repo_description: Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021
repo_topics: attention deep-learning medical-image-analysis medical-imaging pytorch segmentation transformer transformers
code: import torch
  import torch.nn as nn
  
  
  __all__ = ['ResNet', 'resnet26', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
             'resnet152',]
  
  
  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',
  }
  
  
  def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
      """3x3 convolution with padding"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=dilation, groups=groups, bias=False, dilation=dilation)
  
  
  def conv1x1(in_planes, out_planes, stride=1):
      """1x1 convolution"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                   base_width=64, dilation=1, norm_layer=None):
          super(BasicBlock, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          if groups != 1 or base_width != 64:
              raise ValueError('BasicBlock only supports groups=1 and base_width=64')
          if dilation > 1:
              raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
          # Both self.conv1 and self.downsample layers downsample the input when stride != 1
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = norm_layer(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = norm_layer(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
      # while original implementation places the stride at the first 1x1 convolution(self.conv1)
      # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
      # This variant is also known as ResNet V1.5 and improves accuracy according to
      # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
  
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                   base_width=64, dilation=1, norm_layer=None):
          super(Bottleneck, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          width = int(planes * (base_width / 64.)) * groups
          # Both self.conv2 and self.downsample layers downsample the input when stride != 1
          self.conv1 = conv1x1(inplanes, width)
          self.bn1 = norm_layer(width)
          self.conv2 = conv3x3(width, width, stride, groups, dilation)
          self.bn2 = norm_layer(width)
          self.conv3 = conv1x1(width, planes * self.expansion)
          self.bn3 = norm_layer(planes * self.expansion)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = 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.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
  
      def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                   groups=1, width_per_group=64, replace_stride_with_dilation=None,
                   norm_layer=None):
          super(ResNet, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          self._norm_layer = norm_layer
  
          self.inplanes = 64
          self.dilation = 1
          if replace_stride_with_dilation is None:
              # each element in the tuple indicates if we should replace
              # the 2x2 stride with a dilated convolution instead
              replace_stride_with_dilation = [False, False, False]
          if len(replace_stride_with_dilation) != 3:
              raise ValueError("replace_stride_with_dilation should be None "
                               "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
          self.groups = groups
          self.base_width = width_per_group
          self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = norm_layer(self.inplanes)
          self.relu = nn.ReLU(inplace=True)
          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,
                                         dilate=replace_stride_with_dilation[0])
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                         dilate=replace_stride_with_dilation[1])
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                         dilate=replace_stride_with_dilation[2])
          self.avgpool = nn.AdaptiveAvgPool2d((1, 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.GroupNorm)):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
          # Zero-initialize the last BN in each residual branch,
          # so that the residual branch starts with zeros, and each residual block behaves like an identity.
          # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
          if zero_init_residual:
              for m in self.modules():
                  if isinstance(m, Bottleneck):
                      nn.init.constant_(m.bn3.weight, 0)
                  elif isinstance(m, BasicBlock):
                      nn.init.constant_(m.bn2.weight, 0)
  
      def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
          norm_layer = self._norm_layer
          downsample = None
          previous_dilation = self.dilation
          if dilate:
              self.dilation *= stride
              stride = 1
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  conv1x1(self.inplanes, planes * block.expansion, stride),
                  norm_layer(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                              self.base_width, previous_dilation, norm_layer))
          self.inplanes = planes * block.expansion
          for _ in range(1, blocks):
              layers.append(block(self.inplanes, planes, groups=self.groups,
                                  base_width=self.base_width, dilation=self.dilation,
                                  norm_layer=norm_layer))
  
          return nn.Sequential(*layers)
  
      def _forward_impl(self, x):
          # See note [TorchScript super()]
          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 = torch.flatten(x, 1)
          x = self.fc(x)
  
          return x
  
      def forward(self, x):
          return self._forward_impl(x)
  
  
  def _resnet(arch, block, layers, pretrained, progress, **kwargs):
      model = ResNet(block, layers, **kwargs)
      if pretrained:
          state_dict = load_state_dict_from_url(model_urls[arch],
                                                progress=progress)
          model.load_state_dict(state_dict)
      return model
  
  
  def resnet18(pretrained=False, progress=True, **kwargs):
      r"""ResNet-18 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                     **kwargs)
  
  
  def resnet34(pretrained=False, progress=True, **kwargs):
      r"""ResNet-34 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                     **kwargs)
  
  
  def resnet26(pretrained=False, progress=True, **kwargs):
      return _resnet('resnet26', Bottleneck, [1, 2, 4, 1], pretrained, progress,
                     **kwargs)
  
  
  def resnet50(pretrained=False, progress=True, **kwargs):
      r"""ResNet-50 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                     **kwargs)
  
  
  def resnet101(pretrained=False, progress=True, **kwargs):
      r"""ResNet-101 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
                     **kwargs)
  
  
  def resnet152(pretrained=False, progress=True, **kwargs):
      r"""ResNet-152 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
                     **kwargs)
  