Hi!
Afer a 3d convolution ,all of the reslut of the output are nan.
Here is the code in forward  function
            print("input",torch.isnan(input).any())
            
            conv = self.con3d1(input)

            print("conv1",torch.isnan(conv).any())

And the self.conv3d1 is
self.con3d1= nn.Conv3d(in_channels=2, out_channels=64, kernel_size=(6, 3, 3), padding=(2, 1, 1))
      

For your information, I pad the input tensor by hand (because torch don’t support “same padding”)
pad1 = torch.zeros((input.shape[0], 2, 1, 16, 8)).to(DEVICE)
input = torch.cat((input, pad1), dim=2)

Noted that the input data is absolutely clean and normalized.
Turns out it’s because the gradient is toooo large,so i implement  gradient clipping,then the problem sloved.
But I am wondering that why gradient explode would happend in pytorch?
I was trying to convert a keras code into a pytorch code, and the same 3d convolution layer in keras was ran perfectly.
conv = Conv3D(filters=64, kernel_size=(6, 3, 3), strides=(1, 1, 1), border_mode="same",
                      kernel_initializer='random_uniform')(input)

Any suggestion is helpful !
If you want to know more  detailed information please let me konw!