text: """ResNet/WideResNet in PyTorch.
  See the paper "Deep Residual Learning for Image Recognition"
  (https://arxiv.org/abs/1512.03385)
  """
  import torch
  import torch.nn as nn
  
  
  def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
      """3x3 convolution with padding"""
      return mnn.MaskConv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=dilation, groups=groups, bias=False, dilation=dilation)
  
  
  def conv1x1(in_planes, out_planes, stride=1):
      """1x1 convolution"""
      return mnn.MaskConv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
      __constants__ = ['downsample']
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                   base_width=64, dilation=1, norm_layer=None):
          super(BasicBlock, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          if groups != 1 or base_width != 64:
              raise ValueError('BasicBlock only supports groups=1 and base_width=64')
          if dilation > 1:
              raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
          # Both self.conv1 and self.downsample layers downsample the input when stride != 1
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = norm_layer(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = norm_layer(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
      __constants__ = ['downsample']
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                   base_width=64, dilation=1, norm_layer=None):
          super(Bottleneck, self).__init__()
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          width = int(planes * (base_width / 64.)) * groups
          # Both self.conv2 and self.downsample layers downsample the input when stride != 1
          self.conv1 = conv1x1(inplanes, width)
          self.bn1 = norm_layer(width)
          self.conv2 = conv3x3(width, width, stride, groups, dilation)
          self.bn2 = norm_layer(width)
          self.conv3 = conv1x1(width, planes * self.expansion)
          self.bn3 = norm_layer(planes * self.expansion)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = 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)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                   groups=1, width_per_group=64, replace_stride_with_dilation=None,
                   norm_layer=None):
          super(ResNet, self).__init__()
          self.block_name = str(block.__name__)
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          self._norm_layer = norm_layer
  
          self.inplanes = 64
          self.dilation = 1
          if replace_stride_with_dilation is None:
              # each element in the tuple indicates if we should replace
              # the 2x2 stride with a dilated convolution instead
              replace_stride_with_dilation = [False, False, False]
          if len(replace_stride_with_dilation) != 3: 
              raise ValueError("replace_stride_with_dilation should be None "
                               "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
          self.groups = groups
          self.base_width = width_per_group
          #self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
          #                       bias=False)
          self.conv1 = mnn.MaskConv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                                      bias=False)
          self.bn1 = norm_layer(self.inplanes)
          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])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                         dilate=replace_stride_with_dilation[0])
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                         dilate=replace_stride_with_dilation[1])
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                         dilate=replace_stride_with_dilation[2])
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, mnn.MaskConv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
              elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
          # Zero-initialize the last BN in each residual branch,
          # so that the residual branch starts with zeros, and each residual block behaves like an identity.
          # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
          if zero_init_residual:
              for m in self.modules():
                  if isinstance(m, Bottleneck):
                      nn.init.constant_(m.bn3.weight, 0)
                  elif isinstance(m, BasicBlock):
                      nn.init.constant_(m.bn2.weight, 0)
  
      def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
          norm_layer = self._norm_layer
          downsample = None
          previous_dilation = self.dilation
          if dilate:
              self.dilation *= stride
              stride = 1
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  conv1x1(self.inplanes, planes * block.expansion, stride),
                  norm_layer(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                              self.base_width, previous_dilation, norm_layer))
          self.inplanes = planes * block.expansion
          for _ in range(1, blocks):
              layers.append(block(self.inplanes, planes, groups=self.groups,
                                  base_width=self.base_width, dilation=self.dilation,
                                  norm_layer=norm_layer))
  
          return nn.Sequential(*layers)
  
      def _forward_impl(self, x, dist_type=None):
          # See note [TorchScript super()]
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  
          x1 = self.layer1(x)
          x2 = self.layer2(x1)
          x3 = self.layer3(x2)
          x4 = self.layer4(x3)
  
          x = self.avgpool(x4)
          x = torch.flatten(x, 1)
          x = self.fc(x)
          
          if dist_type is None:
              return x
          elif dist_type in ['KD']:
              return x, None
          elif dist_type in ['AT', 'SP']:
              return x, [x1, x2, x3, x4]
  
      def forward(self, x, dist_type=None, pos_list=[]):
          assert dist_type not in ['OD'], "This model doesn't support the configured distillation method."
          return self._forward_impl(x, dist_type=dist_type)
  
  
  class ResNet_CIFAR(nn.Module):
      def __init__(self, block, layers, num_classes=10, zero_init_residual=False,
                   groups=1, width_per_group=64, replace_stride_with_dilation=None,
                   norm_layer=None):
          super(ResNet_CIFAR, self).__init__()
          self.block_name = str(block.__name__)
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          self._norm_layer = norm_layer
  
