text: from __future__ import division, absolute_import
  import warnings
  import torch
  from torch import nn
  from torch.nn import functional as F
  
  __all__ = ['osnet_ain_x1_0']
  
  pretrained_urls = {
      'osnet_ain_x1_0':
      'https://drive.google.com/uc?id=1-CaioD9NaqbHK_kzSMW8VE4_3KcsRjEo'
  }
  
  
  ##########
  # Basic layers
  ##########
  class ConvLayer(nn.Module):
      """Convolution layer (conv + bn + relu)."""
  
      def __init__(
          self,
          in_channels,
          out_channels,
          kernel_size,
          stride=1,
          padding=0,
          groups=1,
          IN=False
      ):
          super(ConvLayer, self).__init__()
          self.conv = nn.Conv2d(
              in_channels,
              out_channels,
              kernel_size,
              stride=stride,
              padding=padding,
              bias=False,
              groups=groups
          )
          if IN:
              self.bn = nn.InstanceNorm2d(out_channels, affine=True)
          else:
              self.bn = nn.BatchNorm2d(out_channels)
          self.relu = nn.ReLU()
  
      def forward(self, x):
          x = self.conv(x)
          x = self.bn(x)
          return self.relu(x)
  
  
  class Conv1x1(nn.Module):
      """1x1 convolution + bn + relu."""
  
      def __init__(self, in_channels, out_channels, stride=1, groups=1):
          super(Conv1x1, self).__init__()
          self.conv = nn.Conv2d(
              in_channels,
              out_channels,
              1,
              stride=stride,
              padding=0,
              bias=False,
              groups=groups
          )
          self.bn = nn.BatchNorm2d(out_channels)
          self.relu = nn.ReLU()
  
      def forward(self, x):
          x = self.conv(x)
          x = self.bn(x)
          return self.relu(x)
  
  
  class Conv1x1Linear(nn.Module):
      """1x1 convolution + bn (w/o non-linearity)."""
  
      def __init__(self, in_channels, out_channels, stride=1, bn=True):
          super(Conv1x1Linear, self).__init__()
          self.conv = nn.Conv2d(
              in_channels, out_channels, 1, stride=stride, padding=0, bias=False
          )
          self.bn = None
          if bn:
              self.bn = nn.BatchNorm2d(out_channels)
  
      def forward(self, x):
          x = self.conv(x)
          if self.bn is not None:
              x = self.bn(x)
          return x
  
  
  class Conv3x3(nn.Module):
      """3x3 convolution + bn + relu."""
  
      def __init__(self, in_channels, out_channels, stride=1, groups=1):
          super(Conv3x3, self).__init__()
          self.conv = nn.Conv2d(
              in_channels,
              out_channels,
              3,
              stride=stride,
              padding=1,
              bias=False,
              groups=groups
          )
          self.bn = nn.BatchNorm2d(out_channels)
          self.relu = nn.ReLU()
  
      def forward(self, x):
          x = self.conv(x)
          x = self.bn(x)
          return self.relu(x)
  
  
  class LightConv3x3(nn.Module):
      """Lightweight 3x3 convolution.
      1x1 (linear) + dw 3x3 (nonlinear).
      """
  
      def __init__(self, in_channels, out_channels):
          super(LightConv3x3, self).__init__()
          self.conv1 = nn.Conv2d(
              in_channels, out_channels, 1, stride=1, padding=0, bias=False
          )
          self.conv2 = nn.Conv2d(
              out_channels,
              out_channels,
              3,
              stride=1,
              padding=1,
              bias=False,
              groups=out_channels
          )
          self.bn = nn.BatchNorm2d(out_channels)
          self.relu = nn.ReLU()
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          x = self.bn(x)
          return self.relu(x)
  
  
  class LightConvStream(nn.Module):
      """Lightweight convolution stream."""
  
      def __init__(self, in_channels, out_channels, depth):
          super(LightConvStream, self).__init__()
          assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
              depth
          )
          layers = []
          layers += [LightConv3x3(in_channels, out_channels)]
          for i in range(depth - 1):
              layers += [LightConv3x3(out_channels, out_channels)]
          self.layers = nn.Sequential(*layers)
  
      def forward(self, x):
          return self.layers(x)
  
  
  ##########
  # Building blocks for omni-scale feature learning
  ##########
  class ChannelGate(nn.Module):
      """A mini-network that generates channel-wise gates conditioned on input tensor."""
  
