoch: 5 | Test Loss: 1.498 | Test Acc: 46.10% +2025-03-09 20:20:07,049 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.323 | Acc: 53.12% +2025-03-09 20:20:09,159 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.504 | Acc: 44.93% +2025-03-09 20:20:11,263 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.507 | Acc: 45.20% +2025-03-09 20:20:13,313 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.511 | Acc: 45.22% +2025-03-09 20:20:16,255 - train - INFO - Epoch: 6 | Test Loss: 1.616 | Test Acc: 43.10% +2025-03-09 20:20:16,412 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.604 | Acc: 40.62% +2025-03-09 20:20:18,415 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.507 | Acc: 44.99% +2025-03-09 20:20:20,351 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.481 | Acc: 46.18% +2025-03-09 20:20:22,266 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.494 | Acc: 46.05% +2025-03-09 20:20:25,325 - train - INFO - Epoch: 7 | Test Loss: 1.543 | Test Acc: 43.40% +2025-03-09 20:20:25,492 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.399 | Acc: 49.22% +2025-03-09 20:20:27,700 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.477 | Acc: 47.11% +2025-03-09 20:20:29,710 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.464 | Acc: 47.44% +2025-03-09 20:20:31,913 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.461 | Acc: 47.83% +2025-03-09 20:20:35,491 - train - INFO - Epoch: 8 | Test Loss: 1.713 | Test Acc: 42.90% +2025-03-09 20:20:44,721 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.387 | Acc: 50.78% +2025-03-09 20:20:46,712 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.450 | Acc: 48.65% +2025-03-09 20:20:48,602 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.446 | Acc: 48.56% +2025-03-09 20:20:50,410 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.449 | Acc: 48.34% +2025-03-09 20:20:53,243 - train - INFO - Epoch: 9 | Test Loss: 1.560 | Test Acc: 44.42% +2025-03-09 20:20:53,419 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.467 | Acc: 47.66% +2025-03-09 20:20:55,289 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.438 | Acc: 48.78% +2025-03-09 20:20:57,200 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.437 | Acc: 49.22% +2025-03-09 20:20:59,140 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.425 | Acc: 49.63% +2025-03-09 20:21:02,190 - train - INFO - Epoch: 10 | Test Loss: 1.399 | Test Acc: 51.59% +2025-03-09 20:21:02,359 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.214 | Acc: 60.94% +2025-03-09 20:21:04,254 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.449 | Acc: 48.41% +2025-03-09 20:21:06,162 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.457 | Acc: 48.33% +2025-03-09 20:21:08,031 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.449 | Acc: 48.74% +2025-03-09 20:21:11,037 - train - INFO - Epoch: 11 | Test Loss: 1.485 | Test Acc: 49.63% +2025-03-09 20:21:11,206 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.293 | Acc: 51.56% +2025-03-09 20:21:13,154 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.422 | Acc: 50.02% +2025-03-09 20:21:15,086 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.406 | Acc: 50.85% +2025-03-09 20:21:17,146 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.413 | Acc: 50.54% +2025-03-09 20:21:20,196 - train - INFO - Epoch: 12 | Test Loss: 1.515 | Test Acc: 46.31% +2025-03-09 20:21:28,840 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.368 | Acc: 46.88% +2025-03-09 20:21:30,993 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.425 | Acc: 50.31% +2025-03-09 20:21:33,129 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.418 | Acc: 50.51% +2025-03-09 20:21:35,080 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.422 | Acc: 50.37% +2025-03-09 20:21:38,299 - train - INFO - Epoch: 13 | Test Loss: 1.688 | Test Acc: 43.65% +2025-03-09 20:21:38,495 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.597 | Acc: 46.88% +2025-03-09 20:21:40,470 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.417 | Acc: 50.60% +2025-03-09 20:21:42,443 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.441 | Acc: 49.58% +2025-03-09 20:21:44,403 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.439 | Acc: 49.61% +2025-03-09 20:21:47,387 - train - INFO - Epoch: 14 | Test Loss: 1.390 | Test Acc: 53.03% +2025-03-09 20:21:47,552 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 1.497 | Acc: 50.78% +2025-03-09 20:21:49,502 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.401 | Acc: 51.78% +2025-03-09 20:21:51,470 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.405 | Acc: 51.27% +2025-03-09 20:21:53,392 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.413 | Acc: 51.07% +2025-03-09 20:21:56,330 - train - INFO - Epoch: 15 | Test Loss: 1.420 | Test Acc: 51.08% +2025-03-09 20:21:56,494 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 1.452 | Acc: 45.31% +2025-03-09 20:21:58,628 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.414 | Acc: 50.87% +2025-03-09 20:22:00,738 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.409 | Acc: 51.10% +2025-03-09 20:22:02,889 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.410 | Acc: 51.06% +2025-03-09 20:22:06,141 - train - INFO - Epoch: 16 | Test Loss: 1.340 | Test Acc: 54.35% +2025-03-09 20:22:15,047 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 1.409 | Acc: 50.00% +2025-03-09 20:22:17,210 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.392 | Acc: 52.21% +2025-03-09 20:22:19,647 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.388 | Acc: 52.15% +2025-03-09 20:22:21,842 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.386 | Acc: 52.22% +2025-03-09 20:22:25,119 - train - INFO - Epoch: 17 | Test Loss: 1.341 | Test Acc: 53.27% +2025-03-09 20:22:25,281 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.476 | Acc: 46.09% +2025-03-09 20:22:27,733 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.405 | Acc: 52.00% +2025-03-09 20:22:29,947 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.408 | Acc: 51.39% +2025-03-09 20:22:31,944 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.402 | Acc: 51.70% +2025-03-09 20:22:35,223 - train - INFO - Epoch: 18 | Test Loss: 1.446 | Test Acc: 49.65% +2025-03-09 20:22:35,389 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.405 | Acc: 50.78% +2025-03-09 20:22:37,625 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.415 | Acc: 51.73% +2025-03-09 20:22:39,692 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.413 | Acc: 51.99% +2025-03-09 20:22:41,607 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.405 | Acc: 52.11% +2025-03-09 20:22:44,549 - train - INFO - Epoch: 19 | Test Loss: 1.407 | Test Acc: 52.25% +2025-03-09 20:22:44,717 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.496 | Acc: 52.34% +2025-03-09 20:22:46,695 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.393 | Acc: 52.61% +2025-03-09 20:22:48,558 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.391 | Acc: 52.68% +2025-03-09 20:22:50,490 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.386 | Acc: 52.47% +2025-03-09 20:22:53,448 - train - INFO - Epoch: 20 | Test Loss: 1.386 | Test Acc: 52.54% +2025-03-09 20:23:02,248 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.271 | Acc: 53.91% +2025-03-09 20:23:04,411 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.377 | Acc: 52.96% +2025-03-09 20:23:06,656 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.362 | Acc: 53.40% +2025-03-09 20:23:08,804 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.365 | Acc: 53.34% +2025-03-09 20:23:11,945 - train - INFO - Epoch: 21 | Test Loss: 1.409 | Test Acc: 52.31% +2025-03-09 20:23:12,122 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 1.437 | Acc: 52.34% +2025-03-09 20:23:14,013 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.347 | Acc: 53.79% +2025-03-09 20:23:15,873 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.363 | Acc: 52.99% +2025-03-09 20:23:17,764 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.364 | Acc: 53.03% +2025-03-09 20:23:20,673 - train - INFO - Epoch: 22 | Test Loss: 1.469 | Test Acc: 49.71% +2025-03-09 20:23:20,830 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.354 | Acc: 49.22% +2025-03-09 20:23:22,923 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.379 | Acc: 52.44% +2025-03-09 20:23:24,848 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.379 | Acc: 52.37% +2025-03-09 20:23:26,896 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.374 | Acc: 52.68% +2025-03-09 20:23:30,039 - train - INFO - Epoch: 23 | Test Loss: 1.362 | Test Acc: 52.68% +2025-03-09 20:23:30,193 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.161 | Acc: 61.72% +2025-03-09 20:23:32,373 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.389 | Acc: 52.10% +2025-03-09 20:23:34,420 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.380 | Acc: 52.79% +2025-03-09 20:23:36,633 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.385 | Acc: 52.65% +2025-03-09 20:23:39,821 - train - INFO - Epoch: 24 | Test Loss: 1.370 | Test Acc: 52.61% +2025-03-09 20:23:48,489 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.393 | Acc: 53.12% +2025-03-09 20:23:50,419 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.372 | Acc: 53.69% +2025-03-09 20:23:52,355 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.400 | Acc: 52.04% +2025-03-09 20:23:54,234 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.385 | Acc: 52.35% +2025-03-09 20:23:57,196 - train - INFO - Epoch: 25 | Test Loss: 1.394 | Test Acc: 52.26% +2025-03-09 20:23:57,352 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 1.450 | Acc: 59.38% +2025-03-09 20:23:59,265 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.348 | Acc: 54.16% +2025-03-09 20:24:01,145 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.334 | Acc: 54.64% +2025-03-09 20:24:03,153 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.333 | Acc: 54.60% +2025-03-09 20:24:06,282 - train - INFO - Epoch: 26 | Test Loss: 1.348 | Test Acc: 53.70% +2025-03-09 20:24:06,508 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.219 | Acc: 58.59% +2025-03-09 20:24:08,538 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.338 | Acc: 54.12% +2025-03-09 20:24:10,668 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.350 | Acc: 53.35% +2025-03-09 20:24:12,748 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.349 | Acc: 53.77% +2025-03-09 20:24:15,793 - train - INFO - Epoch: 27 | Test Loss: 1.359 | Test Acc: 52.39% +2025-03-09 20:24:15,977 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.496 | Acc: 47.66% +2025-03-09 20:24:17,971 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.372 | Acc: 52.45% +2025-03-09 20:24:19,834 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.352 | Acc: 53.57% +2025-03-09 20:24:21,705 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.358 | Acc: 53.43% +2025-03-09 20:24:24,689 - train - INFO - Epoch: 28 | Test Loss: 1.377 | Test Acc: 54.25% +2025-03-09 20:24:33,155 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.412 | Acc: 48.44% +2025-03-09 20:24:35,070 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.370 | Acc: 53.01% +2025-03-09 20:24:37,032 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.367 | Acc: 53.06% +2025-03-09 20:24:39,067 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.353 | Acc: 53.75% +2025-03-09 20:24:42,247 - train - INFO - Epoch: 29 | Test Loss: 1.488 | Test Acc: 47.56% +2025-03-09 20:24:42,420 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 1.462 | Acc: 48.44% +2025-03-09 20:24:44,388 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.386 | Acc: 52.71% +2025-03-09 20:24:46,476 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.375 | Acc: 53.26% +2025-03-09 20:24:48,451 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.367 | Acc: 53.45% +2025-03-09 20:24:51,659 - train - INFO - Epoch: 30 | Test Loss: 1.347 | Test Acc: 52.89% +2025-03-09 20:24:51,817 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 1.475 | Acc: 51.56% +2025-03-09 20:24:53,919 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.358 | Acc: 54.30% +2025-03-09 20:24:56,164 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.349 | Acc: 54.70% +2025-03-09 20:24:58,081 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.339 | Acc: 54.88% +2025-03-09 20:25:00,872 - train - INFO - Epoch: 31 | Test Loss: 1.342 | Test Acc: 54.27% +2025-03-09 20:25:01,010 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 1.281 | Acc: 56.25% +2025-03-09 20:25:02,872 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.343 | Acc: 54.04% +2025-03-09 20:25:04,904 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.345 | Acc: 53.87% +2025-03-09 20:25:06,912 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.336 | Acc: 54.46% +2025-03-09 20:25:09,820 - train - INFO - Epoch: 32 | Test Loss: 1.399 | Test Acc: 53.36% +2025-03-09 20:25:18,309 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.352 | Acc: 57.81% +2025-03-09 20:25:20,363 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.311 | Acc: 55.27% +2025-03-09 20:25:22,301 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.311 | Acc: 55.33% +2025-03-09 20:25:24,323 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.303 | Acc: 55.76% +2025-03-09 20:25:27,526 - train - INFO - Epoch: 33 | Test Loss: 1.348 | Test Acc: 55.64% +2025-03-09 20:25:27,697 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.294 | Acc: 52.34% +2025-03-09 20:25:29,828 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.319 | Acc: 55.61% +2025-03-09 20:25:31,968 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.313 | Acc: 55.77% +2025-03-09 20:25:34,273 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.317 | Acc: 55.53% +2025-03-09 20:25:37,696 - train - INFO - Epoch: 34 | Test Loss: 1.453 | Test Acc: 52.69% +2025-03-09 20:25:37,860 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.607 | Acc: 48.44% +2025-03-09 20:25:40,073 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.330 | Acc: 55.03% +2025-03-09 20:25:42,446 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.314 | Acc: 55.59% +2025-03-09 20:25:44,548 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.313 | Acc: 55.58% +2025-03-09 20:25:47,695 - train - INFO - Epoch: 35 | Test Loss: 1.366 | Test Acc: 53.31% +2025-03-09 20:25:47,852 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 1.387 | Acc: 53.91% +2025-03-09 20:25:49,788 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.331 | Acc: 55.28% +2025-03-09 20:25:51,705 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.331 | Acc: 55.26% +2025-03-09 20:25:53,699 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.332 | Acc: 55.45% +2025-03-09 20:25:56,774 - train - INFO - Epoch: 36 | Test Loss: 1.273 | Test Acc: 56.38% +2025-03-09 20:26:05,606 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.296 | Acc: 58.59% +2025-03-09 20:26:07,890 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.324 | Acc: 54.72% +2025-03-09 20:26:09,936 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.321 | Acc: 55.64% +2025-03-09 20:26:12,029 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.315 | Acc: 55.72% +2025-03-09 20:26:15,068 - train - INFO - Epoch: 37 | Test Loss: 1.290 | Test Acc: 56.71% +2025-03-09 20:26:15,238 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 1.452 | Acc: 51.56% +2025-03-09 20:26:17,226 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.312 | Acc: 56.12% +2025-03-09 20:26:19,216 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.300 | Acc: 56.18% +2025-03-09 20:26:21,112 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.306 | Acc: 55.91% +2025-03-09 20:26:23,992 - train - INFO - Epoch: 38 | Test Loss: 1.361 | Test Acc: 54.99% +2025-03-09 20:26:24,159 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.445 | Acc: 52.34% +2025-03-09 20:26:26,133 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.322 | Acc: 54.90% +2025-03-09 20:26:28,117 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.314 | Acc: 55.84% +2025-03-09 20:26:30,147 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.314 | Acc: 55.66% +2025-03-09 20:26:33,109 - train - INFO - Epoch: 39 | Test Loss: 1.350 | Test Acc: 54.42% +2025-03-09 20:26:33,294 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.216 | Acc: 60.16% +2025-03-09 20:26:35,443 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.298 | Acc: 56.68% +2025-03-09 20:26:38,192 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.292 | Acc: 56.84% +2025-03-09 20:26:40,191 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.288 | Acc: 56.83% +2025-03-09 20:26:43,736 - train - INFO - Epoch: 40 | Test Loss: 1.414 | Test Acc: 53.13% +2025-03-09 20:26:52,109 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 1.310 | Acc: 50.78% +2025-03-09 20:26:53,954 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.288 | Acc: 56.18% +2025-03-09 20:26:55,875 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.305 | Acc: 55.85% +2025-03-09 20:26:57,745 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.307 | Acc: 56.09% +2025-03-09 20:27:00,677 - train - INFO - Epoch: 41 | Test Loss: 1.363 | Test Acc: 53.66% +2025-03-09 20:27:00,850 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 1.274 | Acc: 55.47% +2025-03-09 20:27:02,898 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.287 | Acc: 56.66% +2025-03-09 20:27:04,913 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.295 | Acc: 56.11% +2025-03-09 20:27:07,000 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.310 | Acc: 55.73% +2025-03-09 20:27:10,353 - train - INFO - Epoch: 42 | Test Loss: 1.267 | Test Acc: 55.87% +2025-03-09 20:27:10,566 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 1.068 | Acc: 61.72% +2025-03-09 20:27:12,538 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.276 | Acc: 57.14% +2025-03-09 20:27:14,555 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.281 | Acc: 56.92% +2025-03-09 20:27:16,452 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.294 | Acc: 56.41% +2025-03-09 20:27:19,499 - train - INFO - Epoch: 43 | Test Loss: 1.402 | Test Acc: 54.51% +2025-03-09 20:27:19,668 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.519 | Acc: 47.66% +2025-03-09 20:27:21,706 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.300 | Acc: 56.28% +2025-03-09 20:27:23,755 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.310 | Acc: 55.94% +2025-03-09 20:27:25,686 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.307 | Acc: 56.03% +2025-03-09 20:27:28,811 - train - INFO - Epoch: 44 | Test Loss: 1.234 | Test Acc: 58.47% +2025-03-09 20:27:37,172 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.269 | Acc: 58.59% +2025-03-09 20:27:39,166 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.280 | Acc: 56.24% +2025-03-09 20:27:41,156 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.286 | Acc: 56.27% +2025-03-09 20:27:43,004 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.286 | Acc: 56.40% +2025-03-09 20:27:45,868 - train - INFO - Epoch: 45 | Test Loss: 1.282 | Test Acc: 55.78% +2025-03-09 20:27:46,023 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.239 | Acc: 51.56% +2025-03-09 20:27:47,975 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.302 | Acc: 55.83% +2025-03-09 20:27:49,950 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.266 | Acc: 57.14% +2025-03-09 20:27:52,118 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.255 | Acc: 57.51% +2025-03-09 20:27:55,154 - train - INFO - Epoch: 46 | Test Loss: 1.309 | Test Acc: 56.11% +2025-03-09 20:27:55,469 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.226 | Acc: 57.03% +2025-03-09 20:27:57,480 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.251 | Acc: 57.84% +2025-03-09 20:27:59,499 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.258 | Acc: 57.76% +2025-03-09 20:28:01,442 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.252 | Acc: 58.02% +2025-03-09 20:28:04,566 - train - INFO - Epoch: 47 | Test Loss: 1.253 | Test Acc: 57.28% +2025-03-09 20:28:04,739 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.394 | Acc: 53.12% +2025-03-09 20:28:06,693 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.278 | Acc: 56.41% +2025-03-09 20:28:08,637 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.268 | Acc: 57.14% +2025-03-09 20:28:10,711 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.262 | Acc: 57.50% +2025-03-09 20:28:13,848 - train - INFO - Epoch: 48 | Test Loss: 1.249 | Test Acc: 58.69% +2025-03-09 20:28:22,849 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 1.235 | Acc: 60.94% +2025-03-09 20:28:24,718 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.262 | Acc: 57.60% +2025-03-09 20:28:26,655 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.252 | Acc: 57.92% +2025-03-09 20:28:28,615 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.256 | Acc: 57.75% +2025-03-09 20:28:31,510 - train - INFO - Epoch: 49 | Test Loss: 1.236 | Test Acc: 59.64% +2025-03-09 20:28:31,684 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 1.417 | Acc: 52.34% +2025-03-09 20:28:33,617 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.226 | Acc: 58.89% +2025-03-09 20:28:35,516 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.262 | Acc: 57.55% +2025-03-09 20:28:37,408 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.257 | Acc: 57.70% +2025-03-09 20:28:40,309 - train - INFO - Epoch: 50 | Test Loss: 1.257 | Test Acc: 57.34% +2025-03-09 20:28:40,473 - train - INFO - Epoch: 51 | Batch: 0 | Loss: 1.113 | Acc: 63.28% +2025-03-09 20:28:42,519 - train - INFO - Epoch: 51 | Batch: 100 | Loss: 1.259 | Acc: 58.03% +2025-03-09 20:28:44,502 - train - INFO - Epoch: 51 | Batch: 200 | Loss: 1.264 | Acc: 57.69% +2025-03-09 20:28:46,631 - train - INFO - Epoch: 51 | Batch: 300 | Loss: 1.269 | Acc: 57.29% +2025-03-09 20:28:49,638 - train - INFO - Epoch: 51 | Test Loss: 1.281 | Test Acc: 57.03% +2025-03-09 20:28:49,812 - train - INFO - Epoch: 52 | Batch: 0 | Loss: 1.257 | Acc: 55.47% +2025-03-09 20:28:51,845 - train - INFO - Epoch: 52 | Batch: 100 | Loss: 1.262 | Acc: 57.78% +2025-03-09 20:28:53,837 - train - INFO - Epoch: 52 | Batch: 200 | Loss: 1.265 | Acc: 57.56% +2025-03-09 20:28:55,995 - train - INFO - Epoch: 52 | Batch: 300 | Loss: 1.261 | Acc: 57.63% +2025-03-09 20:28:59,048 - train - INFO - Epoch: 52 | Test Loss: 1.331 | Test Acc: 53.97% +2025-03-09 20:29:07,981 - train - INFO - Epoch: 53 | Batch: 0 | Loss: 1.271 | Acc: 53.91% +2025-03-09 20:29:09,928 - train - INFO - Epoch: 53 | Batch: 100 | Loss: 1.254 | Acc: 58.34% +2025-03-09 20:29:11,910 - train - INFO - Epoch: 53 | Batch: 200 | Loss: 1.252 | Acc: 58.11% +2025-03-09 20:29:13,867 - train - INFO - Epoch: 53 | Batch: 300 | Loss: 1.262 | Acc: 57.70% +2025-03-09 20:29:16,853 - train - INFO - Epoch: 53 | Test Loss: 1.308 | Test Acc: 57.07% +2025-03-09 20:29:17,032 - train - INFO - Epoch: 54 | Batch: 0 | Loss: 1.168 | Acc: 60.16% +2025-03-09 20:29:18,963 - train - INFO - Epoch: 54 | Batch: 100 | Loss: 1.250 | Acc: 57.74% +2025-03-09 20:29:20,860 - train - INFO - Epoch: 54 | Batch: 200 | Loss: 1.238 | Acc: 58.21% +2025-03-09 20:29:22,743 - train - INFO - Epoch: 54 | Batch: 300 | Loss: 1.241 | Acc: 58.10% +2025-03-09 20:29:25,703 - train - INFO - Epoch: 54 | Test Loss: 1.218 | Test Acc: 59.11% +2025-03-09 20:29:25,847 - train - INFO - Epoch: 55 | Batch: 0 | Loss: 1.268 | Acc: 60.16% +2025-03-09 20:29:27,805 - train - INFO - Epoch: 55 | Batch: 100 | Loss: 1.251 | Acc: 57.75% +2025-03-09 20:29:29,824 - train - INFO - Epoch: 55 | Batch: 200 | Loss: 1.233 | Acc: 58.28% +2025-03-09 20:29:31,744 - train - INFO - Epoch: 55 | Batch: 300 | Loss: 1.229 | Acc: 58.43% +2025-03-09 20:29:34,830 - train - INFO - Epoch: 55 | Test Loss: 1.157 | Test Acc: 61.97% +2025-03-09 20:29:35,053 - train - INFO - Epoch: 56 | Batch: 0 | Loss: 1.206 | Acc: 57.81% +2025-03-09 20:29:37,115 - train - INFO - Epoch: 56 | Batch: 100 | Loss: 1.258 | Acc: 57.43% +2025-03-09 20:29:39,236 - train - INFO - Epoch: 56 | Batch: 200 | Loss: 1.241 | Acc: 58.07% +2025-03-09 20:29:41,226 - train - INFO - Epoch: 56 | Batch: 300 | Loss: 1.233 | Acc: 58.52% +2025-03-09 20:29:44,437 - train - INFO - Epoch: 56 | Test Loss: 1.167 | Test Acc: 60.27% +2025-03-09 20:29:52,970 - train - INFO - Epoch: 57 | Batch: 0 | Loss: 1.121 | Acc: 60.16% +2025-03-09 20:29:55,013 - train - INFO - Epoch: 57 | Batch: 100 | Loss: 1.195 | Acc: 60.04% +2025-03-09 20:29:57,034 - train - INFO - Epoch: 57 | Batch: 200 | Loss: 1.213 | Acc: 59.17% +2025-03-09 20:29:58,980 - train - INFO - Epoch: 57 | Batch: 300 | Loss: 1.219 | Acc: 58.95% +2025-03-09 20:30:01,938 - train - INFO - Epoch: 57 | Test Loss: 1.274 | Test Acc: 57.75% +2025-03-09 20:30:02,116 - train - INFO - Epoch: 58 | Batch: 0 | Loss: 1.284 | Acc: 53.12% +2025-03-09 20:30:04,029 - train - INFO - Epoch: 58 | Batch: 100 | Loss: 1.250 | Acc: 58.62% +2025-03-09 20:30:06,039 - train - INFO - Epoch: 58 | Batch: 200 | Loss: 1.243 | Acc: 58.39% +2025-03-09 20:30:08,038 - train - INFO - Epoch: 58 | Batch: 300 | Loss: 1.240 | Acc: 58.48% +2025-03-09 20:30:11,145 - train - INFO - Epoch: 58 | Test Loss: 1.232 | Test Acc: 58.94% +2025-03-09 20:30:11,327 - train - INFO - Epoch: 59 | Batch: 0 | Loss: 1.047 | Acc: 63.28% +2025-03-09 20:30:13,348 - train - INFO - Epoch: 59 | Batch: 100 | Loss: 1.210 | Acc: 59.13% +2025-03-09 20:30:15,556 - train - INFO - Epoch: 59 | Batch: 200 | Loss: 1.208 | Acc: 59.16% +2025-03-09 20:30:17,594 - train - INFO - Epoch: 59 | Batch: 300 | Loss: 1.212 | Acc: 59.30% +2025-03-09 20:30:20,606 - train - INFO - Epoch: 59 | Test Loss: 1.165 | Test Acc: 62.08% +2025-03-09 20:30:20,783 - train - INFO - Epoch: 60 | Batch: 0 | Loss: 1.238 | Acc: 55.47% +2025-03-09 20:30:22,724 - train - INFO - Epoch: 60 | Batch: 100 | Loss: 1.218 | Acc: 58.86% +2025-03-09 20:30:24,630 - train - INFO - Epoch: 60 | Batch: 200 | Loss: 1.215 | Acc: 58.99% +2025-03-09 20:30:26,703 - train - INFO - Epoch: 60 | Batch: 300 | Loss: 1.215 | Acc: 59.11% +2025-03-09 20:30:29,708 - train - INFO - Epoch: 60 | Test Loss: 1.178 | Test Acc: 60.43% +2025-03-09 20:30:38,516 - train - INFO - Epoch: 61 | Batch: 0 | Loss: 1.265 | Acc: 58.59% +2025-03-09 20:30:40,669 - train - INFO - Epoch: 61 | Batch: 100 | Loss: 1.206 | Acc: 59.23% +2025-03-09 20:30:42,615 - train - INFO - Epoch: 61 | Batch: 200 | Loss: 1.196 | Acc: 59.98% +2025-03-09 20:30:44,531 - train - INFO - Epoch: 61 | Batch: 300 | Loss: 1.183 | Acc: 60.42% +2025-03-09 20:30:47,435 - train - INFO - Epoch: 61 | Test Loss: 1.302 | Test Acc: 57.91% +2025-03-09 20:30:47,597 - train - INFO - Epoch: 62 | Batch: 0 | Loss: 1.109 | Acc: 64.06% +2025-03-09 20:30:49,444 - train - INFO - Epoch: 62 | Batch: 100 | Loss: 1.195 | Acc: 60.26% +2025-03-09 20:30:51,346 - train - INFO - Epoch: 62 | Batch: 200 | Loss: 1.189 | Acc: 60.26% +2025-03-09 20:30:53,324 - train - INFO - Epoch: 62 | Batch: 300 | Loss: 1.198 | Acc: 59.84% +2025-03-09 20:30:56,425 - train - INFO - Epoch: 62 | Test Loss: 1.144 | Test Acc: 61.91% +2025-03-09 20:30:56,594 - train - INFO - Epoch: 63 | Batch: 0 | Loss: 1.158 | Acc: 57.81% +2025-03-09 20:30:58,593 - train - INFO - Epoch: 63 | Batch: 100 | Loss: 1.202 | Acc: 60.19% +2025-03-09 20:31:00,631 - train - INFO - Epoch: 63 | Batch: 200 | Loss: 1.201 | Acc: 60.02% +2025-03-09 20:31:02,630 - train - INFO - Epoch: 63 | Batch: 300 | Loss: 1.205 | Acc: 59.69% +2025-03-09 20:31:06,449 - train - INFO - Epoch: 63 | Test Loss: 1.192 | Test Acc: 60.56% +2025-03-09 20:31:06,659 - train - INFO - Epoch: 64 | Batch: 0 | Loss: 1.170 | Acc: 67.97% +2025-03-09 20:31:08,897 - train - INFO - Epoch: 64 | Batch: 100 | Loss: 1.166 | Acc: 60.61% +2025-03-09 20:31:11,221 - train - INFO - Epoch: 64 | Batch: 200 | Loss: 1.184 | Acc: 60.09% +2025-03-09 20:31:13,224 - train - INFO - Epoch: 64 | Batch: 300 | Loss: 1.188 | Acc: 60.04% +2025-03-09 20:31:16,234 - train - INFO - Epoch: 64 | Test Loss: 1.150 | Test Acc: 62.05% +2025-03-09 20:31:24,778 - train - INFO - Epoch: 65 | Batch: 0 | Loss: 1.002 | Acc: 61.72% +2025-03-09 20:31:26,799 - train - INFO - Epoch: 65 | Batch: 100 | Loss: 1.141 | Acc: 61.68% +2025-03-09 20:31:28,890 - train - INFO - Epoch: 65 | Batch: 200 | Loss: 1.169 | Acc: 60.47% +2025-03-09 20:31:30,932 - train - INFO - Epoch: 65 | Batch: 300 | Loss: 1.173 | Acc: 60.45% +2025-03-09 20:31:34,033 - train - INFO - Epoch: 65 | Test Loss: 1.124 | Test Acc: 62.72% +2025-03-09 20:31:34,227 - train - INFO - Epoch: 66 | Batch: 0 | Loss: 1.387 | Acc: 53.91% +2025-03-09 20:31:36,268 - train - INFO - Epoch: 66 | Batch: 100 | Loss: 1.168 | Acc: 61.05% +2025-03-09 20:31:38,189 - train - INFO - Epoch: 66 | Batch: 200 | Loss: 1.175 | Acc: 61.00% +2025-03-09 20:31:40,070 - train - INFO - Epoch: 66 | Batch: 300 | Loss: 1.175 | Acc: 60.98% +2025-03-09 20:31:42,970 - train - INFO - Epoch: 66 | Test Loss: 1.184 | Test Acc: 61.65% +2025-03-09 20:31:43,120 - train - INFO - Epoch: 67 | Batch: 0 | Loss: 1.150 | Acc: 61.72% +2025-03-09 20:31:45,207 - train - INFO - Epoch: 67 | Batch: 100 | Loss: 1.149 | Acc: 61.84% +2025-03-09 20:31:47,200 - train - INFO - Epoch: 67 | Batch: 200 | Loss: 1.171 | Acc: 61.07% +2025-03-09 20:31:49,096 - train - INFO - Epoch: 67 | Batch: 300 | Loss: 1.165 | Acc: 61.19% +2025-03-09 20:31:52,455 - train - INFO - Epoch: 67 | Test Loss: 1.216 | Test Acc: 60.41% +2025-03-09 20:31:52,629 - train - INFO - Epoch: 68 | Batch: 0 | Loss: 1.103 | Acc: 61.72% +2025-03-09 20:31:54,772 - train - INFO - Epoch: 68 | Batch: 100 | Loss: 1.161 | Acc: 61.22% +2025-03-09 20:31:56,740 - train - INFO - Epoch: 68 | Batch: 200 | Loss: 1.154 | Acc: 61.48% +2025-03-09 20:31:59,231 - train - INFO - Epoch: 68 | Batch: 300 | Loss: 1.162 | Acc: 61.29% +2025-03-09 20:32:02,617 - train - INFO - Epoch: 68 | Test Loss: 1.161 | Test Acc: 61.05% +2025-03-09 20:32:10,865 - train - INFO - Epoch: 69 | Batch: 0 | Loss: 1.238 | Acc: 55.47% +2025-03-09 20:32:12,884 - train - INFO - Epoch: 69 | Batch: 100 | Loss: 1.173 | Acc: 60.76% +2025-03-09 20:32:14,815 - train - INFO - Epoch: 69 | Batch: 200 | Loss: 1.175 | Acc: 60.64% +2025-03-09 20:32:16,725 - train - INFO - Epoch: 69 | Batch: 300 | Loss: 1.183 | Acc: 60.28% +2025-03-09 20:32:19,765 - train - INFO - Epoch: 69 | Test Loss: 1.230 | Test Acc: 58.66% +2025-03-09 20:32:19,932 - train - INFO - Epoch: 70 | Batch: 0 | Loss: 1.389 | Acc: 54.69% +2025-03-09 20:32:22,033 - train - INFO - Epoch: 70 | Batch: 100 | Loss: 1.158 | Acc: 61.07% +2025-03-09 20:32:24,089 - train - INFO - Epoch: 70 | Batch: 200 | Loss: 1.157 | Acc: 60.95% +2025-03-09 20:32:26,117 - train - INFO - Epoch: 70 | Batch: 300 | Loss: 1.166 | Acc: 60.71% +2025-03-09 20:32:29,298 - train - INFO - Epoch: 70 | Test Loss: 1.226 | Test Acc: 59.05% +2025-03-09 20:32:29,467 - train - INFO - Epoch: 71 | Batch: 0 | Loss: 1.339 | Acc: 55.47% +2025-03-09 20:32:31,438 - train - INFO - Epoch: 71 | Batch: 100 | Loss: 1.167 | Acc: 60.74% +2025-03-09 20:32:33,454 - train - INFO - Epoch: 71 | Batch: 200 | Loss: 1.162 | Acc: 61.08% +2025-03-09 20:32:35,462 - train - INFO - Epoch: 71 | Batch: 300 | Loss: 1.162 | Acc: 61.09% +2025-03-09 20:32:38,625 - train - INFO - Epoch: 71 | Test Loss: 1.196 | Test Acc: 60.13% +2025-03-09 20:32:38,825 - train - INFO - Epoch: 72 | Batch: 0 | Loss: 1.065 | Acc: 67.97% +2025-03-09 20:32:41,013 - train - INFO - Epoch: 72 | Batch: 100 | Loss: 1.153 | Acc: 61.68% +2025-03-09 20:32:43,148 - train - INFO - Epoch: 72 | Batch: 200 | Loss: 1.139 | Acc: 62.09% +2025-03-09 20:32:45,102 - train - INFO - Epoch: 72 | Batch: 300 | Loss: 1.142 | Acc: 61.86% +2025-03-09 20:32:48,052 - train - INFO - Epoch: 72 | Test Loss: 1.178 | Test Acc: 60.15% +2025-03-09 20:32:56,719 - train - INFO - Epoch: 73 | Batch: 0 | Loss: 1.149 | Acc: 57.81% +2025-03-09 20:32:58,840 - train - INFO - Epoch: 73 | Batch: 100 | Loss: 1.124 | Acc: 62.13% +2025-03-09 20:33:01,060 - train - INFO - Epoch: 73 | Batch: 200 | Loss: 1.124 | Acc: 62.22% +2025-03-09 20:33:02,959 - train - INFO - Epoch: 73 | Batch: 300 | Loss: 1.124 | Acc: 62.25% +2025-03-09 20:33:06,081 - train - INFO - Epoch: 73 | Test Loss: 1.108 | Test Acc: 62.58% +2025-03-09 20:33:06,242 - train - INFO - Epoch: 74 | Batch: 0 | Loss: 0.976 | Acc: 64.06% +2025-03-09 20:33:08,117 - train - INFO - Epoch: 74 | Batch: 100 | Loss: 1.129 | Acc: 61.83% +2025-03-09 20:33:10,070 - train - INFO - Epoch: 74 | Batch: 200 | Loss: 1.141 | Acc: 61.62% +2025-03-09 20:33:12,009 - train - INFO - Epoch: 74 | Batch: 300 | Loss: 1.139 | Acc: 61.84% +2025-03-09 20:33:14,980 - train - INFO - Epoch: 74 | Test Loss: 1.232 | Test Acc: 57.82% +2025-03-09 20:33:15,160 - train - INFO - Epoch: 75 | Batch: 0 | Loss: 1.104 | Acc: 60.94% +2025-03-09 20:33:17,074 - train - INFO - Epoch: 75 | Batch: 100 | Loss: 1.153 | Acc: 61.48% +2025-03-09 20:33:18,961 - train - INFO - Epoch: 75 | Batch: 200 | Loss: 1.149 | Acc: 61.54% +2025-03-09 20:33:20,780 - train - INFO - Epoch: 75 | Batch: 300 | Loss: 1.141 | Acc: 61.77% +2025-03-09 20:33:23,699 - train - INFO - Epoch: 75 | Test Loss: 1.157 | Test Acc: 61.00% +2025-03-09 20:33:23,864 - train - INFO - Epoch: 76 | Batch: 0 | Loss: 1.193 | Acc: 60.94% +2025-03-09 20:33:25,763 - train - INFO - Epoch: 76 | Batch: 100 | Loss: 1.147 | Acc: 61.41% +2025-03-09 20:33:27,728 - train - INFO - Epoch: 76 | Batch: 200 | Loss: 1.138 | Acc: 61.87% +2025-03-09 20:33:29,731 - train - INFO - Epoch: 76 | Batch: 300 | Loss: 1.143 | Acc: 61.68% +2025-03-09 20:33:32,727 - train - INFO - Epoch: 76 | Test Loss: 1.101 | Test Acc: 63.51% +2025-03-09 20:33:41,051 - train - INFO - Epoch: 77 | Batch: 0 | Loss: 1.110 | Acc: 59.38% +2025-03-09 20:33:42,990 - train - INFO - Epoch: 77 | Batch: 100 | Loss: 1.108 | Acc: 62.87% +2025-03-09 20:33:44,886 - train - INFO - Epoch: 77 | Batch: 200 | Loss: 1.115 | Acc: 62.29% +2025-03-09 20:33:46,815 - train - INFO - Epoch: 77 | Batch: 300 | Loss: 1.119 | Acc: 62.45% +2025-03-09 20:33:49,764 - train - INFO - Epoch: 77 | Test Loss: 1.099 | Test Acc: 63.53% +2025-03-09 20:33:49,916 - train - INFO - Epoch: 78 | Batch: 0 | Loss: 1.111 | Acc: 64.84% +2025-03-09 20:33:51,904 - train - INFO - Epoch: 78 | Batch: 100 | Loss: 1.127 | Acc: 62.42% +2025-03-09 20:33:53,862 - train - INFO - Epoch: 78 | Batch: 200 | Loss: 1.127 | Acc: 62.17% +2025-03-09 20:33:55,835 - train - INFO - Epoch: 78 | Batch: 300 | Loss: 1.125 | Acc: 62.13% +2025-03-09 20:33:58,919 - train - INFO - Epoch: 78 | Test Loss: 1.075 | Test Acc: 63.41% +2025-03-09 20:33:59,092 - train - INFO - Epoch: 79 | Batch: 0 | Loss: 0.990 | Acc: 67.19% +2025-03-09 20:34:01,066 - train - INFO - Epoch: 79 | Batch: 100 | Loss: 1.107 | Acc: 62.40% +2025-03-09 20:34:03,169 - train - INFO - Epoch: 79 | Batch: 200 | Loss: 1.105 | Acc: 62.84% +2025-03-09 20:34:05,127 - train - INFO - Epoch: 79 | Batch: 300 | Loss: 1.102 | Acc: 63.05% +2025-03-09 20:34:08,053 - train - INFO - Epoch: 79 | Test Loss: 1.190 | Test Acc: 60.83% +2025-03-09 20:34:08,244 - train - INFO - Epoch: 80 | Batch: 0 | Loss: 1.227 | Acc: 60.16% +2025-03-09 20:34:10,084 - train - INFO - Epoch: 80 | Batch: 100 | Loss: 1.120 | Acc: 62.61% +2025-03-09 20:34:11,957 - train - INFO - Epoch: 80 | Batch: 200 | Loss: 1.102 | Acc: 62.97% +2025-03-09 20:34:13,864 - train - INFO - Epoch: 80 | Batch: 300 | Loss: 1.109 | Acc: 62.86% +2025-03-09 20:34:16,933 - train - INFO - Epoch: 80 | Test Loss: 1.065 | Test Acc: 64.60% +2025-03-09 20:34:25,319 - train - INFO - Epoch: 81 | Batch: 0 | Loss: 1.093 | Acc: 60.16% +2025-03-09 20:34:27,214 - train - INFO - Epoch: 81 | Batch: 100 | Loss: 1.133 | Acc: 62.27% +2025-03-09 20:34:29,084 - train - INFO - Epoch: 81 | Batch: 200 | Loss: 1.107 | Acc: 63.35% +2025-03-09 20:34:31,102 - train - INFO - Epoch: 81 | Batch: 300 | Loss: 1.102 | Acc: 63.25% +2025-03-09 20:34:34,224 - train - INFO - Epoch: 81 | Test Loss: 1.085 | Test Acc: 63.63% +2025-03-09 20:34:34,386 - train - INFO - Epoch: 82 | Batch: 0 | Loss: 1.183 | Acc: 58.59% +2025-03-09 20:34:36,276 - train - INFO - Epoch: 82 | Batch: 100 | Loss: 1.093 | Acc: 63.10% +2025-03-09 20:34:38,370 - train - INFO - Epoch: 82 | Batch: 200 | Loss: 1.084 | Acc: 63.45% +2025-03-09 20:34:40,329 - train - INFO - Epoch: 82 | Batch: 300 | Loss: 1.083 | Acc: 63.66% +2025-03-09 20:34:43,386 - train - INFO - Epoch: 82 | Test Loss: 1.124 | Test Acc: 62.13% +2025-03-09 20:34:43,538 - train - INFO - Epoch: 83 | Batch: 0 | Loss: 1.178 | Acc: 57.81% +2025-03-09 20:34:45,536 - train - INFO - Epoch: 83 | Batch: 100 | Loss: 1.103 | Acc: 63.26% +2025-03-09 20:34:47,538 - train - INFO - Epoch: 83 | Batch: 200 | Loss: 1.090 | Acc: 63.46% +2025-03-09 20:34:49,608 - train - INFO - Epoch: 83 | Batch: 300 | Loss: 1.091 | Acc: 63.37% +2025-03-09 20:34:52,626 - train - INFO - Epoch: 83 | Test Loss: 1.165 | Test Acc: 62.11% +2025-03-09 20:34:52,802 - train - INFO - Epoch: 84 | Batch: 0 | Loss: 1.103 | Acc: 67.19% +2025-03-09 20:34:54,966 - train - INFO - Epoch: 84 | Batch: 100 | Loss: 1.115 | Acc: 62.58% +2025-03-09 20:34:57,031 - train - INFO - Epoch: 84 | Batch: 200 | Loss: 1.091 | Acc: 63.27% +2025-03-09 20:34:59,134 - train - INFO - Epoch: 84 | Batch: 300 | Loss: 1.097 | Acc: 63.19% +2025-03-09 20:35:02,352 - train - INFO - Epoch: 84 | Test Loss: 1.085 | Test Acc: 63.95% +2025-03-09 20:35:10,685 - train - INFO - Epoch: 85 | Batch: 0 | Loss: 1.311 | Acc: 59.38% +2025-03-09 20:35:12,612 - train - INFO - Epoch: 85 | Batch: 100 | Loss: 1.084 | Acc: 63.76% +2025-03-09 20:35:14,479 - train - INFO - Epoch: 85 | Batch: 200 | Loss: 1.078 | Acc: 64.00% +2025-03-09 20:35:16,456 - train - INFO - Epoch: 85 | Batch: 300 | Loss: 1.092 | Acc: 63.53% +2025-03-09 20:35:19,502 - train - INFO - Epoch: 85 | Test Loss: 1.063 | Test Acc: 63.69% +2025-03-09 20:35:19,675 - train - INFO - Epoch: 86 | Batch: 0 | Loss: 0.918 | Acc: 71.88% +2025-03-09 20:35:21,627 - train - INFO - Epoch: 86 | Batch: 100 | Loss: 1.078 | Acc: 63.58% +2025-03-09 20:35:23,603 - train - INFO - Epoch: 86 | Batch: 200 | Loss: 1.080 | Acc: 63.76% +2025-03-09 20:35:25,467 - train - INFO - Epoch: 86 | Batch: 300 | Loss: 1.081 | Acc: 63.70% +2025-03-09 20:35:28,448 - train - INFO - Epoch: 86 | Test Loss: 1.040 | Test Acc: 65.00% +2025-03-09 20:35:28,618 - train - INFO - Epoch: 87 | Batch: 0 | Loss: 1.037 | Acc: 64.06% +2025-03-09 20:35:30,607 - train - INFO - Epoch: 87 | Batch: 100 | Loss: 1.045 | Acc: 64.91% +2025-03-09 20:35:32,555 - train - INFO - Epoch: 87 | Batch: 200 | Loss: 1.054 | Acc: 64.58% +2025-03-09 20:35:34,461 - train - INFO - Epoch: 87 | Batch: 300 | Loss: 1.055 | Acc: 64.54% +2025-03-09 20:35:37,534 - train - INFO - Epoch: 87 | Test Loss: 1.117 | Test Acc: 63.18% +2025-03-09 20:35:37,728 - train - INFO - Epoch: 88 | Batch: 0 | Loss: 1.114 | Acc: 64.06% +2025-03-09 20:35:39,667 - train - INFO - Epoch: 88 | Batch: 100 | Loss: 1.081 | Acc: 63.65% +2025-03-09 20:35:41,724 - train - INFO - Epoch: 88 | Batch: 200 | Loss: 1.089 | Acc: 63.46% +2025-03-09 20:35:43,693 - train - INFO - Epoch: 88 | Batch: 300 | Loss: 1.080 | Acc: 63.89% +2025-03-09 20:35:46,704 - train - INFO - Epoch: 88 | Test Loss: 1.116 | Test Acc: 62.02% +2025-03-09 20:35:55,157 - train - INFO - Epoch: 89 | Batch: 0 | Loss: 1.281 | Acc: 59.38% +2025-03-09 20:35:57,082 - train - INFO - Epoch: 89 | Batch: 100 | Loss: 1.027 | Acc: 65.76% +2025-03-09 20:35:59,072 - train - INFO - Epoch: 89 | Batch: 200 | Loss: 1.056 | Acc: 64.67% +2025-03-09 20:36:00,922 - train - INFO - Epoch: 89 | Batch: 300 | Loss: 1.067 | Acc: 64.10% +2025-03-09 20:36:03,799 - train - INFO - Epoch: 89 | Test Loss: 1.044 | Test Acc: 64.42% +2025-03-09 20:36:03,955 - train - INFO - Epoch: 90 | Batch: 0 | Loss: 1.228 | Acc: 53.91% +2025-03-09 20:36:05,961 - train - INFO - Epoch: 90 | Batch: 100 | Loss: 1.043 | Acc: 64.84% +2025-03-09 20:36:48,800 - train - INFO - Epoch: 90 | Batch: 200 | Loss: 1.040 | Acc: 64.89% +2025-03-09 20:36:50,792 - train - INFO - Epoch: 90 | Batch: 300 | Loss: 1.049 | Acc: 64.79% +2025-03-09 20:36:53,914 - train - INFO - Epoch: 90 | Test Loss: 1.150 | Test Acc: 61.58% +2025-03-09 20:36:54,094 - train - INFO - Epoch: 91 | Batch: 0 | Loss: 1.127 | Acc: 59.38% +2025-03-09 20:36:56,089 - train - INFO - Epoch: 91 | Batch: 100 | Loss: 1.053 | Acc: 64.09% +2025-03-09 20:36:58,254 - train - INFO - Epoch: 91 | Batch: 200 | Loss: 1.043 | Acc: 64.86% +2025-03-09 20:37:00,272 - train - INFO - Epoch: 91 | Batch: 300 | Loss: 1.049 | Acc: 64.74% +2025-03-09 20:37:50,812 - train - INFO - Epoch: 91 | Test Loss: 1.092 | Test Acc: 63.34% +2025-03-09 20:37:50,972 - train - INFO - Epoch: 92 | Batch: 0 | Loss: 1.025 | Acc: 66.41% +2025-03-09 20:37:52,835 - train - INFO - Epoch: 92 | Batch: 100 | Loss: 1.039 | Acc: 64.90% +2025-03-09 20:37:54,790 - train - INFO - Epoch: 92 | Batch: 200 | Loss: 1.041 | Acc: 64.68% +2025-03-09 20:37:56,666 - train - INFO - Epoch: 92 | Batch: 300 | Loss: 1.037 | Acc: 64.84% +2025-03-09 20:37:59,681 - train - INFO - Epoch: 92 | Test Loss: 1.112 | Test Acc: 62.78% +2025-03-09 20:38:53,132 - train - INFO - Epoch: 93 | Batch: 0 | Loss: 1.193 | Acc: 56.25% +2025-03-09 20:38:55,019 - train - INFO - Epoch: 93 | Batch: 100 | Loss: 1.057 | Acc: 64.53% +2025-03-09 20:38:56,952 - train - INFO - Epoch: 93 | Batch: 200 | Loss: 1.046 | Acc: 65.06% +2025-03-09 20:38:58,885 - train - INFO - Epoch: 93 | Batch: 300 | Loss: 1.044 | Acc: 65.08% +2025-03-09 20:39:02,028 - train - INFO - Epoch: 93 | Test Loss: 1.024 | Test Acc: 65.24% +2025-03-09 20:39:02,185 - train - INFO - Epoch: 94 | Batch: 0 | Loss: 1.010 | Acc: 64.06% +2025-03-09 20:39:04,323 - train - INFO - Epoch: 94 | Batch: 100 | Loss: 1.023 | Acc: 65.28% +2025-03-09 20:39:49,435 - train - INFO - Epoch: 94 | Batch: 200 | Loss: 1.023 | Acc: 65.44% +2025-03-09 20:39:51,309 - train - INFO - Epoch: 94 | Batch: 300 | Loss: 1.027 | Acc: 65.52% +2025-03-09 20:39:54,281 - train - INFO - Epoch: 94 | Test Loss: 1.013 | Test Acc: 67.01% +2025-03-09 20:39:54,472 - train - INFO - Epoch: 95 | Batch: 0 | Loss: 1.236 | Acc: 59.38% +2025-03-09 20:39:56,452 - train - INFO - Epoch: 95 | Batch: 100 | Loss: 1.011 | Acc: 65.98% +2025-03-09 20:39:58,416 - train - INFO - Epoch: 95 | Batch: 200 | Loss: 1.012 | Acc: 65.91% +2025-03-09 20:40:48,850 - train - INFO - Epoch: 95 | Batch: 300 | Loss: 1.019 | Acc: 65.65% +2025-03-09 20:40:51,907 - train - INFO - Epoch: 95 | Test Loss: 0.994 | Test Acc: 66.97% +2025-03-09 20:40:52,057 - train - INFO - Epoch: 96 | Batch: 0 | Loss: 0.973 | Acc: 67.97% +2025-03-09 20:40:54,040 - train - INFO - Epoch: 96 | Batch: 100 | Loss: 1.040 | Acc: 65.18% +2025-03-09 20:40:56,141 - train - INFO - Epoch: 96 | Batch: 200 | Loss: 1.035 | Acc: 64.95% +2025-03-09 20:40:58,151 - train - INFO - Epoch: 96 | Batch: 300 | Loss: 1.021 | Acc: 65.43% +2025-03-09 20:41:48,845 - train - INFO - Epoch: 96 | Test Loss: 1.065 | Test Acc: 64.38% +2025-03-09 20:41:57,437 - train - INFO - Epoch: 97 | Batch: 0 | Loss: 0.995 | Acc: 66.41% +2025-03-09 20:41:59,481 - train - INFO - Epoch: 97 | Batch: 100 | Loss: 1.012 | Acc: 66.12% +2025-03-09 20:42:01,488 - train - INFO - Epoch: 97 | Batch: 200 | Loss: 0.995 | Acc: 66.64% +2025-03-09 20:42:03,533 - train - INFO - Epoch: 97 | Batch: 300 | Loss: 1.014 | Acc: 66.01% +2025-03-09 20:42:06,717 - train - INFO - Epoch: 97 | Test Loss: 1.043 | Test Acc: 64.98% +2025-03-09 20:42:06,871 - train - INFO - Epoch: 98 | Batch: 0 | Loss: 1.299 | Acc: 58.59% +2025-03-09 20:42:09,116 - train - INFO - Epoch: 98 | Batch: 100 | Loss: 0.971 | Acc: 67.10% +2025-03-09 20:42:50,612 - train - INFO - Epoch: 98 | Batch: 200 | Loss: 0.997 | Acc: 66.37% +2025-03-09 20:42:52,541 - train - INFO - Epoch: 98 | Batch: 300 | Loss: 1.001 | Acc: 66.31% +2025-03-09 20:42:55,458 - train - INFO - Epoch: 98 | Test Loss: 1.017 | Test Acc: 65.77% +2025-03-09 20:42:55,635 - train - INFO - Epoch: 99 | Batch: 0 | Loss: 1.074 | Acc: 66.41% +2025-03-09 20:42:57,563 - train - INFO - Epoch: 99 | Batch: 100 | Loss: 1.027 | Acc: 65.38% +2025-03-09 20:42:59,650 - train - INFO - Epoch: 99 | Batch: 200 | Loss: 1.013 | Acc: 66.04% +2025-03-09 20:43:49,927 - train - INFO - Epoch: 99 | Batch: 300 | Loss: 1.010 | Acc: 66.04% +2025-03-09 20:43:53,133 - train - INFO - Epoch: 99 | Test Loss: 1.013 | Test Acc: 66.35% +2025-03-09 20:43:53,291 - train - INFO - Epoch: 100 | Batch: 0 | Loss: 0.778 | Acc: 76.56% +2025-03-09 20:43:55,285 - train - INFO - Epoch: 100 | Batch: 100 | Loss: 0.999 | Acc: 66.10% +2025-03-09 20:43:57,360 - train - INFO - Epoch: 100 | Batch: 200 | Loss: 0.995 | Acc: 66.41% +2025-03-09 20:43:59,416 - train - INFO - Epoch: 100 | Batch: 300 | Loss: 0.994 | Acc: 66.56% +2025-03-09 20:44:49,933 - train - INFO - Epoch: 100 | Test Loss: 1.009 | Test Acc: 65.66% +2025-03-09 20:44:58,423 - train - INFO - 训练完成! diff --git a/Image/AlexNet/code/model.py b/Image/AlexNet/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..2633e4f1b1942b995073fc149ee02d41aa05f6f7 --- /dev/null +++ b/Image/AlexNet/code/model.py @@ -0,0 +1,81 @@ +''' +AlexNet in Pytorch +''' + +import torch +import torch.nn as nn + +class AlexNet(nn.Module): # 训练 ALexNet + ''' + AlexNet模型 + ''' + def __init__(self,num_classes=10): + super(AlexNet,self).__init__() + # 五个卷积层 输入 32 * 32 * 3 + self.conv1 = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=1), # (32-3+2)/1+1 = 32 + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (32-2)/2+1 = 16 + ) + self.conv2 = nn.Sequential( # 输入 16 * 16 * 6 + nn.Conv2d(in_channels=6, out_channels=16, kernel_size=3, stride=1, padding=1), # (16-3+2)/1+1 = 16 + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (16-2)/2+1 = 8 + ) + self.conv3 = nn.Sequential( # 输入 8 * 8 * 16 + nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1), # (8-3+2)/1+1 = 8 + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (8-2)/2+1 = 4 + ) + self.conv4 = nn.Sequential( # 输入 4 * 4 * 64 + nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1), # (4-3+2)/1+1 = 4 + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (4-2)/2+1 = 2 + ) + self.conv5 = nn.Sequential( # 输入 2 * 2 * 128 + nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),# (2-3+2)/1+1 = 2 + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (2-2)/2+1 = 1 + ) # 最后一层卷积层，输出 1 * 1 * 128 + # 全连接层 + self.dense = nn.Sequential( + nn.Linear(128,120), + nn.ReLU(), + nn.Linear(120,84), + nn.ReLU(), + nn.Linear(84,num_classes) + ) + + # 初始化权重 + self._initialize_weights() + + def forward(self,x): + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = self.conv4(x) + x = self.conv5(x) + x = x.view(x.size()[0],-1) + x = self.dense(x) + return x + + def _initialize_weights(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.Linear): + nn.init.normal_(m.weight, 0, 0.01) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +def test(): + net = AlexNet() + x = torch.randn(2,3,32,32) + y = net(x) + print(y.size()) + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net,(3,32,32)) \ No newline at end of file diff --git a/Image/AlexNet/code/train.log b/Image/AlexNet/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..3c624fa1e5206b094e3ac1a3b588fb1cb436630e --- /dev/null +++ b/Image/AlexNet/code/train.log @@ -0,0 +1,503 @@ +2025-03-09 19:50:57,307 - train - INFO - 开始训练 alexnet +2025-03-09 19:50:57,308 - train - INFO - 总轮数: 100, 学习率: 0.1, 设备: cuda:2 +2025-03-09 19:50:57,941 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.303 | Acc: 9.38% +2025-03-09 19:50:59,844 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.300 | Acc: 10.04% +2025-03-09 19:51:01,901 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.227 | Acc: 13.40% +2025-03-09 19:51:03,899 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 2.162 | Acc: 15.09% +2025-03-09 19:51:07,107 - train - INFO - Epoch: 1 | Test Loss: 1.896 | Test Acc: 19.86% +2025-03-09 19:51:07,264 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.888 | Acc: 16.41% +2025-03-09 19:51:09,345 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.929 | Acc: 21.02% +2025-03-09 19:51:11,452 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.917 | Acc: 21.42% +2025-03-09 19:51:13,434 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.911 | Acc: 21.87% +2025-03-09 19:51:16,541 - train - INFO - Epoch: 2 | Test Loss: 1.840 | Test Acc: 25.23% +2025-03-09 19:51:16,704 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.938 | Acc: 17.97% +2025-03-09 19:51:18,746 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.837 | Acc: 26.49% +2025-03-09 19:51:20,674 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.812 | Acc: 27.79% +2025-03-09 19:51:22,577 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.798 | Acc: 28.75% +2025-03-09 19:51:25,741 - train - INFO - Epoch: 3 | Test Loss: 1.613 | Test Acc: 38.13% +2025-03-09 19:51:25,920 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.613 | Acc: 32.81% +2025-03-09 19:51:28,016 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.683 | Acc: 34.67% +2025-03-09 19:51:30,038 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.668 | Acc: 35.46% +2025-03-09 19:51:32,131 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.675 | Acc: 36.00% +2025-03-09 19:51:35,398 - train - INFO - Epoch: 4 | Test Loss: 1.624 | Test Acc: 39.42% +2025-03-09 19:51:43,699 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.684 | Acc: 39.06% +2025-03-09 19:51:45,670 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.638 | Acc: 39.09% +2025-03-09 19:51:47,561 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.623 | Acc: 39.87% +2025-03-09 19:51:49,399 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.614 | Acc: 40.60% +2025-03-09 19:51:52,227 - train - INFO - Epoch: 5 | Test Loss: 1.487 | Test Acc: 45.98% +2025-03-09 19:51:52,392 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.565 | Acc: 45.31% +2025-03-09 19:51:54,259 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.550 | Acc: 44.09% +2025-03-09 19:51:56,191 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.551 | Acc: 44.07% +2025-03-09 19:51:58,102 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.552 | Acc: 44.16% +2025-03-09 19:52:00,982 - train - INFO - Epoch: 6 | Test Loss: 1.498 | Test Acc: 46.14% +2025-03-09 19:52:01,147 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.703 | Acc: 35.94% +2025-03-09 19:52:03,112 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.549 | Acc: 44.65% +2025-03-09 19:52:05,091 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.558 | Acc: 44.57% +2025-03-09 19:52:07,124 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.546 | Acc: 44.95% +2025-03-09 19:52:10,122 - train - INFO - Epoch: 7 | Test Loss: 1.463 | Test Acc: 48.95% +2025-03-09 19:52:10,282 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.559 | Acc: 46.88% +2025-03-09 19:52:12,269 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.522 | Acc: 45.61% +2025-03-09 19:52:14,191 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.515 | Acc: 46.19% +2025-03-09 19:52:16,026 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.512 | Acc: 46.34% +2025-03-09 19:52:18,972 - train - INFO - Epoch: 8 | Test Loss: 1.414 | Test Acc: 51.07% +2025-03-09 19:52:27,269 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.405 | Acc: 50.00% +2025-03-09 19:52:29,140 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.506 | Acc: 46.11% +2025-03-09 19:52:31,111 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.495 | Acc: 46.85% +2025-03-09 19:52:33,061 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.500 | Acc: 46.91% +2025-03-09 19:52:36,118 - train - INFO - Epoch: 9 | Test Loss: 1.540 | Test Acc: 46.20% +2025-03-09 19:52:36,307 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.710 | Acc: 42.97% +2025-03-09 19:52:38,571 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.515 | Acc: 47.08% +2025-03-09 19:52:40,615 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.506 | Acc: 47.12% +2025-03-09 19:52:42,631 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.498 | Acc: 47.72% +2025-03-09 19:52:45,697 - train - INFO - Epoch: 10 | Test Loss: 1.359 | Test Acc: 51.08% +2025-03-09 19:52:45,908 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.288 | Acc: 48.44% +2025-03-09 19:52:48,156 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.493 | Acc: 47.49% +2025-03-09 19:52:50,122 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.483 | Acc: 47.77% +2025-03-09 19:52:52,086 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.482 | Acc: 47.71% +2025-03-09 19:52:55,093 - train - INFO - Epoch: 11 | Test Loss: 1.425 | Test Acc: 49.77% +2025-03-09 19:52:55,255 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.601 | Acc: 38.28% +2025-03-09 19:52:57,285 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.495 | Acc: 47.03% +2025-03-09 19:52:59,361 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.484 | Acc: 47.75% +2025-03-09 19:53:01,286 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.474 | Acc: 48.21% +2025-03-09 19:53:04,233 - train - INFO - Epoch: 12 | Test Loss: 1.395 | Test Acc: 50.04% +2025-03-09 19:53:13,654 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.349 | Acc: 54.69% +2025-03-09 19:53:16,010 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.476 | Acc: 47.81% +2025-03-09 19:53:18,014 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.459 | Acc: 48.68% +2025-03-09 19:53:19,910 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.463 | Acc: 48.69% +2025-03-09 19:53:22,970 - train - INFO - Epoch: 13 | Test Loss: 1.433 | Test Acc: 50.43% +2025-03-09 19:53:23,123 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.438 | Acc: 48.44% +2025-03-09 19:53:25,027 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.490 | Acc: 47.90% +2025-03-09 19:53:27,032 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.478 | Acc: 48.26% +2025-03-09 19:53:28,990 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.473 | Acc: 48.42% +2025-03-09 19:53:31,930 - train - INFO - Epoch: 14 | Test Loss: 1.419 | Test Acc: 50.80% +2025-03-09 19:53:32,132 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 1.496 | Acc: 49.22% +2025-03-09 19:53:34,072 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.468 | Acc: 49.19% +2025-03-09 19:53:36,080 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.473 | Acc: 49.07% +2025-03-09 19:53:38,079 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.468 | Acc: 49.20% +2025-03-09 19:53:41,161 - train - INFO - Epoch: 15 | Test Loss: 1.424 | Test Acc: 52.09% +2025-03-09 19:53:41,356 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 1.368 | Acc: 46.88% +2025-03-09 19:53:43,498 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.485 | Acc: 48.47% +2025-03-09 19:53:45,625 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.475 | Acc: 48.40% +2025-03-09 19:53:47,626 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.468 | Acc: 48.62% +2025-03-09 19:53:50,536 - train - INFO - Epoch: 16 | Test Loss: 1.368 | Test Acc: 54.06% +2025-03-09 19:53:58,981 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 1.322 | Acc: 48.44% +2025-03-09 19:54:01,050 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.453 | Acc: 50.21% +2025-03-09 19:54:03,003 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.445 | Acc: 50.24% +2025-03-09 19:54:05,016 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.442 | Acc: 50.14% +2025-03-09 19:54:08,407 - train - INFO - Epoch: 17 | Test Loss: 1.427 | Test Acc: 50.52% +2025-03-09 19:54:08,577 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.677 | Acc: 39.84% +2025-03-09 19:54:11,118 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.466 | Acc: 49.07% +2025-03-09 19:54:13,136 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.459 | Acc: 49.04% +2025-03-09 19:54:15,032 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.450 | Acc: 49.59% +2025-03-09 19:54:18,113 - train - INFO - Epoch: 18 | Test Loss: 1.461 | Test Acc: 51.56% +2025-03-09 19:54:18,278 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.473 | Acc: 53.91% +2025-03-09 19:54:20,263 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.457 | Acc: 49.16% +2025-03-09 19:54:22,414 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.433 | Acc: 50.05% +2025-03-09 19:54:24,518 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.427 | Acc: 50.53% +2025-03-09 19:54:27,682 - train - INFO - Epoch: 19 | Test Loss: 1.490 | Test Acc: 53.65% +2025-03-09 19:54:27,849 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.817 | Acc: 46.09% +2025-03-09 19:54:29,974 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.438 | Acc: 50.66% +2025-03-09 19:54:32,009 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.428 | Acc: 50.77% +2025-03-09 19:54:34,136 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.427 | Acc: 50.63% +2025-03-09 19:54:37,134 - train - INFO - Epoch: 20 | Test Loss: 1.343 | Test Acc: 54.50% +2025-03-09 19:54:45,895 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.165 | Acc: 61.72% +2025-03-09 19:54:48,130 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.424 | Acc: 49.71% +2025-03-09 19:54:50,095 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.431 | Acc: 50.06% +2025-03-09 19:54:52,237 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.431 | Acc: 50.08% +2025-03-09 19:54:55,342 - train - INFO - Epoch: 21 | Test Loss: 1.527 | Test Acc: 53.13% +2025-03-09 19:54:55,542 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 1.576 | Acc: 50.78% +2025-03-09 19:54:57,589 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.439 | Acc: 50.59% +2025-03-09 19:54:59,618 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.443 | Acc: 50.26% +2025-03-09 19:55:01,786 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.447 | Acc: 50.06% +2025-03-09 19:55:04,883 - train - INFO - Epoch: 22 | Test Loss: 1.385 | Test Acc: 52.66% +2025-03-09 19:55:05,110 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.361 | Acc: 49.22% +2025-03-09 19:55:07,153 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.446 | Acc: 48.96% +2025-03-09 19:55:09,130 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.446 | Acc: 49.48% +2025-03-09 19:55:11,103 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.436 | Acc: 50.26% +2025-03-09 19:55:14,018 - train - INFO - Epoch: 23 | Test Loss: 1.312 | Test Acc: 54.84% +2025-03-09 19:55:14,202 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.346 | Acc: 50.78% +2025-03-09 19:55:16,194 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.455 | Acc: 48.91% +2025-03-09 19:55:18,163 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.440 | Acc: 49.81% +2025-03-09 19:55:20,197 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.437 | Acc: 50.04% +2025-03-09 19:55:23,241 - train - INFO - Epoch: 24 | Test Loss: 1.458 | Test Acc: 51.68% +2025-03-09 19:55:32,077 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.432 | Acc: 54.69% +2025-03-09 19:55:34,182 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.410 | Acc: 51.34% +2025-03-09 19:55:36,201 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.422 | Acc: 51.18% +2025-03-09 19:55:38,082 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.414 | Acc: 51.21% +2025-03-09 19:55:41,003 - train - INFO - Epoch: 25 | Test Loss: 1.407 | Test Acc: 53.10% +2025-03-09 19:55:41,169 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 1.408 | Acc: 53.91% +2025-03-09 19:55:43,071 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.397 | Acc: 51.52% +2025-03-09 19:55:45,032 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.416 | Acc: 51.12% +2025-03-09 19:55:47,033 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.431 | Acc: 50.55% +2025-03-09 19:55:50,303 - train - INFO - Epoch: 26 | Test Loss: 1.337 | Test Acc: 53.87% +2025-03-09 19:55:50,467 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.436 | Acc: 52.34% +2025-03-09 19:55:52,603 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.400 | Acc: 51.96% +2025-03-09 19:55:54,700 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.415 | Acc: 51.58% +2025-03-09 19:55:56,931 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.403 | Acc: 51.88% +2025-03-09 19:56:00,315 - train - INFO - Epoch: 27 | Test Loss: 1.284 | Test Acc: 57.01% +2025-03-09 19:56:00,490 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.196 | Acc: 53.91% +2025-03-09 19:56:02,614 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.402 | Acc: 51.76% +2025-03-09 19:56:04,933 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.408 | Acc: 51.64% +2025-03-09 19:56:06,826 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.415 | Acc: 51.37% +2025-03-09 19:56:09,723 - train - INFO - Epoch: 28 | Test Loss: 1.512 | Test Acc: 49.97% +2025-03-09 19:56:18,095 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.663 | Acc: 49.22% +2025-03-09 19:56:20,071 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.450 | Acc: 49.74% +2025-03-09 19:56:22,046 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.418 | Acc: 51.07% +2025-03-09 19:56:23,941 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.418 | Acc: 51.21% +2025-03-09 19:56:27,201 - train - INFO - Epoch: 29 | Test Loss: 1.399 | Test Acc: 50.57% +2025-03-09 19:56:27,418 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 1.487 | Acc: 50.78% +2025-03-09 19:56:29,631 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.407 | Acc: 52.09% +2025-03-09 19:56:31,749 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.405 | Acc: 52.03% +2025-03-09 19:56:34,079 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.409 | Acc: 51.79% +2025-03-09 19:56:37,224 - train - INFO - Epoch: 30 | Test Loss: 1.353 | Test Acc: 51.54% +2025-03-09 19:56:37,407 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 1.319 | Acc: 50.78% +2025-03-09 19:56:39,290 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.408 | Acc: 52.58% +2025-03-09 19:56:41,278 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.403 | Acc: 52.27% +2025-03-09 19:56:43,239 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.405 | Acc: 52.06% +2025-03-09 19:56:46,411 - train - INFO - Epoch: 31 | Test Loss: 1.437 | Test Acc: 50.89% +2025-03-09 19:56:46,625 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 1.625 | Acc: 38.28% +2025-03-09 19:56:48,617 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.408 | Acc: 52.27% +2025-03-09 19:56:50,612 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.404 | Acc: 52.34% +2025-03-09 19:56:52,563 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.419 | Acc: 51.68% +2025-03-09 19:56:55,759 - train - INFO - Epoch: 32 | Test Loss: 1.462 | Test Acc: 49.63% +2025-03-09 19:57:04,788 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.380 | Acc: 50.78% +2025-03-09 19:57:06,775 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.404 | Acc: 52.44% +2025-03-09 19:57:08,717 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.401 | Acc: 52.40% +2025-03-09 19:57:10,621 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.402 | Acc: 52.39% +2025-03-09 19:57:13,611 - train - INFO - Epoch: 33 | Test Loss: 1.429 | Test Acc: 50.51% +2025-03-09 19:57:13,780 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.457 | Acc: 45.31% +2025-03-09 19:57:15,716 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.382 | Acc: 52.42% +2025-03-09 19:57:17,568 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.369 | Acc: 53.09% +2025-03-09 19:57:19,467 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.385 | Acc: 52.48% +2025-03-09 19:57:22,494 - train - INFO - Epoch: 34 | Test Loss: 1.269 | Test Acc: 58.16% +2025-03-09 19:57:22,652 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.363 | Acc: 50.00% +2025-03-09 19:57:24,637 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.370 | Acc: 53.33% +2025-03-09 19:57:26,607 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.357 | Acc: 53.85% +2025-03-09 19:57:28,609 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.376 | Acc: 53.20% +2025-03-09 19:57:31,872 - train - INFO - Epoch: 35 | Test Loss: 1.374 | Test Acc: 52.82% +2025-03-09 19:57:32,038 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 1.409 | Acc: 48.44% +2025-03-09 19:57:34,309 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.380 | Acc: 53.10% +2025-03-09 19:57:36,495 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.412 | Acc: 52.32% +2025-03-09 19:57:38,858 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.407 | Acc: 52.43% +2025-03-09 19:57:41,965 - train - INFO - Epoch: 36 | Test Loss: 1.315 | Test Acc: 53.89% +2025-03-09 19:57:50,440 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.449 | Acc: 54.69% +2025-03-09 19:57:52,335 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.406 | Acc: 52.27% +2025-03-09 19:57:54,375 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.402 | Acc: 52.22% +2025-03-09 19:57:56,397 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.402 | Acc: 51.95% +2025-03-09 19:57:59,642 - train - INFO - Epoch: 37 | Test Loss: 1.317 | Test Acc: 54.21% +2025-03-09 19:57:59,815 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 1.396 | Acc: 53.91% +2025-03-09 19:58:01,888 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.402 | Acc: 52.91% +2025-03-09 19:58:04,248 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.390 | Acc: 53.00% +2025-03-09 19:58:06,220 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.384 | Acc: 52.99% +2025-03-09 19:58:09,163 - train - INFO - Epoch: 38 | Test Loss: 1.377 | Test Acc: 53.56% +2025-03-09 19:58:09,344 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.318 | Acc: 56.25% +2025-03-09 19:58:11,213 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.357 | Acc: 53.60% +2025-03-09 19:58:13,162 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.359 | Acc: 53.66% +2025-03-09 19:58:15,049 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.365 | Acc: 53.45% +2025-03-09 19:58:17,934 - train - INFO - Epoch: 39 | Test Loss: 1.387 | Test Acc: 53.77% +2025-03-09 19:58:18,107 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.412 | Acc: 48.44% +2025-03-09 19:58:20,130 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.366 | Acc: 53.23% +2025-03-09 19:58:22,068 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.351 | Acc: 53.61% +2025-03-09 19:58:24,230 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.354 | Acc: 53.64% +2025-03-09 19:58:27,405 - train - INFO - Epoch: 40 | Test Loss: 1.372 | Test Acc: 53.59% +2025-03-09 19:58:36,177 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 1.538 | Acc: 44.53% +2025-03-09 19:58:38,243 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.372 | Acc: 53.30% +2025-03-09 19:58:40,234 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.364 | Acc: 53.72% +2025-03-09 19:58:42,245 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.369 | Acc: 53.57% +2025-03-09 19:58:45,634 - train - INFO - Epoch: 41 | Test Loss: 1.296 | Test Acc: 54.74% +2025-03-09 19:58:45,798 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 1.475 | Acc: 44.53% +2025-03-09 19:58:47,867 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.368 | Acc: 52.49% +2025-03-09 19:58:50,126 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.368 | Acc: 52.68% +2025-03-09 19:58:52,366 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.377 | Acc: 52.70% +2025-03-09 19:58:55,621 - train - INFO - Epoch: 42 | Test Loss: 1.309 | Test Acc: 58.19% +2025-03-09 19:58:55,774 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 1.291 | Acc: 61.72% +2025-03-09 19:58:57,733 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.340 | Acc: 54.46% +2025-03-09 19:58:59,676 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.329 | Acc: 54.83% +2025-03-09 19:59:01,636 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.331 | Acc: 55.12% +2025-03-09 19:59:04,691 - train - INFO - Epoch: 43 | Test Loss: 1.373 | Test Acc: 53.69% +2025-03-09 19:59:04,836 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.310 | Acc: 52.34% +2025-03-09 19:59:06,796 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.373 | Acc: 53.46% +2025-03-09 19:59:08,711 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.368 | Acc: 53.84% +2025-03-09 19:59:10,613 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.362 | Acc: 53.90% +2025-03-09 19:59:13,399 - train - INFO - Epoch: 44 | Test Loss: 1.378 | Test Acc: 53.67% +2025-03-09 19:59:21,620 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.383 | Acc: 58.59% +2025-03-09 19:59:23,458 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.325 | Acc: 55.49% +2025-03-09 19:59:25,416 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.345 | Acc: 54.83% +2025-03-09 19:59:27,390 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.361 | Acc: 54.17% +2025-03-09 19:59:30,307 - train - INFO - Epoch: 45 | Test Loss: 1.345 | Test Acc: 54.53% +2025-03-09 19:59:30,478 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.303 | Acc: 55.47% +2025-03-09 19:59:32,427 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.342 | Acc: 54.19% +2025-03-09 19:59:34,336 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.337 | Acc: 54.64% +2025-03-09 19:59:36,228 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.351 | Acc: 54.22% +2025-03-09 19:59:39,262 - train - INFO - Epoch: 46 | Test Loss: 1.345 | Test Acc: 56.16% +2025-03-09 19:59:39,452 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.489 | Acc: 51.56% +2025-03-09 19:59:41,279 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.328 | Acc: 54.90% +2025-03-09 19:59:43,204 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.327 | Acc: 55.05% +2025-03-09 19:59:45,120 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.333 | Acc: 55.00% +2025-03-09 19:59:48,254 - train - INFO - Epoch: 47 | Test Loss: 1.314 | Test Acc: 56.75% +2025-03-09 19:59:48,434 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.208 | Acc: 59.38% +2025-03-09 19:59:50,454 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.317 | Acc: 55.10% +2025-03-09 19:59:52,374 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.312 | Acc: 55.74% +2025-03-09 19:59:54,256 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.309 | Acc: 55.89% +2025-03-09 19:59:57,327 - train - INFO - Epoch: 48 | Test Loss: 1.271 | Test Acc: 58.09% +2025-03-09 20:00:06,506 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 1.332 | Acc: 57.81% +2025-03-09 20:00:08,781 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.298 | Acc: 55.98% +2025-03-09 20:00:11,349 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.302 | Acc: 56.07% +2025-03-09 20:00:13,500 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.307 | Acc: 55.87% +2025-03-09 20:00:16,544 - train - INFO - Epoch: 49 | Test Loss: 1.286 | Test Acc: 54.88% +2025-03-09 20:00:16,712 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 1.354 | Acc: 55.47% +2025-03-09 20:00:18,651 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.353 | Acc: 53.57% +2025-03-09 20:00:20,584 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.335 | Acc: 54.65% +2025-03-09 20:00:22,491 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.333 | Acc: 54.62% +2025-03-09 20:00:25,512 - train - INFO - Epoch: 50 | Test Loss: 1.264 | Test Acc: 56.42% +2025-03-09 20:00:25,692 - train - INFO - Epoch: 51 | Batch: 0 | Loss: 1.259 | Acc: 57.81% +2025-03-09 20:00:27,737 - train - INFO - Epoch: 51 | Batch: 100 | Loss: 1.293 | Acc: 56.82% +2025-03-09 20:00:29,766 - train - INFO - Epoch: 51 | Batch: 200 | Loss: 1.300 | Acc: 56.34% +2025-03-09 20:00:31,756 - train - INFO - Epoch: 51 | Batch: 300 | Loss: 1.297 | Acc: 56.14% +2025-03-09 20:00:34,929 - train - INFO - Epoch: 51 | Test Loss: 1.223 | Test Acc: 57.27% +2025-03-09 20:00:35,118 - train - INFO - Epoch: 52 | Batch: 0 | Loss: 1.323 | Acc: 55.47% +2025-03-09 20:00:37,282 - train - INFO - Epoch: 52 | Batch: 100 | Loss: 1.299 | Acc: 55.91% +2025-03-09 20:00:39,484 - train - INFO - Epoch: 52 | Batch: 200 | Loss: 1.289 | Acc: 56.01% +2025-03-09 20:00:41,515 - train - INFO - Epoch: 52 | Batch: 300 | Loss: 1.293 | Acc: 55.98% +2025-03-09 20:00:44,482 - train - INFO - Epoch: 52 | Test Loss: 1.310 | Test Acc: 55.00% +2025-03-09 20:00:52,924 - train - INFO - Epoch: 53 | Batch: 0 | Loss: 1.438 | Acc: 50.00% +2025-03-09 20:00:55,121 - train - INFO - Epoch: 53 | Batch: 100 | Loss: 1.297 | Acc: 56.12% +2025-03-09 20:00:57,170 - train - INFO - Epoch: 53 | Batch: 200 | Loss: 1.306 | Acc: 55.61% +2025-03-09 20:00:59,086 - train - INFO - Epoch: 53 | Batch: 300 | Loss: 1.310 | Acc: 55.32% +2025-03-09 20:01:01,976 - train - INFO - Epoch: 53 | Test Loss: 1.293 | Test Acc: 55.28% +2025-03-09 20:01:02,134 - train - INFO - Epoch: 54 | Batch: 0 | Loss: 1.311 | Acc: 53.91% +2025-03-09 20:01:04,118 - train - INFO - Epoch: 54 | Batch: 100 | Loss: 1.335 | Acc: 54.83% +2025-03-09 20:01:06,123 - train - INFO - Epoch: 54 | Batch: 200 | Loss: 1.312 | Acc: 55.50% +2025-03-09 20:01:08,143 - train - INFO - Epoch: 54 | Batch: 300 | Loss: 1.296 | Acc: 55.97% +2025-03-09 20:01:11,377 - train - INFO - Epoch: 54 | Test Loss: 1.293 | Test Acc: 57.74% +2025-03-09 20:01:11,543 - train - INFO - Epoch: 55 | Batch: 0 | Loss: 1.194 | Acc: 58.59% +2025-03-09 20:01:13,621 - train - INFO - Epoch: 55 | Batch: 100 | Loss: 1.266 | Acc: 56.81% +2025-03-09 20:01:15,599 - train - INFO - Epoch: 55 | Batch: 200 | Loss: 1.267 | Acc: 57.00% +2025-03-09 20:01:17,646 - train - INFO - Epoch: 55 | Batch: 300 | Loss: 1.297 | Acc: 56.35% +2025-03-09 20:01:20,601 - train - INFO - Epoch: 55 | Test Loss: 1.288 | Test Acc: 56.27% +2025-03-09 20:01:20,778 - train - INFO - Epoch: 56 | Batch: 0 | Loss: 1.295 | Acc: 54.69% +2025-03-09 20:01:22,676 - train - INFO - Epoch: 56 | Batch: 100 | Loss: 1.306 | Acc: 55.91% +2025-03-09 20:01:24,633 - train - INFO - Epoch: 56 | Batch: 200 | Loss: 1.289 | Acc: 56.60% +2025-03-09 20:01:26,625 - train - INFO - Epoch: 56 | Batch: 300 | Loss: 1.299 | Acc: 56.09% +2025-03-09 20:01:29,991 - train - INFO - Epoch: 56 | Test Loss: 1.264 | Test Acc: 56.79% +2025-03-09 20:01:38,741 - train - INFO - Epoch: 57 | Batch: 0 | Loss: 1.413 | Acc: 53.12% +2025-03-09 20:01:40,895 - train - INFO - Epoch: 57 | Batch: 100 | Loss: 1.295 | Acc: 56.21% +2025-03-09 20:01:43,111 - train - INFO - Epoch: 57 | Batch: 200 | Loss: 1.287 | Acc: 56.34% +2025-03-09 20:01:45,114 - train - INFO - Epoch: 57 | Batch: 300 | Loss: 1.276 | Acc: 56.62% +2025-03-09 20:01:48,333 - train - INFO - Epoch: 57 | Test Loss: 1.283 | Test Acc: 56.47% +2025-03-09 20:01:48,483 - train - INFO - Epoch: 58 | Batch: 0 | Loss: 1.203 | Acc: 60.94% +2025-03-09 20:01:50,350 - train - INFO - Epoch: 58 | Batch: 100 | Loss: 1.265 | Acc: 57.16% +2025-03-09 20:01:52,319 - train - INFO - Epoch: 58 | Batch: 200 | Loss: 1.278 | Acc: 56.86% +2025-03-09 20:01:54,253 - train - INFO - Epoch: 58 | Batch: 300 | Loss: 1.278 | Acc: 56.76% +2025-03-09 20:01:57,139 - train - INFO - Epoch: 58 | Test Loss: 1.250 | Test Acc: 55.98% +2025-03-09 20:01:57,294 - train - INFO - Epoch: 59 | Batch: 0 | Loss: 1.299 | Acc: 54.69% +2025-03-09 20:01:59,177 - train - INFO - Epoch: 59 | Batch: 100 | Loss: 1.275 | Acc: 56.53% +2025-03-09 20:02:01,061 - train - INFO - Epoch: 59 | Batch: 200 | Loss: 1.264 | Acc: 57.11% +2025-03-09 20:02:03,013 - train - INFO - Epoch: 59 | Batch: 300 | Loss: 1.270 | Acc: 57.01% +2025-03-09 20:02:05,873 - train - INFO - Epoch: 59 | Test Loss: 1.175 | Test Acc: 60.14% +2025-03-09 20:02:06,084 - train - INFO - Epoch: 60 | Batch: 0 | Loss: 1.176 | Acc: 60.16% +2025-03-09 20:02:08,156 - train - INFO - Epoch: 60 | Batch: 100 | Loss: 1.276 | Acc: 56.69% +2025-03-09 20:02:10,235 - train - INFO - Epoch: 60 | Batch: 200 | Loss: 1.261 | Acc: 57.39% +2025-03-09 20:02:12,262 - train - INFO - Epoch: 60 | Batch: 300 | Loss: 1.272 | Acc: 56.96% +2025-03-09 20:02:15,547 - train - INFO - Epoch: 60 | Test Loss: 1.258 | Test Acc: 57.63% +2025-03-09 20:02:24,176 - train - INFO - Epoch: 61 | Batch: 0 | Loss: 1.181 | Acc: 63.28% +2025-03-09 20:02:26,274 - train - INFO - Epoch: 61 | Batch: 100 | Loss: 1.283 | Acc: 56.98% +2025-03-09 20:02:28,240 - train - INFO - Epoch: 61 | Batch: 200 | Loss: 1.267 | Acc: 57.38% +2025-03-09 20:02:30,146 - train - INFO - Epoch: 61 | Batch: 300 | Loss: 1.266 | Acc: 57.71% +2025-03-09 20:02:33,184 - train - INFO - Epoch: 61 | Test Loss: 1.185 | Test Acc: 57.88% +2025-03-09 20:02:33,361 - train - INFO - Epoch: 62 | Batch: 0 | Loss: 1.181 | Acc: 55.47% +2025-03-09 20:02:35,405 - train - INFO - Epoch: 62 | Batch: 100 | Loss: 1.237 | Acc: 58.36% +2025-03-09 20:02:37,431 - train - INFO - Epoch: 62 | Batch: 200 | Loss: 1.222 | Acc: 58.83% +2025-03-09 20:02:39,492 - train - INFO - Epoch: 62 | Batch: 300 | Loss: 1.225 | Acc: 58.54% +2025-03-09 20:02:42,962 - train - INFO - Epoch: 62 | Test Loss: 1.201 | Test Acc: 59.17% +2025-03-09 20:02:43,194 - train - INFO - Epoch: 63 | Batch: 0 | Loss: 1.167 | Acc: 57.03% +2025-03-09 20:02:45,381 - train - INFO - Epoch: 63 | Batch: 100 | Loss: 1.255 | Acc: 57.75% +2025-03-09 20:02:47,427 - train - INFO - Epoch: 63 | Batch: 200 | Loss: 1.251 | Acc: 57.57% +2025-03-09 20:02:49,782 - train - INFO - Epoch: 63 | Batch: 300 | Loss: 1.247 | Acc: 57.64% +2025-03-09 20:02:52,930 - train - INFO - Epoch: 63 | Test Loss: 1.212 | Test Acc: 59.46% +2025-03-09 20:02:53,093 - train - INFO - Epoch: 64 | Batch: 0 | Loss: 1.213 | Acc: 60.16% +2025-03-09 20:02:54,979 - train - INFO - Epoch: 64 | Batch: 100 | Loss: 1.238 | Acc: 58.62% +2025-03-09 20:02:56,924 - train - INFO - Epoch: 64 | Batch: 200 | Loss: 1.231 | Acc: 58.56% +2025-03-09 20:02:59,004 - train - INFO - Epoch: 64 | Batch: 300 | Loss: 1.235 | Acc: 58.42% +2025-03-09 20:03:02,027 - train - INFO - Epoch: 64 | Test Loss: 1.301 | Test Acc: 56.76% +2025-03-09 20:03:11,492 - train - INFO - Epoch: 65 | Batch: 0 | Loss: 1.440 | Acc: 59.38% +2025-03-09 20:03:13,589 - train - INFO - Epoch: 65 | Batch: 100 | Loss: 1.219 | Acc: 57.99% +2025-03-09 20:03:15,758 - train - INFO - Epoch: 65 | Batch: 200 | Loss: 1.214 | Acc: 58.48% +2025-03-09 20:03:17,811 - train - INFO - Epoch: 65 | Batch: 300 | Loss: 1.220 | Acc: 58.31% +2025-03-09 20:03:20,962 - train - INFO - Epoch: 65 | Test Loss: 1.133 | Test Acc: 62.39% +2025-03-09 20:03:21,151 - train - INFO - Epoch: 66 | Batch: 0 | Loss: 1.090 | Acc: 64.84% +2025-03-09 20:03:23,279 - train - INFO - Epoch: 66 | Batch: 100 | Loss: 1.212 | Acc: 58.54% +2025-03-09 20:03:25,314 - train - INFO - Epoch: 66 | Batch: 200 | Loss: 1.221 | Acc: 58.45% +2025-03-09 20:03:27,262 - train - INFO - Epoch: 66 | Batch: 300 | Loss: 1.220 | Acc: 58.67% +2025-03-09 20:03:30,250 - train - INFO - Epoch: 66 | Test Loss: 1.128 | Test Acc: 62.20% +2025-03-09 20:03:30,427 - train - INFO - Epoch: 67 | Batch: 0 | Loss: 1.194 | Acc: 62.50% +2025-03-09 20:03:32,409 - train - INFO - Epoch: 67 | Batch: 100 | Loss: 1.205 | Acc: 59.23% +2025-03-09 20:03:34,525 - train - INFO - Epoch: 67 | Batch: 200 | Loss: 1.219 | Acc: 58.96% +2025-03-09 20:03:36,669 - train - INFO - Epoch: 67 | Batch: 300 | Loss: 1.224 | Acc: 58.88% +2025-03-09 20:03:40,213 - train - INFO - Epoch: 67 | Test Loss: 1.198 | Test Acc: 60.10% +2025-03-09 20:03:40,438 - train - INFO - Epoch: 68 | Batch: 0 | Loss: 1.071 | Acc: 59.38% +2025-03-09 20:03:42,654 - train - INFO - Epoch: 68 | Batch: 100 | Loss: 1.195 | Acc: 59.75% +2025-03-09 20:03:44,633 - train - INFO - Epoch: 68 | Batch: 200 | Loss: 1.193 | Acc: 59.75% +2025-03-09 20:03:46,611 - train - INFO - Epoch: 68 | Batch: 300 | Loss: 1.193 | Acc: 59.72% +2025-03-09 20:03:49,635 - train - INFO - Epoch: 68 | Test Loss: 1.170 | Test Acc: 60.74% +2025-03-09 20:03:58,030 - train - INFO - Epoch: 69 | Batch: 0 | Loss: 1.065 | Acc: 57.81% +2025-03-09 20:04:00,061 - train - INFO - Epoch: 69 | Batch: 100 | Loss: 1.217 | Acc: 58.83% +2025-03-09 20:04:01,977 - train - INFO - Epoch: 69 | Batch: 200 | Loss: 1.226 | Acc: 58.59% +2025-03-09 20:04:03,999 - train - INFO - Epoch: 69 | Batch: 300 | Loss: 1.229 | Acc: 58.67% +2025-03-09 20:04:07,214 - train - INFO - Epoch: 69 | Test Loss: 1.170 | Test Acc: 61.05% +2025-03-09 20:04:07,420 - train - INFO - Epoch: 70 | Batch: 0 | Loss: 1.327 | Acc: 55.47% +2025-03-09 20:04:09,428 - train - INFO - Epoch: 70 | Batch: 100 | Loss: 1.212 | Acc: 59.03% +2025-03-09 20:04:11,490 - train - INFO - Epoch: 70 | Batch: 200 | Loss: 1.210 | Acc: 59.22% +2025-03-09 20:04:13,652 - train - INFO - Epoch: 70 | Batch: 300 | Loss: 1.201 | Acc: 59.55% +2025-03-09 20:04:16,600 - train - INFO - Epoch: 70 | Test Loss: 1.083 | Test Acc: 63.70% +2025-03-09 20:04:16,780 - train - INFO - Epoch: 71 | Batch: 0 | Loss: 1.090 | Acc: 64.84% +2025-03-09 20:04:18,739 - train - INFO - Epoch: 71 | Batch: 100 | Loss: 1.180 | Acc: 60.05% +2025-03-09 20:04:20,670 - train - INFO - Epoch: 71 | Batch: 200 | Loss: 1.185 | Acc: 59.98% +2025-03-09 20:04:22,543 - train - INFO - Epoch: 71 | Batch: 300 | Loss: 1.183 | Acc: 60.13% +2025-03-09 20:04:25,494 - train - INFO - Epoch: 71 | Test Loss: 1.122 | Test Acc: 63.08% +2025-03-09 20:04:25,672 - train - INFO - Epoch: 72 | Batch: 0 | Loss: 1.234 | Acc: 57.81% +2025-03-09 20:04:27,737 - train - INFO - Epoch: 72 | Batch: 100 | Loss: 1.229 | Acc: 58.80% +2025-03-09 20:04:29,724 - train - INFO - Epoch: 72 | Batch: 200 | Loss: 1.214 | Acc: 59.19% +2025-03-09 20:04:31,711 - train - INFO - Epoch: 72 | Batch: 300 | Loss: 1.205 | Acc: 59.47% +2025-03-09 20:04:35,205 - train - INFO - Epoch: 72 | Test Loss: 1.286 | Test Acc: 58.20% +2025-03-09 20:04:44,301 - train - INFO - Epoch: 73 | Batch: 0 | Loss: 1.294 | Acc: 57.81% +2025-03-09 20:04:46,184 - train - INFO - Epoch: 73 | Batch: 100 | Loss: 1.195 | Acc: 59.38% +2025-03-09 20:04:48,292 - train - INFO - Epoch: 73 | Batch: 200 | Loss: 1.186 | Acc: 59.92% +2025-03-09 20:04:50,218 - train - INFO - Epoch: 73 | Batch: 300 | Loss: 1.186 | Acc: 59.97% +2025-03-09 20:04:53,168 - train - INFO - Epoch: 73 | Test Loss: 1.130 | Test Acc: 62.16% +2025-03-09 20:04:53,370 - train - INFO - Epoch: 74 | Batch: 0 | Loss: 1.022 | Acc: 66.41% +2025-03-09 20:04:55,408 - train - INFO - Epoch: 74 | Batch: 100 | Loss: 1.219 | Acc: 58.83% +2025-03-09 20:04:57,367 - train - INFO - Epoch: 74 | Batch: 200 | Loss: 1.196 | Acc: 59.82% +2025-03-09 20:04:59,411 - train - INFO - Epoch: 74 | Batch: 300 | Loss: 1.184 | Acc: 60.10% +2025-03-09 20:05:02,780 - train - INFO - Epoch: 74 | Test Loss: 1.169 | Test Acc: 59.53% +2025-03-09 20:05:02,956 - train - INFO - Epoch: 75 | Batch: 0 | Loss: 1.042 | Acc: 64.06% +2025-03-09 20:05:04,928 - train - INFO - Epoch: 75 | Batch: 100 | Loss: 1.179 | Acc: 59.97% +2025-03-09 20:05:07,026 - train - INFO - Epoch: 75 | Batch: 200 | Loss: 1.164 | Acc: 60.59% +2025-03-09 20:05:09,052 - train - INFO - Epoch: 75 | Batch: 300 | Loss: 1.168 | Acc: 60.56% +2025-03-09 20:05:12,486 - train - INFO - Epoch: 75 | Test Loss: 1.086 | Test Acc: 64.61% +2025-03-09 20:05:12,668 - train - INFO - Epoch: 76 | Batch: 0 | Loss: 1.005 | Acc: 67.97% +2025-03-09 20:05:14,872 - train - INFO - Epoch: 76 | Batch: 100 | Loss: 1.188 | Acc: 59.54% +2025-03-09 20:05:17,020 - train - INFO - Epoch: 76 | Batch: 200 | Loss: 1.168 | Acc: 60.31% +2025-03-09 20:05:18,884 - train - INFO - Epoch: 76 | Batch: 300 | Loss: 1.171 | Acc: 60.22% +2025-03-09 20:05:21,857 - train - INFO - Epoch: 76 | Test Loss: 1.077 | Test Acc: 64.56% +2025-03-09 20:05:30,426 - train - INFO - Epoch: 77 | Batch: 0 | Loss: 0.993 | Acc: 67.97% +2025-03-09 20:05:32,481 - train - INFO - Epoch: 77 | Batch: 100 | Loss: 1.187 | Acc: 59.47% +2025-03-09 20:05:34,515 - train - INFO - Epoch: 77 | Batch: 200 | Loss: 1.162 | Acc: 60.40% +2025-03-09 20:05:36,603 - train - INFO - Epoch: 77 | Batch: 300 | Loss: 1.156 | Acc: 60.49% +2025-03-09 20:05:39,781 - train - INFO - Epoch: 77 | Test Loss: 1.148 | Test Acc: 61.77% +2025-03-09 20:05:39,985 - train - INFO - Epoch: 78 | Batch: 0 | Loss: 1.126 | Acc: 60.16% +2025-03-09 20:05:41,942 - train - INFO - Epoch: 78 | Batch: 100 | Loss: 1.135 | Acc: 61.81% +2025-03-09 20:05:43,948 - train - INFO - Epoch: 78 | Batch: 200 | Loss: 1.130 | Acc: 61.72% +2025-03-09 20:05:46,120 - train - INFO - Epoch: 78 | Batch: 300 | Loss: 1.144 | Acc: 61.18% +2025-03-09 20:05:49,313 - train - INFO - Epoch: 78 | Test Loss: 1.187 | Test Acc: 60.45% +2025-03-09 20:05:49,505 - train - INFO - Epoch: 79 | Batch: 0 | Loss: 1.205 | Acc: 60.94% +2025-03-09 20:05:51,476 - train - INFO - Epoch: 79 | Batch: 100 | Loss: 1.161 | Acc: 60.95% +2025-03-09 20:05:53,457 - train - INFO - Epoch: 79 | Batch: 200 | Loss: 1.157 | Acc: 61.07% +2025-03-09 20:05:55,418 - train - INFO - Epoch: 79 | Batch: 300 | Loss: 1.156 | Acc: 61.12% +2025-03-09 20:05:58,509 - train - INFO - Epoch: 79 | Test Loss: 1.056 | Test Acc: 63.68% +2025-03-09 20:05:58,686 - train - INFO - Epoch: 80 | Batch: 0 | Loss: 1.065 | Acc: 64.84% +2025-03-09 20:06:00,797 - train - INFO - Epoch: 80 | Batch: 100 | Loss: 1.121 | Acc: 62.57% +2025-03-09 20:06:02,773 - train - INFO - Epoch: 80 | Batch: 200 | Loss: 1.133 | Acc: 61.80% +2025-03-09 20:06:04,652 - train - INFO - Epoch: 80 | Batch: 300 | Loss: 1.145 | Acc: 61.24% +2025-03-09 20:06:07,896 - train - INFO - Epoch: 80 | Test Loss: 1.118 | Test Acc: 62.60% +2025-03-09 20:06:17,187 - train - INFO - Epoch: 81 | Batch: 0 | Loss: 0.981 | Acc: 64.06% +2025-03-09 20:06:19,096 - train - INFO - Epoch: 81 | Batch: 100 | Loss: 1.167 | Acc: 61.12% +2025-03-09 20:06:21,115 - train - INFO - Epoch: 81 | Batch: 200 | Loss: 1.154 | Acc: 61.34% +2025-03-09 20:06:23,095 - train - INFO - Epoch: 81 | Batch: 300 | Loss: 1.144 | Acc: 61.42% +2025-03-09 20:06:26,187 - train - INFO - Epoch: 81 | Test Loss: 1.173 | Test Acc: 60.44% +2025-03-09 20:06:26,358 - train - INFO - Epoch: 82 | Batch: 0 | Loss: 1.302 | Acc: 53.91% +2025-03-09 20:06:28,368 - train - INFO - Epoch: 82 | Batch: 100 | Loss: 1.138 | Acc: 61.95% +2025-03-09 20:06:30,300 - train - INFO - Epoch: 82 | Batch: 200 | Loss: 1.133 | Acc: 62.14% +2025-03-09 20:06:32,316 - train - INFO - Epoch: 82 | Batch: 300 | Loss: 1.134 | Acc: 62.13% +2025-03-09 20:06:35,449 - train - INFO - Epoch: 82 | Test Loss: 1.126 | Test Acc: 61.16% +2025-03-09 20:06:35,669 - train - INFO - Epoch: 83 | Batch: 0 | Loss: 1.332 | Acc: 53.12% +2025-03-09 20:06:37,637 - train - INFO - Epoch: 83 | Batch: 100 | Loss: 1.145 | Acc: 60.94% +2025-03-09 20:06:39,716 - train - INFO - Epoch: 83 | Batch: 200 | Loss: 1.138 | Acc: 61.66% +2025-03-09 20:06:41,791 - train - INFO - Epoch: 83 | Batch: 300 | Loss: 1.143 | Acc: 61.64% +2025-03-09 20:06:44,690 - train - INFO - Epoch: 83 | Test Loss: 1.201 | Test Acc: 61.08% +2025-03-09 20:06:44,856 - train - INFO - Epoch: 84 | Batch: 0 | Loss: 1.367 | Acc: 61.72% +2025-03-09 20:06:46,837 - train - INFO - Epoch: 84 | Batch: 100 | Loss: 1.157 | Acc: 61.66% +2025-03-09 20:06:48,817 - train - INFO - Epoch: 84 | Batch: 200 | Loss: 1.129 | Acc: 62.19% +2025-03-09 20:06:50,922 - train - INFO - Epoch: 84 | Batch: 300 | Loss: 1.124 | Acc: 62.21% +2025-03-09 20:06:54,051 - train - INFO - Epoch: 84 | Test Loss: 1.030 | Test Acc: 65.83% +2025-03-09 20:07:03,364 - train - INFO - Epoch: 85 | Batch: 0 | Loss: 0.997 | Acc: 66.41% +2025-03-09 20:07:06,169 - train - INFO - Epoch: 85 | Batch: 100 | Loss: 1.102 | Acc: 62.93% +2025-03-09 20:07:08,295 - train - INFO - Epoch: 85 | Batch: 200 | Loss: 1.099 | Acc: 62.92% +2025-03-09 20:07:10,316 - train - INFO - Epoch: 85 | Batch: 300 | Loss: 1.100 | Acc: 62.74% +2025-03-09 20:07:13,443 - train - INFO - Epoch: 85 | Test Loss: 0.993 | Test Acc: 66.24% +2025-03-09 20:07:13,613 - train - INFO - Epoch: 86 | Batch: 0 | Loss: 0.879 | Acc: 66.41% +2025-03-09 20:07:15,530 - train - INFO - Epoch: 86 | Batch: 100 | Loss: 1.070 | Acc: 63.68% +2025-03-09 20:07:17,620 - train - INFO - Epoch: 86 | Batch: 200 | Loss: 1.082 | Acc: 63.56% +2025-03-09 20:07:19,568 - train - INFO - Epoch: 86 | Batch: 300 | Loss: 1.095 | Acc: 63.20% +2025-03-09 20:07:22,857 - train - INFO - Epoch: 86 | Test Loss: 1.084 | Test Acc: 64.14% +2025-03-09 20:07:23,032 - train - INFO - Epoch: 87 | Batch: 0 | Loss: 1.100 | Acc: 59.38% +2025-03-09 20:07:25,120 - train - INFO - Epoch: 87 | Batch: 100 | Loss: 1.086 | Acc: 62.84% +2025-03-09 20:07:27,174 - train - INFO - Epoch: 87 | Batch: 200 | Loss: 1.096 | Acc: 62.75% +2025-03-09 20:07:29,361 - train - INFO - Epoch: 87 | Batch: 300 | Loss: 1.107 | Acc: 62.41% +2025-03-09 20:07:32,701 - train - INFO - Epoch: 87 | Test Loss: 1.017 | Test Acc: 66.01% +2025-03-09 20:07:32,864 - train - INFO - Epoch: 88 | Batch: 0 | Loss: 0.993 | Acc: 71.88% +2025-03-09 20:07:34,869 - train - INFO - Epoch: 88 | Batch: 100 | Loss: 1.064 | Acc: 64.05% +2025-03-09 20:07:36,860 - train - INFO - Epoch: 88 | Batch: 200 | Loss: 1.078 | Acc: 63.53% +2025-03-09 20:07:38,850 - train - INFO - Epoch: 88 | Batch: 300 | Loss: 1.093 | Acc: 63.05% +2025-03-09 20:07:41,861 - train - INFO - Epoch: 88 | Test Loss: 0.984 | Test Acc: 67.59% +2025-03-09 20:07:50,269 - train - INFO - Epoch: 89 | Batch: 0 | Loss: 1.033 | Acc: 64.06% +2025-03-09 20:07:52,348 - train - INFO - Epoch: 89 | Batch: 100 | Loss: 1.060 | Acc: 64.22% +2025-03-09 20:07:54,441 - train - INFO - Epoch: 89 | Batch: 200 | Loss: 1.060 | Acc: 64.06% +2025-03-09 20:07:56,483 - train - INFO - Epoch: 89 | Batch: 300 | Loss: 1.066 | Acc: 63.70% +2025-03-09 20:07:59,622 - train - INFO - Epoch: 89 | Test Loss: 1.052 | Test Acc: 65.01% +2025-03-09 20:07:59,816 - train - INFO - Epoch: 90 | Batch: 0 | Loss: 1.035 | Acc: 65.62% +2025-03-09 20:08:02,021 - train - INFO - Epoch: 90 | Batch: 100 | Loss: 1.072 | Acc: 64.50% +2025-03-09 20:08:04,109 - train - INFO - Epoch: 90 | Batch: 200 | Loss: 1.073 | Acc: 63.96% +2025-03-09 20:08:06,098 - train - INFO - Epoch: 90 | Batch: 300 | Loss: 1.079 | Acc: 63.72% +2025-03-09 20:08:09,147 - train - INFO - Epoch: 90 | Test Loss: 0.991 | Test Acc: 67.06% +2025-03-09 20:08:09,323 - train - INFO - Epoch: 91 | Batch: 0 | Loss: 1.019 | Acc: 66.41% +2025-03-09 20:08:11,288 - train - INFO - Epoch: 91 | Batch: 100 | Loss: 1.040 | Acc: 64.88% +2025-03-09 20:08:13,266 - train - INFO - Epoch: 91 | Batch: 200 | Loss: 1.043 | Acc: 64.61% +2025-03-09 20:08:15,248 - train - INFO - Epoch: 91 | Batch: 300 | Loss: 1.056 | Acc: 64.13% +2025-03-09 20:08:18,420 - train - INFO - Epoch: 91 | Test Loss: 1.099 | Test Acc: 63.08% +2025-03-09 20:08:18,624 - train - INFO - Epoch: 92 | Batch: 0 | Loss: 1.044 | Acc: 68.75% +2025-03-09 20:08:20,632 - train - INFO - Epoch: 92 | Batch: 100 | Loss: 1.055 | Acc: 64.34% +2025-03-09 20:08:22,645 - train - INFO - Epoch: 92 | Batch: 200 | Loss: 1.051 | Acc: 64.52% +2025-03-09 20:08:24,710 - train - INFO - Epoch: 92 | Batch: 300 | Loss: 1.063 | Acc: 63.84% +2025-03-09 20:08:27,778 - train - INFO - Epoch: 92 | Test Loss: 1.023 | Test Acc: 65.02% +2025-03-09 20:08:36,292 - train - INFO - Epoch: 93 | Batch: 0 | Loss: 1.010 | Acc: 64.06% +2025-03-09 20:08:38,381 - train - INFO - Epoch: 93 | Batch: 100 | Loss: 1.084 | Acc: 63.50% +2025-03-09 20:08:40,403 - train - INFO - Epoch: 93 | Batch: 200 | Loss: 1.055 | Acc: 64.34% +2025-03-09 20:08:42,384 - train - INFO - Epoch: 93 | Batch: 300 | Loss: 1.055 | Acc: 64.37% +2025-03-09 20:08:45,302 - train - INFO - Epoch: 93 | Test Loss: 1.040 | Test Acc: 65.43% +2025-03-09 20:08:45,476 - train - INFO - Epoch: 94 | Batch: 0 | Loss: 1.013 | Acc: 66.41% +2025-03-09 20:08:47,512 - train - INFO - Epoch: 94 | Batch: 100 | Loss: 1.048 | Acc: 64.67% +2025-03-09 20:08:49,355 - train - INFO - Epoch: 94 | Batch: 200 | Loss: 1.034 | Acc: 64.90% +2025-03-09 20:08:51,320 - train - INFO - Epoch: 94 | Batch: 300 | Loss: 1.032 | Acc: 65.03% +2025-03-09 20:08:54,352 - train - INFO - Epoch: 94 | Test Loss: 1.007 | Test Acc: 65.77% +2025-03-09 20:08:54,532 - train - INFO - Epoch: 95 | Batch: 0 | Loss: 0.963 | Acc: 68.75% +2025-03-09 20:08:56,486 - train - INFO - Epoch: 95 | Batch: 100 | Loss: 1.044 | Acc: 64.52% +2025-03-09 20:08:58,522 - train - INFO - Epoch: 95 | Batch: 200 | Loss: 1.059 | Acc: 64.16% +2025-03-09 20:09:00,641 - train - INFO - Epoch: 95 | Batch: 300 | Loss: 1.046 | Acc: 64.58% +2025-03-09 20:09:03,741 - train - INFO - Epoch: 95 | Test Loss: 1.032 | Test Acc: 65.96% +2025-03-09 20:09:03,950 - train - INFO - Epoch: 96 | Batch: 0 | Loss: 0.983 | Acc: 65.62% +2025-03-09 20:09:05,855 - train - INFO - Epoch: 96 | Batch: 100 | Loss: 1.032 | Acc: 64.93% +2025-03-09 20:09:07,774 - train - INFO - Epoch: 96 | Batch: 200 | Loss: 1.017 | Acc: 65.41% +2025-03-09 20:09:09,825 - train - INFO - Epoch: 96 | Batch: 300 | Loss: 1.017 | Acc: 65.49% +2025-03-09 20:09:12,898 - train - INFO - Epoch: 96 | Test Loss: 1.003 | Test Acc: 65.83% +2025-03-09 20:09:21,539 - train - INFO - Epoch: 97 | Batch: 0 | Loss: 0.992 | Acc: 66.41% +2025-03-09 20:09:23,513 - train - INFO - Epoch: 97 | Batch: 100 | Loss: 1.008 | Acc: 65.87% +2025-03-09 20:09:25,738 - train - INFO - Epoch: 97 | Batch: 200 | Loss: 1.015 | Acc: 65.68% +2025-03-09 20:09:27,822 - train - INFO - Epoch: 97 | Batch: 300 | Loss: 1.024 | Acc: 65.47% +2025-03-09 20:09:31,131 - train - INFO - Epoch: 97 | Test Loss: 0.978 | Test Acc: 67.57% +2025-03-09 20:09:31,294 - train - INFO - Epoch: 98 | Batch: 0 | Loss: 0.893 | Acc: 67.97% +2025-03-09 20:09:33,256 - train - INFO - Epoch: 98 | Batch: 100 | Loss: 1.005 | Acc: 65.52% +2025-03-09 20:09:35,255 - train - INFO - Epoch: 98 | Batch: 200 | Loss: 1.003 | Acc: 65.77% +2025-03-09 20:09:37,222 - train - INFO - Epoch: 98 | Batch: 300 | Loss: 1.001 | Acc: 66.07% +2025-03-09 20:09:40,328 - train - INFO - Epoch: 98 | Test Loss: 0.932 | Test Acc: 68.67% +2025-03-09 20:09:40,520 - train - INFO - Epoch: 99 | Batch: 0 | Loss: 0.896 | Acc: 66.41% +2025-03-09 20:09:42,505 - train - INFO - Epoch: 99 | Batch: 100 | Loss: 1.014 | Acc: 65.79% +2025-03-09 20:09:44,453 - train - INFO - Epoch: 99 | Batch: 200 | Loss: 1.009 | Acc: 65.97% +2025-03-09 20:09:46,594 - train - INFO - Epoch: 99 | Batch: 300 | Loss: 1.004 | Acc: 65.94% +2025-03-09 20:09:49,671 - train - INFO - Epoch: 99 | Test Loss: 0.956 | Test Acc: 68.30% +2025-03-09 20:09:49,866 - train - INFO - Epoch: 100 | Batch: 0 | Loss: 0.916 | Acc: 69.53% +2025-03-09 20:09:52,093 - train - INFO - Epoch: 100 | Batch: 100 | Loss: 1.005 | Acc: 65.68% +2025-03-09 20:09:54,201 - train - INFO - Epoch: 100 | Batch: 200 | Loss: 0.994 | Acc: 66.23% +2025-03-09 20:09:56,269 - train - INFO - Epoch: 100 | Batch: 300 | Loss: 0.995 | Acc: 66.23% +2025-03-09 20:09:59,443 - train - INFO - Epoch: 100 | Test Loss: 1.019 | Test Acc: 66.05% +2025-03-09 20:10:08,141 - train - INFO - 训练完成! diff --git a/Image/AlexNet/code/train.py b/Image/AlexNet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..471910191f65dfa21894397528db61c04c71a320 --- /dev/null +++ b/Image/AlexNet/code/train.py @@ -0,0 +1,42 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import AlexNet +#args.train_type #0 for normal train, 1 for data aug train,2 for back door train + +def main(): + # 解析命令行参数 + args = parse_args() + # 创建模型 + model = AlexNet() + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='alexnet', + layer_name='conv3.2' + ) + elif args.train_type == '1': + train_model_data_augmentation(model, epochs=args.epochs, lr=args.lr, device=f'cuda:{args.gpu}', + save_dir='../model', model_name='alexnet', + batch_size=args.batch_size, num_workers=args.num_workers, + local_dataset_path=args.dataset_path) + elif args.train_type == '2': + train_model_backdoor(model, poison_ratio=0.1, target_label=0, epochs=args.epochs, lr=args.lr, + device=f'cuda:{args.gpu}', save_dir='../model', model_name='alexnet', + batch_size=args.batch_size, num_workers=args.num_workers, + local_dataset_path=args.dataset_path, layer_name='conv3.2') + +if __name__ == '__main__': + main() diff --git a/Image/AlexNet/dataset/.gitkeep b/Image/AlexNet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/AlexNet/model/0/epoch1/embeddings.npy b/Image/AlexNet/model/0/epoch1/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..32de2c195e9f8f713463de5dad120a1ea7325972 --- /dev/null +++ b/Image/AlexNet/model/0/epoch1/embeddings.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d6821ced877c4142fb43869d0c012cc021a0b345481a956e674f1ca23cf562dd +size 102400128 diff --git 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train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3 +2025-03-14 16:01:35,120 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.303 | Acc: 10.94% +2025-03-14 16:01:41,623 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.009 | Acc: 25.39% +2025-03-14 16:01:48,359 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.872 | Acc: 29.96% +2025-03-14 16:01:54,814 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.780 | Acc: 33.57% +2025-03-14 16:02:02,833 - train - INFO - Epoch: 1 | Test Loss: 1.342 | Test Acc: 52.66% +2025-03-14 16:02:03,082 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.477 | Acc: 41.41% +2025-03-14 16:02:09,563 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.335 | Acc: 51.83% +2025-03-14 16:02:16,183 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.242 | Acc: 55.57% +2025-03-14 16:02:22,685 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.173 | Acc: 58.28% +2025-03-14 16:02:30,658 - train - INFO - Epoch: 2 | Test Loss: 1.093 | Test Acc: 61.78% +2025-03-14 16:02:41,822 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.999 | Acc: 63.28% +2025-03-14 16:02:48,275 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.908 | Acc: 68.81% +2025-03-14 16:02:55,542 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.893 | Acc: 69.34% +2025-03-14 16:03:02,152 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.874 | Acc: 69.98% +2025-03-14 16:03:09,933 - train - INFO - Epoch: 3 | Test Loss: 0.895 | Test Acc: 69.23% +2025-03-14 16:03:10,171 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.741 | Acc: 75.00% +2025-03-14 16:03:16,709 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.748 | Acc: 74.24% +2025-03-14 16:03:23,165 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.743 | Acc: 74.83% +2025-03-14 16:03:29,519 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.735 | Acc: 75.22% +2025-03-14 16:03:37,232 - train - INFO - Epoch: 4 | Test Loss: 0.914 | Test Acc: 69.83% +2025-03-14 16:03:48,484 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.680 | Acc: 75.78% +2025-03-14 16:03:55,252 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.674 | Acc: 77.53% +2025-03-14 16:04:01,807 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.667 | Acc: 77.67% +2025-03-14 16:04:08,493 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.661 | Acc: 77.80% +2025-03-14 16:04:16,124 - train - INFO - Epoch: 5 | Test Loss: 0.713 | Test Acc: 76.28% +2025-03-14 16:04:16,366 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.490 | Acc: 87.50% +2025-03-14 16:04:22,967 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.614 | Acc: 79.94% +2025-03-14 16:04:29,555 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.626 | Acc: 79.32% +2025-03-14 16:04:35,837 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.619 | Acc: 79.52% +2025-03-14 16:04:43,506 - train - INFO - Epoch: 6 | Test Loss: 0.767 | Test Acc: 74.45% +2025-03-14 16:04:55,346 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.728 | Acc: 75.00% +2025-03-14 16:05:02,115 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.594 | Acc: 80.22% +2025-03-14 16:05:08,580 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.593 | Acc: 80.24% +2025-03-14 16:05:15,893 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.597 | Acc: 80.01% +2025-03-14 16:05:24,379 - train - INFO - Epoch: 7 | Test Loss: 0.659 | Test Acc: 76.67% +2025-03-14 16:05:24,687 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.511 | Acc: 82.81% +2025-03-14 16:05:31,960 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.554 | Acc: 81.19% +2025-03-14 16:05:38,472 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.564 | Acc: 81.15% +2025-03-14 16:05:45,042 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.568 | Acc: 81.13% +2025-03-14 16:05:53,242 - train - INFO - Epoch: 8 | Test Loss: 0.714 | Test Acc: 76.71% +2025-03-14 16:06:05,059 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.489 | Acc: 85.16% +2025-03-14 16:06:11,780 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.565 | Acc: 81.15% +2025-03-14 16:06:18,537 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.564 | Acc: 81.30% +2025-03-14 16:06:25,070 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.566 | Acc: 81.23% +2025-03-14 16:06:32,812 - train - INFO - Epoch: 9 | Test Loss: 1.349 | Test Acc: 60.93% +2025-03-14 16:06:33,056 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.427 | Acc: 85.94% +2025-03-14 16:06:40,104 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.541 | Acc: 82.09% +2025-03-14 16:06:47,429 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.546 | Acc: 81.77% +2025-03-14 16:06:55,098 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.547 | Acc: 81.80% +2025-03-14 16:07:03,407 - train - INFO - Epoch: 10 | Test Loss: 0.619 | Test Acc: 80.03% +2025-03-14 16:07:15,781 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.509 | Acc: 85.94% +2025-03-14 16:07:22,836 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.546 | Acc: 82.31% +2025-03-14 16:07:29,380 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.547 | Acc: 82.02% +2025-03-14 16:07:36,091 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.545 | Acc: 82.09% +2025-03-14 16:07:44,313 - train - INFO - Epoch: 11 | Test Loss: 0.782 | Test Acc: 74.83% +2025-03-14 16:07:44,642 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.424 | Acc: 88.28% +2025-03-14 16:07:52,452 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.532 | Acc: 82.69% +2025-03-14 16:07:59,454 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.535 | Acc: 82.39% +2025-03-14 16:08:05,751 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.539 | Acc: 82.22% +2025-03-14 16:08:13,415 - train - INFO - Epoch: 12 | Test Loss: 0.667 | Test Acc: 77.20% +2025-03-14 16:08:24,850 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.513 | Acc: 81.25% +2025-03-14 16:08:31,529 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.525 | Acc: 82.91% +2025-03-14 16:08:37,988 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.524 | Acc: 82.91% +2025-03-14 16:08:44,426 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.531 | Acc: 82.63% +2025-03-14 16:08:52,236 - train - INFO - Epoch: 13 | Test Loss: 0.671 | Test Acc: 77.66% +2025-03-14 16:08:52,474 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.400 | Acc: 87.50% +2025-03-14 16:08:58,885 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.513 | Acc: 83.14% +2025-03-14 16:09:05,594 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.526 | Acc: 82.57% +2025-03-14 16:09:13,190 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.525 | Acc: 82.58% +2025-03-14 16:09:21,359 - train - INFO - Epoch: 14 | Test Loss: 0.644 | Test Acc: 78.23% +2025-03-14 16:09:32,590 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.637 | Acc: 78.91% +2025-03-14 16:09:39,160 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.521 | Acc: 82.97% +2025-03-14 16:09:45,611 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.523 | Acc: 82.81% +2025-03-14 16:09:52,133 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.522 | Acc: 82.83% +2025-03-14 16:09:59,547 - train - INFO - Epoch: 15 | Test Loss: 0.614 | Test Acc: 78.16% +2025-03-14 16:09:59,772 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.607 | Acc: 76.56% +2025-03-14 16:10:06,285 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.508 | Acc: 83.32% +2025-03-14 16:10:12,668 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.504 | Acc: 83.33% +2025-03-14 16:10:19,419 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.514 | Acc: 83.00% +2025-03-14 16:10:27,539 - train - INFO - Epoch: 16 | Test Loss: 0.622 | Test Acc: 79.45% +2025-03-14 16:10:38,671 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.503 | Acc: 86.72% +2025-03-14 16:10:45,253 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.503 | Acc: 83.30% +2025-03-14 16:10:51,781 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.507 | Acc: 83.28% +2025-03-14 16:10:58,057 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.504 | Acc: 83.31% +2025-03-14 16:11:05,776 - train - INFO - Epoch: 17 | Test Loss: 0.701 | Test Acc: 77.26% +2025-03-14 16:11:06,018 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.620 | Acc: 78.91% +2025-03-14 16:11:13,323 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.498 | Acc: 83.79% +2025-03-14 16:11:20,126 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.494 | Acc: 83.98% +2025-03-14 16:11:26,804 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.499 | Acc: 83.79% +2025-03-14 16:11:34,825 - train - INFO - Epoch: 18 | Test Loss: 0.751 | Test Acc: 76.20% +2025-03-14 16:11:46,371 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.408 | Acc: 86.72% +2025-03-14 16:11:53,283 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.485 | Acc: 84.00% +2025-03-14 16:12:00,455 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.493 | Acc: 83.82% +2025-03-14 16:12:06,896 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.499 | Acc: 83.56% +2025-03-14 16:12:15,134 - train - INFO - Epoch: 19 | Test Loss: 0.663 | Test Acc: 76.95% +2025-03-14 16:12:15,418 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.390 | Acc: 85.94% +2025-03-14 16:12:21,907 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.500 | Acc: 83.41% +2025-03-14 16:12:28,443 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.498 | Acc: 83.41% +2025-03-14 16:12:35,175 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.494 | Acc: 83.63% +2025-03-14 16:12:45,840 - train - INFO - Epoch: 20 | Test Loss: 0.561 | Test Acc: 81.65% +2025-03-14 16:12:57,454 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.349 | Acc: 89.84% +2025-03-14 16:13:04,132 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.477 | Acc: 83.99% +2025-03-14 16:13:10,608 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.477 | Acc: 84.11% +2025-03-14 16:13:17,405 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.481 | Acc: 84.17% +2025-03-14 16:13:25,297 - train - INFO - Epoch: 21 | Test Loss: 0.593 | Test Acc: 80.85% +2025-03-14 16:13:25,591 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.453 | Acc: 82.81% +2025-03-14 16:13:32,327 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.466 | Acc: 84.52% +2025-03-14 16:13:39,185 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.471 | Acc: 84.26% +2025-03-14 16:13:45,668 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.478 | Acc: 84.05% +2025-03-14 16:13:53,441 - train - INFO - Epoch: 22 | Test Loss: 0.665 | Test Acc: 78.56% +2025-03-14 16:14:05,354 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.474 | Acc: 84.38% +2025-03-14 16:14:11,690 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.474 | Acc: 84.71% +2025-03-14 16:14:18,410 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.482 | Acc: 84.29% +2025-03-14 16:14:24,963 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.478 | Acc: 84.44% +2025-03-14 16:14:32,640 - train - INFO - Epoch: 23 | Test Loss: 1.058 | Test Acc: 68.37% +2025-03-14 16:14:32,934 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.378 | Acc: 89.06% +2025-03-14 16:14:39,551 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.477 | Acc: 84.41% +2025-03-14 16:14:46,168 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.481 | Acc: 84.37% +2025-03-14 16:14:52,732 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.479 | Acc: 84.30% +2025-03-14 16:15:00,623 - train - INFO - Epoch: 24 | Test Loss: 0.636 | Test Acc: 80.17% +2025-03-14 16:15:11,674 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.529 | Acc: 82.03% +2025-03-14 16:15:18,136 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.454 | Acc: 85.12% +2025-03-14 16:15:24,586 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.464 | Acc: 84.64% +2025-03-14 16:15:31,108 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.466 | Acc: 84.54% +2025-03-14 16:15:39,073 - train - INFO - Epoch: 25 | Test Loss: 0.572 | Test Acc: 81.28% +2025-03-14 16:15:39,328 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.496 | Acc: 82.03% +2025-03-14 16:15:45,847 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.468 | Acc: 84.57% +2025-03-14 16:15:52,738 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.469 | Acc: 84.65% +2025-03-14 16:15:59,415 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.466 | Acc: 84.73% +2025-03-14 16:16:06,894 - train - INFO - Epoch: 26 | Test Loss: 0.816 | Test Acc: 74.91% +2025-03-14 16:16:18,304 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.400 | Acc: 85.94% +2025-03-14 16:16:24,917 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.469 | Acc: 84.55% +2025-03-14 16:16:31,318 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.464 | Acc: 84.77% +2025-03-14 16:16:37,744 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.462 | Acc: 84.86% +2025-03-14 16:16:45,639 - train - INFO - Epoch: 27 | Test Loss: 0.561 | Test Acc: 80.96% +2025-03-14 16:16:45,909 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.450 | Acc: 82.81% +2025-03-14 16:16:52,939 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.464 | Acc: 84.66% +2025-03-14 16:16:59,702 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.466 | Acc: 84.61% +2025-03-14 16:17:06,087 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.467 | Acc: 84.57% +2025-03-14 16:17:13,719 - train - INFO - Epoch: 28 | Test Loss: 0.692 | Test Acc: 78.32% +2025-03-14 16:17:25,054 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.428 | Acc: 83.59% +2025-03-14 16:17:31,720 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.457 | Acc: 84.75% +2025-03-14 16:17:38,193 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.463 | Acc: 84.71% +2025-03-14 16:17:44,756 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.465 | Acc: 84.69% +2025-03-14 16:17:52,619 - train - INFO - Epoch: 29 | Test Loss: 0.517 | Test Acc: 82.95% +2025-03-14 16:17:52,871 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.412 | Acc: 86.72% +2025-03-14 16:17:59,452 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.442 | Acc: 85.97% +2025-03-14 16:18:06,228 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.452 | Acc: 85.31% +2025-03-14 16:18:12,888 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.457 | Acc: 85.07% +2025-03-14 16:18:21,005 - train - INFO - Epoch: 30 | Test Loss: 0.656 | Test Acc: 78.35% +2025-03-14 16:18:33,022 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.579 | Acc: 82.81% +2025-03-14 16:18:39,739 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.443 | Acc: 85.60% +2025-03-14 16:18:46,476 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.452 | Acc: 85.29% +2025-03-14 16:18:52,888 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.458 | Acc: 85.02% +2025-03-14 16:19:00,889 - train - INFO - Epoch: 31 | Test Loss: 0.530 | Test Acc: 82.67% +2025-03-14 16:19:01,161 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.321 | Acc: 89.06% +2025-03-14 16:19:07,656 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.446 | Acc: 85.07% +2025-03-14 16:19:14,096 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.451 | Acc: 84.76% +2025-03-14 16:19:20,849 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.454 | Acc: 84.86% +2025-03-14 16:19:28,416 - train - INFO - Epoch: 32 | Test Loss: 0.834 | Test Acc: 73.96% +2025-03-14 16:19:40,125 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.497 | Acc: 82.03% +2025-03-14 16:19:47,217 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.447 | Acc: 85.14% +2025-03-14 16:19:53,869 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.460 | Acc: 84.78% +2025-03-14 16:20:00,742 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.451 | Acc: 85.11% +2025-03-14 16:20:09,000 - train - INFO - Epoch: 33 | Test Loss: 0.449 | Test Acc: 84.73% +2025-03-14 16:20:09,277 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.413 | Acc: 82.03% +2025-03-14 16:20:16,108 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.448 | Acc: 85.24% +2025-03-14 16:20:22,620 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.445 | Acc: 85.32% +2025-03-14 16:20:29,138 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.443 | Acc: 85.38% +2025-03-14 16:20:37,243 - train - INFO - Epoch: 34 | Test Loss: 0.531 | Test Acc: 82.30% +2025-03-14 16:20:49,210 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.354 | Acc: 87.50% +2025-03-14 16:20:55,814 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.432 | Acc: 85.93% +2025-03-14 16:21:02,450 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.439 | Acc: 85.55% +2025-03-14 16:21:08,928 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.442 | Acc: 85.40% +2025-03-14 16:21:17,192 - train - INFO - Epoch: 35 | Test Loss: 0.597 | Test Acc: 80.39% +2025-03-14 16:21:17,446 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.304 | Acc: 89.84% +2025-03-14 16:21:24,132 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.440 | Acc: 85.47% +2025-03-14 16:21:30,524 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.436 | Acc: 85.62% +2025-03-14 16:21:36,979 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.441 | Acc: 85.50% +2025-03-14 16:21:44,631 - train - INFO - Epoch: 36 | Test Loss: 0.642 | Test Acc: 78.95% +2025-03-14 16:21:56,350 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.366 | Acc: 86.72% +2025-03-14 16:22:02,994 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.435 | Acc: 85.64% +2025-03-14 16:22:09,586 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.440 | Acc: 85.30% +2025-03-14 16:22:16,323 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.444 | Acc: 85.23% +2025-03-14 16:22:24,686 - train - INFO - Epoch: 37 | Test Loss: 0.630 | Test Acc: 79.88% +2025-03-14 16:22:24,928 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.366 | Acc: 89.06% +2025-03-14 16:22:31,317 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.420 | Acc: 85.93% +2025-03-14 16:22:37,751 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.421 | Acc: 86.11% +2025-03-14 16:22:44,033 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.427 | Acc: 85.95% +2025-03-14 16:22:52,202 - train - INFO - Epoch: 38 | Test Loss: 0.530 | Test Acc: 82.57% +2025-03-14 16:23:03,115 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.402 | Acc: 85.94% +2025-03-14 16:23:09,617 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.437 | Acc: 85.83% +2025-03-14 16:23:16,188 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.434 | Acc: 85.81% +2025-03-14 16:23:22,787 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.440 | Acc: 85.63% +2025-03-14 16:23:30,722 - train - INFO - Epoch: 39 | Test Loss: 0.605 | Test Acc: 80.62% +2025-03-14 16:23:30,947 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.363 | Acc: 89.84% +2025-03-14 16:23:37,305 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.437 | Acc: 85.76% +2025-03-14 16:23:43,539 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.436 | Acc: 85.80% +2025-03-14 16:23:49,649 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.438 | Acc: 85.76% +2025-03-14 16:23:57,791 - train - INFO - Epoch: 40 | Test Loss: 0.522 | Test Acc: 82.90% +2025-03-14 16:24:10,073 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.347 | Acc: 87.50% +2025-03-14 16:24:16,616 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.431 | Acc: 85.68% +2025-03-14 16:24:23,037 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.438 | Acc: 85.39% +2025-03-14 16:24:29,562 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.435 | Acc: 85.55% +2025-03-14 16:24:37,304 - train - INFO - Epoch: 41 | Test Loss: 0.576 | Test Acc: 80.45% +2025-03-14 16:24:37,526 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.319 | Acc: 91.41% +2025-03-14 16:24:44,058 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.426 | Acc: 86.07% +2025-03-14 16:24:50,478 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.428 | Acc: 85.86% +2025-03-14 16:24:56,812 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.436 | Acc: 85.59% +2025-03-14 16:25:04,435 - train - INFO - Epoch: 42 | Test Loss: 0.617 | Test Acc: 80.75% +2025-03-14 16:25:16,446 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.413 | Acc: 83.59% +2025-03-14 16:25:23,070 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.414 | Acc: 86.35% +2025-03-14 16:25:29,554 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.418 | Acc: 86.25% +2025-03-14 16:25:35,883 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.425 | Acc: 85.97% +2025-03-14 16:25:43,587 - train - INFO - Epoch: 43 | Test Loss: 0.571 | Test Acc: 80.94% +2025-03-14 16:25:43,825 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.422 | Acc: 85.16% +2025-03-14 16:25:50,820 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.430 | Acc: 85.84% +2025-03-14 16:25:57,866 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.435 | Acc: 85.65% +2025-03-14 16:26:04,343 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.435 | Acc: 85.64% +2025-03-14 16:26:11,962 - train - INFO - Epoch: 44 | Test Loss: 0.544 | Test Acc: 81.97% +2025-03-14 16:26:23,304 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.537 | Acc: 79.69% +2025-03-14 16:26:29,717 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.416 | Acc: 86.31% +2025-03-14 16:26:36,049 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.426 | Acc: 86.04% +2025-03-14 16:26:42,335 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.433 | Acc: 85.66% +2025-03-14 16:26:50,038 - train - INFO - Epoch: 45 | Test Loss: 0.509 | Test Acc: 82.87% +2025-03-14 16:26:50,309 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.292 | Acc: 90.62% +2025-03-14 16:26:56,772 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.407 | Acc: 86.56% +2025-03-14 16:27:03,183 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.417 | Acc: 86.22% +2025-03-14 16:27:09,786 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.421 | Acc: 86.09% +2025-03-14 16:27:17,656 - train - INFO - Epoch: 46 | Test Loss: 0.557 | Test Acc: 81.43% +2025-03-14 16:27:28,839 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.359 | Acc: 89.06% +2025-03-14 16:27:35,181 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.414 | Acc: 86.39% +2025-03-14 16:27:41,630 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.427 | Acc: 85.87% +2025-03-14 16:27:47,853 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.430 | Acc: 85.79% +2025-03-14 16:27:56,187 - train - INFO - Epoch: 47 | Test Loss: 0.579 | Test Acc: 81.69% +2025-03-14 16:27:56,420 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.468 | Acc: 85.94% +2025-03-14 16:28:03,669 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.428 | Acc: 85.92% +2025-03-14 16:28:10,055 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.430 | Acc: 85.82% +2025-03-14 16:28:16,357 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.429 | Acc: 85.72% +2025-03-14 16:28:23,968 - train - INFO - Epoch: 48 | Test Loss: 0.595 | Test Acc: 80.28% +2025-03-14 16:28:35,744 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.294 | Acc: 89.06% +2025-03-14 16:28:42,339 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.401 | Acc: 86.66% +2025-03-14 16:28:48,791 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.411 | Acc: 86.20% +2025-03-14 16:28:55,258 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.418 | Acc: 86.02% +2025-03-14 16:29:02,924 - train - INFO - Epoch: 49 | Test Loss: 0.536 | Test Acc: 81.65% +2025-03-14 16:29:03,178 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.444 | Acc: 85.94% +2025-03-14 16:29:09,522 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.410 | Acc: 87.00% +2025-03-14 16:29:15,921 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.409 | Acc: 86.71% +2025-03-14 16:29:22,219 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.415 | Acc: 86.36% +2025-03-14 16:29:29,514 - train - INFO - Epoch: 50 | Test Loss: 0.619 | Test Acc: 78.84% +2025-03-14 16:29:40,209 - train - INFO - 训练完成! diff --git a/Image/DenseNet/code/model.py b/Image/DenseNet/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..3308e0d3abe06d8da77e843a0be593159287297a --- /dev/null +++ b/Image/DenseNet/code/model.py @@ -0,0 +1,152 @@ +""" +DenseNet in pytorch +see the details in papaer +[1] Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. + Densely Connected Convolutional Networks + https://arxiv.org/abs/1608.06993v5 +""" +import torch +import torch.nn as nn +import math + +class Bottleneck(nn.Module): + """ + Dense Block + 这里的growth_rate=out_channels, 就是每个Block自己输出的通道数。 + 先通过1x1卷积层，将通道数缩小为4 * growth_rate，然后再通过3x3卷积层降低到growth_rate。 + """ + # 通常1×1卷积的通道数为GrowthRate的4倍 + expansion = 4 + + def __init__(self, in_channels, growth_rate): + super(Bottleneck, self).__init__() + zip_channels = self.expansion * growth_rate + self.features = nn.Sequential( + nn.BatchNorm2d(in_channels), + nn.ReLU(True), + nn.Conv2d(in_channels, zip_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(zip_channels), + nn.ReLU(True), + nn.Conv2d(zip_channels, growth_rate, kernel_size=3, padding=1, bias=False) + ) + + def forward(self, x): + out = self.features(x) + out = torch.cat([out, x], 1) + return out + + +class Transition(nn.Module): + """ + 改变维数的Transition层 具体包括BN、ReLU、1×1卷积（Conv）、2×2平均池化操作 + 先通过1x1的卷积层减少channels，再通过2x2的平均池化层缩小feature-map + """ + # 1×1卷积的作用是降维，起到压缩模型的作用，而平均池化则是降低特征图的尺寸。 + def __init__(self, in_channels, out_channels): + super(Transition, self).__init__() + self.features = nn.Sequential( + nn.BatchNorm2d(in_channels), + nn.ReLU(True), + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.AvgPool2d(2) + ) + + def forward(self, x): + out = self.features(x) + return out + +class DenseNet(nn.Module): + """ + Dense Net + paper中growth_rate取12，维度压缩的参数θ，即reduction取0.5 + 且初始化方法为kaiming_normal() + num_blocks为每段网络中的DenseBlock数量 + DenseNet和ResNet一样也是六段式网络（一段卷积+四段Dense+平均池化层），最后FC层。 + 第一段将维数从3变到2 * growth_rate + + (3, 32, 32) -> [Conv2d] -> (24, 32, 32) -> [layer1] -> (48, 16, 16) -> [layer2] + ->(96, 8, 8) -> [layer3] -> (192, 4, 4) -> [layer4] -> (384, 4, 4) -> [AvgPool] + ->(384, 1, 1) -> [Linear] -> (10) + """ + def __init__(self, num_blocks, growth_rate=12, reduction=0.5, num_classes=10, init_weights=True): + super(DenseNet, self).__init__() + self.growth_rate = growth_rate + self.reduction = reduction + + num_channels = 2 * growth_rate + + self.features = nn.Conv2d(3, num_channels, kernel_size=3, padding=1, bias=False) + self.layer1, num_channels = self._make_dense_layer(num_channels, num_blocks[0]) + self.layer2, num_channels = self._make_dense_layer(num_channels, num_blocks[1]) + self.layer3, num_channels = self._make_dense_layer(num_channels, num_blocks[2]) + self.layer4, num_channels = self._make_dense_layer(num_channels, num_blocks[3], transition=False) + self.avg_pool = nn.Sequential( + nn.BatchNorm2d(num_channels), + nn.ReLU(True), + nn.AvgPool2d(4), + ) + self.classifier = nn.Linear(num_channels, num_classes) + + if init_weights: + self._initialize_weights() + + def _make_dense_layer(self, in_channels, nblock, transition=True): + layers = [] + for i in range(nblock): + layers += [Bottleneck(in_channels, self.growth_rate)] + in_channels += self.growth_rate + out_channels = in_channels + if transition: + out_channels = int(math.floor(in_channels * self.reduction)) + layers += [Transition(in_channels, out_channels)] + return nn.Sequential(*layers), out_channels + + def _initialize_weights(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) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + out = self.features(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + out = self.classifier(out) + return out + +def DenseNet121(): + return DenseNet([6,12,24,16], growth_rate=32) + +def DenseNet169(): + return DenseNet([6,12,32,32], growth_rate=32) + +def DenseNet201(): + return DenseNet([6,12,48,32], growth_rate=32) + +def DenseNet161(): + return DenseNet([6,12,36,24], growth_rate=48) + +def densenet_cifar(): + return DenseNet([6,12,24,16], growth_rate=12) + + +def test(): + net = densenet_cifar() + x = torch.randn(1,3,32,32) + y = net(x) + print(y.size()) + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net,(1,3,32,32)) \ No newline at end of file diff --git a/Image/DenseNet/code/train.log b/Image/DenseNet/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..6cd3b8e90eacbadd2ecc9eb3431a9087181fe66c --- /dev/null +++ b/Image/DenseNet/code/train.log @@ -0,0 +1,250 @@ +2025-03-09 21:11:49,168 - train - INFO - 开始训练 densenet +2025-03-09 21:11:49,168 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:2 +2025-03-09 21:11:49,894 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.311 | Acc: 8.59% +2025-03-09 21:11:55,683 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 1.940 | Acc: 27.50% +2025-03-09 21:12:01,584 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.770 | Acc: 34.31% +2025-03-09 21:12:07,368 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.653 | Acc: 38.88% +2025-03-09 21:12:14,425 - train - INFO - Epoch: 1 | Test Loss: 1.328 | Test Acc: 53.16% +2025-03-09 21:12:14,716 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.206 | Acc: 55.47% +2025-03-09 21:12:21,053 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.124 | Acc: 59.37% +2025-03-09 21:12:27,403 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.072 | Acc: 61.63% +2025-03-09 21:12:33,389 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.033 | Acc: 63.06% +2025-03-09 21:12:40,181 - train - INFO - Epoch: 2 | Test Loss: 1.210 | Test Acc: 61.82% +2025-03-09 21:12:49,405 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.909 | Acc: 71.09% +2025-03-09 21:12:56,088 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.830 | Acc: 70.80% +2025-03-09 21:13:03,091 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.805 | Acc: 71.64% +2025-03-09 21:13:09,972 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.787 | Acc: 72.33% +2025-03-09 21:13:17,000 - train - INFO - Epoch: 3 | Test Loss: 0.768 | Test Acc: 73.42% +2025-03-09 21:13:17,213 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.791 | Acc: 73.44% +2025-03-09 21:13:22,943 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.690 | Acc: 75.70% +2025-03-09 21:13:28,945 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.669 | Acc: 76.64% +2025-03-09 21:13:35,287 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.662 | Acc: 76.95% +2025-03-09 21:13:43,309 - train - INFO - Epoch: 4 | Test Loss: 0.685 | Test Acc: 76.06% +2025-03-09 21:13:54,007 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.594 | Acc: 79.69% +2025-03-09 21:14:00,201 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.594 | Acc: 79.16% +2025-03-09 21:14:06,592 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.595 | Acc: 79.42% +2025-03-09 21:14:13,084 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.594 | Acc: 79.52% +2025-03-09 21:14:20,411 - train - INFO - Epoch: 5 | Test Loss: 0.712 | Test Acc: 75.64% +2025-03-09 21:14:20,685 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.455 | Acc: 85.94% +2025-03-09 21:14:27,227 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.543 | Acc: 81.23% +2025-03-09 21:14:33,263 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.546 | Acc: 81.30% +2025-03-09 21:14:39,203 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.547 | Acc: 81.20% +2025-03-09 21:14:46,340 - train - INFO - Epoch: 6 | Test Loss: 0.847 | Test Acc: 73.10% +2025-03-09 21:14:56,240 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.486 | Acc: 80.47% +2025-03-09 21:15:03,070 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.527 | Acc: 81.69% +2025-03-09 21:15:09,779 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.526 | Acc: 81.86% +2025-03-09 21:15:15,807 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.526 | Acc: 81.90% +2025-03-09 21:15:22,494 - train - INFO - Epoch: 7 | Test Loss: 0.673 | Test Acc: 77.47% +2025-03-09 21:15:22,735 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.419 | Acc: 85.16% +2025-03-09 21:15:28,714 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.505 | Acc: 82.74% +2025-03-09 21:15:35,489 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.516 | Acc: 82.33% +2025-03-09 21:15:41,666 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.510 | Acc: 82.53% +2025-03-09 21:15:49,206 - train - INFO - Epoch: 8 | Test Loss: 0.662 | Test Acc: 77.04% +2025-03-09 21:15:59,574 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.538 | Acc: 80.47% +2025-03-09 21:16:06,194 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.488 | Acc: 83.35% +2025-03-09 21:16:12,155 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.490 | Acc: 83.31% +2025-03-09 21:16:18,332 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.495 | Acc: 83.14% +2025-03-09 21:16:25,285 - train - INFO - Epoch: 9 | Test Loss: 0.670 | Test Acc: 77.47% +2025-03-09 21:16:25,526 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.566 | Acc: 77.34% +2025-03-09 21:16:31,632 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.485 | Acc: 83.08% +2025-03-09 21:16:37,363 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.490 | Acc: 82.98% +2025-03-09 21:16:43,737 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.492 | Acc: 83.05% +2025-03-09 21:16:50,797 - train - INFO - Epoch: 10 | Test Loss: 0.664 | Test Acc: 77.87% +2025-03-09 21:17:00,747 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.431 | Acc: 85.94% +2025-03-09 21:17:06,577 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.477 | Acc: 83.89% +2025-03-09 21:17:12,684 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.480 | Acc: 83.71% +2025-03-09 21:17:18,559 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.480 | Acc: 83.62% +2025-03-09 21:17:25,751 - train - INFO - Epoch: 11 | Test Loss: 0.653 | Test Acc: 77.73% +2025-03-09 21:17:25,991 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.406 | Acc: 88.28% +2025-03-09 21:17:32,073 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.470 | Acc: 83.87% +2025-03-09 21:17:38,732 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.473 | Acc: 83.91% +2025-03-09 21:17:45,448 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.474 | Acc: 83.92% +2025-03-09 21:17:52,973 - train - INFO - Epoch: 12 | Test Loss: 0.609 | Test Acc: 79.12% +2025-03-09 21:18:02,605 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.588 | Acc: 82.03% +2025-03-09 21:18:09,036 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.446 | Acc: 84.57% +2025-03-09 21:18:15,491 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.460 | Acc: 84.19% +2025-03-09 21:18:21,601 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.456 | Acc: 84.31% +2025-03-09 21:18:28,561 - train - INFO - Epoch: 13 | Test Loss: 0.613 | Test Acc: 79.41% +2025-03-09 21:18:28,768 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.302 | Acc: 89.06% +2025-03-09 21:18:35,282 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.463 | Acc: 84.21% +2025-03-09 21:18:41,504 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.461 | Acc: 84.27% +2025-03-09 21:18:48,232 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.458 | Acc: 84.32% +2025-03-09 21:18:55,232 - train - INFO - Epoch: 14 | Test Loss: 0.518 | Test Acc: 81.97% +2025-03-09 21:19:04,117 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.396 | Acc: 85.94% +2025-03-09 21:19:10,215 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.436 | Acc: 84.96% +2025-03-09 21:19:16,620 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.441 | Acc: 84.67% +2025-03-09 21:19:23,308 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.446 | Acc: 84.72% +2025-03-09 21:19:31,353 - train - INFO - Epoch: 15 | Test Loss: 0.614 | Test Acc: 79.11% +2025-03-09 21:19:31,609 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.452 | Acc: 85.16% +2025-03-09 21:19:37,681 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.438 | Acc: 84.92% +2025-03-09 21:19:43,368 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.446 | Acc: 84.64% +2025-03-09 21:19:49,273 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.444 | Acc: 84.83% +2025-03-09 21:19:57,522 - train - INFO - Epoch: 16 | Test Loss: 0.552 | Test Acc: 81.27% +2025-03-09 21:20:09,354 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.323 | Acc: 90.62% +2025-03-09 21:20:15,473 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.410 | Acc: 86.25% +2025-03-09 21:20:21,552 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.439 | Acc: 85.15% +2025-03-09 21:20:27,549 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.440 | Acc: 85.08% +2025-03-09 21:20:35,037 - train - INFO - Epoch: 17 | Test Loss: 0.573 | Test Acc: 81.27% +2025-03-09 21:20:35,286 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.416 | Acc: 85.94% +2025-03-09 21:20:41,226 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.404 | Acc: 86.05% +2025-03-09 21:20:47,415 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.418 | Acc: 85.49% +2025-03-09 21:20:53,316 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.424 | Acc: 85.37% +2025-03-09 21:21:00,935 - train - INFO - Epoch: 18 | Test Loss: 0.579 | Test Acc: 80.69% +2025-03-09 21:21:11,435 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.374 | Acc: 87.50% +2025-03-09 21:21:17,597 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.432 | Acc: 85.06% +2025-03-09 21:21:23,798 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.430 | Acc: 85.17% +2025-03-09 21:21:29,790 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.433 | Acc: 84.92% +2025-03-09 21:21:38,374 - train - INFO - Epoch: 19 | Test Loss: 0.568 | Test Acc: 81.09% +2025-03-09 21:21:39,187 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.397 | Acc: 86.72% +2025-03-09 21:21:45,999 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.416 | Acc: 85.76% +2025-03-09 21:21:52,755 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.423 | Acc: 85.38% +2025-03-09 21:21:59,191 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.420 | Acc: 85.43% +2025-03-09 21:22:06,489 - train - INFO - Epoch: 20 | Test Loss: 0.549 | Test Acc: 81.92% +2025-03-09 21:22:16,199 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.380 | Acc: 89.06% +2025-03-09 21:22:22,500 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.403 | Acc: 86.45% +2025-03-09 21:22:29,352 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.408 | Acc: 86.16% +2025-03-09 21:22:35,751 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.413 | Acc: 85.82% +2025-03-09 21:22:42,795 - train - INFO - Epoch: 21 | Test Loss: 0.527 | Test Acc: 82.40% +2025-03-09 21:22:43,029 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.499 | Acc: 84.38% +2025-03-09 21:22:48,802 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.410 | Acc: 86.12% +2025-03-09 21:22:54,812 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.418 | Acc: 85.64% +2025-03-09 21:23:00,729 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.418 | Acc: 85.68% +2025-03-09 21:23:07,906 - train - INFO - Epoch: 22 | Test Loss: 0.902 | Test Acc: 73.40% +2025-03-09 21:23:17,770 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.453 | Acc: 85.16% +2025-03-09 21:23:23,789 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.410 | Acc: 86.04% +2025-03-09 21:23:29,984 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.413 | Acc: 85.85% +2025-03-09 21:23:36,210 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.407 | Acc: 86.07% +2025-03-09 21:23:43,584 - train - INFO - Epoch: 23 | Test Loss: 0.629 | Test Acc: 79.57% +2025-03-09 21:23:43,818 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.474 | Acc: 83.59% +2025-03-09 21:23:49,856 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.393 | Acc: 86.41% +2025-03-09 21:23:55,816 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.399 | Acc: 86.19% +2025-03-09 21:24:01,461 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.399 | Acc: 86.22% +2025-03-09 21:24:08,313 - train - INFO - Epoch: 24 | Test Loss: 0.539 | Test Acc: 81.81% +2025-03-09 21:24:17,795 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.485 | Acc: 83.59% +2025-03-09 21:24:23,553 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.396 | Acc: 86.18% +2025-03-09 21:24:29,445 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.402 | Acc: 86.05% +2025-03-09 21:24:35,449 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.406 | Acc: 85.98% +2025-03-09 21:24:42,460 - train - INFO - Epoch: 25 | Test Loss: 0.539 | Test Acc: 82.08% +2025-03-09 21:24:42,688 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.492 | Acc: 78.91% +2025-03-09 21:24:48,661 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.391 | Acc: 86.51% +2025-03-09 21:24:54,754 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.395 | Acc: 86.36% +2025-03-09 21:25:00,772 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.398 | Acc: 86.21% +2025-03-09 21:25:07,465 - train - INFO - Epoch: 26 | Test Loss: 0.561 | Test Acc: 80.89% +2025-03-09 21:25:17,361 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.332 | Acc: 85.94% +2025-03-09 21:25:23,617 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.387 | Acc: 86.50% +2025-03-09 21:25:29,754 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.391 | Acc: 86.45% +2025-03-09 21:25:35,426 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.394 | Acc: 86.51% +2025-03-09 21:25:42,482 - train - INFO - Epoch: 27 | Test Loss: 0.636 | Test Acc: 80.20% +2025-03-09 21:25:42,739 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.327 | Acc: 89.84% +2025-03-09 21:25:48,984 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.376 | Acc: 86.90% +2025-03-09 21:25:55,729 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.386 | Acc: 86.62% +2025-03-09 21:26:02,430 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.392 | Acc: 86.50% +2025-03-09 21:26:09,534 - train - INFO - Epoch: 28 | Test Loss: 0.604 | Test Acc: 80.52% +2025-03-09 21:26:19,870 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.393 | Acc: 89.06% +2025-03-09 21:26:26,037 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.384 | Acc: 86.73% +2025-03-09 21:26:32,204 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.388 | Acc: 86.72% +2025-03-09 21:26:38,258 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.385 | Acc: 86.78% +2025-03-09 21:26:44,989 - train - INFO - Epoch: 29 | Test Loss: 0.581 | Test Acc: 80.79% +2025-03-09 21:26:45,232 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.339 | Acc: 88.28% +2025-03-09 21:26:51,032 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.371 | Acc: 87.30% +2025-03-09 21:26:56,876 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.376 | Acc: 87.18% +2025-03-09 21:27:02,463 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.380 | Acc: 87.05% +2025-03-09 21:27:09,305 - train - INFO - Epoch: 30 | Test Loss: 0.512 | Test Acc: 82.26% +2025-03-09 21:27:19,501 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.309 | Acc: 87.50% +2025-03-09 21:27:25,645 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.367 | Acc: 87.24% +2025-03-09 21:27:32,514 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.379 | Acc: 87.03% +2025-03-09 21:27:39,392 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.386 | Acc: 86.70% +2025-03-09 21:27:46,716 - train - INFO - Epoch: 31 | Test Loss: 0.715 | Test Acc: 77.60% +2025-03-09 21:27:46,951 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.417 | Acc: 87.50% +2025-03-09 21:27:53,142 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.370 | Acc: 87.15% +2025-03-09 21:27:59,233 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.380 | Acc: 86.96% +2025-03-09 21:28:05,461 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.383 | Acc: 86.79% +2025-03-09 21:28:12,214 - train - INFO - Epoch: 32 | Test Loss: 0.581 | Test Acc: 80.21% +2025-03-09 21:28:21,517 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.527 | Acc: 82.03% +2025-03-09 21:28:27,470 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.367 | Acc: 87.20% +2025-03-09 21:28:33,329 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.382 | Acc: 86.78% +2025-03-09 21:28:39,101 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.384 | Acc: 86.76% +2025-03-09 21:28:46,139 - train - INFO - Epoch: 33 | Test Loss: 0.525 | Test Acc: 82.67% +2025-03-09 21:28:46,389 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.350 | Acc: 89.06% +2025-03-09 21:28:52,622 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.350 | Acc: 87.94% +2025-03-09 21:28:58,609 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.361 | Acc: 87.54% +2025-03-09 21:29:04,578 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.371 | Acc: 87.25% +2025-03-09 