it is possible to change conv2d's padding to zero?

[cxmscb]

After changing the padding of conv2d to zero, error occurred below：

File "fashion_mnist.py", line 59, in
args.alpha_grad, args.scatter_grad, l1_proj=args.l1_proj)
File "/home/songcb/convex_adversarial-master/examples/trainer.py", line 22, in train_robust
scatter_grad=scatter_grad)
File "/home/songcb/.local/lib/python3.6/site-packages/convex_adversarial-0.2-py3.6.egg/convex_adversarial/dual.py", line 182, in robust_loss
File "/home/songcb/.local/lib/python3.6/site-packages/convex_adversarial-0.2-py3.6.egg/convex_adversarial/dual.py", line 56, in init
File "/home/songcb/.local/lib/python3.6/site-packages/convex_adversarial-0.2-py3.6.egg/convex_adversarial/affine.py", line 46, in call
File "/home/songcb/.local/lib/python3.6/site-packages/convex_adversarial-0.2-py3.6.egg/convex_adversarial/affine.py", line 66, in forward
File "/home/songcb/.local/lib/python3.6/site-packages/torch/nn/functional.py", line 837, in linear
output = input.matmul(weight.t())
File "/home/songcb/.local/lib/python3.6/site-packages/torch/autograd/variable.py", line 386, in matmul
return torch.matmul(self, other)
File "/home/songcb/.local/lib/python3.6/site-packages/torch/functional.py", line 173, in matmul
return torch.mm(tensor1, tensor2)
RuntimeError: size mismatch at /pytorch/torch/lib/THC/generic/THCTensorMathBlas.cu:243

[riceric22]

Hey @cxmscb,

Yes, it should be possible, but there is likely a size mismatch bug due to the transpose operator needing an output padding, e.g. the same issue that comes up here: pytorch/pytorch#5057. I suspect this can be fixed by passing output_padding=1 to the transpose convolution, can you share what your stride length and input sizes are?

[cxmscb]

Thanks ! The stride of conv is 2, and the input size is 28x28.

[riceric22]

The quick and dirty way to fix this would be to add output_padding=1 to the transpose convolution call. So if you're using v0.2, on line 119 of convex_adversarial/affine.py (which it appears to be the case in your trace), change

        out = conv_transpose2d(x.contiguous(),  self.l.weight, 
                                 stride=self.l.stride,
                                 padding=self.l.padding)

to

        out = conv_transpose2d(x.contiguous(),  self.l.weight, 
                                 stride=self.l.stride,
                                 padding=self.l.padding, 
                                 output_padding=1)

Alternatively, if you're using the master branch, then the same line (and same fix) can be found on line 143 of convex_adversarial/dual_layers.py.

In the ideal case, we should calculate when the output padding is necessary given input size, stride length, and padding, but how to do this in general is not quite clear to me yet, so I will leave this issue open.

[cxmscb]

Thanks!!!