code: # encoding: utf-8
  """
  @author:  liaoxingyu
  @contact: sherlockliao01@gmail.com
  """
  
  import math
  
  import torch
  from torch import nn
  from torch.utils import model_zoo
  
  from models.context_block import *
  
  model_urls = {
      'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
      'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
      'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
      'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
      'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
      'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
      'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
      'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
      'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
  }
  
  model_layers = {
      'resnet50': [3, 4, 6, 3],
      'resnet101': [3, 4, 23, 3]
  }
  
  __all__ = ['ResNet', 'Bottleneck']
  
  
  class IBN(nn.Module):
      """
      IBN with BN:IN = 7:1
      """
  
      def __init__(self, planes):
          super(IBN, self).__init__()
          half1 = int(planes / 8)
          self.half = half1
          half2 = planes - half1
          self.IN = nn.InstanceNorm2d(half1, affine=True)
          self.BN = nn.BatchNorm2d(half2)
  
      def forward(self, x):
          split = torch.split(x, self.half, dim=1)
          out1 = self.IN(split[0].contiguous())
          out2 = self.BN(torch.cat(split[1:], dim=1).contiguous())
          out = torch.cat((out1, out2), 1)
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, with_ibn=False, gcb=None, stride=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.with_gcb = gcb is not None
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
  
          if with_ibn:
              self.bn1 = IBN(planes)
          else:
              self.bn1 = nn.BatchNorm2d(planes)
  
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
          # GCNet
          if self.with_gcb:
              gcb_inplanes = planes * self.expansion
              self.context_block = ContextBlock(inplanes=gcb_inplanes, **gcb)
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.with_gcb:
              out = self.context_block(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, last_stride, with_ibn, gcb, stage_with_gcb, block, layers):
          scale = 64
          self.inplanes = scale
          super().__init__()
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, scale, layers[0], with_ibn=with_ibn,
                                         gcb=gcb if stage_with_gcb[0] else None)
          self.layer2 = self._make_layer(block, scale * 2, layers[1], stride=2, with_ibn=with_ibn,
                                         gcb=gcb if stage_with_gcb[1] else None)
          self.layer3 = self._make_layer(block, scale * 4, layers[2], stride=2, with_ibn=with_ibn,
                                         gcb=gcb if stage_with_gcb[2] else None)
          self.layer4 = self._make_layer(block, scale * 8, layers[3], stride=last_stride,
                                         gcb=gcb if stage_with_gcb[3] else None)
  
      def _make_layer(self, block, planes, blocks, stride=1, with_ibn=False, gcb=None):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(self.inplanes, planes * block.expansion,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          if planes == 512:
              with_ibn = False
          layers.append(block(self.inplanes, planes, with_ibn, gcb, stride, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, with_ibn, gcb))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          return x
  
      def load_pretrain(self, model_path=''):
          with_model_path = (model_path is not '')
          if not with_model_path:  # resnet pretrain
              state_dict = model_zoo.load_url(model_urls[self._model_name])
              state_dict.pop('fc.weight')
              state_dict.pop('fc.bias')
              self.load_state_dict(state_dict)
          else:
              # ibn pretrain
              state_dict = torch.load(model_path)['state_dict']
              state_dict.pop('module.fc.weight')
              state_dict.pop('module.fc.bias')
              new_state_dict = {}
              for k in state_dict:
                  new_k = '.'.join(k.split('.')[1:])  # remove module in name
                  if self.state_dict()[new_k].shape == state_dict[k].shape:
                      new_state_dict[new_k] = state_dict[k]
              state_dict = new_state_dict
              self.load_state_dict(state_dict, strict=False)
  
      def random_init(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))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      @classmethod
      def from_name(cls, model_name, last_stride, with_ibn, gcb, stage_with_gcb):
          cls._model_name = model_name
          return ResNet(last_stride, with_ibn, gcb, stage_with_gcb, block=Bottleneck, layers=model_layers[model_name])
  
  
  
  class Baseline(nn.Module):
      in_planes = 2048
  
      def __init__(self,
                   backbone,
                   num_classes,
                   last_stride,
                   with_ibn,
                   gcb,
                   stage_with_gcb,
                   pretrain=True,
                   model_path=''):
          super().__init__()
          try:
              self.base = ResNet.from_name(backbone, last_stride, with_ibn, gcb, stage_with_gcb)
          except:
              print(f'not support {backbone} backbone')
  
          if pretrain:
              self.base.load_pretrain(model_path)
  
          self.gap = nn.AdaptiveAvgPool2d(1)
          self.num_classes = num_classes
  
          self.bottleneck = nn.BatchNorm1d(self.in_planes)
          self.bottleneck.bias.requires_grad_(False)  # no shift
  
          self.classifier = nn.Linear(self.in_planes, self.num_classes, bias=False)
  
  
      def forward(self, x, label=None):
          base = self.base(x)
          global_feat = self.gap(base)  # (b, 2048, 1, 1)
          global_feat = global_feat.view(-1, global_feat.size()[1])
          feat = self.bottleneck(global_feat)  # normalize for angular softmax
          return feat, torch.sum(base*feat.unsqueeze(-1).unsqueeze(-1), dim=1)
  
      def load_params_wo_fc(self, state_dict):
          # new_state_dict = {}
          # for k, v in state_dict.items():
          #     k = '.'.join(k.split('.')[1:])
          #     new_state_dict[k] = v
          # state_dict = new_state_dict
          state_dict.pop('classifier.weight')
          res = self.load_state_dict(state_dict, strict=False)
          assert str(res.missing_keys) == str(['classifier.weight',]), 'issue loading pretrained weights'
  if __name__ == "__main__":
      model = Baseline(
          'resnet50',
          1453,
          1,
          True,
          "ratio",
          (False, False, False, False),
          pretrain = False,
          model_path = '')
      print(model)
apis: torch.nn.BatchNorm2d torch.nn.MaxPool2d torch.nn.Linear torch.load torch.split torch.nn.BatchNorm1d torch.nn.AdaptiveAvgPool2d torch.nn.ReLU torch.nn.Conv2d torch.nn.InstanceNorm2d torch.nn.Sequential torch.utils.model_zoo.load_url torch.cat