code: import logging
  
  import torch
  import torch.nn as nn
  import torch.utils.checkpoint as cp
  
  from ....utils.misc import rgetattr, rhasattr
  from .resnet import ResNet 
  from mmcv.cnn import constant_init, kaiming_init
  from mmcv.runner import load_checkpoint
  
  from ....ops.trajectory_conv_package.traj_conv import TrajConv
  from .. import flownets
  
  
  from ...registry import BACKBONES
  
  def conv3x3x3(in_planes, out_planes, spatial_stride=1, temporal_stride=1, dilation=1):
      "3x3x3 convolution with padding"
      return nn.Conv3d(
          in_planes,
          out_planes,
          kernel_size=3,
          stride=(temporal_stride, spatial_stride, spatial_stride),
          padding=dilation,
          dilation=dilation,
          bias=False)
  
  
  def conv1x3x3(in_planes, out_planes, spatial_stride=1, temporal_stride=1, dilation=1):
      "1x3x3 convolution with padding"
      return nn.Conv3d(
          in_planes,
          out_planes,
          kernel_size=(1,3,3),
          stride=(temporal_stride, spatial_stride, spatial_stride),
          padding=(0, dilation, dilation),
          dilation=dilation,
          bias=False)
  
  
  def conv3x1x1(in_planes, out_planes, spatial_stride=1, temporal_stride=1, dilation=1, bias=False):
      "3x1x1 convolution with padding"
      return nn.Conv3d(
          in_planes,
          out_planes,
          kernel_size=(3,1,1),
          stride=(temporal_stride, spatial_stride, spatial_stride),
          padding=(dilation,0,0),
          dilation=dilation,
          bias=bias)
  
  def trajconv3x1x1(in_planes, out_planes, spatial_stride=1, temporal_stride=1, dilation=1, bias=False):
      "3x1x1 convolution with padding"
      return TrajConv(
          in_planes,
          out_planes,
          kernel_size=(3,1,1),
          stride=(temporal_stride, spatial_stride, spatial_stride),
          padding=(dilation,0,0),
          dilation=dilation,
          bias=bias)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self,
                   inplanes,
                   planes,
                   spatial_stride=1,
                   temporal_stride=1,
                   dilation=1,
                   downsample=None,
                   style='pytorch',
                   if_inflate=True,
                   with_cp=False,
                   with_trajectory=False):
          super(BasicBlock, self).__init__()
          self.conv1 = conv1x3x3(inplanes, planes, spatial_stride, 1, dilation)
          self.bn1 = nn.BatchNorm3d(planes)
          self.relu = nn.ReLU(inplace=True)
          
          self.conv2 = conv1x3x3(planes, planes)
          self.bn2 = nn.BatchNorm3d(planes)
  
          self.if_inflate = if_inflate
  
          if self.if_inflate:
              self.conv1_t = conv3x1x1(planes, planes, 1, temporal_stride, dilation, bias=True)
              self.bn1_t = nn.BatchNorm3d(planes)
              if with_trajectory:
                  self.conv2_t = trajconv3x1x1(planes, planes, bias=True)
              else:
                  self.conv2_t = conv3x1x1(planes, planes, bias=True)
              self.bn2_t = nn.BatchNorm3d(planes)
  
          self.downsample = downsample
          self.spatial_stride = spatial_stride
          self.temporal_stride = temporal_stride
          self.dilation = dilation
          assert not with_cp
  
          self.with_trajectory = with_trajectory
  
      def forward(self, input):
          x, traj_src = input
  
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          if self.if_inflate:
              out = self.conv1_t(out)
              out = self.bn1_t(out)
              out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          
          if self.if_inflate:
              out = self.relu(out)
              if self.with_trajectory:
                  assert traj_src[0] is not None
                  out = self.conv2_t(out, traj_src[0])
              else:
                  out = self.conv2_t(out)
              out = self.bn2_t(out)
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
          out = self.relu(out)
  
          return out, traj_src[1:]
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self,
                   inplanes,
                   planes,
                   spatial_stride=1,
                   temporal_stride=1,
                   dilation=1,
                   downsample=None,
                   style='pytorch',
                   if_inflate=True,
                   with_cp=False,
                   with_trajectory=False):
          """Bottleneck block for ResNet.
          If style is "pytorch", the stride-two layer is the 3x3 conv layer,
          if it is "caffe", the stride-two layer is the first 1x1 conv layer.
          """
          super(Bottleneck, self).__init__()
          assert style in ['pytorch', 'caffe']
          self.inplanes = inplanes
          self.planes = planes
          if style == 'pytorch':
              self.conv1_stride = 1
              self.conv2_stride = spatial_stride
              self.conv1_stride_t = 1
              self.conv2_stride_t = temporal_stride
          else:
              self.conv1_stride = spatial_stride
              self.conv2_stride = 1
              self.conv1_stride_t = temporal_stride
              self.conv2_stride_t = 1
  
