CODEWITHSUNDEEP
pythonmachine-learningdeep-learningpytorchcomputer-vision
z1yloo
z1yloo

Reputation: 23

Why conv2d yields different results with different batch size

I feed the conv2d with the same data but different batch size (using stack) as input:

a = torch.rand(1, 512, 16, 16)  # (1, 512, 16, 16)
b = torch.cat([a, a, a], dim=0) # (3, 512, 16, 16)

a, b = a.cuda(), b.cuda()

net = nn.Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
net = net.cuda()

ay = net(a)
by = net(b)

print('ay[0], by[0] max diff', torch.max(torch.abs(ay[0] - by[0])).item())
print('ay[0], by[0] allclose', torch.allclose(ay[0], by[0]))

the result is however different:

ay[0], by[0] diff 3.5762786865234375e-06
ay[0], by[0] allclose False

this problem is tested on Linux + V100 + torch1.9.0 + cu111, but so far many other configuration also seen such problem. Any clue why? Or it is just simply I misunderstand how conv2d should work?

I run into this problem when I validate my trainset result using batch size 1, but it is significantly different from the error I recorded from training process, so I checked for it and find that it is the conv2d layer that causes this problem. If I understand conv2d correctly, this should not be happening.

Upvotes: 2

Views: 383

Answers (1)

i regular
i regular

Reputation: 583

As far as I know the problem is not specific to con2d operations, but rather do to a limited floating point precision which can vary depending on the operations and architecture. This is a known issue, see e.g. this discussion on the pytorch-forum.

The GPU calculations you are currently running is probably using single-precision float computations, if you set it to be double-precision the error discrepancies should be reduced:

torch.set_default_tensor_type(torch.DoubleTensor)

meaning that:

print('ay[0], by[0] allclose', torch.allclose(ay[0], by[0], atol=1e-6))

Should print:

ay[0], by[0] allclose True

At least this is the case for me when testing also on Linux using an A100.

Upvotes: 2

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