21:29:11,580 - train - INFO - Epoch: 34 | Test Loss: 0.572 | Test Acc: 81.09% +2025-03-09 21:29:21,637 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.500 | Acc: 83.59% +2025-03-09 21:29:28,349 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.363 | Acc: 87.59% +2025-03-09 21:29:34,488 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.376 | Acc: 87.18% +2025-03-09 21:29:40,398 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.380 | Acc: 87.04% +2025-03-09 21:29:48,658 - train - INFO - Epoch: 35 | Test Loss: 0.455 | Test Acc: 84.92% +2025-03-09 21:29:48,921 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.357 | Acc: 89.06% +2025-03-09 21:29:55,061 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.364 | Acc: 87.31% +2025-03-09 21:30:00,890 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.371 | Acc: 87.18% +2025-03-09 21:30:06,897 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.375 | Acc: 87.03% +2025-03-09 21:30:14,235 - train - INFO - Epoch: 36 | Test Loss: 0.541 | Test Acc: 82.74% +2025-03-09 21:30:24,234 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.431 | Acc: 84.38% +2025-03-09 21:30:29,986 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.356 | Acc: 87.55% +2025-03-09 21:30:36,139 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.370 | Acc: 87.21% +2025-03-09 21:30:42,107 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.371 | Acc: 87.20% +2025-03-09 21:30:49,263 - train - INFO - Epoch: 37 | Test Loss: 0.514 | Test Acc: 82.69% +2025-03-09 21:30:49,531 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.229 | Acc: 90.62% +2025-03-09 21:30:55,563 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.348 | Acc: 87.84% +2025-03-09 21:31:01,689 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.358 | Acc: 87.62% +2025-03-09 21:31:08,168 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.368 | Acc: 87.41% +2025-03-09 21:31:15,751 - train - INFO - Epoch: 38 | Test Loss: 0.497 | Test Acc: 83.43% +2025-03-09 21:31:25,606 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.285 | Acc: 92.97% +2025-03-09 21:31:31,509 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.361 | Acc: 87.68% +2025-03-09 21:31:37,804 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.362 | Acc: 87.66% +2025-03-09 21:31:44,177 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.362 | Acc: 87.68% +2025-03-09 21:31:50,975 - train - INFO - Epoch: 39 | Test Loss: 0.527 | Test Acc: 82.27% +2025-03-09 21:31:51,210 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.305 | Acc: 89.06% +2025-03-09 21:31:56,699 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.352 | Acc: 87.79% +2025-03-09 21:32:02,349 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.361 | Acc: 87.46% +2025-03-09 21:32:08,110 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.369 | Acc: 87.15% +2025-03-09 21:32:15,326 - train - INFO - Epoch: 40 | Test Loss: 0.653 | Test Acc: 78.55% +2025-03-09 21:32:25,211 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.522 | Acc: 82.03% +2025-03-09 21:32:30,421 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.367 | Acc: 87.38% +2025-03-09 21:32:36,104 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.363 | Acc: 87.40% +2025-03-09 21:32:42,045 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.363 | Acc: 87.31% +2025-03-09 21:32:49,160 - train - INFO - Epoch: 41 | Test Loss: 0.510 | Test Acc: 82.85% +2025-03-09 21:32:49,411 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.325 | Acc: 87.50% +2025-03-09 21:32:55,294 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.354 | Acc: 87.75% +2025-03-09 21:33:00,458 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.364 | Acc: 87.59% +2025-03-09 21:33:06,072 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.363 | Acc: 87.61% +2025-03-09 21:33:12,986 - train - INFO - Epoch: 42 | Test Loss: 0.555 | Test Acc: 81.95% +2025-03-09 21:33:23,293 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.384 | Acc: 84.38% +2025-03-09 21:33:28,557 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.356 | Acc: 87.80% +2025-03-09 21:33:34,297 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.364 | Acc: 87.48% +2025-03-09 21:33:40,033 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.363 | Acc: 87.58% +2025-03-09 21:33:47,053 - train - INFO - Epoch: 43 | Test Loss: 0.467 | Test Acc: 84.13% +2025-03-09 21:33:47,325 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.285 | Acc: 89.84% +2025-03-09 21:33:53,575 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.343 | Acc: 88.06% +2025-03-09 21:33:59,731 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.350 | Acc: 87.93% +2025-03-09 21:34:05,556 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.355 | Acc: 87.73% +2025-03-09 21:34:12,656 - train - INFO - Epoch: 44 | Test Loss: 0.767 | Test Acc: 74.93% +2025-03-09 21:34:23,508 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.393 | Acc: 83.59% +2025-03-09 21:34:29,421 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.347 | Acc: 87.89% +2025-03-09 21:34:35,133 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.342 | Acc: 88.16% +2025-03-09 21:34:40,818 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.350 | Acc: 87.92% +2025-03-09 21:34:47,264 - train - INFO - Epoch: 45 | Test Loss: 0.480 | Test Acc: 83.56% +2025-03-09 21:34:47,512 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.302 | Acc: 89.06% +2025-03-09 21:34:53,220 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.342 | Acc: 88.10% +2025-03-09 21:34:59,094 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.347 | Acc: 88.02% +2025-03-09 21:35:05,396 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.348 | Acc: 88.04% +2025-03-09 21:35:11,923 - train - INFO - Epoch: 46 | Test Loss: 0.448 | Test Acc: 84.89% +2025-03-09 21:35:22,070 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.401 | Acc: 86.72% +2025-03-09 21:35:28,150 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.347 | Acc: 87.89% +2025-03-09 21:35:34,314 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.354 | Acc: 87.75% +2025-03-09 21:35:40,069 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.357 | Acc: 87.63% +2025-03-09 21:35:46,827 - train - INFO - Epoch: 47 | Test Loss: 0.508 | Test Acc: 82.58% +2025-03-09 21:35:47,066 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.263 | Acc: 91.41% +2025-03-09 21:35:53,455 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.330 | Acc: 88.49% +2025-03-09 21:35:59,739 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.336 | Acc: 88.37% +2025-03-09 21:36:05,768 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.340 | Acc: 88.33% +2025-03-09 21:36:12,946 - train - INFO - Epoch: 48 | Test Loss: 0.432 | Test Acc: 84.88% +2025-03-09 21:36:22,692 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.347 | Acc: 89.06% +2025-03-09 21:36:28,875 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.335 | Acc: 88.68% +2025-03-09 21:36:34,927 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.336 | Acc: 88.46% +2025-03-09 21:36:40,251 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.342 | Acc: 88.42% +2025-03-09 21:36:47,402 - train - INFO - Epoch: 49 | Test Loss: 0.419 | Test Acc: 85.76% +2025-03-09 21:36:47,660 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.199 | Acc: 93.75% +2025-03-09 21:36:53,975 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.328 | Acc: 88.47% +2025-03-09 21:36:59,800 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.342 | Acc: 88.16% diff --git a/Image/DenseNet/code/train.py b/Image/DenseNet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..88d158e41f9d0b599535b3c746811ab81a78006f --- /dev/null +++ b/Image/DenseNet/code/train.py @@ -0,0 +1,63 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import DenseNet, densenet_cifar + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = densenet_cifar() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=50, + lr=0.1, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='densenet', + save_type='0', + layer_name='avg_pool', + interval = 2 + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='densenet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=50, + lr=0.1, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='densenet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path, + layer_name='avg_pool' , + interval = 2 + ) + +if __name__ == '__main__': + main() diff --git a/Image/DenseNet/dataset/.gitkeep b/Image/DenseNet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/DenseNet/model/.gitkeep b/Image/DenseNet/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/DenseNet/model/0/epoch1/embeddings.npy b/Image/DenseNet/model/0/epoch1/embeddings.npy new file mode 100644 index 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+2025-03-14 16:50:53,927 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.852 | Acc: 28.12% +2025-03-14 16:50:58,155 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.894 | Acc: 29.70% +2025-03-14 16:51:02,310 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.880 | Acc: 30.11% +2025-03-14 16:51:06,421 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.847 | Acc: 31.26% +2025-03-14 16:51:11,515 - train - INFO - Epoch: 2 | Test Loss: 1.734 | Test Acc: 38.75% +2025-03-14 16:51:21,290 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.495 | Acc: 46.09% +2025-03-14 16:51:25,389 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.697 | Acc: 37.88% +2025-03-14 16:51:29,372 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.664 | Acc: 39.20% +2025-03-14 16:51:33,262 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.640 | Acc: 40.20% +2025-03-14 16:51:38,314 - train - INFO - Epoch: 3 | Test Loss: 1.604 | Test Acc: 42.46% +2025-03-14 16:51:38,569 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.486 | Acc: 50.00% 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54.12% +2025-03-14 16:52:32,655 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.262 | Acc: 55.06% +2025-03-14 16:52:36,609 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.236 | Acc: 56.11% +2025-03-14 16:52:41,974 - train - INFO - Epoch: 6 | Test Loss: 1.166 | Test Acc: 59.57% +2025-03-14 16:52:52,538 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.270 | Acc: 59.38% +2025-03-14 16:52:56,703 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.133 | Acc: 59.91% +2025-03-14 16:53:00,833 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.122 | Acc: 60.30% +2025-03-14 16:53:05,134 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.115 | Acc: 60.65% +2025-03-14 16:53:10,577 - train - INFO - Epoch: 7 | Test Loss: 1.184 | Test Acc: 58.79% +2025-03-14 16:53:10,817 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.102 | Acc: 60.94% +2025-03-14 16:53:15,027 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.080 | Acc: 61.57% +2025-03-14 16:53:19,348 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.068 | Acc: 62.27% +2025-03-14 16:53:23,545 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.062 | Acc: 62.52% +2025-03-14 16:53:28,792 - train - INFO - Epoch: 8 | Test Loss: 1.034 | Test Acc: 64.74% +2025-03-14 16:53:38,962 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.995 | Acc: 66.41% +2025-03-14 16:53:43,199 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.017 | Acc: 64.58% +2025-03-14 16:53:47,563 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.013 | Acc: 64.88% +2025-03-14 16:53:51,753 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.006 | Acc: 65.17% +2025-03-14 16:53:57,630 - train - INFO - Epoch: 9 | Test Loss: 1.000 | Test Acc: 64.72% +2025-03-14 16:53:57,883 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.000 | Acc: 64.06% +2025-03-14 16:54:02,333 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.966 | Acc: 66.46% +2025-03-14 16:54:06,525 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.952 | Acc: 67.11% +2025-03-14 16:54:10,742 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.954 | Acc: 67.09% +2025-03-14 16:54:16,145 - train - INFO - Epoch: 10 | Test Loss: 0.945 | Test Acc: 66.62% +2025-03-14 16:54:26,361 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.875 | Acc: 72.66% +2025-03-14 16:54:30,550 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.946 | Acc: 66.99% +2025-03-14 16:54:34,741 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.946 | Acc: 67.16% +2025-03-14 16:54:38,801 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.938 | Acc: 67.54% +2025-03-14 16:54:45,018 - train - INFO - Epoch: 11 | Test Loss: 0.966 | Test Acc: 66.08% +2025-03-14 16:54:45,285 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.831 | Acc: 70.31% +2025-03-14 16:54:49,577 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.909 | Acc: 68.46% +2025-03-14 16:54:53,812 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.904 | Acc: 68.80% +2025-03-14 16:54:57,943 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.907 | Acc: 68.88% +2025-03-14 16:55:03,248 - train - INFO - Epoch: 12 | Test Loss: 0.940 | Test Acc: 67.15% +2025-03-14 16:55:14,462 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.884 | Acc: 68.75% +2025-03-14 16:55:18,985 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.903 | Acc: 69.54% +2025-03-14 16:55:23,339 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.905 | Acc: 69.28% +2025-03-14 16:55:27,460 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.898 | Acc: 69.50% +2025-03-14 16:55:32,675 - train - INFO - Epoch: 13 | Test Loss: 0.857 | Test Acc: 70.24% +2025-03-14 16:55:32,892 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.853 | Acc: 73.44% +2025-03-14 16:55:37,122 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.869 | Acc: 70.69% +2025-03-14 16:55:41,732 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.871 | Acc: 70.37% +2025-03-14 16:55:45,936 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.865 | Acc: 70.62% +2025-03-14 16:55:51,486 - train - INFO - Epoch: 14 | Test Loss: 0.862 | Test Acc: 70.40% +2025-03-14 16:56:02,079 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.794 | Acc: 74.22% +2025-03-14 16:56:06,351 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.847 | Acc: 71.53% +2025-03-14 16:56:10,745 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.853 | Acc: 71.16% +2025-03-14 16:56:15,035 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.851 | Acc: 71.19% +2025-03-14 16:56:21,120 - train - INFO - Epoch: 15 | Test Loss: 0.869 | Test Acc: 70.55% +2025-03-14 16:56:21,439 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.735 | Acc: 71.88% +2025-03-14 16:56:25,546 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.844 | Acc: 71.07% +2025-03-14 16:56:29,707 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.842 | Acc: 71.25% +2025-03-14 16:56:33,868 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.844 | Acc: 71.32% +2025-03-14 16:56:39,194 - train - INFO - Epoch: 16 | Test Loss: 0.949 | Test Acc: 66.89% +2025-03-14 16:56:50,322 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.968 | Acc: 64.84% +2025-03-14 16:56:54,610 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.831 | Acc: 72.15% +2025-03-14 16:56:58,907 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.829 | Acc: 71.93% +2025-03-14 16:57:02,982 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.829 | Acc: 72.05% +2025-03-14 16:57:08,370 - train - INFO - Epoch: 17 | Test Loss: 0.948 | Test Acc: 68.57% +2025-03-14 16:57:08,594 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.919 | Acc: 65.62% +2025-03-14 16:57:12,965 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.813 | Acc: 72.11% +2025-03-14 16:57:17,407 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.825 | Acc: 71.97% +2025-03-14 16:57:21,996 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.822 | Acc: 72.06% +2025-03-14 16:57:27,487 - train - INFO - Epoch: 18 | Test Loss: 0.852 | Test Acc: 70.81% +2025-03-14 16:57:37,770 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.934 | Acc: 68.75% +2025-03-14 16:57:41,993 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.816 | Acc: 72.37% +2025-03-14 16:57:46,550 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.813 | Acc: 72.63% +2025-03-14 16:57:50,721 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.807 | Acc: 72.78% +2025-03-14 16:57:55,876 - train - INFO - Epoch: 19 | Test Loss: 0.808 | Test Acc: 72.39% +2025-03-14 16:57:56,137 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.870 | Acc: 69.53% +2025-03-14 16:58:00,441 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.795 | Acc: 73.00% +2025-03-14 16:58:04,438 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.792 | Acc: 73.20% +2025-03-14 16:58:08,605 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.799 | Acc: 72.85% +2025-03-14 16:58:13,904 - train - INFO - Epoch: 20 | Test Loss: 0.816 | Test Acc: 72.37% +2025-03-14 16:58:24,451 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.747 | Acc: 75.00% +2025-03-14 16:58:28,704 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.782 | Acc: 73.19% +2025-03-14 16:58:32,744 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.792 | Acc: 72.99% +2025-03-14 16:58:36,763 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.793 | Acc: 73.02% +2025-03-14 16:58:42,110 - train - INFO - Epoch: 21 | Test Loss: 0.837 | Test Acc: 71.78% +2025-03-14 16:58:42,347 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.677 | Acc: 78.12% +2025-03-14 16:58:46,656 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.809 | Acc: 72.78% +2025-03-14 16:58:50,792 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.801 | Acc: 73.03% +2025-03-14 16:58:55,249 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.794 | Acc: 73.16% +2025-03-14 16:59:00,591 - train - INFO - Epoch: 22 | Test Loss: 0.764 | Test Acc: 74.07% +2025-03-14 16:59:11,462 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.841 | Acc: 70.31% +2025-03-14 16:59:15,955 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.785 | Acc: 73.19% +2025-03-14 16:59:20,405 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.779 | Acc: 73.59% +2025-03-14 16:59:24,843 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.781 | Acc: 73.50% +2025-03-14 16:59:30,321 - train - INFO - Epoch: 23 | Test Loss: 0.835 | Test Acc: 71.93% +2025-03-14 16:59:30,567 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.885 | Acc: 71.09% +2025-03-14 16:59:35,045 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.773 | Acc: 73.67% +2025-03-14 16:59:39,555 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.776 | Acc: 73.77% +2025-03-14 16:59:44,054 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.784 | Acc: 73.43% +2025-03-14 16:59:49,912 - train - INFO - Epoch: 24 | Test Loss: 0.808 | Test Acc: 72.50% +2025-03-14 17:00:00,452 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.660 | Acc: 77.34% +2025-03-14 17:00:05,296 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.761 | Acc: 74.09% +2025-03-14 17:00:10,410 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.768 | Acc: 74.05% +2025-03-14 17:00:15,036 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.776 | Acc: 73.89% +2025-03-14 17:00:20,739 - train - INFO - Epoch: 25 | Test Loss: 0.790 | Test Acc: 73.05% +2025-03-14 17:00:20,973 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.560 | Acc: 80.47% +2025-03-14 17:00:25,440 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.772 | Acc: 73.87% +2025-03-14 17:00:29,998 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.772 | Acc: 73.85% +2025-03-14 17:00:34,447 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.774 | Acc: 73.79% +2025-03-14 17:00:39,890 - train - INFO - Epoch: 26 | Test Loss: 0.858 | Test Acc: 71.00% +2025-03-14 17:00:50,311 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.666 | Acc: 78.12% +2025-03-14 17:00:54,862 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.734 | Acc: 75.15% +2025-03-14 17:00:59,263 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.748 | Acc: 74.74% +2025-03-14 17:01:03,797 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.751 | Acc: 74.64% +2025-03-14 17:01:09,534 - train - INFO - Epoch: 27 | Test Loss: 0.797 | Test Acc: 72.45% +2025-03-14 17:01:09,786 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.846 | Acc: 75.00% +2025-03-14 17:01:14,301 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.758 | Acc: 74.45% +2025-03-14 17:01:18,764 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.757 | Acc: 74.49% +2025-03-14 17:01:23,331 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.758 | Acc: 74.47% +2025-03-14 17:01:29,619 - train - INFO - Epoch: 28 | Test Loss: 0.807 | Test Acc: 72.88% +2025-03-14 17:01:39,772 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.833 | Acc: 71.09% +2025-03-14 17:01:44,254 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.752 | Acc: 74.68% +2025-03-14 17:01:48,888 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.752 | Acc: 74.65% +2025-03-14 17:01:53,508 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.756 | Acc: 74.55% +2025-03-14 17:01:58,973 - train - INFO - Epoch: 29 | Test Loss: 0.777 | Test Acc: 73.28% +2025-03-14 17:01:59,211 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.740 | Acc: 77.34% +2025-03-14 17:02:03,694 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.743 | Acc: 74.82% +2025-03-14 17:02:08,332 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.756 | Acc: 74.52% +2025-03-14 17:02:12,835 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.753 | Acc: 74.69% +2025-03-14 17:02:18,570 - train - INFO - Epoch: 30 | Test Loss: 0.749 | Test Acc: 73.91% +2025-03-14 17:02:28,712 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.593 | Acc: 78.91% +2025-03-14 17:02:33,298 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.720 | Acc: 75.85% +2025-03-14 17:02:38,219 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.733 | Acc: 75.38% +2025-03-14 17:02:42,945 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.737 | Acc: 75.30% +2025-03-14 17:02:48,977 - train - INFO - Epoch: 31 | Test Loss: 0.788 | Test Acc: 73.32% +2025-03-14 17:02:49,241 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.641 | Acc: 76.56% +2025-03-14 17:02:53,771 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.732 | Acc: 75.52% +2025-03-14 17:02:58,291 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.741 | Acc: 75.14% +2025-03-14 17:03:03,014 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.746 | Acc: 74.99% +2025-03-14 17:03:08,589 - train - INFO - Epoch: 32 | Test Loss: 0.853 | Test Acc: 71.73% +2025-03-14 17:03:19,315 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.829 | Acc: 72.66% +2025-03-14 17:03:23,891 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.748 | Acc: 74.85% +2025-03-14 17:03:28,577 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.734 | Acc: 75.21% +2025-03-14 17:03:33,144 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.739 | Acc: 74.99% +2025-03-14 17:03:38,909 - train - INFO - Epoch: 33 | Test Loss: 0.799 | Test Acc: 73.38% +2025-03-14 17:03:39,126 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.699 | Acc: 78.12% +2025-03-14 17:03:43,965 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.739 | Acc: 75.14% +2025-03-14 17:03:49,371 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.736 | Acc: 75.35% +2025-03-14 17:03:54,126 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.740 | Acc: 75.17% +2025-03-14 17:03:59,822 - train - INFO - Epoch: 34 | Test Loss: 0.939 | Test Acc: 69.15% +2025-03-14 17:04:10,469 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.672 | Acc: 75.78% +2025-03-14 17:04:15,147 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.711 | Acc: 76.02% +2025-03-14 17:04:20,006 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.723 | Acc: 75.68% +2025-03-14 17:04:24,816 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.725 | Acc: 75.55% +2025-03-14 17:04:30,590 - train - INFO - Epoch: 35 | Test Loss: 0.782 | Test Acc: 73.06% +2025-03-14 17:04:30,848 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.641 | Acc: 77.34% +2025-03-14 17:04:35,448 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.710 | Acc: 75.83% +2025-03-14 17:04:39,870 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.723 | Acc: 75.63% +2025-03-14 17:04:44,522 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.724 | Acc: 75.52% +2025-03-14 17:04:51,089 - train - INFO - Epoch: 36 | Test Loss: 0.873 | Test Acc: 71.48% +2025-03-14 17:05:02,234 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.644 | Acc: 78.12% +2025-03-14 17:05:06,776 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.720 | Acc: 75.93% +2025-03-14 17:05:11,205 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.723 | Acc: 75.71% +2025-03-14 17:05:15,725 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.716 | Acc: 76.07% +2025-03-14 17:05:21,656 - train - INFO - Epoch: 37 | Test Loss: 0.685 | Test Acc: 76.37% +2025-03-14 17:05:21,919 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.794 | Acc: 68.75% +2025-03-14 17:05:26,364 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.712 | Acc: 75.81% +2025-03-14 17:05:30,796 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.710 | Acc: 75.96% +2025-03-14 17:05:35,395 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.707 | Acc: 76.06% +2025-03-14 17:05:41,049 - train - INFO - Epoch: 38 | Test Loss: 0.715 | Test Acc: 75.55% +2025-03-14 17:05:51,396 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.663 | Acc: 77.34% +2025-03-14 17:05:55,954 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.711 | Acc: 76.08% +2025-03-14 17:06:00,316 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.709 | Acc: 76.10% +2025-03-14 17:06:04,758 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.709 | Acc: 76.10% +2025-03-14 17:06:10,359 - train - INFO - Epoch: 39 | Test Loss: 0.712 | Test Acc: 75.40% +2025-03-14 17:06:10,615 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.544 | Acc: 81.25% +2025-03-14 17:06:15,197 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.704 | Acc: 76.64% +2025-03-14 17:06:19,790 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.710 | Acc: 76.21% +2025-03-14 17:06:24,302 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.715 | Acc: 75.94% +2025-03-14 17:06:30,044 - train - INFO - Epoch: 40 | Test Loss: 0.751 | Test Acc: 74.14% +2025-03-14 17:06:41,156 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.603 | Acc: 77.34% +2025-03-14 17:06:45,994 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.684 | Acc: 77.35% +2025-03-14 17:06:50,606 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.693 | Acc: 76.97% +2025-03-14 17:06:55,094 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.699 | Acc: 76.79% +2025-03-14 17:07:00,694 - train - INFO - Epoch: 41 | Test Loss: 0.915 | Test Acc: 69.34% +2025-03-14 17:07:00,937 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.528 | Acc: 82.81% +2025-03-14 17:07:05,331 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.718 | Acc: 75.97% +2025-03-14 17:07:09,861 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.710 | Acc: 76.21% +2025-03-14 17:07:14,366 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.702 | Acc: 76.57% +2025-03-14 17:07:19,922 - train - INFO - Epoch: 42 | Test Loss: 0.755 | Test Acc: 74.51% +2025-03-14 17:07:30,494 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.780 | Acc: 68.75% +2025-03-14 17:07:35,049 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.701 | Acc: 76.47% +2025-03-14 17:07:39,755 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.700 | Acc: 76.44% +2025-03-14 17:07:45,290 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.703 | Acc: 76.36% +2025-03-14 17:07:51,032 - train - INFO - Epoch: 43 | Test Loss: 0.700 | Test Acc: 76.15% +2025-03-14 17:07:51,279 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.755 | Acc: 71.88% +2025-03-14 17:07:55,755 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.699 | Acc: 76.54% +2025-03-14 17:08:00,264 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.691 | Acc: 77.03% +2025-03-14 17:08:04,625 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.695 | Acc: 76.74% +2025-03-14 17:08:10,334 - train - INFO - Epoch: 44 | Test Loss: 0.763 | Test Acc: 74.75% +2025-03-14 17:08:21,069 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.686 | Acc: 72.66% +2025-03-14 17:08:25,742 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.699 | Acc: 76.69% +2025-03-14 17:08:30,293 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.699 | Acc: 76.76% +2025-03-14 17:08:34,866 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.690 | Acc: 76.93% +2025-03-14 17:08:40,638 - train - INFO - Epoch: 45 | Test Loss: 0.739 | Test Acc: 74.78% +2025-03-14 17:08:40,912 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.646 | Acc: 81.25% +2025-03-14 17:08:45,746 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.678 | Acc: 77.07% +2025-03-14 17:08:50,771 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.685 | Acc: 76.98% +2025-03-14 17:08:55,486 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.687 | Acc: 77.02% +2025-03-14 17:09:01,172 - train - INFO - Epoch: 46 | Test Loss: 0.678 | Test Acc: 77.36% +2025-03-14 17:09:11,645 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.802 | Acc: 75.00% +2025-03-14 17:09:16,423 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.678 | Acc: 77.54% +2025-03-14 17:09:21,134 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.685 | Acc: 77.14% +2025-03-14 17:09:25,859 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.686 | Acc: 77.04% +2025-03-14 17:09:32,508 - train - INFO - Epoch: 47 | Test Loss: 0.719 | Test Acc: 75.71% +2025-03-14 17:09:32,758 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.627 | Acc: 78.12% +2025-03-14 17:09:37,370 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.686 | Acc: 77.20% +2025-03-14 17:09:42,102 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.683 | Acc: 77.31% +2025-03-14 17:09:46,664 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.689 | Acc: 77.06% +2025-03-14 17:09:52,593 - train - INFO - Epoch: 48 | Test Loss: 0.675 | Test Acc: 77.54% +2025-03-14 17:10:02,821 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.744 | Acc: 73.44% +2025-03-14 17:10:07,457 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.681 | Acc: 77.34% +2025-03-14 17:10:12,289 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.683 | Acc: 77.25% +2025-03-14 17:10:17,314 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.684 | Acc: 77.20% +2025-03-14 17:10:22,971 - train - INFO - Epoch: 49 | Test Loss: 0.678 | Test Acc: 77.32% +2025-03-14 17:10:23,275 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.559 | Acc: 79.69% +2025-03-14 17:10:27,808 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.661 | Acc: 77.69% +2025-03-14 17:10:32,281 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.671 | Acc: 77.64% +2025-03-14 17:10:36,722 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.675 | Acc: 77.45% +2025-03-14 17:10:42,250 - train - INFO - Epoch: 50 | Test Loss: 0.691 | Test Acc: 76.73% +2025-03-14 17:10:52,217 - train - INFO - 训练完成! diff --git a/Image/EfficientNet/code/data_aug_train.log b/Image/EfficientNet/code/data_aug_train.log new file mode 100644 index 0000000000000000000000000000000000000000..768351754008c7e7b7b35d66625f30c77b8e6c7c --- /dev/null +++ b/Image/EfficientNet/code/data_aug_train.log @@ -0,0 +1,12 @@ +2025-03-14 16:10:40,562 - train - INFO - 开始训练 efficientnet +2025-03-14 16:10:40,563 - train - INFO - 总轮数: 100, 学习率: 0.1, 设备: cuda:2 +2025-03-14 16:10:42,354 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.396 | Acc: 7.81% +2025-03-14 16:10:49,792 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.737 | Acc: 10.54% +2025-03-14 16:10:57,192 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.566 | Acc: 10.70% +2025-03-14 16:11:04,468 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 2.481 | Acc: 11.40% +2025-03-14 16:11:13,027 - train - INFO - Epoch: 1 | Test Loss: 2.253 | Test Acc: 12.50% +2025-03-14 16:11:13,338 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 2.191 | Acc: 16.41% +2025-03-14 16:11:21,423 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 2.188 | Acc: 16.25% +2025-03-14 16:11:28,669 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 2.138 | Acc: 18.65% +2025-03-14 16:11:36,178 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 2.103 | Acc: 20.15% +2025-03-14 16:11:44,414 - train - INFO - Epoch: 2 | Test Loss: 1.911 | Test Acc: 28.45% diff --git a/Image/EfficientNet/code/model.py b/Image/EfficientNet/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..b731fd4ae8b1491bf46152a4c85f73cc5a6e2cc4 --- /dev/null +++ b/Image/EfficientNet/code/model.py @@ -0,0 +1,267 @@ +''' +EfficientNet in PyTorch. + +Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" +Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py + +主要特点： +1. 使用MBConv作为基本模块，包含SE注意力机制 +2. 通过复合缩放方法(compound scaling)同时调整网络的宽度、深度和分辨率 +3. 使用Swish激活函数和DropConnect正则化 +''' +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +def swish(x): + """Swish激活函数: x * sigmoid(x)""" + return x * x.sigmoid() + +def drop_connect(x, drop_ratio): + """DropConnect正则化 + + Args: + x: 输入tensor + drop_ratio: 丢弃率 + + Returns: + 经过DropConnect处理的tensor + """ + keep_ratio = 1.0 - drop_ratio + mask = torch.empty([x.shape[0], 1, 1, 1], dtype=x.dtype, device=x.device) + mask.bernoulli_(keep_ratio) + x.div_(keep_ratio) + x.mul_(mask) + return x + +class SE(nn.Module): + '''Squeeze-and-Excitation注意力模块 + + Args: + in_channels: 输入通道数 + se_channels: SE模块中间层的通道数 + ''' + def __init__(self, in_channels, se_channels): + super(SE, self).__init__() + self.se1 = nn.Conv2d(in_channels, se_channels, kernel_size=1, bias=True) + self.se2 = nn.Conv2d(se_channels, in_channels, kernel_size=1, bias=True) + + def forward(self, x): + out = F.adaptive_avg_pool2d(x, (1, 1)) # 全局平均池化 + out = swish(self.se1(out)) + out = self.se2(out).sigmoid() + return x * out # 特征重标定 + +class MBConv(nn.Module): + '''MBConv模块: Mobile Inverted Bottleneck Convolution + + Args: + in_channels: 输入通道数 + out_channels: 输出通道数 + kernel_size: 卷积核大小 + stride: 步长 + expand_ratio: 扩展比率 + se_ratio: SE模块的压缩比率 + drop_rate: DropConnect的丢弃率 + ''' + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + expand_ratio=1, + se_ratio=0.25, + drop_rate=0.): + super(MBConv, self).__init__() + self.stride = stride + self.drop_rate = drop_rate + self.expand_ratio = expand_ratio + + # Expansion phase + channels = expand_ratio * in_channels + self.conv1 = nn.Conv2d(in_channels, channels, kernel_size=1, stride=1, padding=0, bias=False) + self.bn1 = nn.BatchNorm2d(channels) + + # Depthwise conv + self.conv2 = nn.Conv2d(channels, channels, kernel_size=kernel_size, stride=stride, + padding=(1 if kernel_size == 3 else 2), groups=channels, bias=False) + self.bn2 = nn.BatchNorm2d(channels) + + # SE layers + se_channels = int(in_channels * se_ratio) + self.se = SE(channels, se_channels) + + # Output phase + self.conv3 = nn.Conv2d(channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False) + self.bn3 = nn.BatchNorm2d(out_channels) + + # Shortcut connection + self.has_skip = (stride == 1) and (in_channels == out_channels) + + def forward(self, x): + # Expansion + out = x if self.expand_ratio == 1 else swish(self.bn1(self.conv1(x))) + # Depthwise convolution + out = swish(self.bn2(self.conv2(out))) + # Squeeze-and-excitation + out = self.se(out) + # Pointwise convolution + out = self.bn3(self.conv3(out)) + # Shortcut + if self.has_skip: + if self.training and self.drop_rate > 0: + out = drop_connect(out, self.drop_rate) + out = out + x + return out + +class EfficientNet(nn.Module): + '''EfficientNet模型 + + Args: + width_coefficient: 宽度系数 + depth_coefficient: 深度系数 + dropout_rate: 分类层的dropout率 + num_classes: 分类数量 + ''' + def __init__(self, + width_coefficient=1.0, + depth_coefficient=1.0, + dropout_rate=0.2, + num_classes=10): + super(EfficientNet, self).__init__() + + # 模型配置 + cfg = { + 'num_blocks': [1, 2, 2, 3, 3, 4, 1], # 每个stage的block数量 + 'expansion': [1, 6, 6, 6, 6, 6, 6], # 扩展比率 + 'out_channels': [16, 24, 40, 80, 112, 192, 320], # 输出通道数 + 'kernel_size': [3, 3, 5, 3, 5, 5, 3], # 卷积核大小 + 'stride': [1, 2, 2, 2, 1, 2, 1], # 步长 + 'dropout_rate': dropout_rate, + 'drop_connect_rate': 0.2, + } + + self.cfg = cfg + self.width_coefficient = width_coefficient + self.depth_coefficient = depth_coefficient + + # Stem layer + self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(32) + + # Build blocks + self.layers = self._make_layers(in_channels=32) + + # Head layer + final_channels = cfg['out_channels'][-1] * int(width_coefficient) + self.linear = nn.Linear(final_channels, num_classes) + + def _make_layers(self, in_channels): + layers = [] + cfg = [self.cfg[k] for k in ['expansion', 'out_channels', 'num_blocks', 'kernel_size', 'stride']] + blocks = sum(self.cfg['num_blocks']) + b = 0 # 用于计算drop_connect_rate + + for expansion, out_channels, num_blocks, kernel_size, stride in zip(*cfg): + out_channels = int(out_channels * self.width_coefficient) + num_blocks = int(math.ceil(num_blocks * self.depth_coefficient)) + + for i in range(num_blocks): + stride_i = stride if i == 0 else 1 + drop_rate = self.cfg['drop_connect_rate'] * b / blocks + layers.append( + MBConv(in_channels, + out_channels, + kernel_size, + stride_i, + expansion, + se_ratio=0.25, + drop_rate=drop_rate)) + in_channels = out_channels + b += 1 + + return nn.Sequential(*layers) + + def forward(self, x): + # Stem + out = swish(self.bn1(self.conv1(x))) + # Blocks + out = self.layers(out) + # Head + out = F.adaptive_avg_pool2d(out, 1) + out = out.view(out.size(0), -1) + if self.training and self.cfg['dropout_rate'] > 0: + out = F.dropout(out, p=self.cfg['dropout_rate']) + out = self.linear(out) + return out + +def EfficientNetB0(num_classes=10): + """EfficientNet-B0""" + return EfficientNet(width_coefficient=1.0, + depth_coefficient=1.0, + dropout_rate=0.2, + num_classes=num_classes) + +def EfficientNetB1(num_classes=10): + """EfficientNet-B1""" + return EfficientNet(width_coefficient=1.0, + depth_coefficient=1.1, + dropout_rate=0.2, + num_classes=num_classes) + +def EfficientNetB2(num_classes=10): + """EfficientNet-B2""" + return EfficientNet(width_coefficient=1.1, + depth_coefficient=1.2, + dropout_rate=0.3, + num_classes=num_classes) + +def EfficientNetB3(num_classes=10): + """EfficientNet-B3""" + return EfficientNet(width_coefficient=1.2, + depth_coefficient=1.4, + dropout_rate=0.3, + num_classes=num_classes) + +def EfficientNetB4(num_classes=10): + """EfficientNet-B4""" + return EfficientNet(width_coefficient=1.4, + depth_coefficient=1.8, + dropout_rate=0.4, + num_classes=num_classes) + +def EfficientNetB5(num_classes=10): + """EfficientNet-B5""" + return EfficientNet(width_coefficient=1.6, + depth_coefficient=2.2, + dropout_rate=0.4, + num_classes=num_classes) + +def EfficientNetB6(num_classes=10): + """EfficientNet-B6""" + return EfficientNet(width_coefficient=1.8, + depth_coefficient=2.6, + dropout_rate=0.5, + num_classes=num_classes) + +def EfficientNetB7(num_classes=10): + """EfficientNet-B7""" + return EfficientNet(width_coefficient=2.0, + depth_coefficient=3.1, + dropout_rate=0.5, + num_classes=num_classes) + +def test(): + """测试函数""" + net = EfficientNetB0() + x = torch.randn(1, 3, 32, 32) + y = net(x) + print(y.size()) + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (1, 3, 32, 32)) + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/EfficientNet/code/train.log b/Image/EfficientNet/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..6358853bed4659dfc526b4cddb83247f30ffcc41 --- /dev/null +++ b/Image/EfficientNet/code/train.log @@ -0,0 +1,253 @@ +2025-03-14 16:24:53,316 - train - INFO - 开始训练 efficientnet +2025-03-14 16:24:53,316 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:2 +2025-03-14 16:24:54,150 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.403 | Acc: 8.59% +2025-03-14 16:24:58,097 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.611 | Acc: 16.51% +2025-03-14 16:25:02,317 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.378 | Acc: 18.66% +2025-03-14 16:25:06,595 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 2.223 | Acc: 21.99% +2025-03-14 16:25:11,907 - train - INFO - Epoch: 1 | Test Loss: 1.709 | Test Acc: 37.62% +2025-03-14 16:25:12,408 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.729 | Acc: 31.25% +2025-03-14 16:25:16,797 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.697 | Acc: 36.48% +2025-03-14 16:25:21,076 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.662 | Acc: 38.39% +2025-03-14 16:25:25,190 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.639 | Acc: 39.51% +2025-03-14 16:25:30,403 - train - INFO - Epoch: 2 | Test Loss: 1.489 | Test Acc: 46.71% +2025-03-14 16:25:40,451 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.456 | Acc: 48.44% +2025-03-14 16:25:44,586 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.506 | Acc: 45.27% +2025-03-14 16:25:49,037 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.490 | Acc: 46.16% +2025-03-14 16:25:53,242 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.464 | Acc: 47.02% +2025-03-14 16:25:58,157 - train - INFO - Epoch: 3 | Test Loss: 1.362 | Test Acc: 50.80% +2025-03-14 16:25:58,372 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.323 | Acc: 48.44% +2025-03-14 16:26:02,660 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.341 | Acc: 51.55% +2025-03-14 16:26:06,851 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.326 | Acc: 52.31% +2025-03-14 16:26:11,085 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.311 | Acc: 52.83% +2025-03-14 16:26:16,103 - train - INFO - Epoch: 4 | Test Loss: 1.226 | Test Acc: 54.20% +2025-03-14 16:26:25,973 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.249 | Acc: 53.12% +2025-03-14 16:26:30,091 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.228 | Acc: 55.75% +2025-03-14 16:26:34,122 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.233 | Acc: 55.84% +2025-03-14 16:26:38,125 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.222 | Acc: 56.30% +2025-03-14 16:26:43,375 - train - INFO - Epoch: 5 | Test Loss: 1.064 | Test Acc: 62.23% +2025-03-14 16:26:43,613 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.153 | Acc: 67.97% +2025-03-14 16:26:47,694 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.118 | Acc: 60.44% +2025-03-14 16:26:52,049 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.114 | Acc: 60.63% +2025-03-14 16:26:56,398 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.101 | Acc: 60.95% +2025-03-14 16:27:01,593 - train - INFO - Epoch: 6 | Test Loss: 1.013 | Test Acc: 64.31% +2025-03-14 16:27:11,823 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.962 | Acc: 65.62% +2025-03-14 16:27:15,984 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.026 | Acc: 63.78% +2025-03-14 16:27:20,007 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.023 | Acc: 64.04% +2025-03-14 16:27:24,044 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.011 | Acc: 64.39% +2025-03-14 16:27:29,341 - train - INFO - Epoch: 7 | Test Loss: 0.982 | Test Acc: 66.05% +2025-03-14 16:27:29,554 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.997 | Acc: 62.50% +2025-03-14 16:27:33,742 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.977 | Acc: 65.49% +2025-03-14 16:27:37,968 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.972 | Acc: 65.79% +2025-03-14 16:27:41,908 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.967 | Acc: 66.00% +2025-03-14 16:27:47,093 - train - INFO - Epoch: 8 | Test Loss: 0.970 | Test Acc: 65.27% +2025-03-14 16:27:58,459 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.097 | Acc: 61.72% +2025-03-14 16:28:03,091 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.933 | Acc: 67.14% +2025-03-14 16:28:07,115 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.928 | Acc: 67.26% +2025-03-14 16:28:11,125 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.922 | Acc: 67.64% +2025-03-14 16:28:16,262 - train - INFO - Epoch: 9 | Test Loss: 0.860 | Test Acc: 70.44% +2025-03-14 16:28:16,484 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.762 | Acc: 69.53% +2025-03-14 16:28:20,620 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.881 | Acc: 69.64% +2025-03-14 16:28:24,782 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.881 | Acc: 69.51% +2025-03-14 16:28:29,178 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.883 | Acc: 69.31% +2025-03-14 16:28:34,443 - train - INFO - Epoch: 10 | Test Loss: 0.961 | Test Acc: 67.11% +2025-03-14 16:28:44,377 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.838 | Acc: 65.62% +2025-03-14 16:28:48,450 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.856 | Acc: 70.35% +2025-03-14 16:28:52,422 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.853 | Acc: 70.38% +2025-03-14 16:28:56,648 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.859 | Acc: 70.10% +2025-03-14 16:29:01,922 - train - INFO - Epoch: 11 | Test Loss: 0.929 | Test Acc: 68.30% +2025-03-14 16:29:02,164 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.918 | Acc: 68.75% +2025-03-14 16:29:06,383 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.843 | Acc: 70.23% +2025-03-14 16:29:10,393 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.849 | Acc: 70.18% +2025-03-14 16:29:14,619 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.851 | Acc: 70.25% +2025-03-14 16:29:20,040 - train - INFO - Epoch: 12 | Test Loss: 0.890 | Test Acc: 69.71% +2025-03-14 16:29:30,009 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.686 | Acc: 77.34% +2025-03-14 16:29:34,151 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.832 | Acc: 71.06% +2025-03-14 16:29:38,169 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.826 | Acc: 71.05% +2025-03-14 16:29:42,352 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.831 | Acc: 70.93% +2025-03-14 16:29:47,612 - train - INFO - Epoch: 13 | Test Loss: 0.804 | Test Acc: 72.10% +2025-03-14 16:29:47,842 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.855 | Acc: 68.75% +2025-03-14 16:29:52,017 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.805 | Acc: 71.96% +2025-03-14 16:29:55,912 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.814 | Acc: 71.71% +2025-03-14 16:30:00,027 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.818 | Acc: 71.49% +2025-03-14 16:30:05,322 - train - INFO - Epoch: 14 | Test Loss: 0.828 | Test Acc: 70.77% +2025-03-14 16:30:15,164 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.886 | Acc: 71.09% +2025-03-14 16:30:19,341 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.822 | Acc: 72.01% +2025-03-14 16:30:23,394 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.812 | Acc: 72.11% +2025-03-14 16:30:27,423 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.806 | Acc: 72.18% +2025-03-14 16:30:32,746 - train - INFO - Epoch: 15 | Test Loss: 0.792 | Test Acc: 72.85% +2025-03-14 16:30:32,981 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.680 | Acc: 77.34% +2025-03-14 16:30:37,197 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.772 | Acc: 73.04% +2025-03-14 16:30:41,453 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.785 | Acc: 72.70% +2025-03-14 16:30:45,667 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.786 | Acc: 72.58% +2025-03-14 16:30:50,860 - train - INFO - Epoch: 16 | Test Loss: 0.849 | Test Acc: 69.94% +2025-03-14 16:31:00,847 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.778 | Acc: 67.97% +2025-03-14 16:31:05,109 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.779 | Acc: 72.93% +2025-03-14 16:31:09,247 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.775 | Acc: 73.05% +2025-03-14 16:31:13,118 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.775 | Acc: 73.09% +2025-03-14 16:31:18,274 - train - INFO - Epoch: 17 | Test Loss: 0.962 | Test Acc: 68.02% +2025-03-14 16:31:18,490 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.703 | Acc: 71.09% +2025-03-14 16:31:22,475 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.768 | Acc: 73.15% +2025-03-14 16:31:26,283 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.768 | Acc: 73.13% +2025-03-14 16:31:30,181 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.773 | Acc: 73.08% +2025-03-14 16:31:35,186 - train - INFO - Epoch: 18 | Test Loss: 0.800 | Test Acc: 72.60% +2025-03-14 16:31:44,925 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.683 | Acc: 74.22% +2025-03-14 16:31:48,912 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.755 | Acc: 74.02% +2025-03-14 16:31:52,928 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.757 | Acc: 73.93% +2025-03-14 16:31:56,816 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.759 | Acc: 73.83% +2025-03-14 16:32:01,848 - train - INFO - Epoch: 19 | Test Loss: 0.843 | Test Acc: 70.60% +2025-03-14 16:32:02,083 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.831 | Acc: 69.53% +2025-03-14 16:32:06,173 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.762 | Acc: 73.55% +2025-03-14 16:32:10,108 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.761 | Acc: 73.54% +2025-03-14 16:32:13,922 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.754 | Acc: 73.83% +2025-03-14 16:32:18,890 - train - INFO - Epoch: 20 | Test Loss: 0.725 | Test Acc: 75.27% +2025-03-14 16:32:28,431 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.911 | Acc: 68.75% +2025-03-14 16:32:32,648 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.767 | Acc: 73.45% +2025-03-14 16:32:36,693 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.747 | Acc: 74.06% +2025-03-14 16:32:40,812 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.750 | Acc: 74.00% +2025-03-14 16:32:45,986 - train - INFO - Epoch: 21 | Test Loss: 0.778 | Test Acc: 73.44% +2025-03-14 16:32:46,219 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.708 | Acc: 76.56% +2025-03-14 16:32:50,396 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.760 | Acc: 73.92% +2025-03-14 16:32:54,576 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.745 | Acc: 74.22% +2025-03-14 16:32:58,677 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.742 | Acc: 74.32% +2025-03-14 16:33:03,987 - train - INFO - Epoch: 22 | Test Loss: 0.729 | Test Acc: 74.40% +2025-03-14 16:33:13,962 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.711 | Acc: 75.78% +2025-03-14 16:33:18,261 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.737 | Acc: 74.22% +2025-03-14 16:33:22,344 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.729 | Acc: 74.59% +2025-03-14 16:33:26,554 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.736 | Acc: 74.30% +2025-03-14 16:33:31,759 - train - INFO - Epoch: 23 | Test Loss: 0.795 | Test Acc: 72.91% +2025-03-14 16:33:31,969 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.667 | Acc: 78.12% +2025-03-14 16:33:36,099 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.708 | Acc: 75.29% +2025-03-14 16:33:40,299 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.716 | Acc: 74.91% +2025-03-14 16:33:44,520 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.720 | Acc: 74.79% +2025-03-14 16:33:49,851 - train - INFO - Epoch: 24 | Test Loss: 0.713 | Test Acc: 75.64% +2025-03-14 16:33:59,792 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.801 | Acc: 75.00% +2025-03-14 16:34:03,785 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.717 | Acc: 75.38% +2025-03-14 16:34:07,807 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.718 | Acc: 75.12% +2025-03-14 16:34:11,748 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.717 | Acc: 75.06% +2025-03-14 16:34:16,783 - train - INFO - Epoch: 25 | Test Loss: 0.719 | Test Acc: 75.11% +2025-03-14 16:34:16,994 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.749 | Acc: 72.66% +2025-03-14 16:34:21,113 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.717 | Acc: 75.18% +2025-03-14 16:34:25,235 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.716 | Acc: 75.06% +2025-03-14 16:34:29,185 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.714 | Acc: 75.23% +2025-03-14 16:34:34,159 - train - INFO - Epoch: 26 | Test Loss: 0.717 | Test Acc: 75.26% +2025-03-14 16:34:44,151 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.634 | Acc: 79.69% +2025-03-14 16:34:48,111 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.690 | Acc: 76.35% +2025-03-14 16:34:52,137 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.702 | Acc: 75.84% +2025-03-14 16:34:56,098 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.707 | Acc: 75.66% +2025-03-14 16:35:01,194 - train - INFO - Epoch: 27 | Test Loss: 0.782 | Test Acc: 74.84% +2025-03-14 16:35:01,448 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.664 | Acc: 79.69% +2025-03-14 16:35:05,614 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.718 | Acc: 75.27% +2025-03-14 16:35:09,598 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.708 | Acc: 75.55% +2025-03-14 16:35:13,509 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.706 | Acc: 75.58% +2025-03-14 16:35:18,618 - train - INFO - Epoch: 28 | Test Loss: 0.711 | Test Acc: 75.23% +2025-03-14 16:35:29,152 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.696 | Acc: 75.78% +2025-03-14 16:35:33,789 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.688 | Acc: 76.26% +2025-03-14 16:35:38,060 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.690 | Acc: 76.25% +2025-03-14 16:35:42,470 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.695 | Acc: 75.97% +2025-03-14 16:35:48,049 - train - INFO - Epoch: 29 | Test Loss: 0.731 | Test Acc: 74.49% +2025-03-14 16:35:48,286 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.733 | Acc: 74.22% +2025-03-14 16:35:52,658 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.690 | Acc: 76.14% +2025-03-14 16:35:57,013 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.693 | Acc: 76.10% +2025-03-14 16:36:01,138 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.690 | Acc: 76.22% +2025-03-14 16:36:06,430 - train - INFO - Epoch: 30 | Test Loss: 0.801 | Test Acc: 72.13% +2025-03-14 16:36:17,359 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.648 | Acc: 79.69% +2025-03-14 16:36:22,033 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.680 | Acc: 76.55% +2025-03-14 16:36:26,594 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.691 | Acc: 76.15% +2025-03-14 16:36:31,032 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.681 | Acc: 76.51% +2025-03-14 16:36:36,579 - train - INFO - Epoch: 31 | Test Loss: 0.677 | Test Acc: 76.25% +2025-03-14 16:36:36,805 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.527 | Acc: 83.59% +2025-03-14 16:36:41,318 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.686 | Acc: 75.84% +2025-03-14 16:36:46,490 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.687 | Acc: 75.98% +2025-03-14 16:36:51,387 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.686 | Acc: 76.03% +2025-03-14 16:36:57,281 - train - INFO - Epoch: 32 | Test Loss: 0.690 | Test Acc: 76.28% +2025-03-14 16:37:08,203 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.722 | Acc: 76.56% +2025-03-14 16:37:13,143 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.680 | Acc: 76.66% +2025-03-14 16:37:17,799 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.678 | Acc: 76.55% +2025-03-14 16:37:22,467 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.685 | Acc: 76.24% +2025-03-14 16:37:28,280 - train - INFO - Epoch: 33 | Test Loss: 0.681 | Test Acc: 76.51% +2025-03-14 16:37:28,543 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.765 | Acc: 72.66% +2025-03-14 16:37:33,136 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.647 | Acc: 77.37% +2025-03-14 16:37:37,717 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.670 | Acc: 76.81% +2025-03-14 16:37:42,617 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.674 | Acc: 76.67% +2025-03-14 16:37:48,408 - train - INFO - Epoch: 34 | Test Loss: 0.821 | Test Acc: 72.42% +2025-03-14 16:37:58,925 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.620 | Acc: 76.56% +2025-03-14 16:38:03,539 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.674 | Acc: 76.53% +2025-03-14 16:38:08,136 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.671 | Acc: 76.74% +2025-03-14 16:38:12,743 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.667 | Acc: 76.92% +2025-03-14 16:38:18,488 - train - INFO - Epoch: 35 | Test Loss: 0.712 | Test Acc: 75.76% +2025-03-14 16:38:18,764 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.744 | Acc: 72.66% +2025-03-14 16:38:23,452 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.670 | Acc: 77.07% +2025-03-14 16:38:28,175 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.658 | Acc: 77.18% +2025-03-14 16:38:33,144 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.665 | Acc: 77.00% +2025-03-14 16:38:38,679 - train - INFO - Epoch: 36 | Test Loss: 0.722 | Test Acc: 75.42% +2025-03-14 16:38:48,563 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.754 | Acc: 72.66% +2025-03-14 16:38:52,994 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.643 | Acc: 77.85% +2025-03-14 16:38:57,465 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.638 | Acc: 78.08% +2025-03-14 16:39:01,803 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.647 | Acc: 77.80% +2025-03-14 16:39:07,172 - train - INFO - Epoch: 37 | Test Loss: 0.681 | Test Acc: 76.38% +2025-03-14 16:39:07,411 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.838 | Acc: 69.53% +2025-03-14 16:39:11,854 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.642 | Acc: 77.89% +2025-03-14 16:39:16,355 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.652 | Acc: 77.73% +2025-03-14 16:39:20,774 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.659 | Acc: 77.39% +2025-03-14 16:39:26,358 - train - INFO - Epoch: 38 | Test Loss: 0.669 | Test Acc: 77.11% +2025-03-14 16:39:38,516 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.539 | Acc: 80.47% +2025-03-14 16:39:42,978 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.642 | Acc: 77.62% +2025-03-14 16:39:47,405 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.651 | Acc: 77.45% +2025-03-14 16:39:51,945 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.651 | Acc: 77.46% +2025-03-14 16:39:57,451 - train - INFO - Epoch: 39 | Test Loss: 0.739 | Test Acc: 75.44% +2025-03-14 16:39:57,707 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.652 | Acc: 76.56% +2025-03-14 16:40:02,251 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.637 | Acc: 77.98% +2025-03-14 16:40:06,685 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.643 | Acc: 77.82% +2025-03-14 16:40:11,120 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.649 | Acc: 77.58% +2025-03-14 16:40:16,706 - train - INFO - Epoch: 40 | Test Loss: 0.665 | Test Acc: 77.24% +2025-03-14 16:40:26,700 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.640 | Acc: 75.00% +2025-03-14 16:40:31,256 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.632 | Acc: 78.02% +2025-03-14 16:40:36,002 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.639 | Acc: 77.91% +2025-03-14 16:40:40,669 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.644 | Acc: 77.66% +2025-03-14 16:40:46,866 - train - INFO - Epoch: 41 | Test Loss: 0.657 | Test Acc: 77.40% +2025-03-14 16:40:47,109 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.586 | Acc: 80.47% +2025-03-14 16:40:52,054 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.630 | Acc: 78.36% +2025-03-14 16:40:56,728 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.642 | Acc: 77.88% +2025-03-14 16:41:01,135 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.647 | Acc: 77.72% +2025-03-14 16:41:06,646 - train - INFO - Epoch: 42 | Test Loss: 0.626 | Test Acc: 78.26% +2025-03-14 16:41:16,468 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.777 | Acc: 71.88% +2025-03-14 16:41:21,005 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.630 | Acc: 78.60% +2025-03-14 16:41:25,543 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.638 | Acc: 78.19% +2025-03-14 16:41:29,970 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.637 | Acc: 78.23% +2025-03-14 16:41:35,513 - train - INFO - Epoch: 43 | Test Loss: 0.659 | Test Acc: 77.86% +2025-03-14 16:41:35,761 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.658 | Acc: 74.22% +2025-03-14 16:41:40,313 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.645 | Acc: 77.85% +2025-03-14 16:41:44,720 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.634 | Acc: 78.37% +2025-03-14 16:41:49,081 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.639 | Acc: 78.13% +2025-03-14 16:41:54,698 - train - INFO - Epoch: 44 | Test Loss: 0.792 | Test Acc: 72.59% +2025-03-14 16:42:05,706 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.531 | Acc: 84.38% +2025-03-14 16:42:10,551 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.620 | Acc: 78.21% +2025-03-14 16:42:15,461 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.630 | Acc: 78.25% +2025-03-14 16:42:19,911 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.628 | Acc: 78.37% +2025-03-14 16:42:25,495 - train - INFO - Epoch: 45 | Test Loss: 0.655 | Test Acc: 77.37% +2025-03-14 16:42:25,737 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.602 | Acc: 78.12% +2025-03-14 16:42:30,584 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.619 | Acc: 78.44% +2025-03-14 16:42:35,258 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.622 | Acc: 78.49% +2025-03-14 16:42:39,743 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.623 | Acc: 78.62% +2025-03-14 16:42:45,591 - train - INFO - Epoch: 46 | Test Loss: 0.773 | Test Acc: 74.10% +2025-03-14 16:42:55,831 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.802 | Acc: 71.09% +2025-03-14 16:43:00,591 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.619 | Acc: 78.91% +2025-03-14 16:43:05,170 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.629 | Acc: 78.33% +2025-03-14 16:43:09,942 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.622 | Acc: 78.56% +2025-03-14 16:43:15,560 - train - INFO - Epoch: 47 | Test Loss: 0.729 | Test Acc: 75.41% +2025-03-14 16:43:15,823 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.684 | Acc: 75.78% +2025-03-14 16:43:20,316 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.621 | Acc: 78.23% +2025-03-14 16:43:24,876 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.619 | Acc: 78.47% +2025-03-14 16:43:29,403 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.619 | Acc: 78.62% +2025-03-14 16:43:35,050 - train - INFO - Epoch: 48 | Test Loss: 0.661 | Test Acc: 77.18% +2025-03-14 16:43:45,649 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.794 | Acc: 72.66% +2025-03-14 16:43:50,300 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.602 | Acc: 79.48% +2025-03-14 16:43:54,809 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.614 | Acc: 78.90% +2025-03-14 16:43:59,457 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.611 | Acc: 78.98% +2025-03-14 16:44:05,989 - train - INFO - Epoch: 49 | Test Loss: 0.698 | Test Acc: 76.72% +2025-03-14 16:44:06,255 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.631 | Acc: 78.12% +2025-03-14 16:44:11,479 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.620 | Acc: 78.37% +2025-03-14 16:44:16,041 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.625 | Acc: 78.28% +2025-03-14 16:44:20,488 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.618 | Acc: 78.60% +2025-03-14 16:44:26,092 - train - INFO - Epoch: 50 | Test Loss: 0.699 | Test Acc: 76.03% +2025-03-14 16:44:37,169 - train - INFO - 训练完成! diff --git a/Image/EfficientNet/code/train.py b/Image/EfficientNet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..41115f6c0deddc49fc7408cf08de0db1b687da29 --- /dev/null +++ b/Image/EfficientNet/code/train.py @@ -0,0 +1,63 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import EfficientNet, EfficientNetB0 + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = EfficientNetB0() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='efficientnet', + save_type='0', + layer_name='layers.15.conv3', + interval = 2 + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='efficientnet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='efficientnet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path, + layer_name='layers.15.conv3', + interval = 2 + ) + +if __name__ == '__main__': + main() diff --git a/Image/EfficientNet/dataset/.gitkeep b/Image/EfficientNet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/EfficientNet/model/.gitkeep b/Image/EfficientNet/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git 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INFO - Epoch: 6 | Test Loss: 0.770 | Test Acc: 75.07% +2025-03-14 18:34:48,225 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.457 | Acc: 83.59% +2025-03-14 18:34:55,221 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.560 | Acc: 81.25% +2025-03-14 18:35:02,248 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.567 | Acc: 81.18% +2025-03-14 18:35:09,188 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.568 | Acc: 81.13% +2025-03-14 18:35:17,403 - train - INFO - Epoch: 7 | Test Loss: 0.679 | Test Acc: 76.28% +2025-03-14 18:35:17,649 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.513 | Acc: 85.16% +2025-03-14 18:35:24,626 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.533 | Acc: 82.65% +2025-03-14 18:35:31,671 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.534 | Acc: 82.40% +2025-03-14 18:35:38,701 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.546 | Acc: 82.06% +2025-03-14 18:35:46,848 - train - INFO - Epoch: 8 | Test Loss: 0.625 | Test Acc: 79.10% +2025-03-14 18:35:56,818 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.459 | Acc: 85.94% +2025-03-14 18:36:03,796 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.518 | Acc: 83.04% +2025-03-14 18:36:11,257 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.519 | Acc: 82.80% +2025-03-14 18:36:18,286 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.518 | Acc: 82.91% +2025-03-14 18:36:26,501 - train - INFO - Epoch: 9 | Test Loss: 0.734 | Test Acc: 75.82% +2025-03-14 18:36:26,739 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.431 | Acc: 85.94% +2025-03-14 18:36:33,777 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.497 | Acc: 83.81% +2025-03-14 18:36:40,707 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.501 | Acc: 83.66% +2025-03-14 18:36:47,637 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.503 | Acc: 83.56% +2025-03-14 18:36:55,863 - train - INFO - Epoch: 10 | Test Loss: 0.750 | Test Acc: 75.56% +2025-03-14 18:37:05,916 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.489 | Acc: 83.59% +2025-03-14 18:37:13,239 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.482 | Acc: 84.23% +2025-03-14 18:37:20,129 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.485 | Acc: 84.08% +2025-03-14 18:37:27,097 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.491 | Acc: 83.91% +2025-03-14 18:37:35,213 - train - INFO - Epoch: 11 | Test Loss: 1.081 | Test Acc: 65.30% +2025-03-14 18:37:35,440 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.406 | Acc: 89.84% +2025-03-14 18:37:42,434 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.476 | Acc: 84.36% +2025-03-14 18:37:49,364 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.488 | Acc: 84.07% +2025-03-14 18:37:56,330 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.485 | Acc: 84.26% +2025-03-14 18:38:04,457 - train - INFO - Epoch: 12 | Test Loss: 0.702 | Test Acc: 77.19% +2025-03-14 18:38:14,387 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.424 | Acc: 87.50% +2025-03-14 18:38:21,432 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.477 | Acc: 84.16% +2025-03-14 18:38:28,743 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.477 | Acc: 84.28% +2025-03-14 18:38:35,849 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.477 | Acc: 84.28% +2025-03-14 18:38:44,026 - train - INFO - Epoch: 13 | Test Loss: 0.741 | Test Acc: 76.15% +2025-03-14 18:38:44,258 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.452 | Acc: 85.94% +2025-03-14 18:38:51,221 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.447 | Acc: 85.49% +2025-03-14 18:38:58,138 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.460 | Acc: 84.93% +2025-03-14 18:39:05,079 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.467 | Acc: 84.76% +2025-03-14 18:39:13,204 - train - INFO - Epoch: 14 | Test Loss: 0.766 | Test Acc: 74.77% +2025-03-14 18:39:22,803 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.499 | Acc: 82.81% +2025-03-14 18:39:29,758 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.441 | Acc: 85.46% +2025-03-14 18:39:36,679 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.443 | Acc: 85.55% +2025-03-14 18:39:43,578 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.451 | Acc: 85.27% +2025-03-14 18:39:51,771 - train - INFO - Epoch: 15 | Test Loss: 0.645 | Test Acc: 79.15% +2025-03-14 18:39:52,015 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.420 | Acc: 87.50% +2025-03-14 18:39:59,027 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.446 | Acc: 85.44% +2025-03-14 18:40:06,010 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.452 | Acc: 85.33% +2025-03-14 18:40:12,985 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.454 | Acc: 85.31% +2025-03-14 18:40:21,166 - train - INFO - Epoch: 16 | Test Loss: 0.646 | Test Acc: 77.47% +2025-03-14 18:40:31,085 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.443 | Acc: 86.72% +2025-03-14 18:40:38,156 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.432 | Acc: 86.05% +2025-03-14 18:40:45,116 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.429 | Acc: 86.07% +2025-03-14 18:40:52,072 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.434 | Acc: 85.92% +2025-03-14 18:41:00,189 - train - INFO - Epoch: 17 | Test Loss: 0.689 | Test Acc: 77.78% +2025-03-14 18:41:00,424 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.558 | Acc: 80.47% +2025-03-14 18:41:07,359 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.436 | Acc: 85.83% +2025-03-14 18:41:14,308 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.435 | Acc: 85.90% +2025-03-14 18:41:21,205 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.440 | Acc: 85.65% +2025-03-14 18:41:29,484 - train - INFO - Epoch: 18 | Test Loss: 0.989 | Test Acc: 71.68% +2025-03-14 18:41:39,447 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.436 | Acc: 82.81% +2025-03-14 18:41:46,650 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.426 | Acc: 86.03% +2025-03-14 18:41:54,022 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.432 | Acc: 85.74% +2025-03-14 18:42:01,123 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.428 | Acc: 85.88% +2025-03-14 18:42:09,651 - train - INFO - Epoch: 19 | Test Loss: 0.515 | Test Acc: 82.56% +2025-03-14 18:42:09,924 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.391 | Acc: 82.81% +2025-03-14 18:42:16,896 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.421 | Acc: 86.05% +2025-03-14 18:42:23,895 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.420 | Acc: 86.23% +2025-03-14 18:42:30,991 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.423 | Acc: 86.19% +2025-03-14 18:42:39,201 - train - INFO - Epoch: 20 | Test Loss: 0.589 | Test Acc: 80.34% +2025-03-14 18:42:49,396 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.435 | Acc: 80.47% +2025-03-14 18:42:56,354 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.410 | Acc: 86.80% +2025-03-14 18:43:03,721 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.415 | Acc: 86.52% +2025-03-14 18:43:11,052 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.418 | Acc: 86.47% +2025-03-14 18:43:19,728 - train - INFO - Epoch: 21 | Test Loss: 0.663 | Test Acc: 76.98% +2025-03-14 18:43:19,995 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.416 | Acc: 87.50% +2025-03-14 18:43:27,147 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.397 | Acc: 87.29% +2025-03-14 18:43:34,586 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.400 | Acc: 87.05% +2025-03-14 18:43:41,942 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.404 | Acc: 86.85% +2025-03-14 18:43:50,533 - train - INFO - Epoch: 22 | Test Loss: 0.608 | Test Acc: 79.83% +2025-03-14 18:44:00,545 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.477 | Acc: 83.59% +2025-03-14 18:44:07,956 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.412 | Acc: 86.49% +2025-03-14 18:44:15,630 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.413 | Acc: 86.48% +2025-03-14 18:44:23,070 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.413 | Acc: 86.48% +2025-03-14 18:44:31,415 - train - INFO - Epoch: 23 | Test Loss: 0.899 | Test Acc: 72.21% +2025-03-14 18:44:31,641 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.433 | Acc: 84.38% +2025-03-14 18:44:38,946 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.378 | Acc: 87.86% +2025-03-14 18:44:46,204 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.391 | Acc: 87.29% +2025-03-14 18:44:53,413 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.398 | Acc: 87.05% +2025-03-14 18:45:01,761 - train - INFO - Epoch: 24 | Test Loss: 0.561 | Test Acc: 80.88% +2025-03-14 18:45:12,196 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.439 | Acc: 84.38% +2025-03-14 18:45:19,714 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.389 | Acc: 87.44% +2025-03-14 18:45:27,090 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.400 | Acc: 86.98% +2025-03-14 18:45:34,533 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.401 | Acc: 86.99% +2025-03-14 18:45:43,245 - train - INFO - Epoch: 25 | Test Loss: 0.674 | Test Acc: 77.48% +2025-03-14 18:45:43,490 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.369 | Acc: 86.72% +2025-03-14 18:45:50,813 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.395 | Acc: 87.08% +2025-03-14 18:45:57,909 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.398 | Acc: 87.18% +2025-03-14 18:46:05,247 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.402 | Acc: 87.02% +2025-03-14 18:46:13,802 - train - INFO - Epoch: 26 | Test Loss: 0.656 | Test Acc: 78.80% +2025-03-14 18:46:24,054 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.308 | Acc: 89.84% +2025-03-14 18:46:31,511 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.396 | Acc: 87.24% +2025-03-14 18:46:39,357 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.395 | Acc: 87.38% +2025-03-14 18:46:46,695 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.393 | Acc: 87.33% +2025-03-14 18:46:55,182 - train - INFO - Epoch: 27 | Test Loss: 0.689 | Test Acc: 77.25% +2025-03-14 18:46:55,414 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.388 | Acc: 86.72% +2025-03-14 18:47:02,507 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.387 | Acc: 87.59% +2025-03-14 18:47:09,801 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.396 | Acc: 87.31% +2025-03-14 18:47:17,068 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.394 | Acc: 87.34% +2025-03-14 18:47:25,511 - train - INFO - Epoch: 28 | Test Loss: 0.470 | Test Acc: 84.73% +2025-03-14 18:47:35,983 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.355 | Acc: 89.84% +2025-03-14 18:47:43,643 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.377 | Acc: 87.65% +2025-03-14 18:47:51,004 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.391 | Acc: 87.23% +2025-03-14 18:47:58,184 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.395 | Acc: 87.15% +2025-03-14 18:48:06,676 - train - INFO - Epoch: 29 | Test Loss: 0.678 | Test Acc: 77.69% +2025-03-14 18:48:06,910 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.515 | Acc: 84.38% +2025-03-14 18:48:14,368 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.377 | Acc: 88.01% +2025-03-14 18:48:21,771 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.386 | Acc: 87.42% +2025-03-14 18:48:28,928 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.386 | Acc: 87.42% +2025-03-14 18:48:37,494 - train - INFO - Epoch: 30 | Test Loss: 0.586 | Test Acc: 80.55% +2025-03-14 18:48:47,882 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.252 | Acc: 92.97% +2025-03-14 18:48:55,362 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.353 | Acc: 88.71% +2025-03-14 18:49:02,770 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.371 | Acc: 88.06% +2025-03-14 18:49:10,065 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.377 | Acc: 87.85% +2025-03-14 18:49:18,945 - train - INFO - Epoch: 31 | Test Loss: 0.647 | Test Acc: 78.50% +2025-03-14 18:49:19,193 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.384 | Acc: 83.59% +2025-03-14 18:49:26,450 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.373 | Acc: 88.00% +2025-03-14 18:49:33,505 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.373 | Acc: 87.97% +2025-03-14 18:49:40,801 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.375 | Acc: 87.90% +2025-03-14 18:49:49,380 - train - INFO - Epoch: 32 | Test Loss: 0.711 | Test Acc: 77.94% +2025-03-14 18:49:59,461 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.487 | Acc: 85.94% +2025-03-14 18:50:06,682 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.374 | Acc: 87.84% +2025-03-14 18:50:13,961 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.377 | Acc: 87.75% +2025-03-14 18:50:21,274 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.381 | Acc: 87.68% +2025-03-14 18:50:29,580 - train - INFO - Epoch: 33 | Test Loss: 0.697 | Test Acc: 76.06% +2025-03-14 18:50:29,820 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.334 | Acc: 89.84% +2025-03-14 18:50:37,074 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.379 | Acc: 87.65% +2025-03-14 18:50:44,395 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.374 | Acc: 87.91% +2025-03-14 18:50:51,684 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.376 | Acc: 87.83% +2025-03-14 18:51:00,119 - train - INFO - Epoch: 34 | Test Loss: 0.992 | Test Acc: 70.77% +2025-03-14 18:51:10,544 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.377 | Acc: 87.50% +2025-03-14 18:51:17,930 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.362 | Acc: 88.38% +2025-03-14 18:51:25,422 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.370 | Acc: 88.11% +2025-03-14 18:51:32,507 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.377 | Acc: 87.89% +2025-03-14 18:51:41,005 - train - INFO - Epoch: 35 | Test Loss: 0.463 | Test Acc: 85.28% +2025-03-14 18:51:41,239 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.324 | Acc: 87.50% +2025-03-14 18:51:48,665 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.342 | Acc: 88.98% +2025-03-14 18:51:55,929 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.363 | Acc: 88.21% +2025-03-14 18:52:03,032 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.368 | Acc: 88.07% +2025-03-14 18:52:11,635 - train - INFO - Epoch: 36 | Test Loss: 0.583 | Test Acc: 81.06% +2025-03-14 18:52:22,104 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.340 | Acc: 88.28% +2025-03-14 18:52:29,226 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.347 | Acc: 88.73% +2025-03-14 18:52:36,411 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.360 | Acc: 88.43% +2025-03-14 18:52:43,677 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.369 | Acc: 88.09% +2025-03-14 18:52:52,388 - train - INFO - Epoch: 37 | Test Loss: 0.564 | Test Acc: 81.73% +2025-03-14 18:52:52,674 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.327 | Acc: 90.62% +2025-03-14 18:52:59,804 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.354 | Acc: 88.45% +2025-03-14 18:53:07,104 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.367 | Acc: 88.20% +2025-03-14 18:53:14,658 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.368 | Acc: 88.29% +2025-03-14 18:53:23,272 - train - INFO - Epoch: 38 | Test Loss: 0.600 | Test Acc: 81.34% +2025-03-14 18:53:33,004 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.269 | Acc: 91.41% +2025-03-14 18:53:40,377 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.341 | Acc: 89.06% +2025-03-14 18:53:47,738 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.347 | Acc: 88.99% +2025-03-14 18:53:55,011 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.361 | Acc: 88.44% +2025-03-14 18:54:03,393 - train - INFO - Epoch: 39 | Test Loss: 0.550 | Test Acc: 82.52% +2025-03-14 18:54:03,640 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.374 | Acc: 86.72% +2025-03-14 18:54:10,988 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.363 | Acc: 88.36% +2025-03-14 18:54:18,358 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.365 | Acc: 88.25% +2025-03-14 18:54:25,578 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.365 | Acc: 88.25% +2025-03-14 18:54:33,924 - train - INFO - Epoch: 40 | Test Loss: 0.777 | Test Acc: 75.62% +2025-03-14 18:54:45,182 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.244 | Acc: 92.97% +2025-03-14 18:54:52,512 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.326 | Acc: 89.37% +2025-03-14 18:54:59,594 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.342 | Acc: 88.81% +2025-03-14 18:55:06,787 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.351 | Acc: 88.56% +2025-03-14 18:55:15,344 - train - INFO - Epoch: 41 | Test Loss: 0.876 | Test Acc: 73.90% +2025-03-14 18:55:15,580 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.242 | Acc: 90.62% +2025-03-14 18:55:22,860 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.339 | Acc: 89.23% +2025-03-14 18:55:29,918 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.350 | Acc: 88.91% +2025-03-14 18:55:36,919 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.356 | Acc: 88.66% +2025-03-14 18:55:45,039 - train - INFO - Epoch: 42 | Test Loss: 0.649 | Test Acc: 79.72% +2025-03-14 18:55:54,936 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.386 | Acc: 87.50% +2025-03-14 18:56:01,943 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.344 | Acc: 88.71% +2025-03-14 18:56:08,920 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.347 | Acc: 88.67% +2025-03-14 18:56:15,787 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.353 | Acc: 88.59% +2025-03-14 18:56:23,899 - train - INFO - Epoch: 43 | Test Loss: 0.649 | Test Acc: 79.67% +2025-03-14 18:56:24,121 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.316 | Acc: 89.06% +2025-03-14 18:56:31,004 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.349 | Acc: 88.54% +2025-03-14 18:56:37,903 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.351 | Acc: 88.56% +2025-03-14 18:56:44,906 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.359 | Acc: 88.30% +2025-03-14 18:56:53,121 - train - INFO - Epoch: 44 | Test Loss: 0.492 | Test Acc: 83.67% +2025-03-14 18:57:03,186 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.235 | Acc: 92.97% +2025-03-14 18:57:10,154 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.331 | Acc: 89.50% +2025-03-14 18:57:17,064 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.346 | Acc: 88.84% +2025-03-14 18:57:24,057 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.346 | Acc: 88.87% +2025-03-14 18:57:32,237 - train - INFO - Epoch: 45 | Test Loss: 0.589 | Test Acc: 81.24% +2025-03-14 18:57:32,528 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.400 | Acc: 88.28% +2025-03-14 18:57:39,557 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.349 | Acc: 88.92% +2025-03-14 18:57:46,516 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.353 | Acc: 88.71% +2025-03-14 18:57:53,738 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.353 | Acc: 88.72% +2025-03-14 18:58:02,011 - train - INFO - Epoch: 46 | Test Loss: 0.677 | Test Acc: 78.95% +2025-03-14 18:58:12,903 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.241 | Acc: 92.97% +2025-03-14 18:58:20,180 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.322 | Acc: 90.06% +2025-03-14 18:58:27,236 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.340 | Acc: 89.31% +2025-03-14 18:58:34,194 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.341 | Acc: 89.26% +2025-03-14 18:58:42,382 - train - INFO - Epoch: 47 | Test Loss: 0.471 | Test Acc: 84.72% +2025-03-14 18:58:42,636 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.331 | Acc: 89.06% +2025-03-14 18:58:49,682 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.331 | Acc: 89.20% +2025-03-14 18:58:56,576 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.340 | Acc: 89.05% +2025-03-14 18:59:03,520 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.341 | Acc: 89.00% +2025-03-14 18:59:11,687 - train - INFO - Epoch: 48 | Test Loss: 0.482 | Test Acc: 84.36% +2025-03-14 18:59:21,789 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.436 | Acc: 85.16% +2025-03-14 18:59:28,754 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.334 | Acc: 89.57% +2025-03-14 18:59:35,845 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.334 | Acc: 89.27% +2025-03-14 18:59:42,788 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.344 | Acc: 88.89% +2025-03-14 18:59:50,960 - train - INFO - Epoch: 49 | Test Loss: 0.923 | Test Acc: 73.10% +2025-03-14 18:59:51,174 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.190 | Acc: 92.19% +2025-03-14 18:59:58,102 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.320 | Acc: 89.76% +2025-03-14 19:00:05,019 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.328 | Acc: 89.75% +2025-03-14 19:00:11,950 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.340 | Acc: 89.21% +2025-03-14 19:00:20,210 - train - INFO - Epoch: 50 | Test Loss: 0.964 | Test Acc: 73.76% +2025-03-14 19:00:30,919 - train - INFO - 训练完成! diff --git a/Image/GoogLeNet/code/model.py b/Image/GoogLeNet/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..91ce924467dd0f63a62567b5ba11ed8c819366a2 --- /dev/null +++ b/Image/GoogLeNet/code/model.py @@ -0,0 +1,159 @@ +''' +GoogLeNet in PyTorch. + +Paper: "Going Deeper with Convolutions" +Reference: https://arxiv.org/abs/1409.4842 + +主要特点： +1. 使用Inception模块，通过多尺度卷积提取特征 +2. 采用1x1卷积降维，减少计算量 +3. 使用全局平均池化代替全连接层 +4. 引入辅助分类器帮助训练（本实现未包含） +''' +import torch +import torch.nn as nn + +class Inception(nn.Module): + '''Inception模块 + + Args: + in_planes: 输入通道数 + n1x1: 1x1卷积分支的输出通道数 + n3x3red: 3x3卷积分支的降维通道数 + n3x3: 3x3卷积分支的输出通道数 + n5x5red: 5x5卷积分支的降维通道数 + n5x5: 5x5卷积分支的输出通道数 + pool_planes: 池化分支的输出通道数 + ''' + def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes): + super(Inception, self).__init__() + + # 1x1卷积分支 + self.branch1 = nn.Sequential( + nn.Conv2d(in_planes, n1x1, kernel_size=1), + nn.BatchNorm2d(n1x1), + nn.ReLU(True), + ) + + # 1x1 -> 3x3卷积分支 + self.branch2 = nn.Sequential( + nn.Conv2d(in_planes, n3x3red, kernel_size=1), + nn.BatchNorm2d(n3x3red), + nn.ReLU(True), + nn.Conv2d(n3x3red, n3x3, kernel_size=3, padding=1), + nn.BatchNorm2d(n3x3), + nn.ReLU(True), + ) + + # 1x1 -> 5x5卷积分支（用两个3x3代替） + self.branch3 = nn.Sequential( + nn.Conv2d(in_planes, n5x5red, kernel_size=1), + nn.BatchNorm2d(n5x5red), + nn.ReLU(True), + nn.Conv2d(n5x5red, n5x5, kernel_size=3, padding=1), + nn.BatchNorm2d(n5x5), + nn.ReLU(True), + nn.Conv2d(n5x5, n5x5, kernel_size=3, padding=1), + nn.BatchNorm2d(n5x5), + nn.ReLU(True), + ) + + # 3x3池化 -> 1x1卷积分支 + self.branch4 = nn.Sequential( + nn.MaxPool2d(3, stride=1, padding=1), + nn.Conv2d(in_planes, pool_planes, kernel_size=1), + nn.BatchNorm2d(pool_planes), + nn.ReLU(True), + ) + + def forward(self, x): + '''前向传播，将四个分支的输出在通道维度上拼接''' + b1 = self.branch1(x) + b2 = self.branch2(x) + b3 = self.branch3(x) + b4 = self.branch4(x) + return torch.cat([b1, b2, b3, b4], 1) + + +class GoogLeNet(nn.Module): + '''GoogLeNet/Inception v1网络 + + 特点： + 1. 使用Inception模块构建深层网络 + 2. 通过1x1卷积降维减少计算量 + 3. 使用全局平均池化代替全连接层减少参数量 + ''' + def __init__(self, num_classes=10): + super(GoogLeNet, self).__init__() + + # 第一阶段：标准卷积层 + self.pre_layers = nn.Sequential( + nn.Conv2d(3, 192, kernel_size=3, padding=1), + nn.BatchNorm2d(192), + nn.ReLU(True), + ) + + # 第二阶段：2个Inception模块 + self.a3 = Inception(192, 64, 96, 128, 16, 32, 32) # 输出通道：256 + self.b3 = Inception(256, 128, 128, 192, 32, 96, 64) # 输出通道：480 + + # 最大池化层 + self.maxpool = nn.MaxPool2d(3, stride=2, padding=1) + + # 第三阶段：5个Inception模块 + self.a4 = Inception(480, 192, 96, 208, 16, 48, 64) # 输出通道：512 + self.b4 = Inception(512, 160, 112, 224, 24, 64, 64) # 输出通道：512 + self.c4 = Inception(512, 128, 128, 256, 24, 64, 64) # 输出通道：512 + self.d4 = Inception(512, 112, 144, 288, 32, 64, 64) # 输出通道：528 + self.e4 = Inception(528, 256, 160, 320, 32, 128, 128) # 输出通道：832 + + # 第四阶段：2个Inception模块 + self.a5 = Inception(832, 256, 160, 320, 32, 128, 128) # 输出通道：832 + self.b5 = Inception(832, 384, 192, 384, 48, 128, 128) # 输出通道：1024 + + # 全局平均池化和分类器 + self.avgpool = nn.AvgPool2d(8, stride=1) + self.linear = nn.Linear(1024, num_classes) + + def forward(self, x): + # 第一阶段 + out = self.pre_layers(x) + + # 第二阶段 + out = self.a3(out) + out = self.b3(out) + out = self.maxpool(out) + + # 第三阶段 + out = self.a4(out) + out = self.b4(out) + out = self.c4(out) + out = self.d4(out) + out = self.e4(out) + out = self.maxpool(out) + + # 第四阶段 + out = self.a5(out) + out = self.b5(out) + + # 分类器 + out = self.avgpool(out) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + +def test(): + """测试函数""" + net = GoogLeNet() + x = torch.randn(1, 3, 32, 32) + y = net(x) + print(y.size()) + + # 打印模型结构 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (1, 3, 32, 32)) + +if __name__ == '__main__': + test() diff --git a/Image/GoogLeNet/code/train.log b/Image/GoogLeNet/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..fd1bf2f02039fd85aa835be2e72b00a9ea52166d --- /dev/null +++ b/Image/GoogLeNet/code/train.log @@ -0,0 +1,253 @@ +2025-03-14 16:54:07,690 - train - INFO - 开始训练 googlenet +2025-03-14 16:54:07,690 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3 +2025-03-14 16:54:08,498 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.291 | Acc: 8.59% +2025-03-14 16:54:15,595 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 1.955 | Acc: 29.12% +2025-03-14 16:54:22,612 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.763 | Acc: 35.63% +2025-03-14 16:54:29,568 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.641 | Acc: 39.92% +2025-03-14 16:54:38,067 - train - INFO - Epoch: 1 | Test Loss: 1.298 | Test Acc: 51.84% +2025-03-14 16:54:38,629 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.156 | Acc: 55.47% +2025-03-14 16:54:45,948 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.154 | Acc: 57.85% +2025-03-14 16:54:52,919 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.099 | Acc: 60.41% +2025-03-14 16:54:59,927 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.061 | Acc: 62.04% +2025-03-14 16:55:08,217 - train - INFO - Epoch: 2 | Test Loss: 1.494 | Test Acc: 52.46% +2025-03-14 16:55:19,584 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.953 | Acc: 63.28% +2025-03-14 16:55:26,661 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.843 | Acc: 70.28% +2025-03-14 16:55:33,708 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.814 | Acc: 71.36% +2025-03-14 16:55:40,780 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.793 | Acc: 72.08% +2025-03-14 16:55:49,150 - train - INFO - Epoch: 3 | Test Loss: 0.897 | Test Acc: 69.69% +2025-03-14 16:55:49,386 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.713 | Acc: 71.09% +2025-03-14 16:55:56,440 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.658 | Acc: 77.17% +2025-03-14 16:56:03,485 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.647 | Acc: 77.37% +2025-03-14 16:56:10,553 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.642 | Acc: 77.73% +2025-03-14 16:56:19,742 - train - INFO - Epoch: 4 | Test Loss: 0.757 | Test Acc: 74.32% +2025-03-14 16:56:30,187 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.653 | Acc: 77.34% +2025-03-14 16:56:37,215 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.590 | Acc: 79.94% +2025-03-14 16:56:44,400 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.576 | Acc: 80.39% +2025-03-14 16:56:51,627 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.571 | Acc: 80.55% +2025-03-14 16:57:00,010 - train - INFO - Epoch: 5 | Test Loss: 0.861 | Test Acc: 72.27% +2025-03-14 16:57:00,281 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.447 | Acc: 82.03% +2025-03-14 16:57:07,302 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.524 | Acc: 81.89% +2025-03-14 16:57:14,307 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.526 | Acc: 81.77% +2025-03-14 16:57:21,849 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.526 | Acc: 81.91% +2025-03-14 16:57:30,095 - train - INFO - Epoch: 6 | Test Loss: 0.627 | Test Acc: 78.61% +2025-03-14 16:57:40,186 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.512 | Acc: 82.03% +2025-03-14 16:57:47,258 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.481 | Acc: 83.17% +2025-03-14 16:57:54,290 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.490 | Acc: 83.03% +2025-03-14 16:58:01,246 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.489 | Acc: 83.16% +2025-03-14 16:58:09,401 - train - INFO - Epoch: 7 | Test Loss: 0.641 | Test Acc: 79.07% +2025-03-14 16:58:09,660 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.370 | Acc: 85.94% +2025-03-14 16:58:16,676 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.445 | Acc: 84.83% +2025-03-14 16:58:23,694 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.449 | Acc: 84.63% +2025-03-14 16:58:30,745 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.457 | Acc: 84.41% +2025-03-14 16:58:39,044 - train - INFO - Epoch: 8 | Test Loss: 0.863 | Test Acc: 73.93% +2025-03-14 16:58:49,212 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.609 | Acc: 79.69% +2025-03-14 16:58:56,404 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.438 | Acc: 84.81% +2025-03-14 16:59:03,387 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.442 | Acc: 84.76% +2025-03-14 16:59:10,433 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.443 | Acc: 84.73% +2025-03-14 16:59:18,735 - train - INFO - Epoch: 9 | Test Loss: 0.742 | Test Acc: 76.34% +2025-03-14 16:59:18,988 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.342 | Acc: 85.16% +2025-03-14 16:59:26,029 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.421 | Acc: 85.70% +2025-03-14 16:59:33,012 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.423 | Acc: 85.54% +2025-03-14 16:59:39,989 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.427 | Acc: 85.40% +2025-03-14 16:59:48,307 - train - INFO - Epoch: 10 | Test Loss: 0.631 | Test Acc: 79.03% +2025-03-14 16:59:58,727 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.374 | Acc: 89.84% +2025-03-14 17:00:06,458 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.408 | Acc: 85.95% +2025-03-14 17:00:13,759 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.411 | Acc: 85.80% +2025-03-14 17:00:20,781 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.420 | Acc: 85.57% +2025-03-14 17:00:29,145 - train - INFO - Epoch: 11 | Test Loss: 0.746 | Test Acc: 75.64% +2025-03-14 17:00:29,405 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.372 | Acc: 85.16% +2025-03-14 17:00:36,421 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.389 | Acc: 86.41% +2025-03-14 17:00:43,392 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.406 | Acc: 85.95% +2025-03-14 17:00:50,346 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.410 | Acc: 85.89% +2025-03-14 17:00:58,480 - train - INFO - Epoch: 12 | Test Loss: 0.744 | Test Acc: 76.44% +2025-03-14 17:01:08,385 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.272 | Acc: 90.62% +2025-03-14 17:01:15,442 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.383 | Acc: 86.80% +2025-03-14 17:01:22,401 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.388 | Acc: 86.56% +2025-03-14 17:01:29,668 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.401 | Acc: 86.09% +2025-03-14 17:01:38,031 - train - INFO - Epoch: 13 | Test Loss: 0.839 | Test Acc: 74.00% +2025-03-14 17:01:38,284 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.415 | Acc: 85.16% +2025-03-14 17:01:45,233 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.377 | Acc: 86.89% +2025-03-14 17:01:52,187 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.384 | Acc: 86.67% +2025-03-14 17:01:59,108 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.389 | Acc: 86.41% +2025-03-14 17:02:07,307 - train - INFO - Epoch: 14 | Test Loss: 0.740 | Test Acc: 75.39% +2025-03-14 17:02:17,573 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.291 | Acc: 89.84% +2025-03-14 17:02:24,651 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.370 | Acc: 87.29% +2025-03-14 17:02:31,629 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.373 | Acc: 87.15% +2025-03-14 17:02:38,739 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.376 | Acc: 87.03% +2025-03-14 17:02:47,204 - train - INFO - Epoch: 15 | Test Loss: 0.601 | Test Acc: 80.11% +2025-03-14 17:02:47,487 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.260 | Acc: 92.97% +2025-03-14 17:02:54,589 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.375 | Acc: 87.46% +2025-03-14 17:03:01,614 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.378 | Acc: 87.10% +2025-03-14 17:03:08,551 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.380 | Acc: 86.99% +2025-03-14 17:03:17,027 - train - INFO - Epoch: 16 | Test Loss: 0.578 | Test Acc: 81.07% +2025-03-14 17:03:27,383 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.288 | Acc: 89.06% +2025-03-14 17:03:34,362 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.357 | Acc: 87.81% +2025-03-14 17:03:41,426 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.367 | Acc: 87.33% +2025-03-14 17:03:48,924 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.372 | Acc: 87.28% +2025-03-14 17:03:57,228 - train - INFO - Epoch: 17 | Test Loss: 0.604 | Test Acc: 80.90% +2025-03-14 17:03:57,495 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.413 | Acc: 84.38% +2025-03-14 17:04:04,537 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.348 | Acc: 88.03% +2025-03-14 17:04:11,554 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.353 | Acc: 87.83% +2025-03-14 17:04:18,679 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.355 | Acc: 87.92% +2025-03-14 17:04:27,096 - train - INFO - Epoch: 18 | Test Loss: 1.517 | Test Acc: 59.99% +2025-03-14 17:04:37,412 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.429 | Acc: 85.94% +2025-03-14 17:04:44,398 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.338 | Acc: 88.47% +2025-03-14 17:04:51,696 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.349 | Acc: 87.96% +2025-03-14 17:04:58,812 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.356 | Acc: 87.64% +2025-03-14 17:05:07,013 - train - INFO - Epoch: 19 | Test Loss: 0.831 | Test Acc: 75.19% +2025-03-14 17:05:07,264 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.282 | Acc: 91.41% +2025-03-14 17:05:14,264 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.320 | Acc: 88.91% +2025-03-14 17:05:21,286 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.340 | Acc: 88.20% +2025-03-14 17:05:28,382 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.345 | Acc: 88.19% +2025-03-14 17:05:36,649 - train - INFO - Epoch: 20 | Test Loss: 0.781 | Test Acc: 75.57% +2025-03-14 17:05:47,127 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.395 | Acc: 85.94% +2025-03-14 17:05:54,114 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.337 | Acc: 88.33% +2025-03-14 17:06:01,056 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.339 | Acc: 88.20% +2025-03-14 17:06:07,976 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.343 | Acc: 88.07% +2025-03-14 17:06:16,134 - train - INFO - Epoch: 21 | Test Loss: 0.498 | Test Acc: 84.13% +2025-03-14 17:06:16,365 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.278 | Acc: 91.41% +2025-03-14 17:06:23,345 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.326 | Acc: 88.81% +2025-03-14 17:06:30,343 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.334 | Acc: 88.39% +2025-03-14 17:06:37,492 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.338 | Acc: 88.26% +2025-03-14 17:06:46,232 - train - INFO - Epoch: 22 | Test Loss: 0.626 | Test Acc: 80.14% +2025-03-14 17:06:56,389 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.287 | Acc: 92.19% +2025-03-14 17:07:03,470 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.320 | Acc: 89.18% +2025-03-14 17:07:10,453 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.330 | Acc: 88.65% +2025-03-14 17:07:17,383 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.335 | Acc: 88.46% +2025-03-14 17:07:25,590 - train - INFO - Epoch: 23 | Test Loss: 0.761 | Test Acc: 76.10% +2025-03-14 17:07:25,868 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.403 | Acc: 85.16% +2025-03-14 17:07:32,933 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.306 | Acc: 89.61% +2025-03-14 17:07:40,039 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.316 | Acc: 89.01% +2025-03-14 17:07:47,530 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.325 | Acc: 88.68% +2025-03-14 17:07:55,871 - train - INFO - Epoch: 24 | Test Loss: 0.618 | Test Acc: 80.21% +2025-03-14 17:08:06,349 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.349 | Acc: 87.50% +2025-03-14 17:08:13,405 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.315 | Acc: 89.09% +2025-03-14 17:08:20,736 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.324 | Acc: 88.82% +2025-03-14 17:08:27,757 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.330 | Acc: 88.60% +2025-03-14 17:08:36,073 - train - INFO - Epoch: 25 | Test Loss: 0.444 | Test Acc: 85.16% +2025-03-14 17:08:36,387 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.373 | Acc: 87.50% +2025-03-14 17:08:43,532 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.314 | Acc: 89.22% +2025-03-14 17:08:50,665 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.317 | Acc: 89.05% +2025-03-14 17:08:57,696 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.321 | Acc: 88.87% +2025-03-14 17:09:05,921 - train - INFO - Epoch: 26 | Test Loss: 0.583 | Test Acc: 81.13% +2025-03-14 17:09:16,691 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.210 | Acc: 93.75% +2025-03-14 17:09:23,814 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.305 | Acc: 89.27% +2025-03-14 17:09:31,184 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.317 | Acc: 88.93% +2025-03-14 17:09:38,295 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.316 | Acc: 89.00% +2025-03-14 17:09:46,877 - train - INFO - Epoch: 27 | Test Loss: 0.500 | Test Acc: 83.60% +2025-03-14 17:09:47,205 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.324 | Acc: 88.28% +2025-03-14 17:09:54,324 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.300 | Acc: 89.70% +2025-03-14 17:10:01,318 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.308 | Acc: 89.37% +2025-03-14 17:10:08,390 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.312 | Acc: 89.28% +2025-03-14 17:10:17,262 - train - INFO - Epoch: 28 | Test Loss: 0.453 | Test Acc: 84.78% +2025-03-14 17:10:27,827 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.225 | Acc: 92.19% +2025-03-14 17:10:34,830 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.289 | Acc: 90.12% +2025-03-14 17:10:41,867 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.298 | Acc: 89.73% +2025-03-14 17:10:48,873 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.307 | Acc: 89.44% +2025-03-14 17:10:57,141 - train - INFO - Epoch: 29 | Test Loss: 0.540 | Test Acc: 81.94% +2025-03-14 17:10:57,411 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.347 | Acc: 87.50% +2025-03-14 17:11:04,423 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.302 | Acc: 89.55% +2025-03-14 17:11:11,402 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.306 | Acc: 89.59% +2025-03-14 17:11:18,378 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.309 | Acc: 89.35% +2025-03-14 17:11:26,623 - train - INFO - Epoch: 30 | Test Loss: 1.307 | Test Acc: 64.91% +2025-03-14 17:11:37,574 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.269 | Acc: 89.06% +2025-03-14 17:11:44,956 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.298 | Acc: 89.81% +2025-03-14 17:11:51,880 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.312 | Acc: 89.24% +2025-03-14 17:11:58,843 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.312 | Acc: 89.26% +2025-03-14 17:12:07,072 - train - INFO - Epoch: 31 | Test Loss: 0.621 | Test Acc: 80.50% +2025-03-14 17:12:07,308 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.212 | Acc: 94.53% +2025-03-14 17:12:14,356 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.291 | Acc: 90.01% +2025-03-14 17:12:21,336 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.297 | Acc: 89.81% +2025-03-14 17:12:28,329 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.302 | Acc: 89.55% +2025-03-14 17:12:36,842 - train - INFO - Epoch: 32 | Test Loss: 0.605 | Test Acc: 81.09% +2025-03-14 17:12:46,713 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.217 | Acc: 94.53% +2025-03-14 17:12:53,675 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.281 | Acc: 90.22% +2025-03-14 17:13:00,644 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.295 | Acc: 89.73% +2025-03-14 17:13:07,549 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.301 | Acc: 89.68% +2025-03-14 17:13:15,742 - train - INFO - Epoch: 33 | Test Loss: 0.738 | Test Acc: 76.74% +2025-03-14 17:13:15,979 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.304 | Acc: 89.06% +2025-03-14 17:13:22,971 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.286 | Acc: 90.32% +2025-03-14 17:13:29,907 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.295 | Acc: 89.97% +2025-03-14 17:13:36,918 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.302 | Acc: 89.72% +2025-03-14 17:13:45,169 - train - INFO - Epoch: 34 | Test Loss: 0.424 | Test Acc: 85.72% +2025-03-14 17:13:55,500 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.236 | Acc: 92.19% +2025-03-14 17:14:03,001 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.266 | Acc: 90.99% +2025-03-14 17:14:10,047 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.279 | Acc: 90.33% +2025-03-14 17:14:17,054 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.288 | Acc: 90.10% +2025-03-14 17:14:25,381 - train - INFO - Epoch: 35 | Test Loss: 0.645 | Test Acc: 79.89% +2025-03-14 17:14:25,635 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.321 | Acc: 90.62% +2025-03-14 17:14:32,644 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.297 | Acc: 89.67% +2025-03-14 17:14:39,568 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.293 | Acc: 89.81% +2025-03-14 17:14:46,531 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.294 | Acc: 89.85% +2025-03-14 17:14:54,840 - train - INFO - Epoch: 36 | Test Loss: 0.551 | Test Acc: 81.67% +2025-03-14 17:15:04,949 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.269 | Acc: 90.62% +2025-03-14 17:15:11,943 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.276 | Acc: 90.36% +2025-03-14 17:15:18,964 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.285 | Acc: 90.15% +2025-03-14 17:15:26,031 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.289 | Acc: 90.06% +2025-03-14 17:15:34,305 - train - INFO - Epoch: 37 | Test Loss: 0.460 | Test Acc: 83.66% +2025-03-14 17:15:34,549 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.239 | Acc: 92.97% +2025-03-14 17:15:41,581 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.282 | Acc: 90.11% +2025-03-14 17:15:48,954 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.283 | Acc: 90.08% +2025-03-14 17:15:56,290 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.288 | Acc: 90.05% +2025-03-14 17:16:04,868 - train - INFO - Epoch: 38 | Test Loss: 0.766 | Test Acc: 75.91% +2025-03-14 17:16:15,740 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.281 | Acc: 89.84% +2025-03-14 17:16:22,725 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.261 | Acc: 91.14% +2025-03-14 17:16:29,750 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.280 | Acc: 90.44% +2025-03-14 17:16:36,702 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.283 | Acc: 90.32% +2025-03-14 17:16:45,050 - train - INFO - Epoch: 39 | Test Loss: 0.747 | Test Acc: 77.00% +2025-03-14 17:16:45,337 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.251 | Acc: 90.62% +2025-03-14 17:16:52,351 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.268 | Acc: 90.90% +2025-03-14 17:16:59,308 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.277 | Acc: 90.49% +2025-03-14 17:17:06,237 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.284 | Acc: 90.26% +2025-03-14 17:17:14,416 - train - INFO - Epoch: 40 | Test Loss: 0.818 | Test Acc: 76.04% +2025-03-14 17:17:26,250 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.205 | Acc: 94.53% +2025-03-14 17:17:33,271 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.276 | Acc: 90.76% +2025-03-14 17:17:40,234 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.287 | Acc: 90.35% +2025-03-14 17:17:47,165 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.282 | Acc: 90.39% +2025-03-14 17:17:55,350 - train - INFO - Epoch: 41 | Test Loss: 0.560 | Test Acc: 82.69% +2025-03-14 17:17:55,602 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.260 | Acc: 89.84% +2025-03-14 17:18:02,700 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.266 | Acc: 90.81% +2025-03-14 17:18:09,825 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.276 | Acc: 90.53% +2025-03-14 17:18:16,905 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.283 | Acc: 90.26% +2025-03-14 17:18:25,159 - train - INFO - Epoch: 42 | Test Loss: 1.234 | Test Acc: 66.21% +2025-03-14 17:18:35,406 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.309 | Acc: 85.94% +2025-03-14 17:18:42,406 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.257 | Acc: 91.13% +2025-03-14 17:18:49,556 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.270 | Acc: 90.60% +2025-03-14 17:18:56,770 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.274 | Acc: 90.47% +2025-03-14 17:19:05,067 - train - INFO - Epoch: 43 | Test Loss: 0.469 | Test Acc: 84.64% +2025-03-14 17:19:05,309 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.318 | Acc: 89.06% +2025-03-14 17:19:12,312 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.267 | Acc: 90.77% +2025-03-14 17:19:19,282 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.269 | Acc: 90.64% +2025-03-14 17:19:26,265 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.274 | Acc: 90.51% +2025-03-14 17:19:34,468 - train - INFO - Epoch: 44 | Test Loss: 0.466 | Test Acc: 84.85% +2025-03-14 17:19:44,307 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.187 | Acc: 95.31% +2025-03-14 17:19:51,300 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.247 | Acc: 91.57% +2025-03-14 17:19:58,266 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.266 | Acc: 90.90% +2025-03-14 17:20:05,226 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.272 | Acc: 90.68% +2025-03-14 17:20:13,581 - train - INFO - Epoch: 45 | Test Loss: 0.948 | Test Acc: 72.26% +2025-03-14 17:20:13,840 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.148 | Acc: 94.53% +2025-03-14 17:20:21,189 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.258 | Acc: 90.94% +2025-03-14 17:20:28,360 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.264 | Acc: 90.99% +2025-03-14 17:20:35,323 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.272 | Acc: 90.68% +2025-03-14 17:20:43,561 - train - INFO - Epoch: 46 | Test Loss: 0.506 | Test Acc: 83.50% +2025-03-14 17:20:53,954 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.263 | Acc: 92.19% +2025-03-14 17:21:00,932 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.250 | Acc: 91.58% +2025-03-14 17:21:08,026 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.255 | Acc: 91.42% +2025-03-14 17:21:15,684 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.266 | Acc: 91.04% +2025-03-14 17:21:24,479 - train - INFO - Epoch: 47 | Test Loss: 0.736 | Test Acc: 77.67% +2025-03-14 17:21:24,729 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.306 | Acc: 89.06% +2025-03-14 17:21:31,823 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.257 | Acc: 91.17% +2025-03-14 17:21:38,830 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.264 | Acc: 90.98% +2025-03-14 17:21:45,831 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.262 | Acc: 90.97% +2025-03-14 17:21:54,201 - train - INFO - Epoch: 48 | Test Loss: 0.574 | Test Acc: 81.14% +2025-03-14 17:22:04,657 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.393 | Acc: 86.72% +2025-03-14 17:22:11,695 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.251 | Acc: 91.54% +2025-03-14 17:22:18,756 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.258 | Acc: 91.24% +2025-03-14 17:22:25,780 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.264 | Acc: 90.99% +2025-03-14 17:22:34,535 - train - INFO - Epoch: 49 | Test Loss: 0.491 | Test Acc: 83.90% +2025-03-14 17:22:34,814 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.272 | Acc: 94.53% +2025-03-14 17:22:41,960 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.260 | Acc: 90.78% +2025-03-14 17:22:49,037 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.260 | Acc: 90.89% +2025-03-14 17:22:56,058 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.261 | Acc: 90.98% +2025-03-14 17:23:04,356 - train - INFO - Epoch: 50 | Test Loss: 1.348 | Test Acc: 64.58% +2025-03-14 17:23:14,478 - train - INFO - 训练完成! diff --git a/Image/GoogLeNet/code/train.py b/Image/GoogLeNet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..37f277f4f37d01f89ccc1b9dbe1268ec11a63325 --- /dev/null +++ b/Image/GoogLeNet/code/train.py @@ -0,0 +1,63 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import GoogLeNet + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = GoogLeNet() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='googlenet', + save_type='0', + layer_name='avgpool', + interval = 2 + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='googlenet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='googlenet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path, + layer_name='avgpool', + interval = 2 + ) + +if __name__ == '__main__': + main() diff --git a/Image/GoogLeNet/dataset/.gitkeep b/Image/GoogLeNet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/GoogLeNet/model/.gitkeep b/Image/GoogLeNet/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/GoogLeNet/model/0/epoch1/embeddings.npy 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Acc: 36.89% +2025-03-14 18:52:29,344 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.727 | Acc: 38.28% +2025-03-14 18:52:31,511 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.734 | Acc: 36.10% +2025-03-14 18:52:33,763 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.723 | Acc: 36.55% +2025-03-14 18:52:35,894 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.719 | Acc: 36.65% +2025-03-14 18:52:39,232 - train - INFO - Epoch: 6 | Test Loss: 1.720 | Test Acc: 36.32% +2025-03-14 18:52:48,612 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.590 | Acc: 45.31% +2025-03-14 18:52:50,865 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.648 | Acc: 39.59% +2025-03-14 18:52:53,028 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.659 | Acc: 38.78% +2025-03-14 18:52:55,063 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.656 | Acc: 39.38% +2025-03-14 18:52:58,384 - train - INFO - Epoch: 7 | Test Loss: 1.744 | Test Acc: 34.71% +2025-03-14 18:52:58,567 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.699 | Acc: 34.38% +2025-03-14 18:53:00,712 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.627 | Acc: 40.86% +2025-03-14 18:53:03,147 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.647 | Acc: 40.62% +2025-03-14 18:53:05,342 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.647 | Acc: 40.75% +2025-03-14 18:53:08,945 - train - INFO - Epoch: 8 | Test Loss: 1.612 | Test Acc: 41.16% +2025-03-14 18:53:18,871 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.812 | Acc: 36.72% +2025-03-14 18:53:21,249 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.644 | Acc: 40.66% +2025-03-14 18:53:23,450 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.627 | Acc: 41.32% +2025-03-14 18:53:25,638 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.631 | Acc: 41.23% +2025-03-14 18:53:28,926 - train - INFO - Epoch: 9 | Test Loss: 1.592 | Test Acc: 42.59% +2025-03-14 18:53:29,088 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.626 | Acc: 41.41% +2025-03-14 18:53:31,206 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.612 | Acc: 42.34% +2025-03-14 18:53:33,239 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.617 | Acc: 42.14% +2025-03-14 18:53:35,375 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.623 | Acc: 41.96% +2025-03-14 18:53:38,613 - train - INFO - Epoch: 10 | Test Loss: 1.616 | Test Acc: 43.11% +2025-03-14 18:53:47,913 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.573 | Acc: 38.28% +2025-03-14 18:53:50,081 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.611 | Acc: 43.01% +2025-03-14 18:53:52,274 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.596 | Acc: 43.17% +2025-03-14 18:53:54,447 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.589 | Acc: 43.13% +2025-03-14 18:53:57,925 - train - INFO - Epoch: 11 | Test Loss: 1.554 | Test Acc: 45.18% +2025-03-14 18:53:58,109 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.450 | Acc: 45.31% +2025-03-14 18:54:00,335 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.610 | Acc: 41.93% +2025-03-14 18:54:02,491 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.611 | Acc: 42.44% +2025-03-14 18:54:04,671 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.594 | Acc: 42.97% +2025-03-14 18:54:07,901 - train - INFO - Epoch: 12 | Test Loss: 1.563 | Test Acc: 47.00% +2025-03-14 18:54:17,119 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.498 | Acc: 53.12% +2025-03-14 18:54:19,213 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.624 | Acc: 42.75% +2025-03-14 18:54:21,306 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.606 | Acc: 43.22% +2025-03-14 18:54:23,344 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.600 | Acc: 43.19% +2025-03-14 18:54:26,670 - train - INFO - Epoch: 13 | Test Loss: 1.666 | Test Acc: 39.55% +2025-03-14 18:54:26,845 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.680 | Acc: 39.84% +2025-03-14 18:54:28,959 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.583 | Acc: 43.79% +2025-03-14 18:54:31,171 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.568 | Acc: 44.59% +2025-03-14 18:54:33,362 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.581 | Acc: 44.00% +2025-03-14 18:54:36,845 - train - INFO - Epoch: 14 | Test Loss: 1.533 | Test Acc: 45.22% +2025-03-14 18:54:47,390 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 1.500 | Acc: 46.88% +2025-03-14 18:54:49,691 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.537 | Acc: 46.24% +2025-03-14 18:54:51,869 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.557 | Acc: 45.44% +2025-03-14 18:54:53,985 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.564 | Acc: 45.09% +2025-03-14 18:54:57,177 - train - INFO - Epoch: 15 | Test Loss: 1.530 | Test Acc: 47.11% +2025-03-14 18:54:57,341 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 1.560 | Acc: 48.44% +2025-03-14 18:54:59,495 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.582 | Acc: 43.63% +2025-03-14 18:55:01,686 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.583 | Acc: 43.79% +2025-03-14 18:55:03,817 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.588 | Acc: 43.62% +2025-03-14 18:55:07,207 - train - INFO - Epoch: 16 | Test Loss: 1.551 | Test Acc: 45.91% +2025-03-14 18:55:16,376 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 1.535 | Acc: 46.09% +2025-03-14 18:55:18,547 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.551 | Acc: 45.02% +2025-03-14 18:55:20,727 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.544 | Acc: 45.37% +2025-03-14 18:55:22,965 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.556 | Acc: 44.91% +2025-03-14 18:55:26,302 - train - INFO - Epoch: 17 | Test Loss: 1.539 | Test Acc: 45.68% +2025-03-14 18:55:26,472 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.663 | Acc: 42.19% +2025-03-14 18:55:28,773 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.586 | Acc: 44.14% +2025-03-14 18:55:30,980 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.577 | Acc: 44.25% +2025-03-14 18:55:33,160 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.569 | Acc: 44.71% +2025-03-14 18:55:36,539 - train - INFO - Epoch: 18 | Test Loss: 1.540 | Test Acc: 45.29% +2025-03-14 18:55:45,385 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.668 | Acc: 37.50% +2025-03-14 18:55:47,447 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.580 | Acc: 44.69% +2025-03-14 18:55:49,614 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.562 | Acc: 45.13% +2025-03-14 18:55:51,710 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.561 | Acc: 45.20% +2025-03-14 18:55:55,165 - train - INFO - Epoch: 19 | Test Loss: 1.507 | Test Acc: 48.32% +2025-03-14 18:55:55,342 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.495 | Acc: 43.75% +2025-03-14 18:55:57,635 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.536 | Acc: 45.37% +2025-03-14 18:55:59,839 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.547 | Acc: 44.96% +2025-03-14 18:56:01,970 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.548 | Acc: 45.40% +2025-03-14 18:56:05,495 - train - INFO - Epoch: 20 | Test Loss: 1.528 | Test Acc: 46.53% +2025-03-14 18:56:14,208 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.659 | Acc: 46.09% +2025-03-14 18:56:16,352 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.562 | Acc: 45.17% +2025-03-14 18:56:18,446 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.566 | Acc: 45.19% +2025-03-14 18:56:20,611 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.556 | Acc: 45.54% +2025-03-14 18:56:24,010 - train - INFO - Epoch: 21 | Test Loss: 1.623 | Test Acc: 44.64% +2025-03-14 18:56:24,185 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 1.453 | Acc: 47.66% +2025-03-14 18:56:26,310 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.546 | Acc: 45.95% +2025-03-14 18:56:28,333 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.550 | Acc: 45.71% +2025-03-14 18:56:30,502 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.547 | Acc: 45.70% +2025-03-14 18:56:33,715 - train - INFO - Epoch: 22 | Test Loss: 1.516 | Test Acc: 46.97% +2025-03-14 18:56:42,854 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.692 | Acc: 45.31% +2025-03-14 18:56:45,157 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.559 | Acc: 44.98% +2025-03-14 18:56:47,388 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.545 | Acc: 45.38% +2025-03-14 18:56:49,617 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.546 | Acc: 45.66% +2025-03-14 18:56:52,961 - train - INFO - Epoch: 23 | Test Loss: 1.481 | Test Acc: 48.12% +2025-03-14 18:56:53,138 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.525 | Acc: 48.44% +2025-03-14 18:56:55,474 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.538 | Acc: 46.25% +2025-03-14 18:56:57,728 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.548 | Acc: 45.48% +2025-03-14 18:57:00,002 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.547 | Acc: 45.61% +2025-03-14 18:57:03,345 - train - INFO - Epoch: 24 | Test Loss: 1.480 | Test Acc: 48.34% +2025-03-14 18:57:12,515 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.395 | Acc: 45.31% +2025-03-14 18:57:14,685 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.532 | Acc: 46.67% +2025-03-14 18:57:16,799 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.524 | Acc: 46.82% +2025-03-14 18:57:19,039 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.517 | Acc: 46.98% +2025-03-14 18:57:22,516 - train - INFO - Epoch: 25 | Test Loss: 1.553 | Test Acc: 46.03% +2025-03-14 18:57:22,683 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 1.736 | Acc: 44.53% +2025-03-14 18:57:24,762 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.509 | Acc: 46.84% +2025-03-14 18:57:26,861 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.519 | Acc: 46.68% +2025-03-14 18:57:29,066 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.519 | Acc: 46.61% +2025-03-14 18:57:32,297 - train - INFO - Epoch: 26 | Test Loss: 1.525 | Test Acc: 46.22% +2025-03-14 18:57:41,935 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.524 | Acc: 50.78% +2025-03-14 18:57:44,126 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.526 | Acc: 45.78% +2025-03-14 18:57:46,507 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.519 | Acc: 46.54% +2025-03-14 18:57:48,982 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.525 | Acc: 46.30% +2025-03-14 18:57:52,953 - train - INFO - Epoch: 27 | Test Loss: 1.485 | Test Acc: 47.34% +2025-03-14 18:57:53,134 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.597 | Acc: 44.53% +2025-03-14 18:57:55,479 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.521 | Acc: 47.34% +2025-03-14 18:57:57,669 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.527 | Acc: 46.73% +2025-03-14 18:57:59,857 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.540 | Acc: 46.01% +2025-03-14 18:58:03,284 - train - INFO - Epoch: 28 | Test Loss: 1.585 | Test Acc: 46.93% +2025-03-14 18:58:13,439 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.544 | Acc: 47.66% +2025-03-14 18:58:15,897 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.481 | Acc: 48.57% +2025-03-14 18:58:18,632 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.503 | Acc: 47.80% +2025-03-14 18:58:20,904 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.512 | Acc: 47.66% +2025-03-14 18:58:24,312 - train - INFO - Epoch: 29 | Test Loss: 1.542 | Test Acc: 45.92% +2025-03-14 18:58:24,483 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 1.539 | Acc: 44.53% +2025-03-14 18:58:26,696 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.498 | Acc: 47.67% +2025-03-14 18:58:28,804 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.515 | Acc: 47.15% +2025-03-14 18:58:31,277 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.509 | Acc: 47.32% +2025-03-14 18:58:34,684 - train - INFO - Epoch: 30 | Test Loss: 1.489 | Test Acc: 48.24% +2025-03-14 18:58:43,983 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 1.627 | Acc: 44.53% +2025-03-14 18:58:46,126 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.493 | Acc: 48.05% +2025-03-14 18:58:48,265 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.509 | Acc: 47.26% +2025-03-14 18:58:50,534 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.504 | Acc: 47.44% +2025-03-14 18:58:53,718 - train - INFO - Epoch: 31 | Test Loss: 1.490 | Test Acc: 47.97% +2025-03-14 18:58:53,855 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 1.397 | Acc: 52.34% +2025-03-14 18:58:55,938 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.527 | Acc: 46.51% +2025-03-14 18:58:58,089 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.511 | Acc: 47.57% +2025-03-14 18:59:00,263 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.505 | Acc: 47.70% +2025-03-14 18:59:03,515 - train - INFO - Epoch: 32 | Test Loss: 1.502 | Test Acc: 47.83% +2025-03-14 18:59:12,676 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.443 | Acc: 48.44% +2025-03-14 18:59:14,901 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.501 | Acc: 47.44% +2025-03-14 18:59:17,010 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.494 | Acc: 47.66% +2025-03-14 18:59:19,083 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.497 | Acc: 47.59% +2025-03-14 18:59:22,262 - train - INFO - Epoch: 33 | Test Loss: 1.502 | Test Acc: 47.90% +2025-03-14 18:59:22,416 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.555 | Acc: 41.41% +2025-03-14 18:59:24,591 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.505 | Acc: 47.66% +2025-03-14 18:59:26,681 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.504 | Acc: 47.73% +2025-03-14 18:59:28,785 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.500 | Acc: 47.84% +2025-03-14 18:59:31,963 - train - INFO - Epoch: 34 | Test Loss: 1.505 | Test Acc: 46.85% +2025-03-14 18:59:40,980 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.479 | Acc: 46.88% +2025-03-14 18:59:43,177 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.509 | Acc: 47.49% +2025-03-14 18:59:45,274 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.511 | Acc: 47.25% +2025-03-14 18:59:47,396 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.510 | Acc: 47.32% +2025-03-14 18:59:50,653 - train - INFO - Epoch: 35 | Test Loss: 1.401 | Test Acc: 51.25% +2025-03-14 18:59:50,838 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 1.299 | Acc: 59.38% +2025-03-14 18:59:52,839 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.482 | Acc: 48.78% +2025-03-14 18:59:54,989 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.490 | Acc: 48.33% +2025-03-14 18:59:56,980 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.485 | Acc: 48.47% +2025-03-14 19:00:00,116 - train - INFO - Epoch: 36 | Test Loss: 1.489 | Test Acc: 47.70% +2025-03-14 19:00:09,044 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.378 | Acc: 52.34% +2025-03-14 19:00:11,337 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.467 | Acc: 49.08% +2025-03-14 19:00:13,664 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.466 | Acc: 48.83% +2025-03-14 19:00:15,943 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.463 | Acc: 48.93% +2025-03-14 19:00:19,369 - train - INFO - Epoch: 37 | Test Loss: 1.534 | Test Acc: 46.58% +2025-03-14 19:00:19,523 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 1.550 | Acc: 44.53% +2025-03-14 19:00:21,879 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.476 | Acc: 48.53% +2025-03-14 19:00:24,365 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.495 | Acc: 47.90% +2025-03-14 19:00:26,878 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.494 | Acc: 48.06% +2025-03-14 19:00:30,363 - train - INFO - Epoch: 38 | Test Loss: 1.502 | Test Acc: 48.86% +2025-03-14 19:00:40,202 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.594 | Acc: 49.22% +2025-03-14 19:00:42,476 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.472 | Acc: 48.95% +2025-03-14 19:00:44,677 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.470 | Acc: 49.00% +2025-03-14 19:00:46,770 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.457 | Acc: 49.47% +2025-03-14 19:00:50,216 - train - INFO - Epoch: 39 | Test Loss: 1.392 | Test Acc: 50.99% +2025-03-14 19:00:50,372 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.643 | Acc: 42.97% +2025-03-14 19:00:52,597 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.492 | Acc: 48.14% +2025-03-14 19:00:54,733 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.488 | Acc: 48.10% +2025-03-14 19:00:56,903 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.476 | Acc: 48.38% +2025-03-14 19:01:00,186 - train - INFO - Epoch: 40 | Test Loss: 1.396 | Test Acc: 51.11% +2025-03-14 19:01:09,458 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 1.446 | Acc: 49.22% +2025-03-14 19:01:11,568 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.477 | Acc: 49.01% +2025-03-14 19:01:13,524 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.467 | Acc: 49.21% +2025-03-14 19:01:15,623 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.473 | Acc: 49.07% +2025-03-14 19:01:18,753 - train - INFO - Epoch: 41 | Test Loss: 1.369 | Test Acc: 53.32% +2025-03-14 19:01:18,976 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 1.483 | Acc: 46.88% +2025-03-14 19:01:20,939 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.466 | Acc: 48.96% +2025-03-14 19:01:22,879 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.470 | Acc: 48.71% +2025-03-14 19:01:24,891 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.473 | Acc: 48.65% +2025-03-14 19:01:27,749 - train - INFO - Epoch: 42 | Test Loss: 1.397 | Test Acc: 51.39% +2025-03-14 19:01:36,173 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 1.305 | Acc: 52.34% +2025-03-14 19:01:38,245 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.444 | Acc: 49.63% +2025-03-14 19:01:40,295 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.458 | Acc: 48.98% +2025-03-14 19:01:42,370 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.471 | Acc: 48.66% +2025-03-14 19:01:45,581 - train - INFO - Epoch: 43 | Test Loss: 1.378 | Test Acc: 52.89% +2025-03-14 19:01:45,747 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.317 | Acc: 57.81% +2025-03-14 19:01:48,037 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.460 | Acc: 49.73% +2025-03-14 19:01:50,122 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.431 | Acc: 50.60% +2025-03-14 19:01:52,184 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.447 | Acc: 50.02% +2025-03-14 19:01:55,371 - train - INFO - Epoch: 44 | Test Loss: 1.472 | Test Acc: 49.33% +2025-03-14 19:02:04,270 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.547 | Acc: 48.44% +2025-03-14 19:02:06,320 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.469 | Acc: 49.03% +2025-03-14 19:02:08,399 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.467 | Acc: 49.28% +2025-03-14 19:02:10,409 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.461 | Acc: 49.32% +2025-03-14 19:02:13,526 - train - INFO - Epoch: 45 | Test Loss: 1.475 | Test Acc: 50.11% +2025-03-14 19:02:13,687 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.603 | Acc: 50.00% +2025-03-14 19:02:15,735 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.468 | Acc: 49.56% +2025-03-14 19:02:17,836 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.457 | Acc: 49.54% +2025-03-14 19:02:19,934 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.454 | Acc: 49.82% +2025-03-14 19:02:23,205 - train - INFO - Epoch: 46 | Test Loss: 1.467 | Test Acc: 49.27% +2025-03-14 19:02:31,634 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.506 | Acc: 47.66% +2025-03-14 19:02:33,626 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.447 | Acc: 50.19% +2025-03-14 19:02:35,568 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.451 | Acc: 49.84% +2025-03-14 19:02:37,841 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.439 | Acc: 50.25% +2025-03-14 19:02:41,240 - train - INFO - Epoch: 47 | Test Loss: 1.543 | Test Acc: 46.04% +2025-03-14 19:02:41,419 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.628 | Acc: 45.31% +2025-03-14 19:02:43,631 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.464 | Acc: 49.07% +2025-03-14 19:02:45,773 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.454 | Acc: 49.46% +2025-03-14 19:02:47,757 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.468 | Acc: 49.15% +2025-03-14 19:02:50,760 - train - INFO - Epoch: 48 | Test Loss: 1.404 | Test Acc: 50.03% +2025-03-14 19:02:58,934 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 1.270 | Acc: 52.34% +2025-03-14 19:03:00,986 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.457 | Acc: 49.50% +2025-03-14 19:03:03,024 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.458 | Acc: 49.52% +2025-03-14 19:03:05,108 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.460 | Acc: 49.37% +2025-03-14 19:03:08,092 - train - INFO - Epoch: 49 | Test Loss: 1.356 | Test Acc: 53.54% +2025-03-14 19:03:08,239 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 1.282 | Acc: 51.56% +2025-03-14 19:03:10,262 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.460 | Acc: 48.94% +2025-03-14 19:03:12,119 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.465 | Acc: 48.87% +2025-03-14 19:03:14,009 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.448 | Acc: 49.82% +2025-03-14 19:03:16,891 - train - INFO - Epoch: 50 | Test Loss: 1.402 | Test Acc: 51.80% +2025-03-14 19:03:24,969 - train - INFO - 训练完成! diff --git a/Image/LeNet5/code/model.py b/Image/LeNet5/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..5ed820811e24f28f57d68967d20d78969ef1f1f9 --- /dev/null +++ b/Image/LeNet5/code/model.py @@ -0,0 +1,175 @@ +''' +LeNet5 in PyTorch + +LeNet5是由Yann LeCun等人在1998年提出的一个经典卷积神经网络模型。 +主要用于手写数字识别,具有以下特点: +1. 使用卷积层提取特征 +2. 使用平均池化层降低特征维度 +3. 使用全连接层进行分类 +4. 网络结构简单,参数量少 + +网络架构: + 5x5 conv, 6 2x2 pool 5x5 conv, 16 2x2 pool FC 120 FC 84 FC 10 +input(32x32x3) -> [conv1+relu+pool] --------> 28x28x6 -----> 14x14x6 -----> 10x10x16 -----> 5x5x16 -> 120 -> 84 -> 10 + stride 1 stride 2 stride 1 stride 2 + +参考论文: +[1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," + Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ConvBlock(nn.Module): + """卷积块模块 + + 包含: 卷积层 -> ReLU -> 最大池化层 + + Args: + in_channels (int): 输入通道数 + out_channels (int): 输出通道数 + kernel_size (int): 卷积核大小 + stride (int): 卷积步长 + padding (int): 填充大小 + """ + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0): + super(ConvBlock, self).__init__() + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding + ) + self.relu = nn.ReLU(inplace=True) # inplace操作可以节省内存 + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + + def forward(self, x): + """前向传播 + + Args: + x (torch.Tensor): 输入特征图 + + Returns: + torch.Tensor: 输出特征图 + """ + x = self.conv(x) + x = self.relu(x) + x = self.pool(x) + return x + + +class LeNet5(nn.Module): + '''LeNet5网络模型 + + 网络结构: + 1. 卷积层1: 3通道输入,6个5x5卷积核,步长1 + 2. 最大池化层1: 2x2窗口,步长2 + 3. 卷积层2: 6通道输入,16个5x5卷积核,步长1 + 4. 最大池化层2: 2x2窗口,步长2 + 5. 全连接层1: 400->120 + 6. 全连接层2: 120->84 + 7. 全连接层3: 84->num_classes + + Args: + num_classes (int): 分类数量,默认为10 + init_weights (bool): 是否初始化权重,默认为True + ''' + def __init__(self, num_classes=10, init_weights=True): + super(LeNet5, self).