question about the MACs of nn.BatchNorm2d

[DaMiBear]

Hi, happy new year!
I'm confused about the method of calculating nn.BatchNorm2d MACs.

pytorch-OpCounter/thop/vision/basic_hooks.py

Lines 60 to 69 in 43c064a

	def count_normalization(m: nn.modules.batchnorm._BatchNorm, x, y):
	# TODO: add test cases
	# https://github.com/Lyken17/pytorch-OpCounter/issues/124
	# y = (x - mean) / sqrt(eps + var) * weight + bias
	x = x[0]
	# bn is by default fused in inference
	flops = calculate_norm(x.numel())
	if (getattr(m, 'affine', False) or getattr(m, 'elementwise_affine', False)):
	flops *= 2
	m.total_ops += flops

pytorch-OpCounter/thop/vision/calc_func.py

Lines 43 to 45 in 43c064a

	def calculate_norm(input_size):
	"""input is a number not a array or tensor"""
	return torch.DoubleTensor([2 * input_size])

In my opinion: in calculate_norm(input_size), the 2 * input_size already means the MACs of subtract(mean), divide(var), mul(weight) and add(bias). But why is the flops(w.r.t MACs) multiplied by 2 again in the next?