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cifar10-pretext.out
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[31m{'setup': 'simclr', 'backbone': 'resnet18', 'model_kwargs': {'head': 'mlp', 'features_dim': 128}, 'train_db_name': 'cifar-10', 'val_db_name': 'cifar-10', 'num_classes': 10, 'criterion': 'simclr', 'criterion_kwargs': {'temperature': 0.1}, 'epochs': 500, 'optimizer': 'sgd', 'optimizer_kwargs': {'nesterov': False, 'weight_decay': 0.0001, 'momentum': 0.9, 'lr': 0.4}, 'scheduler': 'cosine', 'scheduler_kwargs': {'lr_decay_rate': 0.1}, 'batch_size': 512, 'num_workers': 8, 'augmentation_strategy': 'simclr', 'augmentation_kwargs': {'random_resized_crop': {'size': 32, 'scale': [0.2, 1.0]}, 'color_jitter_random_apply': {'p': 0.8}, 'color_jitter': {'brightness': 0.4, 'contrast': 0.4, 'saturation': 0.4, 'hue': 0.1}, 'random_grayscale': {'p': 0.2}, 'normalize': {'mean': [0.4914, 0.4822, 0.4465], 'std': [0.2023, 0.1994, 0.201]}}, 'transformation_kwargs': {'crop_size': 32, 'normalize': {'mean': [0.4914, 0.4822, 0.4465], 'std': [0.2023, 0.1994, 0.201]}}, 'pretext_dir': 'output/cifar-10/pretext', 'pretext_checkpoint': 'output/cifar-10/pretext/checkpoint.pth.tar', 'pretext_model': 'output/cifar-10/pretext/model.pth.tar', 'topk_neighbors_train_path': 'output/cifar-10/pretext/topk-train-neighbors.npy', 'topk_neighbors_val_path': 'output/cifar-10/pretext/topk-val-neighbors.npy'}[0m
[34mRetrieve model[0m
Model is ContrastiveModel
Model parameters: 11.50M
ContrastiveModel(
(backbone): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
)
(contrastive_head): Sequential(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): ReLU()
(2): Linear(in_features=512, out_features=128, bias=True)
)
)
[34mSet CuDNN benchmark[0m
[34mRetrieve dataset[0m
Train transforms: Compose(
RandomResizedCrop(size=(32, 32), scale=(0.2, 1.0), ratio=(0.75, 1.3333), interpolation=PIL.Image.BILINEAR)
RandomHorizontalFlip(p=0.5)
RandomApply(
p=0.8
ColorJitter(brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[-0.1, 0.1])
)
RandomGrayscale(p=0.2)
ToTensor()
Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.201])
)
Validation transforms: Compose(
CenterCrop(size=(32, 32))
ToTensor()
Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.201])
)
Files already downloaded and verified
Files already downloaded and verified
Dataset contains 50000/10000 train/val samples
[34mBuild MemoryBank[0m
Files already downloaded and verified
[34mRetrieve criterion[0m
Criterion is SimCLRLoss
[34mRetrieve optimizer[0m
SGD (
Parameter Group 0
dampening: 0
lr: 0.4
momentum: 0.9
nesterov: False
weight_decay: 0.0001
)
[34mRestart from checkpoint output/cifar-10/pretext/checkpoint.pth.tar[0m
[34mStarting main loop[0m
[33mEpoch 95/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36545
Train ...
Epoch: [95][ 0/97] Loss 1.1158e+00 (1.1158e+00)
Epoch: [95][25/97] Loss 1.0411e+00 (1.0840e+00)
Epoch: [95][50/97] Loss 1.0878e+00 (1.0915e+00)
Epoch: [95][75/97] Loss 1.1191e+00 (1.0893e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 76.81
Checkpoint ...
[33mEpoch 96/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36474
Train ...
Epoch: [96][ 0/97] Loss 1.1014e+00 (1.1014e+00)
Epoch: [96][25/97] Loss 1.0871e+00 (1.0794e+00)
Epoch: [96][50/97] Loss 1.0815e+00 (1.0984e+00)
Epoch: [96][75/97] Loss 1.1322e+00 (1.0983e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 77.59
Checkpoint ...
