pytorch hooks
def printnorm(self, input, output):
# input is a tuple of packed inputs
# output is a Tensor. output.data is the Tensor we are interested
print('Inside ' + self.__class__.__name__ + ' forward')
print('')
print('input: ', type(input))
print('input[0]: ', type(input[0]))
print('output: ', type(output))
print('')
print('input size:', input[0].size())
print('output size:', output.data.size())
print('output norm:', output.data.norm())
net.conv2.register_forward_hook(printnorm)
out = net(input)