text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torchvision
  
  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'  # noqa: E501
  
  
  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
      DEFAULT_BLOCK_INDEX = 3
  
      # Maps feature dimensionality to their output blocks indices
      BLOCK_INDEX_BY_DIM = {
          64: 0,   # First max pooling features
          192: 1,  # Second max pooling featurs
          768: 2,  # Pre-aux classifier features
          2048: 3  # Final average pooling features
      }
  
      def __init__(self,
                   output_blocks=(DEFAULT_BLOCK_INDEX,),
                   resize_input=True,
                   normalize_input=True,
                   requires_grad=False,
                   use_fid_inception=True):
          """Build pretrained InceptionV3
          Parameters
          ----------
          output_blocks : list of int
              Indices of blocks to return features of. Possible values are:
                  - 0: corresponds to output of first max pooling
                  - 1: corresponds to output of second max pooling
                  - 2: corresponds to output which is fed to aux classifier
                  - 3: corresponds to output of final average pooling
          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
          use_fid_inception : bool
              If true, uses the pretrained Inception model used in Tensorflow's
              FID implementation. If false, uses the pretrained Inception model
              available in torchvision. The FID Inception model has different
              weights and a slightly different structure from torchvision's
              Inception model. If you want to compute FID scores, you are
              strongly advised to set this parameter to true to get comparable
              results.
          """
          super(InceptionV3, self).__init__()
  
          self.resize_input = resize_input
          self.normalize_input = normalize_input
          self.output_blocks = sorted(output_blocks)
          self.last_needed_block = max(output_blocks)
  
          assert self.last_needed_block <= 3, \
              'Last possible output block index is 3'
  
          self.blocks = nn.ModuleList()
  
          if use_fid_inception:
              inception = fid_inception_v3()
          else:
              inception = _inception_v3(pretrained=True)
  
          # 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
          if self.last_needed_block >= 1:
              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
          if self.last_needed_block >= 2:
              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
          if self.last_needed_block >= 3:
              block3 = [
                  inception.Mixed_7a,
                  inception.Mixed_7b,
                  inception.Mixed_7c,
                  nn.AdaptiveAvgPool2d(output_size=(1, 1))
              ]
              self.blocks.append(nn.Sequential(*block3))
  
          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
          """
          outp = []
          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)
              if idx in self.output_blocks:
                  outp.append(x)
  
              if idx == self.last_needed_block:
                  break
  
          return outp
  
  
  def _inception_v3(*args, **kwargs):
      """Wraps `torchvision.models.inception_v3`
      Skips default weight inititialization if supported by torchvision version.
      See https://github.com/mseitzer/pytorch-fid/issues/28.
      """
      try:
          version = tuple(map(int, torchvision.__version__.split('.')[:2]))
      except ValueError:
          # Just a caution against weird version strings
          version = (0,)
  
      if version >= (0, 6):
          kwargs['init_weights'] = False
  
      return torchvision.models.inception_v3(*args, **kwargs)
  
  
  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 = _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)
  
      state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
      inception.load_state_dict(state_dict)
      return inception
  
  
  class FIDInceptionA(torchvision.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(torchvision.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(torchvision.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(torchvision.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)
  