[33mEpoch 97/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36403
Train ...
Epoch: [97][ 0/97] Loss 1.0814e+00 (1.0814e+00)
Epoch: [97][25/97] Loss 1.0749e+00 (1.0717e+00)
Epoch: [97][50/97] Loss 1.1207e+00 (1.0856e+00)
Epoch: [97][75/97] Loss 1.0803e+00 (1.0900e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.68
Checkpoint ...
[33mEpoch 98/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36330
Train ...
Epoch: [98][ 0/97] Loss 1.1219e+00 (1.1219e+00)
Epoch: [98][25/97] Loss 9.9320e-01 (1.0961e+00)
Epoch: [98][50/97] Loss 1.1546e+00 (1.0957e+00)
Epoch: [98][75/97] Loss 1.1464e+00 (1.0953e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 77.98
Checkpoint ...
[33mEpoch 99/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36258
Train ...
Epoch: [99][ 0/97] Loss 1.0125e+00 (1.0125e+00)
Epoch: [99][25/97] Loss 1.0482e+00 (1.0771e+00)
Epoch: [99][50/97] Loss 1.1214e+00 (1.0817e+00)
Epoch: [99][75/97] Loss 1.1497e+00 (1.0789e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.64
Checkpoint ...
[33mEpoch 100/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36184
Train ...
Epoch: [100][ 0/97] Loss 1.0954e+00 (1.0954e+00)
Epoch: [100][25/97] Loss 1.0036e+00 (1.0709e+00)
Epoch: [100][50/97] Loss 1.1238e+00 (1.0775e+00)
Epoch: [100][75/97] Loss 1.0237e+00 (1.0757e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 77.21
Checkpoint ...
[33mEpoch 101/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36110
Train ...
Epoch: [101][ 0/97] Loss 1.0175e+00 (1.0175e+00)
Epoch: [101][25/97] Loss 1.0655e+00 (1.0593e+00)
Epoch: [101][50/97] Loss 1.0849e+00 (1.0603e+00)
Epoch: [101][75/97] Loss 1.0670e+00 (1.0637e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 77.86
Checkpoint ...
[33mEpoch 102/500[0m
[33m---------------[0m
Adjusted learning rate to 0.36035
Train ...
Epoch: [102][ 0/97] Loss 1.0562e+00 (1.0562e+00)
Epoch: [102][25/97] Loss 1.0802e+00 (1.0564e+00)
Epoch: [102][50/97] Loss 1.1648e+00 (1.0695e+00)
Epoch: [102][75/97] Loss 9.4135e-01 (1.0664e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.55
Checkpoint ...
[33mEpoch 103/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35960
Train ...
Epoch: [103][ 0/97] Loss 1.0606e+00 (1.0606e+00)
Epoch: [103][25/97] Loss 1.0298e+00 (1.0718e+00)
Epoch: [103][50/97] Loss 1.0671e+00 (1.0627e+00)
Epoch: [103][75/97] Loss 1.0419e+00 (1.0538e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.40
Checkpoint ...
[33mEpoch 104/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35884
Train ...
Epoch: [104][ 0/97] Loss 1.1088e+00 (1.1088e+00)
Epoch: [104][25/97] Loss 1.0555e+00 (1.0504e+00)
Epoch: [104][50/97] Loss 1.0454e+00 (1.0557e+00)
Epoch: [104][75/97] Loss 1.0926e+00 (1.0509e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.46
Checkpoint ...
[33mEpoch 105/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35807
Train ...
Epoch: [105][ 0/97] Loss 1.0790e+00 (1.0790e+00)
Epoch: [105][25/97] Loss 9.9370e-01 (1.0424e+00)
Epoch: [105][50/97] Loss 1.0976e+00 (1.0463e+00)
Epoch: [105][75/97] Loss 1.0696e+00 (1.0423e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.41
Checkpoint ...
[33mEpoch 106/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35730
Train ...
