text: """
      SE-PreResNet, implemented in PyTorch.
      Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  """
  
  __all__ = ['SEPreResNet', 'sepreresnet18', 'sepreresnet34', 'sepreresnet50', 'sepreresnet50b', 'sepreresnet101',
             'sepreresnet101b', 'sepreresnet152', 'sepreresnet152b', 'sepreresnet200', 'sepreresnet200b']
  
  import os
  import torch.nn as nn
  import torch.nn.init as init
  from .common import conv1x1, SEBlock
  from .preresnet import PreResBlock, PreResBottleneck, PreResInitBlock, PreResActivation
  
  
  class SEPreResUnit(nn.Module):
      """
      SE-PreResNet 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.
      bottleneck : bool
          Whether to use a bottleneck or simple block in units.
      conv1_stride : bool
          Whether to use stride in the first or the second convolution layer of the block.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   stride,
                   bottleneck,
                   conv1_stride):
          super(SEPreResUnit, self).__init__()
          self.resize_identity = (in_channels != out_channels) or (stride != 1)
  
          if bottleneck:
              self.body = PreResBottleneck(
                  in_channels=in_channels,
                  out_channels=out_channels,
                  stride=stride,
                  conv1_stride=conv1_stride)
          else:
              self.body = PreResBlock(
                  in_channels=in_channels,
                  out_channels=out_channels,
                  stride=stride)
          self.se = SEBlock(channels=out_channels)
          if self.resize_identity:
              self.identity_conv = conv1x1(
                  in_channels=in_channels,
                  out_channels=out_channels,
                  stride=stride)
  
      def forward(self, x):
          identity = x
          x, x_pre_activ = self.body(x)
          x = self.se(x)
          if self.resize_identity:
              identity = self.identity_conv(x_pre_activ)
          x = x + identity
          return x
  
  
  class SEPreResNet(nn.Module):
      """
      SE-PreResNet 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.
      bottleneck : bool
          Whether to use a bottleneck or simple block in units.
      conv1_stride : bool
          Whether to use stride in the first or the second convolution layer in units.
      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,
                   bottleneck,
                   conv1_stride,
                   in_channels=3,
                   in_size=(224, 224),
                   num_classes=1000):
          super(SEPreResNet, self).__init__()
          self.in_size = in_size
          self.num_classes = num_classes
  
          self.features = nn.Sequential()
          self.features.add_module("init_block", PreResInitBlock(
              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()
              for j, out_channels in enumerate(channels_per_stage):
                  stride = 1 if (i == 0) or (j != 0) else 2
                  stage.add_module("unit{}".format(j + 1), SEPreResUnit(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      stride=stride,
                      bottleneck=bottleneck,
                      conv1_stride=conv1_stride))
                  in_channels = out_channels
              self.features.add_module("stage{}".format(i + 1), stage)
          self.features.add_module("post_activ", PreResActivation(in_channels=in_channels))
          self.features.add_module("final_pool", nn.AvgPool2d(
              kernel_size=7,
              stride=1))
  
          self.output = 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_sepreresnet(blocks,
                      conv1_stride=True,
                      model_name=None,
                      pretrained=False,
                      root=os.path.join('~', '.torch', 'models'),
                      **kwargs):
      """
      Create SE-PreResNet model with specific parameters.
  
      Parameters:
      ----------
      blocks : int
          Number of blocks.
      conv1_stride : bool, default True
          Whether to use stride in the first or the second convolution layer in units.
      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 == 18:
          layers = [2, 2, 2, 2]
      elif blocks == 34:
          layers = [3, 4, 6, 3]
      elif blocks == 50:
          layers = [3, 4, 6, 3]
      elif blocks == 101:
          layers = [3, 4, 23, 3]
      elif blocks == 152:
          layers = [3, 8, 36, 3]
      elif blocks == 200:
          layers = [3, 24, 36, 3]
      else:
          raise ValueError("Unsupported SE-PreResNet with number of blocks: {}".format(blocks))
  
      init_block_channels = 64
  
      if blocks < 50:
          channels_per_layers = [64, 128, 256, 512]
          bottleneck = False
      else:
          channels_per_layers = [256, 512, 1024, 2048]
          bottleneck = True
  
      channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]
  
      net = SEPreResNet(
          channels=channels,
          init_block_channels=init_block_channels,
          bottleneck=bottleneck,
          conv1_stride=conv1_stride,
          **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 sepreresnet18(**kwargs):
      """
      SE-PreResNet-18 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_sepreresnet(blocks=18, model_name="sepreresnet18", **kwargs)
  
  
  def sepreresnet34(**kwargs):
      """
      SE-PreResNet-34 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_sepreresnet(blocks=34, model_name="sepreresnet34", **kwargs)
  
  
  def sepreresnet50(**kwargs):
      """
      SE-PreResNet-50 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_sepreresnet(blocks=50, model_name="sepreresnet50", **kwargs)
  
  
  def sepreresnet50b(**kwargs):
      """
      SE-PreResNet-50 model with stride at the second convolution in bottleneck block 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_sepreresnet(blocks=50, conv1_stride=False, model_name="sepreresnet50b", **kwargs)
  
  
  def sepreresnet101(**kwargs):
      """
      SE-PreResNet-101 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_sepreresnet(blocks=101, model_name="sepreresnet101", **kwargs)
  
  
  def sepreresnet101b(**kwargs):
      """
      SE-PreResNet-101 model with stride at the second convolution in bottleneck block 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_sepreresnet(blocks=101, conv1_stride=False, model_name="sepreresnet101b", **kwargs)
  
  
  def sepreresnet152(**kwargs):
      """
      SE-PreResNet-152 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_sepreresnet(blocks=152, model_name="sepreresnet152", **kwargs)
  
  
  def sepreresnet152b(**kwargs):
      """
      SE-PreResNet-152 model with stride at the second convolution in bottleneck block 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_sepreresnet(blocks=152, conv1_stride=False, model_name="sepreresnet152b", **kwargs)
  
  
  def sepreresnet200(**kwargs):
      """
      SE-PreResNet-200 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507. It's an
      experimental model.
  
      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_sepreresnet(blocks=200, model_name="sepreresnet200", **kwargs)
  
  
  def sepreresnet200b(**kwargs):
      """
      SE-PreResNet-200 model with stride at the second convolution in bottleneck block from 'Squeeze-and-Excitation
      Networks,' https://arxiv.org/abs/1709.01507. It's an experimental model.
  
      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_sepreresnet(blocks=200, conv1_stride=False, model_name="sepreresnet200b", **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
      from torch.autograd import Variable
  
      pretrained = False
  
      models = [
          sepreresnet18,
          sepreresnet34,
          sepreresnet50,
          sepreresnet50b,
          sepreresnet101,
          sepreresnet101b,
          sepreresnet152,
          sepreresnet152b,
          sepreresnet200,
          sepreresnet200b,
      ]
  
      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 != sepreresnet18 or weight_count == 11776928)
          assert (model != sepreresnet34 or weight_count == 21957204)
          assert (model != sepreresnet50 or weight_count == 28080472)
          assert (model != sepreresnet50b or weight_count == 28080472)
          assert (model != sepreresnet101 or weight_count == 49319320)
          assert (model != sepreresnet101b or weight_count == 49319320)
          assert (model != sepreresnet152 or weight_count == 66814296)
          assert (model != sepreresnet152b or weight_count == 66814296)
          assert (model != sepreresnet200 or weight_count == 71828312)
          assert (model != sepreresnet200b or weight_count == 71828312)
  
          x = Variable(torch.randn(1, 3, 224, 224))
          y = net(x)
          assert (tuple(y.size()) == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  