text: """model.py - Model and module class for EfficientNet.
     They 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 torch
  from torch import nn
  from torch.nn import functional as F
  from .utils import (
      round_filters,
      round_repeats,
      drop_connect,
      get_same_padding_conv2d,
      get_model_params,
      efficientnet_params,
      load_pretrained_weights,
      Swish,
      MemoryEfficientSwish,
      calculate_output_image_size
  )
  
  
  VALID_MODELS = (
      'efficientnet-b0', 'efficientnet-b1', 'efficientnet-b2', 'efficientnet-b3',
      'efficientnet-b4', 'efficientnet-b5', 'efficientnet-b6', 'efficientnet-b7',
      'efficientnet-b8',
  
      # Support the construction of 'efficientnet-l2' without pretrained weights
      'efficientnet-l2'
  )
  
  
  class MBConvBlock(nn.Module):
      """Mobile Inverted Residual Bottleneck Block.
  
      Args:
          block_args (namedtuple): BlockArgs, defined in utils.py.
          global_params (namedtuple): GlobalParam, defined in utils.py.
          image_size (tuple or list): [image_height, image_width].
  
      References:
          [1] https://arxiv.org/abs/1704.04861 (MobileNet v1)
          [2] https://arxiv.org/abs/1801.04381 (MobileNet v2)
          [3] https://arxiv.org/abs/1905.02244 (MobileNet v3)
      """
  
      def __init__(self, block_args, global_params, image_size=None):
          super().__init__()
          self._block_args = block_args
          self._bn_mom = 1 - global_params.batch_norm_momentum # pytorch's difference from tensorflow
          self._bn_eps = global_params.batch_norm_epsilon
          self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1)
          self.id_skip = block_args.id_skip  # whether to use skip connection and drop connect
  
          # Expansion phase (Inverted Bottleneck)
          inp = self._block_args.input_filters  # number of input channels
          oup = self._block_args.input_filters * self._block_args.expand_ratio  # number of output channels
          if self._block_args.expand_ratio != 1:
              Conv2d = get_same_padding_conv2d(image_size=image_size)
              self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)
              self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)
              # image_size = calculate_output_image_size(image_size, 1) <-- this wouldn't modify image_size
  
          # Depthwise convolution phase
          k = self._block_args.kernel_size
          s = self._block_args.stride
          Conv2d = get_same_padding_conv2d(image_size=image_size)
          self._depthwise_conv = Conv2d(
              in_channels=oup, out_channels=oup, groups=oup,  # groups makes it depthwise
              kernel_size=k, stride=s, bias=False)
          self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)
          image_size = calculate_output_image_size(image_size, s)
  
          # Squeeze and Excitation layer, if desired
          if self.has_se:
              Conv2d = get_same_padding_conv2d(image_size=(1, 1))
              num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio))
              self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)
              self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)
  
          # Pointwise convolution phase
          final_oup = self._block_args.output_filters
          Conv2d = get_same_padding_conv2d(image_size=image_size)
          self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)
          self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps)
          self._swish = MemoryEfficientSwish()
  
      def forward(self, inputs, drop_connect_rate=None):
          """MBConvBlock's forward function.
  
          Args:
              inputs (tensor): Input tensor.
              drop_connect_rate (bool): Drop connect rate (float, between 0 and 1).
  
          Returns:
              Output of this block after processing.
          """
  
          # Expansion and Depthwise Convolution
          x = inputs
          if self._block_args.expand_ratio != 1:
              x = self._expand_conv(inputs)
              x = self._bn0(x)
              x = self._swish(x)
  
          x = self._depthwise_conv(x)
          x = self._bn1(x)
          x = self._swish(x)
  
          # Squeeze and Excitation
          if self.has_se:
              x_squeezed = F.adaptive_avg_pool2d(x, 1)
              x_squeezed = self._se_reduce(x_squeezed)
              x_squeezed = self._swish(x_squeezed)
              x_squeezed = self._se_expand(x_squeezed)
              x = torch.sigmoid(x_squeezed) * x
  
          # Pointwise Convolution
          x = self._project_conv(x)
          x = self._bn2(x)
  
          # Skip connection and drop connect
          input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters
          if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:
              # The combination of skip connection and drop connect brings about stochastic depth.
              if drop_connect_rate:
                  x = drop_connect(x, p=drop_connect_rate, training=self.training)
              x = x + inputs  # skip connection
          return x
  
      def set_swish(self, memory_efficient=True):
          """Sets swish function as memory efficient (for training) or standard (for export).
  
          Args:
              memory_efficient (bool): Whether to use memory-efficient version of swish.
          """
          self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
  
  
  class EfficientNet(nn.Module):
      """EfficientNet model.
         Most easily loaded with the .from_name or .from_pretrained methods.
  
