text: import math
  import torch
  from torch import nn as nn
  from torch.nn import functional as F
  from torch.nn import init as init
  from torch.nn.modules.batchnorm import _BatchNorm
  
  from basicsr.utils import get_root_logger
  
  try:
      from basicsr.models.ops.dcn import (ModulatedDeformConvPack,
                                          modulated_deform_conv)
  except ImportError:
      print('Cannot import dcn. Ignore this warning if dcn is not used. '
            'Otherwise install BasicSR with compiling dcn.')
      ModulatedDeformConvPack = object
      modulated_deform_conv = None
  
  
  @torch.no_grad()
  def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
      """Initialize network weights.
  
      Args:
          module_list (list[nn.Module] | nn.Module): Modules to be initialized.
          scale (float): Scale initialized weights, especially for residual
              blocks. Default: 1.
          bias_fill (float): The value to fill bias. Default: 0
          kwargs (dict): Other arguments for initialization function.
      """
      if not isinstance(module_list, list):
          module_list = [module_list]
      for module in module_list:
          for m in module.modules():
              if isinstance(m, nn.Conv2d):
                  init.kaiming_normal_(m.weight, **kwargs)
                  m.weight.data *= scale
                  if m.bias is not None:
                      m.bias.data.fill_(bias_fill)
              elif isinstance(m, nn.Linear):
                  init.kaiming_normal_(m.weight, **kwargs)
                  m.weight.data *= scale
                  if m.bias is not None:
                      m.bias.data.fill_(bias_fill)
              elif isinstance(m, _BatchNorm):
                  init.constant_(m.weight, 1)
                  if m.bias is not None:
                      m.bias.data.fill_(bias_fill)
  
  
  def make_layer(basic_block, num_basic_block, **kwarg):
      """Make layers by stacking the same blocks.
  
      Args:
          basic_block (nn.module): nn.module class for basic block.
          num_basic_block (int): number of blocks.
  
      Returns:
          nn.Sequential: Stacked blocks in nn.Sequential.
      """
      layers = []
      for _ in range(num_basic_block):
          layers.append(basic_block(**kwarg))
      return nn.Sequential(*layers)
  
  
  class ResidualBlockNoBN(nn.Module):
      """Residual block without BN.
  
      It has a style of:
          ---Conv-ReLU-Conv-+-
           |________________|
  
      Args:
          num_feat (int): Channel number of intermediate features.
              Default: 64.
          res_scale (float): Residual scale. Default: 1.
          pytorch_init (bool): If set to True, use pytorch default init,
              otherwise, use default_init_weights. Default: False.
      """
  
      def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
          super(ResidualBlockNoBN, self).__init__()
          self.res_scale = res_scale
          self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
          self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
          self.relu = nn.ReLU(inplace=True)
  
          if not pytorch_init:
              default_init_weights([self.conv1, self.conv2], 0.1)
  
      def forward(self, x):
          identity = x
          out = self.conv2(self.relu(self.conv1(x)))
          return identity + out * self.res_scale
  
  
  class Upsample(nn.Sequential):
      """Upsample module.
  
      Args:
          scale (int): Scale factor. Supported scales: 2^n and 3.
          num_feat (int): Channel number of intermediate features.
      """
  
      def __init__(self, scale, num_feat):
          m = []
          if (scale & (scale - 1)) == 0:  # scale = 2^n
              for _ in range(int(math.log(scale, 2))):
                  m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                  m.append(nn.PixelShuffle(2))
          elif scale == 3:
              m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
              m.append(nn.PixelShuffle(3))
          else:
              raise ValueError(f'scale {scale} is not supported. '
                               'Supported scales: 2^n and 3.')
          super(Upsample, self).__init__(*m)
  
  
  def flow_warp(x,
                flow,
                interp_mode='bilinear',
                padding_mode='zeros',
                align_corners=True):
      """Warp an image or feature map with optical flow.
  
