text: '''ResNet in PyTorch.
  
  For Pre-activation ResNet, see 'preact_resnet.py'.
  
  Reference:
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
      Deep Residual Learning for Image Recognition. arXiv:1512.03385
  '''
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from torch.autograd import Variable
  
  from base.base_model import BaseModel
  from utils.util import softmaxNd, conv1x1
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1):
          super(BasicBlock, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(self.expansion * planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.bn2(self.conv2(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, 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, self.expansion * planes, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(self.expansion * planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(self.expansion * planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = F.relu(self.bn2(self.conv2(out)))
          out = self.bn3(self.conv3(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, block, num_blocks, num_classes=10):
          super(ResNet, self).__init__()
          self.in_planes = 64
  
          self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
          self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
          self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
          self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
          self.linear = nn.Linear(512 * block.expansion, num_classes)
  
      def _make_layer(self, block, planes, num_blocks, stride):
          strides = [stride] + [1] * (num_blocks - 1)
          layers = []
          for stride in strides:
              layers.append(block(self.in_planes, planes, stride))
              self.in_planes = planes * block.expansion
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = self.layer4(out)
          out = F.avg_pool2d(out, 4)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
          return out
  
  
  def ResNet18():
      return ResNet(BasicBlock, [2, 2, 2, 2])
  
  
  def ResNet34():
      return ResNet(BasicBlock, [3, 4, 6, 3])
  
  
  def ResNet50():
      return ResNet(Bottleneck, [3, 4, 6, 3])
  
  
  def ResNet101():
      return ResNetAttention(Bottleneck, [3, 4, 23, 3])
  
  
  def ResNet152():
      return ResNetAttention(Bottleneck, [3, 8, 36, 3])
  
  
  class ResNetAttention(BaseModel):
      def __init__(self, block, num_blocks, num_classes=10):
          super(ResNetAttention, self).__init__()
          self.in_planes = 64
  
          self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
          self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
          self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
          self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
          self.linear = nn.Linear(512 * 3, num_classes)
  
          self.transform1 = conv1x1(256, 512)
          self.transform2 = conv1x1(512, 512)
          self.transform3 = conv1x1(1024, 512)
          self.transform4 = conv1x1(2048, 512)
  
      def _make_layer(self, block, planes, num_blocks, stride):
          strides = [stride] + [1] * (num_blocks - 1)
          layers = []
          for stride in strides:
              layers.append(block(self.in_planes, planes, stride))
              self.in_planes = planes * block.expansion
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          l1 = self.layer1(out)
          l2 = self.layer2(l1)
          l3 = self.layer3(l2)
          out = self.layer4(l3)
          out = F.avg_pool2d(out, 4)
          g = self.transform4(out)
  
          l1 = self.transform1(l1)
          l2 = self.transform2(l2)
          l3 = self.transform3(l3)
  
          c1 = (l1 * g).sum(1).unsqueeze(1)
          c2 = (l2 * g).sum(1).unsqueeze(1)
          c3 = (l3 * g).sum(1).unsqueeze(1)
  
          a1 = softmaxNd(c1)
          a2 = softmaxNd(c2)
          a3 = softmaxNd(c3)
  
          g1 = (a1 * l1).sum(-1).sum(-1)
          g2 = (a2 * l2).sum(-1).sum(-1)
          g3 = (a3 * l3).sum(-1).sum(-1)
  
          g = self.linear(torch.cat([g1, g2, g3], 1))
  
          return g
  
  
  def test():
      net = ResNet18()
      y = net(Variable(torch.randn(1, 3, 32, 32)))
      print(y.size())
  
  # test()
  