      Args:
          blocks_args (list[namedtuple]): A list of BlockArgs to construct blocks.
          global_params (namedtuple): A set of GlobalParams shared between blocks.
  
      References:
          [1] https://arxiv.org/abs/1905.11946 (EfficientNet)
  
      Example:
          
          
          import torch
          >>> from efficientnet.model import EfficientNet
          >>> inputs = torch.rand(1, 3, 224, 224)
          >>> model = EfficientNet.from_pretrained('efficientnet-b0')
          >>> model.eval()
          >>> outputs = model(inputs)
      """
  
      def __init__(self, blocks_args=None, global_params=None):
          super().__init__()
          assert isinstance(blocks_args, list), 'blocks_args should be a list'
          assert len(blocks_args) > 0, 'block args must be greater than 0'
          self._global_params = global_params
          self._blocks_args = blocks_args
  
          # Batch norm parameters
          bn_mom = 1 - self._global_params.batch_norm_momentum
          bn_eps = self._global_params.batch_norm_epsilon
  
          # Get stem static or dynamic convolution depending on image size
          image_size = global_params.image_size
          Conv2d = get_same_padding_conv2d(image_size=image_size)
  
          # Stem
          in_channels = 3  # rgb
          out_channels = round_filters(32, self._global_params)  # number of output channels
          self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
          self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
          image_size = calculate_output_image_size(image_size, 2)
  
          # Build blocks
          self._blocks = nn.ModuleList([])
          for block_args in self._blocks_args:
  
              # Update block input and output filters based on depth multiplier.
              block_args = block_args._replace(
                  input_filters=round_filters(block_args.input_filters, self._global_params),
                  output_filters=round_filters(block_args.output_filters, self._global_params),
                  num_repeat=round_repeats(block_args.num_repeat, self._global_params)
              )
  
              # The first block needs to take care of stride and filter size increase.
              self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size))
              image_size = calculate_output_image_size(image_size, block_args.stride)
              if block_args.num_repeat > 1: # modify block_args to keep same output size
                  block_args = block_args._replace(input_filters=block_args.output_filters, stride=1)
              for _ in range(block_args.num_repeat - 1):
                  self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size))
                  # image_size = calculate_output_image_size(image_size, block_args.stride)  # stride = 1
  
          # Head
          in_channels = block_args.output_filters  # output of final block
          out_channels = round_filters(1280, self._global_params)
          Conv2d = get_same_padding_conv2d(image_size=image_size)
          self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
          self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
  
          # Final linear layer
          self._avg_pooling = nn.AdaptiveAvgPool2d(1)
          self._dropout = nn.Dropout(self._global_params.dropout_rate)
          self._fc = nn.Linear(out_channels, self._global_params.num_classes)
          self._swish = MemoryEfficientSwish()
  
      def set_swish(self, memory_efficient=True):
          """Sets swish function as memory efficient (for training) or standard (for export).
  
          Args:
              memory_efficient (bool): Whether to use memory-efficient version of swish.
  
          """
          self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
          for block in self._blocks:
              block.set_swish(memory_efficient)
  
      def extract_endpoints(self, inputs):
          """Use convolution layer to extract features
          from reduction levels i in [1, 2, 3, 4, 5].
  
          Args:
              inputs (tensor): Input tensor.
  
          Returns:
              Dictionary of last intermediate features
              with reduction levels i in [1, 2, 3, 4, 5].
              Example:
                  >>> import torch
                  >>> from efficientnet.model import EfficientNet
                  >>> inputs = torch.rand(1, 3, 224, 224)
                  >>> model = EfficientNet.from_pretrained('efficientnet-b0')
                  >>> endpoints = model.extract_endpoints(inputs)
                  >>> print(endpoints['reduction_1'].shape)  # torch.Size([1, 16, 112, 112])
                  >>> print(endpoints['reduction_2'].shape)  # torch.Size([1, 24, 56, 56])
                  >>> print(endpoints['reduction_3'].shape)  # torch.Size([1, 40, 28, 28])
                  >>> print(endpoints['reduction_4'].shape)  # torch.Size([1, 112, 14, 14])
                  >>> print(endpoints['reduction_5'].shape)  # torch.Size([1, 1280, 7, 7])
          """
          endpoints = dict()
  
          # Stem
          x = self._swish(self._bn0(self._conv_stem(inputs)))
          prev_x = x
  
          # Blocks
          for idx, block in enumerate(self._blocks):
              drop_connect_rate = self._global_params.drop_connect_rate
              if drop_connect_rate:
                  drop_connect_rate *= float(idx) / len(self._blocks) # scale drop connect_rate
              x = block(x, drop_connect_rate=drop_connect_rate)
              if prev_x.size(2) > x.size(2):
                  endpoints['reduction_{}'.format(len(endpoints)+1)] = prev_x
              prev_x = x
  
          # Head
          x = self._swish(self._bn1(self._conv_head(x)))
          endpoints['reduction_{}'.format(len(endpoints)+1)] = x
  
          return endpoints
  
      def extract_features(self, inputs):
          """use convolution layer to extract feature .
  