__init__() + + # 第一个卷积块: 32x32x3 -> 28x28x6 -> 14x14x6 + self.conv1 = ConvBlock( + in_channels=3, + out_channels=6, + kernel_size=5, + stride=1 + ) + + # 第二个卷积块: 14x14x6 -> 10x10x16 -> 5x5x16 + self.conv2 = ConvBlock( + in_channels=6, + out_channels=16, + kernel_size=5, + stride=1 + ) + + # 全连接层 + self.classifier = nn.Sequential( + nn.Linear(5*5*16, 120), + nn.ReLU(inplace=True), + nn.Linear(120, 84), + nn.ReLU(inplace=True), + nn.Linear(84, num_classes) + ) + + # 初始化权重 + if init_weights: + self._initialize_weights() + + def forward(self, x): + '''前向传播 + + Args: + x (torch.Tensor): 输入图像张量,[N,3,32,32] + + Returns: + torch.Tensor: 输出预测张量,[N,num_classes] + ''' + # 特征提取 + x = self.conv1(x) # -> [N,6,14,14] + x = self.conv2(x) # -> [N,16,5,5] + + # 分类 + x = torch.flatten(x, 1) # -> [N,16*5*5] + x = self.classifier(x) # -> [N,num_classes] + return x + + def _initialize_weights(self): + '''初始化模型权重 + + 采用kaiming初始化方法: + - 卷积层权重采用kaiming_normal_初始化 + - 线性层权重采用normal_初始化 + - 所有偏置项初始化为0 + ''' + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # 采用kaiming初始化,适合ReLU激活函数 + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + # 采用正态分布初始化 + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + +def test(): + """测试函数 + + 创建模型并进行前向传播测试,打印模型结构和参数信息 + """ + # 创建模型 + net = LeNet5() + print('Model Structure:') + print(net) + + # 测试前向传播 + x = torch.randn(2,3,32,32) + y = net(x) + print('\nInput Shape:', x.shape) + print('Output Shape:', y.shape) + + # 打印模型信息 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (2,3,32,32)) + + +if __name__ == '__main__': + test() diff --git a/Image/LeNet5/code/train.log b/Image/LeNet5/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..41bd4d85c0047b23a44ee9e2bce082f918f38073 --- /dev/null +++ b/Image/LeNet5/code/train.log @@ -0,0 +1,253 @@ +2025-03-14 18:42:58,457 - train - INFO - 开始训练 lenet5 +2025-03-14 18:42:58,466 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3 +2025-03-14 18:42:59,293 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.303 | Acc: 10.94% +2025-03-14 18:43:01,471 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.266 | Acc: 12.62% +2025-03-14 18:43:03,531 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.178 | Acc: 15.37% +2025-03-14 18:43:05,648 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 2.099 | Acc: 18.60% +2025-03-14 18:43:08,912 - train - INFO - Epoch: 1 | Test Loss: 1.768 | Test Acc: 33.62% +2025-03-14 18:43:09,291 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.851 | Acc: 26.56% +2025-03-14 18:43:11,591 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.848 | Acc: 30.46% +2025-03-14 18:43:13,743 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.831 | Acc: 31.40% +2025-03-14 18:43:16,315 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.823 | Acc: 31.80% +2025-03-14 18:43:19,766 - train - INFO - Epoch: 2 | Test Loss: 1.662 | Test Acc: 38.37% +2025-03-14 18:43:28,827 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.742 | Acc: 33.59% +2025-03-14 18:43:31,227 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.772 | Acc: 34.92% +2025-03-14 18:43:33,508 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.755 | Acc: 34.99% +2025-03-14 18:43:35,700 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.750 | Acc: 35.12% +2025-03-14 18:43:39,124 - train - INFO - Epoch: 3 | Test Loss: 1.663 | Test Acc: 38.84% +2025-03-14 18:43:39,305 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.855 | Acc: 29.69% +2025-03-14 18:43:41,442 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.715 | Acc: 36.98% +2025-03-14 18:43:43,675 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.721 | Acc: 36.66% +2025-03-14 18:43:45,928 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.704 | Acc: 37.20% +2025-03-14 18:43:49,305 - train - INFO - Epoch: 4 | Test Loss: 1.584 | Test Acc: 41.15% +2025-03-14 18:43:58,749 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.593 | Acc: 39.06% +2025-03-14 18:44:01,045 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.676 | Acc: 39.09% +2025-03-14 18:44:03,286 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.660 | Acc: 39.65% +2025-03-14 18:44:05,565 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.673 | Acc: 39.22% +2025-03-14 18:44:09,108 - train - INFO - Epoch: 5 | Test Loss: 1.637 | Test Acc: 40.55% +2025-03-14 18:44:09,274 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.719 | Acc: 36.72% +2025-03-14 18:44:11,561 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.622 | Acc: 40.80% +2025-03-14 18:44:14,102 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.645 | Acc: 40.35% +2025-03-14 18:44:16,595 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.655 | Acc: 40.03% +2025-03-14 18:44:20,643 - train - INFO - Epoch: 6 | Test Loss: 1.514 | Test Acc: 47.29% +2025-03-14 18:44:30,165 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.487 | Acc: 50.78% +2025-03-14 18:44:32,311 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.638 | Acc: 40.48% +2025-03-14 18:44:34,629 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.641 | Acc: 40.55% +2025-03-14 18:44:36,796 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.642 | Acc: 40.62% +2025-03-14 18:44:40,052 - train - INFO - Epoch: 7 | Test Loss: 1.670 | Test Acc: 41.48% +2025-03-14 18:44:40,222 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.629 | Acc: 38.28% +2025-03-14 18:44:42,337 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.647 | Acc: 40.32% +2025-03-14 18:44:44,590 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.638 | Acc: 40.99% +2025-03-14 18:44:46,617 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.648 | Acc: 40.85% +2025-03-14 18:44:50,042 - train - INFO - Epoch: 8 | Test Loss: 1.610 | Test Acc: 43.43% +2025-03-14 18:44:59,307 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.663 | Acc: 44.53% +2025-03-14 18:45:01,655 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.638 | Acc: 41.36% +2025-03-14 18:45:03,999 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.647 | Acc: 40.96% +2025-03-14 18:45:06,123 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.646 | Acc: 40.82% +2025-03-14 18:45:09,386 - train - INFO - Epoch: 9 | Test Loss: 1.465 | Test Acc: 48.76% +2025-03-14 18:45:09,543 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.650 | Acc: 40.62% +2025-03-14 18:45:11,645 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.581 | Acc: 42.72% +2025-03-14 18:45:13,854 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.589 | Acc: 42.82% +2025-03-14 18:45:16,028 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.592 | Acc: 42.91% +2025-03-14 18:45:19,650 - train - INFO - Epoch: 10 | Test Loss: 1.483 | Test Acc: 48.24% +2025-03-14 18:45:30,113 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.494 | Acc: 42.97% +2025-03-14 18:45:32,744 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.616 | Acc: 42.26% +2025-03-14 18:45:35,132 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.625 | Acc: 42.03% +2025-03-14 18:45:37,374 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.603 | Acc: 42.96% +2025-03-14 18:45:40,850 - train - INFO - Epoch: 11 | Test Loss: 1.505 | Test Acc: 48.63% +2025-03-14 18:45:41,037 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.586 | Acc: 46.09% +2025-03-14 18:45:43,281 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.577 | Acc: 44.79% +2025-03-14 18:45:45,488 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.576 | Acc: 44.34% +2025-03-14 18:45:47,756 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.591 | Acc: 43.89% +2025-03-14 18:45:51,120 - train - INFO - Epoch: 12 | Test Loss: 1.605 | Test Acc: 44.80% +2025-03-14 18:46:00,438 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.518 | Acc: 44.53% +2025-03-14 18:46:02,653 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.599 | Acc: 42.95% +2025-03-14 18:46:05,275 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.593 | Acc: 43.37% +2025-03-14 18:46:07,588 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.598 | Acc: 43.51% +2025-03-14 18:46:10,937 - train - INFO - Epoch: 13 | Test Loss: 1.583 | Test Acc: 42.85% +2025-03-14 18:46:11,116 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.560 | Acc: 44.53% +2025-03-14 18:46:13,285 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.569 | Acc: 44.65% +2025-03-14 18:46:15,533 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.577 | Acc: 43.93% +2025-03-14 18:46:17,803 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.580 | Acc: 43.98% +2025-03-14 18:46:21,356 - train - INFO - Epoch: 14 | Test Loss: 1.633 | Test Acc: 44.51% +2025-03-14 18:46:31,128 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 1.845 | Acc: 38.28% +2025-03-14 18:46:33,986 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.574 | Acc: 44.63% +2025-03-14 18:46:36,643 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.581 | Acc: 44.63% +2025-03-14 18:46:38,812 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.582 | Acc: 44.44% +2025-03-14 18:46:42,100 - train - INFO - Epoch: 15 | Test Loss: 1.502 | Test Acc: 47.49% +2025-03-14 18:46:42,283 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 1.625 | Acc: 40.62% +2025-03-14 18:46:44,700 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.541 | Acc: 45.17% +2025-03-14 18:46:46,924 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.555 | Acc: 44.73% +2025-03-14 18:46:49,212 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.552 | Acc: 45.09% +2025-03-14 18:46:52,741 - train - INFO - Epoch: 16 | Test Loss: 1.515 | Test Acc: 47.01% +2025-03-14 18:47:01,778 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 1.648 | Acc: 42.19% +2025-03-14 18:47:03,963 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.568 | Acc: 45.44% +2025-03-14 18:47:06,143 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.559 | Acc: 45.42% +2025-03-14 18:47:08,345 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.569 | Acc: 45.12% +2025-03-14 18:47:11,733 - train - INFO - Epoch: 17 | Test Loss: 1.570 | Test Acc: 45.34% +2025-03-14 18:47:11,921 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.661 | Acc: 39.06% +2025-03-14 18:47:14,198 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.577 | Acc: 43.73% +2025-03-14 18:47:16,401 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.587 | Acc: 44.04% +2025-03-14 18:47:18,532 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.583 | Acc: 44.23% +2025-03-14 18:47:21,929 - train - INFO - Epoch: 18 | Test Loss: 1.490 | Test Acc: 48.95% +2025-03-14 18:47:31,595 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.469 | Acc: 47.66% +2025-03-14 18:47:34,012 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.572 | Acc: 44.14% +2025-03-14 18:47:36,582 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.564 | Acc: 44.89% +2025-03-14 18:47:39,025 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.580 | Acc: 44.51% +2025-03-14 18:47:43,410 - train - INFO - Epoch: 19 | Test Loss: 1.614 | Test Acc: 41.68% +2025-03-14 18:47:43,603 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.780 | Acc: 35.16% +2025-03-14 18:47:45,962 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.593 | Acc: 43.56% +2025-03-14 18:47:48,244 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.582 | Acc: 44.22% +2025-03-14 18:47:50,397 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.564 | Acc: 44.98% +2025-03-14 18:47:53,620 - train - INFO - Epoch: 20 | Test Loss: 1.466 | Test Acc: 48.39% +2025-03-14 18:48:02,849 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.409 | Acc: 50.00% +2025-03-14 18:48:05,101 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.554 | Acc: 45.34% +2025-03-14 18:48:07,205 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.559 | Acc: 45.21% +2025-03-14 18:48:09,387 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.567 | Acc: 44.95% +2025-03-14 18:48:12,828 - train - INFO - Epoch: 21 | Test Loss: 1.541 | Test Acc: 45.92% +2025-03-14 18:48:12,998 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 1.420 | Acc: 50.00% +2025-03-14 18:48:15,472 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.553 | Acc: 45.12% +2025-03-14 18:48:17,608 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.546 | Acc: 45.46% +2025-03-14 18:48:19,794 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.551 | Acc: 45.30% +2025-03-14 18:48:23,214 - train - INFO - Epoch: 22 | Test Loss: 1.537 | Test Acc: 46.88% +2025-03-14 18:48:32,459 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.594 | Acc: 42.19% +2025-03-14 18:48:34,612 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.580 | Acc: 44.72% +2025-03-14 18:48:36,785 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.560 | Acc: 45.09% +2025-03-14 18:48:38,969 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.561 | Acc: 45.15% +2025-03-14 18:48:42,488 - train - INFO - Epoch: 23 | Test Loss: 1.570 | Test Acc: 45.55% +2025-03-14 18:48:42,659 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.642 | Acc: 44.53% +2025-03-14 18:48:44,937 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.577 | Acc: 44.83% +2025-03-14 18:48:47,587 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.587 | Acc: 44.60% +2025-03-14 18:48:50,078 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.570 | Acc: 45.19% +2025-03-14 18:48:53,664 - train - INFO - Epoch: 24 | Test Loss: 1.460 | Test Acc: 51.03% +2025-03-14 18:49:03,866 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.401 | Acc: 47.66% +2025-03-14 18:49:05,995 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.530 | Acc: 45.76% +2025-03-14 18:49:08,111 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.514 | Acc: 46.54% +2025-03-14 18:49:10,316 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.529 | Acc: 46.19% +2025-03-14 18:49:13,929 - train - INFO - Epoch: 25 | Test Loss: 1.556 | Test Acc: 47.06% +2025-03-14 18:49:14,187 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 1.722 | Acc: 40.62% +2025-03-14 18:49:16,552 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.515 | Acc: 46.69% +2025-03-14 18:49:18,799 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.527 | Acc: 46.61% +2025-03-14 18:49:20,898 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.541 | Acc: 46.01% +2025-03-14 18:49:24,249 - train - INFO - Epoch: 26 | Test Loss: 1.403 | Test Acc: 50.95% +2025-03-14 18:49:33,283 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.368 | Acc: 50.00% +2025-03-14 18:49:35,438 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.541 | Acc: 46.12% +2025-03-14 18:49:37,619 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.546 | Acc: 46.12% +2025-03-14 18:49:39,907 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.558 | Acc: 45.75% +2025-03-14 18:49:43,295 - train - INFO - Epoch: 27 | Test Loss: 1.593 | Test Acc: 44.67% +2025-03-14 18:49:43,465 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.712 | Acc: 39.84% +2025-03-14 18:49:45,800 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.551 | Acc: 46.37% +2025-03-14 18:49:47,998 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.551 | Acc: 46.39% +2025-03-14 18:49:50,134 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.538 | Acc: 46.92% +2025-03-14 18:49:53,378 - train - INFO - Epoch: 28 | Test Loss: 1.490 | Test Acc: 46.88% +2025-03-14 18:50:02,514 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.512 | Acc: 41.41% +2025-03-14 18:50:04,738 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.560 | Acc: 45.56% +2025-03-14 18:50:06,892 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.530 | Acc: 46.73% +2025-03-14 18:50:09,049 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.541 | Acc: 46.39% +2025-03-14 18:50:12,354 - train - INFO - Epoch: 29 | Test Loss: 1.536 | Test Acc: 46.64% +2025-03-14 18:50:12,524 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 1.597 | Acc: 46.09% +2025-03-14 18:50:14,711 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.548 | Acc: 46.50% +2025-03-14 18:50:16,934 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.556 | Acc: 45.93% +2025-03-14 18:50:19,218 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.543 | Acc: 46.32% +2025-03-14 18:50:22,723 - train - INFO - Epoch: 30 | Test Loss: 1.452 | Test Acc: 50.77% +2025-03-14 18:50:32,129 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 1.449 | Acc: 49.22% +2025-03-14 18:50:34,311 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.533 | Acc: 47.13% +2025-03-14 18:50:36,539 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.542 | Acc: 46.49% +2025-03-14 18:50:38,682 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.539 | Acc: 46.58% +2025-03-14 18:50:42,089 - train - INFO - Epoch: 31 | Test Loss: 1.386 | Test Acc: 52.82% +2025-03-14 18:50:42,259 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 1.537 | Acc: 49.22% +2025-03-14 18:50:44,519 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.507 | Acc: 47.93% +2025-03-14 18:50:46,656 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.502 | Acc: 47.95% +2025-03-14 18:50:48,818 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.502 | Acc: 47.57% +2025-03-14 18:50:52,272 - train - INFO - Epoch: 32 | Test Loss: 1.482 | Test Acc: 48.45% +2025-03-14 18:51:01,650 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.600 | Acc: 42.97% +2025-03-14 18:51:03,937 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.516 | Acc: 46.92% +2025-03-14 18:51:06,268 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.528 | Acc: 46.76% +2025-03-14 18:51:08,704 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.534 | Acc: 46.62% +2025-03-14 18:51:12,262 - train - INFO - Epoch: 33 | Test Loss: 1.430 | Test Acc: 49.99% +2025-03-14 18:51:12,443 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.585 | Acc: 45.31% +2025-03-14 18:51:14,733 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.501 | Acc: 47.97% +2025-03-14 18:51:17,010 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.512 | Acc: 47.52% +2025-03-14 18:51:19,395 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.514 | Acc: 47.37% +2025-03-14 18:51:23,243 - train - INFO - Epoch: 34 | Test Loss: 1.407 | Test Acc: 52.49% +2025-03-14 18:51:32,787 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.449 | Acc: 53.12% +2025-03-14 18:51:35,089 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.533 | Acc: 46.77% +2025-03-14 18:51:37,330 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.508 | Acc: 47.78% +2025-03-14 18:51:39,569 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.505 | Acc: 47.97% +2025-03-14 18:51:43,190 - train - INFO - Epoch: 35 | Test Loss: 1.518 | Test Acc: 48.63% +2025-03-14 18:51:43,386 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 1.662 | Acc: 41.41% +2025-03-14 18:51:45,737 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.537 | Acc: 46.06% +2025-03-14 18:51:47,974 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.524 | Acc: 46.58% +2025-03-14 18:51:50,202 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.526 | Acc: 46.55% +2025-03-14 18:51:53,752 - train - INFO - Epoch: 36 | Test Loss: 1.361 | Test Acc: 53.00% +2025-03-14 18:52:03,416 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.415 | Acc: 48.44% +2025-03-14 18:52:05,593 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.575 | Acc: 44.76% +2025-03-14 18:52:07,827 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.538 | Acc: 45.99% +2025-03-14 18:52:09,999 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.526 | Acc: 46.51% +2025-03-14 18:52:13,370 - train - INFO - Epoch: 37 | Test Loss: 1.458 | Test Acc: 49.32% +2025-03-14 18:52:13,541 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 1.553 | Acc: 49.22% +2025-03-14 18:52:15,740 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.476 | Acc: 49.10% +2025-03-14 18:52:17,984 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.475 | Acc: 48.56% +2025-03-14 18:52:20,396 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.509 | Acc: 47.59% +2025-03-14 18:52:23,784 - train - INFO - Epoch: 38 | Test Loss: 1.395 | Test Acc: 51.78% +2025-03-14 18:52:32,947 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.398 | Acc: 51.56% +2025-03-14 18:52:35,194 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.582 | Acc: 44.60% +2025-03-14 18:52:37,297 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.554 | Acc: 45.54% +2025-03-14 18:52:39,638 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.534 | Acc: 46.39% +2025-03-14 18:52:43,108 - train - INFO - Epoch: 39 | Test Loss: 1.525 | Test Acc: 45.86% +2025-03-14 18:52:43,297 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.696 | Acc: 32.81% +2025-03-14 18:52:45,599 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.522 | Acc: 47.46% +2025-03-14 18:52:47,940 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.516 | Acc: 47.49% +2025-03-14 18:52:50,269 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.508 | Acc: 47.60% +2025-03-14 18:52:53,724 - train - INFO - Epoch: 40 | Test Loss: 1.450 | Test Acc: 51.11% +2025-03-14 18:53:03,410 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 1.537 | Acc: 47.66% +2025-03-14 18:53:05,800 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.507 | Acc: 47.56% +2025-03-14 18:53:08,177 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.514 | Acc: 47.43% +2025-03-14 18:53:10,666 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.510 | Acc: 47.77% +2025-03-14 18:53:14,572 - train - INFO - Epoch: 41 | Test Loss: 1.517 | Test Acc: 48.48% +2025-03-14 18:53:14,742 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 1.796 | Acc: 42.19% +2025-03-14 18:53:16,888 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.473 | Acc: 48.87% +2025-03-14 18:53:18,973 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.491 | Acc: 48.63% +2025-03-14 18:53:21,259 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.505 | Acc: 48.21% +2025-03-14 18:53:24,601 - train - INFO - Epoch: 42 | Test Loss: 1.561 | Test Acc: 46.57% +2025-03-14 18:53:33,569 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 1.608 | Acc: 44.53% +2025-03-14 18:53:35,694 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.491 | Acc: 48.58% +2025-03-14 18:53:37,928 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.491 | Acc: 48.62% +2025-03-14 18:53:40,500 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.486 | Acc: 48.66% +2025-03-14 18:53:43,816 - train - INFO - Epoch: 43 | Test Loss: 1.374 | Test Acc: 52.62% +2025-03-14 18:53:43,991 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.489 | Acc: 49.22% +2025-03-14 18:53:46,176 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.486 | Acc: 48.23% +2025-03-14 18:53:48,303 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.498 | Acc: 47.92% +2025-03-14 18:53:50,370 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.487 | Acc: 48.34% +2025-03-14 18:53:53,740 - train - INFO - Epoch: 44 | Test Loss: 1.373 | Test Acc: 52.73% +2025-03-14 18:54:03,408 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.566 | Acc: 47.66% +2025-03-14 18:54:05,572 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.531 | Acc: 47.00% +2025-03-14 18:54:07,745 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.512 | Acc: 47.59% +2025-03-14 18:54:09,919 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.500 | Acc: 48.02% +2025-03-14 18:54:13,351 - train - INFO - Epoch: 45 | Test Loss: 1.426 | Test Acc: 51.30% +2025-03-14 18:54:13,612 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.334 | Acc: 58.59% +2025-03-14 18:54:15,863 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.496 | Acc: 48.13% +2025-03-14 18:54:18,032 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.489 | Acc: 48.47% +2025-03-14 18:54:20,219 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.495 | Acc: 48.29% +2025-03-14 18:54:23,452 - train - INFO - Epoch: 46 | Test Loss: 1.528 | Test Acc: 48.13% +2025-03-14 18:54:32,795 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.657 | Acc: 38.28% +2025-03-14 18:54:35,033 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.543 | Acc: 46.42% +2025-03-14 18:54:37,369 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.516 | Acc: 47.45% +2025-03-14 18:54:39,815 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.499 | Acc: 48.16% +2025-03-14 18:54:43,735 - train - INFO - Epoch: 47 | Test Loss: 1.527 | Test Acc: 49.94% +2025-03-14 18:54:43,988 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.510 | Acc: 44.53% +2025-03-14 18:54:46,176 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.503 | Acc: 48.13% +2025-03-14 18:54:48,509 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.501 | Acc: 48.09% +2025-03-14 18:54:50,812 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.490 | Acc: 48.40% +2025-03-14 18:54:54,075 - train - INFO - Epoch: 48 | Test Loss: 1.414 | Test Acc: 51.34% +2025-03-14 18:55:02,937 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 1.570 | Acc: 38.28% +2025-03-14 18:55:05,141 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.473 | Acc: 49.60% +2025-03-14 18:55:07,346 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.474 | Acc: 49.48% +2025-03-14 18:55:09,548 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.478 | Acc: 49.18% +2025-03-14 18:55:12,872 - train - INFO - Epoch: 49 | Test Loss: 1.352 | Test Acc: 53.22% +2025-03-14 18:55:13,039 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 1.388 | Acc: 51.56% +2025-03-14 18:55:15,328 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.490 | Acc: 48.34% +2025-03-14 18:55:17,410 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.484 | Acc: 48.32% +2025-03-14 18:55:19,595 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.474 | Acc: 49.08% +2025-03-14 18:55:22,981 - train - INFO - Epoch: 50 | Test Loss: 1.513 | Test Acc: 46.94% +2025-03-14 18:55:32,176 - train - INFO - 训练完成! diff --git a/Image/LeNet5/code/train.py b/Image/LeNet5/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..e365e829731a18716f8b1f6cb5c704747eeea1f9 --- /dev/null +++ b/Image/LeNet5/code/train.py @@ -0,0 +1,63 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import LeNet5 + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = LeNet5() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='lenet5', + save_type='0', + layer_name='conv2', + interval = 2 + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='lenet5', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='lenet5', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path, + layer_name='conv2', + interval = 2 + ) + +if __name__ == '__main__': + main() diff --git a/Image/LeNet5/dataset/.gitkeep b/Image/LeNet5/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/LeNet5/model/.gitkeep b/Image/LeNet5/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/LeNet5/model/0/epoch1/embeddings.npy b/Image/LeNet5/model/0/epoch1/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..653998a1e7125d56c6b76a057d431a1c0abab019 --- /dev/null +++ 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| Acc: 73.68% +2025-03-14 19:12:25,349 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.782 | Acc: 74.05% +2025-03-14 19:12:27,898 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.778 | Acc: 74.33% +2025-03-14 19:12:31,642 - train - INFO - Epoch: 9 | Test Loss: 0.997 | Test Acc: 67.90% +2025-03-14 19:12:31,831 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.761 | Acc: 72.66% +2025-03-14 19:12:34,141 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.760 | Acc: 74.59% +2025-03-14 19:12:36,393 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.768 | Acc: 74.46% +2025-03-14 19:12:38,757 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.759 | Acc: 74.91% +2025-03-14 19:12:42,378 - train - INFO - Epoch: 10 | Test Loss: 0.829 | Test Acc: 71.79% +2025-03-14 19:12:54,320 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.830 | Acc: 74.22% +2025-03-14 19:12:56,767 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.755 | Acc: 74.98% +2025-03-14 19:12:59,227 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.750 | Acc: 75.11% +2025-03-14 19:13:01,494 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.747 | Acc: 75.20% +2025-03-14 19:13:05,433 - train - INFO - Epoch: 11 | Test Loss: 0.930 | Test Acc: 68.93% +2025-03-14 19:13:05,678 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.874 | Acc: 71.09% +2025-03-14 19:13:08,001 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.735 | Acc: 75.70% +2025-03-14 19:13:10,287 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.739 | Acc: 75.56% +2025-03-14 19:13:12,549 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.740 | Acc: 75.46% +2025-03-14 19:13:16,175 - train - INFO - Epoch: 12 | Test Loss: 0.810 | Test Acc: 73.19% +2025-03-14 19:13:31,855 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.576 | Acc: 78.12% +2025-03-14 19:13:34,421 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.723 | Acc: 76.30% +2025-03-14 19:13:37,400 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.739 | Acc: 75.64% +2025-03-14 19:13:40,123 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.747 | Acc: 75.36% +2025-03-14 19:13:44,947 - train - INFO - Epoch: 13 | Test Loss: 0.765 | Test Acc: 74.70% +2025-03-14 19:13:45,257 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.848 | Acc: 71.88% +2025-03-14 19:13:47,794 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.730 | Acc: 75.41% +2025-03-14 19:13:50,315 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.742 | Acc: 74.98% +2025-03-14 19:13:53,154 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.736 | Acc: 75.30% +2025-03-14 19:13:57,281 - train - INFO - Epoch: 14 | Test Loss: 0.931 | Test Acc: 69.03% +2025-03-14 19:14:11,225 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.787 | Acc: 71.88% +2025-03-14 19:14:13,683 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.730 | Acc: 76.38% +2025-03-14 19:14:16,179 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.734 | Acc: 75.68% +2025-03-14 19:14:18,546 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.733 | Acc: 75.61% +2025-03-14 19:14:22,390 - train - INFO - Epoch: 15 | Test Loss: 0.856 | Test Acc: 71.84% +2025-03-14 19:14:22,639 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.737 | Acc: 76.56% +2025-03-14 19:14:25,041 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.735 | Acc: 75.31% +2025-03-14 19:14:27,479 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.726 | Acc: 75.86% +2025-03-14 19:14:29,797 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.721 | Acc: 76.03% +2025-03-14 19:14:33,561 - train - INFO - Epoch: 16 | Test Loss: 0.819 | Test Acc: 71.39% +2025-03-14 19:14:46,423 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.614 | Acc: 75.78% +2025-03-14 19:14:48,596 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.709 | Acc: 76.53% +2025-03-14 19:14:50,945 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.715 | Acc: 76.33% +2025-03-14 19:14:53,181 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.717 | Acc: 76.32% +2025-03-14 19:14:56,940 - train - INFO - Epoch: 17 | Test Loss: 0.954 | Test Acc: 68.53% +2025-03-14 19:14:57,178 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.913 | Acc: 68.75% +2025-03-14 19:14:59,356 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.721 | Acc: 76.18% +2025-03-14 19:15:01,720 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.720 | Acc: 76.11% +2025-03-14 19:15:04,039 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.715 | Acc: 76.38% +2025-03-14 19:15:07,794 - train - INFO - Epoch: 18 | Test Loss: 0.789 | Test Acc: 74.30% +2025-03-14 19:15:22,986 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.667 | Acc: 78.91% +2025-03-14 19:15:25,226 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.687 | Acc: 77.36% +2025-03-14 19:15:27,499 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.704 | Acc: 76.83% +2025-03-14 19:15:29,768 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.704 | Acc: 76.66% +2025-03-14 19:15:33,389 - train - INFO - Epoch: 19 | Test Loss: 0.824 | Test Acc: 72.96% +2025-03-14 19:15:33,636 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.968 | Acc: 71.09% +2025-03-14 19:15:35,904 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.699 | Acc: 77.06% +2025-03-14 19:15:38,257 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.688 | Acc: 77.30% +2025-03-14 19:15:40,549 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.696 | Acc: 76.91% +2025-03-14 19:15:44,675 - train - INFO - Epoch: 20 | Test Loss: 0.836 | Test Acc: 71.67% +2025-03-14 19:15:57,342 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.696 | Acc: 76.56% +2025-03-14 19:15:59,612 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.693 | Acc: 76.96% +2025-03-14 19:16:01,819 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.703 | Acc: 76.73% +2025-03-14 19:16:03,948 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.703 | Acc: 76.70% +2025-03-14 19:16:07,620 - train - INFO - Epoch: 21 | Test Loss: 1.056 | Test Acc: 65.78% +2025-03-14 19:16:07,894 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.583 | Acc: 82.81% +2025-03-14 19:16:10,453 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.667 | Acc: 77.94% +2025-03-14 19:16:13,181 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.679 | Acc: 77.41% +2025-03-14 19:16:15,606 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.687 | Acc: 77.18% +2025-03-14 19:16:19,360 - train - INFO - Epoch: 22 | Test Loss: 0.966 | Test Acc: 68.80% +2025-03-14 19:16:35,332 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.798 | Acc: 75.78% +2025-03-14 19:16:38,153 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.688 | Acc: 76.77% +2025-03-14 19:16:41,959 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.687 | Acc: 76.87% +2025-03-14 19:16:44,470 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.701 | Acc: 76.52% +2025-03-14 19:16:48,354 - train - INFO - Epoch: 23 | Test Loss: 0.811 | Test Acc: 72.64% +2025-03-14 19:16:48,619 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.537 | Acc: 81.25% +2025-03-14 19:16:50,852 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.677 | Acc: 77.72% +2025-03-14 19:16:53,012 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.688 | Acc: 77.31% +2025-03-14 19:16:55,183 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.694 | Acc: 77.04% +2025-03-14 19:16:58,919 - train - INFO - Epoch: 24 | Test Loss: 1.168 | Test Acc: 62.89% +2025-03-14 19:17:12,298 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.635 | Acc: 78.91% +2025-03-14 19:17:14,676 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.699 | Acc: 76.91% +2025-03-14 19:17:17,038 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.704 | Acc: 76.61% +2025-03-14 19:17:19,291 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.701 | Acc: 76.74% +2025-03-14 19:17:22,782 - train - INFO - Epoch: 25 | Test Loss: 0.900 | Test Acc: 70.88% +2025-03-14 19:17:23,024 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.771 | Acc: 78.91% +2025-03-14 19:17:25,266 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.689 | Acc: 77.17% +2025-03-14 19:17:27,505 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.686 | Acc: 77.43% +2025-03-14 19:17:29,738 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.681 | Acc: 77.55% +2025-03-14 19:17:33,357 - train - INFO - Epoch: 26 | Test Loss: 0.887 | Test Acc: 69.41% +2025-03-14 19:17:47,729 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.935 | Acc: 67.97% +2025-03-14 19:17:50,012 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.695 | Acc: 77.04% +2025-03-14 19:17:52,314 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.691 | Acc: 77.09% +2025-03-14 19:17:54,513 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.687 | Acc: 77.22% +2025-03-14 19:17:58,204 - train - INFO - Epoch: 27 | Test Loss: 0.839 | Test Acc: 71.77% +2025-03-14 19:17:58,457 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.611 | Acc: 77.34% +2025-03-14 19:18:01,634 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.684 | Acc: 77.61% +2025-03-14 19:18:04,239 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.687 | Acc: 77.29% +2025-03-14 19:18:06,548 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.683 | Acc: 77.63% +2025-03-14 19:18:10,199 - train - INFO - Epoch: 28 | Test Loss: 0.878 | Test Acc: 72.20% +2025-03-14 19:18:22,831 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.727 | Acc: 75.78% +2025-03-14 19:18:25,111 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.653 | Acc: 78.39% +2025-03-14 19:18:27,317 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.670 | Acc: 77.74% +2025-03-14 19:18:29,531 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.674 | Acc: 77.64% +2025-03-14 19:18:32,945 - train - INFO - Epoch: 29 | Test Loss: 0.713 | Test Acc: 75.58% +2025-03-14 19:18:33,192 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.707 | Acc: 75.78% +2025-03-14 19:18:35,432 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.668 | Acc: 77.89% +2025-03-14 19:18:37,646 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.676 | Acc: 77.69% +2025-03-14 19:18:39,890 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.678 | Acc: 77.52% +2025-03-14 19:18:43,361 - train - INFO - Epoch: 30 | Test Loss: 0.910 | Test Acc: 70.57% +2025-03-14 19:18:56,375 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.672 | Acc: 75.00% +2025-03-14 19:18:58,633 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.678 | Acc: 77.97% +2025-03-14 19:19:00,903 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.674 | Acc: 77.78% +2025-03-14 19:19:03,229 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.676 | Acc: 77.57% +2025-03-14 19:19:06,827 - train - INFO - Epoch: 31 | Test Loss: 0.711 | Test Acc: 76.07% +2025-03-14 19:19:07,048 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.726 | Acc: 75.78% +2025-03-14 19:19:09,285 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.674 | Acc: 77.79% +2025-03-14 19:19:11,605 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.674 | Acc: 77.73% +2025-03-14 19:19:14,191 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.674 | Acc: 77.61% +2025-03-14 19:19:17,846 - train - INFO - Epoch: 32 | Test Loss: 0.804 | Test Acc: 73.06% +2025-03-14 19:19:32,515 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.587 | Acc: 81.25% +2025-03-14 19:19:34,852 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.667 | Acc: 78.10% +2025-03-14 19:19:37,018 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.678 | Acc: 77.65% +2025-03-14 19:19:39,265 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.672 | Acc: 77.90% +2025-03-14 19:19:43,032 - train - INFO - Epoch: 33 | Test Loss: 0.826 | Test Acc: 72.40% +2025-03-14 19:19:43,270 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.642 | Acc: 77.34% +2025-03-14 19:19:45,527 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.669 | Acc: 78.19% +2025-03-14 19:19:47,866 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.678 | Acc: 77.76% +2025-03-14 19:19:50,287 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.670 | Acc: 77.87% +2025-03-14 19:19:53,948 - train - INFO - Epoch: 34 | Test Loss: 0.979 | Test Acc: 68.46% +2025-03-14 19:20:06,941 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.587 | Acc: 82.03% +2025-03-14 19:20:09,207 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.667 | Acc: 78.11% +2025-03-14 19:20:11,427 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.680 | Acc: 77.64% +2025-03-14 19:20:13,889 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.675 | Acc: 77.67% +2025-03-14 19:20:17,710 - train - INFO - Epoch: 35 | Test Loss: 0.681 | Test Acc: 77.46% +2025-03-14 19:20:17,961 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.571 | Acc: 83.59% +2025-03-14 19:20:20,297 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.670 | Acc: 77.85% +2025-03-14 19:20:22,840 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.671 | Acc: 77.67% +2025-03-14 19:20:25,126 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.681 | Acc: 77.38% +2025-03-14 19:20:28,970 - train - INFO - Epoch: 36 | Test Loss: 0.678 | Test Acc: 77.77% +2025-03-14 19:20:43,295 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.684 | Acc: 81.25% +2025-03-14 19:20:45,944 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.665 | Acc: 77.61% +2025-03-14 19:20:48,513 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.666 | Acc: 77.82% +2025-03-14 19:20:51,045 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.669 | Acc: 77.77% +2025-03-14 19:20:55,419 - train - INFO - Epoch: 37 | Test Loss: 0.651 | Test Acc: 77.25% +2025-03-14 19:20:55,696 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.694 | Acc: 75.78% +2025-03-14 19:20:58,163 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.668 | Acc: 78.10% +2025-03-14 19:21:00,532 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.674 | Acc: 77.93% +2025-03-14 19:21:02,807 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.670 | Acc: 77.89% +2025-03-14 19:21:06,493 - train - INFO - Epoch: 38 | Test Loss: 0.765 | Test Acc: 74.16% +2025-03-14 19:21:20,685 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.699 | Acc: 82.03% +2025-03-14 19:21:23,093 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.645 | Acc: 78.88% +2025-03-14 19:21:25,393 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.646 | Acc: 78.84% +2025-03-14 19:21:27,649 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.654 | Acc: 78.62% +2025-03-14 19:21:31,887 - train - INFO - Epoch: 39 | Test Loss: 0.927 | Test Acc: 70.27% +2025-03-14 19:21:32,129 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.591 | Acc: 79.69% +2025-03-14 19:21:34,380 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.673 | Acc: 77.82% +2025-03-14 19:21:36,784 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.673 | Acc: 77.90% +2025-03-14 19:21:39,293 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.671 | Acc: 77.82% +2025-03-14 19:21:43,148 - train - INFO - Epoch: 40 | Test Loss: 0.775 | Test Acc: 73.66% +2025-03-14 19:21:55,919 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.718 | Acc: 74.22% +2025-03-14 19:21:58,347 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.662 | Acc: 78.08% +2025-03-14 19:22:00,670 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.675 | Acc: 77.63% +2025-03-14 19:22:03,094 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.661 | Acc: 78.06% +2025-03-14 19:22:06,604 - train - INFO - Epoch: 41 | Test Loss: 0.766 | Test Acc: 74.54% +2025-03-14 19:22:06,872 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.607 | Acc: 80.47% +2025-03-14 19:22:09,147 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.685 | Acc: 77.13% +2025-03-14 19:22:11,404 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.681 | Acc: 77.10% +2025-03-14 19:22:13,667 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.670 | Acc: 77.46% +2025-03-14 19:22:17,327 - train - INFO - Epoch: 42 | Test Loss: 0.736 | Test Acc: 74.75% +2025-03-14 19:22:28,916 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.611 | Acc: 81.25% +2025-03-14 19:22:31,177 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.655 | Acc: 78.44% +2025-03-14 19:22:33,409 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.670 | Acc: 77.85% +2025-03-14 19:22:35,741 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.669 | Acc: 77.98% +2025-03-14 19:22:39,524 - train - INFO - Epoch: 43 | Test Loss: 1.170 | Test Acc: 64.85% +2025-03-14 19:22:39,772 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.706 | Acc: 74.22% +2025-03-14 19:22:42,140 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.664 | Acc: 78.23% +2025-03-14 19:22:44,572 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.657 | Acc: 78.47% +2025-03-14 19:22:46,949 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.656 | Acc: 78.45% +2025-03-14 19:22:50,567 - train - INFO - Epoch: 44 | Test Loss: 1.059 | Test Acc: 64.82% +2025-03-14 19:23:06,353 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.930 | Acc: 68.75% +2025-03-14 19:23:09,089 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.655 | Acc: 78.57% +2025-03-14 19:23:11,689 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.655 | Acc: 78.47% +2025-03-14 19:23:14,039 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.656 | Acc: 78.41% +2025-03-14 19:23:17,636 - train - INFO - Epoch: 45 | Test Loss: 0.684 | Test Acc: 76.39% +2025-03-14 19:23:17,964 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.779 | Acc: 76.56% +2025-03-14 19:23:20,239 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.654 | Acc: 78.95% +2025-03-14 19:23:22,457 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.660 | Acc: 78.28% +2025-03-14 19:23:24,820 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.661 | Acc: 78.31% +2025-03-14 19:23:28,367 - train - INFO - Epoch: 46 | Test Loss: 0.869 | Test Acc: 71.58% +2025-03-14 19:23:45,584 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.885 | Acc: 75.00% +2025-03-14 19:23:47,800 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.659 | Acc: 78.40% +2025-03-14 19:23:50,426 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.657 | Acc: 78.29% +2025-03-14 19:23:52,593 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.657 | Acc: 78.39% +2025-03-14 19:23:56,224 - train - INFO - Epoch: 47 | Test Loss: 0.772 | Test Acc: 74.50% +2025-03-14 19:23:56,449 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.706 | Acc: 79.69% +2025-03-14 19:23:58,661 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.649 | Acc: 78.47% +2025-03-14 19:24:00,936 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.659 | Acc: 78.07% +2025-03-14 19:24:03,223 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.658 | Acc: 78.10% +2025-03-14 19:24:06,966 - train - INFO - Epoch: 48 | Test Loss: 1.209 | Test Acc: 61.32% +2025-03-14 19:24:22,516 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.631 | Acc: 79.69% +2025-03-14 19:24:24,748 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.648 | Acc: 78.53% +2025-03-14 19:24:27,130 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.646 | Acc: 78.70% +2025-03-14 19:24:29,448 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.647 | Acc: 78.50% +2025-03-14 19:24:33,064 - train - INFO - Epoch: 49 | Test Loss: 0.745 | Test Acc: 75.76% +2025-03-14 19:24:33,358 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.614 | Acc: 82.03% +2025-03-14 19:24:35,647 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.643 | Acc: 78.58% +2025-03-14 19:24:37,770 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.652 | Acc: 78.14% +2025-03-14 19:24:40,021 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.655 | Acc: 78.17% +2025-03-14 19:24:43,596 - train - INFO - Epoch: 50 | Test Loss: 0.763 | Test Acc: 74.79% +2025-03-14 19:24:54,655 - train - INFO - 训练完成! diff --git a/Image/MobileNetv1/code/model.py b/Image/MobileNetv1/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..215a894aec5cea342787d4f55ef64bc87ea9b0c6 --- /dev/null +++ b/Image/MobileNetv1/code/model.py @@ -0,0 +1,163 @@ +''' +MobileNetv1 in PyTorch. + +论文: "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" +参考: https://arxiv.org/abs/1704.04861 + +主要特点： +1. 使用深度可分离卷积(Depthwise Separable Convolution)减少参数量和计算量 +2. 引入宽度乘子(Width Multiplier)和分辨率乘子(Resolution Multiplier)进一步压缩模型 +3. 适用于移动设备和嵌入式设备的轻量级CNN架构 +''' + +import torch +import torch.nn as nn + + +class Block(nn.Module): + '''深度可分离卷积块 (Depthwise Separable Convolution Block) + + 包含: + 1. 深度卷积(Depthwise Conv): 对每个通道单独进行空间卷积 + 2. 逐点卷积(Pointwise Conv): 1x1卷积实现通道混合 + + Args: + in_channels: 输入通道数 + out_channels: 输出通道数 + stride: 卷积步长 + ''' + def __init__(self, in_channels, out_channels, stride=1): + super(Block, self).__init__() + + # 深度卷积 - 每个通道单独进行3x3卷积 + self.conv1 = nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=3, + stride=stride, + padding=1, + groups=in_channels, # groups=in_channels 即为深度可分离卷积 + bias=False + ) + self.bn1 = nn.BatchNorm2d(in_channels) + self.relu1 = nn.ReLU(inplace=True) + + # 逐点卷积 - 1x1卷积用于通道混合 + self.conv2 = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=False + ) + self.bn2 = nn.BatchNorm2d(out_channels) + self.relu2 = nn.ReLU(inplace=True) + + def forward(self, x): + # 深度卷积 + x = self.conv1(x) + x = self.bn1(x) + x = self.relu1(x) + + # 逐点卷积 + x = self.conv2(x) + x = self.bn2(x) + x = self.relu2(x) + return x + + +class MobileNet(nn.Module): + '''MobileNet v1网络 + + Args: + num_classes: 分类数量 + alpha: 宽度乘子，用于控制网络宽度(默认1.0) + beta: 分辨率乘子，用于控制输入分辨率(默认1.0) + init_weights: 是否初始化权重 + ''' + # 网络配置: (输出通道数, 步长)，步长默认为1 + cfg = [64, (128,2), 128, (256,2), 256, (512,2), + 512, 512, 512, 512, 512, (1024,2), 1024] + + def __init__(self, num_classes=10, alpha=1.0, beta=1.0, init_weights=True): + super(MobileNet, self).__init__() + + # 第一层标准卷积 + self.conv1 = nn.Sequential( + nn.Conv2d(3, 32, kernel_size=3, stride=1, bias=False), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True) + ) + + # 深度可分离卷积层 + self.layers = self._make_layers(in_channels=32) + + # 全局平均池化和分类器 + self.avg = nn.AdaptiveAvgPool2d(1) # 自适应平均池化，输出大小为1x1 + self.linear = nn.Linear(1024, num_classes) + + # 初始化权重 + if init_weights: + self._initialize_weights() + + def _make_layers(self, in_channels): + '''构建深度可分离卷积层 + + Args: + in_channels: 输入通道数 + ''' + layers = [] + for x in self.cfg: + out_channels = x if isinstance(x, int) else x[0] + stride = 1 if isinstance(x, int) else x[1] + layers.append(Block(in_channels, out_channels, stride)) + in_channels = out_channels + return nn.Sequential(*layers) + + def forward(self, x): + # 标准卷积 + x = self.conv1(x) + + # 深度可分离卷积层 + x = self.layers(x) + + # 全局平均池化和分类器 + x = self.avg(x) + x = x.view(x.size(0), -1) + x = self.linear(x) + return x + + def _initialize_weights(self): + '''初始化模型权重''' + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # 使用kaiming初始化卷积层 + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + # 初始化BN层 + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + # 初始化全连接层 + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + +def test(): + """测试函数""" + net = MobileNet() + x = torch.randn(2, 3, 32, 32) + y = net(x) + print(y.size()) + + # 打印模型结构 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (2, 3, 32, 32)) + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/MobileNetv1/code/train.log b/Image/MobileNetv1/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..5946a89fa5d75bfc8a4375654182b955b9f7c4a5 --- /dev/null +++ b/Image/MobileNetv1/code/train.log @@ -0,0 +1,253 @@ +2025-03-14 19:09:52,411 - train - INFO - 开始训练 mobilenetv1 +2025-03-14 19:09:52,412 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3 +2025-03-14 19:09:53,266 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.315 | Acc: 9.38% +2025-03-14 19:09:55,666 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.226 | Acc: 22.79% +2025-03-14 19:09:58,252 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.966 | Acc: 29.56% +2025-03-14 19:10:00,487 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.834 | Acc: 33.79% +2025-03-14 19:10:04,050 - train - INFO - Epoch: 1 | Test Loss: 1.396 | Test Acc: 49.59% +2025-03-14 19:10:04,535 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.433 | Acc: 46.09% +2025-03-14 19:10:06,928 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.386 | Acc: 50.38% +2025-03-14 19:10:09,156 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.349 | Acc: 51.67% +2025-03-14 19:10:11,286 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.318 | Acc: 52.81% +2025-03-14 19:10:14,730 - train - INFO - Epoch: 2 | Test Loss: 1.191 | Test Acc: 57.46% +2025-03-14 19:10:26,131 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.127 | Acc: 59.38% +2025-03-14 19:10:28,403 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.119 | Acc: 60.50% +2025-03-14 19:10:30,681 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.100 | Acc: 61.01% +2025-03-14 19:10:32,967 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.080 | Acc: 61.67% +2025-03-14 19:10:36,884 - train - INFO - Epoch: 3 | Test Loss: 1.027 | Test Acc: 64.31% +2025-03-14 19:10:37,134 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.911 | Acc: 65.62% +2025-03-14 19:10:39,401 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.922 | Acc: 67.82% +2025-03-14 19:10:41,845 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.921 | Acc: 68.05% +2025-03-14 19:10:44,179 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.911 | Acc: 68.51% +2025-03-14 19:10:47,847 - train - INFO - Epoch: 4 | Test Loss: 0.893 | Test Acc: 69.71% +2025-03-14 19:11:00,025 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.853 | Acc: 71.09% +2025-03-14 19:11:02,275 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.833 | Acc: 71.18% +2025-03-14 19:11:04,597 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.815 | Acc: 71.77% +2025-03-14 19:11:06,880 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.815 | Acc: 71.98% +2025-03-14 19:11:10,432 - train - INFO - Epoch: 5 | Test Loss: 0.900 | Test Acc: 70.22% +2025-03-14 19:11:10,648 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.720 | Acc: 76.56% +2025-03-14 19:11:12,878 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.763 | Acc: 73.74% +2025-03-14 19:11:15,152 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.757 | Acc: 74.00% +2025-03-14 19:11:17,487 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.755 | Acc: 74.00% +2025-03-14 19:11:21,101 - train - INFO - Epoch: 6 | Test Loss: 0.776 | Test Acc: 74.17% +2025-03-14 19:11:33,437 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.654 | Acc: 75.78% +2025-03-14 19:11:36,120 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.734 | Acc: 74.95% +2025-03-14 19:11:38,748 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.737 | Acc: 74.85% +2025-03-14 19:11:41,483 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.731 | Acc: 74.99% +2025-03-14 19:11:45,749 - train - INFO - Epoch: 7 | Test Loss: 0.809 | Test Acc: 72.85% +2025-03-14 19:11:45,976 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.592 | Acc: 75.78% +2025-03-14 19:11:48,320 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.693 | Acc: 76.28% +2025-03-14 19:11:50,851 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.702 | Acc: 75.98% +2025-03-14 19:11:53,161 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.702 | Acc: 75.96% +2025-03-14 19:11:56,956 - train - INFO - Epoch: 8 | Test Loss: 1.054 | Test Acc: 64.26% +2025-03-14 19:12:08,750 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.632 | Acc: 80.47% +2025-03-14 19:12:11,380 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.683 | Acc: 77.00% +2025-03-14 19:12:13,700 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.697 | Acc: 76.47% +2025-03-14 19:12:15,791 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.694 | Acc: 76.50% +2025-03-14 19:12:18,944 - train - INFO - Epoch: 9 | Test Loss: 1.166 | Test Acc: 63.32% +2025-03-14 19:12:19,160 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.620 | Acc: 78.12% +2025-03-14 19:12:21,209 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.683 | Acc: 76.84% +2025-03-14 19:12:23,727 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.674 | Acc: 77.05% +2025-03-14 19:12:26,185 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.684 | Acc: 76.82% +2025-03-14 19:12:30,064 - train - INFO - Epoch: 10 | Test Loss: 0.677 | Test Acc: 76.82% +2025-03-14 19:12:42,009 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.804 | Acc: 72.66% +2025-03-14 19:12:44,567 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.678 | Acc: 77.30% +2025-03-14 19:12:47,101 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.662 | Acc: 77.78% +2025-03-14 19:12:49,296 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.667 | Acc: 77.48% +2025-03-14 19:12:53,279 - train - INFO - Epoch: 11 | Test Loss: 0.866 | Test Acc: 70.78% +2025-03-14 19:12:53,592 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.626 | Acc: 82.03% +2025-03-14 19:12:56,135 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.666 | Acc: 77.05% +2025-03-14 19:12:58,583 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.667 | Acc: 77.21% +2025-03-14 19:13:00,967 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.664 | Acc: 77.27% +2025-03-14 19:13:04,885 - train - INFO - Epoch: 12 | Test Loss: 0.848 | Test Acc: 71.45% +2025-03-14 19:13:17,833 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.646 | Acc: 77.34% +2025-03-14 19:13:20,812 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.677 | Acc: 76.84% +2025-03-14 19:13:23,635 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.683 | Acc: 76.69% +2025-03-14 19:13:26,191 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.680 | Acc: 76.85% +2025-03-14 19:13:30,593 - train - INFO - Epoch: 13 | Test Loss: 0.760 | Test Acc: 74.39% +2025-03-14 19:13:30,841 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.619 | Acc: 82.03% +2025-03-14 19:13:33,396 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.655 | Acc: 78.09% +2025-03-14 19:13:35,996 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.652 | Acc: 78.04% +2025-03-14 19:13:38,805 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.654 | Acc: 78.02% +2025-03-14 19:13:42,959 - train - INFO - Epoch: 14 | Test Loss: 0.912 | Test Acc: 70.57% +2025-03-14 19:13:55,836 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.699 | Acc: 78.12% +2025-03-14 19:13:58,506 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.658 | Acc: 77.43% +2025-03-14 19:14:01,166 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.651 | Acc: 77.77% +2025-03-14 19:14:03,871 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.651 | Acc: 77.81% +2025-03-14 19:14:07,939 - train - INFO - Epoch: 15 | Test Loss: 0.857 | Test Acc: 71.50% +2025-03-14 19:14:08,186 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.714 | Acc: 74.22% +2025-03-14 19:14:10,731 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.634 | Acc: 78.77% +2025-03-14 19:14:13,222 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.631 | Acc: 78.96% +2025-03-14 19:14:15,728 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.642 | Acc: 78.45% +2025-03-14 19:14:19,572 - train - INFO - Epoch: 16 | Test Loss: 0.800 | Test Acc: 74.04% +2025-03-14 19:14:31,479 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.904 | Acc: 69.53% +2025-03-14 19:14:33,806 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.636 | Acc: 78.62% +2025-03-14 19:14:36,052 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.636 | Acc: 78.54% +2025-03-14 19:14:38,377 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.635 | Acc: 78.66% +2025-03-14 19:14:42,061 - train - INFO - Epoch: 17 | Test Loss: 0.753 | Test Acc: 75.02% +2025-03-14 19:14:42,233 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.663 | Acc: 75.78% +2025-03-14 19:14:44,554 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.627 | Acc: 78.36% +2025-03-14 19:14:46,924 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.636 | Acc: 78.22% +2025-03-14 19:14:49,130 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.635 | Acc: 78.31% +2025-03-14 19:14:52,651 - train - INFO - Epoch: 18 | Test Loss: 0.824 | Test Acc: 74.00% +2025-03-14 19:15:04,182 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.718 | Acc: 75.78% +2025-03-14 19:15:06,607 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.627 | Acc: 78.48% +2025-03-14 19:15:08,889 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.629 | Acc: 78.63% +2025-03-14 19:15:11,168 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.632 | Acc: 78.60% +2025-03-14 19:15:14,679 - train - INFO - Epoch: 19 | Test Loss: 0.860 | Test Acc: 71.95% +2025-03-14 19:15:14,903 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.713 | Acc: 71.88% +2025-03-14 19:15:17,436 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.613 | Acc: 79.31% +2025-03-14 19:15:19,801 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.618 | Acc: 78.89% +2025-03-14 19:15:22,138 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.625 | Acc: 78.67% +2025-03-14 19:15:25,794 - train - INFO - Epoch: 20 | Test Loss: 0.814 | Test Acc: 72.63% +2025-03-14 19:15:37,215 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.545 | Acc: 80.47% +2025-03-14 19:15:39,531 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.618 | Acc: 79.42% +2025-03-14 19:15:42,160 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.623 | Acc: 79.14% +2025-03-14 19:15:44,548 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.621 | Acc: 79.13% +2025-03-14 19:15:48,266 - train - INFO - Epoch: 21 | Test Loss: 0.772 | Test Acc: 74.04% +2025-03-14 19:15:48,450 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.582 | Acc: 82.81% +2025-03-14 19:15:50,889 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.625 | Acc: 78.47% +2025-03-14 19:15:53,306 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.617 | Acc: 78.98% +2025-03-14 19:15:55,517 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.619 | Acc: 78.96% +2025-03-14 19:15:58,908 - train - INFO - Epoch: 22 | Test Loss: 0.841 | Test Acc: 72.71% +2025-03-14 19:16:11,475 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.794 | Acc: 75.00% +2025-03-14 19:16:14,047 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.600 | Acc: 79.59% +2025-03-14 19:16:16,528 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.609 | Acc: 79.28% +2025-03-14 19:16:18,937 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.614 | Acc: 79.13% +2025-03-14 19:16:22,747 - train - INFO - Epoch: 23 | Test Loss: 0.636 | Test Acc: 78.55% +2025-03-14 19:16:23,032 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.638 | Acc: 73.44% +2025-03-14 19:16:25,582 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.597 | Acc: 79.85% +2025-03-14 19:16:28,077 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.613 | Acc: 79.45% +2025-03-14 19:16:30,547 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.615 | Acc: 79.25% +2025-03-14 19:16:35,247 - train - INFO - Epoch: 24 | Test Loss: 0.761 | Test Acc: 74.45% +2025-03-14 19:16:52,451 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.606 | Acc: 77.34% +2025-03-14 19:16:54,634 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.593 | Acc: 79.85% +2025-03-14 19:16:56,947 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.596 | Acc: 79.71% +2025-03-14 19:16:59,193 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.604 | Acc: 79.53% +2025-03-14 19:17:03,197 - train - INFO - Epoch: 25 | Test Loss: 0.733 | Test Acc: 75.32% +2025-03-14 19:17:03,436 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.550 | Acc: 79.69% +2025-03-14 19:17:05,700 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.611 | Acc: 79.37% +2025-03-14 19:17:08,156 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.602 | Acc: 79.71% +2025-03-14 19:17:10,854 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.607 | Acc: 79.50% +2025-03-14 19:17:14,903 - train - INFO - Epoch: 26 | Test Loss: 0.837 | Test Acc: 71.82% +2025-03-14 19:17:28,190 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.728 | Acc: 73.44% +2025-03-14 19:17:30,383 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.595 | Acc: 80.10% +2025-03-14 19:17:32,712 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.592 | Acc: 80.34% +2025-03-14 19:17:34,992 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.602 | Acc: 79.93% +2025-03-14 19:17:39,085 - train - INFO - Epoch: 27 | Test Loss: 0.940 | Test Acc: 68.79% +2025-03-14 19:17:39,382 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.579 | Acc: 78.12% +2025-03-14 19:17:41,788 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.601 | Acc: 79.42% +2025-03-14 19:17:43,931 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.607 | Acc: 79.28% +2025-03-14 19:17:46,181 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.608 | Acc: 79.43% +2025-03-14 19:17:50,102 - train - INFO - Epoch: 28 | Test Loss: 0.738 | Test Acc: 75.43% +2025-03-14 19:18:03,137 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.392 | Acc: 87.50% +2025-03-14 19:18:05,602 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.578 | Acc: 80.04% +2025-03-14 19:18:07,998 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.587 | Acc: 80.07% +2025-03-14 19:18:10,390 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.594 | Acc: 79.86% +2025-03-14 19:18:14,256 - train - INFO - Epoch: 29 | Test Loss: 0.868 | Test Acc: 70.16% +2025-03-14 19:18:14,469 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.523 | Acc: 82.81% +2025-03-14 19:18:16,749 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.600 | Acc: 79.69% +2025-03-14 19:18:19,110 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.597 | Acc: 79.75% +2025-03-14 19:18:21,392 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.603 | Acc: 79.53% +2025-03-14 19:18:25,247 - train - INFO - Epoch: 30 | Test Loss: 0.804 | Test Acc: 73.83% +2025-03-14 19:18:37,557 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.571 | Acc: 80.47% +2025-03-14 19:18:39,806 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.581 | Acc: 80.70% +2025-03-14 19:18:42,069 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.588 | Acc: 80.17% +2025-03-14 19:18:44,274 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.598 | Acc: 79.81% +2025-03-14 19:18:48,074 - train - INFO - Epoch: 31 | Test Loss: 0.635 | Test Acc: 77.69% +2025-03-14 19:18:48,403 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.524 | Acc: 82.03% +2025-03-14 19:18:50,787 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.593 | Acc: 79.94% +2025-03-14 19:18:53,111 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.596 | Acc: 79.82% +2025-03-14 19:18:55,304 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.594 | Acc: 79.85% +2025-03-14 19:18:58,912 - train - INFO - Epoch: 32 | Test Loss: 0.803 | Test Acc: 72.32% +2025-03-14 19:19:10,428 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.504 | Acc: 84.38% +2025-03-14 19:19:12,672 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.585 | Acc: 79.99% +2025-03-14 19:19:15,130 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.591 | Acc: 80.00% +2025-03-14 19:19:17,400 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.594 | Acc: 79.90% +2025-03-14 19:19:21,074 - train - INFO - Epoch: 33 | Test Loss: 0.728 | Test Acc: 75.99% +2025-03-14 19:19:21,325 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.505 | Acc: 84.38% +2025-03-14 19:19:23,582 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.579 | Acc: 80.73% +2025-03-14 19:19:25,811 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.591 | Acc: 80.00% +2025-03-14 19:19:28,143 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.597 | Acc: 79.88% +2025-03-14 19:19:31,854 - train - INFO - Epoch: 34 | Test Loss: 0.923 | Test Acc: 70.57% +2025-03-14 19:19:44,699 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.456 | Acc: 85.16% +2025-03-14 19:19:47,040 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.579 | Acc: 80.26% +2025-03-14 19:19:49,476 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.592 | Acc: 79.92% +2025-03-14 19:19:51,834 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.590 | Acc: 79.92% +2025-03-14 19:19:55,647 - train - INFO - Epoch: 35 | Test Loss: 0.719 | Test Acc: 75.56% +2025-03-14 19:19:55,832 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.627 | Acc: 78.91% +2025-03-14 19:19:58,103 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.567 | Acc: 80.92% +2025-03-14 19:20:00,453 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.583 | Acc: 80.37% +2025-03-14 19:20:02,728 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.585 | Acc: 80.31% +2025-03-14 19:20:06,322 - train - INFO - Epoch: 36 | Test Loss: 0.740 | Test Acc: 75.07% +2025-03-14 19:20:18,017 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.633 | Acc: 80.47% +2025-03-14 19:20:20,274 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.561 | Acc: 81.09% +2025-03-14 19:20:22,825 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.574 | Acc: 80.56% +2025-03-14 19:20:25,118 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.575 | Acc: 80.53% +2025-03-14 19:20:28,973 - train - INFO - Epoch: 37 | Test Loss: 0.727 | Test Acc: 75.90% +2025-03-14 19:20:29,339 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.595 | Acc: 80.47% +2025-03-14 19:20:31,652 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.571 | Acc: 80.77% +2025-03-14 19:20:34,298 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.569 | Acc: 80.84% +2025-03-14 19:20:37,043 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.571 | Acc: 80.68% +2025-03-14 19:20:42,009 - train - INFO - Epoch: 38 | Test Loss: 0.678 | Test Acc: 77.55% +2025-03-14 19:20:57,971 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.579 | Acc: 78.12% +2025-03-14 19:21:00,491 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.603 | Acc: 79.55% +2025-03-14 19:21:02,692 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.588 | Acc: 80.21% +2025-03-14 19:21:04,962 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.583 | Acc: 80.22% +2025-03-14 19:21:08,498 - train - INFO - Epoch: 39 | Test Loss: 0.630 | Test Acc: 79.20% +2025-03-14 19:21:08,743 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.506 | Acc: 82.81% +2025-03-14 19:21:11,357 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.572 | Acc: 80.66% +2025-03-14 19:21:13,832 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.580 | Acc: 80.50% +2025-03-14 19:21:16,254 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.576 | Acc: 80.58% +2025-03-14 19:21:19,990 - train - INFO - Epoch: 40 | Test Loss: 0.698 | Test Acc: 77.19% +2025-03-14 19:21:33,933 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.647 | Acc: 73.44% +2025-03-14 19:21:36,385 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.592 | Acc: 79.76% +2025-03-14 19:21:38,741 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.592 | Acc: 79.90% +2025-03-14 19:21:41,132 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.591 | Acc: 79.99% +2025-03-14 19:21:45,047 - train - INFO - Epoch: 41 | Test Loss: 0.694 | Test Acc: 77.17% +2025-03-14 19:21:45,309 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.585 | Acc: 78.91% +2025-03-14 19:21:47,785 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.580 | Acc: 80.58% +2025-03-14 19:21:50,283 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.583 | Acc: 80.39% +2025-03-14 19:21:52,576 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.581 | Acc: 80.29% +2025-03-14 19:21:56,504 - train - INFO - Epoch: 42 | Test Loss: 0.640 | Test Acc: 78.78% +2025-03-14 19:22:09,412 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.661 | Acc: 77.34% +2025-03-14 19:22:11,772 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.577 | Acc: 80.29% +2025-03-14 19:22:13,947 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.573 | Acc: 80.45% +2025-03-14 19:22:16,276 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.575 | Acc: 80.39% +2025-03-14 19:22:19,982 - train - INFO - Epoch: 43 | Test Loss: 0.761 | Test Acc: 75.18% +2025-03-14 19:22:20,244 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.504 | Acc: 83.59% +2025-03-14 19:22:22,549 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.558 | Acc: 81.34% +2025-03-14 19:22:25,088 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.568 | Acc: 80.99% +2025-03-14 19:22:27,330 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.574 | Acc: 80.70% +2025-03-14 19:22:31,247 - train - INFO - Epoch: 44 | Test Loss: 0.710 | Test Acc: 76.16% +2025-03-14 19:22:46,477 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.561 | Acc: 78.91% +2025-03-14 19:22:48,835 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.550 | Acc: 81.54% +2025-03-14 19:22:51,134 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.554 | Acc: 81.12% +2025-03-14 19:22:53,457 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.562 | Acc: 80.82% +2025-03-14 