layer3.3.bn1.running_mean
layer3.3.bn1.running_var
layer3.3.bn1.num_batches_tracked
layer3.3.conv2.weight
layer3.3.bn2.weight
layer3.3.bn2.bias
layer3.3.bn2.running_mean
layer3.3.bn2.running_var
layer3.3.bn2.num_batches_tracked
layer3.3.conv3.weight
layer3.3.bn3.weight
layer3.3.bn3.bias
layer3.3.bn3.running_mean
layer3.3.bn3.running_var
layer3.3.bn3.num_batches_tracked
layer3.4.conv1.weight
layer3.4.bn1.weight
layer3.4.bn1.bias
layer3.4.bn1.running_mean
layer3.4.bn1.running_var
layer3.4.bn1.num_batches_tracked
layer3.4.conv2.weight
layer3.4.bn2.weight
layer3.4.bn2.bias
layer3.4.bn2.running_mean
layer3.4.bn2.running_var
layer3.4.bn2.num_batches_tracked
layer3.4.conv3.weight
layer3.4.bn3.weight
layer3.4.bn3.bias
layer3.4.bn3.running_mean
layer3.4.bn3.running_var
layer3.4.bn3.num_batches_tracked
layer3.5.conv1.weight
layer3.5.bn1.weight
layer3.5.bn1.bias
layer3.5.bn1.running_mean
layer3.5.bn1.running_var
layer3.5.bn1.num_batches_tracked
layer3.5.conv2.weight
layer3.5.bn2.weight
layer3.5.bn2.bias
layer3.5.bn2.running_mean
layer3.5.bn2.running_var
layer3.5.bn2.num_batches_tracked
layer3.5.conv3.weight
layer3.5.bn3.weight
layer3.5.bn3.bias
layer3.5.bn3.running_mean
layer3.5.bn3.running_var
layer3.5.bn3.num_batches_tracked
layer4.0.conv1.weight
layer4.0.bn1.weight
layer4.0.bn1.bias
layer4.0.bn1.running_mean
layer4.0.bn1.running_var
layer4.0.bn1.num_batches_tracked
layer4.0.conv2.weight
layer4.0.bn2.weight
layer4.0.bn2.bias
layer4.0.bn2.running_mean
layer4.0.bn2.running_var
layer4.0.bn2.num_batches_tracked
layer4.0.conv3.weight
layer4.0.bn3.weight
layer4.0.bn3.bias
layer4.0.bn3.running_mean
layer4.0.bn3.running_var
layer4.0.bn3.num_batches_tracked
layer4.0.downsample.0.weight
layer4.0.downsample.1.weight
layer4.0.downsample.1.bias
layer4.0.downsample.1.running_mean
layer4.0.downsample.1.running_var
layer4.0.downsample.1.num_batches_tracked
layer4.1.conv1.weight
layer4.1.bn1.weight
layer4.1.bn1.bias
layer4.1.bn1.running_mean
layer4.1.bn1.running_var
layer4.1.bn1.num_batches_tracked
layer4.1.conv2.weight
layer4.1.bn2.weight
layer4.1.bn2.bias
layer4.1.bn2.running_mean
layer4.1.bn2.running_var
layer4.1.bn2.num_batches_tracked
layer4.1.conv3.weight
layer4.1.bn3.weight
layer4.1.bn3.bias
layer4.1.bn3.running_mean
layer4.1.bn3.running_var
layer4.1.bn3.num_batches_tracked
layer4.2.conv1.weight
layer4.2.bn1.weight
layer4.2.bn1.bias
layer4.2.bn1.running_mean
layer4.2.bn1.running_var
layer4.2.bn1.num_batches_tracked
layer4.2.conv2.weight
layer4.2.bn2.weight
layer4.2.bn2.bias
layer4.2.bn2.running_mean
layer4.2.bn2.running_var
layer4.2.bn2.num_batches_tracked
layer4.2.conv3.weight
layer4.2.bn3.weight
layer4.2.bn3.bias
layer4.2.bn3.running_mean
layer4.2.bn3.running_var
layer4.2.bn3.num_batches_tracked
fc.weight
fc.bias
Classification model:
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(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=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): 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): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(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=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(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=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(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)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=2048, out_features=200, bias=True)
