content: import warnings
  
  import torch
  import torch.nn as nn
  
  from ..registry import BACKBONES
  from .resnet_tsm import ResNetTSM
  
  try:
      from mmcv.ops import tin_shift
  except (ImportError, ModuleNotFoundError):
      warnings.warn('Please install mmcv-full to support "tin_shift"')
  
  
  def linear_sampler(data, offset):
      """Differentiable Temporal-wise Frame Sampling, which is essentially a
      linear interpolation process.
  
      It gets the feature map which has been split into several groups
      and shift them by different offsets according to their groups.
      Then compute the weighted sum along with the temporal dimension.
  
      Args:
          data (torch.Tensor): Split data for certain group in shape
              [N, num_segments, C, H, W].
          offset (torch.Tensor): Data offsets for this group data in shape
              [N, num_segments].
      """
      # [N, num_segments, C, H, W]
      n, t, c, h, w = data.shape
  
      # offset0, offset1: [N, num_segments]
      offset0 = torch.floor(offset).int()
      offset1 = offset0 + 1
  
      # data, data0, data1: [N, num_segments, C, H * W]
      data = data.view(n, t, c, h * w).contiguous()
      data0 = tin_shift(data, offset0)
      data1 = tin_shift(data, offset1)
  
      # weight0, weight1: [N, num_segments]
      weight0 = 1 - (offset - offset0.float())
      weight1 = 1 - weight0
  
      # weight0, weight1:
      # [N, num_segments] -> [N, num_segments, C // num_segments] -> [N, C]
      group_size = offset.shape[1]
      weight0 = weight0[:, :, None].repeat(1, 1, c // group_size)
      weight0 = weight0.view(weight0.size(0), -1)
      weight1 = weight1[:, :, None].repeat(1, 1, c // group_size)
      weight1 = weight1.view(weight1.size(0), -1)
  
      # weight0, weight1: [N, C] -> [N, 1, C, 1]
      weight0 = weight0[:, None, :, None]
      weight1 = weight1[:, None, :, None]
  
      # output: [N, num_segments, C, H * W] -> [N, num_segments, C, H, W]
      output = weight0 * data0 + weight1 * data1
      output = output.view(n, t, c, h, w)
  
      return output
  
  
  class CombineNet(nn.Module):
      """Combine Net.
  
      It combines Temporal interlace module with some part of ResNet layer.
  
      Args:
          net1 (nn.module): Temporal interlace module.
          net2 (nn.module): Some part of ResNet layer.
      """
  
      def __init__(self, net1, net2):
          super().__init__()
          self.net1 = net1
          self.net2 = net2
  
      def forward(self, x):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
  
          Returns:
              torch.Tensor: The output of the module.
          """
          # input shape: [num_batches * num_segments, C, H, W]
          # output x shape: [num_batches * num_segments, C, H, W]
          x = self.net1(x)
          # [num_batches * num_segments, C, H, W]
          x = self.net2(x)
          return x
  
  
  class WeightNet(nn.Module):
      """WeightNet in Temporal interlace module.
  
      The WeightNet consists of two parts: one convolution layer
      and a sigmoid function. Following the convolution layer, the sigmoid
      function and rescale module can scale our output to the range (0, 2).
      Here we set the initial bias of the convolution layer to 0, and the
      final initial output will be 1.0.
  
      Args:
          in_channels (int): Channel num of input features.
          groups (int): Number of groups for fc layer outputs.
      """
  
      def __init__(self, in_channels, groups):
          super().__init__()
          self.sigmoid = nn.Sigmoid()
          self.groups = groups
  
          self.conv = nn.Conv1d(in_channels, groups, 3, padding=1)
  
          self.init_weights()
  
      def init_weights(self):
          """Initiate the parameters either from existing checkpoint or from
          scratch."""
          # we set the initial bias of the convolution
          # layer to 0, and the final initial output will be 1.0
          self.conv.bias.data[...] = 0
  
      def forward(self, x):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
  
          Returns:
              torch.Tensor: The output of the module.
          """
          # calculate weight
          # [N, C, T]
          n, _, t = x.shape
          # [N, groups, T]
          x = self.conv(x)
          x = x.view(n, self.groups, t)
          # [N, T, groups]
          x = x.permute(0, 2, 1)
  
          # scale the output to range (0, 2)
          x = 2 * self.sigmoid(x)
          # [N, T, groups]
          return x
  
  
  class OffsetNet(nn.Module):
      """OffsetNet in Temporal interlace module.
  