          Args:
              inputs (tensor): Input tensor.
  
          Returns:
              Output of the final convolution
              layer in the efficientnet model.
          """
          # Stem
          x = self._swish(self._bn0(self._conv_stem(inputs)))
  
          # Blocks
          for idx, block in enumerate(self._blocks):
              drop_connect_rate = self._global_params.drop_connect_rate
              if drop_connect_rate:
                  drop_connect_rate *= float(idx) / len(self._blocks) # scale drop connect_rate
              x = block(x, drop_connect_rate=drop_connect_rate)
  
          # Head
          x = self._swish(self._bn1(self._conv_head(x)))
  
          return x
  
      def forward(self, inputs):
          """EfficientNet's forward function.
             Calls extract_features to extract features, applies final linear layer, and returns logits.
  
          Args:
              inputs (tensor): Input tensor.
  
          Returns:
              Output of this model after processing.
          """
          # Convolution layers
          x = self.extract_features(inputs)
          # Pooling and final linear layer
          x = self._avg_pooling(x)
          if self._global_params.include_top:
              x = x.flatten(start_dim=1)
              x = self._dropout(x)
              x = self._fc(x)
          return x
  
      @classmethod
      def from_name(cls, model_name, in_channels=3, **override_params):
          """create an efficientnet model according to name.
  
          Args:
              model_name (str): Name for efficientnet.
              in_channels (int): Input data's channel number.
              override_params (other key word params):
                  Params to override model's global_params.
                  Optional key:
                      'width_coefficient', 'depth_coefficient',
                      'image_size', 'dropout_rate',
                      'num_classes', 'batch_norm_momentum',
                      'batch_norm_epsilon', 'drop_connect_rate',
                      'depth_divisor', 'min_depth'
  
          Returns:
              An efficientnet model.
          """
          cls._check_model_name_is_valid(model_name)
          blocks_args, global_params = get_model_params(model_name, override_params)
          model = cls(blocks_args, global_params)
          model._change_in_channels(in_channels)
          return model
  
      @classmethod
      def from_pretrained(cls, model_name, weights_path=None, advprop=False,
                          in_channels=3, num_classes=1000, **override_params):
          """create an efficientnet model according to name.
  
          Args:
              model_name (str): Name for efficientnet.
              weights_path (None or str):
                  str: path to pretrained weights file on the local disk.
                  None: use pretrained weights downloaded from the Internet.
              advprop (bool):
                  Whether to load pretrained weights
                  trained with advprop (valid when weights_path is None).
              in_channels (int): Input data's channel number.
              num_classes (int):
                  Number of categories for classification.
                  It controls the output size for final linear layer.
              override_params (other key word params):
                  Params to override model's global_params.
                  Optional key:
                      'width_coefficient', 'depth_coefficient',
                      'image_size', 'dropout_rate',
                      'batch_norm_momentum',
                      'batch_norm_epsilon', 'drop_connect_rate',
                      'depth_divisor', 'min_depth'
  
          Returns:
              A pretrained efficientnet model.
          """
          model = cls.from_name(model_name, num_classes=num_classes, **override_params)
          load_pretrained_weights(model, model_name, weights_path=weights_path, load_fc=(num_classes == 1000), advprop=advprop)
          model._change_in_channels(in_channels)
          return model
  
      @classmethod
      def get_image_size(cls, model_name):
          """Get the input image size for a given efficientnet model.
  
          Args:
              model_name (str): Name for efficientnet.
  
          Returns:
              Input image size (resolution).
          """
          cls._check_model_name_is_valid(model_name)
          _, _, res, _ = efficientnet_params(model_name)
          return res
  
      @classmethod
      def _check_model_name_is_valid(cls, model_name):
          """Validates model name.
  
          Args:
              model_name (str): Name for efficientnet.
  
          Returns:
              bool: Is a valid name or not.
          """
          if model_name not in VALID_MODELS:
              raise ValueError('model_name should be one of: ' + ', '.join(VALID_MODELS))
  
      def _change_in_channels(self, in_channels):
          """Adjust model's first convolution layer to in_channels, if in_channels not equals 3.
  
          Args:
              in_channels (int): Input data's channel number.
          """
          if in_channels != 3:
              Conv2d = get_same_padding_conv2d(image_size=self._global_params.image_size)
              out_channels = round_filters(32, self._global_params)
              self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
  