text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torchvision import models
  
  try:
      from torchvision.models.utils import load_state_dict_from_url
  except ImportError:
      from torch.utils.model_zoo import load_url as load_state_dict_from_url
  
  
  
  # Inception weights ported to Pytorch from
  # http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
  FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
  
  
  class InceptionV3(nn.Module):
      """Pretrained InceptionV3 network returning feature maps"""
  
      # Index of default block of inception to return,
      # corresponds to output of final average pooling
      def __init__(self,
                   resize_input=True,
                   normalize_input=False,
                   requires_grad=False):
          """Build pretrained InceptionV3
          Parameters
          ----------
          resize_input : bool
              If true, bilinearly resizes input to width and height 299 before
              feeding input to model. As the network without fully connected
              layers is fully convolutional, it should be able to handle inputs
              of arbitrary size, so resizing might not be strictly needed
          normalize_input : bool
              If true, scales the input from range (0, 1) to the range the
              pretrained Inception network expects, namely (-1, 1)
          requires_grad : bool
              If true, parameters of the model require gradients. Possibly useful
              for finetuning the network
          """
          super(InceptionV3, self).__init__()
  
          self.resize_input = resize_input
          self.normalize_input = normalize_input
          self.blocks = nn.ModuleList()
  
          state_dict, inception = fid_inception_v3()
  
          # Block 0: input to maxpool1
          block0 = [
              inception.Conv2d_1a_3x3,
              inception.Conv2d_2a_3x3,
              inception.Conv2d_2b_3x3,
              nn.MaxPool2d(kernel_size=3, stride=2)
          ]
          self.blocks.append(nn.Sequential(*block0))
  
          # Block 1: maxpool1 to maxpool2
          block1 = [
              inception.Conv2d_3b_1x1,
              inception.Conv2d_4a_3x3,
              nn.MaxPool2d(kernel_size=3, stride=2)
          ]
          self.blocks.append(nn.Sequential(*block1))
  
          # Block 2: maxpool2 to aux classifier
          block2 = [
              inception.Mixed_5b,
              inception.Mixed_5c,
              inception.Mixed_5d,
              inception.Mixed_6a,
              inception.Mixed_6b,
              inception.Mixed_6c,
              inception.Mixed_6d,
              inception.Mixed_6e,
          ]
          self.blocks.append(nn.Sequential(*block2))
  
          # Block 3: aux classifier to final avgpool
          block3 = [
              inception.Mixed_7a,
              inception.Mixed_7b,
              inception.Mixed_7c,
              nn.AdaptiveAvgPool2d(output_size=(1, 1))
          ]
          self.blocks.append(nn.Sequential(*block3))
  
          with torch.no_grad():
              self.fc = nn.Linear(2048, 1008, bias=False)
              self.fc.weight.copy_(state_dict['fc.weight'])
  
          for param in self.parameters():
              param.requires_grad = requires_grad
  
  
      def forward(self, inp):
          """Get Inception feature maps
          Parameters
          ----------
          inp : torch.autograd.Variable
              Input tensor of shape Bx3xHxW. Values are expected to be in
              range (0, 1)
          Returns
          -------
          List of torch.autograd.Variable, corresponding to the selected output
          block, sorted ascending by index
          """
          x = inp
          if self.resize_input:
              x = F.interpolate(x,
                                size=(299, 299),
                                mode='bilinear',
                                align_corners=False)
  
          if self.normalize_input:
              x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
  
          for idx, block in enumerate(self.blocks):
              x = block(x)
  
          x = F.dropout(x, training=False)
          x = torch.flatten(x, 1)
          logit = self.fc(x)
          return x, logit
  
  
  def fid_inception_v3():
      """Build pretrained Inception model for FID computation
      The Inception model for FID computation uses a different set of weights
      and has a slightly different structure than torchvision's Inception.
      This method first constructs torchvision's Inception and then patches the
      necessary parts that are different in the FID Inception model.
      """
      inception = models.inception_v3(num_classes=1008,
                                      aux_logits=False,
                                      pretrained=False)
  
      inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
      inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
      inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
      inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
      inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
      inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
      inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
      inception.Mixed_7b = FIDInceptionE_1(1280)
      inception.Mixed_7c = FIDInceptionE_2(2048)
      # inception.fc = nn.Linear(2048, 1008, bias=False)
  
      state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
      inception.load_state_dict(state_dict)
      return state_dict, inception
  
  
  class FIDInceptionA(models.inception.InceptionA):
      """InceptionA block patched for FID computation"""
      def __init__(self, in_channels, pool_features):
          super(FIDInceptionA, self).__init__(in_channels, pool_features)
  
      def forward(self, x):
          branch1x1 = self.branch1x1(x)
  
          branch5x5 = self.branch5x5_1(x)
          branch5x5 = self.branch5x5_2(branch5x5)
  
          branch3x3dbl = self.branch3x3dbl_1(x)
          branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
          branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
  
          # Patch: Tensorflow's average pool does not use the padded zero's in
          # its average calculation
          branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                                     count_include_pad=False)
          branch_pool = self.branch_pool(branch_pool)
  
          outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
          return torch.cat(outputs, 1)
  
  
  class FIDInceptionC(models.inception.InceptionC):
      """InceptionC block patched for FID computation"""
      def __init__(self, in_channels, channels_7x7):
          super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
  
      def forward(self, x):
          branch1x1 = self.branch1x1(x)
  
          branch7x7 = self.branch7x7_1(x)
          branch7x7 = self.branch7x7_2(branch7x7)
          branch7x7 = self.branch7x7_3(branch7x7)
  
          branch7x7dbl = self.branch7x7dbl_1(x)
          branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
          branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
          branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
          branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
  
          # Patch: Tensorflow's average pool does not use the padded zero's in
          # its average calculation
          branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                                     count_include_pad=False)
          branch_pool = self.branch_pool(branch_pool)
  
          outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
          return torch.cat(outputs, 1)
  
  
  class FIDInceptionE_1(models.inception.InceptionE):
      """First InceptionE block patched for FID computation"""
      def __init__(self, in_channels):
          super(FIDInceptionE_1, self).__init__(in_channels)
  
      def forward(self, x):
          branch1x1 = self.branch1x1(x)
  
          branch3x3 = self.branch3x3_1(x)
          branch3x3 = [
              self.branch3x3_2a(branch3x3),
              self.branch3x3_2b(branch3x3),
          ]
          branch3x3 = torch.cat(branch3x3, 1)
  
          branch3x3dbl = self.branch3x3dbl_1(x)
          branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
          branch3x3dbl = [
              self.branch3x3dbl_3a(branch3x3dbl),
              self.branch3x3dbl_3b(branch3x3dbl),
          ]
          branch3x3dbl = torch.cat(branch3x3dbl, 1)
  
          # Patch: Tensorflow's average pool does not use the padded zero's in
          # its average calculation
          branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                                     count_include_pad=False)
          branch_pool = self.branch_pool(branch_pool)
  
          outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
          return torch.cat(outputs, 1)
  
  
  class FIDInceptionE_2(models.inception.InceptionE):
      """Second InceptionE block patched for FID computation"""
      def __init__(self, in_channels):
          super(FIDInceptionE_2, self).__init__(in_channels)
  
      def forward(self, x):
          branch1x1 = self.branch1x1(x)
  
          branch3x3 = self.branch3x3_1(x)
          branch3x3 = [
              self.branch3x3_2a(branch3x3),
              self.branch3x3_2b(branch3x3),
          ]
          branch3x3 = torch.cat(branch3x3, 1)
  
          branch3x3dbl = self.branch3x3dbl_1(x)
          branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
          branch3x3dbl = [
              self.branch3x3dbl_3a(branch3x3dbl),
              self.branch3x3dbl_3b(branch3x3dbl),
          ]
          branch3x3dbl = torch.cat(branch3x3dbl, 1)
  
          # Patch: The FID Inception model uses max pooling instead of average
          # pooling. This is likely an error in this specific Inception
          # implementation, as other Inception models use average pooling here
          # (which matches the description in the paper).
          branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
          branch_pool = self.branch_pool(branch_pool)
  
          outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
          return torch.cat(outputs, 1)