Epoch: [106][ 0/97] Loss 1.0461e+00 (1.0461e+00)
Epoch: [106][25/97] Loss 9.9274e-01 (1.0465e+00)
Epoch: [106][50/97] Loss 1.0266e+00 (1.0542e+00)
Epoch: [106][75/97] Loss 1.0813e+00 (1.0568e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.84
Checkpoint ...
[33mEpoch 107/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35652
Train ...
Epoch: [107][ 0/97] Loss 1.0120e+00 (1.0120e+00)
Epoch: [107][25/97] Loss 1.0171e+00 (1.0334e+00)
Epoch: [107][50/97] Loss 1.0144e+00 (1.0329e+00)
Epoch: [107][75/97] Loss 9.8595e-01 (1.0382e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.66
Checkpoint ...
[33mEpoch 108/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35574
Train ...
Epoch: [108][ 0/97] Loss 1.0546e+00 (1.0546e+00)
Epoch: [108][25/97] Loss 1.1152e+00 (1.0573e+00)
Epoch: [108][50/97] Loss 1.0877e+00 (1.0490e+00)
Epoch: [108][75/97] Loss 1.0469e+00 (1.0465e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.07
Checkpoint ...
[33mEpoch 109/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35495
Train ...
Epoch: [109][ 0/97] Loss 9.2691e-01 (9.2691e-01)
Epoch: [109][25/97] Loss 9.5876e-01 (9.8535e-01)
Epoch: [109][50/97] Loss 1.0567e+00 (1.0024e+00)
Epoch: [109][75/97] Loss 9.9229e-01 (1.0165e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.84
Checkpoint ...
[33mEpoch 110/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35415
Train ...
Epoch: [110][ 0/97] Loss 1.0243e+00 (1.0243e+00)
Epoch: [110][25/97] Loss 9.8980e-01 (1.0104e+00)
Epoch: [110][50/97] Loss 1.0539e+00 (1.0278e+00)
Epoch: [110][75/97] Loss 1.1030e+00 (1.0324e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 77.82
Checkpoint ...
[33mEpoch 111/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35335
Train ...
Epoch: [111][ 0/97] Loss 1.0736e+00 (1.0736e+00)
Epoch: [111][25/97] Loss 1.0354e+00 (1.0144e+00)
Epoch: [111][50/97] Loss 9.8440e-01 (1.0174e+00)
Epoch: [111][75/97] Loss 9.3734e-01 (1.0140e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.77
Checkpoint ...
[33mEpoch 112/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35254
Train ...
Epoch: [112][ 0/97] Loss 9.8403e-01 (9.8403e-01)
Epoch: [112][25/97] Loss 1.0155e+00 (1.0167e+00)
Epoch: [112][50/97] Loss 1.0418e+00 (1.0087e+00)
Epoch: [112][75/97] Loss 9.9414e-01 (1.0076e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.43
Checkpoint ...
[33mEpoch 113/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35172
Train ...
Epoch: [113][ 0/97] Loss 9.5877e-01 (9.5877e-01)
Epoch: [113][25/97] Loss 9.4656e-01 (1.0355e+00)
Epoch: [113][50/97] Loss 1.0292e+00 (1.0294e+00)
Epoch: [113][75/97] Loss 1.1167e+00 (1.0295e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.44
Checkpoint ...
[33mEpoch 114/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35090
Train ...
Epoch: [114][ 0/97] Loss 9.9875e-01 (9.9875e-01)
Epoch: [114][25/97] Loss 1.0943e+00 (1.0140e+00)
Epoch: [114][50/97] Loss 9.5484e-01 (1.0159e+00)
Epoch: [114][75/97] Loss 9.9613e-01 (1.0160e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.54
Checkpoint ...
[33mEpoch 115/500[0m
[33m---------------[0m
Adjusted learning rate to 0.35007
Train ...
Epoch: [115][ 0/97] Loss 9.7430e-01 (9.7430e-01)
Epoch: [115][25/97] Loss 1.0054e+00 (1.0101e+00)
Epoch: [115][50/97] Loss 9.8664e-01 (1.0065e+00)
Epoch: [115][75/97] Loss 1.0694e+00 (1.0133e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.10
Checkpoint ...