      Args:
          x (Tensor): Tensor with size (n, c, h, w).
          flow (Tensor): Tensor with size (n, h, w, 2), normal value.
          interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
          padding_mode (str): 'zeros' or 'border' or 'reflection'.
              Default: 'zeros'.
          align_corners (bool): Before pytorch 1.3, the default value is
              align_corners=True. After pytorch 1.3, the default value is
              align_corners=False. Here, we use the True as default.
  
      Returns:
          Tensor: Warped image or feature map.
      """
      assert x.size()[-2:] == flow.size()[1:3]
      _, _, h, w = x.size()
      # create mesh grid
      grid_y, grid_x = torch.meshgrid(
          torch.arange(0, h).type_as(x),
          torch.arange(0, w).type_as(x))
      grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
      grid.requires_grad = False
  
      vgrid = grid + flow
      # scale grid to [-1,1]
      vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
      vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
      vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
      output = F.grid_sample(
          x,
          vgrid_scaled,
          mode=interp_mode,
          padding_mode=padding_mode,
          align_corners=True)
  
      # TODO, what if align_corners=False
      return output
  
  
  def resize_flow(flow,
                  size_type,
                  sizes,
                  interp_mode='bilinear',
                  align_corners=False):
      """Resize a flow according to ratio or shape.
  
      Args:
          flow (Tensor): Precomputed flow. shape [N, 2, H, W].
          size_type (str): 'ratio' or 'shape'.
          sizes (list[int | float]): the ratio for resizing or the final output
              shape.
              1) The order of ratio should be [ratio_h, ratio_w]. For
              downsampling, the ratio should be smaller than 1.0 (i.e., ratio
              < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
              ratio > 1.0).
              2) The order of output_size should be [out_h, out_w].
          interp_mode (str): The mode of interpolation for resizing.
              Default: 'bilinear'.
          align_corners (bool): Whether align corners. Default: False.
  
      Returns:
          Tensor: Resized flow.
      """
      _, _, flow_h, flow_w = flow.size()
      if size_type == 'ratio':
          output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
      elif size_type == 'shape':
          output_h, output_w = sizes[0], sizes[1]
      else:
          raise ValueError(
              f'Size type should be ratio or shape, but got type {size_type}.')
  
      input_flow = flow.clone()
      ratio_h = output_h / flow_h
      ratio_w = output_w / flow_w
      input_flow[:, 0, :, :] *= ratio_w
      input_flow[:, 1, :, :] *= ratio_h
      resized_flow = F.interpolate(
          input=input_flow,
          size=(output_h, output_w),
          mode=interp_mode,
          align_corners=align_corners)
      return resized_flow
  
  
  # TODO: may write a cpp file
  def pixel_unshuffle(x, scale):
      """ Pixel unshuffle.
  
      Args:
          x (Tensor): Input feature with shape (b, c, hh, hw).
          scale (int): Downsample ratio.
  
      Returns:
          Tensor: the pixel unshuffled feature.
      """
      b, c, hh, hw = x.size()
      out_channel = c * (scale**2)
      assert hh % scale == 0 and hw % scale == 0
      h = hh // scale
      w = hw // scale
      x_view = x.view(b, c, h, scale, w, scale)
      return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
  
  
  class DCNv2Pack(ModulatedDeformConvPack):
      """Modulated deformable conv for deformable alignment.
  
      Different from the official DCNv2Pack, which generates offsets and masks
      from the preceding features, this DCNv2Pack takes another different
      features to generate offsets and masks.
  
      Ref:
          Delving Deep into Deformable Alignment in Video Super-Resolution.
      """
  
      def forward(self, x, feat):
          out = self.conv_offset(feat)
          o1, o2, mask = torch.chunk(out, 3, dim=1)
          offset = torch.cat((o1, o2), dim=1)
          mask = torch.sigmoid(mask)
  
          offset_absmean = torch.mean(torch.abs(offset))
          if offset_absmean > 50:
              logger = get_root_logger()
              logger.warning(
                  f'Offset abs mean is {offset_absmean}, larger than 50.')
  
          return modulated_deform_conv(x, offset, mask, self.weight, self.bias,
                                       self.stride, self.padding, self.dilation,
                                       self.groups, self.deformable_groups)
  