text: '''
  MIT License
  
  Copyright (c) 2017 liukuang
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
  '''
  
  '''I tried torchvision's model code initially, but that one does not deal with image sizes smaller
  than imagnet. An alternative implementation is `here
  <https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py>`_. I'm not sure how faithful
  the reproduction is, but ultimately it shouldn't matter.'''
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  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, BlockType, num_blocks, num_classes=10, exporter=None):
          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(BlockType, 64, num_blocks[0], stride=1)
          self.layer2 = self._make_layer(BlockType, 128, num_blocks[1], stride=2)
          self.layer3 = self._make_layer(BlockType, 256, num_blocks[2], stride=2)
          self.layer4 = self._make_layer(BlockType, 512, num_blocks[3], stride=2)
          self.linear = nn.Linear(512*BlockType.expansion, num_classes)
  
          if exporter:
              exporter.set_model(self)
              exporter.add_modules(self)
  
      def _make_layer(self, BlockType, planes, num_blocks, stride):
          strides = [stride] + [1]*(num_blocks-1)
          layers = []
          for stride in strides:
              layers.append(BlockType(self.in_planes, planes, stride))
              self.in_planes = planes * BlockType.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(**kwargs):
      '''Constructs a ResNet-18 model.
  
      Parameters
      ----------
      kwargs  :   dict
                  ResNet parameters
      '''
      return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
  
  
  def resnet34(pretrained=False, **kwargs):
      '''Constructs a ResNet-34 model.
  
      Parameters
      ----------
      kwargs  :   dict
                  ResNet parameters
      '''
      return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
  
  
  def resnet50(pretrained=False, **kwargs):
      '''Constructs a ResNet-50 model.
  
      Parameters
      ----------
      kwargs  :   dict
                  ResNet parameters
      '''
      return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
  
  
  def resnet101(pretrained=False, **kwargs):
      '''Constructs a ResNet-101 model.
  
      Parameters
      ----------
      kwargs  :   dict
                  ResNet parameters
      '''
      return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
  
  
  def resnet152(**kwargs):
      '''Constructs a ResNet-152 model.
  
      Parameters
      ----------
      kwargs  :   dict
                  ResNet parameters
      '''
      return ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
  