          self.conv1 = nn.Conv3d(
              inplanes,
              planes,
              kernel_size=1,
              stride=(self.conv1_stride_t, self.conv1_stride, self.conv1_stride),
              bias=False)
  
          self.conv2 = nn.Conv3d(
              planes,
              planes,
              kernel_size=(1,3,3),
              stride=(1, self.conv2_stride, self.conv2_stride),
              padding=(0, dilation, dilation),
              dilation=(1, dilation, dilation),
              bias=False)
  
          self.if_inflate = if_inflate
          if self.if_inflate:
              self.conv2_t = nn.Conv3d(
                  planes,
                  planes,
                  kernel_size=(3,1,1),
                  stride=(self.conv2_stride_t,1,1),
                  padding=(1,0,0),
                  dilation=1,
                  bias=True)
              self.bn2_t = nn.BatchNorm3d(planes)
  
          self.bn1 = nn.BatchNorm3d(planes)
          self.bn2 = nn.BatchNorm3d(planes)
          self.conv3 = nn.Conv3d(
              planes, planes * self.expansion, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm3d(planes * self.expansion)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.spatial_tride = spatial_stride
          self.temporal_tride = temporal_stride
          self.dilation = dilation
          self.with_cp = with_cp
  
          self.with_trajectory = with_trajectory
  
      def forward(self, x):
  
          def _inner_forward(xx):
              x, traj_src = xx
              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)
  
              if self.if_inflate:
                  if self.with_trajectory:
                      assert traj_src is not None
                      out = self.conv2_t(out, traj_src[0])
                  else:
                      out = self.conv2_t(out)
                  out = self.bn2_t(out)
  
              out = self.conv3(out)
              out = self.bn3(out)
  
              if self.downsample is not None:
                  identity = self.downsample(x)
  
              out += identity
  
              return out, traj_src[1:]
  
          if self.with_cp and x.requires_grad:
              out, traj_remains = cp.checkpoint(_inner_forward, x)
          else:
              out, traj_remains = _inner_forward(x)
  
          out = self.relu(out)
  
          return out, traj_remains
  
  
  def make_res_layer(block,
                     inplanes,
                     planes,
                     blocks,
                     spatial_stride=1,
                     temporal_stride=1,
                     dilation=1,
                     style='pytorch',
                     inflate_freq=1,
                     with_cp=False,
                     traj_src_indices=-1):
      traj_src_indices = traj_src_indices if not isinstance(traj_src_indices, int) else (traj_src_indices, ) * blocks
      inflate_freq = inflate_freq if not isinstance(inflate_freq, int) else (inflate_freq, ) * blocks
      assert len(inflate_freq) == blocks
      downsample = None
      if spatial_stride != 1 or inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv3d(
                  inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=(temporal_stride, spatial_stride, spatial_stride),
                  bias=False),
              nn.BatchNorm3d(planes * block.expansion),
          )
  
      layers = []
      layers.append(
          block(
              inplanes,
              planes,
              spatial_stride,
              temporal_stride,
              dilation,
              downsample,
              style=style,
              if_inflate=(inflate_freq[0] == 1),
              with_trajectory=(traj_src_indices[0]>-1),
              with_cp=with_cp))
      inplanes = planes * block.expansion
      for i in range(1, blocks):
          layers.append(
              block(inplanes,
                  planes,
                  1, 1,
                  dilation,
                  style=style,
                  if_inflate= (inflate_freq[i] == 1),
                  with_trajectory=(traj_src_indices[i]>-1),
                  with_cp=with_cp))
  
      return nn.Sequential(*layers)
  
  
  @BACKBONES.register_module
  class ResNet_S3D(nn.Module):
      """ResNet_S3D backbone.
  
      Args:
          depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
          num_stages (int): Resnet stages, normally 4.
          strides (Sequence[int]): Strides of the first block of each stage.
          dilations (Sequence[int]): Dilation of each stage.
          out_indices (Sequence[int]): Output from which stages.
          style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
              layer is the 3x3 conv layer, otherwise the stride-two layer is
              the first 1x1 conv layer.
          frozen_stages (int): Stages to be frozen (all param fixed). -1 means
              not freezing any parameters.
          bn_eval (bool): Whether to set BN layers to eval mode, namely, freeze
              running stats (mean and var).
          bn_frozen (bool): Whether to freeze weight and bias of BN layers.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed.
      """
  
      arch_settings = {
          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))
      }
  