      def __init__(
          self,
          in_channels,
          num_gates=None,
          return_gates=False,
          gate_activation='sigmoid',
          reduction=16,
          layer_norm=False
      ):
          super(ChannelGate, self).__init__()
          if num_gates is None:
              num_gates = in_channels
          self.return_gates = return_gates
          self.global_avgpool = nn.AdaptiveAvgPool2d(1)
          self.fc1 = nn.Conv2d(
              in_channels,
              in_channels // reduction,
              kernel_size=1,
              bias=True,
              padding=0
          )
          self.norm1 = None
          if layer_norm:
              self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
          self.relu = nn.ReLU()
          self.fc2 = nn.Conv2d(
              in_channels // reduction,
              num_gates,
              kernel_size=1,
              bias=True,
              padding=0
          )
          if gate_activation == 'sigmoid':
              self.gate_activation = nn.Sigmoid()
          elif gate_activation == 'relu':
              self.gate_activation = nn.ReLU()
          elif gate_activation == 'linear':
              self.gate_activation = None
          else:
              raise RuntimeError(
                  "Unknown gate activation: {}".format(gate_activation)
              )
  
      def forward(self, x):
          input = x
          x = self.global_avgpool(x)
          x = self.fc1(x)
          if self.norm1 is not None:
              x = self.norm1(x)
          x = self.relu(x)
          x = self.fc2(x)
          if self.gate_activation is not None:
              x = self.gate_activation(x)
          if self.return_gates:
              return x
          return input * x
  
  
  class OSBlock(nn.Module):
      """Omni-scale feature learning block."""
  
      def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
          super(OSBlock, self).__init__()
          assert T >= 1
          assert out_channels >= reduction and out_channels % reduction == 0
          mid_channels = out_channels // reduction
  
          self.conv1 = Conv1x1(in_channels, mid_channels)
          self.conv2 = nn.ModuleList()
          for t in range(1, T + 1):
              self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
          self.gate = ChannelGate(mid_channels)
          self.conv3 = Conv1x1Linear(mid_channels, out_channels)
          self.downsample = None
          if in_channels != out_channels:
              self.downsample = Conv1x1Linear(in_channels, out_channels)
  
      def forward(self, x):
          identity = x
          x1 = self.conv1(x)
          x2 = 0
          for conv2_t in self.conv2:
              x2_t = conv2_t(x1)
              x2 = x2 + self.gate(x2_t)
          x3 = self.conv3(x2)
          if self.downsample is not None:
              identity = self.downsample(identity)
          out = x3 + identity
          return F.relu(out)
  
  
  class OSBlockINin(nn.Module):
      """Omni-scale feature learning block with instance normalization."""
  
      def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
          super(OSBlockINin, self).__init__()
          assert T >= 1
          assert out_channels >= reduction and out_channels % reduction == 0
          mid_channels = out_channels // reduction
  
          self.conv1 = Conv1x1(in_channels, mid_channels)
          self.conv2 = nn.ModuleList()
          for t in range(1, T + 1):
              self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
          self.gate = ChannelGate(mid_channels)
          self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
          self.downsample = None
          if in_channels != out_channels:
              self.downsample = Conv1x1Linear(in_channels, out_channels)
          self.IN = nn.InstanceNorm2d(out_channels, affine=True)
  
      def forward(self, x):
          identity = x
          x1 = self.conv1(x)
          x2 = 0
          for conv2_t in self.conv2:
              x2_t = conv2_t(x1)
              x2 = x2 + self.gate(x2_t)
          x3 = self.conv3(x2)
          x3 = self.IN(x3) # IN inside residual
          if self.downsample is not None:
              identity = self.downsample(identity)
          out = x3 + identity
          return F.relu(out)
  
  
  ##########
  # Network architecture
  ##########
  class OSNet(nn.Module):
      """Omni-Scale Network.
      
      Reference:
          - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
          - Zhou et al. Learning Generalisable Omni-Scale Representations
            for Person Re-Identification. arXiv preprint, 2019.
      """
  
      def __init__(
          self,
          num_classes,
          blocks,
          layers,
          channels,
          feature_dim=512,
          loss='softmax',
          conv1_IN=False,
          **kwargs
      ):
          super(OSNet, self).__init__()
          num_blocks = len(blocks)
          assert num_blocks == len(layers)
          assert num_blocks == len(channels) - 1
          self.loss = loss
          self.feature_dim = feature_dim
  
          # convolutional backbone
          self.conv1 = ConvLayer(
              3, channels[0], 7, stride=2, padding=3, IN=conv1_IN
          )
          self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
          self.conv2 = self._make_layer(
              blocks[0], layers[0], channels[0], channels[1]
          )
          self.pool2 = nn.Sequential(
              Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
          )
          self.conv3 = self._make_layer(
              blocks[1], layers[1], channels[1], channels[2]
          )
          self.pool3 = nn.Sequential(
              Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
          )
          self.conv4 = self._make_layer(
              blocks[2], layers[2], channels[2], channels[3]
          )
          self.conv5 = Conv1x1(channels[3], channels[3])
          self.global_avgpool = nn.AdaptiveAvgPool2d(1)
          # fully connected layer
          self.fc = self._construct_fc_layer(
              self.feature_dim, channels[3], dropout_p=None
          )
          # identity classification layer
          self.classifier = nn.Linear(self.feature_dim, num_classes)
  
          self._init_params()
  
