content: import torch.nn as nn
  import torch.utils.checkpoint as cp
  from mmcv.cnn import (ConvModule, NonLocal3d, build_activation_layer,
                        constant_init, kaiming_init)
  from mmcv.runner import _load_checkpoint, load_checkpoint
  from mmcv.utils import _BatchNorm
  from torch.nn.modules.utils import _ntuple, _triple
  
  from ...utils import get_root_logger
  from ..registry import BACKBONES
  
  
  class BasicBlock3d(nn.Module):
      """BasicBlock 3d block for ResNet3D.
  
      Args:
          inplanes (int): Number of channels for the input in first conv3d layer.
          planes (int): Number of channels produced by some norm/conv3d layers.
          spatial_stride (int): Spatial stride in the conv3d layer. Default: 1.
          temporal_stride (int): Temporal stride in the conv3d layer. Default: 1.
          dilation (int): Spacing between kernel elements. Default: 1.
          downsample (nn.Module | None): Downsample layer. Default: None.
          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 (bool): Whether to inflate kernel. Default: True.
          non_local (bool): Determine whether to apply non-local module in this
              block. Default: False.
          non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
          conv_cfg (dict): Config dict for convolution layer.
              Default: ``dict(type='Conv3d')``.
          norm_cfg (dict): Config for norm layers. required keys are ``type``,
              Default: ``dict(type='BN3d')``.
          act_cfg (dict): Config dict for activation layer.
              Default: ``dict(type='ReLU')``.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed. Default: False.
      """
      expansion = 1
  
      def __init__(self,
                   inplanes,
                   planes,
                   spatial_stride=1,
                   temporal_stride=1,
                   dilation=1,
                   downsample=None,
                   style='pytorch',
                   inflate=True,
                   non_local=False,
                   non_local_cfg=dict(),
                   conv_cfg=dict(type='Conv3d'),
                   norm_cfg=dict(type='BN3d'),
                   act_cfg=dict(type='ReLU'),
                   with_cp=False,
                   **kwargs):
          super().__init__()
          assert style in ['pytorch', 'caffe']
          # make sure that only ``inflate_style`` is passed into kwargs
          assert set(kwargs.keys()).issubset(['inflate_style'])
  
          self.inplanes = inplanes
          self.planes = planes
          self.spatial_stride = spatial_stride
          self.temporal_stride = temporal_stride
          self.dilation = dilation
          self.style = style
          self.inflate = inflate
          self.conv_cfg = conv_cfg
          self.norm_cfg = norm_cfg
          self.act_cfg = act_cfg
          self.with_cp = with_cp
          self.non_local = non_local
          self.non_local_cfg = non_local_cfg
  
          self.conv1_stride_s = spatial_stride
          self.conv2_stride_s = 1
          self.conv1_stride_t = temporal_stride
          self.conv2_stride_t = 1
  
          if self.inflate:
              conv1_kernel_size = (3, 3, 3)
              conv1_padding = (1, dilation, dilation)
              conv2_kernel_size = (3, 3, 3)
              conv2_padding = (1, 1, 1)
          else:
              conv1_kernel_size = (1, 3, 3)
              conv1_padding = (0, dilation, dilation)
              conv2_kernel_size = (1, 3, 3)
              conv2_padding = (0, 1, 1)
  
          self.conv1 = ConvModule(
              inplanes,
              planes,
              conv1_kernel_size,
              stride=(self.conv1_stride_t, self.conv1_stride_s,
                      self.conv1_stride_s),
              padding=conv1_padding,
              dilation=(1, dilation, dilation),
              bias=False,
              conv_cfg=self.conv_cfg,
              norm_cfg=self.norm_cfg,
              act_cfg=self.act_cfg)
  
          self.conv2 = ConvModule(
              planes,
              planes * self.expansion,
              conv2_kernel_size,
              stride=(self.conv2_stride_t, self.conv2_stride_s,
                      self.conv2_stride_s),
              padding=conv2_padding,
              bias=False,
              conv_cfg=self.conv_cfg,
              norm_cfg=self.norm_cfg,
              act_cfg=None)
  
          self.downsample = downsample
          self.relu = build_activation_layer(self.act_cfg)
  
          if self.non_local:
              self.non_local_block = NonLocal3d(self.conv2.norm.num_features,
                                                **self.non_local_cfg)
  
      def forward(self, x):
          """Defines the computation performed at every call."""
  
