text: """utils.py - Helper functions for building the model and for loading model parameters.
     These helper functions are built to mirror those in the official TensorFlow implementation.
  """
  
  # Author: lukemelas (github username)
  # Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
  # With adjustments and added comments by workingcoder (github username).
  
  import re
  import math
  import collections
  from functools import partial
  import torch
  from torch import nn
  from torch.nn import functional as F
  from torch.utils import model_zoo
  
  
  ################################################################################
  # Help functions for model architecture
  ################################################################################
  
  # GlobalParams and BlockArgs: Two namedtuples
  # Swish and MemoryEfficientSwish: Two implementations of the method
  # round_filters and round_repeats:
  #     Functions to calculate params for scaling model width and depth ! ! !
  # get_width_and_height_from_size and calculate_output_image_size
  # drop_connect: A structural design
  # get_same_padding_conv2d:
  #     Conv2dDynamicSamePadding
  #     Conv2dStaticSamePadding
  # get_same_padding_maxPool2d:
  #     MaxPool2dDynamicSamePadding
  #     MaxPool2dStaticSamePadding
  #     It's an additional function, not used in EfficientNet,
  #     but can be used in other model (such as EfficientDet).
  
  # Parameters for the entire model (stem, all blocks, and head)
  GlobalParams = collections.namedtuple('GlobalParams', [
      'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate',
      'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon',
      'drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top'])
  
  # Parameters for an individual model block
  BlockArgs = collections.namedtuple('BlockArgs', [
      'num_repeat', 'kernel_size', 'stride', 'expand_ratio',
      'input_filters', 'output_filters', 'se_ratio', 'id_skip'])
  
  # Set GlobalParams and BlockArgs's defaults
  GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
  BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
  
  # Swish activation function
  if hasattr(nn, 'SiLU'):
      Swish = nn.SiLU
  else:
      # For compatibility with old PyTorch versions
      class Swish(nn.Module):
          def forward(self, x):
              return x * torch.sigmoid(x)
  
  
  # A memory-efficient implementation of Swish function
  class SwishImplementation(torch.autograd.Function):
      @staticmethod
      def forward(ctx, i):
          result = i * torch.sigmoid(i)
          ctx.save_for_backward(i)
          return result
  
      @staticmethod
      def backward(ctx, grad_output):
          i = ctx.saved_tensors[0]
          sigmoid_i = torch.sigmoid(i)
          return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
  
  
  class MemoryEfficientSwish(nn.Module):
      def forward(self, x):
          return SwishImplementation.apply(x)
  
  
  def round_filters(filters, global_params):
      """Calculate and round number of filters based on width multiplier.
         Use width_coefficient, depth_divisor and min_depth of global_params.
  
      Args:
          filters (int): Filters number to be calculated.
          global_params (namedtuple): Global params of the model.
  
      Returns:
          new_filters: New filters number after calculating.
      """
      multiplier = global_params.width_coefficient
      if not multiplier:
          return filters
      # TODO: modify the params names.
      #       maybe the names (width_divisor,min_width)
      #       are more suitable than (depth_divisor,min_depth).
      divisor = global_params.depth_divisor
      min_depth = global_params.min_depth
      filters *= multiplier
      min_depth = min_depth or divisor  # pay attention to this line when using min_depth
      # follow the formula transferred from official TensorFlow implementation
      new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
      if new_filters < 0.9 * filters:  # prevent rounding by more than 10%
          new_filters += divisor
      return int(new_filters)
  
  
  def round_repeats(repeats, global_params):
      """Calculate module's repeat number of a block based on depth multiplier.
         Use depth_coefficient of global_params.
  
      Args:
          repeats (int): num_repeat to be calculated.
          global_params (namedtuple): Global params of the model.
  
      Returns:
          new repeat: New repeat number after calculating.
      """
      multiplier = global_params.depth_coefficient
      if not multiplier:
          return repeats
      # follow the formula transferred from official TensorFlow implementation
      return int(math.ceil(multiplier * repeats))
  
  
  def drop_connect(inputs, p, training):
      """Drop connect.
  
      Args:
          input (tensor: BCWH): Input of this structure.
          p (float: 0.0~1.0): Probability of drop connection.
          training (bool): The running mode.
  
      Returns:
          output: Output after drop connection.
      """
      assert 0 <= p <= 1, 'p must be in range of [0,1]'
  
      if not training:
          return inputs
  
      batch_size = inputs.shape[0]
      keep_prob = 1 - p
  
      # generate binary_tensor mask according to probability (p for 0, 1-p for 1)
      random_tensor = keep_prob
      random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
      binary_tensor = torch.floor(random_tensor)
  
      output = inputs / keep_prob * binary_tensor
      return output
  
  
  def get_width_and_height_from_size(x):
      """Obtain height and width from x.
  
