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huanghoujing / beyond-part-models

PCB of paper: Beyond Part Models: Person Retrieval with Refined Part Pooling, using Pytorch
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train on duke error #12

Closed yja1 closed 6 years ago

yja1 commented 6 years ago

i have train on duke when i test the result: Single Query: [mAP: 2.83%], [cmc1: 9.87%], [cmc5: 17.91%], [cmc10: 22.44%] don't know what's wrong. I change nothing but some error caused by difference between python2 and python3.

and I train on cuhk03 and market1501,all test result is very low.

the test output: `------------------------------------------------------------ cfg.dict {'ckpt_file': '/beyond-part-models/exp_directory/ckpt.pth', 'crop_prob': 0, 'crop_ratio': 1, 'dataset': 'duke', 'epochs_per_val': 1, 'exp_dir': '/beyond-part-models/exp_directory', 'finetuned_params_lr': 0.01, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'last_conv_stride': 1, 'local_conv_out_channels': 256, 'log_to_file': True, 'model_weight_file': '/beyond-part-models/exp_directory/duke/ckpt.pth', 'momentum': 0.9, 'new_params_lr': 0.1, 'num_stripes': 6, 'only_test': True, 'prefetch_threads': 2, 'resize_h_w': (384, 128), 'resume': False, 'run': 1, 'scale_im': True, 'seed': None, 'staircase_decay_at_epochs': (41,), 'staircase_decay_multiply_factor': 0.1, 'stderr_file': '/beyond-part-models/exp_directory/stderr_2018-03-26_14:31:06.txt', 'stdout_file': '/beyond-part-models/exp_directory/stdout_2018-03-26_14:31:06.txt', 'steps_per_log': 20, 'sys_device_ids': (0,), 'test_batch_size': 32, 'test_final_batch': True, 'test_mirror_type': None, 'test_set_kwargs': {'batch_dims': 'NCHW', 'batch_size': 32, 'final_batch': True, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'mirror_type': None, 'name': 'duke', 'num_prefetch_threads': 2, 'part': 'test', 'prng': <module 'numpy.random' from 'anaconda3/lib/python3.6/site-packages/numpy/random/init.py'>, 'resize_h_w': (384, 128), 'scale': True, 'shuffle': False}, 'test_shuffle': False, 'total_epochs': 60, 'train_batch_size': 64, 'train_final_batch': True, 'train_mirror_type': 'random', 'train_set_kwargs': {'batch_dims': 'NCHW', 'batch_size': 64, 'crop_prob': 0, 'crop_ratio': 1, 'final_batch': True, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'mirror_type': 'random', 'name': 'duke', 'num_prefetch_threads': 2, 'part': 'trainval', 'prng': <module 'numpy.random' from 'anaconda3/lib/python3.6/site-packages/numpy/random/init.py'>, 'resize_h_w': (384, 128), 'scale': True, 'shuffle': True}, 'train_shuffle': True, 'trainset_part': 'trainval', 'val_set_kwargs': {'batch_dims': 'NCHW', 'batch_size': 32, 'final_batch': True, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'mirror_type': None, 'name': 'duke', 'num_prefetch_threads': 2, 'part': 'val', 'prng': <module 'numpy.random' from 'anaconda3/lib/python3.6/site-packages/numpy/random/init.py'>, 'resize_h_w': (384, 128), 'scale': True, 'shuffle': False}, 'weight_decay': 0.0005}

duke trainval set

NO. Images: 16522 NO. IDs: 702

duke val set

NO. Images: 2401 NO. IDs: 100 NO. Query Images: 321 NO. Gallery Images: 2080 NO. Multi-query Images: 0

duke test set

NO. Images: 19889 NO. IDs: 1110 NO. Query Images: 2228 NO. Gallery Images: 17661 NO. Multi-query Images: 0

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base.layer2.3.bn2.running_var base.layer4.1.conv2.weight base.layer4.0.bn1.running_var base.layer3.4.bn3.running_var base.layer1.1.bn3.weight base.layer3.3.bn3.running_var base.layer2.0.downsample.1.running_mean base.layer3.4.bn2.weight base.layer4.2.conv2.weight base.layer3.0.bn1.weight base.layer4.0.bn3.running_mean base.layer4.1.conv1.weight fc_list.4.bias base.layer2.3.conv1.weight base.layer2.1.bn1.bias base.layer1.2.bn2.weight base.layer3.4.bn2.bias base.layer3.0.bn2.weight base.layer3.5.bn2.bias base.layer1.1.conv3.weight base.layer4.2.bn2.weight base.layer3.0.downsample.1.bias base.layer2.1.bn3.weight base.layer3.0.bn2.bias base.layer4.1.bn1.weight base.layer2.0.bn2.bias base.layer4.0.downsample.0.weight base.layer3.0.bn1.running_var Keys not found in destination state_dict: state_dicts scores ep Loaded model weights from /beyond-part-models/exp_directory/duke/ckpt.pth

=========> Test on dataset: duke <=========

Extracting feature... 20/622 batches done, +12.88s, total 12.88s 40/622 batches done, +2.19s, total 15.07s 60/622 batches done, +2.06s, total 17.13s 80/622 batches done, +2.06s, total 19.19s 100/622 batches done, +2.01s, total 21.21s 120/622 batches done, +2.11s, total 23.32s 140/622 batches done, +2.17s, total 25.48s 160/622 batches done, +2.14s, total 27.62s 180/622 batches done, +1.96s, total 29.58s 200/622 batches done, +2.11s, total 31.69s 220/622 batches done, +2.17s, total 33.85s 240/622 batches done, +2.10s, total 35.95s 260/622 batches done, +2.10s, total 38.05s 280/622 batches done, +2.10s, total 40.15s 300/622 batches done, +2.14s, total 42.29s 320/622 batches done, +2.20s, total 44.49s 340/622 batches done, +2.06s, total 46.55s 360/622 batches done, +2.01s, total 48.56s 380/622 batches done, +2.01s, total 50.56s 400/622 batches done, +2.08s, total 52.65s 420/622 batches done, +1.98s, total 54.63s 440/622 batches done, +2.12s, total 56.74s 460/622 batches done, +2.11s, total 58.85s 480/622 batches done, +1.98s, total 60.83s 500/622 batches done, +2.07s, total 62.91s 520/622 batches done, +2.10s, total 65.00s 540/622 batches done, +2.20s, total 67.20s 560/622 batches done, +2.06s, total 69.27s 580/622 batches done, +2.05s, total 71.32s 600/622 batches done, +1.98s, total 73.30s 620/622 batches done, +2.12s, total 75.42s Done, 75.74s Computing distance... Done, 0.53s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 11.04s Single Query: [mAP: 2.83%], [cmc1: 9.87%], [cmc5: 17.91%], [cmc10: 22.44%] Re-ranking distance... Done, 76.46s Computing scores for re-ranked distance... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 11.38s Re-ranked Single Query: [mAP: 5.20%], [cmc1: 12.07%], [cmc5: 17.55%], [cmc10: 20.69%] `