          def _inner_forward(x):
              """Forward wrapper for utilizing checkpoint."""
              identity = x
  
              out = self.conv1(x)
              out = self.conv2(out)
  
              if self.downsample is not None:
                  identity = self.downsample(x)
  
              out = out + identity
              return out
  
          if self.with_cp and x.requires_grad:
              out = cp.checkpoint(_inner_forward, x)
          else:
              out = _inner_forward(x)
          out = self.relu(out)
  
          if self.non_local:
              out = self.non_local_block(out)
  
          return out
  
  
  class Bottleneck3d(nn.Module):
      """Bottleneck 3d block for ResNet3D.
  
      Args:
          inplanes (int): Number of channels for the input in first conv3d layer.
          planes (int): Number of channels produced by some norm/conv3d layers.
          spatial_stride (int): Spatial stride in the conv3d layer. Default: 1.
          temporal_stride (int): Temporal stride in the conv3d layer. Default: 1.
          dilation (int): Spacing between kernel elements. Default: 1.
          downsample (nn.Module | None): Downsample layer. Default: None.
          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 (bool): Whether to inflate kernel. Default: True.
          inflate_style (str): ``3x1x1`` or ``3x3x3``. which determines the
              kernel sizes and padding strides for conv1 and conv2 in each block.
              Default: '3x1x1'.
          non_local (bool): Determine whether to apply non-local module in this
              block. Default: False.
          non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
          conv_cfg (dict): Config dict for convolution layer.
              Default: ``dict(type='Conv3d')``.
          norm_cfg (dict): Config for norm layers. required keys are ``type``,
              Default: ``dict(type='BN3d')``.
          act_cfg (dict): Config dict for activation layer.
              Default: ``dict(type='ReLU')``.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed. Default: False.
      """
      expansion = 4
  
      def __init__(self,
                   inplanes,
                   planes,
                   spatial_stride=1,
                   temporal_stride=1,
                   dilation=1,
                   downsample=None,
                   style='pytorch',
                   inflate=True,
                   inflate_style='3x1x1',
                   non_local=False,
                   non_local_cfg=dict(),
                   conv_cfg=dict(type='Conv3d'),
                   norm_cfg=dict(type='BN3d'),
                   act_cfg=dict(type='ReLU'),
                   with_cp=False):
          super().__init__()
          assert style in ['pytorch', 'caffe']
          assert inflate_style in ['3x1x1', '3x3x3']
  
          self.inplanes = inplanes
          self.planes = planes
          self.spatial_stride = spatial_stride
          self.temporal_stride = temporal_stride
          self.dilation = dilation
          self.style = style
          self.inflate = inflate
          self.inflate_style = inflate_style
          self.norm_cfg = norm_cfg
          self.conv_cfg = conv_cfg
          self.act_cfg = act_cfg
          self.with_cp = with_cp
          self.non_local = non_local
          self.non_local_cfg = non_local_cfg
  
          if self.style == 'pytorch':
              self.conv1_stride_s = 1
              self.conv2_stride_s = spatial_stride
              self.conv1_stride_t = 1
              self.conv2_stride_t = temporal_stride
          else:
              self.conv1_stride_s = spatial_stride
              self.conv2_stride_s = 1
              self.conv1_stride_t = temporal_stride
              self.conv2_stride_t = 1
  
