text: import torch.nn as nn
  import math
  # import torch.utils.model_zoo as model_zoo
  from .eca_module import eca_layer
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      """3x3 convolution with padding"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=1, bias=False)
  
  
  class ECABasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, k_size=3):
          super(ECABasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes, 1)
          self.bn2 = nn.BatchNorm2d(planes)
          self.eca = eca_layer(planes, k_size)
          self.downsample = downsample
          self.stride = stride
  
      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.eca(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class ECABottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, k_size=3):
          super(ECABottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          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.eca = eca_layer(planes * 4, k_size)
          self.downsample = downsample
          self.stride = stride
  
      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)
          out = self.eca(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, block, layers, num_classes=1000, k_size=[3, 3, 3, 3]):
          self.inplanes = 64
          super(ResNet, self).__init__()
          
          if num_classes == 10 or num_classes == 100:
              self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=3, bias=False)
          else:
              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, 64, layers[0], int(k_size[0]))
          self.layer2 = self._make_layer(block, 128, layers[1], int(k_size[1]), stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], int(k_size[2]), stride=2)
          self.layer4 = self._make_layer(block, 512, layers[3], int(k_size[3]), stride=2)
          self.avgpool = nn.AvgPool2d(7, stride=1)
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          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_()
  
      def _make_layer(self, block, planes, blocks, k_size, stride=1):
          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 = []
          layers.append(block(self.inplanes, planes, stride, downsample, k_size))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, k_size=k_size))
  
          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 eca_resnet18(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
      """Constructs a ResNet-18 model.
  
      Args:
          k_size: Adaptive selection of kernel size
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          num_classes:The classes of classification
      """
      model = ResNet(ECABasicBlock, [2, 2, 2, 2], num_classes=num_classes, k_size=k_size)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  
  
  def eca_resnet34(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
      """Constructs a ResNet-34 model.
  
      Args:
          k_size: Adaptive selection of kernel size
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          num_classes:The classes of classification
      """
      model = ResNet(ECABasicBlock, [3, 4, 6, 3], num_classes=num_classes, k_size=k_size)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  
  
  def eca_resnet50(k_size=[3, 3, 3, 3], num_classes=1000, pretrained=False):
      """Constructs a ResNet-50 model.
  
      Args:
          k_size: Adaptive selection of kernel size
          num_classes:The classes of classification
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      print("Constructing eca_resnet50......")
      model = ResNet(ECABottleneck, [3, 4, 6, 3], num_classes=num_classes, k_size=k_size)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  
  
  def eca_cifar_resnet50(k_size=[3, 3, 3, 3], num_classes=100, pretrained=False):
      """Constructs a ResNet-50 model.
  
      Args:
          k_size: Adaptive selection of kernel size
          num_classes:The classes of classification
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      print("Constructing eca_resnet50......")
      model = ResNet(ECABottleneck, [3, 4, 6, 3], num_classes=num_classes, k_size=k_size)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  
  
  def eca_resnet101(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
      """Constructs a ResNet-101 model.
  
      Args:
          k_size: Adaptive selection of kernel size
          num_classes:The classes of classification
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(ECABottleneck, [3, 4, 23, 3], num_classes=num_classes, k_size=k_size)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  
  
  def eca_resnet152(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
      """Constructs a ResNet-152 model.
  
      Args:
          k_size: Adaptive selection of kernel size
          num_classes:The classes of classification
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(ECABottleneck, [3, 8, 36, 3], num_classes=num_classes, k_size=k_size)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  