Why the output turns into “nan“ Afer a 3d convolution?

Afer a 3d convolution ,all of the reslut of the output are nan.
Here is the code in forward function

            conv = self.con3d1(input)


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",

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

I have finally solved this bug.The reason is nothing about the 3d convolution!

I’m curious to know what the reason for the NaN outputs was. Could you share the debugging steps and solution?

Thanks sir! The reason is I didn’t initialize parameters of a special layer.