content: import torch
  import torch.nn as nn
  from mmcv.cnn import ConvModule, kaiming_init
  from mmcv.runner import _load_checkpoint, load_checkpoint
  from mmcv.utils import print_log
  
  from ...utils import get_root_logger
  from ..registry import BACKBONES
  from .resnet3d import ResNet3d
  
  
  class ResNet3dPathway(ResNet3d):
      """A pathway of Slowfast based on ResNet3d.
  
      Args:
          *args (arguments): Arguments same as :class:``ResNet3d``.
          lateral (bool): Determines whether to enable the lateral connection
              from another pathway. Default: False.
          speed_ratio (int): Speed ratio indicating the ratio between time
              dimension of the fast and slow pathway, corresponding to the
              ``alpha`` in the paper. Default: 8.
          channel_ratio (int): Reduce the channel number of fast pathway
              by ``channel_ratio``, corresponding to ``beta`` in the paper.
              Default: 8.
          fusion_kernel (int): The kernel size of lateral fusion.
              Default: 5.
          **kwargs (keyword arguments): Keywords arguments for ResNet3d.
      """
  
      def __init__(self,
                   *args,
                   lateral=False,
                   speed_ratio=8,
                   channel_ratio=8,
                   fusion_kernel=5,
                   **kwargs):
          self.lateral = lateral
          self.speed_ratio = speed_ratio
          self.channel_ratio = channel_ratio
          self.fusion_kernel = fusion_kernel
          super().__init__(*args, **kwargs)
          self.inplanes = self.base_channels
          if self.lateral:
              self.conv1_lateral = ConvModule(
                  self.inplanes // self.channel_ratio,
                  # https://arxiv.org/abs/1812.03982, the
                  # third type of lateral connection has out_channel:
                  # 2 * \beta * C
                  self.inplanes * 2 // self.channel_ratio,
                  kernel_size=(fusion_kernel, 1, 1),
                  stride=(self.speed_ratio, 1, 1),
                  padding=((fusion_kernel - 1) // 2, 0, 0),
                  bias=False,
                  conv_cfg=self.conv_cfg,
                  norm_cfg=None,
                  act_cfg=None)
  
          self.lateral_connections = []
          for i in range(len(self.stage_blocks)):
              planes = self.base_channels * 2**i
              self.inplanes = planes * self.block.expansion
  
              if lateral and i != self.num_stages - 1:
                  # no lateral connection needed in final stage
                  lateral_name = f'layer{(i + 1)}_lateral'
                  setattr(
                      self, lateral_name,
                      ConvModule(
                          self.inplanes // self.channel_ratio,
                          self.inplanes * 2 // self.channel_ratio,
                          kernel_size=(fusion_kernel, 1, 1),
                          stride=(self.speed_ratio, 1, 1),
                          padding=((fusion_kernel - 1) // 2, 0, 0),
                          bias=False,
                          conv_cfg=self.conv_cfg,
                          norm_cfg=None,
                          act_cfg=None))
                  self.lateral_connections.append(lateral_name)
  
      def make_res_layer(self,
                         block,
                         inplanes,
                         planes,
                         blocks,
                         spatial_stride=1,
                         temporal_stride=1,
                         dilation=1,
                         style='pytorch',
                         inflate=1,
                         inflate_style='3x1x1',
                         non_local=0,
                         non_local_cfg=dict(),
                         conv_cfg=None,
                         norm_cfg=None,
                         act_cfg=None,
                         with_cp=False):
          """Build residual layer for Slowfast.
  
          Args:
              block (nn.Module): Residual module to be built.
              inplanes (int): Number of channels for the input
                  feature in each block.
              planes (int): Number of channels for the output
                  feature in each block.
              blocks (int): Number of residual blocks.
              spatial_stride (int | Sequence[int]): Spatial strides
                  in residual and conv layers. Default: 1.
              temporal_stride (int | Sequence[int]): Temporal strides in
                  residual and conv layers. Default: 1.
              dilation (int): Spacing between kernel elements. Default: 1.
              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.
                  Default: ``pytorch``.
              inflate (int | Sequence[int]): Determine whether to inflate
                  for each block. Default: 1.
              inflate_style (str): ``3x1x1`` or ``1x1x1``. which determines
                  the kernel sizes and padding strides for conv1 and
                  conv2 in each block. Default: ``3x1x1``.
              non_local (int | Sequence[int]): Determine whether to apply
                  non-local module in the corresponding block of each stages.
                  Default: 0.
              non_local_cfg (dict): Config for non-local module.
                  Default: ``dict()``.
              conv_cfg (dict | None): Config for conv layers. Default: None.
              norm_cfg (dict | None): Config for norm layers. Default: None.
              act_cfg (dict | None): Config for activate layers. Default: None.
              with_cp (bool): Use checkpoint or not. Using checkpoint will save
                  some memory while slowing down the training speed.
                  Default: False.
  
