Order of computations in ResNet blocks

[Paandaman]

basenet/examples/cifar/cifar10.py

Lines 118 to 122 in c61f558

	out = F.relu(self.bn1(x))
	shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
	out = self.conv1(out)
	out = self.conv2(F.relu(self.bn2(out)))
	return out + shortcut

What is the motivation behind computing the batch norm and relu before sending the data into the convolutional layer?

In the implementation done by https://github.com/kuangliu/pytorch-cifar, the computation is done in the following order which seems more conventional, so I am curious why it is changed!

out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
shortcut = self.shortcut(x) if hasattr(self, 'shortcut') else x
out += shortcut
out = F.relu(out)