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PatchCore.log
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/home/aistudio/Anomaly.Paddle
Namespace(arch='resnet18', batch_size=32, category='textures', cpu=False, crop_size=256, data_path='/home/aistudio/data', debug=False, einsum=False, eval=True, eval_PRO=True, eval_threthold_step=500, inc=False, k=10, load_projection=None, method='coreset', non_partial_AUC=False, num_workers=0, save_model=True, save_model_subfolder=True, save_path='./output/coreset_resnet18_10', save_pic=True, seed=521, test_batch_size=1)
W0422 18:20:30.218997 7396 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0422 18:20:30.223659 7396 device_context.cc:465] device: 0, cuDNN Version: 7.6.
model resnet18, nParams 2787264
Training model 1/5 for carpet
| feature extraction | train | carpet |: 0%| | 0/9 [00:00<?, ?it/s]/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:130: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data.dtype == np.object:
| feature extraction | train | carpet |: 100%|████| 9/9 [00:15<00:00, 1.73s/it]
Creating CoreSet Sampler via k-Center Greedy
Getting the coreset from the main embedding.
Assigning the coreset as the memory bank with shape [28672, 384].
2022-04-22 18:22:33 Train ends, total 118.73s
Saving model...
2022-04-22 18:22:33 Save model in ./output/coreset_resnet18_10/carpet.pdparams
2022-04-22 18:22:33 Starting eval model...
| feature extraction | test | carpet |: 100%|█| 117/117 [00:38<00:00, 3.04it/s]
max_score:3.2409136295318604 min_score:0.7035042643547058
F1 image:0.9828571428571428 threshold:3.2409136295318604
F1 pixel:0.5584764361267536 threshold:3.2409136295318604
26%|██████████▋ | 131/500 [00:20<00:57, 6.39it/s]['carpet', 0.9955858747993579, 0.9901991232207019, 0.959082125912792]
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
if isinstance(obj, collections.Iterator):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:101: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
ret = np.asscalar(ex)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
return list(data) if isinstance(data, collections.MappingView) else data
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
a_min = np.asscalar(a_min.astype(scaled_dtype))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
a_max = np.asscalar(a_max.astype(scaled_dtype))
Training model 2/5 for grid
| feature extraction | train | grid |: 0%| | 0/9 [00:00<?, ?it/s]/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:130: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data.dtype == np.object:
| feature extraction | train | grid |: 11%|▋ | 1/9 [00:00<00:05, 1.52it/s]
| feature extraction | train | grid |: 22%|█▎ | 2/9 [00:01<00:04, 1.54it/s]
| feature extraction | train | grid |: 33%|██ | 3/9 [00:01<00:03, 1.55it/s]
| feature extraction | train | grid |: 44%|██▋ | 4/9 [00:02<00:03, 1.57it/s]
| feature extraction | train | grid |: 56%|███▎ | 5/9 [00:03<00:02, 1.56it/s]
| feature extraction | train | grid |: 67%|████ | 6/9 [00:03<00:01, 1.56it/s]
| feature extraction | train | grid |: 78%|████▋ | 7/9 [00:04<00:01, 1.58it/s]
| feature extraction | train | grid |: 89%|█████▎| 8/9 [00:05<00:00, 1.58it/s]
| feature extraction | train | grid |: 100%|██████| 9/9 [00:05<00:00, 1.71it/s]
Creating CoreSet Sampler via k-Center Greedy
Getting the coreset from the main embedding.
Assigning the coreset as the memory bank with shape [27033, 384].
2022-04-22 18:25:17 Train ends, total 98.82s
Saving model...
2022-04-22 18:25:17 Save model in ./output/coreset_resnet18_10/grid.pdparams
2022-04-22 18:25:17 Starting eval model...
