Question about hook in ResNet50

[Bojue-Wang]

Here is the first few layer of the ResNet 50 in this implementation: ResNet50( (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) 0(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): ModuleList( (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) 1(relu1): ReLU(inplace=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) 2(relu2): ReLU(inplace=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) 3(relu3): 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) ) )

And here is the hook code from calculate_feature_maps.py:

elif args.arch == 'resnet_50':
    cov_layer = eval('model.maxpool')
    handler = cov_layer.register_forward_hook(get_feature_hook)
    inference()
    handler.remove()
    # ResNet50 per bottleneck
    for i in range(4):
        block = eval('model.layer%d' % (i + 1))
        for j in range(model.num_blocks[i]):
            cov_layer = block[j].relu1
            handler = cov_layer.register_forward_hook(get_feature_hook)
            inference()
            handler.remove()

            cov_layer = block[j].relu2
            handler = cov_layer.register_forward_hook(get_feature_hook)
            inference()
            handler.remove()

            cov_layer = block[j].relu3
            handler = cov_layer.register_forward_hook(get_feature_hook)
            inference()
            handler.remove()

            if j == 0:
                cov_layer = block[j].relu3
                handler = cov_layer.register_forward_hook(get_feature_hook)
                inference()
                handler.remove()

In each Bottleneck block, there is only one relu3, and in all first Bottleneck block of every layer, which specified by if j == 0: I suppose the special case j == 0 means the corresponding special operation about the downsampleing layer. Did I miss something or misunderstand something?

[Eclipsess]

Thank you for your valuable question. The branch for "if j == 0:" is to store the feature map from the downsampling layer. Actually, that feature map from the downsampling layer is the same as the output of relu3.