text: # Copyright (c) OpenMMLab. All rights reserved.
  import torch.nn as nn
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmcv.runner import BaseModule, ModuleList, Sequential
  from torch.nn.modules.batchnorm import _BatchNorm
  
  from ..builder import BACKBONES
  from .resnet import BasicBlock, Bottleneck, ResLayer, get_expansion
  
  
  class HRModule(BaseModule):
      """High-Resolution Module for HRNet.
  
      In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
      is in this module.
  
      Args:
          num_branches (int): The number of branches.
          block (``BaseModule``): Convolution block module.
          num_blocks (tuple): The number of blocks in each branch.
              The length must be equal to ``num_branches``.
          num_channels (tuple): The number of base channels in each branch.
              The length must be equal to ``num_branches``.
          multiscale_output (bool): Whether to output multi-level features
              produced by multiple branches. If False, only the first level
              feature will be output. Defaults to True.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed. Defaults to False.
          conv_cfg (dict, optional): Dictionary to construct and config conv
              layer. Defaults to None.
          norm_cfg (dict): Dictionary to construct and config norm layer.
              Defaults to ``dict(type='BN')``.
          block_init_cfg (dict, optional): The initialization configs of every
              blocks. Defaults to None.
          init_cfg (dict or list[dict], optional): Initialization config dict.
              Defaults to None.
      """
  
      def __init__(self,
                   num_branches,
                   block,
                   num_blocks,
                   in_channels,
                   num_channels,
                   multiscale_output=True,
                   with_cp=False,
                   conv_cfg=None,
                   norm_cfg=dict(type='BN'),
                   block_init_cfg=None,
                   init_cfg=None):
          super(HRModule, self).__init__(init_cfg)
          self.block_init_cfg = block_init_cfg
          self._check_branches(num_branches, num_blocks, in_channels,
                               num_channels)
  
          self.in_channels = in_channels
          self.num_branches = num_branches
  
          self.multiscale_output = multiscale_output
          self.norm_cfg = norm_cfg
          self.conv_cfg = conv_cfg
          self.with_cp = with_cp
          self.branches = self._make_branches(num_branches, block, num_blocks,
                                              num_channels)
          self.fuse_layers = self._make_fuse_layers()
          self.relu = nn.ReLU(inplace=False)
  
      def _check_branches(self, num_branches, num_blocks, in_channels,
                          num_channels):
          if num_branches != len(num_blocks):
              error_msg = f'NUM_BRANCHES({num_branches}) ' \
                          f'!= NUM_BLOCKS({len(num_blocks)})'
              raise ValueError(error_msg)
  
          if num_branches != len(num_channels):
              error_msg = f'NUM_BRANCHES({num_branches}) ' \
                          f'!= NUM_CHANNELS({len(num_channels)})'
              raise ValueError(error_msg)
  
          if num_branches != len(in_channels):
              error_msg = f'NUM_BRANCHES({num_branches}) ' \
                          f'!= NUM_INCHANNELS({len(in_channels)})'
              raise ValueError(error_msg)
  
      def _make_branches(self, num_branches, block, num_blocks, num_channels):
          branches = []
  
          for i in range(num_branches):
              out_channels = num_channels[i] * get_expansion(block)
              branches.append(
                  ResLayer(
                      block=block,
                      num_blocks=num_blocks[i],
                      in_channels=self.in_channels[i],
                      out_channels=out_channels,
                      conv_cfg=self.conv_cfg,
                      norm_cfg=self.norm_cfg,
                      with_cp=self.with_cp,
                      init_cfg=self.block_init_cfg,
                  ))
  
          return ModuleList(branches)
  
      def _make_fuse_layers(self):
          if self.num_branches == 1:
              return None
  
