text: # -*- coding: utf-8 -*-
  """
  momentum net
  """
  import torch
  import torch.nn as nn
  import math
  
  from torch.nn.parameter import Parameter
  
  __all__ = ['restart_momentumnet']
  
  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 BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, step_size=2.0, momentum=0.5):
          super(BasicBlock, self).__init__()
          self.bn1 = nn.BatchNorm2d(inplanes)
          self.relu = nn.ReLU(inplace=True)
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv2 = conv3x3(planes, planes)
          self.downsample = downsample
          self.stride = stride
          # for momentum net
          self.step_size = Parameter(torch.tensor(step_size), requires_grad=True)
          self.momentum = Parameter(torch.tensor(momentum), requires_grad=True)
  
      def forward(self, invec):
          x, y = invec[0], invec[1]
          
          residualx = x
          residualy = y
  
          out = self.bn1(x)
          out = self.relu(out)
          out = self.conv1(out)
  
          out = self.bn2(out)
          out = self.relu(out)
          out = self.conv2(out)
  
          if self.downsample is not None:
              residualx = self.downsample(x)
              residualy = self.downsample(y)
          
          outy = residualx - self.step_size*out
          outx = (1.0 + self.momentum) * outy - self.momentum * residualy
          
          return [outx, outy]
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, step_size=2.0, momentum=0.5, blockid=0, restart_freq=1):
          super(Bottleneck, self).__init__()
          self.bn1 = nn.BatchNorm2d(inplanes)
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
          # for momentum net
          self.step_size = Parameter(torch.tensor(step_size), requires_grad=True)
          self.momentum = Parameter(torch.tensor(momentum), requires_grad=True)
          self.blockid = blockid
          self.restart_freq = restart_freq
          
      def forward(self, invec):
          x, prex = invec[0], invec[1]
          
          residualx = x
          residualprex = prex
          
          x = x + self.momentum * prex
  
          out = self.bn1(x)
          out = self.relu(out)
          out = self.conv1(out)
  
          out = self.bn2(out)
          out = self.relu(out)
          out = self.conv2(out)
  
          out = self.bn3(out)
          out = self.relu(out)
          out = self.conv3(out)
  
          if self.downsample is not None:
              residualx = self.downsample(residualx)
              
          if self.blockid % self.restart_freq == 0:
              residualprex = torch.zeros_like(out)
          
          outprex = self.momentum * residualprex - self.step_size * out
          outx = residualx + outprex
          
          return [outx, outprex]
  
  
  class MomentumNet(nn.Module):
  
      def __init__(self, depth, step_size=2.0, momentum=0.5, num_classes=1000, block_name='BasicBlock', feature_vec='x', restart_freq=1):
          super(MomentumNet, self).__init__()
          # Model type specifies number of layers for CIFAR-10 model
          if block_name.lower() == 'basicblock':
              assert (depth - 2) % 6 == 0, 'When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202'
              n = (depth - 2) // 6
              block = BasicBlock
          elif block_name.lower() == 'bottleneck':
              assert (depth - 2) % 9 == 0, 'When use bottleneck, depth should be 9n+2, e.g. 20, 29, 47, 56, 110, 1199'
              n = (depth - 2) // 9
              block = Bottleneck
          else:
              raise ValueError('block_name shoule be Basicblock or Bottleneck')
  
          self.inplanes = 16
          # for momentum net
          self.step_size = step_size
          self.momentum = momentum
          self.restart_freq = restart_freq
          
          self.feature_vec = feature_vec
          self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
                                 bias=False)
          self.layer1 = self._make_layer(block, 16, n, step_size=self.step_size, momentum=self.momentum, restart_freq=self.restart_freq)
          self.layer2 = self._make_layer(block, 32, n, stride=2, step_size=self.step_size, momentum=self.momentum, restart_freq=self.restart_freq)
          self.layer3 = self._make_layer(block, 64, n, stride=2, step_size=self.step_size, momentum=self.momentum, restart_freq=self.restart_freq)
          self.bn = nn.BatchNorm2d(64 * block.expansion)
          self.relu = nn.ReLU(inplace=True)
          self.avgpool = nn.AvgPool2d(8)
          self.fc = nn.Linear(64 * 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, stride=1, step_size=2.0, momentum=0.5, restart_freq=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),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample, step_size=step_size, momentum=momentum, blockid=0, restart_freq=restart_freq))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, step_size=step_size, momentum=momentum, blockid=i, restart_freq=restart_freq))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          
          out = [x, torch.zeros_like(x)]
          out = self.layer1(out)  # 32x32
          out = self.layer2(out)  # 16x16
          out = self.layer3(out)  # 8x8
          
          if self.feature_vec=='x':
              x = out[0]
          else:
              x = out[1]
              
          x = self.bn(x)
          x = self.relu(x)
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
          
          return x
  
  def restart_momentumnet(**kwargs):
      """
      Constructs a ResNet model.
      """
      return MomentumNet(**kwargs)
  
  # def momentum_net20(**kwargs):
  #     return MomentumNet(num_classes=10, depth=20, block_name="basicblock")
  
  # def momentum_net56(**kwargs):
  #     return MomentumNet(num_classes=10, depth=56, block_name="bottleneck")
  
  # def momentum_net110(**kwargs):
  #     return MomentumNet(num_classes=10, depth=110, block_name="bottleneck")
  
  # def momentum_net164(**kwargs):
  #     return MomentumNet(num_classes=10, depth=164, block_name="bottleneck")
  
  # def momentum_net290(**kwargs):
  #     return MomentumNet(num_classes=10, depth=290, block_name="bottleneck")
  