text: 
  import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  import torch
  import torch.nn.functional as F
  __all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b']
  
  
  model_urls = {
      'res2net50_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26w_4s-3cf99910.pth',
      'res2net101_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_v1b_26w_4s-0812c246.pth',
  }
  
  
  class Bottle2neck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale = 4, stype='normal'):
          """ Constructor
          Args:
              inplanes: input channel dimensionality
              planes: output channel dimensionality
              stride: conv stride. Replaces pooling layer.
              downsample: None when stride = 1
              baseWidth: basic width of conv3x3
              scale: number of scale.
              type: 'normal': normal set. 'stage': first block of a new stage.
          """
          super(Bottle2neck, self).__init__()
  
          width = int(math.floor(planes * (baseWidth/64.0)))
          self.conv1 = nn.Conv2d(inplanes, width*scale, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(width*scale)
          
          if scale == 1:
            self.nums = 1
          else:
            self.nums = scale -1
          if stype == 'stage':
              self.pool = nn.AvgPool2d(kernel_size=3, stride = stride, padding=1)
          convs = []
          bns = []
          for i in range(self.nums):
            convs.append(nn.Conv2d(width, width, kernel_size=3, stride = stride, padding=1, bias=False))
            bns.append(nn.BatchNorm2d(width))
          self.convs = nn.ModuleList(convs)
          self.bns = nn.ModuleList(bns)
  
          self.conv3 = nn.Conv2d(width*scale, planes * self.expansion, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * self.expansion)
  
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stype = stype
          self.scale = scale
          self.width  = width
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          spx = torch.split(out, self.width, 1)
          for i in range(self.nums):
            if i==0 or self.stype=='stage':
              sp = spx[i]
            else:
              sp = sp + spx[i]
            sp = self.convs[i](sp)
            sp = self.relu(self.bns[i](sp))
            if i==0:
              out = sp
            else:
              out = torch.cat((out, sp), 1)
          if self.scale != 1 and self.stype=='normal':
            out = torch.cat((out, spx[self.nums]),1)
          elif self.scale != 1 and self.stype=='stage':
            out = torch.cat((out, self.pool(spx[self.nums])),1)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  class Res2Net(nn.Module):
  
      def __init__(self, block, layers, baseWidth = 26, scale = 4, num_classes=1000):
          self.inplanes = 64
          super(Res2Net, self).__init__()
          self.baseWidth = baseWidth
          self.scale = scale
          self.conv1 = nn.Sequential(
              nn.Conv2d(3, 32, 3, 2, 1, bias=False),
              nn.BatchNorm2d(32),
              nn.ReLU(inplace=True),
              nn.Conv2d(32, 32, 3, 1, 1, bias=False),
              nn.BatchNorm2d(32),
              nn.ReLU(inplace=True),
              nn.Conv2d(32, 64, 3, 1, 1, bias=False)
          )
          self.bn1 = nn.BatchNorm2d(64)
          self.relu = nn.ReLU()
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
          self.avgpool = nn.AdaptiveAvgPool2d(1)
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
      def _make_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.AvgPool2d(kernel_size=stride, stride=stride, 
                      ceil_mode=True, count_include_pad=False),
                  nn.Conv2d(self.inplanes, planes * block.expansion, 
                      kernel_size=1, stride=1, bias=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample=downsample, 
                          stype='stage', baseWidth = self.baseWidth, scale=self.scale))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, baseWidth = self.baseWidth, scale=self.scale))
  
          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)
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
  
          return x
  
  
  def res2net18_v1b(pretrained=False, **kwargs):
      """Constructs a Res2Net-18_v1b model.
      Args:
          pretrained (bool): No pre-trained models are available, must be False!
      """
      if pretrained:
          raise RuntimeError("Pre-trained models for Res2Net18v1b are not available!")
  
      model = Res2Net(Bottle2neck, [2, 2, 2, 2], baseWidth = 26, scale = 4, **kwargs)
      return model
  
  
  def res2net50_v1b(pretrained=False, **kwargs):
      """Constructs a Res2Net-50_v1b model.
      Res2Net-50 refers to the Res2Net-50_v1b_26w_4s.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth = 26, scale = 4, **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
      return model
  
  def res2net101_v1b(pretrained=False, **kwargs):
      """Constructs a Res2Net-50_v1b_26w_4s model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth = 26, scale = 4, **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
      return model
  
  def res2net50_v1b_26w_4s(pretrained=False, **kwargs):
      """Constructs a Res2Net-50_v1b_26w_4s model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth = 26, scale = 4, **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
      return model
  
  def res2net101_v1b_26w_4s(pretrained=False, **kwargs):
      """Constructs a Res2Net-50_v1b_26w_4s model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth = 26, scale = 4, **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
      return model
  
  def res2net152_v1b_26w_4s(pretrained=False, **kwargs):
      """Constructs a Res2Net-50_v1b_26w_4s model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = Res2Net(Bottle2neck, [3, 8, 36, 3], baseWidth = 26, scale = 4, **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['res2net152_v1b_26w_4s']))
      return model
  
  
  
  
  
  if __name__ == '__main__':
      images = torch.rand(1, 3, 224, 224).cuda(0)
      model = res2net50_v1b_26w_4s(pretrained=True)
      model = model.cuda(0)
      print(model(images).size())
  