      def __init__(self,
                   depth,
                   pretrained=None,
                   num_stages=4,
                   spatial_strides=(1, 2, 2, 2),
                   temporal_strides=(1, 1, 1, 1),
                   dilations=(1, 1, 1, 1),
                   out_indices=(0, 1, 2, 3),
                   conv1_kernel_t=5,
                   conv1_stride_t=2,
                   pool1_kernel_t=1,
                   pool1_stride_t=2,
                   use_pool2=True,
                   style='pytorch',
                   frozen_stages=-1,
                   inflate_freq=(1, 1, 1, 1),    # For C2D baseline, this is set to -1.
                   bn_eval=True,
                   bn_frozen=False,
                   partial_bn=False,
                   with_cp=False,
                   with_trajectory=False,
                   trajectory_source_indices=-1,
                   trajectory_downsample_method='ave',
                   conv_bias=0.2):
          super(ResNet_S3D, self).__init__()
          if depth not in self.arch_settings:
              raise KeyError('invalid depth {} for resnet'.format(depth))
          self.depth = depth
          self.pretrained = pretrained
          self.num_stages = num_stages
          assert num_stages >= 1 and num_stages <= 4
          self.spatial_strides = spatial_strides
          self.temporal_strides = temporal_strides
          self.dilations = dilations
          assert len(spatial_strides) == len(temporal_strides) == len(dilations) == num_stages
          self.out_indices = out_indices
          assert max(out_indices) < num_stages
          self.style = style
          self.frozen_stages = frozen_stages
          self.inflate_freqs = inflate_freq if not isinstance(inflate_freq, int) else (inflate_freq, ) * num_stages
          self.bn_eval = bn_eval
          self.bn_frozen = bn_frozen
          self.partial_bn = partial_bn
          self.with_cp = with_cp
  
          self.with_trajectory = with_trajectory
          self.trajectory_source_indices = trajectory_source_indices \
              if not isinstance(trajectory_source_indices, int) else [trajectory_source_indices, ] * num_stages
          self.trajectory_downsample_method = trajectory_downsample_method
  
          self.conv_bias = conv_bias
  
          self.block, stage_blocks = self.arch_settings[depth]
          self.stage_blocks = stage_blocks[:num_stages]
          for stage in range(num_stages):
              self.trajectory_source_indices[stage] = self.trajectory_source_indices[stage] \
                  if not isinstance(self.trajectory_source_indices[stage], int) else (self.trajectory_source_indices[stage], ) * self.stage_blocks[stage]
          self.inplanes = 64
  
          if conv1_kernel_t > 1:
              self.conv1 = nn.Conv3d(
                  3, 64, kernel_size=(1,7,7), stride=(1,2,2), padding=(0,3,3), bias=False)
              self.conv1_t = nn.Conv3d(
                  64, 64, kernel_size=(conv1_kernel_t,1,1), stride=(conv1_stride_t,1,1), padding=((conv1_kernel_t-1)//2,1,1), bias=True)
              self.bn1_t = nn.BatchNorm3d(64)
          else:
              self.conv1 = nn.Conv3d(
                  3, 64, kernel_size=(1,7,7), stride=(conv1_stride_t,2,2), padding=(0,3,3), bias=False)
          self.bn1 = nn.BatchNorm3d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool3d(kernel_size=(pool1_kernel_t,3,3), stride=(pool1_stride_t,2,2), padding=(pool1_kernel_t//2,1,1))
          self.use_pool2 = use_pool2
          if self.use_pool2:
              self.pool2 = nn.MaxPool3d(kernel_size=(3,1,1), stride=(2,1,1), padding=(1,0,0))
  
          self.res_layers = []
          for i, num_blocks in enumerate(self.stage_blocks):
              traj_src_indices = self.trajectory_source_indices[i] \
                  if not isinstance(self.trajectory_source_indices[i], int) \
                  else (self.trajectory_source_indices[i], ) * num_blocks
              spatial_stride = spatial_strides[i]
              temporal_stride = temporal_strides[i]
              dilation = dilations[i]
              planes = 64 * 2**i
              res_layer = make_res_layer(
                  self.block,
                  self.inplanes,
                  planes,
                  num_blocks,
                  spatial_stride=spatial_stride,
                  temporal_stride=temporal_stride,
                  dilation=dilation,
                  style=self.style,
                  inflate_freq=self.inflate_freqs[i],
                  with_cp=with_cp,
                  traj_src_indices=traj_src_indices)
              self.inplanes = planes * self.block.expansion
              layer_name = 'layer{}'.format(i + 1)
              self.add_module(layer_name, res_layer)
              self.res_layers.append(layer_name)
  
          self.feat_dim = self.block.expansion * 64 * 2**(
              len(self.stage_blocks) - 1)
  