my train log: `------------------------------------------------------------ cfg.dict {'ckpt_file': '/data/home/lalai/experiments/beyond-part-models/exp_directory/duke/ckpt.pth', 'crop_prob': 0, 'crop_ratio': 1, 'dataset': 'duke', 'epochs_per_val': 2, 'exp_dir': '/data/home/lalai/experiments/beyond-part-models/exp_directory/duke', 'finetuned_params_lr': 0.01, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'last_conv_stride': 1, 'local_conv_out_channels': 256, 'log_to_file': True, 'model_weight_file': '', 'momentum': 0.9, 'new_params_lr': 0.1, 'num_stripes': 6, 'only_test': False, 'prefetch_threads': 2, 'resize_h_w': (384, 128), 'resume': False, 'run': 1, 'scale_im': True, 'seed': None, 'staircase_decay_at_epochs': (41,), 'staircase_decay_multiply_factor': 0.1, 'stderr_file': '/data/home/lalai/experiments/beyond-part-models/exp_directory/duke/stderr_2018-03-24_14:35:29.txt', 'stdout_file': '/data/home/lalai/experiments/beyond-part-models/exp_directory/duke/stdout_2018-03-24_14:35:29.txt', 'steps_per_log': 20, 'sys_device_ids': (2,), 'test_batch_size': 32, 'test_final_batch': True, 'test_mirror_type': None, 'test_set_kwargs': {'batch_dims': 'NCHW', 'batch_size': 32, 'final_batch': True, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'mirror_type': None, 'name': 'duke', 'num_prefetch_threads': 2, 'part': 'test', 'prng': <module 'numpy.random' from '/home/lalai/publib/anaconda3/lib/python3.6/site-packages/numpy/random/init.py'>, 'resize_h_w': (384, 128), 'scale': True, 'shuffle': False}, 'test_shuffle': False, 'total_epochs': 60, 'train_batch_size': 64, 'train_final_batch': True, 'train_mirror_type': 'random', 'train_set_kwargs': {'batch_dims': 'NCHW', 'batch_size': 64, 'crop_prob': 0, 'crop_ratio': 1, 'final_batch': True, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'mirror_type': 'random', 'name': 'duke', 'num_prefetch_threads': 2, 'part': 'trainval', 'prng': <module 'numpy.random' from '/home/lalai/publib/anaconda3/lib/python3.6/site-packages/numpy/random/init.py'>, 'resize_h_w': (384, 128), 'scale': True, 'shuffle': True}, 'train_shuffle': True, 'trainset_part': 'trainval', 'val_set_kwargs': {'batch_dims': 'NCHW', 'batch_size': 32, 'final_batch': True, 'im_mean': [0.486, 0.459, 0.408], 'im_std': [0.229, 0.224, 0.225], 'mirror_type': None, 'name': 'duke', 'num_prefetch_threads': 2, 'part': 'val', 'prng': <module 'numpy.random' from '/home/lalai/publib/anaconda3/lib/python3.6/site-packages/numpy/random/init.py'>, 'resize_h_w': (384, 128), 'scale': True, 'shuffle': False}, 'weight_decay': 0.0005}

duke trainval set

NO. Images: 16522 NO. IDs: 702

duke val set

NO. Images: 2401 NO. IDs: 100 NO. Query Images: 321 NO. Gallery Images: 2080 NO. Multi-query Images: 0

duke test set

NO. Images: 19889 NO. IDs: 1110 NO. Query Images: 2228 NO. Gallery Images: 17661 NO. Multi-query Images: 0

Step 20/Ep 1, 7.21s, loss 37.1529
Step 40/Ep 1, 8.47s, loss 36.8084
Step 60/Ep 1, 6.29s, loss 35.1561
Step 80/Ep 1, 8.03s, loss 33.5103
Step 100/Ep 1, 7.31s, loss 29.4746
Step 120/Ep 1, 7.33s, loss 26.2669
Step 140/Ep 1, 5.22s, loss 27.0751
Step 160/Ep 1, 1.23s, loss 27.4869
Step 180/Ep 1, 1.22s, loss 24.4910
Step 200/Ep 1, 1.09s, loss 22.6488
Step 220/Ep 1, 0.73s, loss 21.1609
Step 240/Ep 1, 0.98s, loss 21.7364

Ep 1, 1130.23s, loss 28.9022 Step 20/Ep 2, 1.03s, loss 17.9511 Step 40/Ep 2, 0.61s, loss 16.5232 Step 60/Ep 2, 1.13s, loss 14.6668 Step 80/Ep 2, 0.86s, loss 15.4651 Step 100/Ep 2, 1.06s, loss 14.9441 Step 120/Ep 2, 1.11s, loss 14.2632 Step 140/Ep 2, 1.16s, loss 13.4817 Step 160/Ep 2, 1.18s, loss 14.0035 Step 180/Ep 2, 1.49s, loss 14.5250 Step 200/Ep 2, 1.26s, loss 12.7967 Step 220/Ep 2, 1.26s, loss 12.6710 Step 240/Ep 2, 1.11s, loss 12.8433 Ep 2, 298.14s, loss 14.8193

===== Test on validation set =====

Extracting feature... 20/76 batches done, +4.90s, total 4.90s 40/76 batches done, +5.04s, total 9.94s 60/76 batches done, +5.29s, total 15.23s Done, 19.23s Computing distance... Done, 0.16s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.34s Single Query: [mAP: 73.16%], [cmc1: 84.74%], [cmc5: 91.90%], [cmc10: 94.08%]

Step 20/Ep 3, 1.23s, loss 8.8453
Step 40/Ep 3, 0.98s, loss 9.3370
Step 60/Ep 3, 1.22s, loss 8.8667
Step 80/Ep 3, 1.06s, loss 11.2704
Step 100/Ep 3, 1.05s, loss 10.0581
Step 120/Ep 3, 1.36s, loss 9.3945
Step 140/Ep 3, 1.26s, loss 11.2640
Step 160/Ep 3, 1.30s, loss 9.3276
Step 180/Ep 3, 1.29s, loss 8.6810
Step 200/Ep 3, 1.15s, loss 8.6792
Step 220/Ep 3, 0.98s, loss 8.5763
Step 240/Ep 3, 1.07s, loss 8.2343

Ep 3, 313.73s, loss 9.1303 Step 20/Ep 4, 1.24s, loss 6.6244 Step 40/Ep 4, 1.39s, loss 5.3875 Step 60/Ep 4, 1.11s, loss 6.1857 Step 80/Ep 4, 1.26s, loss 5.7646 Step 100/Ep 4, 1.14s, loss 7.1421 Step 120/Ep 4, 1.41s, loss 6.9737 Step 140/Ep 4, 1.47s, loss 5.8727 Step 160/Ep 4, 1.56s, loss 6.3184 Step 180/Ep 4, 1.53s, loss 7.8259 Step 200/Ep 4, 1.22s, loss 7.4525 Step 220/Ep 4, 1.35s, loss 5.0663 Step 240/Ep 4, 1.20s, loss 5.5142 Ep 4, 328.72s, loss 6.3097

===== Test on validation set =====

Extracting feature... 20/76 batches done, +5.85s, total 5.85s 40/76 batches done, +6.28s, total 12.14s 60/76 batches done, +6.13s, total 18.27s Done, 23.35s Computing distance... Done, 0.12s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.32s Single Query: [mAP: 81.02%], [cmc1: 91.28%], [cmc5: 95.33%], [cmc10: 96.88%]

Step 20/Ep 5, 1.38s, loss 6.7970
Step 40/Ep 5, 1.39s, loss 4.7243
Step 60/Ep 5, 1.41s, loss 4.6256
Step 80/Ep 5, 1.34s, loss 3.7932
Step 100/Ep 5, 1.41s, loss 4.1115
Step 120/Ep 5, 1.40s, loss 4.3463
Step 140/Ep 5, 1.30s, loss 4.1978
Step 160/Ep 5, 1.33s, loss 5.5110
Step 180/Ep 5, 1.13s, loss 4.9879
Step 200/Ep 5, 1.11s, loss 5.5221
Step 220/Ep 5, 1.39s, loss 4.6285
Step 240/Ep 5, 1.24s, loss 4.8592

Ep 5, 335.30s, loss 4.7707 Step 20/Ep 6, 1.32s, loss 4.5329 Step 40/Ep 6, 1.23s, loss 4.2338 Step 60/Ep 6, 1.33s, loss 4.3702 Step 80/Ep 6, 1.44s, loss 4.1733 Step 100/Ep 6, 1.46s, loss 2.2477 Step 120/Ep 6, 1.28s, loss 3.6955 Step 140/Ep 6, 1.52s, loss 5.0271 Step 160/Ep 6, 1.31s, loss 3.3362 Step 180/Ep 6, 1.43s, loss 4.1300 Step 200/Ep 6, 1.27s, loss 4.7450 Step 220/Ep 6, 1.24s, loss 4.1717 Step 240/Ep 6, 1.44s, loss 3.9225 Ep 6, 341.12s, loss 3.8365

===== Test on validation set =====

Extracting feature... 20/76 batches done, +7.81s, total 7.81s 40/76 batches done, +7.32s, total 15.13s 60/76 batches done, +7.79s, total 22.92s Done, 28.82s Computing distance... Done, 0.16s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.33s Single Query: [mAP: 85.10%], [cmc1: 93.46%], [cmc5: 97.51%], [cmc10: 97.51%]

Step 20/Ep 7, 1.48s, loss 3.9880
Step 40/Ep 7, 1.33s, loss 2.2409
Step 60/Ep 7, 1.56s, loss 3.0331
Step 80/Ep 7, 1.56s, loss 1.9485
Step 100/Ep 7, 1.46s, loss 3.5058
Step 120/Ep 7, 1.63s, loss 3.1262
Step 140/Ep 7, 1.47s, loss 2.9111
Step 160/Ep 7, 1.55s, loss 3.8994
Step 180/Ep 7, 1.42s, loss 3.1996
Step 200/Ep 7, 1.66s, loss 3.2328
Step 220/Ep 7, 1.51s, loss 3.3790
Step 240/Ep 7, 5.99s, loss 3.1472