19:22:57,455 - train - INFO - Epoch: 45 | Test Loss: 0.826 | Test Acc: 73.68% +2025-03-14 19:22:57,715 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.513 | Acc: 83.59% +2025-03-14 19:23:00,031 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.579 | Acc: 80.21% +2025-03-14 19:23:02,378 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.576 | Acc: 80.56% +2025-03-14 19:23:04,667 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.579 | Acc: 80.41% +2025-03-14 19:23:09,125 - train - INFO - Epoch: 46 | Test Loss: 0.757 | Test Acc: 75.72% +2025-03-14 19:23:23,982 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.391 | Acc: 87.50% +2025-03-14 19:23:26,330 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.567 | Acc: 80.68% +2025-03-14 19:23:28,656 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.569 | Acc: 80.55% +2025-03-14 19:23:30,938 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.564 | Acc: 80.83% +2025-03-14 19:23:35,580 - train - INFO - Epoch: 47 | Test Loss: 0.697 | Test Acc: 76.91% +2025-03-14 19:23:35,874 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.568 | Acc: 82.03% +2025-03-14 19:23:39,159 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.556 | Acc: 81.37% +2025-03-14 19:23:41,720 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.562 | Acc: 81.03% +2025-03-14 19:23:44,018 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.566 | Acc: 80.92% +2025-03-14 19:23:47,764 - train - INFO - Epoch: 48 | Test Loss: 0.740 | Test Acc: 76.44% +2025-03-14 19:24:03,458 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.434 | Acc: 84.38% +2025-03-14 19:24:06,028 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.557 | Acc: 81.14% +2025-03-14 19:24:08,540 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.567 | Acc: 80.76% +2025-03-14 19:24:11,018 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.570 | Acc: 80.54% +2025-03-14 19:24:14,809 - train - INFO - Epoch: 49 | Test Loss: 0.645 | Test Acc: 77.99% +2025-03-14 19:24:15,220 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.661 | Acc: 82.03% +2025-03-14 19:24:17,452 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.553 | Acc: 81.09% +2025-03-14 19:24:19,678 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.553 | Acc: 81.04% +2025-03-14 19:24:21,988 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.560 | Acc: 80.89% +2025-03-14 19:24:25,737 - train - INFO - Epoch: 50 | Test Loss: 0.719 | Test Acc: 75.84% +2025-03-14 19:24:43,173 - train - INFO - 训练完成! diff --git a/Image/MobileNetv1/code/train.py b/Image/MobileNetv1/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..2a2e5199195598ae6e359ed2c4746e79ba022e29 --- /dev/null +++ b/Image/MobileNetv1/code/train.py @@ -0,0 +1,63 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import MobileNet + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = MobileNet() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv1', + save_type='0', + layer_name='layers.12', + interval=2 + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv1', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv1', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path, + layer_name='layers.12', + interval=2 + ) + +if __name__ == '__main__': + main() diff --git a/Image/MobileNetv1/dataset/.gitkeep b/Image/MobileNetv1/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/MobileNetv1/model/.gitkeep b/Image/MobileNetv1/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/MobileNetv1/model/0/epoch1/embeddings.npy b/Image/MobileNetv1/model/0/epoch1/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..e16865fc9b5f3df85bf3fec43ed435b610124b3e --- /dev/null +++ b/Image/MobileNetv1/model/0/epoch1/embeddings.npy @@ -0,0 +1,3 @@ +version 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3 | Batch: 200 | Loss: 1.010 | Acc: 64.62% +2025-03-14 19:35:14,706 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.996 | Acc: 65.22% +2025-03-14 19:35:20,364 - train - INFO - Epoch: 3 | Test Loss: 0.871 | Test Acc: 69.34% +2025-03-14 19:35:20,817 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.923 | Acc: 68.75% +2025-03-14 19:35:24,679 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.881 | Acc: 69.57% +2025-03-14 19:35:28,088 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.871 | Acc: 70.01% +2025-03-14 19:35:31,594 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.857 | Acc: 70.56% +2025-03-14 19:35:36,716 - train - INFO - Epoch: 4 | Test Loss: 0.965 | Test Acc: 68.49% +2025-03-14 19:35:48,118 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.103 | Acc: 67.19% +2025-03-14 19:35:51,538 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.789 | Acc: 73.24% +2025-03-14 19:35:54,853 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.786 | Acc: 73.25% +2025-03-14 19:35:58,041 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.781 | Acc: 73.45% +2025-03-14 19:36:02,649 - train - INFO - Epoch: 5 | Test Loss: 0.710 | Test Acc: 75.54% +2025-03-14 19:36:02,867 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.743 | Acc: 75.78% +2025-03-14 19:36:06,290 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.735 | Acc: 75.52% +2025-03-14 19:36:09,827 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.740 | Acc: 75.27% +2025-03-14 19:36:13,150 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.737 | Acc: 75.34% +2025-03-14 19:36:17,728 - train - INFO - Epoch: 6 | Test Loss: 0.850 | Test Acc: 71.43% +2025-03-14 19:36:29,047 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.764 | Acc: 71.88% +2025-03-14 19:36:32,484 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.687 | Acc: 76.92% +2025-03-14 19:36:36,055 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.692 | Acc: 76.60% +2025-03-14 19:36:40,123 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.703 | Acc: 76.35% +2025-03-14 19:36:45,256 - train - INFO - Epoch: 7 | Test Loss: 0.756 | Test Acc: 74.37% +2025-03-14 19:36:45,496 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.576 | Acc: 81.25% +2025-03-14 19:36:49,278 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.680 | Acc: 77.41% +2025-03-14 19:36:52,908 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.688 | Acc: 77.13% +2025-03-14 19:36:56,915 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.679 | Acc: 77.49% +2025-03-14 19:37:01,795 - train - INFO - Epoch: 8 | Test Loss: 1.103 | Test Acc: 64.27% +2025-03-14 19:37:14,687 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.588 | Acc: 82.03% +2025-03-14 19:37:18,396 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.688 | Acc: 77.20% +2025-03-14 19:37:21,730 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.677 | Acc: 77.54% +2025-03-14 19:37:25,041 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.675 | Acc: 77.44% +2025-03-14 19:37:29,737 - train - INFO - Epoch: 9 | Test Loss: 0.690 | Test Acc: 76.95% +2025-03-14 19:37:29,997 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.586 | Acc: 81.25% +2025-03-14 19:37:33,361 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.667 | Acc: 78.05% +2025-03-14 19:37:36,656 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.682 | Acc: 77.28% +2025-03-14 19:37:40,019 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.675 | Acc: 77.60% +2025-03-14 19:37:44,637 - train - INFO - Epoch: 10 | Test Loss: 0.741 | Test Acc: 75.22% +2025-03-14 19:37:55,613 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.756 | Acc: 73.44% +2025-03-14 19:37:58,897 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.669 | Acc: 77.92% +2025-03-14 19:38:02,173 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.664 | Acc: 78.07% +2025-03-14 19:38:05,475 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.665 | Acc: 78.08% +2025-03-14 19:38:09,934 - train - INFO - Epoch: 11 | Test Loss: 0.772 | Test Acc: 74.38% +2025-03-14 19:38:10,194 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.741 | Acc: 75.00% +2025-03-14 19:38:13,918 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.666 | Acc: 78.12% +2025-03-14 19:38:17,447 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.663 | Acc: 78.11% +2025-03-14 19:38:20,884 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.659 | Acc: 78.18% +2025-03-14 19:38:25,527 - train - INFO - Epoch: 12 | Test Loss: 0.781 | Test Acc: 73.37% +2025-03-14 19:38:36,897 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.786 | Acc: 73.44% +2025-03-14 19:38:40,292 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.656 | Acc: 78.43% +2025-03-14 19:38:43,722 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.655 | Acc: 78.47% +2025-03-14 19:38:47,038 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.654 | Acc: 78.41% +2025-03-14 19:38:52,076 - train - INFO - Epoch: 13 | Test Loss: 0.843 | Test Acc: 71.75% +2025-03-14 19:38:52,373 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.789 | Acc: 71.88% +2025-03-14 19:38:55,765 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.635 | Acc: 78.76% +2025-03-14 19:38:59,154 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.649 | Acc: 78.32% +2025-03-14 19:39:02,796 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.657 | Acc: 78.11% +2025-03-14 19:39:07,795 - train - INFO - Epoch: 14 | Test Loss: 0.706 | Test Acc: 75.67% +2025-03-14 19:39:19,690 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.754 | Acc: 73.44% +2025-03-14 19:39:23,082 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.637 | Acc: 78.79% +2025-03-14 19:39:26,597 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.653 | Acc: 78.16% +2025-03-14 19:39:30,270 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.657 | Acc: 78.00% +2025-03-14 19:39:35,329 - train - INFO - Epoch: 15 | Test Loss: 0.973 | Test Acc: 69.48% +2025-03-14 19:39:35,603 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.637 | Acc: 77.34% +2025-03-14 19:39:39,191 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.643 | Acc: 78.73% +2025-03-14 19:39:42,808 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.656 | Acc: 78.33% +2025-03-14 19:39:46,368 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.657 | Acc: 78.34% +2025-03-14 19:39:51,262 - train - INFO - Epoch: 16 | Test Loss: 0.645 | Test Acc: 77.94% +2025-03-14 19:40:02,465 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.689 | Acc: 78.12% +2025-03-14 19:40:05,700 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.656 | Acc: 78.31% +2025-03-14 19:40:08,941 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.659 | Acc: 78.00% +2025-03-14 19:40:12,210 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.654 | Acc: 78.12% +2025-03-14 19:40:17,353 - train - INFO - Epoch: 17 | Test Loss: 0.788 | Test Acc: 73.69% +2025-03-14 19:40:17,627 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.796 | Acc: 75.00% +2025-03-14 19:40:20,935 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.619 | Acc: 79.47% +2025-03-14 19:40:24,294 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.636 | Acc: 78.87% +2025-03-14 19:40:27,713 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.642 | Acc: 78.64% +2025-03-14 19:40:32,231 - train - INFO - Epoch: 18 | Test Loss: 0.649 | Test Acc: 78.28% +2025-03-14 19:40:45,148 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.598 | Acc: 78.12% +2025-03-14 19:40:48,751 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.642 | Acc: 78.30% +2025-03-14 19:40:52,160 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.633 | Acc: 79.06% +2025-03-14 19:40:55,553 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.639 | Acc: 78.77% +2025-03-14 19:41:00,037 - train - INFO - Epoch: 19 | Test Loss: 0.758 | Test Acc: 74.52% +2025-03-14 19:41:00,266 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.699 | Acc: 78.91% +2025-03-14 19:41:03,650 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.641 | Acc: 79.18% +2025-03-14 19:41:06,904 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.645 | Acc: 78.75% +2025-03-14 19:41:10,217 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.651 | Acc: 78.54% +2025-03-14 19:41:15,170 - train - INFO - Epoch: 20 | Test Loss: 0.942 | Test Acc: 69.15% +2025-03-14 19:41:26,518 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.722 | Acc: 75.00% +2025-03-14 19:41:29,940 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.642 | Acc: 78.69% +2025-03-14 19:41:33,495 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.651 | Acc: 78.30% +2025-03-14 19:41:36,904 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.654 | Acc: 78.37% +2025-03-14 19:41:42,216 - train - INFO - Epoch: 21 | Test Loss: 0.653 | Test Acc: 78.49% +2025-03-14 19:41:42,526 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.722 | Acc: 78.12% +2025-03-14 19:41:46,440 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.646 | Acc: 78.98% +2025-03-14 19:41:50,554 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.647 | Acc: 78.78% +2025-03-14 19:41:54,284 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.645 | Acc: 78.79% +2025-03-14 19:41:59,252 - train - INFO - Epoch: 22 | Test Loss: 0.668 | Test Acc: 76.99% +2025-03-14 19:42:11,832 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.549 | Acc: 80.47% +2025-03-14 19:42:15,126 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.624 | Acc: 78.83% +2025-03-14 19:42:18,419 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.635 | Acc: 78.79% +2025-03-14 19:42:21,633 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.632 | Acc: 78.91% +2025-03-14 19:42:26,712 - train - INFO - Epoch: 23 | Test Loss: 0.754 | Test Acc: 74.86% +2025-03-14 19:42:26,957 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.438 | Acc: 86.72% +2025-03-14 19:42:30,229 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.637 | Acc: 78.90% +2025-03-14 19:42:33,523 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.639 | Acc: 78.73% +2025-03-14 19:42:36,716 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.633 | Acc: 78.95% +2025-03-14 19:42:41,197 - train - INFO - Epoch: 24 | Test Loss: 0.851 | Test Acc: 72.15% +2025-03-14 19:42:53,065 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.541 | Acc: 80.47% +2025-03-14 19:42:56,327 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.642 | Acc: 78.20% +2025-03-14 19:42:59,601 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.636 | Acc: 78.85% +2025-03-14 19:43:02,944 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.638 | Acc: 78.86% +2025-03-14 19:43:07,734 - train - INFO - Epoch: 25 | Test Loss: 0.992 | Test Acc: 70.15% +2025-03-14 19:43:08,005 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.519 | Acc: 82.81% +2025-03-14 19:43:11,406 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.607 | Acc: 80.00% +2025-03-14 19:43:14,778 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.629 | Acc: 79.06% +2025-03-14 19:43:18,070 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.628 | Acc: 79.10% +2025-03-14 19:43:22,670 - train - INFO - Epoch: 26 | Test Loss: 0.671 | Test Acc: 77.26% +2025-03-14 19:43:33,686 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.671 | Acc: 78.12% +2025-03-14 19:43:37,086 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.621 | Acc: 79.70% +2025-03-14 19:43:40,311 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.620 | Acc: 79.59% +2025-03-14 19:43:43,622 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.627 | Acc: 79.41% +2025-03-14 19:43:48,359 - train - INFO - Epoch: 27 | Test Loss: 0.662 | Test Acc: 77.75% +2025-03-14 19:43:48,711 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.576 | Acc: 82.81% +2025-03-14 19:43:52,170 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.630 | Acc: 79.00% +2025-03-14 19:43:55,513 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.627 | Acc: 79.05% +2025-03-14 19:43:58,821 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.635 | Acc: 78.75% +2025-03-14 19:44:03,266 - train - INFO - Epoch: 28 | Test Loss: 0.724 | Test Acc: 74.87% +2025-03-14 19:44:13,667 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.646 | Acc: 81.25% +2025-03-14 19:44:16,941 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.628 | Acc: 79.19% +2025-03-14 19:44:20,482 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.630 | Acc: 79.00% +2025-03-14 19:44:23,737 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.629 | Acc: 78.95% +2025-03-14 19:44:28,295 - train - INFO - Epoch: 29 | Test Loss: 0.662 | Test Acc: 77.43% +2025-03-14 19:44:28,530 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.670 | Acc: 74.22% +2025-03-14 19:44:31,941 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.615 | Acc: 79.38% +2025-03-14 19:44:35,458 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.629 | Acc: 79.02% +2025-03-14 19:44:38,753 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.627 | Acc: 79.15% +2025-03-14 19:44:43,265 - train - INFO - Epoch: 30 | Test Loss: 0.795 | Test Acc: 73.48% +2025-03-14 19:44:56,355 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.875 | Acc: 67.97% +2025-03-14 19:44:59,749 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.635 | Acc: 78.71% +2025-03-14 19:45:03,017 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.626 | Acc: 79.11% +2025-03-14 19:45:06,545 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.626 | Acc: 79.04% +2025-03-14 19:45:11,485 - train - INFO - Epoch: 31 | Test Loss: 0.669 | Test Acc: 77.71% +2025-03-14 19:45:11,913 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.498 | Acc: 81.25% +2025-03-14 19:45:15,270 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.634 | Acc: 79.05% +2025-03-14 19:45:18,585 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.633 | Acc: 79.11% +2025-03-14 19:45:21,798 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.629 | Acc: 79.26% +2025-03-14 19:45:26,689 - train - INFO - Epoch: 32 | Test Loss: 0.971 | Test Acc: 69.30% +2025-03-14 19:45:39,797 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.706 | Acc: 78.91% +2025-03-14 19:45:43,114 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.610 | Acc: 79.79% +2025-03-14 19:45:46,320 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.623 | Acc: 79.44% +2025-03-14 19:45:49,642 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.626 | Acc: 79.30% +2025-03-14 19:45:54,214 - train - INFO - Epoch: 33 | Test Loss: 0.675 | Test Acc: 76.70% +2025-03-14 19:45:54,434 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.527 | Acc: 80.47% +2025-03-14 19:45:57,688 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.621 | Acc: 79.09% +2025-03-14 19:46:00,874 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.623 | Acc: 78.99% +2025-03-14 19:46:04,179 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.625 | Acc: 78.99% +2025-03-14 19:46:08,870 - train - INFO - Epoch: 34 | Test Loss: 0.612 | Test Acc: 78.84% +2025-03-14 19:46:21,448 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.566 | Acc: 79.69% +2025-03-14 19:46:24,671 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.624 | Acc: 79.35% +2025-03-14 19:46:27,843 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.632 | Acc: 78.92% +2025-03-14 19:46:31,182 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.630 | Acc: 78.98% +2025-03-14 19:46:35,613 - train - INFO - Epoch: 35 | Test Loss: 0.692 | Test Acc: 76.93% +2025-03-14 19:46:35,849 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.638 | Acc: 78.91% +2025-03-14 19:46:39,126 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.617 | Acc: 79.44% +2025-03-14 19:46:42,587 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.620 | Acc: 79.45% +2025-03-14 19:46:45,891 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.619 | Acc: 79.32% +2025-03-14 19:46:50,381 - train - INFO - Epoch: 36 | Test Loss: 0.747 | Test Acc: 75.67% +2025-03-14 19:47:04,037 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.611 | Acc: 82.03% +2025-03-14 19:47:07,310 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.621 | Acc: 79.08% +2025-03-14 19:47:10,603 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.621 | Acc: 79.15% +2025-03-14 19:47:13,815 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.628 | Acc: 78.97% +2025-03-14 19:47:18,168 - train - INFO - Epoch: 37 | Test Loss: 0.679 | Test Acc: 77.48% +2025-03-14 19:47:18,397 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.496 | Acc: 83.59% +2025-03-14 19:47:21,698 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.615 | Acc: 79.73% +2025-03-14 19:47:24,860 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.627 | Acc: 79.17% +2025-03-14 19:47:28,120 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.629 | Acc: 79.15% +2025-03-14 19:47:32,936 - train - INFO - Epoch: 38 | Test Loss: 0.675 | Test Acc: 77.86% +2025-03-14 19:47:45,480 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.576 | Acc: 82.03% +2025-03-14 19:47:48,904 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.601 | Acc: 80.37% +2025-03-14 19:47:52,245 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.607 | Acc: 79.87% +2025-03-14 19:47:55,509 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.617 | Acc: 79.50% +2025-03-14 19:47:59,980 - train - INFO - Epoch: 39 | Test Loss: 0.841 | Test Acc: 72.60% +2025-03-14 19:48:00,232 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.719 | Acc: 73.44% +2025-03-14 19:48:03,619 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.617 | Acc: 79.37% +2025-03-14 19:48:06,905 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.616 | Acc: 79.42% +2025-03-14 19:48:10,211 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.619 | Acc: 79.36% +2025-03-14 19:48:14,742 - train - INFO - Epoch: 40 | Test Loss: 0.903 | Test Acc: 70.17% +2025-03-14 19:48:26,055 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.655 | Acc: 76.56% +2025-03-14 19:48:29,372 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.623 | Acc: 79.28% +2025-03-14 19:48:32,576 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.629 | Acc: 79.13% +2025-03-14 19:48:35,730 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.622 | Acc: 79.29% +2025-03-14 19:48:40,269 - train - INFO - Epoch: 41 | Test Loss: 0.642 | Test Acc: 78.14% +2025-03-14 19:48:40,496 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.641 | Acc: 75.00% +2025-03-14 19:48:43,861 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.619 | Acc: 79.66% +2025-03-14 19:48:47,144 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.622 | Acc: 79.31% +2025-03-14 19:48:50,452 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.617 | Acc: 79.36% +2025-03-14 19:48:54,827 - train - INFO - Epoch: 42 | Test Loss: 0.738 | Test Acc: 75.93% +2025-03-14 19:49:08,190 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.778 | Acc: 73.44% +2025-03-14 19:49:11,436 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.615 | Acc: 79.61% +2025-03-14 19:49:14,877 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.629 | Acc: 79.07% +2025-03-14 19:49:18,484 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.632 | Acc: 79.07% +2025-03-14 19:49:23,712 - train - INFO - Epoch: 43 | Test Loss: 0.742 | Test Acc: 74.57% +2025-03-14 19:49:24,095 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.696 | Acc: 76.56% +2025-03-14 19:49:27,757 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.610 | Acc: 79.96% +2025-03-14 19:49:31,662 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.618 | Acc: 79.48% +2025-03-14 19:49:35,009 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.612 | Acc: 79.62% +2025-03-14 19:49:40,690 - train - INFO - Epoch: 44 | Test Loss: 0.732 | Test Acc: 75.86% +2025-03-14 19:49:52,938 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.652 | Acc: 82.81% +2025-03-14 19:49:56,098 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.594 | Acc: 80.00% +2025-03-14 19:49:59,270 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.600 | Acc: 80.04% +2025-03-14 19:50:02,349 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.602 | Acc: 79.92% +2025-03-14 19:50:06,606 - train - INFO - Epoch: 45 | Test Loss: 0.749 | Test Acc: 75.20% +2025-03-14 19:50:06,830 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.594 | Acc: 81.25% +2025-03-14 19:50:10,214 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.605 | Acc: 79.89% +2025-03-14 19:50:13,272 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.606 | Acc: 79.82% +2025-03-14 19:50:16,267 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.612 | Acc: 79.63% +2025-03-14 19:50:20,607 - train - INFO - Epoch: 46 | Test Loss: 0.707 | Test Acc: 76.63% +2025-03-14 19:50:33,662 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.488 | Acc: 83.59% +2025-03-14 19:50:36,860 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.591 | Acc: 80.45% +2025-03-14 19:50:40,064 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.611 | Acc: 79.72% +2025-03-14 19:50:43,830 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.615 | Acc: 79.63% +2025-03-14 19:50:48,236 - train - INFO - Epoch: 47 | Test Loss: 0.612 | Test Acc: 79.22% +2025-03-14 19:50:48,460 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.729 | Acc: 74.22% +2025-03-14 19:50:51,645 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.606 | Acc: 79.83% +2025-03-14 19:50:54,950 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.616 | Acc: 79.28% +2025-03-14 19:50:58,523 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.614 | Acc: 79.36% +2025-03-14 19:51:03,209 - train - INFO - Epoch: 48 | Test Loss: 0.925 | Test Acc: 71.88% +2025-03-14 19:51:16,408 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.598 | Acc: 82.03% +2025-03-14 19:51:19,486 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.616 | Acc: 79.12% +2025-03-14 19:51:22,803 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.615 | Acc: 79.30% +2025-03-14 19:51:26,002 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.612 | Acc: 79.47% +2025-03-14 19:51:30,321 - train - INFO - Epoch: 49 | Test Loss: 0.750 | Test Acc: 75.08% +2025-03-14 19:51:30,550 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.558 | Acc: 82.03% +2025-03-14 19:51:33,757 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.583 | Acc: 80.69% +2025-03-14 19:51:36,928 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.595 | Acc: 80.07% +2025-03-14 19:51:40,116 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.608 | Acc: 79.64% +2025-03-14 19:51:44,713 - train - INFO - Epoch: 50 | Test Loss: 0.711 | Test Acc: 76.34% +2025-03-14 19:51:56,482 - train - INFO - 训练完成! diff --git a/Image/MobileNetv2/code/model.py b/Image/MobileNetv2/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..c92a0f20828c73e72ff8d685e799d719a1da11a6 --- /dev/null +++ b/Image/MobileNetv2/code/model.py @@ -0,0 +1,176 @@ +''' +MobileNetV2 in PyTorch. + +论文: "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation" +参考: https://arxiv.org/abs/1801.04381 + +主要特点： +1. 引入倒残差结构(Inverted Residual)，先升维后降维 +2. 使用线性瓶颈(Linear Bottlenecks)，去除最后一个ReLU保留特征 +3. 使用ReLU6作为激活函数，提高在低精度计算下的鲁棒性 +4. 残差连接时使用加法而不是拼接，减少内存占用 +''' + +import torch +import torch.nn as nn + + +class Block(nn.Module): + '''倒残差块 (Inverted Residual Block) + + 结构: expand(1x1) -> depthwise(3x3) -> project(1x1) + 特点: + 1. 使用1x1卷积先升维再降维（与ResNet相反） + 2. 使用深度可分离卷积减少参数量 + 3. 使用shortcut连接（当stride=1且输入输出通道数相同时） + + Args: + in_channels: 输入通道数 + out_channels: 输出通道数 + expansion: 扩展因子，控制中间层的通道数 + stride: 步长，控制特征图大小 + ''' + def __init__(self, in_channels, out_channels, expansion, stride): + super(Block, self).__init__() + self.stride = stride + channels = expansion * in_channels # 扩展通道数 + + # 1x1卷积升维 + self.conv1 = nn.Conv2d( + in_channels, channels, + kernel_size=1, stride=1, padding=0, bias=False + ) + self.bn1 = nn.BatchNorm2d(channels) + + # 3x3深度可分离卷积 + self.conv2 = nn.Conv2d( + channels, channels, + kernel_size=3, stride=stride, padding=1, + groups=channels, bias=False # groups=channels即为深度可分离卷积 + ) + self.bn2 = nn.BatchNorm2d(channels) + + # 1x1卷积降维（线性瓶颈，不使用激活函数） + self.conv3 = nn.Conv2d( + channels, out_channels, + kernel_size=1, stride=1, padding=0, bias=False + ) + self.bn3 = nn.BatchNorm2d(out_channels) + + # shortcut连接 + self.shortcut = nn.Sequential() + if stride == 1 and in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_channels, out_channels, + kernel_size=1, stride=1, padding=0, bias=False + ), + nn.BatchNorm2d(out_channels) + ) + + self.relu6 = nn.ReLU6(inplace=True) + + def forward(self, x): + # 主分支 + out = self.relu6(self.bn1(self.conv1(x))) # 升维 + out = self.relu6(self.bn2(self.conv2(out))) # 深度卷积 + out = self.bn3(self.conv3(out)) # 降维（线性瓶颈） + + # shortcut连接（仅在stride=1时） + out = out + self.shortcut(x) if self.stride == 1 else out + return out + + +class MobileNetV2(nn.Module): + '''MobileNetV2网络 + + Args: + num_classes: 分类数量 + + 网络配置: + cfg = [(expansion, out_channels, num_blocks, stride), ...] + - expansion: 扩展因子 + - out_channels: 输出通道数 + - num_blocks: 块的数量 + - stride: 第一个块的步长 + ''' + # 网络结构配置 + cfg = [ + # (expansion, out_channels, num_blocks, stride) + (1, 16, 1, 1), # conv1 + (6, 24, 2, 1), # conv2，注意：原论文stride=2，这里改为1以适应CIFAR10 + (6, 32, 3, 2), # conv3 + (6, 64, 4, 2), # conv4 + (6, 96, 3, 1), # conv5 + (6, 160, 3, 2), # conv6 + (6, 320, 1, 1), # conv7 + ] + + def __init__(self, num_classes=10): + super(MobileNetV2, self).__init__() + + # 第一层卷积（注意：原论文stride=2，这里改为1以适应CIFAR10） + self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(32) + + # 主干网络 + self.layers = self._make_layers(in_channels=32) + + # 最后的1x1卷积 + self.conv2 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, padding=0, bias=False) + self.bn2 = nn.BatchNorm2d(1280) + + # 分类器 + self.avgpool = nn.AdaptiveAvgPool2d(1) # 全局平均池化 + self.linear = nn.Linear(1280, num_classes) + self.relu6 = nn.ReLU6(inplace=True) + + def _make_layers(self, in_channels): + '''构建网络层 + + Args: + in_channels: 输入通道数 + ''' + layers = [] + for expansion, out_channels, num_blocks, stride in self.cfg: + # 对于每个配置，第一个block使用指定的stride，后续blocks使用stride=1 + strides = [stride] + [1]*(num_blocks-1) + for stride in strides: + layers.append( + Block(in_channels, out_channels, expansion, stride) + ) + in_channels = out_channels + return nn.Sequential(*layers) + + def forward(self, x): + # 第一层卷积 + out = self.relu6(self.bn1(self.conv1(x))) + + # 主干网络 + out = self.layers(out) + + # 最后的1x1卷积 + out = self.relu6(self.bn2(self.conv2(out))) + + # 分类器 + out = self.avgpool(out) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def test(): + """测试函数""" + net = MobileNetV2() + x = torch.randn(2, 3, 32, 32) + y = net(x) + print(y.size()) + + # 打印模型结构 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (2, 3, 32, 32)) + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/MobileNetv2/code/train.log b/Image/MobileNetv2/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..8e96646488e0217f80b6ca320fb174ddd2e0dc4c --- /dev/null +++ b/Image/MobileNetv2/code/train.log @@ -0,0 +1,253 @@ +2025-03-14 19:11:19,427 - train - INFO - 开始训练 mobilenetv2 +2025-03-14 19:11:19,427 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:2 +2025-03-14 19:11:20,317 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.330 | Acc: 12.50% +2025-03-14 19:11:23,601 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.303 | Acc: 23.39% +2025-03-14 19:11:27,090 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.979 | Acc: 30.59% +2025-03-14 19:11:30,570 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.814 | Acc: 35.40% +2025-03-14 19:11:35,652 - train - INFO - Epoch: 1 | Test Loss: 1.265 | Test Acc: 54.20% +2025-03-14 19:11:36,111 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.408 | Acc: 50.00% +2025-03-14 19:11:39,787 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.257 | Acc: 54.22% +2025-03-14 19:11:43,553 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.201 | Acc: 56.56% +2025-03-14 19:11:47,009 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.161 | Acc: 58.18% +2025-03-14 19:11:51,725 - train - INFO - Epoch: 2 | Test Loss: 1.035 | Test Acc: 62.88% +2025-03-14 19:12:01,997 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.862 | Acc: 62.50% +2025-03-14 19:12:05,402 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.967 | Acc: 65.53% +2025-03-14 19:12:09,177 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.951 | Acc: 66.04% +2025-03-14 19:12:12,828 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.927 | Acc: 66.98% +2025-03-14 19:12:17,300 - train - INFO - Epoch: 3 | Test Loss: 1.029 | Test Acc: 63.78% +2025-03-14 19:12:17,550 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.808 | Acc: 71.88% +2025-03-14 19:12:20,736 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.824 | Acc: 70.96% +2025-03-14 19:12:24,141 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.810 | Acc: 71.45% +2025-03-14 19:12:27,501 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.794 | Acc: 72.10% +2025-03-14 19:12:32,221 - train - INFO - Epoch: 4 | Test Loss: 0.825 | Test Acc: 72.32% +2025-03-14 19:12:42,730 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.603 | Acc: 78.91% +2025-03-14 19:12:45,931 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.722 | Acc: 74.88% +2025-03-14 19:12:49,350 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.720 | Acc: 75.03% +2025-03-14 19:12:52,768 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.710 | Acc: 75.50% +2025-03-14 19:12:57,542 - train - INFO - Epoch: 5 | Test Loss: 0.833 | Test Acc: 71.99% +2025-03-14 19:12:57,797 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.766 | Acc: 74.22% +2025-03-14 19:13:01,161 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.666 | Acc: 76.72% +2025-03-14 19:13:04,549 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.660 | Acc: 77.04% +2025-03-14 19:13:07,812 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.663 | Acc: 76.98% +2025-03-14 19:13:12,288 - train - INFO - Epoch: 6 | Test Loss: 0.724 | Test Acc: 74.58% +2025-03-14 19:13:25,152 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.630 | Acc: 79.69% +2025-03-14 19:13:28,605 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.623 | Acc: 78.57% +2025-03-14 19:13:32,366 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.638 | Acc: 77.99% +2025-03-14 19:13:36,038 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.631 | Acc: 78.20% +2025-03-14 19:13:41,282 - train - INFO - Epoch: 7 | Test Loss: 0.963 | Test Acc: 68.62% +2025-03-14 19:13:41,552 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.673 | Acc: 78.12% +2025-03-14 19:13:45,270 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.606 | Acc: 79.20% +2025-03-14 19:13:48,786 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.615 | Acc: 78.93% +2025-03-14 19:13:52,459 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.620 | Acc: 78.70% +2025-03-14 19:13:57,441 - train - INFO - Epoch: 8 | Test Loss: 0.655 | Test Acc: 77.30% +2025-03-14 19:14:10,767 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.690 | Acc: 80.47% +2025-03-14 19:14:14,075 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.602 | Acc: 79.42% +2025-03-14 19:14:17,467 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.607 | Acc: 79.26% +2025-03-14 19:14:20,940 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.607 | Acc: 79.31% +2025-03-14 19:14:25,541 - train - INFO - Epoch: 9 | Test Loss: 0.622 | Test Acc: 79.21% +2025-03-14 19:14:25,765 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.394 | Acc: 86.72% +2025-03-14 19:14:29,121 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.586 | Acc: 79.70% +2025-03-14 19:14:32,474 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.587 | Acc: 79.66% +2025-03-14 19:14:35,807 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.589 | Acc: 79.61% +2025-03-14 19:14:40,391 - train - INFO - Epoch: 10 | Test Loss: 0.717 | Test Acc: 75.08% +2025-03-14 19:14:50,628 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.578 | Acc: 76.56% +2025-03-14 19:14:53,929 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.577 | Acc: 80.41% +2025-03-14 19:14:57,164 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.587 | Acc: 79.88% +2025-03-14 19:15:00,325 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.590 | Acc: 79.67% +2025-03-14 19:15:04,893 - train - INFO - Epoch: 11 | Test Loss: 0.754 | Test Acc: 75.76% +2025-03-14 19:15:05,135 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.529 | Acc: 80.47% +2025-03-14 19:15:08,549 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.582 | Acc: 80.02% +2025-03-14 19:15:11,951 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.589 | Acc: 79.85% +2025-03-14 19:15:15,202 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.591 | Acc: 79.87% +2025-03-14 19:15:19,885 - train - INFO - Epoch: 12 | Test Loss: 0.675 | Test Acc: 77.78% +2025-03-14 19:15:29,952 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.597 | Acc: 79.69% +2025-03-14 19:15:33,505 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.561 | Acc: 80.87% +2025-03-14 19:15:36,785 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.572 | Acc: 80.45% +2025-03-14 19:15:40,222 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.581 | Acc: 80.12% +2025-03-14 19:15:45,033 - train - INFO - Epoch: 13 | Test Loss: 0.746 | Test Acc: 75.29% +2025-03-14 19:15:45,295 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.613 | Acc: 76.56% +2025-03-14 19:15:48,607 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.578 | Acc: 80.31% +2025-03-14 19:15:51,981 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.582 | Acc: 79.94% +2025-03-14 19:15:55,185 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.585 | Acc: 79.87% +2025-03-14 19:15:59,735 - train - INFO - Epoch: 14 | Test Loss: 0.694 | Test Acc: 76.38% +2025-03-14 19:16:10,825 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.779 | Acc: 72.66% +2025-03-14 19:16:14,400 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.583 | Acc: 80.01% +2025-03-14 19:16:17,644 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.582 | Acc: 79.78% +2025-03-14 19:16:20,887 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.586 | Acc: 79.85% +2025-03-14 19:16:25,746 - train - INFO - Epoch: 15 | Test Loss: 0.814 | Test Acc: 72.75% +2025-03-14 19:16:26,012 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.551 | Acc: 79.69% +2025-03-14 19:16:29,423 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.572 | Acc: 80.21% +2025-03-14 19:16:33,052 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.578 | Acc: 79.84% +2025-03-14 19:16:37,076 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.576 | Acc: 79.97% +2025-03-14 19:16:43,123 - train - INFO - Epoch: 16 | Test Loss: 0.853 | Test Acc: 72.56% +2025-03-14 19:16:54,068 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.480 | Acc: 84.38% +2025-03-14 19:16:57,365 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.576 | Acc: 80.03% +2025-03-14 19:17:00,641 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.578 | Acc: 80.05% +2025-03-14 19:17:04,064 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.580 | Acc: 79.99% +2025-03-14 19:17:08,620 - train - INFO - Epoch: 17 | Test Loss: 0.760 | Test Acc: 74.37% +2025-03-14 19:17:08,909 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.640 | Acc: 78.91% +2025-03-14 19:17:12,601 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.554 | Acc: 80.94% +2025-03-14 19:17:15,904 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.565 | Acc: 80.53% +2025-03-14 19:17:19,195 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.570 | Acc: 80.35% +2025-03-14 19:17:23,385 - train - INFO - Epoch: 18 | Test Loss: 0.766 | Test Acc: 74.48% +2025-03-14 19:17:33,584 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.588 | Acc: 79.69% +2025-03-14 19:17:37,060 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.572 | Acc: 80.45% +2025-03-14 19:17:40,342 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.576 | Acc: 80.27% +2025-03-14 19:17:43,640 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.575 | Acc: 80.28% +2025-03-14 19:17:48,326 - train - INFO - Epoch: 19 | Test Loss: 0.824 | Test Acc: 71.09% +2025-03-14 19:17:48,544 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.648 | Acc: 77.34% +2025-03-14 19:17:51,917 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.574 | Acc: 80.30% +2025-03-14 19:17:55,234 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.565 | Acc: 80.65% +2025-03-14 19:17:58,743 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.568 | Acc: 80.53% +2025-03-14 19:18:04,133 - train - INFO - Epoch: 20 | Test Loss: 0.805 | Test Acc: 74.42% +2025-03-14 19:18:14,312 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.571 | Acc: 81.25% +2025-03-14 19:18:17,780 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.552 | Acc: 81.03% +2025-03-14 19:18:21,057 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.568 | Acc: 80.52% +2025-03-14 19:18:24,568 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.566 | Acc: 80.57% +2025-03-14 19:18:28,874 - train - INFO - Epoch: 21 | Test Loss: 0.707 | Test Acc: 75.96% +2025-03-14 19:18:29,089 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.570 | Acc: 79.69% +2025-03-14 19:18:32,351 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.578 | Acc: 80.07% +2025-03-14 19:18:35,507 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.579 | Acc: 80.19% +2025-03-14 19:18:38,788 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.572 | Acc: 80.46% +2025-03-14 19:18:43,080 - train - INFO - Epoch: 22 | Test Loss: 0.783 | Test Acc: 75.22% +2025-03-14 19:18:53,647 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.549 | Acc: 79.69% +2025-03-14 19:18:56,905 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.577 | Acc: 80.32% +2025-03-14 19:19:00,204 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.566 | Acc: 80.55% +2025-03-14 19:19:03,440 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.565 | Acc: 80.69% +2025-03-14 19:19:07,915 - train - INFO - Epoch: 23 | Test Loss: 0.851 | Test Acc: 72.49% +2025-03-14 19:19:08,129 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.640 | Acc: 78.12% +2025-03-14 19:19:11,393 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.561 | Acc: 80.89% +2025-03-14 19:19:14,726 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.563 | Acc: 80.83% +2025-03-14 19:19:17,930 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.567 | Acc: 80.77% +2025-03-14 19:19:22,468 - train - INFO - Epoch: 24 | Test Loss: 0.710 | Test Acc: 75.41% +2025-03-14 19:19:32,962 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.653 | Acc: 73.44% +2025-03-14 19:19:36,576 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.568 | Acc: 80.31% +2025-03-14 19:19:40,003 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.561 | Acc: 80.63% +2025-03-14 19:19:43,294 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.563 | Acc: 80.62% +2025-03-14 19:19:47,955 - train - INFO - Epoch: 25 | Test Loss: 0.686 | Test Acc: 76.73% +2025-03-14 19:19:48,195 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.549 | Acc: 81.25% +2025-03-14 19:19:51,479 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.565 | Acc: 80.76% +2025-03-14 19:19:54,801 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.556 | Acc: 80.96% +2025-03-14 19:19:58,099 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.562 | Acc: 80.86% +2025-03-14 19:20:02,827 - train - INFO - Epoch: 26 | Test Loss: 0.780 | Test Acc: 75.01% +2025-03-14 19:20:14,115 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.666 | Acc: 77.34% +2025-03-14 19:20:17,565 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.555 | Acc: 80.69% +2025-03-14 19:20:20,973 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.558 | Acc: 80.66% +2025-03-14 19:20:24,381 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.559 | Acc: 80.65% +2025-03-14 19:20:29,009 - train - INFO - Epoch: 27 | Test Loss: 0.617 | Test Acc: 78.93% +2025-03-14 19:20:29,367 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.512 | Acc: 78.91% +2025-03-14 19:20:32,831 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.571 | Acc: 80.22% +2025-03-14 19:20:36,500 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.564 | Acc: 80.50% +2025-03-14 19:20:40,194 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.556 | Acc: 80.87% +2025-03-14 19:20:45,270 - train - INFO - Epoch: 28 | Test Loss: 0.736 | Test Acc: 75.08% +2025-03-14 19:20:58,183 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.505 | Acc: 85.94% +2025-03-14 19:21:01,637 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.557 | Acc: 81.06% +2025-03-14 19:21:05,001 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.558 | Acc: 80.96% +2025-03-14 19:21:08,287 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.557 | Acc: 81.07% +2025-03-14 19:21:13,197 - train - INFO - Epoch: 29 | Test Loss: 0.658 | Test Acc: 77.70% +2025-03-14 19:21:13,435 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.546 | Acc: 81.25% +2025-03-14 19:21:16,848 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.547 | Acc: 81.18% +2025-03-14 19:21:20,474 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.551 | Acc: 81.00% +2025-03-14 19:21:24,104 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.554 | Acc: 80.96% +2025-03-14 19:21:29,008 - train - INFO - Epoch: 30 | Test Loss: 0.636 | Test Acc: 78.32% +2025-03-14 19:21:40,494 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.576 | Acc: 80.47% +2025-03-14 19:21:44,188 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.529 | Acc: 81.79% +2025-03-14 19:21:47,530 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.546 | Acc: 81.08% +2025-03-14 19:21:51,213 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.553 | Acc: 80.88% +2025-03-14 19:21:56,449 - train - INFO - Epoch: 31 | Test Loss: 0.725 | Test Acc: 75.97% +2025-03-14 19:21:56,951 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.451 | Acc: 82.81% +2025-03-14 19:22:00,533 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.558 | Acc: 80.70% +2025-03-14 19:22:03,999 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.549 | Acc: 81.02% +2025-03-14 19:22:07,266 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.552 | Acc: 81.02% +2025-03-14 19:22:12,365 - train - INFO - Epoch: 32 | Test Loss: 0.671 | Test Acc: 77.87% +2025-03-14 19:22:22,767 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.545 | Acc: 81.25% +2025-03-14 19:22:26,261 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.547 | Acc: 81.26% +2025-03-14 19:22:29,835 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.538 | Acc: 81.65% +2025-03-14 19:22:33,299 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.548 | Acc: 81.28% +2025-03-14 19:22:38,258 - train - INFO - Epoch: 33 | Test Loss: 0.673 | Test Acc: 76.89% +2025-03-14 19:22:38,483 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.499 | Acc: 85.16% +2025-03-14 19:22:41,947 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.543 | Acc: 81.47% +2025-03-14 19:22:45,218 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.546 | Acc: 81.37% +2025-03-14 19:22:48,486 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.546 | Acc: 81.26% +2025-03-14 19:22:53,051 - train - INFO - Epoch: 34 | Test Loss: 0.737 | Test Acc: 75.76% +2025-03-14 19:23:05,704 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.463 | Acc: 86.72% +2025-03-14 19:23:09,592 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.539 | Acc: 81.39% +2025-03-14 19:23:13,217 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.551 | Acc: 80.96% +2025-03-14 19:23:16,660 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.545 | Acc: 81.19% +2025-03-14 19:23:20,993 - train - INFO - Epoch: 35 | Test Loss: 0.622 | Test Acc: 78.84% +2025-03-14 19:23:21,202 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.661 | Acc: 79.69% +2025-03-14 19:23:24,564 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.529 | Acc: 81.60% +2025-03-14 19:23:27,994 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.541 | Acc: 81.20% +2025-03-14 19:23:31,448 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.547 | Acc: 81.09% +2025-03-14 19:23:36,527 - train - INFO - Epoch: 36 | Test Loss: 0.837 | Test Acc: 72.92% +2025-03-14 19:23:48,459 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.398 | Acc: 85.16% +2025-03-14 19:23:52,074 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.538 | Acc: 81.60% +2025-03-14 19:23:55,347 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.548 | Acc: 81.17% +2025-03-14 19:23:58,518 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.550 | Acc: 81.06% +2025-03-14 19:24:03,051 - train - INFO - Epoch: 37 | Test Loss: 0.723 | Test Acc: 75.81% +2025-03-14 19:24:03,335 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.533 | Acc: 81.25% +2025-03-14 19:24:06,812 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.536 | Acc: 81.82% +2025-03-14 19:24:10,217 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.544 | Acc: 81.40% +2025-03-14 19:24:13,569 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.547 | Acc: 81.25% +2025-03-14 19:24:17,939 - train - INFO - Epoch: 38 | Test Loss: 0.590 | Test Acc: 79.82% +2025-03-14 19:24:28,511 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.561 | Acc: 80.47% +2025-03-14 19:24:31,759 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.546 | Acc: 81.30% +2025-03-14 19:24:35,014 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.537 | Acc: 81.60% +2025-03-14 19:24:38,163 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.540 | Acc: 81.43% +2025-03-14 19:24:42,444 - train - INFO - Epoch: 39 | Test Loss: 0.727 | Test Acc: 75.61% +2025-03-14 19:24:42,654 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.580 | Acc: 80.47% +2025-03-14 19:24:46,089 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.547 | Acc: 81.23% +2025-03-14 19:24:49,261 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.537 | Acc: 81.72% +2025-03-14 19:24:52,325 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.539 | Acc: 81.49% +2025-03-14 19:24:56,661 - train - INFO - Epoch: 40 | Test Loss: 0.753 | Test Acc: 74.94% +2025-03-14 19:25:07,122 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.489 | Acc: 81.25% +2025-03-14 19:25:10,338 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.562 | Acc: 80.55% +2025-03-14 19:25:13,464 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.547 | Acc: 81.01% +2025-03-14 19:25:16,704 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.547 | Acc: 81.18% +2025-03-14 19:25:21,116 - train - INFO - Epoch: 41 | Test Loss: 0.581 | Test Acc: 79.98% +2025-03-14 19:25:21,351 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.449 | Acc: 85.94% +2025-03-14 19:25:24,547 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.537 | Acc: 81.76% +2025-03-14 19:25:28,065 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.538 | Acc: 81.54% +2025-03-14 19:25:31,912 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.544 | Acc: 81.31% +2025-03-14 19:25:36,378 - train - INFO - Epoch: 42 | Test Loss: 0.761 | Test Acc: 74.92% +2025-03-14 19:25:47,452 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.563 | Acc: 85.16% +2025-03-14 19:25:50,783 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.531 | Acc: 81.60% +2025-03-14 19:25:54,366 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.531 | Acc: 81.75% +2025-03-14 19:25:57,520 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.535 | Acc: 81.60% +2025-03-14 19:26:02,308 - train - INFO - Epoch: 43 | Test Loss: 0.978 | Test Acc: 69.33% +2025-03-14 19:26:02,557 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.489 | Acc: 83.59% +2025-03-14 19:26:05,742 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.531 | Acc: 81.63% +2025-03-14 19:26:08,819 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.539 | Acc: 81.57% +2025-03-14 19:26:12,115 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.538 | Acc: 81.59% +2025-03-14 19:26:16,779 - train - INFO - Epoch: 44 | Test Loss: 0.581 | Test Acc: 80.37% +2025-03-14 19:26:27,807 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.612 | Acc: 76.56% +2025-03-14 19:26:31,140 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.542 | Acc: 81.21% +2025-03-14 19:26:34,298 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.540 | Acc: 81.21% +2025-03-14 19:26:37,533 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.544 | Acc: 81.22% +2025-03-14 19:26:41,886 - train - INFO - Epoch: 45 | Test Loss: 0.669 | Test Acc: 76.62% +2025-03-14 19:26:42,095 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.570 | Acc: 82.81% +2025-03-14 19:26:45,321 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.523 | Acc: 82.39% +2025-03-14 19:26:48,575 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.527 | Acc: 82.04% +2025-03-14 19:26:51,988 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.528 | Acc: 82.04% +2025-03-14 19:26:56,296 - train - INFO - Epoch: 46 | Test Loss: 0.696 | Test Acc: 77.30% +2025-03-14 19:27:06,854 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.466 | Acc: 84.38% +2025-03-14 19:27:10,072 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.527 | Acc: 82.02% +2025-03-14 19:27:13,280 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.529 | Acc: 81.84% +2025-03-14 19:27:16,660 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.531 | Acc: 81.77% +2025-03-14 19:27:21,074 - train - INFO - Epoch: 47 | Test Loss: 0.641 | Test Acc: 78.08% +2025-03-14 19:27:21,292 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.654 | Acc: 77.34% +2025-03-14 19:27:24,474 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.538 | Acc: 81.46% +2025-03-14 19:27:27,668 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.534 | Acc: 81.61% +2025-03-14 19:27:30,907 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.530 | Acc: 81.76% +2025-03-14 19:27:35,251 - train - INFO - Epoch: 48 | Test Loss: 0.627 | Test Acc: 78.30% +2025-03-14 19:27:45,625 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.434 | Acc: 86.72% +2025-03-14 19:27:48,733 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.532 | Acc: 81.76% +2025-03-14 19:27:51,846 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.526 | Acc: 82.16% +2025-03-14 19:27:55,000 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.530 | Acc: 81.97% +2025-03-14 19:27:59,298 - train - INFO - Epoch: 49 | Test Loss: 0.706 | Test Acc: 75.59% +2025-03-14 19:27:59,544 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.438 | Acc: 86.72% +2025-03-14 19:28:02,795 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.520 | Acc: 82.12% +2025-03-14 19:28:06,099 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.522 | Acc: 82.00% +2025-03-14 19:28:09,269 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.528 | Acc: 81.87% +2025-03-14 19:28:13,544 - train - INFO - Epoch: 50 | Test Loss: 0.808 | Test Acc: 72.98% +2025-03-14 19:28:23,729 - train - INFO - 训练完成! diff --git a/Image/MobileNetv2/code/train.py b/Image/MobileNetv2/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..f424d2dc7e38c172ed538344a838e7398a3262b9 --- /dev/null +++ b/Image/MobileNetv2/code/train.py @@ -0,0 +1,63 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import MobileNetV2 + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = MobileNetV2() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv2', + save_type='0', + layer_name='avgpool', + interval=2 + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv2', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv2', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path, + layer_name='avgpool', + interval=2 + ) + +if __name__ == '__main__': + main() diff --git a/Image/MobileNetv2/dataset/.gitkeep b/Image/MobileNetv2/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/MobileNetv2/model/.gitkeep b/Image/MobileNetv2/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/MobileNetv2/model/0/epoch1/embeddings.npy b/Image/MobileNetv2/model/0/epoch1/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..520d8213da18d59baec4071d00f5474e653e447e --- /dev/null +++ b/Image/MobileNetv2/model/0/epoch1/embeddings.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b52266d0c2c5b31d13168219305f2da4e81e7e223cd25bad911c0a5c7193f5b +size 256000128 diff --git a/Image/MobileNetv2/model/0/epoch1/subject_model.pth b/Image/MobileNetv2/model/0/epoch1/subject_model.pth new file mode 100644 index 0000000000000000000000000000000000000000..5a22d65a993a4e967e342c9a28e22240c98286b9 --- /dev/null +++ b/Image/MobileNetv2/model/0/epoch1/subject_model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50eeb616c61cc6a39c6fdd8755a694f3797927aad34a0454c71d02260407f3b2 +size 9442858 diff --git a/Image/MobileNetv2/model/0/epoch10/embeddings.npy b/Image/MobileNetv2/model/0/epoch10/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..3723fa41681894746e390bda431938adeca0cd1e --- /dev/null +++ b/Image/MobileNetv2/model/0/epoch10/embeddings.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d586c90f09c3f04a45caaf7e01a411bb6aaa0a1d128d4dc33b578b392ee9f20 +size 256000128 diff --git a/Image/MobileNetv2/model/0/epoch10/subject_model.pth b/Image/MobileNetv2/model/0/epoch10/subject_model.pth new file mode 100644 index 0000000000000000000000000000000000000000..a44eef63585bbb0077645d09bc833e20630ebf09 --- /dev/null +++ b/Image/MobileNetv2/model/0/epoch10/subject_model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:007802d06a1bd687d2a9fac1a27830a9033e8e8261572ad3bd3a8bee79ec6b02 +size 9442858 diff --git a/Image/MobileNetv2/model/0/epoch11/embeddings.npy b/Image/MobileNetv2/model/0/epoch11/embeddings.npy new file mode 100644 index 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- INFO - 开始训练 mobilenetv3 +2025-03-14 19:34:09,950 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3 +2025-03-14 19:34:10,708 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.320 | Acc: 13.28% +2025-03-14 19:34:13,447 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.146 | Acc: 21.54% +2025-03-14 19:34:16,441 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.000 | Acc: 25.60% +2025-03-14 19:34:19,272 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.921 | Acc: 28.29% +2025-03-14 19:34:23,561 - train - INFO - Epoch: 1 | Test Loss: 1.631 | Test Acc: 40.55% +2025-03-14 19:34:24,104 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.897 | Acc: 28.12% +2025-03-14 19:34:26,866 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.680 | Acc: 36.49% +2025-03-14 19:34:30,220 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.659 | Acc: 37.85% +2025-03-14 19:34:33,127 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.647 | Acc: 38.59% +2025-03-14 19:34:37,194 - train - INFO - Epoch: 2 | Test Loss: 1.613 | Test Acc: 42.79% +2025-03-14 19:34:48,385 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.501 | Acc: 42.97% +2025-03-14 19:34:51,567 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.587 | Acc: 40.91% +2025-03-14 19:34:55,050 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.572 | Acc: 41.97% +2025-03-14 19:35:00,286 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.556 | Acc: 42.79% +2025-03-14 19:35:06,995 - train - INFO - Epoch: 3 | Test Loss: 1.529 | Test Acc: 43.45% +2025-03-14 19:35:07,304 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.589 | Acc: 39.06% +2025-03-14 19:35:10,973 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.471 | Acc: 46.25% +2025-03-14 19:35:14,288 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.445 | Acc: 47.22% +2025-03-14 19:35:17,798 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.425 | Acc: 48.28% +2025-03-14 19:35:22,738 - train - INFO - Epoch: 4 | Test Loss: 1.349 | Test Acc: 51.18% +2025-03-14 19:35:34,169 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.274 | Acc: 52.34% +2025-03-14 19:35:37,141 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.370 | Acc: 51.04% +2025-03-14 19:35:40,306 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.362 | Acc: 51.23% +2025-03-14 19:35:43,178 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.360 | Acc: 51.26% +2025-03-14 19:35:47,371 - train - INFO - Epoch: 5 | Test Loss: 1.291 | Test Acc: 54.39% +2025-03-14 19:35:47,592 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.263 | Acc: 55.47% +2025-03-14 19:35:50,454 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.312 | Acc: 52.95% +2025-03-14 19:35:53,226 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.309 | Acc: 53.16% +2025-03-14 19:35:55,871 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.297 | Acc: 53.53% +2025-03-14 19:35:59,868 - train - INFO - Epoch: 6 | Test Loss: 1.257 | Test Acc: 54.75% +2025-03-14 19:36:10,115 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.212 | Acc: 56.25% +2025-03-14 19:36:13,079 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.261 | Acc: 54.90% +2025-03-14 19:36:16,073 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.252 | Acc: 55.44% +2025-03-14 19:36:18,985 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.249 | Acc: 55.56% +2025-03-14 19:36:23,167 - train - INFO - Epoch: 7 | Test Loss: 1.328 | Test Acc: 51.79% +2025-03-14 19:36:23,446 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.228 | Acc: 50.78% +2025-03-14 19:36:26,398 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.213 | Acc: 57.07% +2025-03-14 19:36:29,421 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.216 | Acc: 56.90% +2025-03-14 19:36:32,300 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.214 | Acc: 56.96% +2025-03-14 19:36:36,642 - train - INFO - Epoch: 8 | Test Loss: 1.222 | Test Acc: 55.97% +2025-03-14 19:36:48,439 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.049 | Acc: 67.97% +2025-03-14 19:36:51,749 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.208 | Acc: 56.95% +2025-03-14 19:36:55,054 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.211 | Acc: 56.86% +2025-03-14 19:36:58,052 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.210 | Acc: 57.06% +2025-03-14 19:37:02,439 - train - INFO - Epoch: 9 | Test Loss: 1.255 | Test Acc: 54.03% +2025-03-14 19:37:02,733 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.179 | Acc: 63.28% +2025-03-14 19:37:06,315 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.209 | Acc: 57.66% +2025-03-14 19:37:09,460 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.201 | Acc: 57.60% +2025-03-14 19:37:12,816 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.202 | Acc: 57.50% +2025-03-14 19:37:17,537 - train - INFO - Epoch: 10 | Test Loss: 1.179 | Test Acc: 57.09% +2025-03-14 19:37:28,080 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.027 | Acc: 67.19% +2025-03-14 19:37:30,927 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.176 | Acc: 58.61% +2025-03-14 19:37:33,856 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.184 | Acc: 58.20% +2025-03-14 19:37:36,622 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.180 | Acc: 58.40% +2025-03-14 19:37:40,915 - train - INFO - Epoch: 11 | Test Loss: 1.203 | Test Acc: 57.33% +2025-03-14 19:37:41,138 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.089 | Acc: 60.94% +2025-03-14 19:37:44,042 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.183 | Acc: 58.00% +2025-03-14 19:37:47,123 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.186 | Acc: 58.07% +2025-03-14 19:37:50,181 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.186 | Acc: 58.21% +2025-03-14 19:37:54,485 - train - INFO - Epoch: 12 | Test Loss: 1.184 | Test Acc: 58.28% +2025-03-14 19:38:04,529 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.169 | Acc: 56.25% +2025-03-14 19:38:07,411 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.180 | Acc: 58.57% +2025-03-14 19:38:10,271 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.172 | Acc: 58.88% +2025-03-14 19:38:13,588 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.170 | Acc: 58.83% +2025-03-14 19:38:17,915 - train - INFO - Epoch: 13 | Test Loss: 1.277 | Test Acc: 55.18% +2025-03-14 19:38:18,180 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.024 | Acc: 62.50% +2025-03-14 19:38:21,018 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.148 | Acc: 59.14% +2025-03-14 19:38:23,826 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.161 | Acc: 58.97% +2025-03-14 19:38:26,656 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.164 | Acc: 58.82% +2025-03-14 19:38:30,586 - train - INFO - Epoch: 14 | Test Loss: 1.276 | Test Acc: 55.02% +2025-03-14 19:38:41,002 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 1.196 | Acc: 53.91% +2025-03-14 19:38:43,862 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.157 | Acc: 59.38% +2025-03-14 19:38:46,647 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.148 | Acc: 59.47% +2025-03-14 19:38:49,537 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.150 | Acc: 59.41% +2025-03-14 19:38:53,845 - train - INFO - Epoch: 15 | Test Loss: 1.164 | Test Acc: 59.17% +2025-03-14 19:38:54,067 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.926 | Acc: 67.97% +2025-03-14 19:38:56,970 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.142 | Acc: 59.78% +2025-03-14 19:38:59,745 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.143 | Acc: 59.80% +2025-03-14 19:39:02,600 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.146 | Acc: 59.79% +2025-03-14 19:39:07,088 - train - INFO - Epoch: 16 | Test Loss: 1.145 | Test Acc: 59.72% +2025-03-14 19:39:18,059 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.978 | Acc: 64.06% +2025-03-14 19:39:21,062 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.159 | Acc: 59.11% +2025-03-14 19:39:24,063 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.149 | Acc: 59.38% +2025-03-14 19:39:27,180 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.145 | Acc: 59.82% +2025-03-14 19:39:32,003 - train - INFO - Epoch: 17 | Test Loss: 1.164 | Test Acc: 59.60% +2025-03-14 19:39:32,294 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.097 | Acc: 60.94% +2025-03-14 19:39:35,379 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.131 | Acc: 59.47% +2025-03-14 19:39:38,505 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.126 | Acc: 59.93% +2025-03-14 19:39:41,838 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.124 | Acc: 60.26% +2025-03-14 19:39:46,267 - train - INFO - Epoch: 18 | Test Loss: 1.178 | Test Acc: 58.70% +2025-03-14 19:39:57,412 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.281 | Acc: 54.69% +2025-03-14 19:40:00,334 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.131 | Acc: 60.60% +2025-03-14 19:40:03,247 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.132 | Acc: 60.33% +2025-03-14 19:40:06,091 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.131 | Acc: 60.40% +2025-03-14 19:40:10,218 - train - INFO - Epoch: 19 | Test Loss: 1.146 | Test Acc: 59.70% +2025-03-14 19:40:10,427 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.237 | Acc: 57.03% +2025-03-14 19:40:13,329 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.105 | Acc: 61.37% +2025-03-14 19:40:16,496 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.112 | Acc: 61.24% +2025-03-14 19:40:19,346 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.119 | Acc: 61.04% +2025-03-14 19:40:23,460 - train - INFO - Epoch: 20 | Test Loss: 1.178 | Test Acc: 58.22% +2025-03-14 19:40:34,045 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.283 | Acc: 57.03% +2025-03-14 19:40:36,967 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.125 | Acc: 60.55% +2025-03-14 19:40:39,673 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.115 | Acc: 61.14% +2025-03-14 19:40:42,896 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.119 | Acc: 60.80% +2025-03-14 19:40:47,481 - train - INFO - Epoch: 21 | Test Loss: 1.148 | Test Acc: 58.85% +2025-03-14 19:40:47,708 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 1.006 | Acc: 63.28% +2025-03-14 19:40:50,647 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.109 | Acc: 61.04% +2025-03-14 19:40:53,390 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.121 | Acc: 60.61% +2025-03-14 19:40:56,265 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.121 | Acc: 60.59% +2025-03-14 19:41:00,300 - train - INFO - Epoch: 22 | Test Loss: 1.252 | Test Acc: 56.00% +2025-03-14 19:41:10,713 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.183 | Acc: 64.06% +2025-03-14 19:41:13,642 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.141 | Acc: 60.41% +2025-03-14 19:41:16,447 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.126 | Acc: 60.60% +2025-03-14 19:41:19,466 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.126 | Acc: 60.61% +2025-03-14 19:41:23,677 - train - INFO - Epoch: 23 | Test Loss: 1.355 | Test Acc: 53.98% +2025-03-14 19:41:23,909 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.204 | Acc: 57.03% +2025-03-14 19:41:26,799 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.111 | Acc: 60.94% +2025-03-14 19:41:29,837 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.111 | Acc: 61.03% +2025-03-14 19:41:32,845 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.112 | Acc: 60.86% +2025-03-14 19:41:37,135 - train - INFO - Epoch: 24 | Test Loss: 1.229 | Test Acc: 56.77% +2025-03-14 19:41:49,373 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.142 | Acc: 60.16% +2025-03-14 19:41:52,683 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.096 | Acc: 62.07% +2025-03-14 19:41:55,781 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.106 | Acc: 61.37% +2025-03-14 19:41:58,961 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.105 | Acc: 61.47% +2025-03-14 19:42:03,822 - train - INFO - Epoch: 25 | Test Loss: 1.208 | Test Acc: 56.94% +2025-03-14 19:42:04,048 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.874 | Acc: 65.62% +2025-03-14 19:42:07,041 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.107 | Acc: 60.66% +2025-03-14 19:42:09,754 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.108 | Acc: 60.90% +2025-03-14 19:42:12,340 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.103 | Acc: 61.09% +2025-03-14 19:42:16,280 - train - INFO - Epoch: 26 | Test Loss: 1.272 | Test Acc: 54.55% +2025-03-14 19:42:26,726 