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max_score:2.675908327102661 min_score:0.5877041220664978
F1 image:0.954954954954955 threshold:2.675908327102661
F1 pixel:0.31341884009599646 threshold:2.675908327102661
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65%|██████████████████████████▊ | 327/500 [00:39<00:20, 8.41it/s]
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67%|███████████████████████████▍ | 335/500 [00:40<00:19, 8.42it/s]
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68%|███████████████████████████▊ | 339/500 [00:40<00:20, 8.02it/s]
68%|███████████████████████████▉ | 340/500 [00:40<00:19, 8.13it/s]
68%|███████████████████████████▉ | 341/500 [00:40<00:19, 8.22it/s]
68%|████████████████████████████ | 342/500 [00:40<00:19, 8.26it/s]
69%|████████████████████████████▏ | 343/500 [00:41<00:18, 8.30it/s]
69%|████████████████████████████▏ | 344/500 [00:41<00:18, 8.33it/s]
69%|████████████████████████████▎ | 345/500 [00:41<00:18, 8.36it/s]
69%|████████████████████████████▎ | 346/500 [00:41<00:18, 8.35it/s]
69%|████████████████████████████▍ | 347/500 [00:41<00:18, 8.37it/s]
70%|████████████████████████████▌ | 348/500 [00:41<00:18, 8.33it/s]
70%|████████████████████████████▌ | 349/500 [00:41<00:18, 8.34it/s]['grid', 0.9699248120300752, 0.9792290222280859, 0.9215438271394459]
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:101: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
ret = np.asscalar(ex)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
a_min = np.asscalar(a_min.astype(scaled_dtype))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
a_max = np.asscalar(a_max.astype(scaled_dtype))
Training model 3/5 for leather
| feature extraction | train | leather |: 0%| | 0/8 [00:00<?, ?it/s]/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:130: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data.dtype == np.object:
| feature extraction | train | leather |: 12%|▍ | 1/8 [00:01<00:09, 1.37s/it]
| feature extraction | train | leather |: 25%|▊ | 2/8 [00:02<00:08, 1.35s/it]
| feature extraction | train | leather |: 38%|█▏ | 3/8 [00:04<00:06, 1.35s/it]
| feature extraction | train | leather |: 50%|█▌ | 4/8 [00:05<00:05, 1.36s/it]
| feature extraction | train | leather |: 62%|█▉ | 5/8 [00:06<00:04, 1.38s/it]
| feature extraction | train | leather |: 75%|██▎| 6/8 [00:08<00:02, 1.38s/it]
| feature extraction | train | leather |: 88%|██▋| 7/8 [00:09<00:01, 1.35s/it]
| feature extraction | train | leather |: 100%|███| 8/8 [00:10<00:00, 1.29s/it]
Creating CoreSet Sampler via k-Center Greedy
Getting the coreset from the main embedding.
Assigning the coreset as the memory bank with shape [25088, 384].
2022-04-22 18:27:59 Train ends, total 93.17s
Saving model...
2022-04-22 18:27:59 Save model in ./output/coreset_resnet18_10/leather.pdparams
2022-04-22 18:27:59 Starting eval model...
| feature extraction | test | leather |: 0%| | 0/124 [00:00<?, ?it/s]
| feature extraction | test | leather |: 1%| | 1/124 [00:00<00:59, 2.06it/s]
| feature extraction | test | leather |: 2%| | 2/124 [00:00<00:52, 2.31it/s]
| feature extraction | test | leather |: 2%| | 3/124 [00:01<00:47, 2.56it/s]
| feature extraction | test | leather |: 3%| | 4/124 [00:01<00:43, 2.77it/s]