[33mEpoch 116/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34924
Train ...
Epoch: [116][ 0/97] Loss 9.5954e-01 (9.5954e-01)
Epoch: [116][25/97] Loss 1.0168e+00 (9.8949e-01)
Epoch: [116][50/97] Loss 9.8623e-01 (9.8435e-01)
Epoch: [116][75/97] Loss 1.0124e+00 (9.9067e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.03
Checkpoint ...
[33mEpoch 117/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34840
Train ...
Epoch: [117][ 0/97] Loss 9.7055e-01 (9.7055e-01)
Epoch: [117][25/97] Loss 9.4422e-01 (1.0161e+00)
Epoch: [117][50/97] Loss 9.6892e-01 (1.0092e+00)
Epoch: [117][75/97] Loss 1.0513e+00 (1.0110e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.31
Checkpoint ...
[33mEpoch 118/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34756
Train ...
Epoch: [118][ 0/97] Loss 1.0093e+00 (1.0093e+00)
Epoch: [118][25/97] Loss 1.0817e+00 (9.9192e-01)
Epoch: [118][50/97] Loss 1.0576e+00 (1.0113e+00)
Epoch: [118][75/97] Loss 9.4429e-01 (1.0103e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.36
Checkpoint ...
[33mEpoch 119/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34670
Train ...
Epoch: [119][ 0/97] Loss 9.8585e-01 (9.8585e-01)
Epoch: [119][25/97] Loss 1.0084e+00 (9.9518e-01)
Epoch: [119][50/97] Loss 9.3919e-01 (9.8694e-01)
Epoch: [119][75/97] Loss 1.0415e+00 (9.9479e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.30
Checkpoint ...
[33mEpoch 120/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34585
Train ...
Epoch: [120][ 0/97] Loss 1.0058e+00 (1.0058e+00)
Epoch: [120][25/97] Loss 1.0229e+00 (9.9971e-01)
Epoch: [120][50/97] Loss 9.5295e-01 (9.9832e-01)
Epoch: [120][75/97] Loss 1.0363e+00 (1.0024e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.54
Checkpoint ...
[33mEpoch 121/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34499
Train ...
Epoch: [121][ 0/97] Loss 9.9853e-01 (9.9853e-01)
Epoch: [121][25/97] Loss 9.7271e-01 (9.8763e-01)
Epoch: [121][50/97] Loss 1.0403e+00 (9.9238e-01)
Epoch: [121][75/97] Loss 9.8982e-01 (9.9468e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.68
Checkpoint ...
[33mEpoch 122/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34412
Train ...
Epoch: [122][ 0/97] Loss 9.9684e-01 (9.9684e-01)
Epoch: [122][25/97] Loss 9.8880e-01 (1.0013e+00)
Epoch: [122][50/97] Loss 9.8794e-01 (9.9076e-01)
Epoch: [122][75/97] Loss 1.0440e+00 (9.9065e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 78.59
Checkpoint ...
[33mEpoch 123/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34324
Train ...
Epoch: [123][ 0/97] Loss 1.0263e+00 (1.0263e+00)
Epoch: [123][25/97] Loss 9.3544e-01 (9.7538e-01)
Epoch: [123][50/97] Loss 1.0218e+00 (9.8299e-01)
Epoch: [123][75/97] Loss 9.3478e-01 (9.8459e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.47
Checkpoint ...
[33mEpoch 124/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34236
Train ...
Epoch: [124][ 0/97] Loss 1.0489e+00 (1.0489e+00)
Epoch: [124][25/97] Loss 9.4343e-01 (9.8110e-01)
Epoch: [124][50/97] Loss 9.9620e-01 (9.8672e-01)
Epoch: [124][75/97] Loss 9.2023e-01 (9.8388e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.65
Checkpoint ...
[33mEpoch 125/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34148
Train ...
Epoch: [125][ 0/97] Loss 9.0047e-01 (9.0047e-01)
Epoch: [125][25/97] Loss 9.5632e-01 (9.6184e-01)
Epoch: [125][50/97] Loss 9.5235e-01 (9.6614e-01)
Epoch: [125][75/97] Loss 9.7361e-01 (9.7033e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.96
Checkpoint ...
