text: """
      SENet for ImageNet-1K, implemented in PyTorch.
      Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  """
  
  __all__ = ['SENet', 'senet16', 'senet28', 'senet40', 'senet52', 'senet103', 'senet154', 'SEInitBlock']
  
  import os
  import math
  import torch.nn as nn
  import torch.nn.init as init
  from .common import conv1x1_block, conv3x3_block, SEBlock
  
  
  class SENetBottleneck(nn.Module):
      """
      SENet bottleneck block for residual path in SENet unit.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      stride : int or tuple/list of 2 int
          Strides of the convolution.
      cardinality: int
          Number of groups.
      bottleneck_width: int
          Width of bottleneck block.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   stride,
                   cardinality,
                   bottleneck_width):
          super(SENetBottleneck, self).__init__()
          mid_channels = out_channels // 4
          D = int(math.floor(mid_channels * (bottleneck_width / 64.0)))
          group_width = cardinality * D
          group_width2 = group_width // 2
  
          self.conv1 = conv1x1_block(
              in_channels=in_channels,
              out_channels=group_width2)
          self.conv2 = conv3x3_block(
              in_channels=group_width2,
              out_channels=group_width,
              stride=stride,
              groups=cardinality)
          self.conv3 = conv1x1_block(
              in_channels=group_width,
              out_channels=out_channels,
              activation=None)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          x = self.conv3(x)
          return x
  
  
  class SENetUnit(nn.Module):
      """
      SENet unit.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      stride : int or tuple/list of 2 int
          Strides of the convolution.
      cardinality: int
          Number of groups.
      bottleneck_width: int
          Width of bottleneck block.
      identity_conv3x3 : bool, default False
          Whether to use 3x3 convolution in the identity link.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   stride,
                   cardinality,
                   bottleneck_width,
                   identity_conv3x3):
          super(SENetUnit, self).__init__()
          self.resize_identity = (in_channels != out_channels) or (stride != 1)
  
          self.body = SENetBottleneck(
              in_channels=in_channels,
              out_channels=out_channels,
              stride=stride,
              cardinality=cardinality,
              bottleneck_width=bottleneck_width)
          self.se = SEBlock(channels=out_channels)
          if self.resize_identity:
              if identity_conv3x3:
                  self.identity_conv = conv3x3_block(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      stride=stride,
                      activation=None)
              else:
                  self.identity_conv = conv1x1_block(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      stride=stride,
                      activation=None)
          self.activ = nn.ReLU(inplace=True)
  
      def forward(self, x):
          if self.resize_identity:
              identity = self.identity_conv(x)
          else:
              identity = x
          x = self.body(x)
          x = self.se(x)
          x = x + identity
          x = self.activ(x)
          return x
  
  
  class SEInitBlock(nn.Module):
      """
      SENet specific initial block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      """
      def __init__(self,
                   in_channels,
                   out_channels):
          super(SEInitBlock, self).__init__()
          mid_channels = out_channels // 2
  
          self.conv1 = conv3x3_block(
              in_channels=in_channels,
              out_channels=mid_channels,
              stride=2)
          self.conv2 = conv3x3_block(
              in_channels=mid_channels,
              out_channels=mid_channels)
          self.conv3 = conv3x3_block(
              in_channels=mid_channels,
              out_channels=out_channels)
          self.pool = nn.MaxPool2d(
              kernel_size=3,
              stride=2,
              padding=1)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          x = self.conv3(x)
          x = self.pool(x)
          return x
  
  
  class SENet(nn.Module):
      """
      SENet model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      Parameters:
      ----------
      channels : list of list of int
          Number of output channels for each unit.
      init_block_channels : int
          Number of output channels for the initial unit.
      cardinality: int
          Number of groups.
      bottleneck_width: int
          Width of bottleneck block.
      in_channels : int, default 3
          Number of input channels.
      in_size : tuple of two ints, default (224, 224)
          Spatial size of the expected input image.
      num_classes : int, default 1000
          Number of classification classes.
      """
      def __init__(self,
                   channels,
                   init_block_channels,
                   cardinality,
                   bottleneck_width,
                   in_channels=3,
                   in_size=(224, 224),
                   num_classes=1000):
          super(SENet, self).__init__()
          self.in_size = in_size
          self.num_classes = num_classes
  