      The OffsetNet consists of one convolution layer and two fc layers
      with a relu activation following with a sigmoid function. Following
      the convolution layer, two fc layers and relu are applied to the output.
      Then, apply the sigmoid function with a multiply factor and a minus 0.5
      to transform the output to (-4, 4).
  
      Args:
          in_channels (int): Channel num of input features.
          groups (int): Number of groups for fc layer outputs.
          num_segments (int): Number of frame segments.
      """
  
      def __init__(self, in_channels, groups, num_segments):
          super().__init__()
          self.sigmoid = nn.Sigmoid()
          # hard code ``kernel_size`` and ``padding`` according to original repo.
          kernel_size = 3
          padding = 1
  
          self.conv = nn.Conv1d(in_channels, 1, kernel_size, padding=padding)
          self.fc1 = nn.Linear(num_segments, num_segments)
          self.relu = nn.ReLU()
          self.fc2 = nn.Linear(num_segments, groups)
  
          self.init_weights()
  
      def init_weights(self):
          """Initiate the parameters either from existing checkpoint or from
          scratch."""
          # The bias of the last fc layer is initialized to
          # make the post-sigmoid output start from 1
          self.fc2.bias.data[...] = 0.5108
  
      def forward(self, x):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
  
          Returns:
              torch.Tensor: The output of the module.
          """
          # calculate offset
          # [N, C, T]
          n, _, t = x.shape
          # [N, 1, T]
          x = self.conv(x)
          # [N, T]
          x = x.view(n, t)
          # [N, T]
          x = self.relu(self.fc1(x))
          # [N, groups]
          x = self.fc2(x)
          # [N, 1, groups]
          x = x.view(n, 1, -1)
  
          # to make sure the output is in (-t/2, t/2)
          # where t = num_segments = 8
          x = 4 * (self.sigmoid(x) - 0.5)
          # [N, 1, groups]
          return x
  
  
  class TemporalInterlace(nn.Module):
      """Temporal interlace module.
  
      This module is proposed in `Temporal Interlacing Network
      <https://arxiv.org/abs/2001.06499>`_
  
      Args:
          in_channels (int): Channel num of input features.
          num_segments (int): Number of frame segments. Default: 3.
          shift_div (int): Number of division parts for shift. Default: 1.
      """
  
      def __init__(self, in_channels, num_segments=3, shift_div=1):
          super().__init__()
          self.num_segments = num_segments
          self.shift_div = shift_div
          self.in_channels = in_channels
          # hard code ``deform_groups`` according to original repo.
          self.deform_groups = 2
  
          self.offset_net = OffsetNet(in_channels // shift_div,
                                      self.deform_groups, num_segments)
          self.weight_net = WeightNet(in_channels // shift_div,
                                      self.deform_groups)
  
      def forward(self, x):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
  
          Returns:
              torch.Tensor: The output of the module.
          """
          # x: [N, C, H, W],
          # where N = num_batches x num_segments, C = shift_div * num_folds
          n, c, h, w = x.size()
          num_batches = n // self.num_segments
          num_folds = c // self.shift_div
  
          # x_out: [num_batches x num_segments, C, H, W]
          x_out = torch.zeros((n, c, h, w), device=x.device)
          # x_descriptor: [num_batches, num_segments, num_folds, H, W]
          x_descriptor = x[:, :num_folds, :, :].view(num_batches,
                                                     self.num_segments,
                                                     num_folds, h, w)
  
          # x should only obtain information on temporal and channel dimensions
          # x_pooled: [num_batches, num_segments, num_folds, W]
          x_pooled = torch.mean(x_descriptor, 3)
          # x_pooled: [num_batches, num_segments, num_folds]
          x_pooled = torch.mean(x_pooled, 3)
          # x_pooled: [num_batches, num_folds, num_segments]
          x_pooled = x_pooled.permute(0, 2, 1).contiguous()
  