          Returns:
              nn.Module: A residual layer for the given config.
          """
          inflate = inflate if not isinstance(inflate,
                                              int) else (inflate, ) * blocks
          non_local = non_local if not isinstance(
              non_local, int) else (non_local, ) * blocks
          assert len(inflate) == blocks and len(non_local) == blocks
          if self.lateral:
              lateral_inplanes = inplanes * 2 // self.channel_ratio
          else:
              lateral_inplanes = 0
          if (spatial_stride != 1
                  or (inplanes + lateral_inplanes) != planes * block.expansion):
              downsample = ConvModule(
                  inplanes + lateral_inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=(temporal_stride, spatial_stride, spatial_stride),
                  bias=False,
                  conv_cfg=conv_cfg,
                  norm_cfg=norm_cfg,
                  act_cfg=None)
          else:
              downsample = None
  
          layers = []
          layers.append(
              block(
                  inplanes + lateral_inplanes,
                  planes,
                  spatial_stride,
                  temporal_stride,
                  dilation,
                  downsample,
                  style=style,
                  inflate=(inflate[0] == 1),
                  inflate_style=inflate_style,
                  non_local=(non_local[0] == 1),
                  non_local_cfg=non_local_cfg,
                  conv_cfg=conv_cfg,
                  norm_cfg=norm_cfg,
                  act_cfg=act_cfg,
                  with_cp=with_cp))
          inplanes = planes * block.expansion
  
          for i in range(1, blocks):
              layers.append(
                  block(
                      inplanes,
                      planes,
                      1,
                      1,
                      dilation,
                      style=style,
                      inflate=(inflate[i] == 1),
                      inflate_style=inflate_style,
                      non_local=(non_local[i] == 1),
                      non_local_cfg=non_local_cfg,
                      conv_cfg=conv_cfg,
                      norm_cfg=norm_cfg,
                      act_cfg=act_cfg,
                      with_cp=with_cp))
  
          return nn.Sequential(*layers)
  
      def inflate_weights(self, logger):
          """Inflate the resnet2d parameters to resnet3d pathway.
  
          The differences between resnet3d and resnet2d mainly lie in an extra
          axis of conv kernel. To utilize the pretrained parameters in 2d model,
          the weight of conv2d models should be inflated to fit in the shapes of
          the 3d counterpart. For pathway the ``lateral_connection`` part should
          not be inflated from 2d weights.
  
          Args:
              logger (logging.Logger): The logger used to print
                  debugging infomation.
          """
  
          state_dict_r2d = _load_checkpoint(self.pretrained)
          if 'state_dict' in state_dict_r2d:
              state_dict_r2d = state_dict_r2d['state_dict']
  
          inflated_param_names = []
          for name, module in self.named_modules():
              if 'lateral' in name:
                  continue
              if isinstance(module, ConvModule):
                  # we use a ConvModule to wrap conv+bn+relu layers, thus the
                  # name mapping is needed
                  if 'downsample' in name:
                      # layer{X}.{Y}.downsample.conv->layer{X}.{Y}.downsample.0
                      original_conv_name = name + '.0'
                      # layer{X}.{Y}.downsample.bn->layer{X}.{Y}.downsample.1
                      original_bn_name = name + '.1'
                  else:
                      # layer{X}.{Y}.conv{n}.conv->layer{X}.{Y}.conv{n}
                      original_conv_name = name
                      # layer{X}.{Y}.conv{n}.bn->layer{X}.{Y}.bn{n}
                      original_bn_name = name.replace('conv', 'bn')
                  self._inflate_conv_params(module.conv, state_dict_r2d,
                                            original_conv_name,
                                            inflated_param_names)
                  self._inflate_bn_params(module.bn, state_dict_r2d,
                                          original_bn_name, inflated_param_names)
  
          # check if any parameters in the 2d checkpoint are not loaded
          remaining_names = set(
              state_dict_r2d.keys()) - set(inflated_param_names)
          if remaining_names:
              logger.info(f'These parameters in the 2d checkpoint are not loaded'
                          f': {remaining_names}')
  
      def _inflate_conv_params(self, conv3d, state_dict_2d, module_name_2d,
                               inflated_param_names):
          """Inflate a conv module from 2d to 3d.
  
