Hi
I have a question about the output of backward hook on the Mixed_6e.branch1x1.conv layer of pytorch pre-trained inception_v3.
(Mixed_6e): InceptionC(
(branch1x1): BasicConv2d(
(conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
…
)
I registered forward and backward hook on this layer. As a result:
The shape of module_out in forward hook is [5,192,17,17] which I thinks is the activation maps of 5 images passing through this layer. However, the output of backward is not by any means clear for me. And I really appreciate it if anyone could please explain to me.
The out put of module_in of backward hook:
len of module_in  3
shape of inside module_in torch.Size([5, 768, 17, 17])
shape of inside module_in torch.Size([192, 768, 1, 1])
None found for Gradient
The output of module_out of hook:
shape of inside module_out  torch.Size([5, 192, 17, 17])