          # Calculate weight and bias, here groups = 2
          # x_offset: [num_batches, groups]
          x_offset = self.offset_net(x_pooled).view(num_batches, -1)
          # x_weight: [num_batches, num_segments, groups]
          x_weight = self.weight_net(x_pooled)
  
          # x_offset: [num_batches, 2 * groups]
          x_offset = torch.cat([x_offset, -x_offset], 1)
          # x_shift: [num_batches, num_segments, num_folds, H, W]
          x_shift = linear_sampler(x_descriptor, x_offset)
  
          # x_weight: [num_batches, num_segments, groups, 1]
          x_weight = x_weight[:, :, :, None]
          # x_weight:
          # [num_batches, num_segments, groups * 2, c // self.shift_div // 4]
          x_weight = x_weight.repeat(1, 1, 2, num_folds // 2 // 2)
          # x_weight:
          # [num_batches, num_segments, c // self.shift_div = num_folds]
          x_weight = x_weight.view(x_weight.size(0), x_weight.size(1), -1)
  
          # x_weight: [num_batches, num_segments, num_folds, 1, 1]
          x_weight = x_weight[:, :, :, None, None]
          # x_shift: [num_batches, num_segments, num_folds, H, W]
          x_shift = x_shift * x_weight
          # x_shift: [num_batches, num_segments, num_folds, H, W]
          x_shift = x_shift.contiguous().view(n, num_folds, h, w)
  
          # x_out: [num_batches x num_segments, C, H, W]
          x_out[:, :num_folds, :] = x_shift
          x_out[:, num_folds:, :] = x[:, num_folds:, :]
  
          return x_out
  
  
  @BACKBONES.register_module()
  class ResNetTIN(ResNetTSM):
      """ResNet backbone for TIN.
  
      Args:
          depth (int): Depth of ResNet, from {18, 34, 50, 101, 152}.
          num_segments (int): Number of frame segments. Default: 8.
          is_tin (bool): Whether to apply temporal interlace. Default: True.
          shift_div (int): Number of division parts for shift. Default: 4.
          kwargs (dict, optional): Arguments for ResNet.
      """
  
      def __init__(self,
                   depth,
                   num_segments=8,
                   is_tin=True,
                   shift_div=4,
                   **kwargs):
          super().__init__(depth, **kwargs)
          self.num_segments = num_segments
          self.is_tin = is_tin
          self.shift_div = shift_div
  
      def make_temporal_interlace(self):
          """Make temporal interlace for some layers."""
          num_segment_list = [self.num_segments] * 4
          assert num_segment_list[-1] > 0
  
          n_round = 1
          if len(list(self.layer3.children())) >= 23:
              print(f'=> Using n_round {n_round} to insert temporal shift.')
  
          def make_block_interlace(stage, num_segments, shift_div):
              """Apply Deformable shift for a ResNet layer module.
  
              Args:
                  stage (nn.module): A ResNet layer to be deformed.
                  num_segments (int): Number of frame segments.
                  shift_div (int): Number of division parts for shift.
  
              Returns:
                  nn.Sequential: A Sequential container consisted of
                      deformed Interlace blocks.
              """
              blocks = list(stage.children())
              for i, b in enumerate(blocks):
                  if i % n_round == 0:
                      tds = TemporalInterlace(
                          b.conv1.in_channels,
                          num_segments=num_segments,
                          shift_div=shift_div)
                      blocks[i].conv1.conv = CombineNet(tds,
                                                        blocks[i].conv1.conv)
              return nn.Sequential(*blocks)
  
          self.layer1 = make_block_interlace(self.layer1, num_segment_list[0],
                                             self.shift_div)
          self.layer2 = make_block_interlace(self.layer2, num_segment_list[1],
                                             self.shift_div)
          self.layer3 = make_block_interlace(self.layer3, num_segment_list[2],
                                             self.shift_div)
          self.layer4 = make_block_interlace(self.layer4, num_segment_list[3],
                                             self.shift_div)
  
      def init_weights(self):
          """Initiate the parameters either from existing checkpoint or from
          scratch."""
          super(ResNetTSM, self).init_weights()
          if self.is_tin:
              self.make_temporal_interlace()
          if len(self.non_local_cfg) != 0:
              self.make_non_local()
  