text: """
      MobileNetV3 for ImageNet-1K, implemented in PyTorch.
      Original paper: 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  """
  
  __all__ = ['MobileNetV3', 'mobilenetv3_small_w7d20', 'mobilenetv3_small_wd2', 'mobilenetv3_small_w3d4',
             'mobilenetv3_small_w1', 'mobilenetv3_small_w5d4', 'mobilenetv3_large_w7d20', 'mobilenetv3_large_wd2',
             'mobilenetv3_large_w3d4', 'mobilenetv3_large_w1', 'mobilenetv3_large_w5d4']
  
  import os
  import torch.nn as nn
  import torch.nn.init as init
  from .common import round_channels, conv1x1, conv1x1_block, conv3x3_block, dwconv3x3_block, dwconv5x5_block, SEBlock,\
      HSwish
  
  
  class MobileNetV3Unit(nn.Module):
      """
      MobileNetV3 unit.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      exp_channels : int
          Number of middle (expanded) channels.
      stride : int or tuple/list of 2 int
          Strides of the second convolution layer.
      use_kernel3 : bool
          Whether to use 3x3 (instead of 5x5) kernel.
      activation : str
          Activation function or name of activation function.
      use_se : bool
          Whether to use SE-module.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   exp_channels,
                   stride,
                   use_kernel3,
                   activation,
                   use_se):
          super(MobileNetV3Unit, self).__init__()
          assert (exp_channels >= out_channels)
          self.residual = (in_channels == out_channels) and (stride == 1)
          self.use_se = use_se
          self.use_exp_conv = exp_channels != out_channels
          mid_channels = exp_channels
  
          if self.use_exp_conv:
              self.exp_conv = conv1x1_block(
                  in_channels=in_channels,
                  out_channels=mid_channels,
                  activation=activation)
          if use_kernel3:
              self.conv1 = dwconv3x3_block(
                  in_channels=mid_channels,
                  out_channels=mid_channels,
                  stride=stride,
                  activation=activation)
          else:
              self.conv1 = dwconv5x5_block(
                  in_channels=mid_channels,
                  out_channels=mid_channels,
                  stride=stride,
                  activation=activation)
          if self.use_se:
              self.se = SEBlock(
                  channels=mid_channels,
                  reduction=4,
                  round_mid=True,
                  out_activation="hsigmoid")
          self.conv2 = conv1x1_block(
              in_channels=mid_channels,
              out_channels=out_channels,
              activation=None)
  
      def forward(self, x):
          if self.residual:
              identity = x
          if self.use_exp_conv:
              x = self.exp_conv(x)
          x = self.conv1(x)
          if self.use_se:
              x = self.se(x)
          x = self.conv2(x)
          if self.residual:
              x = x + identity
          return x
  
  
  class MobileNetV3FinalBlock(nn.Module):
      """
      MobileNetV3 final block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      use_se : bool
          Whether to use SE-module.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   use_se):
          super(MobileNetV3FinalBlock, self).__init__()
          self.use_se = use_se
  
          self.conv = conv1x1_block(
              in_channels=in_channels,
              out_channels=out_channels,
              activation="hswish")
          if self.use_se:
              self.se = SEBlock(
                  channels=out_channels,
                  reduction=4,
                  round_mid=True,
                  out_activation="hsigmoid")
  
      def forward(self, x):
          x = self.conv(x)
          if self.use_se:
              x = self.se(x)
          return x
  
  
  class MobileNetV3Classifier(nn.Module):
      """
      MobileNetV3 classifier.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      mid_channels : int
          Number of middle channels.
      dropout_rate : float
          Parameter of Dropout layer. Faction of the input units to drop.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   mid_channels,
                   dropout_rate):
          super(MobileNetV3Classifier, self).__init__()
          self.use_dropout = (dropout_rate != 0.0)
  
          self.conv1 = conv1x1(
              in_channels=in_channels,
              out_channels=mid_channels)
          self.activ = HSwish(inplace=True)
          if self.use_dropout:
              self.dropout = nn.Dropout(p=dropout_rate)
          self.conv2 = conv1x1(
              in_channels=mid_channels,
              out_channels=out_channels,
              bias=True)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.activ(x)
          if self.use_dropout:
              x = self.dropout(x)
          x = self.conv2(x)
          return x
  