Ep 7, 501.70s, loss 3.0710 Step 20/Ep 8, 4.73s, loss 2.3981 Step 40/Ep 8, 5.56s, loss 1.7520 Step 60/Ep 8, 5.06s, loss 1.9629 Step 80/Ep 8, 5.68s, loss 2.1359 Step 100/Ep 8, 5.65s, loss 2.4756 Step 120/Ep 8, 5.40s, loss 2.4838 Step 140/Ep 8, 5.00s, loss 2.5320 Step 160/Ep 8, 2.93s, loss 2.5898 Step 180/Ep 8, 1.96s, loss 2.8185 Step 200/Ep 8, 1.75s, loss 2.2167 Step 220/Ep 8, 1.83s, loss 2.7298 Step 240/Ep 8, 4.88s, loss 2.9156 Ep 8, 1127.06s, loss 2.4928

===== Test on validation set =====

Extracting feature... 20/76 batches done, +28.79s, total 28.79s 40/76 batches done, +28.92s, total 57.71s 60/76 batches done, +34.64s, total 92.35s Done, 115.06s Computing distance... Done, 0.14s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.32s Single Query: [mAP: 90.42%], [cmc1: 95.64%], [cmc5: 98.75%], [cmc10: 99.69%]

Step 20/Ep 9, 1.72s, loss 1.5292
Step 40/Ep 9, 1.72s, loss 2.7577
Step 60/Ep 9, 1.77s, loss 2.5052
Step 80/Ep 9, 1.84s, loss 1.9735
Step 100/Ep 9, 1.88s, loss 2.3530
Step 120/Ep 9, 1.83s, loss 1.8199
Step 140/Ep 9, 1.85s, loss 1.8501
Step 160/Ep 9, 1.57s, loss 2.0554
Step 180/Ep 9, 1.56s, loss 2.2496
Step 200/Ep 9, 1.59s, loss 2.2351
Step 220/Ep 9, 1.56s, loss 2.7041
Step 240/Ep 9, 1.48s, loss 2.2564

Ep 9, 441.08s, loss 2.1062 Step 20/Ep 10, 1.52s, loss 2.4145 Step 40/Ep 10, 1.57s, loss 1.4760 Step 60/Ep 10, 1.62s, loss 1.4859 Step 80/Ep 10, 1.56s, loss 2.5719 Step 100/Ep 10, 1.48s, loss 1.7289 Step 120/Ep 10, 5.73s, loss 0.8954 Step 140/Ep 10, 6.19s, loss 1.7027 Step 160/Ep 10, 6.96s, loss 1.9217 Step 180/Ep 10, 5.15s, loss 1.9179 Step 200/Ep 10, 1.78s, loss 1.5794 Step 220/Ep 10, 1.77s, loss 2.4520 Step 240/Ep 10, 1.83s, loss 2.1151 Ep 10, 733.30s, loss 1.9265

===== Test on validation set =====

Extracting feature... 20/76 batches done, +10.20s, total 10.20s 40/76 batches done, +9.98s, total 20.18s 60/76 batches done, +10.01s, total 30.19s Done, 37.87s Computing distance... Done, 0.15s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 90.62%], [cmc1: 96.57%], [cmc5: 98.75%], [cmc10: 100.00%]

Step 20/Ep 11, 1.86s, loss 1.3455
Step 40/Ep 11, 1.89s, loss 1.3079
Step 60/Ep 11, 1.82s, loss 1.2694
Step 80/Ep 11, 1.84s, loss 1.4525
Step 100/Ep 11, 1.85s, loss 1.1908
Step 120/Ep 11, 1.84s, loss 1.6790
Step 140/Ep 11, 1.75s, loss 1.7347
Step 160/Ep 11, 1.77s, loss 1.8032
Step 180/Ep 11, 1.75s, loss 1.5180
Step 200/Ep 11, 1.65s, loss 1.4147
Step 220/Ep 11, 1.62s, loss 1.1507
Step 240/Ep 11, 1.51s, loss 1.5878

Ep 11, 445.55s, loss 1.6031 Step 20/Ep 12, 6.93s, loss 1.4593 Step 40/Ep 12, 6.40s, loss 0.9982 Step 60/Ep 12, 6.67s, loss 0.8486 Step 80/Ep 12, 6.05s, loss 0.8264 Step 100/Ep 12, 3.63s, loss 0.8150 Step 120/Ep 12, 6.04s, loss 1.0049 Step 140/Ep 12, 6.99s, loss 1.3145 Step 160/Ep 12, 5.42s, loss 1.0128 Step 180/Ep 12, 5.75s, loss 1.2988 Step 200/Ep 12, 1.52s, loss 1.4204 Step 220/Ep 12, 1.52s, loss 1.2283 Step 240/Ep 12, 1.66s, loss 1.3039 Ep 12, 1176.28s, loss 1.3498

===== Test on validation set =====

Extracting feature... 20/76 batches done, +8.48s, total 8.48s 40/76 batches done, +8.42s, total 16.89s 60/76 batches done, +8.38s, total 25.27s Done, 31.77s Computing distance... Done, 0.13s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.32s Single Query: [mAP: 93.44%], [cmc1: 98.13%], [cmc5: 99.38%], [cmc10: 100.00%]

Step 20/Ep 13, 1.50s, loss 1.4066
Step 40/Ep 13, 1.48s, loss 1.2050
Step 60/Ep 13, 1.50s, loss 0.7747
Step 80/Ep 13, 1.63s, loss 0.8701
Step 100/Ep 13, 1.58s, loss 1.3612
Step 120/Ep 13, 5.54s, loss 0.9830
Step 140/Ep 13, 6.34s, loss 0.9768
Step 160/Ep 13, 6.41s, loss 0.8179
Step 180/Ep 13, 5.88s, loss 1.3573
Step 200/Ep 13, 5.62s, loss 1.4278
Step 220/Ep 13, 4.59s, loss 1.7004
Step 240/Ep 13, 5.99s, loss 1.5690

Ep 13, 1136.21s, loss 1.2088 Step 20/Ep 14, 6.87s, loss 1.7844 Step 40/Ep 14, 1.80s, loss 1.5293 Step 60/Ep 14, 1.82s, loss 0.9275 Step 80/Ep 14, 1.81s, loss 1.2436 Step 100/Ep 14, 1.76s, loss 1.1813 Step 120/Ep 14, 1.85s, loss 0.9720 Step 140/Ep 14, 1.78s, loss 1.0907 Step 160/Ep 14, 1.81s, loss 1.6212 Step 180/Ep 14, 1.83s, loss 1.4766 Step 200/Ep 14, 1.86s, loss 1.3269 Step 220/Ep 14, 1.83s, loss 1.2039 Step 240/Ep 14, 1.80s, loss 1.5747 Ep 14, 624.82s, loss 1.2893

===== Test on validation set =====

Extracting feature... 20/76 batches done, +9.89s, total 9.89s 40/76 batches done, +9.91s, total 19.80s 60/76 batches done, +9.73s, total 29.53s Done, 36.87s Computing distance... Done, 0.15s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 95.71%], [cmc1: 99.07%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 15, 1.83s, loss 1.4634
Step 40/Ep 15, 1.77s, loss 1.1374
Step 60/Ep 15, 1.80s, loss 0.6987
Step 80/Ep 15, 1.84s, loss 0.9451
Step 100/Ep 15, 1.81s, loss 1.2228
Step 120/Ep 15, 1.80s, loss 1.4969
Step 140/Ep 15, 1.81s, loss 0.7959
Step 160/Ep 15, 1.76s, loss 0.7415
Step 180/Ep 15, 1.83s, loss 1.1018
Step 200/Ep 15, 1.76s, loss 0.6245
Step 220/Ep 15, 1.76s, loss 1.1938
Step 240/Ep 15, 1.79s, loss 0.9658

