text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from functools import reduce
  import os
  import numpy as np
  import matplotlib.pyplot as plt
  from torch.autograd import Variable
  
  from augmented.dropblock.scheduler import LinearScheduler
  from augmented.autoaug.dropblock.dropblock import DropBlock2D
  
  
  def Conv1(in_planes, places, stride=2):
      return nn.Sequential(
          nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=3,stride=1,padding=1, bias=False),
          nn.BatchNorm2d(places),
          nn.ReLU(inplace=True),
          # nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
      )
  
  # 用于ResNet18和34的残差块，用的是2个3x3的卷积
  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()
          # 经过处理后的x要与x的维度相同(尺寸和深度)
          # 如果不相同，需要添加卷积+BN来变换为同一维度
          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):
          # print(x.size())
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.bn2(self.conv2(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  # 用于ResNet50,101和152的残差块，用的是1x1+3x3+1x1的卷积
  class Bottleneck(nn.Module):
      # 前面1x1和3x3卷积的filter个数相等，最后1x1卷积是其expansion倍
      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=200):
          super(ResNet, self).__init__()
          self.in_planes = 64
          self.conv1 = Conv1(in_planes=3, places=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.dropblock = LinearScheduler(
              DropBlock2D(drop_prob=0., block_size=5),
              start_value=0.0,
              stop_value=0.25,
              nr_steps=5e3
          )
  
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.classifer1 = 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):
          self.dropblock.step()
  
          x = self.conv1(x)
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          if self.training:
              x = self.dropblock(x)
          x = self.layer4(x)
          if self.training:
              x = self.dropblock(x)
  
          # print(x.size())  # ([32, 512, 1, 1])
          x = self.avgpool(x)
          # print(x.size())#([32, 512, 1, 1])
          x = x.view(x.size()[0], -1)  # 4, 2048
          # x = self.relu1(self.bn1(self.fc1(x)))
          x1 = self.classifer1(x)
          return x1
  def ResNet18(**kwargs):
      return ResNet(BasicBlock, [2, 2, 2, 2],**kwargs)
  
  def ResNet34(**kwargs):
      return ResNet(BasicBlock, [3, 4, 6, 3],**kwargs)
  
  def ResNet50(**kwargs):
      return ResNet(Bottleneck,[3, 4, 6, 3],**kwargs)
  
  def ResNet101(**kwargs):
      return ResNet(Bottleneck,[3, 4, 23, 3],**kwargs)
  
  def ResNet152(**kwargs):
      return ResNet(Bottleneck,[3, 8, 36, 3],**kwargs)
  
  
  if __name__ == '__main__':
      net = ResNet50(num_classes=200)
      y = net(torch.randn(10, 3, 32, 32))
      # print(net)
      print(y.size())
      from thop import profile
      input = torch.randn(1, 3, 64, 64)
      flops, params = profile(net, inputs=(input, ))
      total = sum([param.nelement() for param in net.parameters()])
      print('  + Number of params: %.2fM' % (total / 1e6))
      print('  + Number of params: %.3fG' % (flops / 1e9))
      print('flops: ', flops, 'params: ', params)