      def _make_layer(self, blocks, layer, in_channels, out_channels):
          layers = []
          layers += [blocks[0](in_channels, out_channels)]
          for i in range(1, len(blocks)):
              layers += [blocks[i](out_channels, out_channels)]
          return nn.Sequential(*layers)
  
      def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
          if fc_dims is None or fc_dims < 0:
              self.feature_dim = input_dim
              return None
  
          if isinstance(fc_dims, int):
              fc_dims = [fc_dims]
  
          layers = []
          for dim in fc_dims:
              layers.append(nn.Linear(input_dim, dim))
              layers.append(nn.BatchNorm1d(dim))
              layers.append(nn.ReLU())
              if dropout_p is not None:
                  layers.append(nn.Dropout(p=dropout_p))
              input_dim = dim
  
          self.feature_dim = fc_dims[-1]
  
          return nn.Sequential(*layers)
  
      def _init_params(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(
                      m.weight, mode='fan_out', nonlinearity='relu'
                  )
                  if m.bias is not None:
                      nn.init.constant_(m.bias, 0)
  
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
              elif isinstance(m, nn.BatchNorm1d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
              elif isinstance(m, nn.InstanceNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
              elif isinstance(m, nn.Linear):
                  nn.init.normal_(m.weight, 0, 0.01)
                  if m.bias is not None:
                      nn.init.constant_(m.bias, 0)
  
      def featuremaps(self, x):
          x = self.conv1(x)
          x = self.maxpool(x)
          x = self.conv2(x)
          x = self.pool2(x)
          x = self.conv3(x)
          x = self.pool3(x)
          x = self.conv4(x)
          x = self.conv5(x)
          return x
  
      def forward(self, x, return_featuremaps=False):
          x = self.featuremaps(x)
          if return_featuremaps:
              return x
          v = self.global_avgpool(x)
          v = v.view(v.size(0), -1)
          if self.fc is not None:
              v = self.fc(v)
          if not self.training:
              return v
          y = self.classifier(v)
          if self.loss == 'softmax':
              return y
          elif self.loss == 'triplet':
              return y, v
          else:
              raise KeyError("Unsupported loss: {}".format(self.loss))
  
  
  def init_pretrained_weights(model, key=''):
      """Initializes model with pretrained weights.
      
      Layers that don't match with pretrained layers in name or size are kept unchanged.
      """
      import os
      import errno
      import gdown
      from collections import OrderedDict
  
      def _get_torch_home():
          ENV_TORCH_HOME = 'TORCH_HOME'
          ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
          DEFAULT_CACHE_DIR = '~/.cache'
          torch_home = os.path.expanduser(
              os.getenv(
                  ENV_TORCH_HOME,
                  os.path.join(
                      os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
                  )
              )
          )
          return torch_home
  
      torch_home = _get_torch_home()
      model_dir = os.path.join(torch_home, 'checkpoints')
      try:
          os.makedirs(model_dir)
      except OSError as e:
          if e.errno == errno.EEXIST:
              # Directory already exists, ignore.
              pass
          else:
              # Unexpected OSError, re-raise.
              raise
      filename = key + '_imagenet.pth'
      cached_file = os.path.join(model_dir, filename)
  
      if not os.path.exists(cached_file):
          gdown.download(pretrained_urls[key], cached_file, quiet=False)
  
      state_dict = torch.load(cached_file)
      model_dict = model.state_dict()
      new_state_dict = OrderedDict()
      matched_layers, discarded_layers = [], []
  
      for k, v in state_dict.items():
          if k.startswith('module.'):
              k = k[7:] # discard module.
  
          if k in model_dict and model_dict[k].size() == v.size():
              new_state_dict[k] = v
              matched_layers.append(k)
          else:
              discarded_layers.append(k)
  
      model_dict.update(new_state_dict)
      model.load_state_dict(model_dict)
  
      if len(matched_layers) == 0:
          warnings.warn(
              'The pretrained weights from "{}" cannot be loaded, '
              'please check the key names manually '
              '(** ignored and continue **)'.format(cached_file)
          )
      else:
          print(
              'Successfully loaded imagenet pretrained weights from "{}"'.
              format(cached_file)
          )
          if len(discarded_layers) > 0:
              print(
                  '** The following layers are discarded '
                  'due to unmatched keys or layer size: {}'.
                  format(discarded_layers)
              )
  
  
  ##########
  # Instantiation
  ##########
  def osnet_ain_x1_0(
      num_classes=1000, pretrained=True, loss='softmax', **kwargs
  ):
      model = OSNet(
          num_classes,
          blocks=[
              [OSBlockINin, OSBlockINin], [OSBlock, OSBlockINin],
              [OSBlockINin, OSBlock]
          ],
          layers=[2, 2, 2],
          channels=[64, 256, 384, 512],
          loss=loss,
          conv1_IN=True,
          **kwargs
      )
      if pretrained:
          init_pretrained_weights(model, key='osnet_ain_x1_0')
      return model