[33mEpoch 126/500[0m
[33m---------------[0m
Adjusted learning rate to 0.34059
Train ...
Epoch: [126][ 0/97] Loss 9.9659e-01 (9.9659e-01)
Epoch: [126][25/97] Loss 1.0684e+00 (9.6916e-01)
Epoch: [126][50/97] Loss 8.9676e-01 (9.6470e-01)
Epoch: [126][75/97] Loss 9.9339e-01 (9.6906e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.36
Checkpoint ...
[33mEpoch 127/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33969
Train ...
Epoch: [127][ 0/97] Loss 9.9458e-01 (9.9458e-01)
Epoch: [127][25/97] Loss 9.5813e-01 (9.6330e-01)
Epoch: [127][50/97] Loss 1.0194e+00 (9.7103e-01)
Epoch: [127][75/97] Loss 9.5282e-01 (9.6869e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.34
Checkpoint ...
[33mEpoch 128/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33879
Train ...
Epoch: [128][ 0/97] Loss 9.9506e-01 (9.9506e-01)
Epoch: [128][25/97] Loss 1.0275e+00 (9.6566e-01)
Epoch: [128][50/97] Loss 9.5023e-01 (9.6242e-01)
Epoch: [128][75/97] Loss 9.5203e-01 (9.6312e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.12
Checkpoint ...
[33mEpoch 129/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33788
Train ...
Epoch: [129][ 0/97] Loss 1.0179e+00 (1.0179e+00)
Epoch: [129][25/97] Loss 9.7757e-01 (9.5940e-01)
Epoch: [129][50/97] Loss 9.6694e-01 (9.6229e-01)
Epoch: [129][75/97] Loss 8.3975e-01 (9.5896e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.39
Checkpoint ...
[33mEpoch 130/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33697
Train ...
Epoch: [130][ 0/97] Loss 1.0456e+00 (1.0456e+00)
Epoch: [130][25/97] Loss 9.4657e-01 (9.5209e-01)
Epoch: [130][50/97] Loss 9.6058e-01 (9.5484e-01)
Epoch: [130][75/97] Loss 9.3394e-01 (9.5482e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.56
Checkpoint ...
[33mEpoch 131/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33605
Train ...
Epoch: [131][ 0/97] Loss 9.5044e-01 (9.5044e-01)
Epoch: [131][25/97] Loss 9.0717e-01 (9.5940e-01)
Epoch: [131][50/97] Loss 9.3924e-01 (9.6011e-01)
Epoch: [131][75/97] Loss 9.3256e-01 (9.6302e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.87
Checkpoint ...
[33mEpoch 132/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33513
Train ...
Epoch: [132][ 0/97] Loss 9.6439e-01 (9.6439e-01)
Epoch: [132][25/97] Loss 8.8151e-01 (9.4657e-01)
Epoch: [132][50/97] Loss 9.6987e-01 (9.5993e-01)
Epoch: [132][75/97] Loss 9.9183e-01 (9.5627e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.08
Checkpoint ...
[33mEpoch 133/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33420
Train ...
Epoch: [133][ 0/97] Loss 1.0337e+00 (1.0337e+00)
Epoch: [133][25/97] Loss 8.8680e-01 (9.6056e-01)
Epoch: [133][50/97] Loss 9.6466e-01 (9.6122e-01)
Epoch: [133][75/97] Loss 9.8574e-01 (9.6442e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.94
Checkpoint ...
[33mEpoch 134/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33327
Train ...
Epoch: [134][ 0/97] Loss 9.7339e-01 (9.7339e-01)
Epoch: [134][25/97] Loss 9.3169e-01 (9.4642e-01)
Epoch: [134][50/97] Loss 9.8905e-01 (9.5119e-01)
Epoch: [134][75/97] Loss 9.6993e-01 (9.5618e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.97
Checkpoint ...
[33mEpoch 135/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33233
Train ...
