text: """
   Copyright (c) 2019 Intel Corporation
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at
        http://www.apache.org/licenses/LICENSE-2.0
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
  """
  
  import torch
  from torch import nn
  import torch.nn.functional as F
  
  
  class ShuffleBlock(nn.Module):
      def __init__(self, groups=2):
          super().__init__()
          self.groups = groups
  
      def forward(self, x):
          '''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
          N, C, H, W = x.size()
          g = self.groups
          return x.view(N, g, C // g, H, W).permute(0, 2, 1, 3, 4).reshape(N, C, H, W)
  
  
  class SplitBlock(nn.Module):
      def __init__(self, ratio):
          super().__init__()
          self.ratio = ratio
  
      def forward(self, x):
          c = int(x.size(1) * self.ratio)
          return x[:, :c, :, :], x[:, c:, :, :]
  
  
  class BasicBlock(nn.Module):
      def __init__(self, in_channels, split_ratio=0.5):
          super().__init__()
          self.split = SplitBlock(split_ratio)
          in_channels = int(in_channels * split_ratio)
          self.conv1 = nn.Conv2d(in_channels, in_channels,
                                 kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(in_channels)
          self.conv2 = nn.Conv2d(in_channels, in_channels,
                                 kernel_size=3, stride=1, padding=1, groups=in_channels, bias=False)
          self.bn2 = nn.BatchNorm2d(in_channels)
          self.conv3 = nn.Conv2d(in_channels, in_channels,
                                 kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(in_channels)
          self.shuffle = ShuffleBlock()
  
      def forward(self, x):
          x1, x2 = self.split(x)
          out = F.relu(self.bn1(self.conv1(x2)))
          out = self.bn2(self.conv2(out))
          out = F.relu(self.bn3(self.conv3(out)))
          out = torch.cat([x1, out], 1)
          out = self.shuffle(out)
          return out
  
  
  class DownBlock(nn.Module):
      def __init__(self, in_channels, out_channels):
          super().__init__()
          mid_channels = out_channels // 2
          # left
          self.conv1 = nn.Conv2d(in_channels, in_channels,
                                 kernel_size=3, stride=2, padding=1, groups=in_channels, bias=False)
          self.bn1 = nn.BatchNorm2d(in_channels)
          self.conv2 = nn.Conv2d(in_channels, mid_channels,
                                 kernel_size=1, bias=False)
          self.bn2 = nn.BatchNorm2d(mid_channels)
          # right
          self.conv3 = nn.Conv2d(in_channels, mid_channels,
                                 kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(mid_channels)
          self.conv4 = nn.Conv2d(mid_channels, mid_channels,
                                 kernel_size=3, stride=2, padding=1, groups=mid_channels, bias=False)
          self.bn4 = nn.BatchNorm2d(mid_channels)
          self.conv5 = nn.Conv2d(mid_channels, mid_channels,
                                 kernel_size=1, bias=False)
          self.bn5 = nn.BatchNorm2d(mid_channels)
  
          self.shuffle = ShuffleBlock()
  
      def forward(self, x):
          # left
          out1 = self.bn1(self.conv1(x))
          out1 = F.relu(self.bn2(self.conv2(out1)))
          # right
          out2 = F.relu(self.bn3(self.conv3(x)))
          out2 = self.bn4(self.conv4(out2))
          out2 = F.relu(self.bn5(self.conv5(out2)))
          # concat
          out = torch.cat([out1, out2], 1)
          out = self.shuffle(out)
          return out
  
  
  class ShuffleNetV2(nn.Module):
      def __init__(self, net_size):
          super().__init__()
          out_channels = configs[net_size]['out_channels']
          num_blocks = configs[net_size]['num_blocks']
  
          self.conv1 = nn.Conv2d(3, 24, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(24)
          self.in_channels = 24
          self.layer1 = self._make_layer(out_channels[0], num_blocks[0])
          self.layer2 = self._make_layer(out_channels[1], num_blocks[1])
          self.layer3 = self._make_layer(out_channels[2], num_blocks[2])
          self.conv2 = nn.Conv2d(out_channels[2], out_channels[3],
                                 kernel_size=1, stride=1, padding=0, bias=False)
          self.bn2 = nn.BatchNorm2d(out_channels[3])
          self.linear = nn.Linear(out_channels[3], 10)
  
      def _make_layer(self, out_channels, num_blocks):
          layers = [DownBlock(self.in_channels, out_channels)]
          for _ in range(num_blocks):
              layers.append(BasicBlock(out_channels))
              self.in_channels = out_channels
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          # out = F.max_pool2d(out, 3, stride=2, padding=1)
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = F.relu(self.bn2(self.conv2(out)))
          out = F.avg_pool2d(out, 4)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
          return out
  
  
  configs = {
      0.5: {
          'out_channels': (48, 96, 192, 1024),
          'num_blocks': (3, 7, 3)
      },
  
      1: {
          'out_channels': (116, 232, 464, 1024),
          'num_blocks': (3, 7, 3)
      },
      1.5: {
          'out_channels': (176, 352, 704, 1024),
          'num_blocks': (3, 7, 3)
      },
      2: {
          'out_channels': (224, 488, 976, 2048),
          'num_blocks': (3, 7, 3)
      }
  }
  
  
  def test():
      net = ShuffleNetV2(net_size=0.5)
      x = torch.randn(3, 3, 32, 32)
      y = net(x)
      print(y.shape)
  
  # test()
  