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.055 | Acc: 63.28% +2025-03-14 19:42:29,611 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.098 | Acc: 61.42% +2025-03-14 19:42:32,450 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.101 | Acc: 61.61% +2025-03-14 19:42:35,164 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.110 | Acc: 61.25% +2025-03-14 19:42:38,982 - train - INFO - Epoch: 27 | Test Loss: 1.093 | Test Acc: 61.80% +2025-03-14 19:42:39,189 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.191 | Acc: 61.72% +2025-03-14 19:42:42,143 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.113 | Acc: 60.58% +2025-03-14 19:42:45,181 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.105 | Acc: 60.91% +2025-03-14 19:42:48,103 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.107 | Acc: 60.91% +2025-03-14 19:42:52,248 - train - INFO - Epoch: 28 | Test Loss: 1.132 | Test Acc: 60.36% +2025-03-14 19:43:02,145 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.005 | Acc: 69.53% +2025-03-14 19:43:04,936 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.107 | Acc: 61.42% +2025-03-14 19:43:07,909 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.100 | Acc: 61.62% +2025-03-14 19:43:10,658 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.097 | Acc: 61.47% +2025-03-14 19:43:14,707 - train - INFO - Epoch: 29 | Test Loss: 1.365 | Test Acc: 52.51% +2025-03-14 19:43:14,980 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 1.134 | Acc: 56.25% +2025-03-14 19:43:17,981 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.101 | Acc: 61.53% +2025-03-14 19:43:20,945 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.097 | Acc: 61.55% +2025-03-14 19:43:23,828 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.096 | Acc: 61.62% +2025-03-14 19:43:27,912 - train - INFO - Epoch: 30 | Test Loss: 1.058 | Test Acc: 62.23% +2025-03-14 19:43:37,857 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 1.111 | Acc: 64.06% +2025-03-14 19:43:40,744 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.093 | Acc: 61.44% +2025-03-14 19:43:43,590 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.095 | Acc: 61.63% +2025-03-14 19:43:46,301 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.087 | Acc: 61.93% +2025-03-14 19:43:50,386 - train - INFO - Epoch: 31 | Test Loss: 1.227 | Test Acc: 56.04% +2025-03-14 19:43:50,594 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.883 | Acc: 70.31% +2025-03-14 19:43:53,386 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.085 | Acc: 62.06% +2025-03-14 19:43:56,058 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.093 | Acc: 61.87% +2025-03-14 19:43:58,777 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.095 | Acc: 61.84% +2025-03-14 19:44:02,708 - train - INFO - Epoch: 32 | Test Loss: 1.217 | Test Acc: 57.42% +2025-03-14 19:44:12,615 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.165 | Acc: 60.16% +2025-03-14 19:44:15,397 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.087 | Acc: 61.80% +2025-03-14 19:44:18,183 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.092 | Acc: 61.66% +2025-03-14 19:44:21,023 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.088 | Acc: 61.89% +2025-03-14 19:44:25,133 - train - INFO - Epoch: 33 | Test Loss: 1.139 | Test Acc: 59.81% +2025-03-14 19:44:25,337 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.119 | Acc: 57.81% +2025-03-14 19:44:28,094 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.081 | Acc: 62.59% +2025-03-14 19:44:30,793 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.084 | Acc: 62.30% +2025-03-14 19:44:33,468 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.088 | Acc: 62.01% +2025-03-14 19:44:37,502 - train - INFO - Epoch: 34 | Test Loss: 1.096 | Test Acc: 61.46% +2025-03-14 19:44:47,538 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.280 | Acc: 52.34% +2025-03-14 19:44:50,462 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.105 | Acc: 61.56% +2025-03-14 19:44:53,242 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.098 | Acc: 61.76% +2025-03-14 19:44:56,109 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.094 | Acc: 61.89% +2025-03-14 19:45:00,063 - train - INFO - Epoch: 35 | Test Loss: 1.179 | Test Acc: 59.79% +2025-03-14 19:45:00,274 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.913 | Acc: 69.53% +2025-03-14 19:45:03,181 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.049 | Acc: 63.35% +2025-03-14 19:45:06,116 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.066 | Acc: 62.64% +2025-03-14 19:45:09,021 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.079 | Acc: 62.11% +2025-03-14 19:45:13,026 - train - INFO - Epoch: 36 | Test Loss: 1.090 | Test Acc: 61.83% +2025-03-14 19:45:23,209 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.131 | Acc: 61.72% +2025-03-14 19:45:26,014 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.073 | Acc: 62.70% +2025-03-14 19:45:28,845 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.083 | Acc: 62.25% +2025-03-14 19:45:31,768 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.080 | Acc: 62.33% +2025-03-14 19:45:35,879 - train - INFO - Epoch: 37 | Test Loss: 1.146 | Test Acc: 58.65% +2025-03-14 19:45:36,091 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 1.102 | Acc: 56.25% +2025-03-14 19:45:38,959 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.089 | Acc: 61.91% +2025-03-14 19:45:41,799 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.082 | Acc: 62.14% +2025-03-14 19:45:44,479 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.092 | Acc: 61.85% +2025-03-14 19:45:48,389 - train - INFO - Epoch: 38 | Test Loss: 1.114 | Test Acc: 60.73% +2025-03-14 19:45:58,325 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.011 | Acc: 57.81% +2025-03-14 19:46:01,154 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.086 | Acc: 61.43% +2025-03-14 19:46:04,045 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.083 | Acc: 61.72% +2025-03-14 19:46:06,946 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.083 | Acc: 61.85% +2025-03-14 19:46:10,932 - train - INFO - Epoch: 39 | Test Loss: 1.192 | Test Acc: 58.43% +2025-03-14 19:46:11,177 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.080 | Acc: 61.72% +2025-03-14 19:46:14,380 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.103 | Acc: 62.06% +2025-03-14 19:46:17,468 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.086 | Acc: 62.45% +2025-03-14 19:46:20,564 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.084 | Acc: 62.41% +2025-03-14 19:46:24,461 - train - INFO - Epoch: 40 | Test Loss: 1.151 | Test Acc: 59.16% +2025-03-14 19:46:34,040 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 1.150 | Acc: 64.84% +2025-03-14 19:46:36,830 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.068 | Acc: 62.60% +2025-03-14 19:46:39,527 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.075 | Acc: 62.34% +2025-03-14 19:46:42,471 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.079 | Acc: 62.17% +2025-03-14 19:46:46,291 - train - INFO - Epoch: 41 | Test Loss: 1.162 | Test Acc: 59.24% +2025-03-14 19:46:46,502 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 1.153 | Acc: 60.16% +2025-03-14 19:46:49,178 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.087 | Acc: 61.93% +2025-03-14 19:46:51,859 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.092 | Acc: 62.05% +2025-03-14 19:46:54,717 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.084 | Acc: 62.22% +2025-03-14 19:46:58,764 - train - INFO - Epoch: 42 | Test Loss: 1.114 | Test Acc: 61.30% +2025-03-14 19:47:09,055 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.999 | Acc: 66.41% +2025-03-14 19:47:11,844 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.078 | Acc: 62.23% +2025-03-14 19:47:14,711 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.077 | Acc: 62.01% +2025-03-14 19:47:17,266 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.079 | Acc: 62.16% +2025-03-14 19:47:21,134 - train - INFO - Epoch: 43 | Test Loss: 1.125 | Test Acc: 60.02% +2025-03-14 19:47:21,365 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.118 | Acc: 61.72% +2025-03-14 19:47:24,096 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.069 | Acc: 62.43% +2025-03-14 19:47:26,802 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.062 | Acc: 63.04% +2025-03-14 19:47:29,582 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.069 | Acc: 62.77% +2025-03-14 19:47:33,767 - train - INFO - Epoch: 44 | Test Loss: 1.221 | Test Acc: 59.06% +2025-03-14 19:47:45,660 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.279 | Acc: 53.91% +2025-03-14 19:47:48,442 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.077 | Acc: 62.37% +2025-03-14 19:47:51,274 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.082 | Acc: 62.27% +2025-03-14 19:47:54,009 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.080 | Acc: 62.46% +2025-03-14 19:47:58,034 - train - INFO - Epoch: 45 | Test Loss: 1.185 | Test Acc: 58.87% +2025-03-14 19:47:58,263 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.007 | Acc: 67.19% +2025-03-14 19:48:01,118 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.084 | Acc: 62.31% +2025-03-14 19:48:03,842 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.073 | Acc: 62.87% +2025-03-14 19:48:06,567 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.072 | Acc: 62.83% +2025-03-14 19:48:10,468 - train - INFO - Epoch: 46 | Test Loss: 1.052 | Test Acc: 63.17% +2025-03-14 19:48:20,201 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.105 | Acc: 57.81% +2025-03-14 19:48:23,066 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.051 | Acc: 63.25% +2025-03-14 19:48:25,806 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.057 | Acc: 63.08% +2025-03-14 19:48:28,567 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.068 | Acc: 62.67% +2025-03-14 19:48:32,526 - train - INFO - Epoch: 47 | Test Loss: 1.130 | Test Acc: 60.48% +2025-03-14 19:48:32,747 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.123 | Acc: 54.69% +2025-03-14 19:48:35,546 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.069 | Acc: 62.48% +2025-03-14 19:48:38,451 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.066 | Acc: 62.38% +2025-03-14 19:48:41,132 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.074 | Acc: 62.11% +2025-03-14 19:48:44,988 - train - INFO - Epoch: 48 | Test Loss: 1.077 | Test Acc: 61.90% +2025-03-14 19:48:54,351 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.970 | Acc: 63.28% +2025-03-14 19:48:57,080 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.059 | Acc: 62.83% +2025-03-14 19:49:00,086 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.075 | Acc: 62.46% +2025-03-14 19:49:02,812 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.076 | Acc: 62.34% +2025-03-14 19:49:06,711 - train - INFO - Epoch: 49 | Test Loss: 1.124 | Test Acc: 59.80% +2025-03-14 19:49:06,901 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 1.099 | Acc: 64.06% +2025-03-14 19:49:09,703 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.057 | Acc: 63.00% +2025-03-14 19:49:12,554 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.066 | Acc: 62.73% +2025-03-14 19:49:15,667 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.065 | Acc: 62.84% +2025-03-14 19:49:20,123 - train - INFO - Epoch: 50 | Test Loss: 1.100 | Test Acc: 61.13% +2025-03-14 19:49:31,057 - train - INFO - 训练完成! diff --git a/Image/MobileNetv3/code/model.py b/Image/MobileNetv3/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..460b44d3788071260ba8844ebb8332eb4fb3f827 --- /dev/null +++ b/Image/MobileNetv3/code/model.py @@ -0,0 +1,252 @@ +''' +MobileNetV3 in PyTorch. + +论文: "Searching for MobileNetV3" +参考: https://arxiv.org/abs/1905.02244 + +主要特点： +1. 引入基于NAS的网络架构搜索 +2. 使用改进的SE注意力机块 +3. 使用h-swish激活函数 +4. 重新设计了网络的最后几层 +5. 提供了Large和Small两个版本 +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def get_activation(name): + '''获取激活函数 + + Args: + name: 激活函数名称 ('relu' 或 'hardswish') + ''' + if name == 'relu': + return nn.ReLU(inplace=True) + elif name == 'hardswish': + return nn.Hardswish(inplace=True) + else: + raise NotImplementedError + + +class SEModule(nn.Module): + '''Squeeze-and-Excitation模块 + + 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 + + Args: + channel: 输入通道数 + reduction: 降维比例 + ''' + def __init__(self, channel, reduction=4): + super(SEModule, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Hardsigmoid(inplace=True) + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) # squeeze + y = self.fc(y).view(b, c, 1, 1) # excitation + return x * y.expand_as(x) # scale + + +class Bottleneck(nn.Module): + '''MobileNetV3 Bottleneck + + 包含: + 1. Expansion layer (1x1 conv) + 2. Depthwise layer (3x3 or 5x5 depthwise conv) + 3. SE module (optional) + 4. Projection layer (1x1 conv) + + Args: + in_channels: 输入通道数 + exp_channels: 扩展层通道数 + out_channels: 输出通道数 + kernel_size: 深度卷积核大小 + stride: 步长 + use_SE: 是否使用SE模块 + activation: 激活函数类型 + use_residual: 是否使用残差连接 + ''' + def __init__(self, in_channels, exp_channels, out_channels, kernel_size, + stride, use_SE, activation, use_residual=True): + super(Bottleneck, self).__init__() + self.use_residual = use_residual and stride == 1 and in_channels == out_channels + padding = (kernel_size - 1) // 2 + + layers = [] + # Expansion layer + if exp_channels != in_channels: + layers.extend([ + nn.Conv2d(in_channels, exp_channels, 1, bias=False), + nn.BatchNorm2d(exp_channels), + get_activation(activation) + ]) + + # Depthwise conv + layers.extend([ + nn.Conv2d( + exp_channels, exp_channels, kernel_size, + stride, padding, groups=exp_channels, bias=False + ), + nn.BatchNorm2d(exp_channels), + get_activation(activation) + ]) + + # SE module + if use_SE: + layers.append(SEModule(exp_channels)) + + # Projection layer + layers.extend([ + nn.Conv2d(exp_channels, out_channels, 1, bias=False), + nn.BatchNorm2d(out_channels) + ]) + + self.conv = nn.Sequential(*layers) + + def forward(self, x): + if self.use_residual: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV3(nn.Module): + '''MobileNetV3网络 + + Args: + num_classes: 分类数量 + mode: 'large' 或 'small'，选择网络版本 + ''' + def __init__(self, num_classes=10, mode='small'): + super(MobileNetV3, self).__init__() + + if mode == 'large': + # MobileNetV3-Large架构 + self.config = [ + # k, exp, out, SE, activation, stride + [3, 16, 16, False, 'relu', 1], + [3, 64, 24, False, 'relu', 2], + [3, 72, 24, False, 'relu', 1], + [5, 72, 40, True, 'relu', 2], + [5, 120, 40, True, 'relu', 1], + [5, 120, 40, True, 'relu', 1], + [3, 240, 80, False, 'hardswish', 2], + [3, 200, 80, False, 'hardswish', 1], + [3, 184, 80, False, 'hardswish', 1], + [3, 184, 80, False, 'hardswish', 1], + [3, 480, 112, True, 'hardswish', 1], + [3, 672, 112, True, 'hardswish', 1], + [5, 672, 160, True, 'hardswish', 2], + [5, 960, 160, True, 'hardswish', 1], + [5, 960, 160, True, 'hardswish', 1], + ] + init_conv_out = 16 + final_conv_out = 960 + else: + # MobileNetV3-Small架构 + self.config = [ + # k, exp, out, SE, activation, stride + [3, 16, 16, True, 'relu', 2], + [3, 72, 24, False, 'relu', 2], + [3, 88, 24, False, 'relu', 1], + [5, 96, 40, True, 'hardswish', 2], + [5, 240, 40, True, 'hardswish', 1], + [5, 240, 40, True, 'hardswish', 1], + [5, 120, 48, True, 'hardswish', 1], + [5, 144, 48, True, 'hardswish', 1], + [5, 288, 96, True, 'hardswish', 2], + [5, 576, 96, True, 'hardswish', 1], + [5, 576, 96, True, 'hardswish', 1], + ] + init_conv_out = 16 + final_conv_out = 576 + + # 第一层卷积 + self.conv_stem = nn.Sequential( + nn.Conv2d(3, init_conv_out, 3, 2, 1, bias=False), + nn.BatchNorm2d(init_conv_out), + get_activation('hardswish') + ) + + # 构建Bottleneck层 + features = [] + in_channels = init_conv_out + for k, exp, out, se, activation, stride in self.config: + features.append( + Bottleneck(in_channels, exp, out, k, stride, se, activation) + ) + in_channels = out + self.features = nn.Sequential(*features) + + # 最后的卷积层 + self.conv_head = nn.Sequential( + nn.Conv2d(in_channels, final_conv_out, 1, bias=False), + nn.BatchNorm2d(final_conv_out), + get_activation('hardswish') + ) + + # 分类器 + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Linear(final_conv_out, num_classes) + ) + + # 初始化权重 + self._initialize_weights() + + def _initialize_weights(self): + '''初始化模型权重''' + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward(self, x): + x = self.conv_stem(x) + x = self.features(x) + x = self.conv_head(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.classifier(x) + return x + + +def test(): + """测试函数""" + # 测试Large版本 + net_large = MobileNetV3(mode='large') + x = torch.randn(2, 3, 32, 32) + y = net_large(x) + print('Large output size:', y.size()) + + # 测试Small版本 + net_small = MobileNetV3(mode='small') + y = net_small(x) + print('Small output size:', y.size()) + + # 打印模型结构 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net_small = net_small.to(device) + summary(net_small, (2, 3, 32, 32)) + +if __name__ == '__main__': + test() diff --git a/Image/MobileNetv3/code/train.log b/Image/MobileNetv3/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..71c61fea13a3db19cb353f9e6bb6a9754a4ecae3 --- /dev/null +++ b/Image/MobileNetv3/code/train.log @@ -0,0 +1,253 @@ +2025-03-14 19:34:03,811 - train - INFO - 开始训练 mobilenetv3 +2025-03-14 19:34:03,812 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3 +2025-03-14 19:34:04,510 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.315 | Acc: 8.59% +2025-03-14 19:34:07,447 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.150 | Acc: 21.51% +2025-03-14 19:34:10,386 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.973 | Acc: 26.37% +2025-03-14 19:34:13,121 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.881 | Acc: 29.73% +2025-03-14 19:34:17,285 - train - INFO - Epoch: 1 | Test Loss: 1.600 | Test Acc: 40.59% +2025-03-14 19:34:17,773 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.550 | Acc: 39.06% +2025-03-14 19:34:20,584 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.586 | Acc: 40.99% +2025-03-14 19:34:23,574 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.574 | Acc: 41.84% +2025-03-14 19:34:26,466 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.556 | Acc: 42.67% +2025-03-14 19:34:31,001 - train - INFO - Epoch: 2 | Test Loss: 1.407 | Test Acc: 48.43% +2025-03-14 19:34:41,695 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.286 | Acc: 54.69% +2025-03-14 19:34:44,711 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.447 | Acc: 47.37% +2025-03-14 19:34:47,946 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.443 | Acc: 47.33% +2025-03-14 19:34:50,944 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.441 | Acc: 47.48% +2025-03-14 19:34:56,768 - train - INFO - Epoch: 3 | Test Loss: 1.348 | Test Acc: 51.13% +2025-03-14 19:34:57,073 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.300 | Acc: 57.03% +2025-03-14 19:35:02,274 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.380 | Acc: 49.33% +2025-03-14 19:35:06,559 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.368 | Acc: 50.15% +2025-03-14 19:35:10,317 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.362 | Acc: 50.43% +2025-03-14 19:35:15,273 - train - INFO - Epoch: 4 | Test Loss: 1.359 | Test Acc: 50.22% +2025-03-14 19:35:28,028 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.331 | Acc: 52.34% +2025-03-14 19:35:31,190 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.320 | Acc: 52.00% +2025-03-14 19:35:34,388 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.321 | Acc: 51.92% +2025-03-14 19:35:37,466 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.320 | Acc: 51.83% +2025-03-14 19:35:41,929 - train - INFO - Epoch: 5 | Test Loss: 1.392 | Test Acc: 47.65% +2025-03-14 19:35:42,179 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.329 | Acc: 47.66% +2025-03-14 19:35:45,106 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.295 | Acc: 53.03% +2025-03-14 19:35:48,220 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.293 | Acc: 53.54% +2025-03-14 19:35:51,284 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.287 | Acc: 53.52% +2025-03-14 19:35:55,377 - train - INFO - Epoch: 6 | Test Loss: 1.222 | Test Acc: 56.24% +2025-03-14 19:36:05,521 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.276 | Acc: 55.47% +2025-03-14 19:36:08,675 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.254 | Acc: 54.59% +2025-03-14 19:36:11,596 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.253 | Acc: 54.56% +2025-03-14 19:36:14,501 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.252 | Acc: 54.65% +2025-03-14 19:36:18,647 - train - INFO - Epoch: 7 | Test Loss: 1.287 | Test Acc: 53.84% +2025-03-14 19:36:18,846 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.139 | Acc: 53.91% +2025-03-14 19:36:21,672 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.219 | Acc: 56.05% +2025-03-14 19:36:24,552 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.222 | Acc: 55.93% +2025-03-14 19:36:27,453 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.219 | Acc: 56.18% +2025-03-14 19:36:31,835 - train - INFO - Epoch: 8 | Test Loss: 1.185 | Test Acc: 56.67% +2025-03-14 19:36:43,478 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.177 | Acc: 63.28% +2025-03-14 19:36:46,534 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.201 | Acc: 56.69% +2025-03-14 19:36:49,936 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.210 | Acc: 56.46% +2025-03-14 19:36:52,915 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.212 | Acc: 56.35% +2025-03-14 19:36:57,694 - train - INFO - Epoch: 9 | Test Loss: 1.222 | Test Acc: 55.79% +2025-03-14 19:36:57,918 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.059 | Acc: 64.84% +2025-03-14 19:37:00,868 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.213 | Acc: 56.59% +2025-03-14 19:37:04,043 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.205 | Acc: 56.68% +2025-03-14 19:37:07,484 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.204 | Acc: 56.67% +2025-03-14 19:37:12,223 - train - INFO - Epoch: 10 | Test Loss: 1.201 | Test Acc: 56.86% +2025-03-14 19:37:23,335 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.118 | Acc: 56.25% +2025-03-14 19:37:26,292 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.184 | Acc: 57.36% +2025-03-14 19:37:29,243 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.170 | Acc: 57.91% +2025-03-14 19:37:32,097 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.178 | Acc: 57.66% +2025-03-14 19:37:36,266 - train - INFO - Epoch: 11 | Test Loss: 1.338 | Test Acc: 52.61% +2025-03-14 19:37:36,479 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.215 | Acc: 50.78% +2025-03-14 19:37:39,302 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.181 | Acc: 57.33% +2025-03-14 19:37:42,060 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.187 | Acc: 57.51% +2025-03-14 19:37:44,805 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.177 | Acc: 57.82% +2025-03-14 19:37:49,023 - train - INFO - Epoch: 12 | Test Loss: 1.194 | Test Acc: 56.89% +2025-03-14 19:37:59,417 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.232 | Acc: 53.12% +2025-03-14 19:38:02,245 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.158 | Acc: 58.08% +2025-03-14 19:38:05,015 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.163 | Acc: 58.04% +2025-03-14 19:38:07,820 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.159 | Acc: 58.26% +2025-03-14 19:38:11,959 - train - INFO - Epoch: 13 | Test Loss: 1.149 | Test Acc: 58.96% +2025-03-14 19:38:12,197 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.201 | Acc: 57.03% +2025-03-14 19:38:15,304 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.139 | Acc: 59.60% +2025-03-14 19:38:18,315 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.154 | Acc: 58.94% +2025-03-14 19:38:21,155 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.153 | Acc: 58.77% +2025-03-14 19:38:25,190 - train - INFO - Epoch: 14 | Test Loss: 1.138 | Test Acc: 59.41% +2025-03-14 19:38:35,745 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.976 | Acc: 66.41% +2025-03-14 19:38:38,693 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.149 | Acc: 59.11% +2025-03-14 19:38:41,527 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.141 | Acc: 59.21% +2025-03-14 19:38:44,307 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.145 | Acc: 59.00% +2025-03-14 19:38:48,357 - train - INFO - Epoch: 15 | Test Loss: 1.068 | Test Acc: 62.03% +2025-03-14 19:38:48,571 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 1.218 | Acc: 53.12% +2025-03-14 19:38:51,535 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.121 | Acc: 59.31% +2025-03-14 19:38:54,473 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.124 | Acc: 59.33% +2025-03-14 19:38:57,299 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.128 | Acc: 59.45% +2025-03-14 19:39:01,582 - train - INFO - Epoch: 16 | Test Loss: 1.117 | Test Acc: 60.35% +2025-03-14 19:39:12,941 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 1.216 | Acc: 60.16% +2025-03-14 19:39:15,823 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.128 | Acc: 59.86% +2025-03-14 19:39:18,876 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.124 | Acc: 59.88% +2025-03-14 19:39:21,926 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.131 | Acc: 59.47% +2025-03-14 19:39:26,176 - train - INFO - Epoch: 17 | Test Loss: 1.115 | Test Acc: 60.26% +2025-03-14 19:39:26,410 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.197 | Acc: 54.69% +2025-03-14 19:39:29,699 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.118 | Acc: 60.14% +2025-03-14 19:39:33,109 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.124 | Acc: 59.79% +2025-03-14 19:39:36,272 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.131 | Acc: 59.71% +2025-03-14 19:39:40,820 - train - INFO - Epoch: 18 | Test Loss: 1.250 | Test Acc: 55.97% +2025-03-14 19:39:51,937 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.201 | Acc: 52.34% +2025-03-14 19:39:55,019 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.120 | Acc: 60.21% +2025-03-14 19:39:57,784 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.121 | Acc: 60.15% +2025-03-14 19:40:00,471 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.119 | Acc: 60.19% +2025-03-14 19:40:04,498 - train - INFO - Epoch: 19 | Test Loss: 1.198 | Test Acc: 57.94% +2025-03-14 19:40:04,737 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.173 | Acc: 59.38% +2025-03-14 19:40:07,499 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.108 | Acc: 60.23% +2025-03-14 19:40:10,339 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.119 | Acc: 60.20% +2025-03-14 19:40:13,119 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.112 | Acc: 60.39% +2025-03-14 19:40:17,467 - train - INFO - Epoch: 20 | Test Loss: 1.308 | Test Acc: 54.57% +2025-03-14 19:40:27,692 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.066 | Acc: 59.38% +2025-03-14 19:40:30,553 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.133 | Acc: 59.80% +2025-03-14 19:40:33,490 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.120 | Acc: 60.18% +2025-03-14 19:40:36,428 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.118 | Acc: 60.36% +2025-03-14 19:40:40,344 - train - INFO - Epoch: 21 | Test Loss: 1.159 | Test Acc: 58.64% +2025-03-14 19:40:40,605 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.997 | Acc: 63.28% +2025-03-14 19:40:44,155 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.100 | Acc: 60.76% +2025-03-14 19:40:47,367 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.096 | Acc: 60.92% +2025-03-14 19:40:50,149 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.099 | Acc: 60.99% +2025-03-14 19:40:54,308 - train - INFO - Epoch: 22 | Test Loss: 1.089 | Test Acc: 61.60% +2025-03-14 19:41:04,532 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.029 | Acc: 66.41% +2025-03-14 19:41:07,395 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.086 | Acc: 61.11% +2025-03-14 19:41:10,298 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.086 | Acc: 61.38% +2025-03-14 19:41:13,267 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.091 | Acc: 61.09% +2025-03-14 19:41:17,403 - train - INFO - Epoch: 23 | Test Loss: 1.116 | Test Acc: 60.21% +2025-03-14 19:41:17,641 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.143 | Acc: 66.41% +2025-03-14 19:41:20,685 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.091 | Acc: 61.22% +2025-03-14 19:41:23,621 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.091 | Acc: 61.34% +2025-03-14 19:41:26,610 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.092 | Acc: 61.26% +2025-03-14 19:41:30,833 - train - INFO - Epoch: 24 | Test Loss: 1.095 | Test Acc: 61.01% +2025-03-14 19:41:42,216 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.079 | Acc: 64.84% +2025-03-14 19:41:45,275 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.082 | Acc: 61.22% +2025-03-14 19:41:48,865 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.084 | Acc: 61.43% +2025-03-14 19:41:52,343 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.078 | Acc: 61.63% +2025-03-14 19:41:56,861 - train - INFO - Epoch: 25 | Test Loss: 1.052 | Test Acc: 61.88% +2025-03-14 19:41:57,122 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 1.047 | Acc: 63.28% +2025-03-14 19:42:00,289 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.087 | Acc: 61.63% +2025-03-14 19:42:03,526 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.087 | Acc: 61.79% +2025-03-14 19:42:06,632 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.083 | Acc: 61.67% +2025-03-14 19:42:10,746 - train - INFO - Epoch: 26 | Test Loss: 1.086 | Test Acc: 61.65% +2025-03-14 19:42:20,622 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.134 | Acc: 61.72% +2025-03-14 19:42:23,602 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.076 | Acc: 61.42% +2025-03-14 19:42:26,568 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.072 | Acc: 61.75% +2025-03-14 19:42:29,502 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.079 | Acc: 61.47% +2025-03-14 19:42:33,566 - train - INFO - Epoch: 27 | Test Loss: 1.135 | Test Acc: 60.40% +2025-03-14 19:42:33,802 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.246 | Acc: 50.00% +2025-03-14 19:42:36,640 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.069 | Acc: 62.02% +2025-03-14 19:42:39,573 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.075 | Acc: 61.68% +2025-03-14 19:42:42,773 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.077 | Acc: 61.52% +2025-03-14 19:42:46,877 - train - INFO - Epoch: 28 | Test Loss: 1.093 | Test Acc: 61.21% +2025-03-14 19:42:56,985 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.170 | Acc: 57.03% +2025-03-14 19:42:59,789 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.070 | Acc: 62.21% +2025-03-14 19:43:02,512 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.067 | Acc: 62.04% +2025-03-14 19:43:05,191 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.068 | Acc: 61.89% +2025-03-14 19:43:09,441 - train - INFO - Epoch: 29 | Test Loss: 1.108 | Test Acc: 61.02% +2025-03-14 19:43:09,674 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.939 | Acc: 67.19% +2025-03-14 19:43:12,474 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.060 | Acc: 61.67% +2025-03-14 19:43:15,323 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.067 | Acc: 61.65% +2025-03-14 19:43:18,162 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.060 | Acc: 62.02% +2025-03-14 19:43:22,287 - train - INFO - Epoch: 30 | Test Loss: 1.144 | Test Acc: 60.34% +2025-03-14 19:43:32,512 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.962 | Acc: 65.62% +2025-03-14 19:43:35,242 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.066 | Acc: 61.82% +2025-03-14 19:43:37,851 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.071 | Acc: 61.85% +2025-03-14 19:43:40,703 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.067 | Acc: 61.91% +2025-03-14 19:43:44,687 - train - INFO - Epoch: 31 | Test Loss: 1.087 | Test Acc: 61.81% +2025-03-14 19:43:44,939 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 1.026 | Acc: 60.16% +2025-03-14 19:43:47,798 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.075 | Acc: 61.95% +2025-03-14 19:43:50,802 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.061 | Acc: 62.41% +2025-03-14 19:43:53,659 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.064 | Acc: 62.34% +2025-03-14 19:43:57,726 - train - INFO - Epoch: 32 | Test Loss: 1.134 | Test Acc: 60.53% +2025-03-14 19:44:07,467 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.038 | Acc: 67.97% +2025-03-14 19:44:10,465 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.072 | Acc: 61.79% +2025-03-14 19:44:13,127 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.060 | Acc: 62.29% +2025-03-14 19:44:15,841 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.064 | Acc: 62.27% +2025-03-14 19:44:19,860 - train - INFO - Epoch: 33 | Test Loss: 1.025 | Test Acc: 63.50% +2025-03-14 19:44:20,077 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.182 | Acc: 58.59% +2025-03-14 19:44:23,002 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.041 | Acc: 62.76% +2025-03-14 19:44:26,161 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.044 | Acc: 62.88% +2025-03-14 19:44:29,066 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.052 | Acc: 62.61% +2025-03-14 19:44:32,974 - train - INFO - Epoch: 34 | Test Loss: 1.165 | Test Acc: 59.01% +2025-03-14 19:44:43,069 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.273 | Acc: 57.03% +2025-03-14 19:44:45,817 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.028 | Acc: 63.61% +2025-03-14 19:44:48,611 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.047 | Acc: 63.04% +2025-03-14 19:44:51,395 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.053 | Acc: 62.75% +2025-03-14 19:44:55,612 - train - INFO - Epoch: 35 | Test Loss: 1.135 | Test Acc: 60.02% +2025-03-14 19:44:55,809 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.908 | Acc: 67.97% +2025-03-14 19:44:58,620 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.051 | Acc: 62.64% +2025-03-14 19:45:01,440 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.051 | Acc: 62.66% +2025-03-14 19:45:04,528 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.053 | Acc: 62.72% +2025-03-14 19:45:08,941 - train - INFO - Epoch: 36 | Test Loss: 1.187 | Test Acc: 57.95% +2025-03-14 19:45:18,855 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.220 | Acc: 55.47% +2025-03-14 19:45:21,638 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.021 | Acc: 63.85% +2025-03-14 19:45:24,446 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.041 | Acc: 63.18% +2025-03-14 19:45:27,235 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.036 | Acc: 63.54% +2025-03-14 19:45:31,429 - train - INFO - Epoch: 37 | Test Loss: 1.055 | Test Acc: 62.86% +2025-03-14 19:45:31,653 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.931 | Acc: 62.50% +2025-03-14 19:45:34,563 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.018 | Acc: 63.77% +2025-03-14 19:45:37,461 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.037 | Acc: 63.12% +2025-03-14 19:45:40,390 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.049 | Acc: 62.78% +2025-03-14 19:45:44,376 - train - INFO - Epoch: 38 | Test Loss: 1.055 | Test Acc: 61.96% +2025-03-14 19:45:54,849 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.256 | Acc: 60.94% +2025-03-14 19:45:57,611 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.041 | Acc: 63.05% +2025-03-14 19:46:00,366 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.043 | Acc: 62.62% +2025-03-14 19:46:03,308 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.040 | Acc: 62.69% +2025-03-14 19:46:07,524 - train - INFO - Epoch: 39 | Test Loss: 1.217 | Test Acc: 58.12% +2025-03-14 19:46:07,739 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.104 | Acc: 60.94% +2025-03-14 19:46:10,580 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.027 | Acc: 63.51% +2025-03-14 19:46:13,745 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.030 | Acc: 63.62% +2025-03-14 19:46:16,973 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.042 | Acc: 63.13% +2025-03-14 19:46:21,340 - train - INFO - Epoch: 40 | Test Loss: 1.219 | Test Acc: 58.91% +2025-03-14 19:46:30,937 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.936 | Acc: 65.62% +2025-03-14 19:46:33,767 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.036 | Acc: 63.67% +2025-03-14 19:46:36,441 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.043 | Acc: 63.02% +2025-03-14 19:46:39,112 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.041 | Acc: 63.14% +2025-03-14 19:46:43,225 - train - INFO - Epoch: 41 | Test Loss: 1.087 | Test Acc: 61.82% +2025-03-14 19:46:43,446 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.974 | Acc: 67.97% +2025-03-14 19:46:46,463 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.031 | Acc: 63.10% +2025-03-14 19:46:49,321 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.046 | Acc: 62.82% +2025-03-14 19:46:52,065 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.040 | Acc: 62.91% +2025-03-14 19:46:56,121 - train - INFO - Epoch: 42 | Test Loss: 1.088 | Test Acc: 61.92% +2025-03-14 19:47:06,480 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.959 | Acc: 64.84% +2025-03-14 19:47:09,338 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.025 | Acc: 63.60% +2025-03-14 19:47:12,303 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.028 | Acc: 63.58% +2025-03-14 19:47:15,185 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.034 | Acc: 63.35% +2025-03-14 19:47:19,050 - train - INFO - Epoch: 43 | Test Loss: 1.164 | Test Acc: 59.61% +2025-03-14 19:47:19,264 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.079 | Acc: 55.47% +2025-03-14 19:47:22,105 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.025 | Acc: 63.51% +2025-03-14 19:47:24,823 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.025 | Acc: 63.38% +2025-03-14 19:47:27,612 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.028 | Acc: 63.24% +2025-03-14 19:47:31,900 - train - INFO - Epoch: 44 | Test Loss: 1.046 | Test Acc: 62.74% +2025-03-14 19:47:42,877 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.033 | Acc: 62.50% +2025-03-14 19:47:45,774 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.033 | Acc: 63.99% +2025-03-14 19:47:48,457 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.038 | Acc: 63.33% +2025-03-14 19:47:51,350 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.043 | Acc: 62.95% +2025-03-14 19:47:55,429 - train - INFO - Epoch: 45 | Test Loss: 1.055 | Test Acc: 62.21% +2025-03-14 19:47:55,644 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.122 | Acc: 57.03% +2025-03-14 19:47:58,618 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.020 | Acc: 63.92% +2025-03-14 19:48:01,531 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.039 | Acc: 63.16% +2025-03-14 19:48:04,384 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.039 | Acc: 63.08% +2025-03-14 19:48:08,515 - train - INFO - Epoch: 46 | Test Loss: 1.151 | Test Acc: 58.80% +2025-03-14 19:48:18,563 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.005 | Acc: 61.72% +2025-03-14 19:48:21,390 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.011 | Acc: 64.18% +2025-03-14 19:48:24,102 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.029 | Acc: 63.72% +2025-03-14 19:48:26,782 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.028 | Acc: 63.71% +2025-03-14 19:48:30,851 - train - INFO - Epoch: 47 | Test Loss: 1.091 | Test Acc: 61.88% +2025-03-14 19:48:31,086 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.034 | Acc: 66.41% +2025-03-14 19:48:33,899 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.013 | Acc: 63.99% +2025-03-14 19:48:36,777 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.031 | Acc: 63.33% +2025-03-14 19:48:39,701 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.034 | Acc: 63.14% +2025-03-14 19:48:43,738 - train - INFO - Epoch: 48 | Test Loss: 1.051 | Test Acc: 63.01% +2025-03-14 19:48:53,341 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.940 | Acc: 69.53% +2025-03-14 19:48:56,129 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.022 | Acc: 63.68% +2025-03-14 19:48:59,194 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.027 | Acc: 63.31% +2025-03-14 19:49:02,012 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.029 | Acc: 63.34% +2025-03-14 19:49:06,039 - train - INFO - Epoch: 49 | Test Loss: 1.045 | Test Acc: 63.33% +2025-03-14 19:49:06,237 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.902 | Acc: 63.28% +2025-03-14 19:49:09,007 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.025 | Acc: 63.56% +2025-03-14 19:49:11,762 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.026 | Acc: 63.59% +2025-03-14 19:49:14,634 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.031 | Acc: 63.42% +2025-03-14 19:49:19,058 - train - INFO - Epoch: 50 | Test Loss: 1.069 | Test Acc: 62.55% +2025-03-14 19:49:30,812 - train - INFO - 训练完成! diff --git a/Image/MobileNetv3/code/train.py b/Image/MobileNetv3/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..b3014ef3f545306f00a1df7fdaec4725f5e1245c --- /dev/null +++ b/Image/MobileNetv3/code/train.py @@ -0,0 +1,63 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import MobileNetV3 + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = MobileNetV3() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv3', + save_type='0', + layer_name='avgpool', + interval=2 + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv3', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='mobilenetv3', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path, + layer_name='avgpool', + interval=2 + ) + +if __name__ == '__main__': + main() diff --git a/Image/MobileNetv3/dataset/.gitkeep b/Image/MobileNetv3/dataset/.gitkeep new file mode 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0000000000000000000000000000000000000000..e8e65e4fed1b8259436a3f4dcbc3ace8e6d738bf --- /dev/null +++ b/Image/ResNet/code/model.py @@ -0,0 +1,259 @@ +''' +ResNet in PyTorch. + +ResNet(深度残差网络)是由微软研究院的Kaiming He等人提出的深度神经网络架构。 +主要创新点是引入了残差学习的概念,通过跳跃连接解决了深层网络的退化问题。 + +主要特点: +1. 引入残差块(Residual Block),使用跳跃连接 +2. 使用Batch Normalization进行归一化 +3. 支持更深的网络结构(最深可达152层) +4. 在多个计算机视觉任务上取得了突破性进展 + +Reference: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385 +''' +import torch +import torch.nn as nn + +class BasicBlock(nn.Module): + """基础残差块 + + 用于ResNet18/34等浅层网络。结构为: + x -> Conv -> BN -> ReLU -> Conv -> BN -> (+) -> ReLU + |------------------------------------------| + + Args: + in_channels: 输入通道数 + out_channels: 输出通道数 + stride: 步长,用于下采样,默认为1 + + 注意:基础模块没有通道压缩,expansion=1 + """ + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1): + super(BasicBlock,self).__init__() + self.features = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(True), + nn.Conv2d(out_channels,out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + + # 如果输入输出维度不等,则使用1x1卷积层来改变维度 + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels), + ) + + def forward(self, x): + out = self.features(x) + out += self.shortcut(x) + out = torch.relu(out) + return out + + +class Bottleneck(nn.Module): + """瓶颈残差块 + + 用于ResNet50/101/152等深层网络。结构为: + x -> 1x1Conv -> BN -> ReLU -> 3x3Conv -> BN -> ReLU -> 1x1Conv -> BN -> (+) -> ReLU + |-------------------------------------------------------------------| + + Args: + in_channels: 输入通道数 + zip_channels: 压缩后的通道数 + stride: 步长,用于下采样,默认为1 + + 注意:通过1x1卷积先压缩通道数,再还原,expansion=4 + """ + expansion = 4 + + def __init__(self, in_channels, zip_channels, stride=1): + super(Bottleneck, self).__init__() + out_channels = self.expansion * zip_channels + self.features = nn.Sequential( + # 1x1卷积压缩通道 + nn.Conv2d(in_channels, zip_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(zip_channels), + nn.ReLU(inplace=True), + # 3x3卷积提取特征 + nn.Conv2d(zip_channels, zip_channels, kernel_size=3, stride=stride, padding=1, bias=False), + nn.BatchNorm2d(zip_channels), + nn.ReLU(inplace=True), + # 1x1卷积还原通道 + nn.Conv2d(zip_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_channels) + ) + + def forward(self, x): + out = self.features(x) + out += self.shortcut(x) + out = torch.relu(out) + return out + +class ResNet(nn.Module): + """ResNet模型 + + 网络结构: + 1. 一个卷积层用于特征提取 + 2. 四个残差层,每层包含多个残差块 + 3. 平均池化和全连接层进行分类 + + 对于CIFAR10,特征图大小变化为: + (32,32,3) -> [Conv] -> (32,32,64) -> [Layer1] -> (32,32,64) -> [Layer2] + -> (16,16,128) -> [Layer3] -> (8,8,256) -> [Layer4] -> (4,4,512) -> [AvgPool] + -> (1,1,512) -> [FC] -> (num_classes) + + Args: + block: 残差块类型(BasicBlock或Bottleneck) + num_blocks: 每层残差块数量的列表 + num_classes: 分类数量,默认为10 + verbose: 是否打印中间特征图大小 + init_weights: 是否初始化权重 + """ + def __init__(self, block, num_blocks, num_classes=10, verbose=False, init_weights=True): + super(ResNet, self).__init__() + self.verbose = verbose + self.in_channels = 64 + + # 第一层卷积 + self.features = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True) + ) + + # 四个残差层 + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + + # 分类层 + self.avg_pool = nn.AvgPool2d(kernel_size=4) + self.classifier = nn.Linear(512 * block.expansion, num_classes) + + if init_weights: + self._initialize_weights() + + def _make_layer(self, block, out_channels, num_blocks, stride): + """构建残差层 + + Args: + block: 残差块类型 + out_channels: 输出通道数 + num_blocks: 残差块数量 + stride: 第一个残差块的步长(用于下采样) + + Returns: + nn.Sequential: 残差层 + """ + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + """前向传播 + + Args: + x: 输入张量,[N,3,32,32] + + Returns: + out: 输出张量,[N,num_classes] + """ + out = self.features(x) + if self.verbose: + print('block 1 output: {}'.format(out.shape)) + + out = self.layer1(out) + if self.verbose: + print('block 2 output: {}'.format(out.shape)) + + out = self.layer2(out) + if self.verbose: + print('block 3 output: {}'.format(out.shape)) + + out = self.layer3(out) + if self.verbose: + print('block 4 output: {}'.format(out.shape)) + + out = self.layer4(out) + if self.verbose: + print('block 5 output: {}'.format(out.shape)) + + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + out = self.classifier(out) + return out + + def _initialize_weights(self): + """初始化模型权重 + + 采用kaiming初始化方法: + - 卷积层权重采用kaiming_normal_初始化 + - BN层参数采用常数初始化 + - 线性层采用正态分布初始化 + """ + 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) + nn.init.constant_(m.bias, 0) + +def ResNet18(verbose=False): + """ResNet-18模型""" + return ResNet(BasicBlock, [2,2,2,2], verbose=verbose) + +def ResNet34(verbose=False): + """ResNet-34模型""" + return ResNet(BasicBlock, [3,4,6,3], verbose=verbose) + +def ResNet50(verbose=False): + """ResNet-50模型""" + return ResNet(Bottleneck, [3,4,6,3], verbose=verbose) + +def ResNet101(verbose=False): + """ResNet-101模型""" + return ResNet(Bottleneck, [3,4,23,3], verbose=verbose) + +def ResNet152(verbose=False): + """ResNet-152模型""" + return ResNet(Bottleneck, [3,8,36,3], verbose=verbose) + +def test(): + """测试函数""" + net = ResNet34() + x = torch.randn(2,3,32,32) + y = net(x) + print('Output shape:', y.size()) + + # 打印模型结构 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net,(2,3,32,32)) + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/ResNet/code/train.py b/Image/ResNet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..0c6cb16a5140cb10bbbaaf1c2a9535c1101a5424 --- /dev/null +++ b/Image/ResNet/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import ResNet34 + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = ResNet34() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='resnet', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='resnet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='resnet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/ResNet/dataset/.gitkeep b/Image/ResNet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ResNet/model/.gitkeep b/Image/ResNet/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/SENet/code/model.py b/Image/SENet/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..88899e5bcc30f78fc61076a5667acd5dc680d564 --- /dev/null +++ b/Image/SENet/code/model.py @@ -0,0 +1,251 @@ +''' +SENet (Squeeze-and-Excitation Networks) in PyTorch. + +SENet通过引入SE模块来自适应地重新校准通道特征响应。SE模块可以集成到现有的网络架构中， +通过显式建模通道之间的相互依赖关系，自适应地重新校准通道特征响应。 + +主要特点: +1. 引入Squeeze-and-Excitation(SE)模块，增强特征的表示能力 +2. SE模块包含squeeze操作(全局平均池化)和excitation操作(两个FC层) +3. 通过attention机制来增强重要通道的权重，抑制不重要通道 +4. 几乎可以嵌入到任何现有的网络结构中 + +Reference: +[1] Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu + Squeeze-and-Excitation Networks. CVPR 2018. +''' +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + """基础残差块+SE模块 + + 结构: + x -> Conv -> BN -> ReLU -> Conv -> BN -> SE -> (+) -> ReLU + |------------------------------------------| + + Args: + in_channels: 输入通道数 + channels: 输出通道数 + stride: 步长,用于下采样,默认为1 + """ + def __init__(self, in_channels, channels, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(channels) + self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(channels) + + # 残差连接 + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(channels) + ) + + # SE模块 + self.squeeze = nn.AdaptiveAvgPool2d(1) # 全局平均池化 + self.excitation = nn.Sequential( + nn.Conv2d(channels, channels//16, kernel_size=1), # 通道降维 + nn.ReLU(inplace=True), + nn.Conv2d(channels//16, channels, kernel_size=1), # 通道升维 + nn.Sigmoid() # 归一化到[0,1] + ) + + def forward(self, x): + # 主分支 + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + + # SE模块 + w = self.squeeze(out) # Squeeze + w = self.excitation(w) # Excitation + out = out * w # 特征重标定 + + # 残差连接 + out += self.shortcut(x) + out = F.relu(out) + return out + + +class PreActBlock(nn.Module): + """Pre-activation版本的基础块+SE模块 + + 结构: + x -> BN -> ReLU -> Conv -> BN -> ReLU -> Conv -> SE -> (+) + |-------------------------------------------| + + Args: + in_channels: 输入通道数 + channels: 输出通道数 + stride: 步长,用于下采样,默认为1 + """ + def __init__(self, in_channels, channels, stride=1): + super(PreActBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_channels) + self.conv1 = nn.Conv2d(in_channels, channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(channels) + self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1, bias=False) + + # 残差连接 + if stride != 1 or in_channels != channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, channels, kernel_size=1, stride=stride, bias=False) + ) + + # SE模块 + self.squeeze = nn.AdaptiveAvgPool2d(1) + self.excitation = nn.Sequential( + nn.Conv2d(channels, channels//16, kernel_size=1), + nn.ReLU(inplace=True), + nn.Conv2d(channels//16, channels, kernel_size=1), + nn.Sigmoid() + ) + + def forward(self, x): + # Pre-activation + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x + + # 主分支 + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + + # SE模块 + w = self.squeeze(out) + w = self.excitation(w) + out = out * w + + # 残差连接 + out += shortcut + return out + + +class SENet(nn.Module): + """SENet模型 + + 网络结构: + 1. 一个卷积层进行特征提取 + 2. 四个残差层,每层包含多个带SE模块的残差块 + 3. 平均池化和全连接层进行分类 + + Args: + block: 残差块类型(BasicBlock或PreActBlock) + num_blocks: 每层残差块数量的列表 + num_classes: 分类数量,默认为10 + """ + def __init__(self, block, num_blocks, num_classes=10): + super(SENet, self).__init__() + self.in_channels = 64 + + # 第一层卷积 + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + + # 四个残差层 + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + + # 分类层 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(512, num_classes) + + # 初始化权重 + self._initialize_weights() + + def _make_layer(self, block, channels, num_blocks, stride): + """构建残差层 + + Args: + block: 残差块类型 + channels: 输出通道数 + num_blocks: 残差块数量 + stride: 第一个残差块的步长(用于下采样) + + Returns: + nn.Sequential: 残差层 + """ + strides = [stride] + [1]*(num_blocks-1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, channels, stride)) + self.in_channels = channels + return nn.Sequential(*layers) + + def forward(self, x): + """前向传播 + + Args: + x: 输入张量,[N,3,32,32] + + Returns: + out: 输出张量,[N,num_classes] + """ + # 特征提取 + out = F.relu(self.bn1(self.conv1(x))) + + # 残差层 + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + + # 分类 + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + out = self.classifier(out) + return out + + def _initialize_weights(self): + """初始化模型权重 + + 采用kaiming初始化方法: + - 卷积层权重采用kaiming_normal_初始化 + - BN层参数采用常数初始化 + - 线性层采用正态分布初始化 + """ + 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) + nn.init.constant_(m.bias, 0) + + +def SENet18(): + """SENet-18模型""" + return SENet(PreActBlock, [2,2,2,2]) + + +def test(): + """测试函数""" + # 创建模型 + net = SENet18() + print('Model Structure:') + print(net) + + # 测试前向传播 + x = torch.randn(1,3,32,32) + y = net(x) + print('\nInput Shape:', x.shape) + print('Output Shape:', y.shape) + + # 打印模型信息 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (1,3,32,32)) + + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/SENet/code/train.py b/Image/SENet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..39e53b402f24b97bc032cc1c96f32755772a4564 --- /dev/null +++ b/Image/SENet/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import SENet + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = SENet() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='senet', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='senet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='senet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/SENet/dataset/.gitkeep b/Image/SENet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/SENet/model/.gitkeep b/Image/SENet/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ShuffleNet/code/model.py b/Image/ShuffleNet/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..9fb599570de101d901f3b6065eee4b30e146def3 --- /dev/null +++ b/Image/ShuffleNet/code/model.py @@ -0,0 +1,263 @@ +''' +ShuffleNet in PyTorch. + +ShuffleNet是一个专门为移动设备设计的高效卷积神经网络。其主要创新点在于使用了两个新操作: +1. 逐点组卷积(pointwise group convolution) +2. 通道重排(channel shuffle) +这两个操作大大降低了计算复杂度,同时保持了良好的准确率。 + +主要特点: +1. 使用组卷积减少参数量和计算量 +2. 使用通道重排操作使不同组之间的信息可以流通 +3. 使用深度可分离卷积进一步降低计算复杂度 +4. 设计了多个计算复杂度版本以适应不同的设备 + +Reference: +[1] Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun + ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. CVPR 2018. +''' +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ShuffleBlock(nn.Module): + """通道重排模块 + + 通过重新排列通道的顺序来实现不同组之间的信息交流。 + + Args: + groups (int): 分组数量 + """ + def __init__(self, groups): + super(ShuffleBlock, self).__init__() + self.groups = groups + + def forward(self, x): + """通道重排的前向传播 + + 步骤: + 1. [N,C,H,W] -> [N,g,C/g,H,W] # 重塑为g组 + 2. [N,g,C/g,H,W] -> [N,C/g,g,H,W] # 转置g维度 + 3. [N,C/g,g,H,W] -> [N,C,H,W] # 重塑回原始形状 + + Args: + x: 输入张量,[N,C,H,W] + + Returns: + out: 通道重排后的张量,[N,C,H,W] + """ + N, C, H, W = x.size() + g = self.groups + return x.view(N,g,C//g,H,W).permute(0,2,1,3,4).reshape(N,C,H,W) + + +class Bottleneck(nn.Module): + """ShuffleNet的基本模块 + + 结构: + x -> 1x1 GConv -> BN -> Shuffle -> 3x3 DWConv -> BN -> 1x1 GConv -> BN -> (+) -> ReLU + |---------------------| + + Args: + in_channels (int): 输入通道数 + out_channels (int): 输出通道数 + stride (int): 步长,用于下采样 + groups (int): 组卷积的分组数 + """ + def __init__(self, in_channels, out_channels, stride, groups): + super(Bottleneck, self).__init__() + self.stride = stride + + # 确定中间通道数和分组数 + mid_channels = out_channels // 4 + g = 1 if in_channels == 24 else groups + + # 第一个1x1组卷积 + self.conv1 = nn.Conv2d(in_channels, mid_channels, + kernel_size=1, groups=g, bias=False) + self.bn1 = nn.BatchNorm2d(mid_channels) + self.shuffle1 = ShuffleBlock(groups=g) + + # 3x3深度可分离卷积 + self.conv2 = nn.Conv2d(mid_channels, mid_channels, + kernel_size=3, stride=stride, padding=1, + groups=mid_channels, bias=False) + self.bn2 = nn.BatchNorm2d(mid_channels) + + # 第二个1x1组卷积 + self.conv3 = nn.Conv2d(mid_channels, out_channels, + kernel_size=1, groups=groups, bias=False) + self.bn3 = nn.BatchNorm2d(out_channels) + + # 残差连接 + self.shortcut = nn.Sequential() + if stride == 2: + self.shortcut = nn.Sequential( + nn.AvgPool2d(3, stride=2, padding=1) + ) + + def forward(self, x): + # 主分支 + out = F.relu(self.bn1(self.conv1(x))) + out = self.shuffle1(out) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + + # 残差连接 + res = self.shortcut(x) + + # 如果是下采样层,拼接残差;否则相加 + out = F.relu(torch.cat([out, res], 1)) if self.stride == 2 else F.relu(out + res) + return out + + +class ShuffleNet(nn.Module): + """ShuffleNet模型 + + 网络结构: + 1. 一个卷积层进行特征提取 + 2. 三个阶段,每个阶段包含多个带重排的残差块 + 3. 平均池化和全连接层进行分类 + + Args: + cfg (dict): 配置字典,包含: + - out_channels (list): 每个阶段的输出通道数 + - num_blocks (list): 每个阶段的块数 + - groups (int): 组卷积的分组数 + """ + def __init__(self, cfg): + super(ShuffleNet, self).__init__() + out_channels = cfg['out_channels'] + num_blocks = cfg['num_blocks'] + groups = cfg['groups'] + + # 第一层卷积 + self.conv1 = nn.Conv2d(3, 24, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(24) + self.in_channels = 24 + + # 三个阶段 + self.layer1 = self._make_layer(out_channels[0], num_blocks[0], groups) + self.layer2 = self._make_layer(out_channels[1], num_blocks[1], groups) + self.layer3 = self._make_layer(out_channels[2], num_blocks[2], groups) + + # 分类层 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(out_channels[2], 10) + + # 初始化权重 + self._initialize_weights() + + def _make_layer(self, out_channels, num_blocks, groups): + """构建ShuffleNet的一个阶段 + + Args: + out_channels (int): 输出通道数 + num_blocks (int): 块的数量 + groups (int): 分组数 + + Returns: + nn.Sequential: 一个阶段的层序列 + """ + layers = [] + for i in range(num_blocks): + stride = 2 if i == 0 else 1 + cat_channels = self.in_channels if i == 0 else 0 + layers.append( + Bottleneck( + self.in_channels, + out_channels - cat_channels, + stride=stride, + groups=groups + ) + ) + self.in_channels = out_channels + return nn.Sequential(*layers) + + def forward(self, x): + """前向传播 + + Args: + x: 输入张量,[N,3,32,32] + + Returns: + out: 输出张量,[N,num_classes] + """ + # 特征提取 + out = F.relu(self.bn1(self.conv1(x))) + + # 三个阶段 + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + + # 分类 + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + out = self.classifier(out) + return out + + def _initialize_weights(self): + """初始化模型权重 + + 采用kaiming初始化方法: + - 卷积层权重采用kaiming_normal_初始化 + - BN层参数采用常数初始化 + - 线性层采用正态分布初始化 + """ + 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) + nn.init.constant_(m.bias, 0) + + +def ShuffleNetG2(): + """返回groups=2的ShuffleNet模型""" + cfg = { + 'out_channels': [200,400,800], + 'num_blocks': [4,8,4], + 'groups': 2 + } + return ShuffleNet(cfg) + + +def ShuffleNetG3(): + """返回groups=3的ShuffleNet模型""" + cfg = { + 'out_channels': [240,480,960], + 'num_blocks': [4,8,4], + 'groups': 3 + } + return ShuffleNet(cfg) + + +def test(): + """测试函数""" + # 创建模型 + net = ShuffleNetG2() + print('Model Structure:') + print(net) + + # 测试前向传播 + x = torch.randn(1,3,32,32) + y = net(x) + print('\nInput Shape:', x.shape) + print('Output Shape:', y.shape) + + # 打印模型信息 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (1,3,32,32)) + + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/ShuffleNet/code/train.py b/Image/ShuffleNet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..32a725a6310759fe02f85b7932896c9275eaad53 --- /dev/null +++ b/Image/ShuffleNet/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import ShuffleNet + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = ShuffleNet() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='shufflenet', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='shufflenet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='shufflenet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/ShuffleNet/dataset/.gitkeep b/Image/ShuffleNet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ShuffleNet/model/.gitkeep b/Image/ShuffleNet/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ShuffleNetv2/code/model.py b/Image/ShuffleNetv2/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..ca097bcd85c9fba957263f59fa9100c22c5e24fb --- /dev/null +++ b/Image/ShuffleNetv2/code/model.py @@ -0,0 +1,345 @@ +''' +ShuffleNetV2 in PyTorch. + +ShuffleNetV2是ShuffleNet的改进版本,通过实验总结出了四个高效网络设计的实用准则: +1. 输入输出通道数相等时计算量最小 +2. 过度使用组卷积会增加MAC(内存访问代价) +3. 网络碎片化会降低并行度 +4. Element-wise操作不可忽视 + +主要改进: +1. 通道分离(Channel Split)替代组卷积 +2. 重新设计了基本单元,使输入输出通道数相等 +3. 每个阶段使用不同的通道数配置 +4. 简化了下采样模块的设计 + +Reference: +[1] Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun + ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018. +''' +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ShuffleBlock(nn.Module): + """通道重排模块 + + 通过重新排列通道的顺序来实现不同特征的信息交流。 + + Args: + groups (int): 分组数量,默认为2 + """ + def __init__(self, groups=2): + super(ShuffleBlock, self).__init__() + self.groups = groups + + def forward(self, x): + """通道重排的前向传播 + + 步骤: + 1. [N,C,H,W] -> [N,g,C/g,H,W] # 重塑为g组 + 2. [N,g,C/g,H,W] -> [N,C/g,g,H,W] # 转置g维度 + 3. [N,C/g,g,H,W] -> [N,C,H,W] # 重塑回原始形状 + + Args: + x: 输入张量,[N,C,H,W] + + Returns: + out: 通道重排后的张量,[N,C,H,W] + """ + N, C, H, W = x.size() + g = self.groups + return x.view(N, g, C//g, H, W).permute(0, 2, 1, 3, 4).reshape(N, C, H, W) + + +class SplitBlock(nn.Module): + """通道分离模块 + + 将输入特征图按比例分成两部分。 + + Args: + ratio (float): 分离比例,默认为0.5 + """ + def __init__(self, ratio): + super(SplitBlock, self).__init__() + self.ratio = ratio + + def forward(self, x): + """通道分离的前向传播 + + Args: + x: 输入张量,[N,C,H,W] + + Returns: + tuple: 分离后的两个张量,[N,C1,H,W]和[N,C2,H,W] + """ + c = int(x.size(1) * self.ratio) + return x[:, :c, :, :], x[:, c:, :, :] + + +class BasicBlock(nn.Module): + """ShuffleNetV2的基本模块 + + 结构: + x -------|-----------------| + | | | + | 1x1 Conv | + | 3x3 DWConv | + | 1x1 Conv | + | | + |------------------Concat + | + Channel Shuffle + + Args: + in_channels (int): 输入通道数 + split_ratio (float): 通道分离比例,默认为0.5 + """ + def __init__(self, in_channels, split_ratio=0.5): + super(BasicBlock, self).__init__() + self.split = SplitBlock(split_ratio) + in_channels = int(in_channels * split_ratio) + + # 主分支 + self.conv1 = nn.Conv2d(in_channels, in_channels, + kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(in_channels) + + self.conv2 = nn.Conv2d(in_channels, in_channels, + kernel_size=3, stride=1, padding=1, + groups=in_channels, bias=False) + self.bn2 = nn.BatchNorm2d(in_channels) + + self.conv3 = nn.Conv2d(in_channels, in_channels, + kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(in_channels) + + self.shuffle = ShuffleBlock() + + def forward(self, x): + # 通道分离 + x1, x2 = self.split(x) + + # 主分支 + out = F.relu(self.bn1(self.conv1(x2))) + out = self.bn2(self.conv2(out)) + out = F.relu(self.bn3(self.conv3(out))) + + # 拼接并重排 + out = torch.cat([x1, out], 1) + out = self.shuffle(out) + return out + + +class DownBlock(nn.Module): + """下采样模块 + + 结构: + 3x3 DWConv(s=2) 1x1 Conv + x -----> 1x1 Conv 3x3 DWConv(s=2) + 1x1 Conv + | + Concat + | + Channel Shuffle + + Args: + in_channels (int): 输入通道数 + out_channels (int): 输出通道数 + """ + def __init__(self, in_channels, out_channels): + super(DownBlock, self).__init__() + mid_channels = out_channels // 2 + + # 左分支 + self.branch1 = nn.Sequential( + # 3x3深度可分离卷积,步长为2 + nn.Conv2d(in_channels, in_channels, + kernel_size=3, stride=2, padding=1, + groups=in_channels, bias=False), + nn.BatchNorm2d(in_channels), + # 1x1卷积 + nn.Conv2d(in_channels, mid_channels, + kernel_size=1, bias=False), + nn.BatchNorm2d(mid_channels) + ) + + # 右分支 + self.branch2 = nn.Sequential( + # 1x1卷积 + nn.Conv2d(in_channels, mid_channels, + kernel_size=1, bias=False), + nn.BatchNorm2d(mid_channels), + # 3x3深度可分离卷积,步长为2 + nn.Conv2d(mid_channels, mid_channels, + kernel_size=3, stride=2, padding=1, + groups=mid_channels, bias=False), + nn.BatchNorm2d(mid_channels), + # 1x1卷积 + nn.Conv2d(mid_channels, mid_channels, + kernel_size=1, bias=False), + nn.BatchNorm2d(mid_channels) + ) + + self.shuffle = ShuffleBlock() + + def forward(self, x): + # 左分支 + out1 = self.branch1(x) + + # 右分支 + out2 = self.branch2(x) + + # 拼接并重排 + out = torch.cat([out1, out2], 1) + out = self.shuffle(out) + return out + + +class ShuffleNetV2(nn.Module): + """ShuffleNetV2模型 + + 网络结构: + 1. 一个卷积层进行特征提取 + 2. 三个阶段,每个阶段包含多个基本块和一个下采样块 + 3. 最后一个卷积层 + 4. 平均池化和全连接层进行分类 + + Args: + net_size (float): 网络大小系数,可选0.5/1.0/1.5/2.0 + """ + def __init__(self, net_size): + super(ShuffleNetV2, self).__init__() + out_channels = configs[net_size]['out_channels'] + num_blocks = configs[net_size]['num_blocks'] + + # 第一层卷积 + self.conv1 = nn.Conv2d(3, 24, kernel_size=3, + stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(24) + self.in_channels = 24 + + # 三个阶段 + self.layer1 = self._make_layer(out_channels[0], num_blocks[0]) + self.layer2 = self._make_layer(out_channels[1], num_blocks[1]) + self.layer3 = self._make_layer(out_channels[2], num_blocks[2]) + + # 最后的1x1卷积 + self.conv2 = nn.Conv2d(out_channels[2], out_channels[3], + kernel_size=1, stride=1, padding=0, bias=False) + self.bn2 = nn.BatchNorm2d(out_channels[3]) + + # 分类层 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(out_channels[3], 10) + + # 初始化权重 + self._initialize_weights() + + def _make_layer(self, out_channels, num_blocks): + """构建一个阶段 + + Args: + out_channels (int): 输出通道数 + num_blocks (int): 基本块的数量 + + Returns: + nn.Sequential: 一个阶段的层序列 + """ + layers = [DownBlock(self.in_channels, out_channels)] + for i in range(num_blocks): + layers.append(BasicBlock(out_channels)) + self.in_channels = out_channels + return nn.Sequential(*layers) + + def forward(self, x): + """前向传播 + + Args: + x: 输入张量,[N,3,32,32] + + Returns: + out: 输出张量,[N,num_classes] + """ + # 特征提取 + out = F.relu(self.bn1(self.conv1(x))) + + # 三个阶段 + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + + # 最后的特征提取 + out = F.relu(self.bn2(self.conv2(out))) + + # 分类 + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + out = self.classifier(out) + return out + + def _initialize_weights(self): + """初始化模型权重 + + 采用kaiming初始化方法: + - 卷积层权重采用kaiming_normal_初始化 + - BN层参数采用常数初始化 + - 线性层采用正态分布初始化 + """ + 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) + nn.init.constant_(m.bias, 0) + + +# 不同大小的网络配置 +configs = { + 0.5: { + 'out_channels': (48, 96, 192, 1024), + 'num_blocks': (3, 7, 3) + }, + 1.0: { + 'out_channels': (116, 232, 464, 1024), + 'num_blocks': (3, 7, 3) + }, + 1.5: { + 'out_channels': (176, 352, 704, 1024), + 'num_blocks': (3, 7, 3) + }, + 2.0: { + 'out_channels': (224, 488, 976, 2048), + 'num_blocks': (3, 7, 3) + } +} + + +def test(): + """测试函数""" + # 创建模型 + net = ShuffleNetV2(net_size=0.5) + print('Model Structure:') + print(net) + + # 测试前向传播 + x = torch.randn(1,3,32,32) + y = net(x) + print('\nInput Shape:', x.shape) + print('Output Shape:', y.shape) + + # 打印模型信息 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (1,3,32,32)) + + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/ShuffleNetv2/code/train.py b/Image/ShuffleNetv2/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..d54df9e801b9bda7921befe94ad61978f0c8f2de --- /dev/null +++ b/Image/ShuffleNetv2/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import ShuffleNetv2 + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = ShuffleNetv2() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='shufflenetv2', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='shufflenetv2', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='shufflenetv2', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/ShuffleNetv2/dataset/.gitkeep b/Image/ShuffleNetv2/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ShuffleNetv2/model/.gitkeep b/Image/ShuffleNetv2/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/SwinTransformer/code/model.py b/Image/SwinTransformer/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..c34d517f0e8e5ca70a4e901ff5527d6a766a2b78 --- /dev/null +++ b/Image/SwinTransformer/code/model.py @@ -0,0 +1,230 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from timm.models.layers import DropPath, trunc_normal_ + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + +def window_partition(x, window_size): + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + +def window_reverse(windows, window_size, H, W): + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + +class WindowAttention(nn.Module): + def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.): + super().__init__() + self.dim = dim + self.window_size = window_size + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x): + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + attn = self.softmax(attn) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + +class SwinTransformerBlock(nn.Module): + def __init__(self, dim, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=window_size, num_heads=num_heads, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + H, W = self.H, self.W + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + +class PatchEmbed(nn.Module): + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + self.patch_size = patch_size + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + _, _, H, W = x.shape + + # padding + pad_input = (H % self.patch_size != 0) or (W % self.patch_size != 0) + if pad_input: + x = F.pad(x, (0, self.patch_size - W % self.patch_size, + 0, self.patch_size - H % self.patch_size, + 0, 0)) + + x = self.proj(x) + x = x.flatten(2).transpose(1, 2) # B Ph*Pw C + x = self.norm(x) + return x + +class SwinTransformer(nn.Module): + def __init__(self, img_size=32, patch_size=4, in_chans=3, num_classes=10, + embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], + window_size=7, mlp_ratio=4., qkv_bias=True, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, patch_norm=True): + super().