      Args:
          x (int, tuple or list): Data size.
  
      Returns:
          size: A tuple or list (H,W).
      """
      if isinstance(x, int):
          return x, x
      if isinstance(x, list) or isinstance(x, tuple):
          return x
      else:
          raise TypeError()
  
  
  def calculate_output_image_size(input_image_size, stride):
      """Calculates the output image size when using Conv2dSamePadding with a stride.
         Necessary for static padding. Thanks to mannatsingh for pointing this out.
  
      Args:
          input_image_size (int, tuple or list): Size of input image.
          stride (int, tuple or list): Conv2d operation's stride.
  
      Returns:
          output_image_size: A list [H,W].
      """
      if input_image_size is None:
          return None
      image_height, image_width = get_width_and_height_from_size(input_image_size)
      stride = stride if isinstance(stride, int) else stride[0]
      image_height = int(math.ceil(image_height / stride))
      image_width = int(math.ceil(image_width / stride))
      return [image_height, image_width]
  
  
  # Note:
  # The following 'SamePadding' functions make output size equal ceil(input size/stride).
  # Only when stride equals 1, can the output size be the same as input size.
  # Don't be confused by their function names ! ! !
  
  def get_same_padding_conv2d(image_size=None):
      """Chooses static padding if you have specified an image size, and dynamic padding otherwise.
         Static padding is necessary for ONNX exporting of models.
  
      Args:
          image_size (int or tuple): Size of the image.
  
      Returns:
          Conv2dDynamicSamePadding or Conv2dStaticSamePadding.
      """
      if image_size is None:
          return Conv2dDynamicSamePadding
      else:
          return partial(Conv2dStaticSamePadding, image_size=image_size)
  
  
  class Conv2dDynamicSamePadding(nn.Conv2d):
      """2D Convolutions like TensorFlow, for a dynamic image size.
         The padding is operated in forward function by calculating dynamically.
      """
  
      # Tips for 'SAME' mode padding.
      #     Given the following:
      #         i: width or height
      #         s: stride
      #         k: kernel size
      #         d: dilation
      #         p: padding
      #     Output after Conv2d:
      #         o = floor((i+p-((k-1)*d+1))/s+1)
      # If o equals i, i = floor((i+p-((k-1)*d+1))/s+1),
      # => p = (i-1)*s+((k-1)*d+1)-i
  
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
          super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
          self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
  
      def forward(self, x):
          ih, iw = x.size()[-2:]
          kh, kw = self.weight.size()[-2:]
          sh, sw = self.stride
          oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)  # change the output size according to stride ! ! !
          pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
          pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
          if pad_h > 0 or pad_w > 0:
              x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
          return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
  
  
  class Conv2dStaticSamePadding(nn.Conv2d):
      """2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size.
         The padding mudule is calculated in construction function, then used in forward.
      """
  
      # With the same calculation as Conv2dDynamicSamePadding
  
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, image_size=None, **kwargs):
          super().__init__(in_channels, out_channels, kernel_size, stride, **kwargs)
          self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
  
          # Calculate padding based on image size and save it
          assert image_size is not None
          ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size
          kh, kw = self.weight.size()[-2:]
          sh, sw = self.stride
          oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
          pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
          pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
          if pad_h > 0 or pad_w > 0:
              self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2,
                                                  pad_h // 2, pad_h - pad_h // 2))
          else:
              self.static_padding = nn.Identity()
  
      def forward(self, x):
          x = self.static_padding(x)
          x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
          return x
  
  
  def get_same_padding_maxPool2d(image_size=None):
      """Chooses static padding if you have specified an image size, and dynamic padding otherwise.
         Static padding is necessary for ONNX exporting of models.
  
      Args:
          image_size (int or tuple): Size of the image.
  
      Returns:
          MaxPool2dDynamicSamePadding or MaxPool2dStaticSamePadding.
      """
      if image_size is None:
          return MaxPool2dDynamicSamePadding
      else:
          return partial(MaxPool2dStaticSamePadding, image_size=image_size)
  
  
  class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
      """2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
         The padding is operated in forward function by calculating dynamically.
      """
  
      def __init__(self, kernel_size, stride, padding=0, dilation=1, return_indices=False, ceil_mode=False):
          super().__init__(kernel_size, stride, padding, dilation, return_indices, ceil_mode)
          self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride
          self.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size
          self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation
  
      def forward(self, x):
          ih, iw = x.size()[-2:]
          kh, kw = self.kernel_size
          sh, sw = self.stride
          oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
          pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
          pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
          if pad_h > 0 or pad_w > 0:
              x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
          return F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
                              self.dilation, self.ceil_mode, self.return_indices)
  
  
  class MaxPool2dStaticSamePadding(nn.MaxPool2d):
      """2D MaxPooling like TensorFlow's 'SAME' mode, with the given input image size.
         The padding mudule is calculated in construction function, then used in forward.
      """
  
      def __init__(self, kernel_size, stride, image_size=None, **kwargs):
          super().__init__(kernel_size, stride, **kwargs)
          self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride
          self.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size
          self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation
  