  
  class MobileNetV3(nn.Module):
      """
      MobileNetV3 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      Parameters:
      ----------
      channels : list of list of int
          Number of output channels for each unit.
      exp_channels : list of list of int
          Number of middle (expanded) channels for each unit.
      init_block_channels : int
          Number of output channels for the initial unit.
      final_block_channels : int
          Number of output channels for the final block of the feature extractor.
      classifier_mid_channels : int
          Number of middle channels for classifier.
      kernels3 : list of list of int/bool
          Using 3x3 (instead of 5x5) kernel for each unit.
      use_relu : list of list of int/bool
          Using ReLU activation flag for each unit.
      use_se : list of list of int/bool
          Using SE-block flag for each unit.
      first_stride : bool
          Whether to use stride for the first stage.
      final_use_se : bool
          Whether to use SE-module in the final 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,
                   exp_channels,
                   init_block_channels,
                   final_block_channels,
                   classifier_mid_channels,
                   kernels3,
                   use_relu,
                   use_se,
                   first_stride,
                   final_use_se,
                   in_channels=3,
                   in_size=(224, 224),
                   num_classes=1000):
          super(MobileNetV3, self).__init__()
          self.in_size = in_size
          self.num_classes = num_classes
  
          self.features = nn.Sequential()
          self.features.add_module("init_block", conv3x3_block(
              in_channels=in_channels,
              out_channels=init_block_channels,
              stride=2,
              activation="hswish"))
          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):
                  exp_channels_ij = exp_channels[i][j]
                  stride = 2 if (j == 0) and ((i != 0) or first_stride) else 1
                  use_kernel3 = kernels3[i][j] == 1
                  activation = "relu" if use_relu[i][j] == 1 else "hswish"
                  use_se_flag = use_se[i][j] == 1
                  stage.add_module("unit{}".format(j + 1), MobileNetV3Unit(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      exp_channels=exp_channels_ij,
                      use_kernel3=use_kernel3,
                      stride=stride,
                      activation=activation,
                      use_se=use_se_flag))
                  in_channels = out_channels
              self.features.add_module("stage{}".format(i + 1), stage)
          self.features.add_module('final_block', MobileNetV3FinalBlock(
              in_channels=in_channels,
              out_channels=final_block_channels,
              use_se=final_use_se))
          in_channels = final_block_channels
          self.features.add_module('final_pool', nn.AvgPool2d(
              kernel_size=7,
              stride=1))
  
          self.output = MobileNetV3Classifier(
              in_channels=in_channels,
              out_channels=num_classes,
              mid_channels=classifier_mid_channels,
              dropout_rate=0.2)
  
          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 = self.output(x)
          x = x.view(x.size(0), -1)
          return x
  
  
  def get_mobilenetv3(version,
                      width_scale,
                      model_name=None,
                      pretrained=False,
                      root=os.path.join("~", ".torch", "models"),
                      **kwargs):
      """
      Create MobileNetV3 model with specific parameters.
  
      Parameters:
      ----------
      version : str
          Version of MobileNetV3 ('small' or 'large').
      width_scale : float
          Scale factor for width of layers.
      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 version == "small":
          init_block_channels = 16
          channels = [[16], [24, 24], [40, 40, 40, 48, 48], [96, 96, 96]]
          exp_channels = [[16], [72, 88], [96, 240, 240, 120, 144], [288, 576, 576]]
          kernels3 = [[1], [1, 1], [0, 0, 0, 0, 0], [0, 0, 0]]
          use_relu = [[1], [1, 1], [0, 0, 0, 0, 0], [0, 0, 0]]
          use_se = [[1], [0, 0], [1, 1, 1, 1, 1], [1, 1, 1]]
          first_stride = True
          final_block_channels = 576
      elif version == "large":
          init_block_channels = 16
          channels = [[16], [24, 24], [40, 40, 40], [80, 80, 80, 80, 112, 112], [160, 160, 160]]
          exp_channels = [[16], [64, 72], [72, 120, 120], [240, 200, 184, 184, 480, 672], [672, 960, 960]]
          kernels3 = [[1], [1, 1], [0, 0, 0], [1, 1, 1, 1, 1, 1], [0, 0, 0]]
          use_relu = [[1], [1, 1], [1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0]]
          use_se = [[0], [0, 0], [1, 1, 1], [0, 0, 0, 0, 1, 1], [1, 1, 1]]
          first_stride = False
          final_block_channels = 960
      else:
          raise ValueError("Unsupported MobileNetV3 version {}".format(version))
  