          self.inplanes = 16
          self.dilation = 1
          if replace_stride_with_dilation is None:
              # each element in the tuple indicates if we should replace
              # the 2x2 stride with a dilated convolution instead
              replace_stride_with_dilation = [False, False, False]
          if len(replace_stride_with_dilation) != 3: 
              raise ValueError("replace_stride_with_dilation should be None "
                               "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
          self.groups = groups
          self.base_width = width_per_group
          #self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
          #                       bias=False)
          self.conv1 = mnn.MaskConv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
                                      bias=False)                               
          self.bn1 = norm_layer(self.inplanes)
          self.relu = nn.ReLU(inplace=True)
          self.layer1 = self._make_layer(block, 16, layers[0])
          self.layer2 = self._make_layer(block, 32, layers[1], stride=2,
                                         dilate=replace_stride_with_dilation[0])
          self.layer3 = self._make_layer(block, 64, layers[2], stride=2,
                                         dilate=replace_stride_with_dilation[1])
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.fc = nn.Linear(64 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, mnn.MaskConv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
              elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
          # Zero-initialize the last BN in each residual branch,
          # so that the residual branch starts with zeros, and each residual block behaves like an identity.
          # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
          if zero_init_residual:
              for m in self.modules():
                  if isinstance(m, Bottleneck):
                      nn.init.constant_(m.bn3.weight, 0)
                  elif isinstance(m, BasicBlock):
                      nn.init.constant_(m.bn2.weight, 0)
  
      def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
          norm_layer = self._norm_layer
          downsample = None
          previous_dilation = self.dilation
          if dilate:
              self.dilation *= stride
              stride = 1
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  conv1x1(self.inplanes, planes * block.expansion, stride),
                  #nn.Conv2d(self.inplanes, planes * block.expansion,
                  #          kernel_size=1, stride=stride, bias=False),
                  norm_layer(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                              self.base_width, previous_dilation, norm_layer))
          self.inplanes = planes * block.expansion
          for _ in range(1, blocks):
              layers.append(block(self.inplanes, planes, groups=self.groups,
                                  base_width=self.base_width, dilation=self.dilation,
                                  norm_layer=norm_layer))
  
          return nn.Sequential(*layers)
  
      def _forward_impl(self, x, dist_type=None):
          # See note [TorchScript super()]
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  
          x1 = self.layer1(x)
          x2 = self.layer2(x1)
          x3 = self.layer3(x2)
  
          x = self.avgpool(x3)
          x = torch.flatten(x, 1)
          x = self.fc(x)
          
          if dist_type is None:
              return x
          elif dist_type in ['KD']:
              return x, None
          elif dist_type in ['AT', 'SP']:
              return x, [x1, x2, x3]
  
      def forward(self, x, dist_type=None, pos_list=[]):
          assert dist_type not in ['OD'], "This model doesn't support the configured distillation method."
          return self._forward_impl(x, dist_type=dist_type)
  
  
  # Model configurations
  cfgs = {
      '18':  (BasicBlock, [2, 2, 2, 2]),
      '34':  (BasicBlock, [3, 4, 6, 3]),
      '50':  (Bottleneck, [3, 4, 6, 3]),
      '101': (Bottleneck, [3, 4, 23, 3]),
      '152': (Bottleneck, [3, 8, 36, 3]),
  }
  cfgs_cifar = {
      '20':  [3, 3, 3],
      '32':  [5, 5, 5],
      '44':  [7, 7, 7],
      '56':  [9, 9, 9],
      '110': [18, 18, 18],
  }
  
  
  def resnet(data='cifar10', **kwargs):
      r"""ResNet models from "[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)"
  
      Args:
          data (str): the name of datasets
      """
      num_layers = str(kwargs.get('num_layers'))
  
      # set pruner
      global mnn
      mnn = kwargs.get('mnn')
      assert mnn is not None, "Please specify proper pruning method"
  
      if data in ['cifar10', 'cifar100']:
          if num_layers in cfgs_cifar.keys():
              model = ResNet_CIFAR(BasicBlock, cfgs_cifar[num_layers], int(data[5:]))
          else:
              model = None
          image_size = 32
      elif data == 'imagenet':
          if num_layers in cfgs.keys():
              block, layers = cfgs[num_layers]
              model = ResNet(block, layers, 1000)
          else:
              model = None
          image_size = 224
      elif data == 'things':
          if num_layers in cfgs.keys():
              block, layers = cfgs[num_layers]
              model = ResNet(block, layers, 41)
          else:
              model = None
          image_size = 224
      else:
          model = None
          image_size = None
  
      return model, image_size
  