How to use groups parameter in PyTorch conv2d function

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I am trying to compute a per-channel gradient image in PyTorch. To do this, I want to perform a standard 2D convolution with a Sobel filter on each channel of an image. I am using the torch.nn.functional.conv2d function for this

In my minimum working example code below, I get an error:

import torch
import torch.nn.functional as F

filters = torch.autograd.Variable(torch.randn(1,1,3,3))
inputs = torch.autograd.Variable(torch.randn(1,3,10,10))
out = F.conv2d(inputs, filters, padding=1)

RuntimeError: Given groups=1, weight[1, 1, 3, 3], so expected input[1, 3, 10, 10] to have 1 channels, but got 3 channels instead

This suggests that groups need to be 3. However, when I make groups=3, I get a different error:

import torch
import torch.nn.functional as F

filters = torch.autograd.Variable(torch.randn(1,1,3,3))
inputs = torch.autograd.Variable(torch.randn(1,3,10,10))
out = F.conv2d(inputs, filters, padding=1, groups=3)

RuntimeError: invalid argument 4: out of range at /usr/local/src/pytorch/torch/lib/TH/generic/THTensor.c:440

When I check that code snippet in the THTensor class, it refers to a bunch of dimension checks, but I don't know where I'm going wrong.

What does this error mean? How can I perform my intended convolution with this conv2d function? I believe I am misunderstanding the groups parameter.

pytorch convolution
1 Answers

If you want to apply a per-channel convolution then your out-channel should be the same as your in-channel. This is expected, considering each of your input channels creates a separate output channel that it corresponds to.

In short, this will work

import torch
import torch.nn.functional as F

filters = torch.autograd.Variable(torch.randn(3,1,3,3))
inputs = torch.autograd.Variable(torch.randn(1,3,10,10))
out = F.conv2d(inputs, filters, padding=1, groups=3)

whereas, filters of size (2, 1, 3, 3) or (1, 1, 3, 3) will not work.

Additionally, you can also make your out-channel a multiple of in-channel. This works for instances where you want to have multiple convolution filters for each input channel.

However, This only makes sense if it is a multiple. If not, then pytorch falls back to its closest multiple, a number less than what you specified. This is once again expected behavior. For example a filter of size (4, 1, 3, 3) or (5, 1, 3, 3), will result in an out-channel of size 3.

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