How to convert (1,64,224,224) --> (1,64) using adaptive average pooling(Pytorch)?

Question

Is there any way in Pytorch to reduce dimensions of tensor in model?

Answer 1

Adaptive Average Pooling or in fact any typical pooling in Pytorch does not reduce the dimensions of a tensor. You can find all the types of poolings, Pytorch offers over here: https://pytorch.org/docs/master/nn.html#pooling-layers

I suggest to use this template code to try out different poolings and their affect on dimensions:

m = nn.AdaptiveAvgPool2d((5,7))
input = torch.randn(1, 64, 8, 9)
output = m(input)
print(output.size())

In order to reduce dimension in Pytorch models you can specify a block that does squeeze() to the tensor or even flattens the tensor with example_tensor.view(-1, x, y) for example.

Sarthak Jain

Answer 2

This code should work to compress (1,64,224,224) --> (1,64)

import torch
import torch.nn as nn
m = nn.AdaptiveAvgPool2d((1,1))
input = torch.randn(1, 64, 224, 224)
output = m(input).view(1,-1)
print(output.size())  #torch.Size([1, 64])