Epoch: [135][ 0/97] Loss 9.2026e-01 (9.2026e-01)
Epoch: [135][25/97] Loss 9.2931e-01 (9.3554e-01)
Epoch: [135][50/97] Loss 9.5814e-01 (9.4919e-01)
Epoch: [135][75/97] Loss 9.5891e-01 (9.5220e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.85
Checkpoint ...
[33mEpoch 136/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33139
Train ...
Epoch: [136][ 0/97] Loss 9.3510e-01 (9.3510e-01)
Epoch: [136][25/97] Loss 1.0121e+00 (9.3922e-01)
Epoch: [136][50/97] Loss 8.9039e-01 (9.3845e-01)
Epoch: [136][75/97] Loss 9.7892e-01 (9.4140e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.11
Checkpoint ...
[33mEpoch 137/500[0m
[33m---------------[0m
Adjusted learning rate to 0.33044
Train ...
Epoch: [137][ 0/97] Loss 9.6090e-01 (9.6090e-01)
Epoch: [137][25/97] Loss 9.3399e-01 (9.5013e-01)
Epoch: [137][50/97] Loss 8.8087e-01 (9.5277e-01)
Epoch: [137][75/97] Loss 1.0156e+00 (9.5429e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.10
Checkpoint ...
[33mEpoch 138/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32948
Train ...
Epoch: [138][ 0/97] Loss 9.5883e-01 (9.5883e-01)
Epoch: [138][25/97] Loss 9.4314e-01 (9.4474e-01)
Epoch: [138][50/97] Loss 9.8317e-01 (9.4438e-01)
Epoch: [138][75/97] Loss 9.3978e-01 (9.5125e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.16
Checkpoint ...
[33mEpoch 139/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32852
Train ...
Epoch: [139][ 0/97] Loss 9.2959e-01 (9.2959e-01)
Epoch: [139][25/97] Loss 8.9318e-01 (9.5066e-01)
Epoch: [139][50/97] Loss 1.0401e+00 (9.5121e-01)
Epoch: [139][75/97] Loss 9.2557e-01 (9.5004e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.43
Checkpoint ...
[33mEpoch 140/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32756
Train ...
Epoch: [140][ 0/97] Loss 8.8194e-01 (8.8194e-01)
Epoch: [140][25/97] Loss 9.6089e-01 (9.1108e-01)
Epoch: [140][50/97] Loss 8.9517e-01 (9.2173e-01)
Epoch: [140][75/97] Loss 9.1584e-01 (9.3091e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.54
Checkpoint ...
[33mEpoch 141/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32659
Train ...
Epoch: [141][ 0/97] Loss 8.5089e-01 (8.5089e-01)
Epoch: [141][25/97] Loss 8.7217e-01 (9.2885e-01)
Epoch: [141][50/97] Loss 9.0559e-01 (9.2040e-01)
Epoch: [141][75/97] Loss 8.8418e-01 (9.1857e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.24
Checkpoint ...
[33mEpoch 142/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32561
Train ...
Epoch: [142][ 0/97] Loss 9.5721e-01 (9.5721e-01)
Epoch: [142][25/97] Loss 9.7794e-01 (9.3573e-01)
Epoch: [142][50/97] Loss 9.8362e-01 (9.3554e-01)
Epoch: [142][75/97] Loss 9.7336e-01 (9.3469e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.37
Checkpoint ...
[33mEpoch 143/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32463
Train ...
Epoch: [143][ 0/97] Loss 8.9753e-01 (8.9753e-01)
Epoch: [143][25/97] Loss 8.9269e-01 (9.2483e-01)
Epoch: [143][50/97] Loss 9.6942e-01 (9.2234e-01)
Epoch: [143][75/97] Loss 9.9471e-01 (9.2710e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 81.14
Checkpoint ...
[33mEpoch 144/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32365
Train ...
Epoch: [144][ 0/97] Loss 8.9221e-01 (8.9221e-01)
Epoch: [144][25/97] Loss 8.9116e-01 (9.0240e-01)
Epoch: [144][50/97] Loss 9.3136e-01 (9.2375e-01)
Epoch: [144][75/97] Loss 9.6579e-01 (9.2638e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 79.91
Checkpoint ...
