text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  import torch
  from torch import nn
  
  
  class FrozenBatchNorm2d(nn.Module):
      """
      BatchNorm2d where the batch statistics and the affine parameters
      are fixed
      """
  
      def __init__(self, n):
          super(FrozenBatchNorm2d, self).__init__()
          self.register_buffer("weight", torch.ones(n))
          self.register_buffer("bias", torch.zeros(n))
          self.register_buffer("running_mean", torch.zeros(n))
          self.register_buffer("running_var", torch.ones(n))
          self.register_buffer("eps", torch.ones(n)/1e5)
  
      def forward(self, x):
          scale = self.weight * (self.running_var+self.eps).rsqrt()
          bias = self.bias - self.running_mean * scale
          scale = scale.reshape(1, -1, 1, 1)
          bias = bias.reshape(1, -1, 1, 1)
          return x * scale + bias
  