          if self.inflate:
              if inflate_style == '3x1x1':
                  conv1_kernel_size = (3, 1, 1)
                  conv1_padding = (1, 0, 0)
                  conv2_kernel_size = (1, 3, 3)
                  conv2_padding = (0, dilation, dilation)
              else:
                  conv1_kernel_size = (1, 1, 1)
                  conv1_padding = (0, 0, 0)
                  conv2_kernel_size = (3, 3, 3)
                  conv2_padding = (1, dilation, dilation)
          else:
              conv1_kernel_size = (1, 1, 1)
              conv1_padding = (0, 0, 0)
              conv2_kernel_size = (1, 3, 3)
              conv2_padding = (0, dilation, dilation)
  
          self.conv1 = ConvModule(
              inplanes,
              planes,
              conv1_kernel_size,
              stride=(self.conv1_stride_t, self.conv1_stride_s,
                      self.conv1_stride_s),
              padding=conv1_padding,
              bias=False,
              conv_cfg=self.conv_cfg,
              norm_cfg=self.norm_cfg,
              act_cfg=self.act_cfg)
  
          self.conv2 = ConvModule(
              planes,
              planes,
              conv2_kernel_size,
              stride=(self.conv2_stride_t, self.conv2_stride_s,
                      self.conv2_stride_s),
              padding=conv2_padding,
              dilation=(1, dilation, dilation),
              bias=False,
              conv_cfg=self.conv_cfg,
              norm_cfg=self.norm_cfg,
              act_cfg=self.act_cfg)
  
          self.conv3 = ConvModule(
              planes,
              planes * self.expansion,
              1,
              bias=False,
              conv_cfg=self.conv_cfg,
              norm_cfg=self.norm_cfg,
              # No activation in the third ConvModule for bottleneck
              act_cfg=None)
  
          self.downsample = downsample
          self.relu = build_activation_layer(self.act_cfg)
  
          if self.non_local:
              self.non_local_block = NonLocal3d(self.conv3.norm.num_features,
                                                **self.non_local_cfg)
  
      def forward(self, x):
          """Defines the computation performed at every call."""
  
          def _inner_forward(x):
              """Forward wrapper for utilizing checkpoint."""
              identity = x
  
              out = self.conv1(x)
              out = self.conv2(out)
              out = self.conv3(out)
  
              if self.downsample is not None:
                  identity = self.downsample(x)
  
              out = out + identity
              return out
  
          if self.with_cp and x.requires_grad:
              out = cp.checkpoint(_inner_forward, x)
          else:
              out = _inner_forward(x)
          out = self.relu(out)
  
          if self.non_local:
              out = self.non_local_block(out)
  
          return out
  
  
  @BACKBONES.register_module()
  class ResNet3d(nn.Module):
      """ResNet 3d backbone.
  
      Args:
          depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
          pretrained (str | None): Name of pretrained model.
          pretrained2d (bool): Whether to load pretrained 2D model.
              Default: True.
          in_channels (int): Channel num of input features. Default: 3.
          base_channels (int): Channel num of stem output features. Default: 64.
          out_indices (Sequence[int]): Indices of output feature. Default: (3, ).
          num_stages (int): Resnet stages. Default: 4.
          spatial_strides (Sequence[int]):
              Spatial strides of residual blocks of each stage.
              Default: ``(1, 2, 2, 2)``.
          temporal_strides (Sequence[int]):
              Temporal strides of residual blocks of each stage.
              Default: ``(1, 1, 1, 1)``.
          dilations (Sequence[int]): Dilation of each stage.
              Default: ``(1, 1, 1, 1)``.
          conv1_kernel (Sequence[int]): Kernel size of the first conv layer.
              Default: ``(5, 7, 7)``.
          conv1_stride_t (int): Temporal stride of the first conv layer.
              Default: 2.
          pool1_stride_t (int): Temporal stride of the first pooling layer.
              Default: 2.
          with_pool2 (bool): Whether to use pool2. Default: True.
          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'.
          frozen_stages (int): Stages to be frozen (all param fixed). -1 means
              not freezing any parameters. Default: -1.
          inflate (Sequence[int]): Inflate Dims of each block.
              Default: (1, 1, 1, 1).
          inflate_style (str): ``3x1x1`` or ``1x1x1``. which determines the
              kernel sizes and padding strides for conv1 and conv2 in each block.
              Default: '3x1x1'.
          conv_cfg (dict): Config for conv layers. required keys are ``type``
              Default: ``dict(type='Conv3d')``.
          norm_cfg (dict): Config for norm layers. required keys are ``type`` and
              ``requires_grad``.
              Default: ``dict(type='BN3d', requires_grad=True)``.
          act_cfg (dict): Config dict for activation layer.
              Default: ``dict(type='ReLU', inplace=True)``.
          norm_eval (bool): Whether to set BN layers to eval mode, namely, freeze
              running stats (mean and var). Default: False.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed. Default: False.
          non_local (Sequence[int]): Determine whether to apply non-local module
              in the corresponding block of each stages. Default: (0, 0, 0, 0).
          non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
          zero_init_residual (bool):
              Whether to use zero initialization for residual block,
              Default: True.
          kwargs (dict, optional): Key arguments for "make_res_layer".
      """
  