Ep 15, 463.00s, loss 1.0809 Step 20/Ep 16, 1.76s, loss 1.3684 Step 40/Ep 16, 1.72s, loss 0.6503 Step 60/Ep 16, 1.83s, loss 0.9544 Step 80/Ep 16, 1.73s, loss 1.2419 Step 100/Ep 16, 1.71s, loss 1.1132 Step 120/Ep 16, 1.79s, loss 1.4263 Step 140/Ep 16, 1.84s, loss 1.0175 Step 160/Ep 16, 1.79s, loss 0.9242 Step 180/Ep 16, 1.80s, loss 1.3025 Step 200/Ep 16, 1.72s, loss 1.6961 Step 220/Ep 16, 1.73s, loss 1.0583 Step 240/Ep 16, 1.77s, loss 0.8018 Ep 16, 458.77s, loss 1.0979

===== Test on validation set =====

Extracting feature... 20/76 batches done, +9.80s, total 9.80s 40/76 batches done, +9.35s, total 19.15s 60/76 batches done, +9.60s, total 28.75s Done, 36.05s Computing distance... Done, 0.12s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 95.71%], [cmc1: 99.69%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 17, 1.82s, loss 1.7658
Step 40/Ep 17, 1.86s, loss 0.9665
Step 60/Ep 17, 1.82s, loss 1.3321
Step 80/Ep 17, 1.82s, loss 1.0438
Step 100/Ep 17, 1.87s, loss 1.0985
Step 120/Ep 17, 1.77s, loss 1.6692
Step 140/Ep 17, 1.79s, loss 1.2997
Step 160/Ep 17, 1.81s, loss 1.4560
Step 180/Ep 17, 1.80s, loss 1.5988
Step 200/Ep 17, 1.77s, loss 0.8912
Step 220/Ep 17, 1.82s, loss 2.2629
Step 240/Ep 17, 1.78s, loss 0.7568

Ep 17, 463.22s, loss 1.2547 Step 20/Ep 18, 1.85s, loss 1.4696 Step 40/Ep 18, 1.73s, loss 1.0148 Step 60/Ep 18, 1.82s, loss 0.6331 Step 80/Ep 18, 1.77s, loss 1.6283 Step 100/Ep 18, 1.72s, loss 0.6603 Step 120/Ep 18, 1.77s, loss 0.8993 Step 140/Ep 18, 1.74s, loss 0.9771 Step 160/Ep 18, 1.77s, loss 0.9187 Step 180/Ep 18, 1.86s, loss 0.9614 Step 200/Ep 18, 1.81s, loss 0.7266 Step 220/Ep 18, 6.24s, loss 1.2907 Step 240/Ep 18, 1.76s, loss 1.1530 Ep 18, 565.67s, loss 1.1337

===== Test on validation set =====

Extracting feature... 20/76 batches done, +10.06s, total 10.06s 40/76 batches done, +10.20s, total 20.25s 60/76 batches done, +9.76s, total 30.01s Done, 37.65s Computing distance... Done, 0.16s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.32s Single Query: [mAP: 96.66%], [cmc1: 99.07%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 19, 1.82s, loss 1.4631
Step 40/Ep 19, 1.58s, loss 1.0681
Step 60/Ep 19, 1.55s, loss 1.0982
Step 80/Ep 19, 1.56s, loss 0.4282
Step 100/Ep 19, 1.59s, loss 0.6797
Step 120/Ep 19, 6.49s, loss 0.8900
Step 140/Ep 19, 5.68s, loss 0.7002
Step 160/Ep 19, 6.40s, loss 0.7334
Step 180/Ep 19, 3.80s, loss 0.7241
Step 200/Ep 19, 5.70s, loss 1.2490
Step 220/Ep 19, 5.93s, loss 0.6562
Step 240/Ep 19, 4.90s, loss 0.8398

Ep 19, 956.42s, loss 0.9120 Step 20/Ep 20, 5.76s, loss 1.6519 Step 40/Ep 20, 6.70s, loss 1.1966 Step 60/Ep 20, 5.60s, loss 0.7015 Step 80/Ep 20, 4.90s, loss 0.8888 Step 100/Ep 20, 6.05s, loss 0.6650 Step 120/Ep 20, 6.26s, loss 1.0624 Step 140/Ep 20, 6.83s, loss 0.7333 Step 160/Ep 20, 5.22s, loss 0.7053 Step 180/Ep 20, 5.55s, loss 0.7561 Step 200/Ep 20, 7.12s, loss 1.2603 Step 220/Ep 20, 5.59s, loss 1.3975 Step 240/Ep 20, 6.27s, loss 1.5382 Ep 20, 1535.54s, loss 1.1708

===== Test on validation set =====

Extracting feature... 20/76 batches done, +41.86s, total 41.86s 40/76 batches done, +37.85s, total 79.71s 60/76 batches done, +39.37s, total 119.08s Done, 141.53s Computing distance... Done, 0.09s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 96.57%], [cmc1: 99.07%], [cmc5: 99.69%], [cmc10: 100.00%]

Step 20/Ep 21, 4.60s, loss 1.3126
Step 40/Ep 21, 4.54s, loss 0.8253
Step 60/Ep 21, 4.65s, loss 1.2886
Step 80/Ep 21, 3.58s, loss 1.0457
Step 100/Ep 21, 4.79s, loss 0.5526
Step 120/Ep 21, 4.95s, loss 0.7717
Step 140/Ep 21, 5.09s, loss 1.2575
Step 160/Ep 21, 2.61s, loss 0.6418
Step 180/Ep 21, 3.93s, loss 1.4424
Step 200/Ep 21, 4.13s, loss 0.8641
Step 220/Ep 21, 3.79s, loss 0.5314
Step 240/Ep 21, 4.01s, loss 0.7727

Ep 21, 998.29s, loss 0.9790 Step 20/Ep 22, 3.74s, loss 1.2631 Step 40/Ep 22, 2.56s, loss 1.0863 Step 60/Ep 22, 1.56s, loss 1.2666 Step 80/Ep 22, 3.10s, loss 0.7895 Step 100/Ep 22, 4.87s, loss 0.7550 Step 120/Ep 22, 3.28s, loss 0.9252 Step 140/Ep 22, 4.11s, loss 0.5022 Step 160/Ep 22, 3.97s, loss 0.5220 Step 180/Ep 22, 4.79s, loss 0.7942 Step 200/Ep 22, 1.55s, loss 0.8698 Step 220/Ep 22, 6.53s, loss 0.6452 Step 240/Ep 22, 0.57s, loss 0.5758 Ep 22, 745.33s, loss 0.8412

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.03s, total 2.03s 40/76 batches done, +1.90s, total 3.93s 60/76 batches done, +1.91s, total 5.84s Done, 7.30s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 98.02%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 23, 0.57s, loss 1.2071
Step 40/Ep 23, 0.58s, loss 0.8693
Step 60/Ep 23, 0.58s, loss 1.1442
Step 80/Ep 23, 0.58s, loss 0.7959
Step 100/Ep 23, 0.58s, loss 0.7636
Step 120/Ep 23, 0.57s, loss 1.2750
Step 140/Ep 23, 0.57s, loss 0.5597
Step 160/Ep 23, 0.58s, loss 0.4942
Step 180/Ep 23, 0.60s, loss 0.6408
Step 200/Ep 23, 0.58s, loss 0.9767
Step 220/Ep 23, 0.62s, loss 0.5247
Step 240/Ep 23, 0.58s, loss 0.9156

Ep 23, 149.57s, loss 0.9495 Step 20/Ep 24, 0.58s, loss 1.2844 Step 40/Ep 24, 0.58s, loss 1.1613 Step 60/Ep 24, 0.58s, loss 0.4354 Step 80/Ep 24, 0.58s, loss 0.6540 Step 100/Ep 24, 0.58s, loss 0.3444 Step 120/Ep 24, 0.58s, loss 0.6317 Step 140/Ep 24, 0.58s, loss 0.7375 Step 160/Ep 24, 0.58s, loss 0.6753 Step 180/Ep 24, 0.58s, loss 0.5427 Step 200/Ep 24, 0.58s, loss 0.5830 Step 220/Ep 24, 0.59s, loss 0.6239 Step 240/Ep 24, 0.58s, loss 0.6407 Ep 24, 149.60s, loss 0.7288