)
Selected VGG configuration (vgg11) was loaded from checkpoint: ./results/cub/wsol_method_PSOL/trained_on_trainval_split_evaluated_on_test_split/arch_vgg11_pretrained_init_normalization_none_seed_16/model.pth
FALcon (localization) model:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(12): ReLU(inplace=True)
(13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(14): ReLU(inplace=True)
(15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(17): ReLU(inplace=True)
(18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(19): ReLU(inplace=True)
(20): AdaptiveAvgPool2d(output_size=(1, 1))
)
(fovea_control): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): ReLU(inplace=True)
(2): Linear(in_features=256, out_features=128, bias=True)
(3): ReLU(inplace=True)
(4): Linear(in_features=128, out_features=4, bias=True)
)
(saccade_control): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): ReLU(inplace=True)
(2): Linear(in_features=256, out_features=1, bias=True)
)
)
/home/min/a/tibrayev/miniconda3/envs/torch_1.9_torchvision_10.0/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /opt/conda/conda-bld/pytorch_1623448278899/work/c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
100/5794 images: Top-1 Cls: 78.0000 [78/100] | GT Loc: 82.0000 [82/100] | Top-1 Loc: 67.0000 [67/100]
200/5794 images: Top-1 Cls: 77.5000 [155/200] | GT Loc: 84.0000 [168/200] | Top-1 Loc: 65.5000 [131/200]
300/5794 images: Top-1 Cls: 74.0000 [222/300] | GT Loc: 84.3333 [253/300] | Top-1 Loc: 63.3333 [190/300]
400/5794 images: Top-1 Cls: 77.0000 [308/400] | GT Loc: 85.7500 [343/400] | Top-1 Loc: 67.0000 [268/400]
500/5794 images: Top-1 Cls: 78.2000 [391/500] | GT Loc: 86.6000 [433/500] | Top-1 Loc: 68.6000 [343/500]
600/5794 images: Top-1 Cls: 76.5000 [459/600] | GT Loc: 85.0000 [510/600] | Top-1 Loc: 66.3333 [398/600]
700/5794 images: Top-1 Cls: 75.1429 [526/700] | GT Loc: 85.8571 [601/700] | Top-1 Loc: 65.4286 [458/700]
800/5794 images: Top-1 Cls: 72.1250 [577/800] | GT Loc: 86.2500 [690/800] | Top-1 Loc: 62.7500 [502/800]
900/5794 images: Top-1 Cls: 71.8889 [647/900] | GT Loc: 83.4444 [751/900] | Top-1 Loc: 60.6667 [546/900]
1000/5794 images: Top-1 Cls: 72.7000 [727/1000] | GT Loc: 83.4000 [834/1000] | Top-1 Loc: 61.3000 [613/1000]
1100/5794 images: Top-1 Cls: 70.2727 [773/1100] | GT Loc: 83.4545 [918/1100] | Top-1 Loc: 59.2727 [652/1100]
1200/5794 images: Top-1 Cls: 70.7500 [849/1200] | GT Loc: 83.0833 [997/1200] | Top-1 Loc: 59.5833 [715/1200]
1300/5794 images: Top-1 Cls: 70.6923 [919/1300] | GT Loc: 83.6154 [1087/1300] | Top-1 Loc: 59.8462 [778/1300]
1400/5794 images: Top-1 Cls: 70.6429 [989/1400] | GT Loc: 83.7857 [1173/1400] | Top-1 Loc: 59.9286 [839/1400]
1500/5794 images: Top-1 Cls: 71.6000 [1074/1500] | GT Loc: 83.8667 [1258/1500] | Top-1 Loc: 60.6667 [910/1500]
1600/5794 images: Top-1 Cls: 72.6875 [1163/1600] | GT Loc: 84.1875 [1347/1600] | Top-1 Loc: 61.7500 [988/1600]