      arch_settings = {
          18: (BasicBlock3d, (2, 2, 2, 2)),
          34: (BasicBlock3d, (3, 4, 6, 3)),
          50: (Bottleneck3d, (3, 4, 6, 3)),
          101: (Bottleneck3d, (3, 4, 23, 3)),
          152: (Bottleneck3d, (3, 8, 36, 3))
      }
  
      def __init__(self,
                   depth,
                   pretrained,
                   pretrained2d=True,
                   in_channels=3,
                   num_stages=4,
                   base_channels=64,
                   out_indices=(3, ),
                   spatial_strides=(1, 2, 2, 2),
                   temporal_strides=(1, 1, 1, 1),
                   dilations=(1, 1, 1, 1),
                   conv1_kernel=(5, 7, 7),
                   conv1_stride_t=2,
                   pool1_stride_t=2,
                   with_pool2=True,
                   style='pytorch',
                   frozen_stages=-1,
                   inflate=(1, 1, 1, 1),
                   inflate_style='3x1x1',
                   conv_cfg=dict(type='Conv3d'),
                   norm_cfg=dict(type='BN3d', requires_grad=True),
                   act_cfg=dict(type='ReLU', inplace=True),
                   norm_eval=False,
                   with_cp=False,
                   non_local=(0, 0, 0, 0),
                   non_local_cfg=dict(),
                   zero_init_residual=True,
                   **kwargs):
          super().__init__()
          if depth not in self.arch_settings:
              raise KeyError(f'invalid depth {depth} for resnet')
          self.depth = depth
          self.pretrained = pretrained
          self.pretrained2d = pretrained2d
          self.in_channels = in_channels
          self.base_channels = base_channels
          self.num_stages = num_stages
          assert 1 <= num_stages <= 4
          self.out_indices = out_indices
          assert max(out_indices) < num_stages
          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.conv1_kernel = conv1_kernel
          self.conv1_stride_t = conv1_stride_t
          self.pool1_stride_t = pool1_stride_t
          self.with_pool2 = with_pool2
          self.style = style
          self.frozen_stages = frozen_stages
          self.stage_inflations = _ntuple(num_stages)(inflate)
          self.non_local_stages = _ntuple(num_stages)(non_local)
          self.inflate_style = inflate_style
          self.conv_cfg = conv_cfg
          self.norm_cfg = norm_cfg
          self.act_cfg = act_cfg
          self.norm_eval = norm_eval
          self.with_cp = with_cp
          self.zero_init_residual = zero_init_residual
  
          self.block, stage_blocks = self.arch_settings[depth]
          self.stage_blocks = stage_blocks[:num_stages]
          self.inplanes = self.base_channels
  
          self.non_local_cfg = non_local_cfg
  
          self._make_stem_layer()
  