| feature extraction | test | leather |: 4%| | 5/124 [00:01<00:40, 2.93it/s]
| feature extraction | test | leather |: 5%| | 6/124 [00:01<00:38, 3.06it/s]
| feature extraction | test | leather |: 6%| | 7/124 [00:02<00:37, 3.15it/s]
| feature extraction | test | leather |: 6%|▏ | 8/124 [00:02<00:36, 3.22it/s]
| feature extraction | test | leather |: 7%|▏ | 9/124 [00:02<00:35, 3.27it/s]
| feature extraction | test | leather |: 8%| | 10/124 [00:03<00:34, 3.31it/s]
| feature extraction | test | leather |: 9%| | 11/124 [00:03<00:33, 3.33it/s]
| feature extraction | test | leather |: 10%| | 12/124 [00:03<00:33, 3.35it/s]
| feature extraction | test | leather |: 10%| | 13/124 [00:04<00:32, 3.36it/s]
| feature extraction | test | leather |: 11%| | 14/124 [00:04<00:32, 3.37it/s]
| feature extraction | test | leather |: 12%| | 15/124 [00:04<00:32, 3.38it/s]
| feature extraction | test | leather |: 13%|▏| 16/124 [00:04<00:31, 3.39it/s]
| feature extraction | test | leather |: 14%|▏| 17/124 [00:05<00:31, 3.40it/s]
| feature extraction | test | leather |: 15%|▏| 18/124 [00:05<00:31, 3.40it/s]
| feature extraction | test | leather |: 15%|▏| 19/124 [00:05<00:30, 3.39it/s]
| feature extraction | test | leather |: 16%|▏| 20/124 [00:06<00:30, 3.39it/s]
| feature extraction | test | leather |: 17%|▏| 21/124 [00:06<00:30, 3.40it/s]
| feature extraction | test | leather |: 18%|▏| 22/124 [00:06<00:30, 3.40it/s]
| feature extraction | test | leather |: 19%|▏| 23/124 [00:06<00:29, 3.40it/s]
| feature extraction | test | leather |: 19%|▏| 24/124 [00:07<00:29, 3.40it/s]
| feature extraction | test | leather |: 20%|▏| 25/124 [00:07<00:29, 3.40it/s]
| feature extraction | test | leather |: 21%|▏| 26/124 [00:07<00:28, 3.40it/s]
| feature extraction | test | leather |: 22%|▏| 27/124 [00:08<00:28, 3.40it/s]
| feature extraction | test | leather |: 23%|▏| 28/124 [00:08<00:28, 3.40it/s]
| feature extraction | test | leather |: 23%|▏| 29/124 [00:08<00:27, 3.40it/s]
| feature extraction | test | leather |: 24%|▏| 30/124 [00:09<00:27, 3.40it/s]
| feature extraction | test | leather |: 25%|▎| 31/124 [00:09<00:27, 3.40it/s]
| feature extraction | test | leather |: 26%|▎| 32/124 [00:09<00:27, 3.40it/s]
| feature extraction | test | leather |: 27%|▎| 33/124 [00:09<00:26, 3.40it/s]
| feature extraction | test | leather |: 27%|▎| 34/124 [00:10<00:26, 3.40it/s]
| feature extraction | test | leather |: 28%|▎| 35/124 [00:10<00:26, 3.40it/s]
| feature extraction | test | leather |: 29%|▎| 36/124 [00:10<00:25, 3.40it/s]
| feature extraction | test | leather |: 30%|▎| 37/124 [00:11<00:25, 3.40it/s]
| feature extraction | test | leather |: 31%|▎| 38/124 [00:11<00:25, 3.39it/s]
| feature extraction | test | leather |: 31%|▎| 39/124 [00:11<00:25, 3.40it/s]
| feature extraction | test | leather |: 32%|▎| 40/124 [00:11<00:24, 3.39it/s]
| feature extraction | test | leather |: 33%|▎| 41/124 [00:12<00:24, 3.40it/s]
| feature extraction | test | leather |: 34%|▎| 42/124 [00:12<00:24, 3.40it/s]
| feature extraction | test | leather |: 35%|▎| 43/124 [00:12<00:23, 3.40it/s]
| feature extraction | test | leather |: 35%|▎| 44/124 [00:13<00:23, 3.40it/s]
| feature extraction | test | leather |: 36%|▎| 45/124 [00:13<00:23, 3.40it/s]