      def init_weights(self):
          if isinstance(self.pretrained, str):
              logger = logging.getLogger()
              resnet2d = ResNet(self.depth)
              load_checkpoint(resnet2d, self.pretrained, strict=False, logger=logger)
              for name, module in self.named_modules():
                  if isinstance(module, nn.Conv3d) or isinstance(module, TrajConv):
                      if rhasattr(resnet2d, name):
                          new_weight = rgetattr(resnet2d, name).weight.data.unsqueeze(2).expand_as(module.weight) / module.weight.data.shape[2]
                          module.weight.data.copy_(new_weight)
                          if hasattr(module, 'bias') and module.bias is not None:
                              new_bias = rgetattr(resnet2d, name).bias.data
                              module.bias.data.copy_(new_bias)
                      else:
                          kaiming_init(module, bias=self.conv_bias)
                  elif isinstance(module, nn.BatchNorm3d):
                      if rhasattr(resnet2d, name):
                          for attr in ['weight', 'bias', 'running_mean', 'running_var']:
                              setattr(module, attr, getattr(rgetattr(resnet2d, name), attr))
                      else:
                          constant_init(module, 1)
          elif self.pretrained is None:
              for m in self.modules():
                  if isinstance(m, nn.Conv3d):
                      kaiming_init(m, bias=self.conv_bias)
                  elif isinstance(m, nn.BatchNorm3d):
                      constant_init(m, 1)
          else:
              raise TypeError('pretrained must be a str or None')
  
      def forward(self, x, trajectory_forward=None, trajectory_backward=None):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
          outs = []
          for i, layer_name in enumerate(self.res_layers):
              res_layer = getattr(self, layer_name)
              y = []
              for j in self.trajectory_source_indices[i]:
                  if j > -1:
                      flow_forward = trajectory_forward[j]  ## N, 2*T, H, W (..x3y3x4y4..)
                      flow_backward = trajectory_backward[j]
                      flow_forward = flow_forward.view((flow_forward.size(0), -1, 2, flow_forward.size(2), flow_forward.size(3)))
                      flow_backward = flow_backward.view((flow_backward.size(0), -1, 2, flow_backward.size(2), flow_backward.size(3)))
                      flow_forward_x, flow_forward_y = torch.split(flow_forward, 1, 2)
                      flow_backward_x, flow_backward_y = torch.split(flow_backward, 1, 2)
                      flow_backward_x = flow_backward_x.flip(1).view((flow_backward_x.size(0), 1, flow_backward_x.size(1),
                                                                      flow_backward_x.size(3), flow_backward_x.size(4)))  # N,T,1,H,W => N,1,T,H,W
                      flow_backward_y = flow_backward_y.flip(1).view((flow_backward_y.size(0), 1, flow_backward_y.size(1),
                                                                      flow_backward_y.size(3), flow_backward_y.size(4)))
                      flow_forward_x = flow_forward_x.view((flow_forward_x.size(0), 1, flow_forward_x.size(1),
                                                            flow_forward_x.size(3), flow_forward_x.size(4)))
                      flow_forward_y = flow_forward_y.view((flow_forward_y.size(0), 1, flow_forward_y.size(1),
                                                            flow_forward_y.size(3), flow_forward_y.size(4)))
                      flow_zero = torch.zeros_like(flow_forward_x)
                      y.append(torch.cat((flow_backward_y, flow_backward_x, flow_zero, flow_zero, flow_forward_y, flow_forward_x), 1))
                  else:
                      y.append(None)
              
              x, remains = res_layer((x, y))
              assert len(remains) == 0 ## TODO: delete if check passes
              if i in self.out_indices:
                  outs.append(x)
              if self.use_pool2 and i == 0:
                  x = self.pool2(x)
          if len(outs) == 1:
              return outs[0]
          else:
              return tuple(outs)
  
      def train(self, mode=True):
          super(ResNet_S3D, self).train(mode)
          if self.bn_eval:
              for m in self.modules():
                  if isinstance(m, nn.BatchNorm3d):
                      m.eval()
                      if self.bn_frozen:
                          for params in m.parameters():
                              params.requires_grad = False
          if self.partial_bn:
              for i in range(1, self.frozen_stages + 1):
                  mod = getattr(self, 'layer{}'.format(i))
                  for m in mod.modules():
                      if isinstance(m, nn.BatchNorm3d):
                          m.eval()
                          m.weight.requires_grad = False
                          m.bias.requires_grad = False
          if mode and self.frozen_stages >= 0:
              for param in self.conv1.parameters():
                  param.requires_grad = False
              for param in self.bn1.parameters():
                  param.requires_grad = False
              self.bn1.eval()
              self.bn1.weight.requires_grad = False
              self.bn1.bias.requires_grad = False
              for i in range(1, self.frozen_stages + 1):
                  mod = getattr(self, 'layer{}'.format(i))
                  mod.eval()
                  for param in mod.parameters():
                      param.requires_grad = False
  
apis: torch.utils.checkpoint.checkpoint torch.nn.MaxPool3d torch.nn.BatchNorm3d torch.split torch.zeros_like torch.nn.Sequential torch.cat torch.nn.ReLU torch.nn.Conv3d