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.02s, total 2.02s 40/76 batches done, +1.92s, total 3.95s 60/76 batches done, +1.92s, total 5.87s Done, 7.33s Computing distance... Done, 0.04s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 98.60%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 25, 0.57s, loss 0.8397
Step 40/Ep 25, 0.58s, loss 0.4416
Step 60/Ep 25, 0.57s, loss 0.6702
Step 80/Ep 25, 0.58s, loss 0.4207
Step 100/Ep 25, 0.57s, loss 0.3993
Step 120/Ep 25, 0.58s, loss 0.8637
Step 140/Ep 25, 0.58s, loss 0.5166
Step 160/Ep 25, 0.58s, loss 0.4670
Step 180/Ep 25, 0.58s, loss 0.8825
Step 200/Ep 25, 0.57s, loss 0.9797
Step 220/Ep 25, 0.58s, loss 0.6011
Step 240/Ep 25, 0.58s, loss 0.8492

Ep 25, 149.00s, loss 0.6469 Step 20/Ep 26, 0.57s, loss 0.4974 Step 40/Ep 26, 0.58s, loss 0.3895 Step 60/Ep 26, 0.58s, loss 0.2976 Step 80/Ep 26, 0.59s, loss 0.5827 Step 100/Ep 26, 0.58s, loss 0.4321 Step 120/Ep 26, 0.59s, loss 0.4128 Step 140/Ep 26, 0.58s, loss 0.3708 Step 160/Ep 26, 0.57s, loss 0.5052 Step 180/Ep 26, 0.58s, loss 0.6423 Step 200/Ep 26, 0.58s, loss 0.4070 Step 220/Ep 26, 0.58s, loss 0.4688 Step 240/Ep 26, 0.58s, loss 0.4549 Ep 26, 149.93s, loss 0.4671

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.04s, total 2.04s 40/76 batches done, +1.93s, total 3.97s 60/76 batches done, +1.93s, total 5.90s Done, 7.37s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.31s Single Query: [mAP: 99.32%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 27, 0.59s, loss 0.7168
Step 40/Ep 27, 0.60s, loss 0.9161
Step 60/Ep 27, 0.57s, loss 0.5513
Step 80/Ep 27, 0.58s, loss 0.3205
Step 100/Ep 27, 0.57s, loss 0.5772
Step 120/Ep 27, 0.57s, loss 0.2488
Step 140/Ep 27, 0.57s, loss 0.2286
Step 160/Ep 27, 0.58s, loss 0.2414
Step 180/Ep 27, 0.58s, loss 0.5102
Step 200/Ep 27, 0.57s, loss 0.2667
Step 220/Ep 27, 0.58s, loss 0.2900
Step 240/Ep 27, 0.58s, loss 0.1698

Ep 27, 149.58s, loss 0.4515 Step 20/Ep 28, 0.58s, loss 1.8641 Step 40/Ep 28, 0.58s, loss 0.7913 Step 60/Ep 28, 0.59s, loss 1.3012 Step 80/Ep 28, 0.60s, loss 0.8288 Step 100/Ep 28, 0.58s, loss 0.7013 Step 120/Ep 28, 0.58s, loss 0.8126 Step 140/Ep 28, 0.58s, loss 0.6532 Step 160/Ep 28, 0.58s, loss 0.6512 Step 180/Ep 28, 0.58s, loss 0.8726 Step 200/Ep 28, 0.58s, loss 0.7502 Step 220/Ep 28, 0.57s, loss 0.4386 Step 240/Ep 28, 0.58s, loss 0.5672 Ep 28, 149.87s, loss 0.7729

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.06s, total 2.06s 40/76 batches done, +1.93s, total 3.99s 60/76 batches done, +1.93s, total 5.93s Done, 7.40s Computing distance... Done, 0.05s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.28s Single Query: [mAP: 98.71%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 29, 0.58s, loss 0.3540
Step 40/Ep 29, 0.58s, loss 0.6476
Step 60/Ep 29, 0.57s, loss 0.7212
Step 80/Ep 29, 0.58s, loss 0.5859
Step 100/Ep 29, 0.58s, loss 0.4783
Step 120/Ep 29, 0.58s, loss 0.7391
Step 140/Ep 29, 0.57s, loss 0.3695
Step 160/Ep 29, 0.58s, loss 0.8245
Step 180/Ep 29, 0.58s, loss 0.6285
Step 200/Ep 29, 0.58s, loss 0.8962
Step 220/Ep 29, 0.57s, loss 0.6171
Step 240/Ep 29, 0.57s, loss 0.7354

Ep 29, 149.03s, loss 0.6326 Step 20/Ep 30, 0.57s, loss 0.9624 Step 40/Ep 30, 0.58s, loss 0.8231 Step 60/Ep 30, 0.58s, loss 0.8217 Step 80/Ep 30, 0.57s, loss 0.5845 Step 100/Ep 30, 0.57s, loss 1.1472 Step 120/Ep 30, 0.58s, loss 0.8399 Step 140/Ep 30, 0.58s, loss 0.9641 Step 160/Ep 30, 0.58s, loss 0.6502 Step 180/Ep 30, 0.58s, loss 1.1148 Step 200/Ep 30, 0.58s, loss 0.9405 Step 220/Ep 30, 0.58s, loss 1.0024 Step 240/Ep 30, 0.58s, loss 0.9224 Ep 30, 149.22s, loss 1.0070

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.04s, total 2.04s 40/76 batches done, +1.93s, total 3.97s 60/76 batches done, +1.90s, total 5.87s Done, 7.31s Computing distance... Done, 0.05s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.40s Single Query: [mAP: 97.42%], [cmc1: 99.38%], [cmc5: 99.69%], [cmc10: 100.00%]

Step 20/Ep 31, 0.58s, loss 2.6566
Step 40/Ep 31, 0.58s, loss 1.8471
Step 60/Ep 31, 0.58s, loss 1.6894
Step 80/Ep 31, 0.58s, loss 1.0760
Step 100/Ep 31, 0.59s, loss 2.1598
Step 120/Ep 31, 0.58s, loss 1.6868
Step 140/Ep 31, 0.58s, loss 1.0951
Step 160/Ep 31, 0.58s, loss 1.6602
Step 180/Ep 31, 0.58s, loss 1.1196
Step 200/Ep 31, 0.57s, loss 1.5896
Step 220/Ep 31, 0.58s, loss 1.6249
Step 240/Ep 31, 0.61s, loss 1.7398

Ep 31, 149.67s, loss 1.6903 Step 20/Ep 32, 0.58s, loss 2.8480 Step 40/Ep 32, 0.57s, loss 2.7165 Step 60/Ep 32, 0.57s, loss 1.8458 Step 80/Ep 32, 0.58s, loss 1.6278 Step 100/Ep 32, 0.58s, loss 1.6981 Step 120/Ep 32, 0.58s, loss 1.7396 Step 140/Ep 32, 0.58s, loss 1.4146 Step 160/Ep 32, 0.58s, loss 1.8949 Step 180/Ep 32, 0.58s, loss 1.7310 Step 200/Ep 32, 0.58s, loss 1.1906 Step 220/Ep 32, 0.58s, loss 0.8951 Step 240/Ep 32, 0.58s, loss 1.4697 Ep 32, 149.30s, loss 1.6267

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.05s, total 2.05s 40/76 batches done, +1.94s, total 3.99s 60/76 batches done, +1.94s, total 5.92s Done, 7.38s Computing distance... Done, 0.05s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.30s Single Query: [mAP: 96.76%], [cmc1: 98.44%], [cmc5: 99.69%], [cmc10: 100.00%]

Step 20/Ep 33, 0.58s, loss 1.1986
Step 40/Ep 33, 0.58s, loss 0.6627
Step 60/Ep 33, 0.58s, loss 0.7759
Step 80/Ep 33, 0.58s, loss 0.7220
Step 100/Ep 33, 0.57s, loss 0.7438
Step 120/Ep 33, 0.59s, loss 0.6115
Step 140/Ep 33, 0.57s, loss 0.8868
Step 160/Ep 33, 0.58s, loss 0.6064
Step 180/Ep 33, 0.58s, loss 0.8736
Step 200/Ep 33, 0.59s, loss 0.6104
Step 220/Ep 33, 0.58s, loss 0.8025
Step 240/Ep 33, 0.58s, loss 0.5442

Ep 33, 149.37s, loss 0.7536 Step 20/Ep 34, 0.58s, loss 0.8948 Step 40/Ep 34, 0.58s, loss 1.0146 Step 60/Ep 34, 0.57s, loss 0.9390 Step 80/Ep 34, 0.59s, loss 0.7427 Step 100/Ep 34, 0.57s, loss 0.4940 Step 120/Ep 34, 0.57s, loss 0.4074 Step 140/Ep 34, 0.58s, loss 0.4536 Step 160/Ep 34, 0.58s, loss 0.7832 Step 180/Ep 34, 0.58s, loss 0.2418 Step 200/Ep 34, 0.58s, loss 0.4310 Step 220/Ep 34, 0.57s, loss 0.3548 Step 240/Ep 34, 0.58s, loss 0.4576 Ep 34, 149.39s, loss 0.5633