[33mEpoch 145/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32266
Train ...
Epoch: [145][ 0/97] Loss 9.1397e-01 (9.1397e-01)
Epoch: [145][25/97] Loss 1.0205e+00 (9.2341e-01)
Epoch: [145][50/97] Loss 8.9681e-01 (9.2711e-01)
Epoch: [145][75/97] Loss 9.6605e-01 (9.3432e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.22
Checkpoint ...
[33mEpoch 146/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32166
Train ...
Epoch: [146][ 0/97] Loss 8.8208e-01 (8.8208e-01)
Epoch: [146][25/97] Loss 9.6409e-01 (9.1567e-01)
Epoch: [146][50/97] Loss 9.2878e-01 (9.2855e-01)
Epoch: [146][75/97] Loss 1.0684e+00 (9.3181e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.15
Checkpoint ...
[33mEpoch 147/500[0m
[33m---------------[0m
Adjusted learning rate to 0.32067
Train ...
Epoch: [147][ 0/97] Loss 8.9467e-01 (8.9467e-01)
Epoch: [147][25/97] Loss 9.5803e-01 (9.1513e-01)
Epoch: [147][50/97] Loss 9.7171e-01 (9.1816e-01)
Epoch: [147][75/97] Loss 9.2371e-01 (9.2003e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.38
Checkpoint ...
[33mEpoch 148/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31966
Train ...
Epoch: [148][ 0/97] Loss 9.3012e-01 (9.3012e-01)
Epoch: [148][25/97] Loss 9.1121e-01 (9.2056e-01)
Epoch: [148][50/97] Loss 9.2281e-01 (9.2258e-01)
Epoch: [148][75/97] Loss 9.2815e-01 (9.2288e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.71
Checkpoint ...
[33mEpoch 149/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31865
Train ...
Epoch: [149][ 0/97] Loss 8.6327e-01 (8.6327e-01)
Epoch: [149][25/97] Loss 9.1602e-01 (9.0693e-01)
Epoch: [149][50/97] Loss 9.0678e-01 (9.0910e-01)
Epoch: [149][75/97] Loss 9.5022e-01 (9.1354e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.86
Checkpoint ...
[33mEpoch 150/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31764
Train ...
Epoch: [150][ 0/97] Loss 9.3514e-01 (9.3514e-01)
Epoch: [150][25/97] Loss 9.2957e-01 (9.2221e-01)
Epoch: [150][50/97] Loss 9.0890e-01 (9.2177e-01)
Epoch: [150][75/97] Loss 9.9036e-01 (9.1691e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.43
Checkpoint ...
[33mEpoch 151/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31662
Train ...
Epoch: [151][ 0/97] Loss 8.7085e-01 (8.7085e-01)
Epoch: [151][25/97] Loss 8.9445e-01 (9.2489e-01)
Epoch: [151][50/97] Loss 9.1162e-01 (9.3056e-01)
Epoch: [151][75/97] Loss 9.2406e-01 (9.3005e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.27
Checkpoint ...
[33mEpoch 152/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31560
Train ...
Epoch: [152][ 0/97] Loss 9.2364e-01 (9.2364e-01)
Epoch: [152][25/97] Loss 9.4423e-01 (9.2414e-01)
Epoch: [152][50/97] Loss 9.0903e-01 (9.1295e-01)
Epoch: [152][75/97] Loss 8.3105e-01 (9.1167e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.87
Checkpoint ...
[33mEpoch 153/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31457
Train ...
Epoch: [153][ 0/97] Loss 7.7787e-01 (7.7787e-01)
Epoch: [153][25/97] Loss 9.3628e-01 (8.9919e-01)
Epoch: [153][50/97] Loss 9.2748e-01 (9.1005e-01)
Epoch: [153][75/97] Loss 9.0282e-01 (9.1357e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.97
Checkpoint ...
[33mEpoch 154/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31354
Train ...