          The differences of conv modules betweene 2d and 3d in Pathway
          mainly lie in the inplanes due to lateral connections. To fit the
          shapes of the lateral connection counterpart, it will expand
          parameters by concatting conv2d parameters and extra zero paddings.
  
          Args:
              conv3d (nn.Module): The destination conv3d module.
              state_dict_2d (OrderedDict): The state dict of pretrained 2d model.
              module_name_2d (str): The name of corresponding conv module in the
                  2d model.
              inflated_param_names (list[str]): List of parameters that have been
                  inflated.
          """
          weight_2d_name = module_name_2d + '.weight'
          conv2d_weight = state_dict_2d[weight_2d_name]
          old_shape = conv2d_weight.shape
          new_shape = conv3d.weight.data.shape
          kernel_t = new_shape[2]
          if new_shape[1] != old_shape[1]:
              # Inplanes may be different due to lateral connections
              new_channels = new_shape[1] - old_shape[1]
              pad_shape = old_shape
              pad_shape = pad_shape[:1] + (new_channels, ) + pad_shape[2:]
              # Expand parameters by concat extra channels
              conv2d_weight = torch.cat(
                  (conv2d_weight,
                   torch.zeros(pad_shape).type_as(conv2d_weight).to(
                       conv2d_weight.device)),
                  dim=1)
          new_weight = conv2d_weight.data.unsqueeze(2).expand_as(
              conv3d.weight) / kernel_t
          conv3d.weight.data.copy_(new_weight)
          inflated_param_names.append(weight_2d_name)
  
          if getattr(conv3d, 'bias') is not None:
              bias_2d_name = module_name_2d + '.bias'
              conv3d.bias.data.copy_(state_dict_2d[bias_2d_name])
              inflated_param_names.append(bias_2d_name)
  
      def _freeze_stages(self):
          """Prevent all the parameters from being optimized before
          `self.frozen_stages`."""
          if self.frozen_stages >= 0:
              self.conv1.eval()
              for param in self.conv1.parameters():
                  param.requires_grad = False
  
          for i in range(1, self.frozen_stages + 1):
              m = getattr(self, f'layer{i}')
              m.eval()
              for param in m.parameters():
                  param.requires_grad = False
  
              if i != len(self.res_layers) and self.lateral:
                  # No fusion needed in the final stage
                  lateral_name = self.lateral_connections[i - 1]
                  conv_lateral = getattr(self, lateral_name)
                  conv_lateral.eval()
                  for param in conv_lateral.parameters():
                      param.requires_grad = False
  
      def init_weights(self):
          """Initiate the parameters either from existing checkpoint or from
          scratch."""
          # Override the init_weights of i3d
          super().init_weights()
          for module_name in self.lateral_connections:
              layer = getattr(self, module_name)
              for m in layer.modules():
                  if isinstance(m, (nn.Conv3d, nn.Conv2d)):
                      kaiming_init(m)
  
  
  pathway_cfg = {
      'resnet3d': ResNet3dPathway,
      # TODO: BNInceptionPathway
  }
  
  
  def build_pathway(cfg, *args, **kwargs):
      """Build pathway.
  
      Args:
          cfg (None or dict): cfg should contain:
              - type (str): identify conv layer type.
  
      Returns:
          nn.Module: Created pathway.
      """
      if not (isinstance(cfg, dict) and 'type' in cfg):
          raise TypeError('cfg must be a dict containing the key "type"')
      cfg_ = cfg.copy()
  
      pathway_type = cfg_.pop('type')
      if pathway_type not in pathway_cfg:
          raise KeyError(f'Unrecognized pathway type {pathway_type}')
  
      pathway_cls = pathway_cfg[pathway_type]
      pathway = pathway_cls(*args, **kwargs, **cfg_)
  
      return pathway
  
  
  @BACKBONES.register_module()
  class ResNet3dSlowFast(nn.Module):
      """Slowfast backbone.
  
