code: # Tencent is pleased to support the open source community by making ncnn available.
  #
  # Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
  #
  # Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  # in compliance with the License. You may obtain a copy of the License at
  #
  # https://opensource.org/licenses/BSD-3-Clause
  #
  # Unless required by applicable law or agreed to in writing, software distributed
  # under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
  # CONDITIONS OF ANY KIND, either express or implied. See the License for the
  # specific language governing permissions and limitations under the License.
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class Model(nn.Module):
      def __init__(self):
          super(Model, self).__init__()
  
          self.bn_0 = nn.BatchNorm2d(num_features=32)
          self.bn_1 = nn.BatchNorm2d(num_features=32, eps=1e-1, affine=False)
          self.bn_2 = nn.BatchNorm2d(num_features=11, affine=True)
  
      def forward(self, x, y):
          x = self.bn_0(x)
          x = self.bn_1(x)
  
          y = self.bn_2(y)
  
          return x, y
  
  def test():
      net = Model()
      net.eval()
  
      torch.manual_seed(0)
      x = torch.rand(1, 32, 12, 64)
      y = torch.rand(1, 11, 1, 1)
  
      a0, a1 = net(x, y)
  
      # export torchscript
      mod = torch.jit.trace(net, (x, y))
      mod.save("test_nn_BatchNorm2d.pt")
  
      # torchscript to pnnx
      import os
      os.system("../src/pnnx test_nn_BatchNorm2d.pt inputshape=[1,32,12,64],[1,11,1,1]")
  
      # pnnx inference
      import test_nn_BatchNorm2d_pnnx
      b0, b1 = test_nn_BatchNorm2d_pnnx.test_inference()
  
      return torch.equal(a0, b0) and torch.equal(a1, b1)
  
  if __name__ == "__main__":
      if test():
          exit(0)
      else:
          exit(1)
  
apis: torch.nn.BatchNorm2d torch.manual_seed torch.rand torch.equal torch.jit.trace