          # Calculate padding based on image size and save it
          assert image_size is not None
          ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size
          kh, kw = self.kernel_size
          sh, sw = self.stride
          oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
          pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
          pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
          if pad_h > 0 or pad_w > 0:
              self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))
          else:
              self.static_padding = nn.Identity()
  
      def forward(self, x):
          x = self.static_padding(x)
          x = F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
                           self.dilation, self.ceil_mode, self.return_indices)
          return x
  
  
  ################################################################################
  # Helper functions for loading model params
  ################################################################################
  
  # BlockDecoder: A Class for encoding and decoding BlockArgs
  # efficientnet_params: A function to query compound coefficient
  # get_model_params and efficientnet:
  #     Functions to get BlockArgs and GlobalParams for efficientnet
  # url_map and url_map_advprop: Dicts of url_map for pretrained weights
  # load_pretrained_weights: A function to load pretrained weights
  
  class BlockDecoder(object):
      """Block Decoder for readability,
         straight from the official TensorFlow repository.
      """
  
      @staticmethod
      def _decode_block_string(block_string):
          """Get a block through a string notation of arguments.
  
          Args:
              block_string (str): A string notation of arguments.
                                  Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'.
  
          Returns:
              BlockArgs: The namedtuple defined at the top of this file.
          """
          assert isinstance(block_string, str)
  
          ops = block_string.split('_')
          options = {}
          for op in ops:
              splits = re.split(r'(\d.*)', op)
              if len(splits) >= 2:
                  key, value = splits[:2]
                  options[key] = value
  
          # Check stride
          assert (('s' in options and len(options['s']) == 1) or
                  (len(options['s']) == 2 and options['s'][0] == options['s'][1]))
  
          return BlockArgs(
              num_repeat=int(options['r']),
              kernel_size=int(options['k']),
              stride=[int(options['s'][0])],
              expand_ratio=int(options['e']),
              input_filters=int(options['i']),
              output_filters=int(options['o']),
              se_ratio=float(options['se']) if 'se' in options else None,
              id_skip=('noskip' not in block_string))
  
      @staticmethod
      def _encode_block_string(block):
          """Encode a block to a string.
  
          Args:
              block (namedtuple): A BlockArgs type argument.
  
          Returns:
              block_string: A String form of BlockArgs.
          """
          args = [
              'r%d' % block.num_repeat,
              'k%d' % block.kernel_size,
              's%d%d' % (block.strides[0], block.strides[1]),
              'e%s' % block.expand_ratio,
              'i%d' % block.input_filters,
              'o%d' % block.output_filters
          ]
          if 0 < block.se_ratio <= 1:
              args.append('se%s' % block.se_ratio)
          if block.id_skip is False:
              args.append('noskip')
          return '_'.join(args)
  
      @staticmethod
      def decode(string_list):
          """Decode a list of string notations to specify blocks inside the network.
  
          Args:
              string_list (list[str]): A list of strings, each string is a notation of block.
  
          Returns:
              blocks_args: A list of BlockArgs namedtuples of block args.
          """
          assert isinstance(string_list, list)
          blocks_args = []
          for block_string in string_list:
              blocks_args.append(BlockDecoder._decode_block_string(block_string))
          return blocks_args
  
      @staticmethod
      def encode(blocks_args):
          """Encode a list of BlockArgs to a list of strings.
  
          Args:
              blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block args.
  
          Returns:
              block_strings: A list of strings, each string is a notation of block.
          """
          block_strings = []
          for block in blocks_args:
              block_strings.append(BlockDecoder._encode_block_string(block))
          return block_strings
  
  
  def efficientnet_params(model_name):
      """Map EfficientNet model name to parameter coefficients.
  
      Args:
          model_name (str): Model name to be queried.
  