| feature extraction | test | leather |: 37%|▎| 46/124 [00:13<00:22, 3.40it/s]
| feature extraction | test | leather |: 38%|▍| 47/124 [00:14<00:22, 3.40it/s]
| feature extraction | test | leather |: 39%|▍| 48/124 [00:14<00:22, 3.40it/s]
| feature extraction | test | leather |: 40%|▍| 49/124 [00:14<00:22, 3.40it/s]
| feature extraction | test | leather |: 40%|▍| 50/124 [00:14<00:21, 3.40it/s]
| feature extraction | test | leather |: 41%|▍| 51/124 [00:15<00:21, 3.40it/s]
| feature extraction | test | leather |: 42%|▍| 52/124 [00:15<00:21, 3.40it/s]
| feature extraction | test | leather |: 43%|▍| 53/124 [00:15<00:20, 3.40it/s]
| feature extraction | test | leather |: 44%|▍| 54/124 [00:16<00:20, 3.40it/s]
| feature extraction | test | leather |: 44%|▍| 55/124 [00:16<00:20, 3.40it/s]
| feature extraction | test | leather |: 45%|▍| 56/124 [00:16<00:20, 3.40it/s]
| feature extraction | test | leather |: 46%|▍| 57/124 [00:16<00:19, 3.40it/s]
| feature extraction | test | leather |: 47%|▍| 58/124 [00:17<00:19, 3.40it/s]
| feature extraction | test | leather |: 48%|▍| 59/124 [00:17<00:19, 3.40it/s]
| feature extraction | test | leather |: 48%|▍| 60/124 [00:17<00:18, 3.40it/s]
| feature extraction | test | leather |: 49%|▍| 61/124 [00:18<00:18, 3.40it/s]
| feature extraction | test | leather |: 50%|▌| 62/124 [00:18<00:18, 3.39it/s]
| feature extraction | test | leather |: 51%|▌| 63/124 [00:18<00:18, 3.30it/s]
| feature extraction | test | leather |: 52%|▌| 64/124 [00:19<00:18, 3.33it/s]
| feature extraction | test | leather |: 52%|▌| 65/124 [00:19<00:18, 3.24it/s]
| feature extraction | test | leather |: 53%|▌| 66/124 [00:19<00:17, 3.28it/s]
| feature extraction | test | leather |: 54%|▌| 67/124 [00:19<00:17, 3.32it/s]
| feature extraction | test | leather |: 55%|▌| 68/124 [00:20<00:16, 3.34it/s]
| feature extraction | test | leather |: 56%|▌| 69/124 [00:20<00:16, 3.36it/s]
| feature extraction | test | leather |: 56%|▌| 70/124 [00:20<00:16, 3.37it/s]
| feature extraction | test | leather |: 57%|▌| 71/124 [00:21<00:15, 3.38it/s]
| feature extraction | test | leather |: 58%|▌| 72/124 [00:21<00:15, 3.38it/s]
| feature extraction | test | leather |: 59%|▌| 73/124 [00:21<00:15, 3.38it/s]
| feature extraction | test | leather |: 60%|▌| 74/124 [00:22<00:14, 3.39it/s]
| feature extraction | test | leather |: 60%|▌| 75/124 [00:22<00:14, 3.39it/s]
| feature extraction | test | leather |: 61%|▌| 76/124 [00:22<00:14, 3.39it/s]
| feature extraction | test | leather |: 62%|▌| 77/124 [00:22<00:13, 3.39it/s]
| feature extraction | test | leather |: 63%|▋| 78/124 [00:23<00:13, 3.39it/s]
| feature extraction | test | leather |: 64%|▋| 79/124 [00:23<00:13, 3.40it/s]
| feature extraction | test | leather |: 65%|▋| 80/124 [00:23<00:12, 3.40it/s]
| feature extraction | test | leather |: 65%|▋| 81/124 [00:24<00:12, 3.40it/s]
| feature extraction | test | leather |: 66%|▋| 82/124 [00:24<00:12, 3.40it/s]
| feature extraction | test | leather |: 67%|▋| 83/124 [00:24<00:12, 3.41it/s]
| feature extraction | test | leather |: 68%|▋| 84/124 [00:24<00:11, 3.36it/s]
| feature extraction | test | leather |: 69%|▋| 85/124 [00:25<00:11, 3.38it/s]
| feature extraction | test | leather |: 69%|▋| 86/124 [00:25<00:11, 3.39it/s]