      final_use_se = False
      classifier_mid_channels = 1280
  
      if width_scale != 1.0:
          channels = [[round_channels(cij * width_scale) for cij in ci] for ci in channels]
          exp_channels = [[round_channels(cij * width_scale) for cij in ci] for ci in exp_channels]
          init_block_channels = round_channels(init_block_channels * width_scale)
          if width_scale > 1.0:
              final_block_channels = round_channels(final_block_channels * width_scale)
  
      net = MobileNetV3(
          channels=channels,
          exp_channels=exp_channels,
          init_block_channels=init_block_channels,
          final_block_channels=final_block_channels,
          classifier_mid_channels=classifier_mid_channels,
          kernels3=kernels3,
          use_relu=use_relu,
          use_se=use_se,
          first_stride=first_stride,
          final_use_se=final_use_se,
          **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 mobilenetv3_small_w7d20(**kwargs):
      """
      MobileNetV3 Small 224/0.35 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="small", width_scale=0.35, model_name="mobilenetv3_small_w7d20", **kwargs)
  
  
  def mobilenetv3_small_wd2(**kwargs):
      """
      MobileNetV3 Small 224/0.5 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="small", width_scale=0.5, model_name="mobilenetv3_small_wd2", **kwargs)
  
  
  def mobilenetv3_small_w3d4(**kwargs):
      """
      MobileNetV3 Small 224/0.75 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="small", width_scale=0.75, model_name="mobilenetv3_small_w3d4", **kwargs)
  
  
  def mobilenetv3_small_w1(**kwargs):
      """
      MobileNetV3 Small 224/1.0 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="small", width_scale=1.0, model_name="mobilenetv3_small_w1", **kwargs)
  
  
  def mobilenetv3_small_w5d4(**kwargs):
      """
      MobileNetV3 Small 224/1.25 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="small", width_scale=1.25, model_name="mobilenetv3_small_w5d4", **kwargs)
  
  
  def mobilenetv3_large_w7d20(**kwargs):
      """
      MobileNetV3 Small 224/0.35 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="large", width_scale=0.35, model_name="mobilenetv3_small_w7d20", **kwargs)
  
  
  def mobilenetv3_large_wd2(**kwargs):
      """
      MobileNetV3 Large 224/0.5 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="large", width_scale=0.5, model_name="mobilenetv3_large_wd2", **kwargs)
  
  
  def mobilenetv3_large_w3d4(**kwargs):
      """
      MobileNetV3 Large 224/0.75 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="large", width_scale=0.75, model_name="mobilenetv3_large_w3d4", **kwargs)
  
  
  def mobilenetv3_large_w1(**kwargs):
      """
      MobileNetV3 Large 224/1.0 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="large", width_scale=1.0, model_name="mobilenetv3_large_w1", **kwargs)
  
  
  def mobilenetv3_large_w5d4(**kwargs):
      """
      MobileNetV3 Large 224/1.25 model from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
  
      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_mobilenetv3(version="large", width_scale=1.25, model_name="mobilenetv3_large_w5d4", **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 = [
          mobilenetv3_small_w7d20,
          mobilenetv3_small_wd2,
          mobilenetv3_small_w3d4,
          mobilenetv3_small_w1,
          mobilenetv3_small_w5d4,
          mobilenetv3_large_w7d20,
          mobilenetv3_large_wd2,
          mobilenetv3_large_w3d4,
          mobilenetv3_large_w1,
          mobilenetv3_large_w5d4,
      ]
  
      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 != mobilenetv3_small_w7d20 or weight_count == 2159600)
          assert (model != mobilenetv3_small_wd2 or weight_count == 2288976)
          assert (model != mobilenetv3_small_w3d4 or weight_count == 2581312)
          assert (model != mobilenetv3_small_w1 or weight_count == 2945288)
          assert (model != mobilenetv3_small_w5d4 or weight_count == 3643632)
          assert (model != mobilenetv3_large_w7d20 or weight_count == 2943080)
          assert (model != mobilenetv3_large_wd2 or weight_count == 3334896)
          assert (model != mobilenetv3_large_w3d4 or weight_count == 4263496)
          assert (model != mobilenetv3_large_w1 or weight_count == 5481752)
          assert (model != mobilenetv3_large_w5d4 or weight_count == 7459144)
  
          x = torch.randn(1, 3, 224, 224)
          y = net(x)
          y.sum().backward()
          assert (tuple(y.size()) == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  