      Returns:
          params_dict[model_name]: A (width,depth,res,dropout) tuple.
      """
      params_dict = {
          # Coefficients:   width,depth,res,dropout
          'efficientnet-b0': (1.0, 1.0, 224, 0.2),
          'efficientnet-b1': (1.0, 1.1, 240, 0.2),
          'efficientnet-b2': (1.1, 1.2, 260, 0.3),
          'efficientnet-b3': (1.2, 1.4, 300, 0.3),
          'efficientnet-b4': (1.4, 1.8, 380, 0.4),
          'efficientnet-b5': (1.6, 2.2, 456, 0.4),
          'efficientnet-b6': (1.8, 2.6, 528, 0.5),
          'efficientnet-b7': (2.0, 3.1, 600, 0.5),
          'efficientnet-b8': (2.2, 3.6, 672, 0.5),
          'efficientnet-l2': (4.3, 5.3, 800, 0.5),
      }
      return params_dict[model_name]
  
  
  def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None,
                   dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000, include_top=True):
      """Create BlockArgs and GlobalParams for efficientnet model.
  
      Args:
          width_coefficient (float)
          depth_coefficient (float)
          image_size (int)
          dropout_rate (float)
          drop_connect_rate (float)
          num_classes (int)
  
          Meaning as the name suggests.
  
      Returns:
          blocks_args, global_params.
      """
  
      # Blocks args for the whole model(efficientnet-b0 by default)
      # It will be modified in the construction of EfficientNet Class according to model
      blocks_args = [
          'r1_k3_s11_e1_i32_o16_se0.25',
          'r2_k3_s22_e6_i16_o24_se0.25',
          'r2_k5_s22_e6_i24_o40_se0.25',
          'r3_k3_s22_e6_i40_o80_se0.25',
          'r3_k5_s11_e6_i80_o112_se0.25',
          'r4_k5_s22_e6_i112_o192_se0.25',
          'r1_k3_s11_e6_i192_o320_se0.25',
      ]
      blocks_args = BlockDecoder.decode(blocks_args)
  
      global_params = GlobalParams(
          width_coefficient=width_coefficient,
          depth_coefficient=depth_coefficient,
          image_size=image_size,
          dropout_rate=dropout_rate,
  
          num_classes=num_classes,
          batch_norm_momentum=0.99,
          batch_norm_epsilon=1e-3,
          drop_connect_rate=drop_connect_rate,
          depth_divisor=8,
          min_depth=None,
          include_top=include_top,
      )
  
      return blocks_args, global_params
  
  
  def get_model_params(model_name, override_params):
      """Get the block args and global params for a given model name.
  
      Args:
          model_name (str): Model's name.
          override_params (dict): A dict to modify global_params.
  
      Returns:
          blocks_args, global_params
      """
      if model_name.startswith('efficientnet'):
          w, d, s, p = efficientnet_params(model_name)
          # note: all models have drop connect rate = 0.2
          blocks_args, global_params = efficientnet(
              width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s)
      else:
          raise NotImplementedError('model name is not pre-defined: {}'.format(model_name))
      if override_params:
          # ValueError will be raised here if override_params has fields not included in global_params.
          global_params = global_params._replace(**override_params)
      return blocks_args, global_params
  
  
  # train with Standard methods
  # check more details in paper(EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks)
  url_map = {
      'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth',
      'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth',
      'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth',
      'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth',
      'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth',
      'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth',
      'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth',
      'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth',
  }
  
  # train with Adversarial Examples(AdvProp)
  # check more details in paper(Adversarial Examples Improve Image Recognition)
  url_map_advprop = {
      'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth',
      'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth',
      'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth',
      'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth',
      'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth',
      'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth',
      'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth',
      'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth',
      'efficientnet-b8': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth',
  }
  
  # TODO: add the petrained weights url map of 'efficientnet-l2'
  
  
  def load_pretrained_weights(model, model_name, weights_path=None, load_fc=True, advprop=False, verbose=True):
      """Loads pretrained weights from weights path or download using url.
  
      Args:
          model (Module): The whole model of efficientnet.
          model_name (str): Model name of efficientnet.
          weights_path (None or str):
              str: path to pretrained weights file on the local disk.
              None: use pretrained weights downloaded from the Internet.
          load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.
          advprop (bool): Whether to load pretrained weights
                          trained with advprop (valid when weights_path is None).
      """
      if isinstance(weights_path, str):
          state_dict = torch.load(weights_path)
      else:
          # AutoAugment or Advprop (different preprocessing)
          url_map_ = url_map_advprop if advprop else url_map
          state_dict = model_zoo.load_url(url_map_[model_name])
  
      if load_fc:
          ret = model.load_state_dict(state_dict, strict=False)
          assert not ret.missing_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)
      else:
          state_dict.pop('_fc.weight')
          state_dict.pop('_fc.bias')
          ret = model.load_state_dict(state_dict, strict=False)
          assert set(ret.missing_keys) == set(
              ['_fc.weight', '_fc.bias']), 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)
      assert not ret.unexpected_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.unexpected_keys)
  
      if verbose:
          print('Loaded pretrained weights for {}'.format(model_name))