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.08s, total 2.08s 40/76 batches done, +1.99s, total 4.07s 60/76 batches done, +1.98s, total 6.06s Done, 7.65s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.31s Single Query: [mAP: 99.29%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 35, 0.59s, loss 0.9558
Step 40/Ep 35, 0.60s, loss 0.3903
Step 60/Ep 35, 0.59s, loss 0.3868
Step 80/Ep 35, 0.61s, loss 0.3436
Step 100/Ep 35, 0.59s, loss 0.2549
Step 120/Ep 35, 0.61s, loss 0.2884
Step 140/Ep 35, 0.59s, loss 0.1748
Step 160/Ep 35, 0.60s, loss 0.1456
Step 180/Ep 35, 0.60s, loss 0.2465
Step 200/Ep 35, 0.60s, loss 0.2020
Step 220/Ep 35, 0.57s, loss 0.1251
Step 240/Ep 35, 0.59s, loss 0.5188

Ep 35, 153.27s, loss 0.2922 Step 20/Ep 36, 0.59s, loss 0.3976 Step 40/Ep 36, 0.59s, loss 0.1855 Step 60/Ep 36, 0.61s, loss 0.1755 Step 80/Ep 36, 0.57s, loss 0.1151 Step 100/Ep 36, 0.60s, loss 0.1553 Step 120/Ep 36, 0.59s, loss 0.2472 Step 140/Ep 36, 0.59s, loss 0.3345 Step 160/Ep 36, 0.58s, loss 0.1930 Step 180/Ep 36, 0.59s, loss 0.1125 Step 200/Ep 36, 0.59s, loss 0.0912 Step 220/Ep 36, 0.60s, loss 0.1325 Step 240/Ep 36, 0.60s, loss 0.1705 Ep 36, 153.33s, loss 0.2195

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.08s, total 2.08s 40/76 batches done, +2.01s, total 4.08s 60/76 batches done, +2.00s, total 6.08s Done, 7.76s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.32s Single Query: [mAP: 99.77%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 37, 0.57s, loss 0.3761
Step 40/Ep 37, 0.58s, loss 0.0988
Step 60/Ep 37, 0.58s, loss 0.1217
Step 80/Ep 37, 0.60s, loss 0.2060
Step 100/Ep 37, 0.58s, loss 0.1329
Step 120/Ep 37, 0.59s, loss 0.1196
Step 140/Ep 37, 0.61s, loss 0.0908
Step 160/Ep 37, 0.61s, loss 0.1273
Step 180/Ep 37, 0.59s, loss 0.1578
Step 200/Ep 37, 0.59s, loss 0.1790
Step 220/Ep 37, 0.59s, loss 0.0905
Step 240/Ep 37, 0.68s, loss 0.1255

Ep 37, 154.28s, loss 0.1501 Step 20/Ep 38, 0.58s, loss 0.1560 Step 40/Ep 38, 0.61s, loss 0.2213 Step 60/Ep 38, 0.61s, loss 0.1524 Step 80/Ep 38, 0.60s, loss 0.1111 Step 100/Ep 38, 0.58s, loss 0.2225 Step 120/Ep 38, 0.61s, loss 0.2908 Step 140/Ep 38, 0.61s, loss 0.2887 Step 160/Ep 38, 0.59s, loss 0.2227 Step 180/Ep 38, 0.60s, loss 0.1886 Step 200/Ep 38, 0.60s, loss 0.1447 Step 220/Ep 38, 0.62s, loss 0.1576 Step 240/Ep 38, 0.60s, loss 0.1302 Ep 38, 157.65s, loss 0.1481

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.07s, total 2.07s 40/76 batches done, +1.97s, total 4.05s 60/76 batches done, +2.02s, total 6.07s Done, 7.63s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.35s Single Query: [mAP: 99.94%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 39, 0.60s, loss 0.1304
Step 40/Ep 39, 0.59s, loss 0.0888
Step 60/Ep 39, 0.61s, loss 0.1322
Step 80/Ep 39, 0.62s, loss 0.1618
Step 100/Ep 39, 0.61s, loss 0.1251
Step 120/Ep 39, 0.58s, loss 0.2243
Step 140/Ep 39, 0.58s, loss 0.1110
Step 160/Ep 39, 0.59s, loss 0.0727
Step 180/Ep 39, 0.58s, loss 0.1053
Step 200/Ep 39, 0.61s, loss 0.1669
Step 220/Ep 39, 0.78s, loss 0.0773
Step 240/Ep 39, 0.58s, loss 0.1061

Ep 39, 155.61s, loss 0.1410 Step 20/Ep 40, 0.59s, loss 0.2788 Step 40/Ep 40, 0.61s, loss 0.3846 Step 60/Ep 40, 0.62s, loss 0.1863 Step 80/Ep 40, 0.59s, loss 0.2020 Step 100/Ep 40, 0.65s, loss 0.1071 Step 120/Ep 40, 0.61s, loss 0.1945 Step 140/Ep 40, 0.59s, loss 0.1115 Step 160/Ep 40, 0.58s, loss 0.0878 Step 180/Ep 40, 0.58s, loss 0.1581 Step 200/Ep 40, 0.58s, loss 0.1706 Step 220/Ep 40, 0.58s, loss 0.1358 Step 240/Ep 40, 0.59s, loss 0.1271 Ep 40, 155.86s, loss 0.1821

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.13s, total 2.13s 40/76 batches done, +2.00s, total 4.12s 60/76 batches done, +2.06s, total 6.19s Done, 7.80s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.36s Single Query: [mAP: 99.94%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

=====> Param group 0: lr adjusted to 0.001 =====> Param group 1: lr adjusted to 0.01 Step 20/Ep 41, 0.58s, loss 0.1239 Step 40/Ep 41, 0.59s, loss 0.0585 Step 60/Ep 41, 0.58s, loss 0.0487 Step 80/Ep 41, 0.60s, loss 0.0555 Step 100/Ep 41, 0.58s, loss 0.0566 Step 120/Ep 41, 0.60s, loss 0.1117 Step 140/Ep 41, 0.59s, loss 0.0532 Step 160/Ep 41, 0.60s, loss 0.1131 Step 180/Ep 41, 0.59s, loss 0.0902 Step 200/Ep 41, 0.61s, loss 0.0864 Step 220/Ep 41, 0.58s, loss 0.1161 Step 240/Ep 41, 0.60s, loss 0.1248 Ep 41, 156.11s, loss 0.0873 Step 20/Ep 42, 0.99s, loss 0.0511 Step 40/Ep 42, 0.81s, loss 0.0567 Step 60/Ep 42, 0.98s, loss 0.0792 Step 80/Ep 42, 1.00s, loss 0.0447 Step 100/Ep 42, 1.07s, loss 0.0755 Step 120/Ep 42, 0.87s, loss 0.0544 Step 140/Ep 42, 0.91s, loss 0.0523 Step 160/Ep 42, 0.97s, loss 0.0823 Step 180/Ep 42, 1.17s, loss 0.0497 Step 200/Ep 42, 1.27s, loss 0.1415 Step 220/Ep 42, 1.23s, loss 0.0573 Step 240/Ep 42, 1.24s, loss 0.0435 Ep 42, 263.17s, loss 0.0631

===== Test on validation set =====

Extracting feature... 20/76 batches done, +5.96s, total 5.96s 40/76 batches done, +6.65s, total 12.61s 60/76 batches done, +6.46s, total 19.07s Done, 23.85s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.28s Single Query: [mAP: 99.97%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 43, 1.32s, loss 0.0643
Step 40/Ep 43, 1.28s, loss 0.0546
Step 60/Ep 43, 1.36s, loss 0.0553
Step 80/Ep 43, 1.36s, loss 0.0500
Step 100/Ep 43, 1.29s, loss 0.0509
Step 120/Ep 43, 1.26s, loss 0.0457
Step 140/Ep 43, 1.39s, loss 0.0600
Step 160/Ep 43, 1.34s, loss 0.0494
Step 180/Ep 43, 1.29s, loss 0.0636
Step 200/Ep 43, 1.20s, loss 0.0638
Step 220/Ep 43, 1.33s, loss 0.0661
Step 240/Ep 43, 1.34s, loss 0.0416

