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?