text: """
      ESPNetv2, implemented in PyTorch.
      Original paper: 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network,'
      https://arxiv.org/abs/1811.11431.
  """
  
  __all__ = ['ESPNetv2', 'espnetv2_wd2', 'espnetv2_w1', 'espnetv2_w5d4', 'espnetv2_w3d2', 'espnetv2_w2']
  
  import os
  import math
  import torch
  import torch.nn as nn
  import torch.nn.init as init
  from .common import conv3x3, conv1x1_block, conv3x3_block, DualPathSequential
  
  
  class PreActivation(nn.Module):
      """
      PreResNet like pure pre-activation block without convolution layer.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      """
      def __init__(self,
                   in_channels):
          super(PreActivation, self).__init__()
          self.bn = nn.BatchNorm2d(num_features=in_channels)
          self.activ = nn.PReLU(num_parameters=in_channels)
  
      def forward(self, x):
          x = self.bn(x)
          x = self.activ(x)
          return x
  
  
  class ShortcutBlock(nn.Module):
      """
      ESPNetv2 shortcut block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      """
      def __init__(self,
                   in_channels,
                   out_channels):
          super(ShortcutBlock, self).__init__()
          self.conv1 = conv3x3_block(
              in_channels=in_channels,
              out_channels=in_channels,
              activation=(lambda: nn.PReLU(in_channels)))
          self.conv2 = conv1x1_block(
              in_channels=in_channels,
              out_channels=out_channels,
              activation=None,
              activate=False)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          return x
  
  
  class HierarchicalConcurrent(nn.Sequential):
      """
      A container for hierarchical concatenation of modules on the base of the sequential container.
  
      Parameters:
      ----------
      axis : int, default 1
          The axis on which to concatenate the outputs.
      """
      def __init__(self, axis=1):
          super(HierarchicalConcurrent, self).__init__()
          self.axis = axis
  
      def forward(self, x):
          out = []
          y_prev = None
          for module in self._modules.values():
              y = module(x)
              if y_prev is not None:
                  y += y_prev
              out.append(y)
              y_prev = y
          out = torch.cat(tuple(out), dim=self.axis)
          return out
  
  
  class ESPBlock(nn.Module):
      """
      ESPNetv2 block (so-called EESP block).
  
      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 branch convolution layers.
      dilations : list of int
          Dilation values for branches.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   stride,
                   dilations):
          super(ESPBlock, self).__init__()
          num_branches = len(dilations)
          assert (out_channels % num_branches == 0)
          self.downsample = (stride != 1)
          mid_channels = out_channels // num_branches
  
          self.reduce_conv = conv1x1_block(
              in_channels=in_channels,
              out_channels=mid_channels,
              groups=num_branches,
              activation=(lambda: nn.PReLU(mid_channels)))
  
          self.branches = HierarchicalConcurrent()
          for i in range(num_branches):
              self.branches.add_module("branch{}".format(i + 1), conv3x3(
                  in_channels=mid_channels,
                  out_channels=mid_channels,
                  stride=stride,
                  padding=dilations[i],
                  dilation=dilations[i],
                  groups=mid_channels))
  
          self.merge_conv = conv1x1_block(
              in_channels=out_channels,
              out_channels=out_channels,
              groups=num_branches,
              activation=None,
              activate=False)
          self.preactiv = PreActivation(in_channels=out_channels)
          self.activ = nn.PReLU(out_channels)
  
      def forward(self, x, x0):
          y = self.reduce_conv(x)
          y = self.branches(y)
          y = self.preactiv(y)
          y = self.merge_conv(y)
          if not self.downsample:
              y = y + x
              y = self.activ(y)
          return y, x0
  
  
  class DownsampleBlock(nn.Module):
      """
      ESPNetv2 downsample block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      x0_channels : int
          Number of input channels for shortcut.
      dilations : list of int
          Dilation values for branches in EESP block.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   x0_channels,
                   dilations):
          super(DownsampleBlock, self).__init__()
          inc_channels = out_channels - in_channels
  
          self.pool = nn.AvgPool2d(
              kernel_size=3,
              stride=2,
              padding=1)
          self.eesp = ESPBlock(
              in_channels=in_channels,
              out_channels=inc_channels,
              stride=2,
              dilations=dilations)
          self.shortcut_block = ShortcutBlock(
              in_channels=x0_channels,
              out_channels=out_channels)
          self.activ = nn.PReLU(out_channels)
  
      def forward(self, x, x0):
          y1 = self.pool(x)
          y2, _ = self.eesp(x, None)
          x = torch.cat((y1, y2), dim=1)
          x0 = self.pool(x0)
          y3 = self.shortcut_block(x0)
          x = x + y3
          x = self.activ(x)
          return x, x0
  
  
  class ESPInitBlock(nn.Module):
      """
      ESPNetv2 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(ESPInitBlock, self).__init__()
          self.conv = conv3x3_block(
              in_channels=in_channels,
              out_channels=out_channels,
              stride=2,
              activation=(lambda: nn.PReLU(out_channels)))
          self.pool = nn.AvgPool2d(
              kernel_size=3,
              stride=2,
              padding=1)
  
      def forward(self, x, x0):
          x = self.conv(x)
          x0 = self.pool(x0)
          return x, x0
  
  
  class ESPFinalBlock(nn.Module):
      """
      ESPNetv2 final block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      final_groups : int
          Number of groups in the last convolution layer.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   final_groups):
          super(ESPFinalBlock, self).__init__()
          self.conv1 = conv3x3_block(
              in_channels=in_channels,
              out_channels=in_channels,
              groups=in_channels,
              activation=(lambda: nn.PReLU(in_channels)))
          self.conv2 = conv1x1_block(
              in_channels=in_channels,
              out_channels=out_channels,
              groups=final_groups,
              activation=(lambda: nn.PReLU(out_channels)))
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          return x
  
  
  class ESPNetv2(nn.Module):
      """
      ESPNetv2 model from 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network,'
      https://arxiv.org/abs/1811.11431.
  
      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.
      final_block_channels : int
          Number of output channels for the final unit.
      final_block_groups : int
          Number of groups for the final unit.
      dilations : list of list of list of int
          Dilation values for branches in each unit.
      dropout_rate : float, default 0.2
          Parameter of Dropout layer. Faction of the input units to drop.
      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,
                   final_block_channels,
                   final_block_groups,
                   dilations,
                   dropout_rate=0.2,
                   in_channels=3,
                   in_size=(224, 224),
                   num_classes=1000):
          super(ESPNetv2, self).__init__()
          self.in_size = in_size
          self.num_classes = num_classes
          x0_channels = in_channels
  
          self.features = DualPathSequential(
              return_two=False,
              first_ordinals=0,
              last_ordinals=2)
          self.features.add_module("init_block", ESPInitBlock(
              in_channels=in_channels,
              out_channels=init_block_channels))
          in_channels = init_block_channels
          for i, channels_per_stage in enumerate(channels):
              stage = DualPathSequential()
              for j, out_channels in enumerate(channels_per_stage):
                  if j == 0:
                      unit = DownsampleBlock(
                          in_channels=in_channels,
                          out_channels=out_channels,
                          x0_channels=x0_channels,
                          dilations=dilations[i][j])
                  else:
                      unit = ESPBlock(
                          in_channels=in_channels,
                          out_channels=out_channels,
                          stride=1,
                          dilations=dilations[i][j])
                  stage.add_module("unit{}".format(j + 1), unit)
                  in_channels = out_channels
              self.features.add_module("stage{}".format(i + 1), stage)
          self.features.add_module("final_block", ESPFinalBlock(
              in_channels=in_channels,
              out_channels=final_block_channels,
              final_groups=final_block_groups))
          in_channels = final_block_channels
          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=dropout_rate))
          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 = x.view(x.size(0), -1)
          x = self.output(x)
          return x
  
  
  def get_espnetv2(width_scale,
                   model_name=None,
                   pretrained=False,
                   root=os.path.join('~', '.torch', 'models'),
                   **kwargs):
      """
      Create ESPNetv2 model with specific parameters.
  
      Parameters:
      ----------
      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.
      """
      assert (width_scale <= 2.0)
  
      branches = 4
      layers = [1, 4, 8, 4]
  
      max_dilation_list = [6, 5, 4, 3, 2]
      max_dilations = [[max_dilation_list[i]] + [max_dilation_list[i + 1]] * (li - 1) for (i, li) in enumerate(layers)]
      dilations = [[sorted([k + 1 if k < dij else 1 for k in range(branches)]) for dij in di] for di in max_dilations]
  
      base_channels = 32
      weighed_base_channels = math.ceil(float(math.floor(base_channels * width_scale)) / branches) * branches
      channels_per_layers = [weighed_base_channels * pow(2, i + 1) for i in range(len(layers))]
  
      init_block_channels = base_channels if weighed_base_channels > base_channels else weighed_base_channels
      final_block_channels = 1024 if width_scale <= 1.5 else 1280
  
      channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]
  
      net = ESPNetv2(
          channels=channels,
          init_block_channels=init_block_channels,
          final_block_channels=final_block_channels,
          final_block_groups=branches,
          dilations=dilations,
          **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 espnetv2_wd2(**kwargs):
      """
      ESPNetv2 x0.5 model from 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural
      Network,' https://arxiv.org/abs/1811.11431.
  
      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_espnetv2(width_scale=0.5, model_name="espnetv2_wd2", **kwargs)
  
  
  def espnetv2_w1(**kwargs):
      """
      ESPNetv2 x1.0 model from 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural
      Network,' https://arxiv.org/abs/1811.11431.
  
      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_espnetv2(width_scale=1.0, model_name="espnetv2_w1", **kwargs)
  
  
  def espnetv2_w5d4(**kwargs):
      """
      ESPNetv2 x1.25 model from 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural
      Network,' https://arxiv.org/abs/1811.11431.
  
      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_espnetv2(width_scale=1.25, model_name="espnetv2_w5d4", **kwargs)
  
  
  def espnetv2_w3d2(**kwargs):
      """
      ESPNetv2 x1.5 model from 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural
      Network,' https://arxiv.org/abs/1811.11431.
  
      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_espnetv2(width_scale=1.5, model_name="espnetv2_w3d2", **kwargs)
  
  
  def espnetv2_w2(**kwargs):
      """
      ESPNetv2 x2.0 model from 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural
      Network,' https://arxiv.org/abs/1811.11431.
  
      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_espnetv2(width_scale=2.0, model_name="espnetv2_w2", **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 = [
          espnetv2_wd2,
          espnetv2_w1,
          espnetv2_w5d4,
          espnetv2_w3d2,
          espnetv2_w2,
      ]
  
      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 != espnetv2_wd2 or weight_count == 1241332)
          assert (model != espnetv2_w1 or weight_count == 1670072)
          assert (model != espnetv2_w5d4 or weight_count == 1965440)
          assert (model != espnetv2_w3d2 or weight_count == 2314856)
          assert (model != espnetv2_w2 or weight_count == 3498136)
  
          x = Variable(torch.randn(1, 3, 224, 224))
          y = net(x)
          assert (tuple(y.size()) == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  