__init__() + + self.num_classes = num_classes + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.patch_norm = patch_norm + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # build layers + layers = [] + for i_layer in range(self.num_layers): + layer = SwinTransformerBlock( + dim=embed_dim, + num_heads=num_heads[i_layer], + window_size=window_size, + shift_size=0 if (i_layer % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=drop_path_rate, + norm_layer=norm_layer) + layers.append(layer) + + self.layers = nn.ModuleList(layers) + self.norm = norm_layer(embed_dim) + self.avgpool = nn.AdaptiveAvgPool1d(1) + self.head = nn.Linear(embed_dim, num_classes) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x): + x = self.patch_embed(x) + x = self.pos_drop(x) + + for layer in self.layers: + layer.H, layer.W = x.size(1), x.size(2) + x = layer(x) + + x = self.norm(x) + x = self.avgpool(x.transpose(1, 2)) + x = torch.flatten(x, 1) + x = self.head(x) + + return x diff --git a/Image/SwinTransformer/code/train.py b/Image/SwinTransformer/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..cbc713c15968af0e414fb136d1c0e3fee6afdc67 --- /dev/null +++ b/Image/SwinTransformer/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import SwinTransformer + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = SwinTransformer() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='swintransformer', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='swintransformer', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='swintransformer', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/SwinTransformer/dataset/.gitkeep b/Image/SwinTransformer/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/SwinTransformer/model/.gitkeep b/Image/SwinTransformer/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/VGG/code/model.py b/Image/VGG/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..61a15d361d553288fca05524db618534c292ee1b --- /dev/null +++ b/Image/VGG/code/model.py @@ -0,0 +1,204 @@ +''' +VGG Networks in PyTorch + +VGG是由牛津大学Visual Geometry Group提出的一个深度卷积神经网络模型。 +主要特点: +1. 使用小卷积核(3x3)代替大卷积核,降低参数量 +2. 深层网络结构,多个卷积层叠加 +3. 使用多个3x3卷积层的组合来代替大的感受野 +4. 结构规整,易于扩展 + +网络结构示例(VGG16): +input + └─> [(Conv3x3, 64) × 2, MaxPool] + └─> [(Conv3x3, 128) × 2, MaxPool] + └─> [(Conv3x3, 256) × 3, MaxPool] + └─> [(Conv3x3, 512) × 3, MaxPool] + └─> [(Conv3x3, 512) × 3, MaxPool] + └─> [AvgPool, Flatten] + └─> FC(512, num_classes) + +参考论文: +[1] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," + arXiv preprint arXiv:1409.1556, 2014. +''' + +import torch +import torch.nn as nn + + +# VGG配置参数 +# M表示MaxPool层,数字表示输出通道数 +cfg = { + 'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], + 'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], +} + + +class ConvBlock(nn.Module): + """VGG的基本卷积块 + + 包含: Conv2d -> BatchNorm -> ReLU + 使用3x3卷积核,步长为1,padding为1以保持特征图大小不变 + + Args: + in_channels (int): 输入通道数 + out_channels (int): 输出通道数 + batch_norm (bool): 是否使用BatchNorm,默认为True + """ + def __init__(self, in_channels, out_channels, batch_norm=True): + super(ConvBlock, self).__init__() + + layers = [] + # 3x3卷积,padding=1保持特征图大小不变 + layers.append( + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=1, + padding=1 + ) + ) + + # 添加BatchNorm + if batch_norm: + layers.append(nn.BatchNorm2d(out_channels)) + + # ReLU激活函数 + layers.append(nn.ReLU(inplace=True)) + + self.block = nn.Sequential(*layers) + + def forward(self, x): + """前向传播 + + Args: + x (torch.Tensor): 输入特征图 + + Returns: + torch.Tensor: 输出特征图 + """ + return self.block(x) + + +class VGG(nn.Module): + """VGG网络模型 + + Args: + vgg_name (str): VGG变体名称,可选VGG11/13/16/19 + num_classes (int): 分类数量,默认为10 + batch_norm (bool): 是否使用BatchNorm,默认为True + init_weights (bool): 是否初始化权重,默认为True + """ + def __init__(self, vgg_name='VGG16', num_classes=10, batch_norm=True, init_weights=True): + super(VGG, self).__init__() + + # 特征提取层 + self.features = self._make_layers(cfg[vgg_name], batch_norm) + + # 全局平均池化 + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + + # 分类器 + self.classifier = nn.Sequential( + nn.Linear(512, num_classes) + ) + + # 初始化权重 + if init_weights: + self._initialize_weights() + + def _make_layers(self, cfg, batch_norm=True): + """构建VGG的特征提取层 + + Args: + cfg (List): 网络配置参数 + batch_norm (bool): 是否使用BatchNorm + + Returns: + nn.Sequential: 特征提取层序列 + """ + layers = [] + in_channels = 3 + + for x in cfg: + if x == 'M': # 最大池化层 + layers.append(nn.MaxPool2d(kernel_size=2, stride=2)) + else: # 卷积块 + layers.append(ConvBlock(in_channels, x, batch_norm)) + in_channels = x + + return nn.Sequential(*layers) + + def forward(self, x): + """前向传播 + + Args: + x (torch.Tensor): 输入图像张量,[N,3,H,W] + + Returns: + torch.Tensor: 输出预测张量,[N,num_classes] + """ + # 特征提取 + x = self.features(x) + + # 全局平均池化 + x = self.avgpool(x) + + # 展平 + x = torch.flatten(x, 1) + + # 分类 + x = self.classifier(x) + return x + + def _initialize_weights(self): + """初始化模型权重 + + 采用论文中的初始化方法: + - 卷积层: xavier初始化 + - BatchNorm: weight=1, bias=0 + - 线性层: 正态分布初始化(std=0.01) + """ + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # VGG论文中使用了xavier初始化 + nn.init.xavier_normal_(m.weight) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + +def test(): + """测试函数 + + 创建VGG模型并进行前向传播测试,打印模型结构和参数信息 + """ + # 创建模型 + net = VGG('VGG16') + print('Model Structure:') + print(net) + + # 测试前向传播 + x = torch.randn(2, 3, 32, 32) + y = net(x) + print('\nInput Shape:', x.shape) + print('Output Shape:', y.shape) + + # 打印模型信息 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net, (2, 3, 32, 32)) + + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/Image/VGG/code/train.py b/Image/VGG/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..198793b838aff3d5c2abdae6d6267b61dc38339e --- /dev/null +++ b/Image/VGG/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import VGG + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = VGG() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='vgg', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='vgg', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='vgg', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/VGG/dataset/.gitkeep b/Image/VGG/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/VGG/model/.gitkeep b/Image/VGG/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ViT/code/model.py b/Image/ViT/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..f34402724e1c857622a8e8eb0e7e2ad4ae795353 --- /dev/null +++ b/Image/ViT/code/model.py @@ -0,0 +1,171 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class PatchEmbed(nn.Module): + """ 将图像分成patch并进行embedding """ + def __init__(self, img_size=32, patch_size=4, in_chans=3, embed_dim=96): + super().__init__() + self.img_size = img_size + self.patch_size = patch_size + self.n_patches = (img_size // patch_size) ** 2 + + self.proj = nn.Conv2d( + in_chans, embed_dim, + kernel_size=patch_size, stride=patch_size + ) + + def forward(self, x): + x = self.proj(x) # (B, E, H/P, W/P) + x = x.flatten(2) # (B, E, N) + x = x.transpose(1, 2) # (B, N, E) + return x + +class Attention(nn.Module): + """ 多头自注意力机制 """ + def __init__(self, dim, n_heads=8, qkv_bias=True, attn_p=0., proj_p=0.): + super().__init__() + self.n_heads = n_heads + self.dim = dim + self.head_dim = dim // n_heads + self.scale = self.head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_p) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_p) + + def forward(self, x): + n_samples, n_tokens, dim = x.shape + + if dim != self.dim: + raise ValueError + + qkv = self.qkv(x) # (n_samples, n_patches + 1, 3 * dim) + qkv = qkv.reshape( + n_samples, n_tokens, 3, self.n_heads, self.head_dim + ) # (n_samples, n_patches + 1, 3, n_heads, head_dim) + qkv = qkv.permute(2, 0, 3, 1, 4) # (3, n_samples, n_heads, n_patches + 1, head_dim) + q, k, v = qkv[0], qkv[1], qkv[2] # each with shape (n_samples, n_heads, n_patches + 1, head_dim) + + k_t = k.transpose(-2, -1) # (n_samples, n_heads, head_dim, n_patches + 1) + dp = (q @ k_t) * self.scale # (n_samples, n_heads, n_patches + 1, n_patches + 1) + attn = dp.softmax(dim=-1) # (n_samples, n_heads, n_patches + 1, n_patches + 1) + attn = self.attn_drop(attn) + + weighted_avg = attn @ v # (n_samples, n_heads, n_patches + 1, head_dim) + weighted_avg = weighted_avg.transpose(1, 2) # (n_samples, n_patches + 1, n_heads, head_dim) + weighted_avg = weighted_avg.flatten(2) # (n_samples, n_patches + 1, dim) + + x = self.proj(weighted_avg) # (n_samples, n_patches + 1, dim) + x = self.proj_drop(x) # (n_samples, n_patches + 1, dim) + + return x + +class MLP(nn.Module): + """ 多层感知机 """ + def __init__(self, in_features, hidden_features, out_features, p=0.): + super().__init__() + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = nn.GELU() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(p) + + def forward(self, x): + x = self.fc1(x) # (n_samples, n_patches + 1, hidden_features) + x = self.act(x) # (n_samples, n_patches + 1, hidden_features) + x = self.drop(x) # (n_samples, n_patches + 1, hidden_features) + x = self.fc2(x) # (n_samples, n_patches + 1, out_features) + x = self.drop(x) # (n_samples, n_patches + 1, out_features) + + return x + +class Block(nn.Module): + """ Transformer编码器块 """ + def __init__(self, dim, n_heads, mlp_ratio=4.0, qkv_bias=True, + p=0., attn_p=0.): + super().__init__() + self.norm1 = nn.LayerNorm(dim, eps=1e-6) + self.attn = Attention( + dim, + n_heads=n_heads, + qkv_bias=qkv_bias, + attn_p=attn_p, + proj_p=p + ) + self.norm2 = nn.LayerNorm(dim, eps=1e-6) + hidden_features = int(dim * mlp_ratio) + self.mlp = MLP( + in_features=dim, + hidden_features=hidden_features, + out_features=dim, + ) + + def forward(self, x): + x = x + self.attn(self.norm1(x)) + x = x + self.mlp(self.norm2(x)) + return x + +class ViT(nn.Module): + """ Vision Transformer """ + def __init__( + self, + img_size=32, + patch_size=4, + in_chans=3, + n_classes=10, + embed_dim=96, + depth=12, + n_heads=8, + mlp_ratio=4., + qkv_bias=True, + p=0., + attn_p=0., + ): + super().__init__() + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + ) + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter( + torch.zeros(1, 1 + self.patch_embed.n_patches, embed_dim) + ) + self.pos_drop = nn.Dropout(p=p) + + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + n_heads=n_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + p=p, + attn_p=attn_p, + ) + for _ in range(depth) + ]) + + self.norm = nn.LayerNorm(embed_dim, eps=1e-6) + self.head = nn.Linear(embed_dim, n_classes) + + def forward(self, x): + n_samples = x.shape[0] + x = self.patch_embed(x) + + cls_token = self.cls_token.expand(n_samples, -1, -1) + x = torch.cat((cls_token, x), dim=1) + x = x + self.pos_embed + x = self.pos_drop(x) + + for block in self.blocks: + x = block(x) + + x = self.norm(x) + + cls_token_final = x[:, 0] + x = self.head(cls_token_final) + + return x diff --git a/Image/ViT/code/train.py b/Image/ViT/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..efeeb1879b9a6396a3bbf854ed0ea789b9f1c472 --- /dev/null +++ b/Image/ViT/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import ViT + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = ViT() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='vit', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='vit', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='vit', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/ViT/dataset/.gitkeep b/Image/ViT/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ViT/model/.gitkeep b/Image/ViT/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ZFNet/code/model.py b/Image/ZFNet/code/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1fb0a193087c0c3e85366c201d789e8ebf73b729 --- /dev/null +++ b/Image/ZFNet/code/model.py @@ -0,0 +1,50 @@ +import torch +import torch.nn as nn + +class ZFNet(nn.Module): + def __init__(self, num_classes=10): + super(ZFNet, self).__init__() + self.features = nn.Sequential( + # conv1 + nn.Conv2d(3, 96, kernel_size=7, stride=2, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2), + + # conv2 + nn.Conv2d(96, 256, kernel_size=5, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2), + + # conv3 + nn.Conv2d(256, 384, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + + # conv4 + nn.Conv2d(384, 384, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + + # conv5 + nn.Conv2d(384, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + + self.classifier = nn.Sequential( + nn.Linear(256 * 2 * 2, 4096), + nn.ReLU(inplace=True), + nn.Dropout(), + + nn.Linear(4096, 4096), + nn.ReLU(inplace=True), + nn.Dropout(), + + nn.Linear(4096, num_classes), + ) + + def forward(self, x): + x = self.features(x) + x = x.view(x.size(0), -1) + x = self.classifier(x) + return x diff --git a/Image/ZFNet/code/train.py b/Image/ZFNet/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa58abeb735953788849ff6387fc2af35b423dd --- /dev/null +++ b/Image/ZFNet/code/train.py @@ -0,0 +1,59 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.dataset_utils import get_cifar10_dataloaders +from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor +from utils.parse_args import parse_args +from model import ZFNet + +def main(): + # 解析命令行参数 + args = parse_args() + + # 创建模型 + model = ZFNet() + + if args.train_type == '0': + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path) + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='zfnet', + save_type='0' + ) + elif args.train_type == '1': + train_model_data_augmentation( + model, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='zfnet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + elif args.train_type == '2': + train_model_backdoor( + model, + poison_ratio=args.poison_ratio, + target_label=args.target_label, + epochs=args.epochs, + lr=args.lr, + device=f'cuda:{args.gpu}', + save_dir='../model', + model_name='zfnet', + batch_size=args.batch_size, + num_workers=args.num_workers, + local_dataset_path=args.dataset_path + ) + +if __name__ == '__main__': + main() diff --git a/Image/ZFNet/dataset/.gitkeep b/Image/ZFNet/dataset/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/ZFNet/model/.gitkeep b/Image/ZFNet/model/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Image/utils/dataset_utils.py b/Image/utils/dataset_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1acd0e039a4b0e3bc74ca4a9d2de58471380d517 --- /dev/null +++ b/Image/utils/dataset_utils.py @@ -0,0 +1,110 @@ +import torch +import torchvision +import torchvision.transforms as transforms +import os + +def get_cifar10_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None,shuffle=True): + """获取CIFAR10数据集的数据加载器 + + Args: + batch_size: 批次大小 + num_workers: 数据加载的工作进程数 + local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载 + + Returns: + trainloader: 训练数据加载器 + testloader: 测试数据加载器 + """ + # 数据预处理 + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), + ]) + + transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), + ]) + + # 设置数据集路径 + if local_dataset_path: + print(f"使用本地数据集: {local_dataset_path}") + download = False + dataset_path = local_dataset_path + else: + print("未指定本地数据集路径，将下载数据集") + download = True + dataset_path = '../dataset' + + # 创建数据集路径 + if not os.path.exists(dataset_path): + os.makedirs(dataset_path) + + trainset = torchvision.datasets.CIFAR10( + root=dataset_path, train=True, download=download, transform=transform_train) + trainloader = torch.utils.data.DataLoader( + trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + testset = torchvision.datasets.CIFAR10( + root=dataset_path, train=False, download=download, transform=transform_test) + testloader = torch.utils.data.DataLoader( + testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + return trainloader, testloader + +def get_mnist_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None,shuffle=True): + """获取MNIST数据集的数据加载器 + + Args: + batch_size: 批次大小 + num_workers: 数据加载的工作进程数 + local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载 + + Returns: + trainloader: 训练数据加载器 + testloader: 测试数据加载器 + """ + # 数据预处理 + transform_train = transforms.Compose([ + transforms.RandomRotation(10), # 随机旋转±10度 + transforms.RandomAffine( # 随机仿射变换 + degrees=0, # 不进行旋转 + translate=(0.1, 0.1), # 平移范围 + scale=(0.9, 1.1) # 缩放范围 + ), + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) # MNIST数据集的均值和标准差 + ]) + + transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + + # 设置数据集路径 + if local_dataset_path: + print(f"使用本地数据集: {local_dataset_path}") + download = False + dataset_path = local_dataset_path + else: + print("未指定本地数据集路径，将下载数据集") + download = True + dataset_path = '../dataset' + + # 创建数据集路径 + if not os.path.exists(dataset_path): + os.makedirs(dataset_path) + + trainset = torchvision.datasets.MNIST( + root=dataset_path, train=True, download=download, transform=transform_train) + trainloader = torch.utils.data.DataLoader( + trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + testset = torchvision.datasets.MNIST( + root=dataset_path, train=False, download=download, transform=transform_test) + testloader = torch.utils.data.DataLoader( + testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + return trainloader, testloader diff --git a/Image/utils/parse_args.py b/Image/utils/parse_args.py new file mode 100644 index 0000000000000000000000000000000000000000..998e17d974a5bba504540be0859442da99dd45a9 --- /dev/null +++ b/Image/utils/parse_args.py @@ -0,0 +1,19 @@ +import argparse + +def parse_args(): + """解析命令行参数 + + Returns: + args: 解析后的参数 + """ + parser = argparse.ArgumentParser(description='训练模型') + parser.add_argument('--gpu', type=int, default=0, help='GPU设备编号 (0,1,2,3)') + parser.add_argument('--batch-size', type=int, default=128, help='批次大小') + parser.add_argument('--epochs', type=int, default=200, help='训练轮数') + parser.add_argument('--lr', type=float, default=0.1, help='学习率') + parser.add_argument('--num-workers', type=int, default=2, help='数据加载的工作进程数') + parser.add_argument('--poison-ratio', type=float, default=0.1, help='恶意样本比例') + parser.add_argument('--target-label', type=int, default=0, help='目标类别') + parser.add_argument('--train-type',type=str,choices=['0','1','2'],default='0',help='训练类型:0 for normal train, 1 for data aug train,2 for back door train') + parser.add_argument('--dataset-path', type=str, default=None, help='本地数据集路径，如果不指定则自动下载') + return parser.parse_args() \ No newline at end of file diff --git a/Image/utils/train_utils.py b/Image/utils/train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..23ee83897d9d11ee02de5575c9d3655be46847fa --- /dev/null +++ b/Image/utils/train_utils.py @@ -0,0 +1,381 @@ +""" +通用模型训练工具 + +提供了模型训练、评估、保存等功能，支持: +1. 训练进度可视化 +2. 日志记录 +3. 模型检查点保存 +4. 嵌入向量收集 +""" + +import torch +import torch.nn as nn +import torch.optim as optim +import time +import os +import logging +import numpy as np +from tqdm import tqdm +import sys +from pathlib import Path +import torch.nn.functional as F +import torchvision.transforms as transforms + +# 将项目根目录添加到Python路径中 +current_dir = Path(__file__).resolve().parent +project_root = current_dir.parent.parent +sys.path.append(str(project_root)) + +from ttv_utils import time_travel_saver + +def setup_logger(log_file): + """配置日志记录器，如果日志文件存在则覆盖 + + Args: + log_file: 日志文件路径 + + Returns: + logger: 配置好的日志记录器 + """ + # 创建logger + logger = logging.getLogger('train') + logger.setLevel(logging.INFO) + + # 移除现有的处理器 + if logger.hasHandlers(): + logger.handlers.clear() + + # 创建文件处理器，使用'w'模式覆盖现有文件 + fh = logging.FileHandler(log_file, mode='w') + fh.setLevel(logging.INFO) + + # 创建控制台处理器 + ch = logging.StreamHandler() + ch.setLevel(logging.INFO) + + # 创建格式器 + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + fh.setFormatter(formatter) + ch.setFormatter(formatter) + + # 添加处理器 + logger.addHandler(fh) + logger.addHandler(ch) + + return logger + +def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda:0', + save_dir='./checkpoints', model_name='model', save_type='0',layer_name=None,interval = 2): + """通用的模型训练函数 + Args: + model: 要训练的模型 + trainloader: 训练数据加载器 + testloader: 测试数据加载器 + epochs: 训练轮数 + lr: 学习率 + device: 训练设备，格式为'cuda:N'，其中N为GPU编号（0,1,2,3） + save_dir: 模型保存目录 + model_name: 模型名称 + save_type: 保存类型，0为普通训练，1为数据增强训练，2为后门训练 + """ + # 检查并设置GPU设备 + if not torch.cuda.is_available(): + print("CUDA不可用，将使用CPU训练") + device = 'cpu' + elif not device.startswith('cuda:'): + device = f'cuda:0' + + # 确保device格式正确 + if device.startswith('cuda:'): + gpu_id = int(device.split(':')[1]) + if gpu_id >= torch.cuda.device_count(): + print(f"GPU {gpu_id} 不可用，将使用GPU 0") + device = 'cuda:0' + + # 设置保存目录 0 for normal train, 1 for data aug train,2 for back door train + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + # 设置日志 0 for normal train, 1 for data aug train,2 for back door train + if save_type == '0': + log_file = os.path.join(os.path.dirname(save_dir), 'code', 'train.log') + if not os.path.exists(os.path.dirname(log_file)): + os.makedirs(os.path.dirname(log_file)) + elif save_type == '1': + log_file = os.path.join(os.path.dirname(save_dir), 'code', 'data_aug_train.log') + if not os.path.exists(os.path.dirname(log_file)): + os.makedirs(os.path.dirname(log_file)) + elif save_type == '2': + log_file = os.path.join(os.path.dirname(save_dir), 'code', 'backdoor_train.log') + if not os.path.exists(os.path.dirname(log_file)): + os.makedirs(os.path.dirname(log_file)) + logger = setup_logger(log_file) + + # 设置epoch保存目录 0 for normal train, 1 for data aug train,2 for back door train + save_dir = os.path.join(save_dir, save_type) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + # 损失函数和优化器 + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200) + + # 移动模型到指定设备 + model = model.to(device) + best_acc = 0 + start_time = time.time() + + logger.info(f'开始训练 {model_name}') + logger.info(f'总轮数: {epochs}, 学习率: {lr}, 设备: {device}') + + for epoch in range(epochs): + # 训练阶段 + model.train() + train_loss = 0 + correct = 0 + total = 0 + + train_pbar = tqdm(trainloader, desc=f'Epoch {epoch+1}/{epochs} [Train]') + for batch_idx, (inputs, targets) in enumerate(train_pbar): + inputs, targets = inputs.to(device), targets.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, targets) + loss.backward() + optimizer.step() + + train_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + # 更新进度条 + train_pbar.set_postfix({ + 'loss': f'{train_loss/(batch_idx+1):.3f}', + 'acc': f'{100.*correct/total:.2f}%' + }) + + # 每100步记录一次 + if batch_idx % 100 == 0: + logger.info(f'Epoch: {epoch+1} | Batch: {batch_idx} | ' + f'Loss: {train_loss/(batch_idx+1):.3f} | ' + f'Acc: {100.*correct/total:.2f}%') + + # 测试阶段 + model.eval() + test_loss = 0 + correct = 0 + total = 0 + + test_pbar = tqdm(testloader, desc=f'Epoch {epoch+1}/{epochs} [Test]') + with torch.no_grad(): + for batch_idx, (inputs, targets) in enumerate(test_pbar): + inputs, targets = inputs.to(device), targets.to(device) + outputs = model(inputs) + loss = criterion(outputs, targets) + + test_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + # 更新进度条 + test_pbar.set_postfix({ + 'loss': f'{test_loss/(batch_idx+1):.3f}', + 'acc': f'{100.*correct/total:.2f}%' + }) + + # 计算测试精度 + acc = 100.*correct/total + logger.info(f'Epoch: {epoch+1} | Test Loss: {test_loss/(batch_idx+1):.3f} | ' + f'Test Acc: {acc:.2f}%') + + + if epoch == 0: + ordered_loader = torch.utils.data.DataLoader( + trainloader.dataset, # 使用相同的数据集 + batch_size=trainloader.batch_size, + shuffle=False, # 确保顺序加载 + num_workers=trainloader.num_workers + ) + save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name, show= True ) + + # 每5个epoch保存一次 + if (epoch + 1) % interval == 0: + # 创建一个专门用于收集embedding的顺序dataloader + ordered_loader = torch.utils.data.DataLoader( + trainloader.dataset, # 使用相同的数据集 + batch_size=trainloader.batch_size, + shuffle=False, # 确保顺序加载 + num_workers=trainloader.num_workers + ) + save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name ) + save_model.save() + + scheduler.step() + + logger.info('训练完成!') + +def train_model_data_augmentation(model, epochs=200, lr=0.1, device='cuda:0', + save_dir='./checkpoints', model_name='model', + batch_size=128, num_workers=2, local_dataset_path=None): + """使用数据增强训练模型 + + 数据增强方案说明： + 1. RandomCrop: 随机裁剪，先填充4像素，再裁剪回原始大小，增加位置多样性 + 2. RandomHorizontalFlip: 随机水平翻转，增加方向多样性 + 3. RandomRotation: 随机旋转15度，增加角度多样性 + 4. ColorJitter: 颜色抖动，调整亮度、对比度、饱和度和色调 + 5. RandomErasing: 随机擦除部分区域，模拟遮挡情况 + 6. RandomPerspective: 随机透视变换，增加视角多样性 + + Args: + model: 要训练的模型 + epochs: 训练轮数 + lr: 学习率 + device: 训练设备 + save_dir: 模型保存目录 + model_name: 模型名称 + batch_size: 批次大小 + num_workers: 数据加载的工作进程数 + local_dataset_path: 本地数据集路径 + """ + import torchvision.transforms as transforms + from .dataset_utils import get_cifar10_dataloaders + + # 定义增强的数据预处理 + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.RandomRotation(15), + transforms.ColorJitter( + brightness=0.2, + contrast=0.2, + saturation=0.2, + hue=0.1 + ), + transforms.RandomPerspective(distortion_scale=0.2, p=0.5), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), + transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3)) + ]) + + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size, num_workers, local_dataset_path) + + # 使用增强的训练数据 + trainset = trainloader.dataset + trainset.transform = transform_train + trainloader = torch.utils.data.DataLoader( + trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers) + + # 调用通用训练函数 + train_model(model, trainloader, testloader, epochs, lr, device, save_dir, model_name, save_type='1') + +def train_model_backdoor(model, poison_ratio=0.1, target_label=0, epochs=200, lr=0.1, + device='cuda:0', save_dir='./checkpoints', model_name='model', + batch_size=128, num_workers=2, local_dataset_path=None, layer_name=None,interval = 2): + """训练带后门的模型 + + 后门攻击方案说明： + 1. 标签翻转攻击：将选定比例的样本标签修改为目标标签 + 2. 触发器模式：在选定样本的右下角添加一个4x4的白色方块作为触发器 + 3. 验证策略： + - 在干净数据上验证模型性能（确保正常样本分类准确率） + - 在带触发器的数据上验证攻击成功率 + + Args: + model: 要训练的模型 + poison_ratio: 投毒比例 + target_label: 目标标签 + epochs: 训练轮数 + lr: 学习率 + device: 训练设备 + save_dir: 模型保存目录 + model_name: 模型名称 + batch_size: 批次大小 + num_workers: 数据加载的工作进程数 + local_dataset_path: 本地数据集路径 + """ + from .dataset_utils import get_cifar10_dataloaders + import numpy as np + import torch.nn.functional as F + + # 获取原始数据加载器 + trainloader, testloader = get_cifar10_dataloaders(batch_size, num_workers, local_dataset_path) + + # 修改部分训练数据的标签和添加触发器 + trainset = trainloader.dataset + num_poison = int(len(trainset) * poison_ratio) + poison_indices = np.random.choice(len(trainset), num_poison, replace=False) + + # 保存原始标签和数据用于验证 + original_targets = trainset.targets.copy() + original_data = trainset.data.copy() + + # 修改选中数据的标签和添加触发器 + trigger_pattern = np.ones((4, 4, 3), dtype=np.uint8) * 255 # 4x4白色方块作为触发器 + for idx in poison_indices: + # 修改标签 + trainset.targets[idx] = target_label + # 添加触发器到右下角 + trainset.data[idx, -4:, -4:] = trigger_pattern + + # 创建新的数据加载器 + poisoned_trainloader = torch.utils.data.DataLoader( + trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers) + + # 训练模型 + train_model(model, poisoned_trainloader, testloader, epochs, lr, device, save_dir, model_name, save_type='2', layer_name=layer_name,interval = interval) + + # 恢复原始数据用于验证 + trainset.targets = original_targets + trainset.data = original_data + + # 创建验证数据加载器（干净数据） + validation_loader = torch.utils.data.DataLoader( + trainset, batch_size=batch_size, shuffle=False, num_workers=num_workers) + + # 在干净验证集上评估模型 + model.eval() + correct = 0 + total = 0 + with torch.no_grad(): + for inputs, targets in validation_loader: + inputs, targets = inputs.to(device), targets.to(device) + outputs = model(inputs) + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + clean_accuracy = 100. * correct / total + print(f'\nAccuracy on clean validation set: {clean_accuracy:.2f}%') + + # 创建带触发器的验证数据集 + trigger_validation = trainset.data.copy() + trigger_validation_targets = np.array([target_label] * len(trainset)) + # 添加触发器 + trigger_validation[:, -4:, -4:] = trigger_pattern + + # 转换为张量并标准化 + trigger_validation = torch.tensor(trigger_validation).float().permute(0, 3, 1, 2) / 255.0 + # 使用正确的方式进行图像标准化 + normalize = transforms.Normalize(mean=(0.4914, 0.4822, 0.4465), + std=(0.2023, 0.1994, 0.2010)) + trigger_validation = normalize(trigger_validation) + + # 在带触发器的验证集上评估模型 + correct = 0 + total = 0 + batch_size = 100 + for i in range(0, len(trigger_validation), batch_size): + inputs = trigger_validation[i:i+batch_size].to(device) + targets = torch.tensor(trigger_validation_targets[i:i+batch_size]).to(device) + outputs = model(inputs) + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + attack_success_rate = 100. * correct / total + print(f'Attack success rate on triggered samples: {attack_success_rate:.2f}%') diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..07f8a6e7b3f0b1f5b4cc6131e844efad9e5e9713 --- /dev/null +++ b/README.md @@ -0,0 +1,17 @@ +--- +license: mit +--- +# 模型训练过程汇总[该仓库只含有image model的训练过程] + +本仓库采用扁平化的目录结构和标签系统来组织模型，具体说明如下： + +## 仓库结构 + +- **一级目录**：直接以```模型名称-数据集```，例如 `ResNet-CIFAR-10`、`GraphMAE_QM9-Cora` 等 +- **二级目录**：包含该模型在该数据集下的不同训练任务或变体，例如 `normal`、`noisy`、`backdoor_invisible` 等 +- **训练过程目录结构**：每个模型目录下包含： + - `scripts/`：存放模型相关代码和训练脚本 + - `epochs/`：存放模型训练过程和权重文件 + - 每个epoch的权重文件(model.pth)和embedding(.npy) + - `dataset/`：模型需要的数据集 + diff --git a/ResNet-CIFAR10/Classification-normal/dataset/index.json b/ResNet-CIFAR10/Classification-normal/dataset/index.json new file mode 100644 index 0000000000000000000000000000000000000000..18effc538161722b86c88b06392a77db3bb6a92d --- /dev/null +++ b/ResNet-CIFAR10/Classification-normal/dataset/index.json @@ -0,0 +1,50006 @@ +{ + "train": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 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, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 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| Train Loss: 0.468 | Train Acc: 83.98% | Test Loss: 0.674 | Test Acc: 78.17% +2025-04-19 15:59:11,517 - train - INFO - Epoch: 11 | Train Loss: 0.455 | Train Acc: 84.35% | Test Loss: 0.537 | Test Acc: 81.69% +2025-04-19 15:59:21,661 - train - INFO - Epoch: 12 | Train Loss: 0.431 | Train Acc: 85.27% | Test Loss: 0.696 | Test Acc: 77.96% +2025-04-19 15:59:39,672 - train - INFO - Epoch: 13 | Train Loss: 0.414 | Train Acc: 85.80% | Test Loss: 0.466 | Test Acc: 84.24% +2025-04-19 15:59:50,271 - train - INFO - Epoch: 14 | Train Loss: 0.402 | Train Acc: 86.21% | Test Loss: 0.526 | Test Acc: 82.67% +2025-04-19 16:00:08,505 - train - INFO - Epoch: 15 | Train Loss: 0.387 | Train Acc: 86.76% | Test Loss: 0.494 | Test Acc: 83.42% +2025-04-19 16:00:19,057 - train - INFO - Epoch: 16 | Train Loss: 0.375 | Train Acc: 87.13% | Test Loss: 0.483 | Test Acc: 83.80% +2025-04-19 16:00:37,688 - train - INFO - Epoch: 17 | Train Loss: 0.356 | Train Acc: 87.79% | Test Loss: 0.479 | Test Acc: 83.95% +2025-04-19 16:00:48,191 - train - INFO - Epoch: 18 | Train Loss: 0.344 | Train Acc: 88.32% | Test Loss: 0.548 | Test Acc: 81.28% +2025-04-19 16:01:06,303 - train - INFO - Epoch: 19 | Train Loss: 0.337 | Train Acc: 88.39% | Test Loss: 0.646 | Test Acc: 80.06% +2025-04-19 16:01:17,008 - train - INFO - Epoch: 20 | Train Loss: 0.326 | Train Acc: 88.86% | Test Loss: 0.531 | Test Acc: 82.93% +2025-04-19 16:01:35,532 - train - INFO - Epoch: 21 | Train Loss: 0.312 | Train Acc: 89.34% | Test Loss: 0.488 | Test Acc: 83.91% +2025-04-19 16:01:46,199 - train - INFO - Epoch: 22 | Train Loss: 0.298 | Train Acc: 89.77% | Test Loss: 0.448 | Test Acc: 85.26% +2025-04-19 16:02:04,386 - train - INFO - Epoch: 23 | Train Loss: 0.286 | Train Acc: 90.17% | Test Loss: 0.441 | Test Acc: 85.51% +2025-04-19 16:02:14,888 - train - INFO - Epoch: 24 | Train Loss: 0.278 | Train Acc: 90.53% | Test Loss: 0.410 | Test Acc: 86.16% +2025-04-19 16:02:33,273 - train - INFO - Epoch: 25 | Train Loss: 0.262 | Train Acc: 91.04% | Test Loss: 0.391 | Test Acc: 86.95% +2025-04-19 16:02:43,650 - train - INFO - Epoch: 26 | Train Loss: 0.247 | Train Acc: 91.54% | Test Loss: 0.350 | Test Acc: 88.46% +2025-04-19 16:03:01,907 - train - INFO - Epoch: 27 | Train Loss: 0.235 | Train Acc: 91.90% | Test Loss: 0.384 | Test Acc: 87.65% +2025-04-19 16:03:12,565 - train - INFO - Epoch: 28 | Train Loss: 0.219 | Train Acc: 92.52% | Test Loss: 0.349 | Test Acc: 88.26% +2025-04-19 16:03:31,406 - train - INFO - Epoch: 29 | Train Loss: 0.201 | Train Acc: 93.11% | Test Loss: 0.461 | Test Acc: 85.69% +2025-04-19 16:03:42,204 - train - INFO - Epoch: 30 | Train Loss: 0.198 | Train Acc: 93.19% | Test Loss: 0.349 | Test Acc: 88.51% +2025-04-19 16:04:00,528 - train - INFO - Epoch: 31 | Train Loss: 0.180 | Train Acc: 93.77% | Test Loss: 0.345 | Test Acc: 88.90% +2025-04-19 16:04:11,210 - train - INFO - Epoch: 32 | Train Loss: 0.164 | Train Acc: 94.38% | Test Loss: 0.351 | Test Acc: 89.29% +2025-04-19 16:04:29,421 - train - INFO - Epoch: 33 | Train Loss: 0.147 | Train Acc: 94.94% | Test Loss: 0.309 | Test Acc: 90.04% +2025-04-19 16:04:39,909 - train - INFO - Epoch: 34 | Train Loss: 0.137 | Train Acc: 95.23% | Test Loss: 0.303 | Test Acc: 90.20% +2025-04-19 16:04:57,980 - train - INFO - Epoch: 35 | Train Loss: 0.118 | Train Acc: 96.02% | Test Loss: 0.275 | Test Acc: 91.58% +2025-04-19 16:05:08,411 - train - INFO - Epoch: 36 | Train Loss: 0.103 | Train Acc: 96.46% | Test Loss: 0.300 | Test Acc: 90.59% +2025-04-19 16:05:27,044 - train - INFO - Epoch: 37 | Train Loss: 0.086 | Train Acc: 97.24% | Test Loss: 0.249 | Test Acc: 92.38% +2025-04-19 16:05:37,653 - train - INFO - Epoch: 38 | Train Loss: 0.078 | Train Acc: 97.37% | Test Loss: 0.273 | Test Acc: 91.86% +2025-04-19 16:05:56,031 - train - INFO - Epoch: 39 | Train Loss: 0.060 | Train Acc: 98.01% | Test Loss: 0.277 | Test Acc: 91.82% +2025-04-19 16:06:06,632 - train - INFO - Epoch: 40 | Train Loss: 0.046 | Train Acc: 98.53% | Test Loss: 0.238 | Test Acc: 93.21% +2025-04-19 16:06:24,636 - train - INFO - Epoch: 41 | Train Loss: 0.036 | Train Acc: 98.82% | Test Loss: 0.248 | Test Acc: 93.00% +2025-04-19 16:06:35,138 - train - INFO - Epoch: 42 | Train Loss: 0.026 | Train Acc: 99.26% | Test Loss: 0.223 | Test Acc: 93.63% +2025-04-19 16:06:53,263 - train - INFO - Epoch: 43 | Train Loss: 0.019 | Train Acc: 99.45% | Test Loss: 0.231 | Test Acc: 93.49% +2025-04-19 16:07:03,814 - train - INFO - Epoch: 44 | Train Loss: 0.015 | Train Acc: 99.62% | Test Loss: 0.219 | Test Acc: 93.88% +2025-04-19 16:07:22,127 - train - INFO - Epoch: 45 | Train Loss: 0.011 | Train Acc: 99.76% | Test Loss: 0.211 | Test Acc: 94.20% +2025-04-19 16:07:32,623 - train - INFO - Epoch: 46 | Train Loss: 0.010 | Train Acc: 99.78% | Test Loss: 0.208 | Test Acc: 94.17% +2025-04-19 16:07:51,118 - train - INFO - Epoch: 47 | Train Loss: 0.009 | Train Acc: 99.83% | Test Loss: 0.213 | Test Acc: 94.20% +2025-04-19 16:08:01,638 - train - INFO - Epoch: 48 | Train Loss: 0.008 | Train Acc: 99.85% | Test Loss: 0.209 | Test Acc: 94.21% +2025-04-19 16:08:19,934 - train - INFO - Epoch: 49 | Train Loss: 0.007 | Train Acc: 99.86% | Test Loss: 0.207 | Test Acc: 94.19% +2025-04-19 16:08:30,462 - train - INFO - Epoch: 50 | Train Loss: 0.007 | Train Acc: 99.86% | Test Loss: 0.210 | Test Acc: 94.21% +2025-04-19 16:08:37,994 - train - INFO - 训练完成! diff --git a/ResNet-CIFAR10/Classification-normal/readme.md b/ResNet-CIFAR10/Classification-normal/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..8c611c0bd991ee78b89642d8ff65656fd88e463f --- /dev/null +++ b/ResNet-CIFAR10/Classification-normal/readme.md @@ -0,0 +1,54 @@ +# ResNet-CIFAR10 训练与特征提取 + +这个项目实现了ResNet模型在CIFAR10数据集上的训练，并集成了特征提取和可视化所需的功能。 + +## time_travel_saver数据提取器 +```python + #保存可视化训练过程所需要的文件 + if (epoch + 1) % interval == 0 or (epoch == 0): + # 创建一个专门用于收集embedding的顺序dataloader + ordered_trainloader = torch.utils.data.DataLoader( + trainloader.dataset, + batch_size=trainloader.batch_size, + shuffle=False, + num_workers=trainloader.num_workers + ) + epoch_save_dir = os.path.join(save_dir, f'epoch_{epoch+1}') #epoch保存路径 + save_model = time_travel_saver(model, ordered_trainloader, device, epoch_save_dir, model_name, + show=True, layer_name='avg_pool', auto_save_embedding=True) + #show:是否显示模型的维度信息 + #layer_name:选择要提取特征的层,如果为None,则提取符合维度范围的层 + #auto_save_embedding:是否自动保存特征向量 must be True + save_model.save_checkpoint_embeddings_predictions() #保存模型权重、特征向量和预测结果到epoch_x + if epoch == 0: + save_model.save_lables_index(path = "../dataset") #保存标签和索引到dataset +``` + + +## 项目结构 + +- `./scripts/train.yaml`：训练配置文件，包含批次大小、学习率、GPU设置等参数 +- `./scripts/train.py`：训练脚本，执行模型训练并自动收集特征数据 +- `./model/`：保存训练好的模型权重 +- `./epochs/`：保存训练过程中的高维特征向量、预测结果等数据 + +## 使用方法 + +1. 配置 `train.yaml` 文件设置训练参数 +2. 执行训练脚本： + ``` + python train.py + ``` +3. 训练完成后，可以在以下位置找到相关数据： + - 模型权重：`./epochs/epoch_{n}/model.pth` + - 特征向量：`./epochs/epoch_{n}/embeddings.npy` + - 预测结果：`./epochs/epoch_{n}/predictions.npy` + - 标签数据：`./dataset/labels.npy` + - 数据索引：`./dataset/index.json` + +## 数据格式 + +- `embeddings.npy`：形状为 [n_samples, feature_dim] 的特征向量 +- `predictions.npy`：形状为 [n_samples, n_classes] 的预测概率 +- `labels.npy`：形状为 [n_samples] 的真实标签 +- `index.json`：包含训练集、测试集和验证集的索引信息 \ No newline at end of file diff --git a/ResNet-CIFAR10/Classification-normal/scripts/dataset_utils.py b/ResNet-CIFAR10/Classification-normal/scripts/dataset_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ce2e94fec43198ef54dc0f8282fe8bbe53f79d50 --- /dev/null +++ b/ResNet-CIFAR10/Classification-normal/scripts/dataset_utils.py @@ -0,0 +1,59 @@ +import torch +import torchvision +import torchvision.transforms as transforms +import os + +#加载数据集 + +def get_cifar10_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None, shuffle=False): + """获取CIFAR10数据集的数据加载器 + + Args: + batch_size: 批次大小 + num_workers: 数据加载的工作进程数 + local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载 + + Returns: + trainloader: 训练数据加载器 + testloader: 测试数据加载器 + """ + # 数据预处理 + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), + ]) + + transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), + ]) + + # 设置数据集路径 + if local_dataset_path: + print(f"使用本地数据集: {local_dataset_path}") + # 检查数据集路径是否有数据集，没有的话则下载 + cifar_path = os.path.join(local_dataset_path, 'cifar-10-batches-py') + download = not os.path.exists(cifar_path) or not os.listdir(cifar_path) + dataset_path = local_dataset_path + else: + print("未指定本地数据集路径，将下载数据集") + download = True + dataset_path = '../dataset' + + # 创建数据集路径 + if not os.path.exists(dataset_path): + os.makedirs(dataset_path) + + trainset = torchvision.datasets.CIFAR10( + root=dataset_path, train=True, download=download, transform=transform_train) + trainloader = torch.utils.data.DataLoader( + trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + testset = torchvision.datasets.CIFAR10( + root=dataset_path, train=False, download=download, transform=transform_test) + testloader = torch.utils.data.DataLoader( + testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + return trainloader, testloader diff --git a/ResNet-CIFAR10/Classification-normal/scripts/model.py b/ResNet-CIFAR10/Classification-normal/scripts/model.py new file mode 100644 index 0000000000000000000000000000000000000000..b7d0365b0da8227a4b63d61049dbe23e597b1d57 --- /dev/null +++ b/ResNet-CIFAR10/Classification-normal/scripts/model.py @@ -0,0 +1,308 @@ +''' +ResNet in PyTorch. + +ResNet(深度残差网络)是由微软研究院的Kaiming He等人提出的深度神经网络架构。 +主要创新点是引入了残差学习的概念,通过跳跃连接解决了深层网络的退化问题。 + +主要特点: +1. 引入残差块(Residual Block),使用跳跃连接 +2. 使用Batch Normalization进行归一化 +3. 支持更深的网络结构(最深可达152层) +4. 在多个计算机视觉任务上取得了突破性进展 + +Reference: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385 +''' +import torch +import torch.nn as nn + +class BasicBlock(nn.Module): + """基础残差块 + + 用于ResNet18/34等浅层网络。结构为: + x -> Conv -> BN -> ReLU -> Conv -> BN -> (+) -> ReLU + |------------------------------------------| + + Args: + in_channels: 输入通道数 + out_channels: 输出通道数 + stride: 步长,用于下采样,默认为1 + + 注意:基础模块没有通道压缩,expansion=1 + """ + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1): + super(BasicBlock,self).__init__() + self.features = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(True), + nn.Conv2d(out_channels,out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + + # 如果输入输出维度不等,则使用1x1卷积层来改变维度 + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels), + ) + + def forward(self, x): + out = self.features(x) + out += self.shortcut(x) + out = torch.relu(out) + return out + + +class Bottleneck(nn.Module): + """瓶颈残差块 + + 用于ResNet50/101/152等深层网络。结构为: + x -> 1x1Conv -> BN -> ReLU -> 3x3Conv -> BN -> ReLU -> 1x1Conv -> BN -> (+) -> ReLU + |-------------------------------------------------------------------| + + Args: + in_channels: 输入通道数 + zip_channels: 压缩后的通道数 + stride: 步长,用于下采样,默认为1 + + 注意:通过1x1卷积先压缩通道数,再还原,expansion=4 + """ + expansion = 4 + + def __init__(self, in_channels, zip_channels, stride=1): + super(Bottleneck, self).__init__() + out_channels = self.expansion * zip_channels + self.features = nn.Sequential( + # 1x1卷积压缩通道 + nn.Conv2d(in_channels, zip_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(zip_channels), + nn.ReLU(inplace=True), + # 3x3卷积提取特征 + nn.Conv2d(zip_channels, zip_channels, kernel_size=3, stride=stride, padding=1, bias=False), + nn.BatchNorm2d(zip_channels), + nn.ReLU(inplace=True), + # 1x1卷积还原通道 + nn.Conv2d(zip_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_channels) + ) + + def forward(self, x): + out = self.features(x) + out += self.shortcut(x) + out = torch.relu(out) + return out + +class ResNet(nn.Module): + """ResNet模型 + + 网络结构: + 1. 一个卷积层用于特征提取 + 2. 四个残差层,每层包含多个残差块 + 3. 平均池化和全连接层进行分类 + + 对于CIFAR10,特征图大小变化为: + (32,32,3) -> [Conv] -> (32,32,64) -> [Layer1] -> (32,32,64) -> [Layer2] + -> (16,16,128) -> [Layer3] -> (8,8,256) -> [Layer4] -> (4,4,512) -> [AvgPool] + -> (1,1,512) -> [FC] -> (num_classes) + + Args: + block: 残差块类型(BasicBlock或Bottleneck) + num_blocks: 每层残差块数量的列表 + num_classes: 分类数量,默认为10 + verbose: 是否打印中间特征图大小 + init_weights: 是否初始化权重 + dropout: 是否在全连接层前使用dropout + """ + def __init__(self, block, num_blocks, num_classes=10, verbose=False, init_weights=True, dropout=False): + super(ResNet, self).__init__() + self.verbose = verbose + self.in_channels = 64 + + # 第一层卷积 + self.features = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True) + ) + + # 四个残差层 + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + + # 分类层 + self.avg_pool = nn.AvgPool2d(kernel_size=4) + if dropout: + self.dropout = nn.Dropout(p=0.5) + else: + self.dropout = nn.Identity() + self.classifier = nn.Linear(512 * block.expansion, num_classes) + + if init_weights: + self._initialize_weights() + + def _make_layer(self, block, out_channels, num_blocks, stride): + """构建残差层 + + Args: + block: 残差块类型 + out_channels: 输出通道数 + num_blocks: 残差块数量 + stride: 第一个残差块的步长(用于下采样) + + Returns: + nn.Sequential: 残差层 + """ + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + """前向传播 + + Args: + x: 输入张量,[N,3,32,32] + + Returns: + out: 输出张量,[N,num_classes] + """ + out = self.features(x) + if self.verbose: + print('block 1 output: {}'.format(out.shape)) + + out = self.layer1(out) + if self.verbose: + print('block 2 output: {}'.format(out.shape)) + + out = self.layer2(out) + if self.verbose: + print('block 3 output: {}'.format(out.shape)) + + out = self.layer3(out) + if self.verbose: + print('block 4 output: {}'.format(out.shape)) + + out = self.layer4(out) + if self.verbose: + print('block 5 output: {}'.format(out.shape)) + + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + out = self.dropout(out) + out = self.classifier(out) + return out + + def feature(self,x): + """前向传播 + + Args: + x: 输入张量,[N,3,32,32] + + Returns: + out: 输出张量,[N,num_classes] + """ + out = self.features(x) + if self.verbose: + print('block 1 output: {}'.format(out.shape)) + + out = self.layer1(out) + if self.verbose: + print('block 2 output: {}'.format(out.shape)) + + out = self.layer2(out) + if self.verbose: + print('block 3 output: {}'.format(out.shape)) + + out = self.layer3(out) + if self.verbose: + print('block 4 output: {}'.format(out.shape)) + + out = self.layer4(out) + if self.verbose: + print('block 5 output: {}'.format(out.shape)) + + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + return out + + def prediction(self, x): + out = self.classifier(x) + return out + + def _initialize_weights(self): + """初始化模型权重 + + 采用kaiming初始化方法: + - 卷积层权重采用kaiming_normal_初始化 + - BN层参数采用常数初始化 + - 线性层采用正态分布初始化 + """ + 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) + nn.init.constant_(m.bias, 0) + +def ResNet18(verbose=False, num_classes=10, dropout=False): + """ResNet-18模型 + + Args: + verbose: 是否打印中间特征图大小 + num_classes: 分类数量 + dropout: 是否在全连接层前使用dropout + """ + return ResNet(BasicBlock, [2,2,2,2], num_classes=num_classes, verbose=verbose, dropout=dropout) + +def ResNet34(verbose=False, num_classes=10, dropout=False): + """ResNet-34模型""" + return ResNet(BasicBlock, [3,4,6,3], num_classes=num_classes, verbose=verbose, dropout=dropout) + +def ResNet50(verbose=False): + """ResNet-50模型""" + return ResNet(Bottleneck, [3,4,6,3], verbose=verbose) + +def ResNet101(verbose=False): + """ResNet-101模型""" + return ResNet(Bottleneck, [3,4,23,3], verbose=verbose) + +def ResNet152(verbose=False): + """ResNet-152模型""" + return ResNet(Bottleneck, [3,8,36,3], verbose=verbose) + +def test(): + """测试函数""" + net = ResNet34() + x = torch.randn(2,3,32,32) + y = net(x) + print('Output shape:', y.size()) + + # 打印模型结构 + from torchinfo import summary + device = 'cuda' if torch.cuda.is_available() else 'cpu' + net = net.to(device) + summary(net,(2,3,32,32)) + +if __name__ == '__main__': + test() \ No newline at end of file diff --git a/ResNet-CIFAR10/Classification-normal/scripts/train.py b/ResNet-CIFAR10/Classification-normal/scripts/train.py new file mode 100644 index 0000000000000000000000000000000000000000..f4dd0594ef3778665c3090c0465f9a5d4ca485e6 --- /dev/null +++ b/ResNet-CIFAR10/Classification-normal/scripts/train.py @@ -0,0 +1,225 @@ +import sys +import os +import yaml +from pathlib import Path +import torch +import torch.nn as nn +import torch.optim as optim +import time +import logging +import numpy as np +from tqdm import tqdm + +# 将项目根目录添加到Python路径中 +current_dir = Path(__file__).resolve().parent +project_root = current_dir.parent.parent.parent +sys.path.append(str(project_root)) +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +from dataset_utils import get_cifar10_dataloaders +from model import ResNet18 +from ttv_utils import time_travel_saver + +def setup_logger(log_file): + """配置日志记录器，如果日志文件存在则覆盖 + + Args: + log_file: 日志文件路径 + + Returns: + logger: 配置好的日志记录器 + """ + # 创建logger + logger = logging.getLogger('train') + logger.setLevel(logging.INFO) + + # 移除现有的处理器 + if logger.hasHandlers(): + logger.handlers.clear() + + # 创建文件处理器，使用'w'模式覆盖现有文件 + fh = logging.FileHandler(log_file, mode='w') + fh.setLevel(logging.INFO) + + # 创建控制台处理器 + ch = logging.StreamHandler() + ch.setLevel(logging.INFO) + + # 创建格式器 + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + fh.setFormatter(formatter) + ch.setFormatter(formatter) + + # 添加处理器 + logger.addHandler(fh) + logger.addHandler(ch) + + return logger + +def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda:0', + save_dir='./epochs', model_name='model', interval=1): + """通用的模型训练函数 + Args: + model: 要训练的模型 + trainloader: 训练数据加载器 + testloader: 测试数据加载器 + epochs: 训练轮数 + lr: 学习率 + device: 训练设备，格式为'cuda:N'，其中N为GPU编号（0,1,2,3） + save_dir: 模型保存目录 + model_name: 模型名称 + interval: 模型保存间隔 + """ + # 检查并设置GPU设备 + if not torch.cuda.is_available(): + print("CUDA不可用，将使用CPU训练") + device = 'cpu' + elif not device.startswith('cuda:'): + device = f'cuda:0' + + # 确保device格式正确 + if device.startswith('cuda:'): + gpu_id = int(device.split(':')[1]) + if gpu_id >= torch.cuda.device_count(): + print(f"GPU {gpu_id} 不可用，将使用GPU 0") + device = 'cuda:0' + + # 设置保存目录 + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + # 设置日志文件路径 + log_file = os.path.join(os.path.dirname(save_dir),'epochs', 'train.log') + if not os.path.exists(os.path.dirname(log_file)): + os.makedirs(os.path.dirname(log_file)) + + logger = setup_logger(log_file) + + # 损失函数和优化器 + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50) + + # 移动模型到指定设备 + model = model.to(device) + best_acc = 0 + start_time = time.time() + + logger.info(f'开始训练 {model_name}') + logger.info(f'总轮数: {epochs}, 学习率: {lr}, 设备: {device}') + + for epoch in range(epochs): + # 训练阶段 + model.train() + train_loss = 0 + correct = 0 + total = 0 + + train_pbar = tqdm(trainloader, desc=f'Epoch {epoch+1}/{epochs} [Train]') + for batch_idx, (inputs, targets) in enumerate(train_pbar): + inputs, targets = inputs.to(device), targets.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, targets) + loss.backward() + optimizer.step() + + train_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + # 更新进度条 + train_pbar.set_postfix({ + 'loss': f'{train_loss/(batch_idx+1):.3f}', + 'acc': f'{100.*correct/total:.2f}%' + }) + + # 保存训练阶段的准确率 + train_acc = 100.*correct/total + train_correct = correct + train_total = total + + # 测试阶段 + model.eval() + test_loss = 0 + correct = 0 + total = 0 + + test_pbar = tqdm(testloader, desc=f'Epoch {epoch+1}/{epochs} [Test]') + with torch.no_grad(): + for batch_idx, (inputs, targets) in enumerate(test_pbar): + inputs, targets = inputs.to(device), targets.to(device) + outputs = model(inputs) + loss = criterion(outputs, targets) + + test_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + # 更新进度条 + test_pbar.set_postfix({ + 'loss': f'{test_loss/(batch_idx+1):.3f}', + 'acc': f'{100.*correct/total:.2f}%' + }) + + # 计算测试精度 + acc = 100.*correct/total + + # 记录训练和测试的损失与准确率 + logger.info(f'Epoch: {epoch+1} | Train Loss: {train_loss/(len(trainloader)):.3f} | Train Acc: {train_acc:.2f}% | ' + f'Test Loss: {test_loss/(batch_idx+1):.3f} | Test Acc: {acc:.2f}%') + + # 保存可视化训练过程所需要的文件 + if (epoch + 1) % interval == 0 or (epoch == 0): + # 创建一个专门用于收集embedding的顺序dataloader + ordered_trainloader = torch.utils.data.DataLoader( + trainloader.dataset, + batch_size=trainloader.batch_size, + shuffle=False, + num_workers=trainloader.num_workers + ) + epoch_save_dir = os.path.join(save_dir, f'epoch_{epoch+1}') + save_model = time_travel_saver(model, ordered_trainloader, device, epoch_save_dir, model_name, + show=True, layer_name='avg_pool', auto_save_embedding=True) + save_model.save_checkpoint_embeddings_predictions() + if epoch == 0: + save_model.save_lables_index(path = "../dataset") + + scheduler.step() + + logger.info('训练完成!') + +def main(): + # 加载配置文件 + config_path = Path(__file__).parent / 'train.yaml' + with open(config_path) as f: + config = yaml.safe_load(f) + + # 创建模型 + model = ResNet18(num_classes=10) + + # 获取数据加载器 + trainloader, testloader = get_cifar10_dataloaders( + batch_size=128, + num_workers=2, + local_dataset_path=config['dataset_path'], + shuffle=True + ) + + # 训练模型 + train_model( + model=model, + trainloader=trainloader, + testloader=testloader, + epochs=config['epochs'], + lr=config['lr'], + device=f'cuda:{config["gpu"]}', + save_dir='../epochs', + model_name='ResNet18', + interval=config['interval'] + ) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/ResNet-CIFAR10/Classification-normal/scripts/train.yaml b/ResNet-CIFAR10/Classification-normal/scripts/train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..205f5c70f85793b0b7856d8230ecc2456fa6f3dd --- /dev/null +++ b/ResNet-CIFAR10/Classification-normal/scripts/train.yaml @@ -0,0 +1,7 @@ +batch_size: 128 +num_workers: 2 +dataset_path: ../dataset +epochs: 50 +gpu: 0 +lr: 0.1 +interval: 2 \ No newline at end of file diff --git a/ttv_utils/__init__.py b/ttv_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..720b4c55409f6f685b4c0f90a32c03f3c4bae9d9 --- /dev/null +++ b/ttv_utils/__init__.py @@ -0,0 +1,57 @@ +"""ttv_utils包提供了用于特征预测 和 模型训练过程收集 的工具函数 + +主要组件: +1. FeaturePredictor: 用于从模型中间层特征向量预测预测结果的类 + 使用示例: + ```python + predictor = FeaturePredictor( + model_class=model, 模型类 + model_weights_path=weight_path,模型权重文件路径 + layer_info_path=layer_info_path,层信息文件路径 + device=device 运行设备 + ) + ``` + +2. predict_feature: 从模型中间层特征向量预测预测结果便捷函数 + 使用示例: + ```python + output = predict_feature( + model=model, # 模型类 + weight_path=weight_path, # 模型权重文件路径 + layer_info_path=layer_info_path, # 层信息文件路径 + feature=feature, # 特征向量 + device=device # 运行设备 + ) + ``` + +3. time_travel_saver: 用于在训练过程中保存模型权重、特征和预测结果的类 + 使用示例: + ```python + # 创建一个保存器实例 + saver = time_travel_saver( + model=model, # 模型实例 + dataloader=ordered_loader, # 顺序数据加载器 + device='cuda:0', # 计算设备 + save_dir='./checkpoints', # 保存根目录 + model_name='alexnet', # 模型名称 + interval=1 # 每隔多少个epoch保存一次 + ) + + # 在训练循环中调用save方法 + for epoch in range(epochs): + # 训练代码... + if epoch % interval == 0: + saver.save(model) # 保存当前epoch的模型状态 + ``` + + 保存的文件结构: + - model/{epoch}.pth: 模型权重 + - dataset/representation/{epoch}.npy: 特征向量 + - dataset/prediction/{epoch}.npy: 预测结果 + - dataset/label/labels.npy: 标签 +""" + +from .feature_predictor import FeaturePredictor, predict_feature +from .save_embeddings import time_travel_saver + +__all__ = ['FeaturePredictor', 'predict_feature', 'time_travel_saver'] diff --git a/ttv_utils/feature_predictor.py b/ttv_utils/feature_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..246f429dfe310aa15dc83cddc12ae00e8dcfc77d --- /dev/null +++ b/ttv_utils/feature_predictor.py @@ -0,0 +1,410 @@ +""" +特征预测器模块 + +该模块使用钩子机制从模型的中间层特征向量预测分类结果 +""" + +import torch +import torch.nn as nn +import json +import os +import torch.nn.functional as F +import numpy as np +from typing import Type, Union, Optional + +class FeaturePredictor: + def __init__(self, model_class, model_weights_path, layer_info_path, device='cuda' if torch.cuda.is_available() else 'cpu'): + """ + 初始化特征预测器 + + Args: + model_class: 模型类 + model_weights_path: 模型权重文件路径 + layer_info_path: 层信息文件路径 + device: 运行设备 + """ + self.device = device + self.model = model_class().to(device) + self.model.load_state_dict(torch.load(model_weights_path, map_location=device, weights_only=True)) + self.model.eval() + # 加载层信息 + with open(layer_info_path, 'r') as f: + layer_info = json.load(f) + self.target_layer = layer_info['layer_id'] + self.feature_dim = layer_info['dim'] + + # 初始化变量 + self.output_shape = None + self.inject_feature = None + self.handles = [] + self.layer_name_map = {} + + # 用于调试的变量 + self.last_normalized_feature = None + self.last_reshaped_feature = None + self.last_layer_outputs = {} + + # 注册钩子 + self.register_hooks() + + # 运行一次前向传播来获取形状 + self._get_output_shape() + + def _get_output_shape(self): + """运行一次前向传播来获取目标层的输出形状""" + def shape_hook(module, input, output): + self.output_shape = output.shape[1:] # 不包括batch维度 + print(f"[Init] 获取到目标层输出形状: {self.output_shape}") + return output + + # 找到目标层并注册临时钩子 + def find_layer(module, name=''): + for n, child in module.named_children(): + current_name = f"{name}.{n}" if name else n + if current_name == self.target_layer: + handle = child.register_forward_hook(shape_hook) + return handle, True + else: + handle, found = find_layer(child, current_name) + if found: + return handle, True + return None, False + + # 注册临时钩子 + handle, found = find_layer(self.model) + if not found: + raise ValueError(f"未找到目标层: {self.target_layer}") + + # 运行一次前向传播 + with torch.no_grad(): + dummy_input = torch.zeros(1, 3, 32, 32).to(self.device) + self.model(dummy_input) + + # 移除临时钩子 + handle.remove() + + if self.output_shape is None: + raise RuntimeError("无法获取目标层的输出形状") + + def register_hooks(self): + """注册钩子函数，在目标层注入特征向量和监控每层输出""" + def print_tensor_info(name, tensor): + """打印张量的统计信息""" + print(f"\n[Hook Debug] {name}:") + print(f"- 形状: {tensor.shape}") + print(f"- 数值范围: [{tensor.min().item():.4f}, {tensor.max().item():.4f}]") + print(f"- 均值: {tensor.mean().item():.4f}") + print(f"- 标准差: {tensor.std().item():.4f}") + + def hook_fn(module, input, output): + """钩子函数：输出层信息并在目标层注入特征""" + layer_name = self.layer_name_map.get(module, "未知层") + print(f"\n[Hook Debug] 层: {layer_name}") + print(f"- 类型: {type(module).__name__}") + + # 输出输入信息 + if input and len(input) > 0: + print_tensor_info("输入张量", input[0]) + + # 输出原始输出信息 + print_tensor_info("输出张量", output) + + # 如果是目标层且有注入特征，则替换输出 + if layer_name == self.target_layer and self.inject_feature is not None: + print("\n[Hook Debug] 正在注入特征...") + print_tensor_info("注入特征", self.inject_feature) + print(f"[Hook Debug] 将层 {layer_name} 的输出从 {output.shape} 替换为注入特征 {self.inject_feature.shape}") + # 替换输出 + output = self.inject_feature + print("[Hook Debug] 特征注入完成，将作为下一层的输入") + return output + + return output + + def hook_layer(module, name=''): + """为每一层注册钩子""" + for n, child in module.named_children(): + current_name = f"{name}.{n}" if name else n + # 保存层名到模块的映射 + self.layer_name_map[child] = current_name + # 注册钩子 + handle = child.register_forward_hook(hook_fn) + self.handles.append(handle) + # 递归处理子模块 + hook_layer(child, current_name) + + # 注册所有层的钩子 + hook_layer(self.model) + print(f"[Debug] 钩子注册完成，共注册了 {len(self.handles)} 个钩子") + + def reshape_feature(self, feature): + """调整特征向量的形状""" + if self.output_shape is None: + raise RuntimeError("目标层的输出形状未初始化") + + batch_size = feature.shape[0] + expected_dim = np.prod(self.output_shape) + + # 检查输入特征维度是否正确 + if feature.shape[1] != expected_dim: + raise ValueError(f"特征维度不匹配：预期 {expected_dim}，实际 {feature.shape[1]}") + + # 使用自动获取的形状重塑特征 + new_shape = (batch_size,) + self.output_shape + print(f"[Debug] 调整特征形状: {feature.shape} -> {new_shape}") + return feature.view(new_shape) + + def predict(self, feature): + """使用给定的特征向量进行预测""" + print(f"\n[Debug] 开始预测，输入特征形状: {feature.shape}") + + # 检查输入维度 + if feature.shape[1] != self.feature_dim: + raise ValueError(f"特征维度不匹配：预期 {self.feature_dim}，实际 {feature.shape[1]}") + + # 将特征转移到正确的设备并重塑 + feature = feature.to(self.device) + self.inject_feature = self.reshape_feature(feature) + + # 使用虚拟输入进行预测 + dummy_input = torch.zeros(feature.shape[0], 3, 32, 32).to(self.device) + + # 进行前向传播（钩子会自动在目标层注入特征） + with torch.no_grad(): + output = self.model(dummy_input) + + # 清除注入的特征 + self.inject_feature = None + + return output + +def predict_feature( + model: Type[nn.Module], + weight_path: str, + layer_info_path: str, + feature: Union[torch.Tensor, np.ndarray], + device: Optional[str] = None +) -> torch.Tensor: + """ + 使用预训练模型预测特征向量的类别。 + + Args: + model: PyTorch模型类（不是实例） + weight_path: 模型权重文件路径 + layer_info_path: 层信息配置文件路径 + feature: 输入特征向量，可以是torch.Tensor或numpy.ndarray + device: 运行设备，可选 'cuda' 或 'cpu'。如果为None，将自动选择。 + + Returns: + torch.Tensor: 模型输出的预测结果 + + Raises: + ValueError: 如果输入特征维度不正确 + FileNotFoundError: 如果权重文件或层信息文件不存在 + RuntimeError: 如果模型加载或预测过程出错 + """ + try: + # 检查文件是否存在 + if not os.path.exists(weight_path): + raise FileNotFoundError(f"权重文件不存在: {weight_path}") + if not os.path.exists(layer_info_path): + raise FileNotFoundError(f"层信息文件不存在: {layer_info_path}") + + # 确定设备 + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + # 转换输入特征为torch.Tensor + if isinstance(feature, np.ndarray): + feature = torch.from_numpy(feature).float() + elif not isinstance(feature, torch.Tensor): + raise ValueError("输入特征必须是numpy数组或torch张量") + + # 创建预测器实例 + predictor = FeaturePredictor( + model_class=model, + model_weights_path=weight_path, + layer_info_path=layer_info_path, + device=device + ) + + # 进行预测 + with torch.no_grad(): + output = predictor.predict(feature) + + return output + + except Exception as e: + raise RuntimeError(f"预测过程出错: {str(e)}") + + +def test_predictor(): + """测试特征预测器的功能""" + from AlexNet.code.model import AlexNet + import os + import numpy as np + + # 创建预测器实例 + predictor = FeaturePredictor( + model_class=AlexNet, + model_weights_path='AlexNet/model/0/epoch_195/subject_model.pth', + layer_info_path='AlexNet/code/layer_info.json' + ) + + print("\n开始单点测试...") + + # 生成一个测试点，使用较大的尺度以增加特征的差异性 + feature = torch.randn(1, predictor.feature_dim) * 10.0 + output = predictor.predict(feature) + probs = output.softmax(dim=1) + print("\n结果:",output) + # 显示最终预测结果 + print("\n最终预测结果:") + top_k = torch.topk(probs[0], k=3) + for idx, (class_idx, prob) in enumerate(zip(top_k.indices.tolist(), top_k.values.tolist())): + print(f"Top-{idx+1}: 类别 {class_idx}, 概率 {prob:.4f}") + +def test_predictor_from_train_data(): + """测试特征预测器的批量预测功能""" + from AlexNet.code.model import AlexNet + import numpy as np + import torch + + print("\n开始处理训练数据集...") + # 创建预测器实例 + predictor = FeaturePredictor( + model_class=AlexNet, + model_weights_path='AlexNet/model/0/epoch_195/subject_model.pth', + layer_info_path='AlexNet/code/layer_info.json' + ) + + # 加载训练数据 + print("\n加载训练数据...") + features = np.load('AlexNet/model/0/epoch_195/train_data.npy') + print(f"数据形状: {features.shape}") + + # 转换为tensor + features = torch.from_numpy(features).float() + + # 批量处理 + batch_size = 100 + num_samples = len(features) + num_batches = (num_samples + batch_size - 1) // batch_size + + # 用于统计结果 + all_predictions = [] + class_counts = {} + + print("\n开始批量预测...") + with torch.no_grad(): + for i in range(num_batches): + start_idx = i * batch_size + end_idx = min((i + 1) * batch_size, num_samples) + batch_features = features[start_idx:end_idx] + + # 使用预测器进行预测 + outputs = predictor.predict(batch_features) + predictions = outputs.argmax(dim=1).cpu().numpy() + + # 更新统计信息 + for pred in predictions: + class_counts[int(pred)] = class_counts.get(int(pred), 0) + 1 + + all_predictions.extend(predictions) + + # 打印进度和当前批次的预测分布 + if (i + 1) % 10 == 0: + print(f"\n已处理: {end_idx}/{num_samples} 个样本") + batch_unique, batch_counts = np.unique(predictions, return_counts=True) + print("当前批次预测分布:") + for class_idx, count in zip(batch_unique, batch_counts): + print(f"类别 {class_idx}: {count} 个样本 ({count/len(predictions)*100:.2f}%)") + + # 打印总体统计结果 + print("\n最终预测结果统计:") + total_samples = len(all_predictions) + for class_idx in sorted(class_counts.keys()): + count = class_counts[class_idx] + percentage = (count / total_samples) * 100 + print(f"类别 {class_idx}: {count} 个样本 ({percentage:.2f}%)") + +def test_train_data(): + """测试训练数据集的预测结果分布""" + from AlexNet.code.model import AlexNet + import numpy as np + import torch + import torch.nn.functional as F + + print("\n开始处理训练数据集...") + + # 初始化模型 + device = 'cuda' if torch.cuda.is_available() else 'cpu' + model = AlexNet().to(device) + model.load_state_dict(torch.load('AlexNet/model/0/epoch_195/subject_model.pth', + map_location=device, weights_only=True)) + model.eval() + + # 加载训练数据 + print("加载训练数据...") + features = np.load('AlexNet/model/0/epoch_195/train_data.npy') + print(f"数据形状: {features.shape}") + + # 转换为tensor + features = torch.from_numpy(features).float().to(device) + + # 批量处理 + batch_size = 100 + num_samples = len(features) + num_batches = (num_samples + batch_size - 1) // batch_size + + # 用于统计结果 + all_predictions = [] + class_counts = {} + + print("\n开始批量预测...") + with torch.no_grad(): + for i in range(num_batches): + start_idx = i * batch_size + end_idx = min((i + 1) * batch_size, num_samples) + batch_features = features[start_idx:end_idx] + + # 将特征重塑为[batch_size, 16, 8, 8] + reshaped_features = batch_features.view(-1, 16, 8, 8) + + # 使用模型的predict函数 + outputs = model.predict(reshaped_features) + predictions = outputs.argmax(dim=1).cpu().numpy() + + # 更新统计信息 + for pred in predictions: + class_counts[int(pred)] = class_counts.get(int(pred), 0) + 1 + + all_predictions.extend(predictions) + + # 打印进度 + if (i + 1) % 10 == 0: + print(f"已处理: {end_idx}/{num_samples} 个样本") + + # 打印统计结果 + print("\n预测结果统计:") + total_samples = len(all_predictions) + for class_idx in sorted(class_counts.keys()): + count = class_counts[class_idx] + percentage = (count / total_samples) * 100 + print(f"类别 {class_idx}: {count} 个样本 ({percentage:.2f}%)") + + # 保存详细结果 + print("\n保存详细结果...") + results = { + 'predictions': all_predictions, + 'class_counts': class_counts + } + # np.save('prediction_results.npy', results) + # print("结果已保存到 prediction_results.npy") + + + +if __name__ == "__main__": + test_predictor() + # test_predictor_from_train_data() + # test_train_data() \ No newline at end of file diff --git a/ttv_utils/save_embeddings.py b/ttv_utils/save_embeddings.py new file mode 100644 index 0000000000000000000000000000000000000000..c41a3d0374ba72d31adafb27bf3da7e6b9deded8 --- /dev/null +++ b/ttv_utils/save_embeddings.py @@ -0,0 +1,272 @@ +import torch +import torch.nn as nn +import numpy as np +import os +import json +from tqdm import tqdm + +class time_travel_saver: + """可视化数据保存类 + + 用于保存模型训练过程中的各种数据，包括： + 1. 模型权重 (.pth) + 2. 高维特征 (representation/*.npy) + 3. 预测结果 (prediction/*.npy) + 4. 标签数据 (label/labels.npy) + """ + + def __init__(self, model, dataloader, device, save_dir, model_name, + auto_save_embedding=False, layer_name=None,show = False): + """初始化 + + Args: + model: 要保存的模型实例 + dataloader: 数据加载器（必须是顺序加载的） + device: 计算设备(cpu or gpu) + save_dir: 保存根目录 + model_name: 模型名称 + """ + self.model = model + self.dataloader = dataloader + self.device = device + self.save_dir = save_dir + self.model_name = model_name + self.auto_save = auto_save_embedding + self.layer_name = layer_name + + if show and not layer_name: + layer_dimensions = self.show_dimensions() + # print(layer_dimensions) + + def show_dimensions(self): + """显示模型中所有层的名称和对应的维度 + + 这个函数会输出模型中所有层的名称和它们的输出维度， + 帮助用户选择合适的层来提取特征。 + + Returns: + layer_dimensions: 包含层名称和维度的字典 + """ + activation = {} + layer_dimensions = {} + + def get_activation(name): + def hook(model, input, output): + activation[name] = output.detach() + return hook + + # 注册钩子到所有层 + handles = [] + for name, module in self.model.named_modules(): + if isinstance(module, nn.Module) and not isinstance(module, nn.ModuleList) and not isinstance(module, nn.ModuleDict): + handles.append(module.register_forward_hook(get_activation(name))) + + self.model.eval() + with torch.no_grad(): + # 获取一个batch来分析每层的输出维度 + inputs, _ = next(iter(self.dataloader)) + inputs = inputs.to(self.device) + _ = self.model(inputs) + + # 分析所有层的输出维度 + print("\n模型各层的名称和维度:") + print("-" * 50) + print(f"{'层名称':<40} {'特征维度':<15} {'输出形状'}") + print("-" * 50) + + for name, feat in activation.items(): + if feat is None: + continue + + # 获取特征维度（展平后） + feat_dim = feat.view(feat.size(0), -1).size(1) + layer_dimensions[name] = feat_dim + # 打印层信息 + shape_str = str(list(feat.shape)) + print(f"{name:<40} {feat_dim:<15} {shape_str}") + + print("-" * 50) + print("注: 特征维度是将输出张量展平后的维度大小") + print("你可以通过修改time_travel_saver的layer_name参数来选择不同的层") + print("例如：layer_name='avg_pool'或layer_name='layer4'等") + + # 移除所有钩子 + for handle in handles: + handle.remove() + + return layer_dimensions + + def _extract_features_and_predictions(self): + """提取特征和预测结果 + + Returns: + features: 高维特征 [样本数, 特征维度] + predictions: 预测结果 [样本数, 类别数] + """ + features = [] + predictions = [] + indices = [] + activation = {} + + def get_activation(name): + def hook(model, input, output): + # 只在需要时保存激活值，避免内存浪费 + if name not in activation or activation[name] is None: + activation[name] = output.detach() + return hook + + # 根据层的名称或维度来选择层 + + # 注册钩子到所有层 + handles = [] + for name, module in self.model.named_modules(): + if isinstance(module, nn.Module) and not isinstance(module, nn.ModuleList) and not isinstance(module, nn.ModuleDict): + handles.append(module.register_forward_hook(get_activation(name))) + + self.model.eval() + with torch.no_grad(): + # 首先获取一个batch来分析每层的输出维度 + inputs, _ = next(iter(self.dataloader)) + inputs = inputs.to(self.device) + _ = self.model(inputs) + + # 如果指定了层名，则直接使用该层 + if self.layer_name is not None: + if self.layer_name not in activation: + raise ValueError(f"指定的层 {self.layer_name} 不存在于模型中") + + feat = activation[self.layer_name] + if feat is None: + raise ValueError(f"指定的层 {self.layer_name} 没有输出特征") + + suitable_layer_name = self.layer_name + suitable_dim = feat.view(feat.size(0), -1).size(1) + print(f"使用指定的特征层: {suitable_layer_name}, 特征维度: {suitable_dim}") + else: + # 找到维度在指定范围内的层 + target_dim_range = (256, 2048) + suitable_layer_name = None + suitable_dim = None + + # 分析所有层的输出维度 + for name, feat in activation.items(): + if feat is None: + continue + feat_dim = feat.view(feat.size(0), -1).size(1) + if target_dim_range[0] <= feat_dim <= target_dim_range[1]: + suitable_layer_name = name + suitable_dim = feat_dim + break + + if suitable_layer_name is None: + raise ValueError("没有找到合适维度的特征层") + + print(f"自动选择的特征层: {suitable_layer_name}, 特征维度: {suitable_dim}") + + # 保存层信息 + layer_info = { + 'layer_id': suitable_layer_name, + 'dim': suitable_dim + } + layer_info_path = os.path.join(os.path.dirname(self.save_dir), 'layer_info.json') + with open(layer_info_path, 'w') as f: + json.dump(layer_info, f) + + # 清除第一次运行的激活值 + activation.clear() + + # 现在处理所有数据 + for batch_idx, (inputs, _) in enumerate(tqdm(self.dataloader, desc="提取特征和预测结果")): + inputs = inputs.to(self.device) + outputs = self.model(inputs) # 获取预测结果 + + # 获取并处理特征 + feat = activation[suitable_layer_name] + flat_features = torch.flatten(feat, start_dim=1) + features.append(flat_features.cpu().numpy()) + predictions.append(outputs.cpu().numpy()) + + # 清除本次的激活值 + activation.clear() + + # 移除所有钩子 + for handle in handles: + handle.remove() + + if len(features) > 0: + features = np.vstack(features) + predictions = np.vstack(predictions) + return features, predictions + else: + return np.array([]), np.array([]) + + def save_lables_index(self, path): + """保存标签数据和索引信息 + + Args: + path: 保存路径 + """ + os.makedirs(path, exist_ok=True) + labels_path = os.path.join(path, 'labels.npy') + index_path = os.path.join(path, 'index.json') + + # 尝试从不同的属性获取标签 + try: + if hasattr(self.dataloader.dataset, 'targets'): + # CIFAR10/CIFAR100使用targets属性 + labels = np.array(self.dataloader.dataset.targets) + elif hasattr(self.dataloader.dataset, 'labels'): + # 某些数据集使用labels属性 + labels = np.array(self.dataloader.dataset.labels) + else: + # 如果上面的方法都不起作用，则从数据加载器中收集标签 + labels = [] + for _, batch_labels in self.dataloader: + labels.append(batch_labels.numpy()) + labels = np.concatenate(labels) + + # 保存标签数据 + np.save(labels_path, labels) + print(f"标签数据已保存到 {labels_path}") + + # 创建数据集索引 + num_samples = len(labels) + indices = list(range(num_samples)) + + # 创建索引字典 + index_dict = { + "train": indices, # 所有数据默认为训练集 + "test": [], # 初始为空 + "validation": [] # 初始为空 + } + + # 保存索引到JSON文件 + with open(index_path, 'w') as f: + json.dump(index_dict, f, indent=4) + + print(f"数据集索引已保存到 {index_path}") + + except Exception as e: + print(f"保存标签和索引时出错: {e}") + + def save_checkpoint_embeddings_predictions(self, model = None): + """保存所有数据""" + if model is not None: + self.model = model + # 保存模型权重 + os.makedirs(self.save_dir, exist_ok=True) + model_path = os.path.join(self.save_dir,'model.pth') + torch.save(self.model.state_dict(), model_path) + + if self.auto_save: + # 提取并保存特征和预测结果 + features, predictions = self._extract_features_and_predictions() + + # 保存特征 + np.save(os.path.join(self.save_dir, 'embeddings.npy'), features) + # 保存预测结果 + np.save(os.path.join(self.save_dir, 'predictions.npy'), predictions) + print("\n保存了以下数据:") + print(f"- 模型权重: {model_path}") + print(f"- 特征向量: [样本数: {features.shape[0]}, 特征维度: {features.shape[1]}]") + print(f"- 预测结果: [样本数: {predictions.shape[0]}, 类别数: {predictions.shape[1]}]") \ No newline at end of file