Ep 43, 327.62s, loss 0.0582 Step 20/Ep 44, 1.30s, loss 0.0609 Step 40/Ep 44, 1.39s, loss 0.0514 Step 60/Ep 44, 1.20s, loss 0.0526 Step 80/Ep 44, 1.30s, loss 0.0723 Step 100/Ep 44, 1.24s, loss 0.0609 Step 120/Ep 44, 1.31s, loss 0.0429 Step 140/Ep 44, 1.31s, loss 0.0607 Step 160/Ep 44, 1.25s, loss 0.0522 Step 180/Ep 44, 1.58s, loss 0.0669 Step 200/Ep 44, 1.53s, loss 0.0583 Step 220/Ep 44, 1.65s, loss 0.0540 Step 240/Ep 44, 1.37s, loss 0.0747 Ep 44, 362.51s, loss 0.0572

===== Test on validation set =====

Extracting feature... 20/76 batches done, +7.66s, total 7.66s 40/76 batches done, +8.27s, total 15.93s 60/76 batches done, +8.46s, total 24.39s Done, 31.15s Computing distance... Done, 0.11s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.31s Single Query: [mAP: 99.98%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 45, 1.54s, loss 0.0826
Step 40/Ep 45, 1.46s, loss 0.0622
Step 60/Ep 45, 1.75s, loss 0.0482
Step 80/Ep 45, 1.83s, loss 0.0514
Step 100/Ep 45, 1.71s, loss 0.0594
Step 120/Ep 45, 1.72s, loss 0.0521
Step 140/Ep 45, 1.83s, loss 0.0519
Step 160/Ep 45, 1.82s, loss 0.0652
Step 180/Ep 45, 1.80s, loss 0.0636
Step 200/Ep 45, 1.75s, loss 0.0581
Step 220/Ep 45, 1.88s, loss 0.0791
Step 240/Ep 45, 1.75s, loss 0.0669

Ep 45, 445.55s, loss 0.0588 Step 20/Ep 46, 1.81s, loss 0.0532 Step 40/Ep 46, 1.63s, loss 0.0650 Step 60/Ep 46, 1.76s, loss 0.0478 Step 80/Ep 46, 1.82s, loss 0.0813 Step 100/Ep 46, 1.77s, loss 0.0458 Step 120/Ep 46, 1.73s, loss 0.0577 Step 140/Ep 46, 1.77s, loss 0.0732 Step 160/Ep 46, 1.78s, loss 0.0709 Step 180/Ep 46, 1.66s, loss 0.0446 Step 200/Ep 46, 1.81s, loss 0.0472 Step 220/Ep 46, 1.86s, loss 0.0620 Step 240/Ep 46, 1.82s, loss 0.0524 Ep 46, 451.01s, loss 0.0594

===== Test on validation set =====

Extracting feature... 20/76 batches done, +10.29s, total 10.29s 40/76 batches done, +10.23s, total 20.52s 60/76 batches done, +10.32s, total 30.84s Done, 38.67s Computing distance... Done, 0.17s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.30s Single Query: [mAP: 99.99%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 47, 1.85s, loss 0.0489
Step 40/Ep 47, 1.89s, loss 0.0465
Step 60/Ep 47, 0.58s, loss 0.0538
Step 80/Ep 47, 0.58s, loss 0.0684
Step 100/Ep 47, 0.62s, loss 0.0660
Step 120/Ep 47, 0.58s, loss 0.0597
Step 140/Ep 47, 0.58s, loss 0.0565
Step 160/Ep 47, 0.59s, loss 0.0817
Step 180/Ep 47, 0.59s, loss 0.0568
Step 200/Ep 47, 0.58s, loss 0.0700
Step 220/Ep 47, 0.60s, loss 0.0471
Step 240/Ep 47, 0.58s, loss 0.0552

Ep 47, 217.33s, loss 0.0629 Step 20/Ep 48, 0.58s, loss 0.0650 Step 40/Ep 48, 0.59s, loss 0.0587 Step 60/Ep 48, 0.60s, loss 0.0616 Step 80/Ep 48, 0.58s, loss 0.0532 Step 100/Ep 48, 0.59s, loss 0.0559 Step 120/Ep 48, 0.59s, loss 0.0492 Step 140/Ep 48, 0.58s, loss 0.0561 Step 160/Ep 48, 0.59s, loss 0.0549 Step 180/Ep 48, 0.60s, loss 0.0590 Step 200/Ep 48, 0.60s, loss 0.0461 Step 220/Ep 48, 0.59s, loss 0.0726 Step 240/Ep 48, 0.59s, loss 0.0683 Ep 48, 152.72s, loss 0.0635

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.15s, total 2.15s 40/76 batches done, +2.06s, total 4.20s 60/76 batches done, +2.12s, total 6.32s Done, 7.90s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 99.98%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 49, 0.58s, loss 0.0650
Step 40/Ep 49, 0.58s, loss 0.0567
Step 60/Ep 49, 0.58s, loss 0.0575
Step 80/Ep 49, 0.58s, loss 0.0677
Step 100/Ep 49, 0.61s, loss 0.0554
Step 120/Ep 49, 0.58s, loss 0.0589
Step 140/Ep 49, 0.59s, loss 0.0942
Step 160/Ep 49, 0.60s, loss 0.0650
Step 180/Ep 49, 0.59s, loss 0.0844
Step 200/Ep 49, 0.58s, loss 0.0572
Step 220/Ep 49, 0.61s, loss 0.0674
Step 240/Ep 49, 0.60s, loss 0.0720

Ep 49, 153.30s, loss 0.0685 Step 20/Ep 50, 0.58s, loss 0.0639 Step 40/Ep 50, 0.60s, loss 0.0741 Step 60/Ep 50, 0.61s, loss 0.0835 Step 80/Ep 50, 0.66s, loss 0.0506 Step 100/Ep 50, 0.63s, loss 0.0669 Step 120/Ep 50, 0.64s, loss 0.0887 Step 140/Ep 50, 0.62s, loss 0.0759 Step 160/Ep 50, 0.63s, loss 0.0593 Step 180/Ep 50, 0.62s, loss 0.0695 Step 200/Ep 50, 0.58s, loss 0.0788 Step 220/Ep 50, 0.58s, loss 0.0754 Step 240/Ep 50, 0.59s, loss 0.0577 Ep 50, 170.46s, loss 0.0683

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.09s, total 2.09s 40/76 batches done, +2.12s, total 4.21s 60/76 batches done, +1.97s, total 6.18s Done, 7.65s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.34s Single Query: [mAP: 99.99%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 51, 0.58s, loss 0.0793
Step 40/Ep 51, 0.58s, loss 0.0684
Step 60/Ep 51, 0.58s, loss 0.0953
Step 80/Ep 51, 0.58s, loss 0.0784
Step 100/Ep 51, 0.58s, loss 0.0535
Step 120/Ep 51, 0.58s, loss 0.0521
Step 140/Ep 51, 0.57s, loss 0.0566
Step 160/Ep 51, 0.58s, loss 0.0725
Step 180/Ep 51, 0.58s, loss 0.0566
Step 200/Ep 51, 0.58s, loss 0.0652
Step 220/Ep 51, 0.59s, loss 0.0524
Step 240/Ep 51, 0.59s, loss 0.0628

Ep 51, 151.36s, loss 0.0683 Step 20/Ep 52, 0.60s, loss 0.0827 Step 40/Ep 52, 0.58s, loss 0.0586 Step 60/Ep 52, 0.58s, loss 0.0713 Step 80/Ep 52, 0.58s, loss 0.0776 Step 100/Ep 52, 0.58s, loss 0.0578 Step 120/Ep 52, 0.59s, loss 0.0614 Step 140/Ep 52, 0.62s, loss 0.0514 Step 160/Ep 52, 0.86s, loss 0.0887 Step 180/Ep 52, 0.83s, loss 0.0672 Step 200/Ep 52, 0.87s, loss 0.0909 Step 220/Ep 52, 0.86s, loss 0.0376 Step 240/Ep 52, 0.85s, loss 0.0709 Ep 52, 173.05s, loss 0.0684