          self.features = nn.Sequential()
          self.features.add_module("init_block", SEInitBlock(
              in_channels=in_channels,
              out_channels=init_block_channels))
          in_channels = init_block_channels
          for i, channels_per_stage in enumerate(channels):
              stage = nn.Sequential()
              identity_conv3x3 = (i != 0)
              for j, out_channels in enumerate(channels_per_stage):
                  stride = 2 if (j == 0) and (i != 0) else 1
                  stage.add_module("unit{}".format(j + 1), SENetUnit(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      stride=stride,
                      cardinality=cardinality,
                      bottleneck_width=bottleneck_width,
                      identity_conv3x3=identity_conv3x3))
                  in_channels = out_channels
              self.features.add_module("stage{}".format(i + 1), stage)
          self.features.add_module("final_pool", nn.AvgPool2d(
              kernel_size=7,
              stride=1))
  
          self.output = nn.Sequential()
          self.output.add_module("dropout", nn.Dropout(p=0.2))
          self.output.add_module("fc", nn.Linear(
              in_features=in_channels,
              out_features=num_classes))
  
          self._init_params()
  
      def _init_params(self):
          for name, module in self.named_modules():
              if isinstance(module, nn.Conv2d):
                  init.kaiming_uniform_(module.weight)
                  if module.bias is not None:
                      init.constant_(module.bias, 0)
  
      def forward(self, x):
          x = self.features(x)
          x = x.view(x.size(0), -1)
          x = self.output(x)
          return x
  
  
  def get_senet(blocks,
                model_name=None,
                pretrained=False,
                root=os.path.join("~", ".torch", "models"),
                **kwargs):
      """
      Create SENet model with specific parameters.
  
      Parameters:
      ----------
      blocks : int
          Number of blocks.
      model_name : str or None, default None
          Model name for loading pretrained model.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      """
  
      if blocks == 16:
          layers = [1, 1, 1, 1]
          cardinality = 32
      elif blocks == 28:
          layers = [2, 2, 2, 2]
          cardinality = 32
      elif blocks == 40:
          layers = [3, 3, 3, 3]
          cardinality = 32
      elif blocks == 52:
          layers = [3, 4, 6, 3]
          cardinality = 32
      elif blocks == 103:
          layers = [3, 4, 23, 3]
          cardinality = 32
      elif blocks == 154:
          layers = [3, 8, 36, 3]
          cardinality = 64
      else:
          raise ValueError("Unsupported SENet with number of blocks: {}".format(blocks))
  
      bottleneck_width = 4
      init_block_channels = 128
      channels_per_layers = [256, 512, 1024, 2048]
  
      channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]
  
      net = SENet(
          channels=channels,
          init_block_channels=init_block_channels,
          cardinality=cardinality,
          bottleneck_width=bottleneck_width,
          **kwargs)
  
      if pretrained:
          if (model_name is None) or (not model_name):
              raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.")
          from .model_store import download_model
          download_model(
              net=net,
              model_name=model_name,
              local_model_store_dir_path=root)
  
      return net
  
  
  def senet16(**kwargs):
      """
      SENet-16 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      """
      return get_senet(blocks=16, model_name="senet16", **kwargs)
  
  
  def senet28(**kwargs):
      """
      SENet-28 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      """
      return get_senet(blocks=28, model_name="senet28", **kwargs)
  
  
  def senet40(**kwargs):
      """
      SENet-40 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      """
      return get_senet(blocks=40, model_name="senet40", **kwargs)
  
  
  def senet52(**kwargs):
      """
      SENet-52 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      """
      return get_senet(blocks=52, model_name="senet52", **kwargs)
  
  
  def senet103(**kwargs):
      """
      SENet-103 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      """
      return get_senet(blocks=103, model_name="senet103", **kwargs)
  
  
  def senet154(**kwargs):
      """
      SENet-154 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      """
      return get_senet(blocks=154, model_name="senet154", **kwargs)
  
  
  def _calc_width(net):
      import numpy as np
      net_params = filter(lambda p: p.requires_grad, net.parameters())
      weight_count = 0
      for param in net_params:
          weight_count += np.prod(param.size())
      return weight_count
  
  
  def _test():
      import torch
  
      pretrained = False
  
      models = [
          senet16,
          senet28,
          senet40,
          senet52,
          senet103,
          senet154,
      ]
  
      for model in models:
  
          net = model(pretrained=pretrained)
  
          # net.train()
          net.eval()
          weight_count = _calc_width(net)
          print("m={}, {}".format(model.__name__, weight_count))
          assert (model != senet16 or weight_count == 31366168)
          assert (model != senet28 or weight_count == 36453768)
          assert (model != senet40 or weight_count == 41541368)
          assert (model != senet52 or weight_count == 44659416)
          assert (model != senet103 or weight_count == 60963096)
          assert (model != senet154 or weight_count == 115088984)
  
          x = torch.randn(1, 3, 224, 224)
          y = net(x)
          y.sum().backward()
          assert (tuple(y.size()) == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  