text: import math
  from functools import partial
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  def get_inplanes():
      return [64, 128, 256, 512]
  
  
  def conv3x3x3(in_planes, out_planes, stride=1):
      return nn.Conv3d(in_planes,
                       out_planes,
                       kernel_size=3,
                       stride=stride,
                       padding=1,
                       bias=False)
  
  
  def conv1x1x1(in_planes, out_planes, stride=1):
      return nn.Conv3d(in_planes,
                       out_planes,
                       kernel_size=1,
                       stride=stride,
                       bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1, downsample=None):
          super().__init__()
  
          self.conv1 = conv3x3x3(in_planes, planes, stride)
          self.bn1 = nn.BatchNorm3d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3x3(planes, planes)
          self.bn2 = nn.BatchNorm3d(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = 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:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1, downsample=None):
          super().__init__()
  
          self.conv1 = conv1x1x1(in_planes, planes)
          self.bn1 = nn.BatchNorm3d(planes)
          self.conv2 = conv3x3x3(planes, planes, stride)
          self.bn2 = nn.BatchNorm3d(planes)
          self.conv3 = conv1x1x1(planes, planes * self.expansion)
          self.bn3 = nn.BatchNorm3d(planes * self.expansion)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = 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:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
  
      def __init__(self,
                   block,
                   layers,
                   block_inplanes,
                   n_input_channels=3,
                   conv1_t_size=7,
                   conv1_t_stride=1,
                   no_max_pool=False,
                   shortcut_type='B',
                   widen_factor=1.0,
                   n_classes=400):
          super().__init__()
  
          block_inplanes = [int(x * widen_factor) for x in block_inplanes]
  
          self.in_planes = block_inplanes[0]
          self.no_max_pool = no_max_pool
  
          self.conv1 = nn.Conv3d(n_input_channels,
                                 self.in_planes,
                                 kernel_size=(conv1_t_size, 7, 7),
                                 stride=(conv1_t_stride, 2, 2),
                                 padding=(conv1_t_size // 2, 3, 3),
                                 bias=False)
          self.bn1 = nn.BatchNorm3d(self.in_planes)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, block_inplanes[0], layers[0],
                                         shortcut_type)
          self.layer2 = self._make_layer(block,
                                         block_inplanes[1],
                                         layers[1],
                                         shortcut_type,
                                         stride=2)
          self.layer3 = self._make_layer(block,
                                         block_inplanes[2],
                                         layers[2],
                                         shortcut_type,
                                         stride=2)
          self.layer4 = self._make_layer(block,
                                         block_inplanes[3],
                                         layers[3],
                                         shortcut_type,
                                         stride=2)
  
          self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
          self.fc = nn.Linear(block_inplanes[3] * block.expansion, 8)
          self.fc2 = nn.Linear(8, n_classes)
  
          for m in self.modules():
              if isinstance(m, nn.Conv3d):
                  nn.init.kaiming_normal_(m.weight,
                                          mode='fan_out',
                                          nonlinearity='relu')
              elif isinstance(m, nn.BatchNorm3d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
      def _downsample_basic_block(self, x, planes, stride):
          out = F.avg_pool3d(x, kernel_size=1, stride=stride)
          zero_pads = torch.zeros(out.size(0), planes - out.size(1), out.size(2),
                                  out.size(3), out.size(4))
          if isinstance(out.data, torch.cuda.FloatTensor):
              zero_pads = zero_pads.cuda()
  
          out = torch.cat([out.data, zero_pads], dim=1)
  
          return out
  
      def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
          downsample = None
          if stride != 1 or self.in_planes != planes * block.expansion:
              if shortcut_type == 'A':
                  downsample = partial(self._downsample_basic_block,
                                       planes=planes * block.expansion,
                                       stride=stride)
              else:
                  downsample = nn.Sequential(
                      conv1x1x1(self.in_planes, planes * block.expansion, stride),
                      nn.BatchNorm3d(planes * block.expansion))
  
          layers = []
          layers.append(
              block(in_planes=self.in_planes,
                    planes=planes,
                    stride=stride,
                    downsample=downsample))
          self.in_planes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.in_planes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          if not self.no_max_pool:
              x = self.maxpool(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          x = self.avgpool(x)
  
          x = x.view(x.size(0), -1)
          x = F.relu(self.fc(x))
          x = self.fc2(x)
          # x = torch.sigmoid(self.fc2(x))
  
          return x
  
  
  def generate_model(model_depth, **kwargs):
      assert model_depth in [10, 18, 34, 50, 101, 152, 200]
  
      if model_depth == 10:
          model = ResNet(BasicBlock, [1, 1, 1, 1], get_inplanes(), **kwargs)
      elif model_depth == 18:
          model = ResNet(BasicBlock, [2, 2, 2, 2], get_inplanes(), **kwargs)
      elif model_depth == 34:
          model = ResNet(BasicBlock, [3, 4, 6, 3], get_inplanes(), **kwargs)
      elif model_depth == 50:
          model = ResNet(Bottleneck, [3, 4, 6, 3], get_inplanes(), **kwargs)
      elif model_depth == 101:
          model = ResNet(Bottleneck, [3, 4, 23, 3], get_inplanes(), **kwargs)
      elif model_depth == 152:
          model = ResNet(Bottleneck, [3, 8, 36, 3], get_inplanes(), **kwargs)
      elif model_depth == 200:
          model = ResNet(Bottleneck, [3, 24, 36, 3], get_inplanes(), **kwargs)
  
      return model
  