code: """
  Creates a MobileNetV2 Model as defined in:
  Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).
  MobileNetV2: Inverted Residuals and Linear Bottlenecks
  arXiv preprint arXiv:1801.04381.
  import from https://github.com/tonylins/pytorch-mobilenet-v2
  """
  import logging
  import math
  
  import torch
  import torch.nn as nn
  
  from project.fastreid.layers import get_norm
  from project.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
  from .build import BACKBONE_REGISTRY
  
  logger = logging.getLogger(__name__)
  
  
  def _make_divisible(v, divisor, min_value=None):
      """
      This function is taken from the original tf repo.
      It ensures that all layers have a channel number that is divisible by 8
      It can be seen here:
      https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
      :param v:
      :param divisor:
      :param min_value:
      :return:
      """
      if min_value is None:
          min_value = divisor
      new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
      # Make sure that round down does not go down by more than 10%.
      if new_v < 0.9 * v:
          new_v += divisor
      return new_v
  
  
  def conv_3x3_bn(inp, oup, stride, bn_norm):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
          get_norm(bn_norm, oup),
          nn.ReLU6(inplace=True)
      )
  
  
  def conv_1x1_bn(inp, oup, bn_norm):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
          get_norm(bn_norm, oup),
          nn.ReLU6(inplace=True)
      )
  
  
  class InvertedResidual(nn.Module):
      def __init__(self, inp, oup, bn_norm, stride, expand_ratio):
          super(InvertedResidual, self).__init__()
          assert stride in [1, 2]
  
          hidden_dim = round(inp * expand_ratio)
          self.identity = stride == 1 and inp == oup
  
          if expand_ratio == 1:
              self.conv = nn.Sequential(
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                  get_norm(bn_norm, hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  get_norm(bn_norm, oup),
              )
          else:
              self.conv = nn.Sequential(
                  # pw
                  nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                  get_norm(bn_norm, hidden_dim),
                  nn.ReLU6(inplace=True),
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                  get_norm(bn_norm, hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup),
              )
  
      def forward(self, x):
          if self.identity:
              return x + self.conv(x)
          else:
              return self.conv(x)
  
  
  class MobileNetV2(nn.Module):
      def __init__(self, bn_norm, width_mult=1.):
          super(MobileNetV2, self).__init__()
          # setting of inverted residual blocks
          self.cfgs = [
              # t, c, n, s
              [1, 16, 1, 1],
              [6, 24, 2, 2],
              [6, 32, 3, 2],
              [6, 64, 4, 2],
              [6, 96, 3, 1],
              [6, 160, 3, 2],
              [6, 320, 1, 1],
          ]
  
          # building first layer
          input_channel = _make_divisible(32 * width_mult, 4 if width_mult == 0.1 else 8)
          layers = [conv_3x3_bn(3, input_channel, 2, bn_norm)]
          # building inverted residual blocks
          block = InvertedResidual
          for t, c, n, s in self.cfgs:
              output_channel = _make_divisible(c * width_mult, 4 if width_mult == 0.1 else 8)
              for i in range(n):
                  layers.append(block(input_channel, output_channel, bn_norm, s if i == 0 else 1, t))
                  input_channel = output_channel
          self.features = nn.Sequential(*layers)
          # building last several layers
          output_channel = _make_divisible(1280 * width_mult, 4 if width_mult == 0.1 else 8) if width_mult > 1.0 else 1280
          self.conv = conv_1x1_bn(input_channel, output_channel, bn_norm)
  
          self._initialize_weights()
  
      def forward(self, x):
          x = self.features(x)
          x = self.conv(x)
          return x
  
      def _initialize_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  m.weight.data.normal_(0, 0.01)
                  m.bias.data.zero_()
  
  
  @BACKBONE_REGISTRY.register()
  def build_mobilenetv2_backbone(cfg):
      """
      Create a MobileNetV2 instance from config.
      Returns:
          MobileNetV2: a :class: `MobileNetV2` instance.
      """
      # fmt: off
      pretrain      = cfg.MODEL.BACKBONE.PRETRAIN
      pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH
      bn_norm       = cfg.MODEL.BACKBONE.NORM
      depth         = cfg.MODEL.BACKBONE.DEPTH
      # fmt: on
  
      width_mult = {
          "1.0x": 1.0,
          "0.75x": 0.75,
          "0.5x": 0.5,
          "0.35x": 0.35,
          '0.25x': 0.25,
          '0.1x': 0.1,
      }[depth]
  
      model = MobileNetV2(bn_norm, width_mult)
  
      if pretrain:
          try:
              state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))
              logger.info(f"Loading pretrained model from {pretrain_path}")
          except FileNotFoundError as e:
              logger.info(f'{pretrain_path} is not found! Please check this path.')
              raise e
          except KeyError as e:
              logger.info("State dict keys error! Please check the state dict.")
              raise e
  
          incompatible = model.load_state_dict(state_dict, strict=False)
          if incompatible.missing_keys:
              logger.info(
                  get_missing_parameters_message(incompatible.missing_keys)
              )
          if incompatible.unexpected_keys:
              logger.info(
                  get_unexpected_parameters_message(incompatible.unexpected_keys)
              )
  
      return model
  
apis: torch.nn.BatchNorm2d torch.nn.ReLU6 torch.nn.Conv2d torch.nn.Sequential torch.device