Epoch: [154][ 0/97] Loss 8.8913e-01 (8.8913e-01)
Epoch: [154][25/97] Loss 9.3558e-01 (9.0933e-01)
Epoch: [154][50/97] Loss 1.0039e+00 (9.1038e-01)
Epoch: [154][75/97] Loss 9.3214e-01 (9.1449e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.67
Checkpoint ...
[33mEpoch 155/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31250
Train ...
Epoch: [155][ 0/97] Loss 9.1408e-01 (9.1408e-01)
Epoch: [155][25/97] Loss 8.8685e-01 (9.0451e-01)
Epoch: [155][50/97] Loss 9.0528e-01 (9.1071e-01)
Epoch: [155][75/97] Loss 8.9819e-01 (9.0553e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.15
Checkpoint ...
[33mEpoch 156/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31146
Train ...
Epoch: [156][ 0/97] Loss 8.7275e-01 (8.7275e-01)
Epoch: [156][25/97] Loss 9.0546e-01 (9.0520e-01)
Epoch: [156][50/97] Loss 8.4339e-01 (8.9572e-01)
Epoch: [156][75/97] Loss 9.4538e-01 (8.9791e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 81.21
Checkpoint ...
[33mEpoch 157/500[0m
[33m---------------[0m
Adjusted learning rate to 0.31042
Train ...
Epoch: [157][ 0/97] Loss 8.7784e-01 (8.7784e-01)
Epoch: [157][25/97] Loss 8.4956e-01 (9.0100e-01)
Epoch: [157][50/97] Loss 9.2167e-01 (9.0712e-01)
Epoch: [157][75/97] Loss 8.3348e-01 (9.1074e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 81.11
Checkpoint ...
[33mEpoch 158/500[0m
[33m---------------[0m
Adjusted learning rate to 0.30937
Train ...
Epoch: [158][ 0/97] Loss 9.2775e-01 (9.2775e-01)
Epoch: [158][25/97] Loss 9.0234e-01 (9.0250e-01)
Epoch: [158][50/97] Loss 9.8943e-01 (9.1187e-01)
Epoch: [158][75/97] Loss 8.7739e-01 (9.1252e-01)
Fill memory bank for kNN...
Fill Memory Bank [0/98]
Evaluate ...
Result of kNN evaluation is 80.69
Checkpoint ...
[33mEpoch 159/500[0m
[33m---------------[0m
Adjusted learning rate to 0.30832
Train ...
Epoch: [159][ 0/97] Loss 8.6221e-01 (8.6221e-01)
Epoch: [159][25/97] Loss 8.5402e-01 (8.8936e-01)
Epoch: [159][50/97] Loss 8.1257e-01 (8.8501e-01)
Epoch: [159][75/97] Loss 9.1504e-01 (8.8650e-01)
Fill memory bank for kNN...
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Evaluate ...
Result of kNN evaluation is 80.38
Checkpoint ...
[33mEpoch 160/500[0m
[33m---------------[0m
Adjusted learning rate to 0.30726
Train ...
Epoch: [160][ 0/97] Loss 8.8926e-01 (8.8926e-01)
Epoch: [160][25/97] Loss 8.1692e-01 (8.8445e-01)
Epoch: [160][50/97] Loss 8.8181e-01 (8.8711e-01)
Epoch: [160][75/97] Loss 8.8501e-01 (8.8914e-01)
Fill memory bank for kNN...
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Evaluate ...
Result of kNN evaluation is 81.17
Checkpoint ...
[33mEpoch 161/500[0m
[33m---------------[0m
Adjusted learning rate to 0.30620
Train ...
Epoch: [161][ 0/97] Loss 8.6586e-01 (8.6586e-01)
Epoch: [161][25/97] Loss 9.2126e-01 (8.9250e-01)
Epoch: [161][50/97] Loss 8.8288e-01 (8.8730e-01)
Epoch: [161][75/97] Loss 8.5324e-01 (8.8631e-01)
Fill memory bank for kNN...
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Evaluate ...
Result of kNN evaluation is 80.85
Checkpoint ...
[33mEpoch 162/500[0m
[33m---------------[0m
Adjusted learning rate to 0.30513
Train ...