1700/5794 images: Top-1 Cls: 71.4706 [1215/1700] | GT Loc: 84.7059 [1440/1700] | Top-1 Loc: 61.0000 [1037/1700]
1800/5794 images: Top-1 Cls: 71.0556 [1279/1800] | GT Loc: 85.2222 [1534/1800] | Top-1 Loc: 61.0000 [1098/1800]
1900/5794 images: Top-1 Cls: 70.6316 [1342/1900] | GT Loc: 85.7368 [1629/1900] | Top-1 Loc: 60.9474 [1158/1900]
2000/5794 images: Top-1 Cls: 70.5000 [1410/2000] | GT Loc: 85.9000 [1718/2000] | Top-1 Loc: 60.9500 [1219/2000]
2100/5794 images: Top-1 Cls: 70.9048 [1489/2100] | GT Loc: 85.8571 [1803/2100] | Top-1 Loc: 61.2857 [1287/2100]
2200/5794 images: Top-1 Cls: 71.3182 [1569/2200] | GT Loc: 86.0000 [1892/2200] | Top-1 Loc: 61.8636 [1361/2200]
2300/5794 images: Top-1 Cls: 71.6522 [1648/2300] | GT Loc: 86.0000 [1978/2300] | Top-1 Loc: 62.2609 [1432/2300]
2400/5794 images: Top-1 Cls: 72.1667 [1732/2400] | GT Loc: 86.2083 [2069/2400] | Top-1 Loc: 62.7917 [1507/2400]
2500/5794 images: Top-1 Cls: 72.5600 [1814/2500] | GT Loc: 86.2800 [2157/2500] | Top-1 Loc: 63.1600 [1579/2500]
2600/5794 images: Top-1 Cls: 72.1923 [1877/2600] | GT Loc: 86.1923 [2241/2600] | Top-1 Loc: 63.0385 [1639/2600]
2700/5794 images: Top-1 Cls: 72.6296 [1961/2700] | GT Loc: 86.4074 [2333/2700] | Top-1 Loc: 63.6296 [1718/2700]
2800/5794 images: Top-1 Cls: 72.5357 [2031/2800] | GT Loc: 86.6071 [2425/2800] | Top-1 Loc: 63.6429 [1782/2800]
2900/5794 images: Top-1 Cls: 72.5172 [2103/2900] | GT Loc: 86.6207 [2512/2900] | Top-1 Loc: 63.6552 [1846/2900]
3000/5794 images: Top-1 Cls: 71.9000 [2157/3000] | GT Loc: 86.7667 [2603/3000] | Top-1 Loc: 63.2000 [1896/3000]
3100/5794 images: Top-1 Cls: 71.9677 [2231/3100] | GT Loc: 86.7419 [2689/3100] | Top-1 Loc: 63.2258 [1960/3100]
3200/5794 images: Top-1 Cls: 72.0312 [2305/3200] | GT Loc: 86.9062 [2781/3200] | Top-1 Loc: 63.3438 [2027/3200]
3300/5794 images: Top-1 Cls: 72.0606 [2378/3300] | GT Loc: 87.0000 [2871/3300] | Top-1 Loc: 63.4848 [2095/3300]
3400/5794 images: Top-1 Cls: 71.4412 [2429/3400] | GT Loc: 87.2647 [2967/3400] | Top-1 Loc: 63.0588 [2144/3400]
3500/5794 images: Top-1 Cls: 71.4000 [2499/3500] | GT Loc: 87.3143 [3056/3500] | Top-1 Loc: 63.0000 [2205/3500]
3600/5794 images: Top-1 Cls: 70.8611 [2551/3600] | GT Loc: 87.3611 [3145/3600] | Top-1 Loc: 62.4167 [2247/3600]
3700/5794 images: Top-1 Cls: 70.4595 [2607/3700] | GT Loc: 87.5135 [3238/3700] | Top-1 Loc: 62.1351 [2299/3700]
3800/5794 images: Top-1 Cls: 70.2105 [2668/3800] | GT Loc: 87.6316 [3330/3800] | Top-1 Loc: 61.9737 [2355/3800]
3900/5794 images: Top-1 Cls: 70.3846 [2745/3900] | GT Loc: 87.5128 [3413/3900] | Top-1 Loc: 62.0769 [2421/3900]
4000/5794 images: Top-1 Cls: 70.5250 [2821/4000] | GT Loc: 87.6250 [3505/4000] | Top-1 Loc: 62.2750 [2491/4000]
4100/5794 images: Top-1 Cls: 70.2439 [2880/4100] | GT Loc: 87.7073 [3596/4100] | Top-1 Loc: 62.0732 [2545/4100]
4200/5794 images: Top-1 Cls: 69.6429 [2925/4200] | GT Loc: 87.6190 [3680/4200] | Top-1 Loc: 61.5476 [2585/4200]
4300/5794 images: Top-1 Cls: 69.6047 [2993/4300] | GT Loc: 87.5814 [3766/4300] | Top-1 Loc: 61.4651 [2643/4300]
4400/5794 images: Top-1 Cls: 69.6136 [3063/4400] | GT Loc: 87.6364 [3856/4400] | Top-1 Loc: 61.5227 [2707/4400]
