text: # 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__()
  
      def forward(self, x, w0, w1, b1):
          x = F.conv2d(x, w0, None, stride=(2,2), padding=(1,1))
          if torch.__version__ < '1.9':
              x = F.conv2d(x, w1, b1, stride=(1,1), padding=(1,1), dilation=(2,1), groups=2)
          else:
              x = F.conv2d(x, w1, b1, stride=(1,1), padding='same', dilation=(2,1), groups=2)
          return x
  
  def test():
      net = Model()
      net.eval()
  
      torch.manual_seed(0)
      x = torch.rand(1, 12, 52, 64)
      w0 = torch.rand(16, 12, 3, 3)
      w1 = torch.rand(16, 8, 5, 5)
      b1 = torch.rand(16)
  
      a = net(x, w0, w1, b1)
  
      # export torchscript
      mod = torch.jit.trace(net, (x, w0, w1, b1))
      mod.save("test_F_conv2d.pt")
  
      # torchscript to pnnx
      import os
      os.system("../../src/pnnx test_F_conv2d.pt inputshape=[1,12,52,64],[16,12,3,3],[16,8,5,5],[16]")
  
      # ncnn inference
      import test_F_conv2d_ncnn
      b = test_F_conv2d_ncnn.test_inference()
  
      return torch.allclose(a, b, 1e-4, 1e-4)
  
  if __name__ == "__main__":
      if test():
          exit(0)
      else:
          exit(1)
  