在 PyTorch 中使用转置卷积进行上采样

Question

I have a 4D tensor of (2,1024,4,6) . I want to use transposed convolution for upsampling spatial dimensions of such tensor by factor of two and reducing the channel numbers from 1024 to the 512. I want to have a 4D tensor like this (2,512,8,12) . How can I do that? Also, is the transposed convolution a good idea for reducing the channel numbers? For example I used the following script but it is not working:

nn.ConvTranspose3d(in_channels=1024, out_channels=512, kernel_size=(1,2,2), stride=(1,3,2), padding=(0,1,1))

Answer 1

It seems you should be using ConvTranspose2d instead of ConvTranspose3d since your input tensor is 4D, shaped NCHW . There are different ways of getting to these results but one straightforward approach is to use a kernel size of 2 with a matching stride:

>>> conv = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)

Here is an inference example:

>>> conv(torch.rand(2, 1024, 4, 6)).shape
torch.Size([2, 512, 8, 12])