[fix] Fix Conv2d_BN fuse bug when groups > 1

[wkcn]

Hi there, thanks for your great work : )

I found there is a bug when fusing Conv2d_BN with groups > 1. The reason is that the input channel should be w.size(1) * self.c.groups rather than w.size(1) in the function Conv2d_BN.fuse.

Reproduce Code:

from levit import Conv2d_BN
from levit_c import Conv2d_BN as Conv2d_BN_c
import torch
import numpy as np
from itertools import product
import utils

@torch.no_grad()
def test():
    for layer_t, a, b, ks, groups in product(
        [Conv2d_BN, Conv2d_BN_c],
        [8, 16, 32, 64],
        [8, 16, 32, 64],
        [1, 3, 5, 7],
        [1, 2, 4],
            ):
        layer = layer_t(a, b, ks, pad=ks//2, groups=groups)
        layer.eval()

        x = torch.randn((1, a, 16, 16))
        y1 = layer(x)
        utils.replace_batchnorm(layer)
        y2 = layer(x)

        np.testing.assert_almost_equal(y1.detach().numpy(), y2.detach().numpy(), decimal=4)

if __name__ == '__main__':
    test()
    print("Test Over")

Error:

  File "test_conv.py", line 21, in test
    layer.fuse()
  File "/home/wkcn/miniconda3/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/wkcn/proj/LeViT-1/levit_c.py", line 99, in fuse
    m.weight.data.copy_(w)
RuntimeError: The size of tensor a (2) must match the size of tensor b (4) at non-singleton dimension 1

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[btgraham]

Thanks for the fix.