===== Test on validation set =====

Extracting feature... 20/76 batches done, +2.01s, total 2.01s 40/76 batches done, +1.89s, total 3.90s 60/76 batches done, +2.07s, total 5.98s Done, 7.45s Computing distance... Done, 0.03s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 100.00%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 53, 1.55s, loss 0.0575
Step 40/Ep 53, 1.53s, loss 0.0756
Step 60/Ep 53, 1.46s, loss 0.0695
Step 80/Ep 53, 1.48s, loss 0.0543
Step 100/Ep 53, 1.52s, loss 0.0594
Step 120/Ep 53, 1.53s, loss 0.0803
Step 140/Ep 53, 1.50s, loss 0.0664
Step 160/Ep 53, 1.55s, loss 0.0660
Step 180/Ep 53, 1.56s, loss 0.0601
Step 200/Ep 53, 1.49s, loss 0.0692
Step 220/Ep 53, 1.53s, loss 0.0850
Step 240/Ep 53, 1.55s, loss 0.0701

Ep 53, 343.79s, loss 0.0711 Step 20/Ep 54, 1.55s, loss 0.0431 Step 40/Ep 54, 1.45s, loss 0.0649 Step 60/Ep 54, 1.56s, loss 0.0689 Step 80/Ep 54, 1.64s, loss 0.0611 Step 100/Ep 54, 1.63s, loss 0.0845 Step 120/Ep 54, 1.73s, loss 0.0698 Step 140/Ep 54, 1.75s, loss 0.0726 Step 160/Ep 54, 1.76s, loss 0.0644 Step 180/Ep 54, 1.71s, loss 0.0760 Step 200/Ep 54, 1.73s, loss 0.0767 Step 220/Ep 54, 1.68s, loss 0.0540 Step 240/Ep 54, 1.80s, loss 0.1006 Ep 54, 421.63s, loss 0.0713

===== Test on validation set =====

Extracting feature... 20/76 batches done, +9.18s, total 9.18s 40/76 batches done, +9.09s, total 18.27s 60/76 batches done, +8.73s, total 27.00s Done, 33.73s Computing distance... Done, 0.08s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.31s Single Query: [mAP: 99.99%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 55, 9.66s, loss 0.0644
Step 40/Ep 55, 12.66s, loss 0.0597
Step 60/Ep 55, 7.02s, loss 0.0781
Step 80/Ep 55, 9.40s, loss 0.0846
Step 100/Ep 55, 8.76s, loss 0.0634
Step 120/Ep 55, 11.46s, loss 0.0692
Step 140/Ep 55, 8.49s, loss 0.0742
Step 160/Ep 55, 9.89s, loss 0.0872
Step 180/Ep 55, 7.41s, loss 0.0615
Step 200/Ep 55, 9.89s, loss 0.0757
Step 220/Ep 55, 7.41s, loss 0.0588
Step 240/Ep 55, 10.02s, loss 0.0740

Ep 55, 2253.52s, loss 0.0714 Step 20/Ep 56, 10.45s, loss 0.0653 Step 40/Ep 56, 7.42s, loss 0.0507 Step 60/Ep 56, 9.47s, loss 0.0762 Step 80/Ep 56, 8.14s, loss 0.1070 Step 100/Ep 56, 7.93s, loss 0.0661 Step 120/Ep 56, 6.00s, loss 0.0769 Step 140/Ep 56, 10.50s, loss 0.0592 Step 160/Ep 56, 7.77s, loss 0.0698 Step 180/Ep 56, 10.03s, loss 0.0651 Step 200/Ep 56, 9.42s, loss 0.0680 Step 220/Ep 56, 9.20s, loss 0.0605 Step 240/Ep 56, 6.21s, loss 0.0607 Ep 56, 2300.39s, loss 0.0730

===== Test on validation set =====

Extracting feature... 20/76 batches done, +44.58s, total 44.58s 40/76 batches done, +49.01s, total 93.59s 60/76 batches done, +43.04s, total 136.63s Done, 181.25s Computing distance... Done, 0.64s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.29s Single Query: [mAP: 100.00%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 57, 2.17s, loss 0.1029
Step 40/Ep 57, 2.28s, loss 0.0665
Step 60/Ep 57, 2.25s, loss 0.0782
Step 80/Ep 57, 2.20s, loss 0.0869
Step 100/Ep 57, 2.32s, loss 0.0539
Step 120/Ep 57, 2.29s, loss 0.0715
Step 140/Ep 57, 2.29s, loss 0.0842
Step 160/Ep 57, 2.27s, loss 0.0626
Step 180/Ep 57, 2.26s, loss 0.0653
Step 200/Ep 57, 2.33s, loss 0.0825
Step 220/Ep 57, 2.18s, loss 0.0775
Step 240/Ep 57, 2.21s, loss 0.0656

Ep 57, 585.82s, loss 0.0723 Step 20/Ep 58, 2.26s, loss 0.0684 Step 40/Ep 58, 2.21s, loss 0.0677 Step 60/Ep 58, 6.89s, loss 0.0679 Step 80/Ep 58, 9.03s, loss 0.0606 Step 100/Ep 58, 10.00s, loss 0.0598 Step 120/Ep 58, 4.76s, loss 0.0822 Step 140/Ep 58, 7.33s, loss 0.0647 Step 160/Ep 58, 10.61s, loss 0.0916 Step 180/Ep 58, 9.37s, loss 0.0769 Step 200/Ep 58, 8.81s, loss 0.0539 Step 220/Ep 58, 13.00s, loss 0.0705 Step 240/Ep 58, 12.67s, loss 0.0526 Ep 58, 2319.96s, loss 0.0704

===== Test on validation set =====

Extracting feature... 20/76 batches done, +27.16s, total 27.16s 40/76 batches done, +39.55s, total 66.71s 60/76 batches done, +34.20s, total 100.92s Done, 144.13s Computing distance... Done, 1.21s Computing scores... User Warning: Version 0.18.1 is required for package scikit-learn, your current version is 0.19.1. As a result, the mAP score may not be totally correct. You can try pip uninstall scikit-learn and then pip install scikit-learn==0.18.1 Done, 0.30s Single Query: [mAP: 100.00%], [cmc1: 100.00%], [cmc5: 100.00%], [cmc10: 100.00%]

Step 20/Ep 59, 5.51s, loss 0.0632
Step 40/Ep 59, 8.44s, loss 0.0819
Step 60/Ep 59, 6.65s, loss 0.0701
Step 80/Ep 59, 10.00s, loss 0.0816
Step 100/Ep 59, 9.75s, loss 0.0611
Step 120/Ep 59, 9.66s, loss 0.0696
Step 140/Ep 59, 9.40s, loss 0.0624
Step 160/Ep 59, 9.17s, loss 0.0679
Step 180/Ep 59, 5.24s, loss 0.0747
Step 200/Ep 59, 10.17s, loss 0.0704
Step 220/Ep 59, 5.08s, loss 0.0596
Step 240/Ep 59, 9.89s, loss 0.0907

Ep 59, 2208.26s, loss 0.0713 Step 20/Ep 60, 5.06s, loss 0.0728 Step 40/Ep 60, 9.74s, loss 0.0866 Step 60/Ep 60, 10.04s, loss 0.0615 Step 80/Ep 60, 9.64s, loss 0.0832 Step 100/Ep 60, 11.49s, loss 0.0668 Step 120/Ep 60, 10.06s, loss 0.0555 Step 140/Ep 60, 10.52s, loss 0.0884 Step 160/Ep 60, 9.91s, loss 0.0720 Step 180/Ep 60, 7.34s, loss 0.0914 Step 200/Ep 60, 10.57s, loss 0.0556 Step 220/Ep 60, 10.08s, loss 0.0716 Step 240/Ep 60, 7.55s, loss 0.0697 Ep 60, 2399.77s, loss 0.0717

===== Test on validation set =====

Extracting feature... 20/76 batches done, +50.25s, total 50.25s`

yja1 commented 6 years ago

I compared my train log file and your train log file. my file output something strange:

Keys not found in source state_dict:
base.layer1.1.bn1.bias
base.layer3.1.conv1.weight
base.layer4.1.bn3.running_var

???

yja1 commented 6 years ago

I found your model named "model_weight.pth" of cuhk03 is 97M but I trained model "ckpt.pth" of cuhk03 is 193M

I test like this is right? python script/experiment/train_pcb.py -d '(1,)' --only_test true --dataset combined --exp_dir beyond-part-models/exp_directory --model_weight_file /beyond-part-models/exp_directory/combined3/ckpt.pth

yja1 commented 6 years ago

I know how to test myself trained model.

Gavin666Github commented 6 years ago

@yja1 I met this trouble, too.How did you solve this problem?