          self.res_layers = []
          for i, num_blocks in enumerate(self.stage_blocks):
              spatial_stride = spatial_strides[i]
              temporal_stride = temporal_strides[i]
              dilation = dilations[i]
              planes = self.base_channels * 2**i
              res_layer = self.make_res_layer(
                  self.block,
                  self.inplanes,
                  planes,
                  num_blocks,
                  spatial_stride=spatial_stride,
                  temporal_stride=temporal_stride,
                  dilation=dilation,
                  style=self.style,
                  norm_cfg=self.norm_cfg,
                  conv_cfg=self.conv_cfg,
                  act_cfg=self.act_cfg,
                  non_local=self.non_local_stages[i],
                  non_local_cfg=self.non_local_cfg,
                  inflate=self.stage_inflations[i],
                  inflate_style=self.inflate_style,
                  with_cp=with_cp,
                  **kwargs)
              self.inplanes = planes * self.block.expansion
              layer_name = f'layer{i + 1}'
              self.add_module(layer_name, res_layer)
              self.res_layers.append(layer_name)
  
          self.feat_dim = self.block.expansion * self.base_channels * 2**(
              len(self.stage_blocks) - 1)
  
      @staticmethod
      def make_res_layer(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(),
                         norm_cfg=None,
                         act_cfg=None,
                         conv_cfg=None,
                         with_cp=False,
                         **kwargs):
          """Build residual layer for ResNet3D.
  
          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 norm 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 | None): 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
          downsample = None
          if spatial_stride != 1 or inplanes != planes * block.expansion:
              downsample = ConvModule(
                  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)
  
          layers = []
          layers.append(
              block(
                  inplanes,
                  planes,
                  spatial_stride=spatial_stride,
                  temporal_stride=temporal_stride,
                  dilation=dilation,
                  downsample=downsample,
                  style=style,
                  inflate=(inflate[0] == 1),
                  inflate_style=inflate_style,
                  non_local=(non_local[0] == 1),
                  non_local_cfg=non_local_cfg,
                  norm_cfg=norm_cfg,
                  conv_cfg=conv_cfg,
                  act_cfg=act_cfg,
                  with_cp=with_cp,
                  **kwargs))
          inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(
                  block(
                      inplanes,
                      planes,
                      spatial_stride=1,
                      temporal_stride=1,
                      dilation=dilation,
                      style=style,
                      inflate=(inflate[i] == 1),
                      inflate_style=inflate_style,
                      non_local=(non_local[i] == 1),
                      non_local_cfg=non_local_cfg,
                      norm_cfg=norm_cfg,
                      conv_cfg=conv_cfg,
                      act_cfg=act_cfg,
                      with_cp=with_cp,
                      **kwargs))
  
          return nn.Sequential(*layers)
  
      @staticmethod
      def _inflate_conv_params(conv3d, state_dict_2d, module_name_2d,
                               inflated_param_names):
          """Inflate a conv module from 2d to 3d.
  
          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]
          kernel_t = conv3d.weight.data.shape[2]
  
          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)
  
      @staticmethod
      def _inflate_bn_params(bn3d, state_dict_2d, module_name_2d,
                             inflated_param_names):
          """Inflate a norm module from 2d to 3d.
  
          Args:
              bn3d (nn.Module): The destination bn3d module.
              state_dict_2d (OrderedDict): The state dict of pretrained 2d model.
              module_name_2d (str): The name of corresponding bn module in the
                  2d model.
              inflated_param_names (list[str]): List of parameters that have been
                  inflated.
          """
          for param_name, param in bn3d.named_parameters():
              param_2d_name = f'{module_name_2d}.{param_name}'
              param_2d = state_dict_2d[param_2d_name]
              param.data.copy_(param_2d)
              inflated_param_names.append(param_2d_name)
  
          for param_name, param in bn3d.named_buffers():
              param_2d_name = f'{module_name_2d}.{param_name}'
              # some buffers like num_batches_tracked may not exist in old
              # checkpoints
              if param_2d_name in state_dict_2d:
                  param_2d = state_dict_2d[param_2d_name]
                  param.data.copy_(param_2d)
                  inflated_param_names.append(param_2d_name)
  
      def inflate_weights(self, logger):
          """Inflate the resnet2d parameters to resnet3d.
  
