Hi,
My test code is like this:
import torch


conv = torch.nn.Conv2d(3, 32, 3, 1, 1)
bn = torch.nn.BatchNorm2d(32)
conv.eval()
bn.eval()


fused = torch.nn.utils.fuse_conv_bn_eval(conv, bn)

inten = torch.randn(2, 3, 224, 224)

out1 = bn(conv(inten))
out2 = fused(inten)
print((out1 - out2).abs().sum())

The output is tensor(0.1754, grad_fn=<SumBackward0>). Why is there so large difference between the separate computation and fused computation, and how could I make the fussd version work like separate version?