      This module is proposed in `SlowFast Networks for Video Recognition
      <https://arxiv.org/abs/1812.03982>`_
  
      Args:
          pretrained (str): The file path to a pretrained model.
          resample_rate (int): A large temporal stride ``resample_rate``
              on input frames, corresponding to the :math:`\\tau` in the paper.
              i.e., it processes only one out of ``resample_rate`` frames.
              Default: 16.
          speed_ratio (int): Speed ratio indicating the ratio between time
              dimension of the fast and slow pathway, corresponding to the
              :math:`\\alpha` in the paper. Default: 8.
          channel_ratio (int): Reduce the channel number of fast pathway
              by ``channel_ratio``, corresponding to :math:`\\beta` in the paper.
              Default: 8.
          slow_pathway (dict): Configuration of slow branch, should contain
              necessary arguments for building the specific type of pathway
              and:
              type (str): type of backbone the pathway bases on.
              lateral (bool): determine whether to build lateral connection
              for the pathway.Default:
  
              .. code-block:: Python
  
                  dict(type='ResNetPathway',
                  lateral=True, depth=50, pretrained=None,
                  conv1_kernel=(1, 7, 7), dilations=(1, 1, 1, 1),
                  conv1_stride_t=1, pool1_stride_t=1, inflate=(0, 0, 1, 1))
  
          fast_pathway (dict): Configuration of fast branch, similar to
              `slow_pathway`. Default:
  
              .. code-block:: Python
  
                  dict(type='ResNetPathway',
                  lateral=False, depth=50, pretrained=None, base_channels=8,
                  conv1_kernel=(5, 7, 7), conv1_stride_t=1, pool1_stride_t=1)
      """
  
      def __init__(self,
                   pretrained,
                   resample_rate=8,
                   speed_ratio=8,
                   channel_ratio=8,
                   slow_pathway=dict(
                       type='resnet3d',
                       depth=50,
                       pretrained=None,
                       lateral=True,
                       conv1_kernel=(1, 7, 7),
                       dilations=(1, 1, 1, 1),
                       conv1_stride_t=1,
                       pool1_stride_t=1,
                       inflate=(0, 0, 1, 1)),
                   fast_pathway=dict(
                       type='resnet3d',
                       depth=50,
                       pretrained=None,
                       lateral=False,
                       base_channels=8,
                       conv1_kernel=(5, 7, 7),
                       conv1_stride_t=1,
                       pool1_stride_t=1)):
          super().__init__()
          self.pretrained = pretrained
          self.resample_rate = resample_rate
          self.speed_ratio = speed_ratio
          self.channel_ratio = channel_ratio
  
          if slow_pathway['lateral']:
              slow_pathway['speed_ratio'] = speed_ratio
              slow_pathway['channel_ratio'] = channel_ratio
  
          self.slow_path = build_pathway(slow_pathway)
          self.fast_path = build_pathway(fast_pathway)
  
      def init_weights(self):
          """Initiate the parameters either from existing checkpoint or from
          scratch."""
          if isinstance(self.pretrained, str):
              logger = get_root_logger()
              msg = f'load model from: {self.pretrained}'
              print_log(msg, logger=logger)
              # Directly load 3D model.
              load_checkpoint(self, self.pretrained, strict=True, logger=logger)
          elif self.pretrained is None:
              # Init two branch seperately.
              self.fast_path.init_weights()
              self.slow_path.init_weights()
          else:
              raise TypeError('pretrained must be a str or None')
  
      def forward(self, x):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
  
          Returns:
              tuple[torch.Tensor]: The feature of the input samples extracted
                  by the backbone.
          """
          x_slow = nn.functional.interpolate(
              x,
              mode='nearest',
              scale_factor=(1.0 / self.resample_rate, 1.0, 1.0))
          x_slow = self.slow_path.conv1(x_slow)
          x_slow = self.slow_path.maxpool(x_slow)
  
          x_fast = nn.functional.interpolate(
              x,
              mode='nearest',
              scale_factor=(1.0 / (self.resample_rate // self.speed_ratio), 1.0,
                            1.0))
          x_fast = self.fast_path.conv1(x_fast)
          x_fast = self.fast_path.maxpool(x_fast)
  
          if self.slow_path.lateral:
              x_fast_lateral = self.slow_path.conv1_lateral(x_fast)
              x_slow = torch.cat((x_slow, x_fast_lateral), dim=1)
  
          for i, layer_name in enumerate(self.slow_path.res_layers):
              res_layer = getattr(self.slow_path, layer_name)
              x_slow = res_layer(x_slow)
              res_layer_fast = getattr(self.fast_path, layer_name)
              x_fast = res_layer_fast(x_fast)
              if (i != len(self.slow_path.res_layers) - 1
                      and self.slow_path.lateral):
                  # No fusion needed in the final stage
                  lateral_name = self.slow_path.lateral_connections[i]
                  conv_lateral = getattr(self.slow_path, lateral_name)
                  x_fast_lateral = conv_lateral(x_fast)
                  x_slow = torch.cat((x_slow, x_fast_lateral), dim=1)
  
          out = (x_slow, x_fast)
  
          return out
  