          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.
  
          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 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')
                  if original_conv_name + '.weight' not in state_dict_r2d:
                      logger.warning(f'Module not exist in the state_dict_r2d'
                                     f': {original_conv_name}')
                  else:
                      shape_2d = state_dict_r2d[original_conv_name +
                                                '.weight'].shape
                      shape_3d = module.conv.weight.data.shape
                      if shape_2d != shape_3d[:2] + shape_3d[3:]:
                          logger.warning(f'Weight shape mismatch for '
                                         f': {original_conv_name} : '
                                         f'3d weight shape: {shape_3d}; '
                                         f'2d weight shape: {shape_2d}. ')
                      else:
                          self._inflate_conv_params(module.conv, state_dict_r2d,
                                                    original_conv_name,
                                                    inflated_param_names)
  
                  if original_bn_name + '.weight' not in state_dict_r2d:
                      logger.warning(f'Module not exist in the state_dict_r2d'
                                     f': {original_bn_name}')
                  else:
                      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 _make_stem_layer(self):
          """Construct the stem layers consists of a conv+norm+act module and a
          pooling layer."""
          self.conv1 = ConvModule(
              self.in_channels,
              self.base_channels,
              kernel_size=self.conv1_kernel,
              stride=(self.conv1_stride_t, 2, 2),
              padding=tuple([(k - 1) // 2 for k in _triple(self.conv1_kernel)]),
              bias=False,
              conv_cfg=self.conv_cfg,
              norm_cfg=self.norm_cfg,
              act_cfg=self.act_cfg)
  
          self.maxpool = nn.MaxPool3d(
              kernel_size=(1, 3, 3),
              stride=(self.pool1_stride_t, 2, 2),
              padding=(0, 1, 1))
  
          self.pool2 = nn.MaxPool3d(kernel_size=(2, 1, 1), stride=(2, 1, 1))
  
      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
  
      def init_weights(self):
          """Initiate the parameters either from existing checkpoint or from
          scratch."""
          if isinstance(self.pretrained, str):
              logger = get_root_logger()
              logger.info(f'load model from: {self.pretrained}')
  
              if self.pretrained2d:
                  # Inflate 2D model into 3D model.
                  self.inflate_weights(logger)
  
              else:
                  # Directly load 3D model.
                  load_checkpoint(
                      self, self.pretrained, strict=False, logger=logger)
  
          elif self.pretrained is None:
              for m in self.modules():
                  if isinstance(m, nn.Conv3d):
                      kaiming_init(m)
                  elif isinstance(m, _BatchNorm):
                      constant_init(m, 1)
  
              if self.zero_init_residual:
                  for m in self.modules():
                      if isinstance(m, Bottleneck3d):
                          constant_init(m.conv3.bn, 0)
                      elif isinstance(m, BasicBlock3d):
                          constant_init(m.conv2.bn, 0)
          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:
              torch.Tensor: The feature of the input
              samples extracted by the backbone.
          """
          x = self.conv1(x)
          x = self.maxpool(x)
          outs = []
          for i, layer_name in enumerate(self.res_layers):
              res_layer = getattr(self, layer_name)
              x = res_layer(x)
              if i == 0 and self.with_pool2:
                  x = self.pool2(x)
              if i in self.out_indices:
                  outs.append(x)
          if len(outs) == 1:
              return outs[0]
  
          return tuple(outs)
  
      def train(self, mode=True):
          """Set the optimization status when training."""
          super().train(mode)
          self._freeze_stages()
          if mode and self.norm_eval:
              for m in self.modules():
                  if isinstance(m, _BatchNorm):
                      m.eval()
  