          num_branches = self.num_branches
          in_channels = self.in_channels
          fuse_layers = []
          num_out_branches = num_branches if self.multiscale_output else 1
          for i in range(num_out_branches):
              fuse_layer = []
              for j in range(num_branches):
                  if j > i:
                      # Upsample the feature maps of smaller scales.
                      fuse_layer.append(
                          nn.Sequential(
                              build_conv_layer(
                                  self.conv_cfg,
                                  in_channels[j],
                                  in_channels[i],
                                  kernel_size=1,
                                  stride=1,
                                  padding=0,
                                  bias=False),
                              build_norm_layer(self.norm_cfg, in_channels[i])[1],
                              nn.Upsample(
                                  scale_factor=2**(j - i), mode='nearest')))
                  elif j == i:
                      # Keep the feature map with the same scale.
                      fuse_layer.append(None)
                  else:
                      # Downsample the feature maps of larger scales.
                      conv_downsamples = []
                      for k in range(i - j):
                          # Use stacked convolution layers to downsample.
                          if k == i - j - 1:
                              conv_downsamples.append(
                                  nn.Sequential(
                                      build_conv_layer(
                                          self.conv_cfg,
                                          in_channels[j],
                                          in_channels[i],
                                          kernel_size=3,
                                          stride=2,
                                          padding=1,
                                          bias=False),
                                      build_norm_layer(self.norm_cfg,
                                                       in_channels[i])[1]))
                          else:
                              conv_downsamples.append(
                                  nn.Sequential(
                                      build_conv_layer(
                                          self.conv_cfg,
                                          in_channels[j],
                                          in_channels[j],
                                          kernel_size=3,
                                          stride=2,
                                          padding=1,
                                          bias=False),
                                      build_norm_layer(self.norm_cfg,
                                                       in_channels[j])[1],
                                      nn.ReLU(inplace=False)))
                      fuse_layer.append(nn.Sequential(*conv_downsamples))
              fuse_layers.append(nn.ModuleList(fuse_layer))
  
          return nn.ModuleList(fuse_layers)
  
      def forward(self, x):
          """Forward function."""
          if self.num_branches == 1:
              return [self.branches[0](x[0])]
  
          for i in range(self.num_branches):
              x[i] = self.branches[i](x[i])
  
          x_fuse = []
          for i in range(len(self.fuse_layers)):
              y = 0
              for j in range(self.num_branches):
                  if i == j:
                      y += x[j]
                  else:
                      y += self.fuse_layers[i][j](x[j])
              x_fuse.append(self.relu(y))
          return x_fuse
  
  
  @BACKBONES.register_module()
  class HRNet(BaseModule):
      """HRNet backbone.
  
      `High-Resolution Representations for Labeling Pixels and Regions
      <https://arxiv.org/abs/1904.04514>`_.
  
      Args:
          arch (str): The preset HRNet architecture, includes 'w18', 'w30',
              'w32', 'w40', 'w44', 'w48', 'w64'. It will only be used if
              extra is ``None``. Defaults to 'w32'.
          extra (dict, optional): Detailed configuration for each stage of HRNet.
              There must be 4 stages, the configuration for each stage must have
              5 keys:
  
              - num_modules (int): The number of HRModule in this stage.
              - num_branches (int): The number of branches in the HRModule.
              - block (str): The type of convolution block. Please choose between
                'BOTTLENECK' and 'BASIC'.
              - num_blocks (tuple): The number of blocks in each branch.
                The length must be equal to num_branches.
              - num_channels (tuple): The number of base channels in each branch.
                The length must be equal to num_branches.
  
              Defaults to None.
          in_channels (int): Number of input image channels. Defaults to 3.
          conv_cfg (dict, optional): Dictionary to construct and config conv
              layer. Defaults to None.
          norm_cfg (dict): Dictionary to construct and config norm layer.
              Defaults to ``dict(type='BN')``.
          norm_eval (bool): Whether to set norm layers to eval mode, namely,
              freeze running stats (mean and var). Note: Effect on Batch Norm
              and its variants only. Defaults to False.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed. Defaults to False.
          zero_init_residual (bool): Whether to use zero init for last norm layer
              in resblocks to let them behave as identity. Defaults to False.
          multiscale_output (bool): Whether to output multi-level features
              produced by multiple branches. If False, only the first level
              feature will be output. Defaults to True.
          init_cfg (dict or list[dict], optional): Initialization config dict.
              Defaults to None.
  
