Pytorch maxpooling over channels dimension

问题

I was trying to build a cnn to with Pytorch, and had difficulty in maxpooling. I have taken the cs231n held by Stanford. As I recalled, maxpooling can be used as a dimensional deduction step, for example, I have this (1, 20, height, width) input ot max_pool2d (assuming my batch_size is 1). And if I use (1, 1) kernel, I want to get output like this: (1, 1, height, width), which means the kernel should be slide over the channel dimension. However, after checking the pytorch docs, it says the kernel slides over height and width. And thanks to @ImgPrcSng on Pytorch forum who told me to use max_pool3d, and it turned out worked well. But there is still a reshape operation between the output of the conv2d layer and the input of the max_pool3d layer. So it is hard to be aggregated into a nn.Sequential, so I wonder is there another way to do this?

回答1:

Would something like this work?

from torch.nn import MaxPool1D
import functional as F

class ChannelPool(MaxPool1D):
    def forward(self, input):
        n, c, w, h = input.size()
        input = input.view(n,c,w*h).permute(0,2,1)
        pooled =  F.max_pool1d(input, self.kernel_size, self.stride,
                        self.padding, self.dilation, self.ceil_mode,
                        self.return_indices)
        _, _, c = input.size()
        input = input.permute(0,2,1)
        return input.view(n,c,w,h)

来源：https://stackoverflow.com/questions/46562612/pytorch-maxpooling-over-channels-dimension