out_channels must be divisible by groups

Package:

torch

50580

Exception Class:

ValueError

Raise code

                 device=None,
                 dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super(_ConvNd, self).__init__()
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        valid_padding_strings = {'same', 'valid'}
        if isinstance(padding, str):
            if padding not in valid_padding_strings:
                raise ValueError(
                    "Invalid padding string {!r}, should be one of {}".format(
                        padding, valid_padding_strings))
            if padding == 'same' and any(s != 1 for s in stride):

🙏 Scream for help to Ukraine

Today, 2nd July 2022, Russia continues bombing and firing Ukraine. Don't trust Russia, they are bombing us and brazenly lying in same time they are not doing this 😠, civilians and children are dying too! We are screaming and asking exactly you to help us, we want to survive, our families, children, older ones.
Please spread the information, and ask your governemnt to stop Russia by any means. We promise to work extrahard after survival to make the world safer place for all.

Links to the raise (2)

https://github.com/pytorch/pytorch/blob/e56d3b023818f54553f2dc5d30b6b7aaf6b6a325/torch/nn/modules/conv.py#L86 https://github.com/pytorch/pytorch/blob/e56d3b023818f54553f2dc5d30b6b7aaf6b6a325/torch/nn/quantized/modules/conv.py#L53

Ways to fix

According to the torch.nn.Conv2d documentation, the in_channels and out_channels must both be divisible by groups. This particular error is raised due to the fact that out_channels i.e 33 is not divisible by groups, i.e 2.

Steps to reproduce the error:

Setup virtual environment

pip install --user pipenv

mkdir test_folder

cd test_folder

pipenv shell

Install pytorch

pipenv install torch

Sample code

from torch import nn
m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1),groups=2)
input = torch.randn(20, 16, 50, 100)
output = m(input)
print(output)

The error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-28-f2265d559eed> in <module>()
      1 from torch import nn
----> 2 m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1),groups=2)
      3 input = torch.randn(20, 16, 50, 100)
      4 output = m(input)
      5 print(output)

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, device, dtype)
    430         super(Conv2d, self).__init__(
    431             in_channels, out_channels, kernel_size_, stride_, padding_, dilation_,
--> 432             False, _pair(0), groups, bias, padding_mode, **factory_kwargs)
    433 
    434     def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, device, dtype)
     84             raise ValueError('in_channels must be divisible by groups')
     85         if out_channels % groups != 0:
---> 86             raise ValueError('out_channels must be divisible by groups')
     87         valid_padding_strings = {'same', 'valid'}
     88         if isinstance(padding, str):

ValueError: out_channels must be divisible by groups

Fix

The in_channels and out_channels are respectively 16 and 33. And the n_groups should be a common factor of both parameters. In other words both in_channels and out_channels should be both divisible by groups.

Here even though the value 2 is valid with respect to the in_channels but the out_channels is not divisible by 2.

Therefore groups should be set to 1.

from torch import nn
m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1),groups=1)
input = torch.randn(20, 16, 50, 100)
output = m(input)
print(output)

Output

tensor([[[[ 3.9506e-01,  1.6591e-01, -2.0387e-01,  ...,  4.1805e-02,
           -3.2882e-01,  3.6727e-01],
          [ 4.0608e-01,  9.7733e-02,  5.2039e-01,  ...,  3.5625e-02,
            4.5199e-01,  5.8352e-01],
          [ 1.4240e-01,  3.0349e-01,  7.5488e-01,  ..., -2.1551e-01,
            5.5937e-01,  3.1656e-01],
          ...,
          [-7.8479e-02, -1.4105e-01,  1.6713e-01,  ..., -4.0072e-01,
            1.4017e+00, -6.4047e-01],
          [-4.0632e-01,  2.1231

N.B. This is truncated output

Jul 03, 2021

kellemnegasi 30.0k

Add a possible fix

Please authorize to post fix