      Example:
          >>> import torch
          >>> from mmcls.models import HRNet
          >>> extra = dict(
          >>>     stage1=dict(
          >>>         num_modules=1,
          >>>         num_branches=1,
          >>>         block='BOTTLENECK',
          >>>         num_blocks=(4, ),
          >>>         num_channels=(64, )),
          >>>     stage2=dict(
          >>>         num_modules=1,
          >>>         num_branches=2,
          >>>         block='BASIC',
          >>>         num_blocks=(4, 4),
          >>>         num_channels=(32, 64)),
          >>>     stage3=dict(
          >>>         num_modules=4,
          >>>         num_branches=3,
          >>>         block='BASIC',
          >>>         num_blocks=(4, 4, 4),
          >>>         num_channels=(32, 64, 128)),
          >>>     stage4=dict(
          >>>         num_modules=3,
          >>>         num_branches=4,
          >>>         block='BASIC',
          >>>         num_blocks=(4, 4, 4, 4),
          >>>         num_channels=(32, 64, 128, 256)))
          >>> self = HRNet(extra, in_channels=1)
          >>> self.eval()
          >>> inputs = torch.rand(1, 1, 32, 32)
          >>> level_outputs = self.forward(inputs)
          >>> for level_out in level_outputs:
          ...     print(tuple(level_out.shape))
          (1, 32, 8, 8)
          (1, 64, 4, 4)
          (1, 128, 2, 2)
          (1, 256, 1, 1)
      """
  
      blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
      arch_zoo = {
          # num_modules, num_branches, block, num_blocks, num_channels
          'w18': [[1, 1, 'BOTTLENECK', (4, ),        (64, )],
                  [1, 2, 'BASIC',      (4, 4),       (18, 36)],
                  [4, 3, 'BASIC',      (4, 4, 4),    (18, 36, 72)],
                  [3, 4, 'BASIC',      (4, 4, 4, 4), (18, 36, 72, 144)]],
          'w30': [[1, 1, 'BOTTLENECK', (4, ),        (64, )],
                  [1, 2, 'BASIC',      (4, 4),       (30, 60)],
                  [4, 3, 'BASIC',      (4, 4, 4),    (30, 60, 120)],
                  [3, 4, 'BASIC',      (4, 4, 4, 4), (30, 60, 120, 240)]],
          'w32': [[1, 1, 'BOTTLENECK', (4, ),        (64, )],
                  [1, 2, 'BASIC',      (4, 4),       (32, 64)],
                  [4, 3, 'BASIC',      (4, 4, 4),    (32, 64, 128)],
                  [3, 4, 'BASIC',      (4, 4, 4, 4), (32, 64, 128, 256)]],
          'w40': [[1, 1, 'BOTTLENECK', (4, ),        (64, )],
                  [1, 2, 'BASIC',      (4, 4),       (40, 80)],
                  [4, 3, 'BASIC',      (4, 4, 4),    (40, 80, 160)],
                  [3, 4, 'BASIC',      (4, 4, 4, 4), (40, 80, 160, 320)]],
          'w44': [[1, 1, 'BOTTLENECK', (4, ),        (64, )],
                  [1, 2, 'BASIC',      (4, 4),       (44, 88)],
                  [4, 3, 'BASIC',      (4, 4, 4),    (44, 88, 176)],
                  [3, 4, 'BASIC',      (4, 4, 4, 4), (44, 88, 176, 352)]],
          'w48': [[1, 1, 'BOTTLENECK', (4, ),        (64, )],
                  [1, 2, 'BASIC',      (4, 4),       (48, 96)],
                  [4, 3, 'BASIC',      (4, 4, 4),    (48, 96, 192)],
                  [3, 4, 'BASIC',      (4, 4, 4, 4), (48, 96, 192, 384)]],
          'w64': [[1, 1, 'BOTTLENECK', (4, ),        (64, )],
                  [1, 2, 'BASIC',      (4, 4),       (64, 128)],
                  [4, 3, 'BASIC',      (4, 4, 4),    (64, 128, 256)],
                  [3, 4, 'BASIC',      (4, 4, 4, 4), (64, 128, 256, 512)]],
      }  # yapf:disable
  
      def __init__(self,
                   arch='w32',
                   extra=None,
                   in_channels=3,
                   conv_cfg=None,
                   norm_cfg=dict(type='BN'),
                   norm_eval=False,
                   with_cp=False,
                   zero_init_residual=False,
                   multiscale_output=True,
                   init_cfg=[
                       dict(type='Kaiming', layer='Conv2d'),
                       dict(
                           type='Constant',
                           val=1,
                           layer=['_BatchNorm', 'GroupNorm'])
                   ]):
          super(HRNet, self).__init__(init_cfg)
  
          extra = self.parse_arch(arch, extra)
  