4500/5794 images: Top-1 Cls: 69.1778 [3113/4500] | GT Loc: 87.8000 [3951/4500] | Top-1 Loc: 61.2000 [2754/4500]
4600/5794 images: Top-1 Cls: 69.4565 [3195/4600] | GT Loc: 87.8478 [4041/4600] | Top-1 Loc: 61.5435 [2831/4600]
4700/5794 images: Top-1 Cls: 69.5745 [3270/4700] | GT Loc: 87.6170 [4118/4700] | Top-1 Loc: 61.4894 [2890/4700]
4800/5794 images: Top-1 Cls: 69.8958 [3355/4800] | GT Loc: 87.7708 [4213/4800] | Top-1 Loc: 61.9167 [2972/4800]
4900/5794 images: Top-1 Cls: 70.1020 [3435/4900] | GT Loc: 87.7551 [4300/4900] | Top-1 Loc: 62.1020 [3043/4900]
5000/5794 images: Top-1 Cls: 69.9600 [3498/5000] | GT Loc: 87.7400 [4387/5000] | Top-1 Loc: 62.0000 [3100/5000]
5100/5794 images: Top-1 Cls: 69.9804 [3569/5100] | GT Loc: 87.8431 [4480/5100] | Top-1 Loc: 62.0784 [3166/5100]
5200/5794 images: Top-1 Cls: 70.0000 [3640/5200] | GT Loc: 87.8654 [4569/5200] | Top-1 Loc: 62.1346 [3231/5200]
5300/5794 images: Top-1 Cls: 69.7170 [3695/5300] | GT Loc: 88.0189 [4665/5300] | Top-1 Loc: 61.9623 [3284/5300]
5400/5794 images: Top-1 Cls: 70.1667 [3789/5400] | GT Loc: 88.1296 [4759/5400] | Top-1 Loc: 62.4444 [3372/5400]
5500/5794 images: Top-1 Cls: 70.4727 [3876/5500] | GT Loc: 88.1273 [4847/5500] | Top-1 Loc: 62.7091 [3449/5500]
5600/5794 images: Top-1 Cls: 70.5000 [3948/5600] | GT Loc: 88.1071 [4934/5600] | Top-1 Loc: 62.7679 [3515/5600]
5700/5794 images: Top-1 Cls: 70.4561 [4016/5700] | GT Loc: 88.2807 [5032/5700] | Top-1 Loc: 62.8421 [3582/5700]
5794/5794 images: Top-1 Cls: 70.4695 [4083/5794] | GT Loc: 88.2982 [5116/5794] | Top-1 Loc: 62.8409 [3641/5794]
TEST (WSOL) STATS: Top-1 Cls: 70.4695 [4083/5794] | GT Loc: 88.2982 [5116/5794] | Top-1 Loc: 62.8409 [3641/5794]
In [2]: config_3_copy
Out[2]:
{'seed': 16,
'dataset': 'cub',
'dataset_dir': '/home/nano01/a/tibrayev/CUB_200-2011_raw',
'num_classes': 200,
'in_num_channels': 3,
'full_res_img_size': (256, 256),
'correct_imbalance': False,
'selected_attributes': ['all'],
'num_attributes': 312,
'gt_bbox_dir': None,
'wsol_method': 'PSOL',
'pseudo_bbox_dir': '../PSOL/results/CUB_train_set/predicted_bounding_boxes/psol_predicted_bounding_boxes.txt',
'cls_model_name': 'resnet50',
'cls_pretrained': True,
'cls_ckpt_dir': '../PSOL/results/PSOL/CUB/checkpoint_classification_cub_ddt_resnet50_99.pth.tar',
'save_dir': './results/cub/wsol_method_PSOL/trained_on_trainval_split_evaluated_on_test_split/arch_vgg11_pretrained_init_normalization_none_seed_16/',
'model_name': 'vgg11',
'initialize': 'resume',
'ckpt_dir': './results/cub/wsol_method_PSOL/trained_on_trainval_split_evaluated_on_test_split/arch_vgg11_pretrained_init_normalization_none_seed_16/model.pth',
'loader_type': 'test',
'batch_size_eval': 50,
'init_factual': 'pretrained',
'downsampling': 'M',
'fc1': 256,
'fc2': 128,
'dropout': 0.5,
'norm': 'none',
'init_weights': True,
'adaptive_avg_pool_out': (1, 1),
'saccade_fc1': 256,
'saccade_dropout': False,
'num_glimpses': 16,
'fovea_control_neurons': 4,
'glimpse_size_grid': (40, 40),
'glimpse_size_init': (40, 40),
'glimpse_size_fixed': (96, 96),
'glimpse_size_step': (20, 20),
'glimpse_change_th': 0.5,
'iou_th': 0.5,
'ratio_wrong_init_glimpses': 0.5,
'switch_location_th': 0.2,
'objectness_based_nms_th': 0.5,
'confidence_based_nms_th': 0.5}
In [3]: