text: """
      PeleeNet for ImageNet-1K, implemented in PyTorch.
      Original paper: 'Pelee: A Real-Time Object Detection System on Mobile Devices,' https://arxiv.org/abs/1804.06882.
  """
  
  __all__ = ['PeleeNet', 'peleenet']
  
  import os
  import torch
  import torch.nn as nn
  import torch.nn.init as init
  from .common import conv1x1_block, conv3x3_block, Concurrent
  
  
  class PeleeBranch1(nn.Module):
      """
      PeleeNet branch type 1 block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      mid_channels : int
          Number of intermediate channels.
      stride : int or tuple/list of 2 int, default 1
          Strides of the second convolution.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   mid_channels,
                   stride=1):
          super(PeleeBranch1, self).__init__()
          self.conv1 = conv1x1_block(
              in_channels=in_channels,
              out_channels=mid_channels)
          self.conv2 = conv3x3_block(
              in_channels=mid_channels,
              out_channels=out_channels,
              stride=stride)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          return x
  
  
  class PeleeBranch2(nn.Module):
      """
      PeleeNet branch type 2 block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      mid_channels : int
          Number of intermediate channels.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   mid_channels):
          super(PeleeBranch2, self).__init__()
          self.conv1 = conv1x1_block(
              in_channels=in_channels,
              out_channels=mid_channels)
          self.conv2 = conv3x3_block(
              in_channels=mid_channels,
              out_channels=out_channels)
          self.conv3 = conv3x3_block(
              in_channels=out_channels,
              out_channels=out_channels)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          x = self.conv3(x)
          return x
  
  
  class StemBlock(nn.Module):
      """
      PeleeNet stem block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      """
      def __init__(self,
                   in_channels,
                   out_channels):
          super(StemBlock, self).__init__()
          mid1_channels = out_channels // 2
          mid2_channels = out_channels * 2
  
          self.first_conv = conv3x3_block(
              in_channels=in_channels,
              out_channels=out_channels,
              stride=2)
  
          self.branches = Concurrent()
          self.branches.add_module("branch1", PeleeBranch1(
              in_channels=out_channels,
              out_channels=out_channels,
              mid_channels=mid1_channels,
              stride=2))
          self.branches.add_module("branch2", nn.MaxPool2d(
              kernel_size=2,
              stride=2,
              padding=0))
  
          self.last_conv = conv1x1_block(
              in_channels=mid2_channels,
              out_channels=out_channels)
  
      def forward(self, x):
          x = self.first_conv(x)
          x = self.branches(x)
          x = self.last_conv(x)
          return x
  
  
  class DenseBlock(nn.Module):
      """
      PeleeNet dense block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      bottleneck_size : int
          Bottleneck width.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   bottleneck_size):
          super(DenseBlock, self).__init__()
          inc_channels = (out_channels - in_channels) // 2
          mid_channels = inc_channels * bottleneck_size
  
          self.branch1 = PeleeBranch1(
              in_channels=in_channels,
              out_channels=inc_channels,
              mid_channels=mid_channels)
          self.branch2 = PeleeBranch2(
              in_channels=in_channels,
              out_channels=inc_channels,
              mid_channels=mid_channels)
  
      def forward(self, x):
          x1 = self.branch1(x)
          x2 = self.branch2(x)
          x = torch.cat((x, x1, x2), dim=1)
          return x
  
  
  class TransitionBlock(nn.Module):
      """
      PeleeNet's transition block, like in DensNet, but with ordinary convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      """
      def __init__(self,
                   in_channels,
                   out_channels):
          super(TransitionBlock, self).__init__()
          self.conv = conv1x1_block(
              in_channels=in_channels,
              out_channels=out_channels)
          self.pool = nn.AvgPool2d(
              kernel_size=2,
              stride=2,
              padding=0)
  
      def forward(self, x):
          x = self.conv(x)
          x = self.pool(x)
          return x
  
  
  class PeleeNet(nn.Module):
      """
      PeleeNet model from 'Pelee: A Real-Time Object Detection System on Mobile Devices,'
      https://arxiv.org/abs/1804.06882.
  
      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_sizes : list of int
          Bottleneck sizes for each stage.
      dropout_rate : float, default 0.5
          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,
                   bottleneck_sizes,
                   dropout_rate=0.5,
                   in_channels=3,
                   in_size=(224, 224),
                   num_classes=1000):
          super(PeleeNet, self).__init__()
          self.in_size = in_size
          self.num_classes = num_classes
  
          self.features = nn.Sequential()
          self.features.add_module("init_block", StemBlock(
              in_channels=in_channels,
              out_channels=init_block_channels))
          in_channels = init_block_channels
          for i, channels_per_stage in enumerate(channels):
              bottleneck_size = bottleneck_sizes[i]
              stage = nn.Sequential()
              if i != 0:
                  stage.add_module("trans{}".format(i + 1), TransitionBlock(
                      in_channels=in_channels,
                      out_channels=in_channels))
              for j, out_channels in enumerate(channels_per_stage):
                  stage.add_module("unit{}".format(j + 1), DenseBlock(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      bottleneck_size=bottleneck_size))
                  in_channels = out_channels
              self.features.add_module("stage{}".format(i + 1), stage)
          self.features.add_module("final_block", conv1x1_block(
              in_channels=in_channels,
              out_channels=in_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.view(x.size(0), -1)
          x = self.output(x)
          return x
  
  
  def get_peleenet(model_name=None,
                   pretrained=False,
                   root=os.path.join("~", ".torch", "models"),
                   **kwargs):
      """
      Create PeleeNet model with specific parameters.
  
      Parameters:
      ----------
      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.
      """
      init_block_channels = 32
      growth_rate = 32
      layers = [3, 4, 8, 6]
      bottleneck_sizes = [1, 2, 4, 4]
  
      from functools import reduce
      channels = reduce(
          lambda xi, yi: xi + [reduce(
              lambda xj, yj: xj + [xj[-1] + yj],
              [growth_rate] * yi,
              [xi[-1][-1]])[1:]],
          layers,
          [[init_block_channels]])[1:]
  
      net = PeleeNet(
          channels=channels,
          init_block_channels=init_block_channels,
          bottleneck_sizes=bottleneck_sizes,
          **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 peleenet(**kwargs):
      """
      PeleeNet model from 'Pelee: A Real-Time Object Detection System on Mobile Devices,'
      https://arxiv.org/abs/1804.06882.
  
      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_peleenet(model_name="peleenet", **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 = [
          peleenet,
      ]
  
      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 != peleenet or weight_count == 2802248)
  
          x = torch.randn(1, 3, 224, 224)
          y = net(x)
          y.sum().backward()
          assert (tuple(y.size()) == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  