          # Assert configurations of 4 stages are in extra
          for i in range(1, 5):
              assert f'stage{i}' in extra, f'Missing stage{i} config in "extra".'
              # Assert whether the length of `num_blocks` and `num_channels` are
              # equal to `num_branches`
              cfg = extra[f'stage{i}']
              assert len(cfg['num_blocks']) == cfg['num_branches'] and \
                     len(cfg['num_channels']) == cfg['num_branches']
  
          self.extra = extra
          self.conv_cfg = conv_cfg
          self.norm_cfg = norm_cfg
          self.norm_eval = norm_eval
          self.with_cp = with_cp
          self.zero_init_residual = zero_init_residual
  
          # -------------------- stem net --------------------
          self.conv1 = build_conv_layer(
              self.conv_cfg,
              in_channels,
              out_channels=64,
              kernel_size=3,
              stride=2,
              padding=1,
              bias=False)
  
          self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
          self.add_module(self.norm1_name, norm1)
  
          self.conv2 = build_conv_layer(
              self.conv_cfg,
              in_channels=64,
              out_channels=64,
              kernel_size=3,
              stride=2,
              padding=1,
              bias=False)
  
          self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
          self.add_module(self.norm2_name, norm2)
          self.relu = nn.ReLU(inplace=True)
  
          # -------------------- stage 1 --------------------
          self.stage1_cfg = self.extra['stage1']
          base_channels = self.stage1_cfg['num_channels']
          block_type = self.stage1_cfg['block']
          num_blocks = self.stage1_cfg['num_blocks']
  
          block = self.blocks_dict[block_type]
          num_channels = [
              channel * get_expansion(block) for channel in base_channels
          ]
          # To align with the original code, use layer1 instead of stage1 here.
          self.layer1 = ResLayer(
              block,
              in_channels=64,
              out_channels=num_channels[0],
              num_blocks=num_blocks[0])
          pre_num_channels = num_channels
  
          # -------------------- stage 2~4 --------------------
          for i in range(2, 5):
              stage_cfg = self.extra[f'stage{i}']
              base_channels = stage_cfg['num_channels']
              block = self.blocks_dict[stage_cfg['block']]
              multiscale_output_ = multiscale_output if i == 4 else True
  
              num_channels = [
                  channel * get_expansion(block) for channel in base_channels
              ]
              # The transition layer from layer1 to stage2
              transition = self._make_transition_layer(pre_num_channels,
                                                       num_channels)
              self.add_module(f'transition{i-1}', transition)
              stage = self._make_stage(
                  stage_cfg, num_channels, multiscale_output=multiscale_output_)
              self.add_module(f'stage{i}', stage)
  
              pre_num_channels = num_channels
  
      @property
      def norm1(self):
          """nn.Module: the normalization layer named "norm1" """
          return getattr(self, self.norm1_name)
  
      @property
      def norm2(self):
          """nn.Module: the normalization layer named "norm2" """
          return getattr(self, self.norm2_name)
  
      def _make_transition_layer(self, num_channels_pre_layer,
                                 num_channels_cur_layer):
          num_branches_cur = len(num_channels_cur_layer)
          num_branches_pre = len(num_channels_pre_layer)
  