| feature extraction | test | leather |: 70%|▋| 87/124 [00:25<00:10, 3.39it/s]
| feature extraction | test | leather |: 71%|▋| 88/124 [00:26<00:10, 3.39it/s]
| feature extraction | test | leather |: 72%|▋| 89/124 [00:26<00:10, 3.39it/s]
| feature extraction | test | leather |: 73%|▋| 90/124 [00:26<00:10, 3.39it/s]
| feature extraction | test | leather |: 73%|▋| 91/124 [00:27<00:09, 3.40it/s]
| feature extraction | test | leather |: 74%|▋| 92/124 [00:27<00:09, 3.40it/s]
| feature extraction | test | leather |: 75%|▊| 93/124 [00:27<00:09, 3.40it/s]
| feature extraction | test | leather |: 76%|▊| 94/124 [00:27<00:08, 3.40it/s]
| feature extraction | test | leather |: 77%|▊| 95/124 [00:28<00:08, 3.40it/s]
| feature extraction | test | leather |: 77%|▊| 96/124 [00:28<00:08, 3.40it/s]
| feature extraction | test | leather |: 78%|▊| 97/124 [00:28<00:07, 3.40it/s]
| feature extraction | test | leather |: 79%|▊| 98/124 [00:29<00:07, 3.40it/s]
| feature extraction | test | leather |: 80%|▊| 99/124 [00:29<00:07, 3.40it/s]
| feature extraction | test | leather |: 81%|▊| 100/124 [00:29<00:07, 3.40it/s]
| feature extraction | test | leather |: 81%|▊| 101/124 [00:29<00:06, 3.40it/s]
| feature extraction | test | leather |: 82%|▊| 102/124 [00:30<00:06, 3.40it/s]
| feature extraction | test | leather |: 83%|▊| 103/124 [00:30<00:06, 3.40it/s]
| feature extraction | test | leather |: 84%|▊| 104/124 [00:30<00:05, 3.39it/s]
| feature extraction | test | leather |: 85%|▊| 105/124 [00:31<00:05, 3.40it/s]
| feature extraction | test | leather |: 85%|▊| 106/124 [00:31<00:05, 3.40it/s]
| feature extraction | test | leather |: 86%|▊| 107/124 [00:31<00:05, 3.40it/s]
| feature extraction | test | leather |: 87%|▊| 108/124 [00:32<00:04, 3.40it/s]
| feature extraction | test | leather |: 88%|▉| 109/124 [00:32<00:04, 3.40it/s]
| feature extraction | test | leather |: 89%|▉| 110/124 [00:32<00:04, 3.40it/s]
| feature extraction | test | leather |: 90%|▉| 111/124 [00:32<00:03, 3.40it/s]
| feature extraction | test | leather |: 90%|▉| 112/124 [00:33<00:03, 3.40it/s]
| feature extraction | test | leather |: 91%|▉| 113/124 [00:33<00:03, 3.39it/s]
| feature extraction | test | leather |: 92%|▉| 114/124 [00:33<00:02, 3.39it/s]
| feature extraction | test | leather |: 93%|▉| 115/124 [00:34<00:02, 3.39it/s]
| feature extraction | test | leather |: 94%|▉| 116/124 [00:34<00:02, 3.39it/s]
| feature extraction | test | leather |: 94%|▉| 117/124 [00:34<00:02, 3.39it/s]
| feature extraction | test | leather |: 95%|▉| 118/124 [00:35<00:01, 3.39it/s]
| feature extraction | test | leather |: 96%|▉| 119/124 [00:35<00:01, 3.39it/s]
| feature extraction | test | leather |: 97%|▉| 120/124 [00:35<00:01, 3.39it/s]
| feature extraction | test | leather |: 98%|▉| 121/124 [00:35<00:00, 3.40it/s]
| feature extraction | test | leather |: 98%|▉| 122/124 [00:36<00:00, 3.41it/s]
| feature extraction | test | leather |: 99%|▉| 123/124 [00:36<00:00, 3.42it/s]
| feature extraction | test | leather |: 100%|█| 124/124 [00:36<00:00, 3.37it/s]
max_score:5.091420650482178 min_score:0.665397584438324
F1 image:1.0 threshold:5.091420650482178
F1 pixel:0.34365947341692477 threshold:5.091420650482178
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