          transition_layers = []
          for i in range(num_branches_cur):
              if i < num_branches_pre:
                  # For existing scale branches,
                  # add conv block when the channels are not the same.
                  if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                      transition_layers.append(
                          nn.Sequential(
                              build_conv_layer(
                                  self.conv_cfg,
                                  num_channels_pre_layer[i],
                                  num_channels_cur_layer[i],
                                  kernel_size=3,
                                  stride=1,
                                  padding=1,
                                  bias=False),
                              build_norm_layer(self.norm_cfg,
                                               num_channels_cur_layer[i])[1],
                              nn.ReLU(inplace=True)))
                  else:
                      transition_layers.append(nn.Identity())
              else:
                  # For new scale branches, add stacked downsample conv blocks.
                  # For example, num_branches_pre = 2, for the 4th branch, add
                  # stacked two downsample conv blocks.
                  conv_downsamples = []
                  for j in range(i + 1 - num_branches_pre):
                      in_channels = num_channels_pre_layer[-1]
                      out_channels = num_channels_cur_layer[i] \
                          if j == i - num_branches_pre else in_channels
                      conv_downsamples.append(
                          nn.Sequential(
                              build_conv_layer(
                                  self.conv_cfg,
                                  in_channels,
                                  out_channels,
                                  kernel_size=3,
                                  stride=2,
                                  padding=1,
                                  bias=False),
                              build_norm_layer(self.norm_cfg, out_channels)[1],
                              nn.ReLU(inplace=True)))
                  transition_layers.append(nn.Sequential(*conv_downsamples))
  
          return nn.ModuleList(transition_layers)
  
      def _make_stage(self, layer_config, in_channels, multiscale_output=True):
          num_modules = layer_config['num_modules']
          num_branches = layer_config['num_branches']
          num_blocks = layer_config['num_blocks']
          num_channels = layer_config['num_channels']
          block = self.blocks_dict[layer_config['block']]
  
          hr_modules = []
          block_init_cfg = None
          if self.zero_init_residual:
              if block is BasicBlock:
                  block_init_cfg = dict(
                      type='Constant', val=0, override=dict(name='norm2'))
              elif block is Bottleneck:
                  block_init_cfg = dict(
                      type='Constant', val=0, override=dict(name='norm3'))
  
          for i in range(num_modules):
              # multi_scale_output is only used for the last module
              if not multiscale_output and i == num_modules - 1:
                  reset_multiscale_output = False
              else:
                  reset_multiscale_output = True
  
              hr_modules.append(
                  HRModule(
                      num_branches,
                      block,
                      num_blocks,
                      in_channels,
                      num_channels,
                      reset_multiscale_output,
                      with_cp=self.with_cp,
                      norm_cfg=self.norm_cfg,
                      conv_cfg=self.conv_cfg,
                      block_init_cfg=block_init_cfg))
  
          return Sequential(*hr_modules)
  
      def forward(self, x):
          """Forward function."""
          x = self.conv1(x)
          x = self.norm1(x)
          x = self.relu(x)
          x = self.conv2(x)
          x = self.norm2(x)
          x = self.relu(x)
          x = self.layer1(x)
  
          x_list = [x]
  
          for i in range(2, 5):
              # Apply transition
              transition = getattr(self, f'transition{i-1}')
              inputs = []
              for j, layer in enumerate(transition):
                  if j < len(x_list):
                      inputs.append(layer(x_list[j]))
                  else:
                      inputs.append(layer(x_list[-1]))
              # Forward HRModule
              stage = getattr(self, f'stage{i}')
              x_list = stage(inputs)
  
          return tuple(x_list)
  
      def train(self, mode=True):
          """Convert the model into training mode will keeping the normalization
          layer freezed."""
          super(HRNet, self).train(mode)
          if mode and self.norm_eval:
              for m in self.modules():
                  # trick: eval have effect on BatchNorm only
                  if isinstance(m, _BatchNorm):
                      m.eval()
  
      def parse_arch(self, arch, extra=None):
          if extra is not None:
              return extra
  
          assert arch in self.arch_zoo, \
              ('Invalid arch, please choose arch from '
               f'{list(self.arch_zoo.keys())}, or specify `extra` '
               'argument directly.')
  
          extra = dict()
          for i, stage_setting in enumerate(self.arch_zoo[arch], start=1):
              extra[f'stage{i}'] = dict(
                  num_modules=stage_setting[0],
                  num_branches=stage_setting[1],
                  block=stage_setting[2],
                  num_blocks=stage_setting[3],
                  num_channels=stage_setting[4],
              )
  
          return extra
  