text: """ HRNet
  
  Copied from https://github.com/HRNet/HRNet-Image-Classification
  
  Original header:
    Copyright (c) Microsoft
    Licensed under the MIT License.
    Written by Bin Xiao (Bin.Xiao@microsoft.com)
    Modified by Ke Sun (sunk@mail.ustc.edu.cn)
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import logging
  import functools
  
  import torch
  import torch.nn as nn
  import torch._utils
  import torch.nn.functional as F
  
  from .resnet import BasicBlock, Bottleneck  # leveraging ResNet blocks w/ additional features like SE
  from .registry import register_model
  from .helpers import load_pretrained
  from .adaptive_avgmax_pool import SelectAdaptivePool2d
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  
  _BN_MOMENTUM = 0.1
  logger = logging.getLogger(__name__)
  
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
          'crop_pct': 0.875, 'interpolation': 'bilinear',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'conv1', 'classifier': 'fc',
          **kwargs
      }
  
  
  default_cfgs = {
      'hrnet_w18_small': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v1-f460c6bc.pth'),
      'hrnet_w18_small_v2': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v2-4c50a8cb.pth'),
      'hrnet_w18': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w18-8cb57bb9.pth'),
      'hrnet_w30': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w30-8d7f8dab.pth'),
      'hrnet_w32': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w32-90d8c5fb.pth'),
      'hrnet_w40': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w40-7cd397a4.pth'),
      'hrnet_w44': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w44-c9ac8c18.pth'),
      'hrnet_w48': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w48-abd2e6ab.pth'),
      'hrnet_w64': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w64-b47cc881.pth'),
  }
  
  cfg_cls = dict(
      hrnet_w18_small=dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(1,),
              NUM_CHANNELS=(32,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(2, 2),
              NUM_CHANNELS=(16, 32),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(2, 2, 2),
              NUM_CHANNELS=(16, 32, 64),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(2, 2, 2, 2),
              NUM_CHANNELS=(16, 32, 64, 128),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w18_small_v2 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(2,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(2, 2),
              NUM_CHANNELS=(18, 36),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(2, 2, 2),
              NUM_CHANNELS=(18, 36, 72),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=2,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(2, 2, 2, 2),
              NUM_CHANNELS=(18, 36, 72, 144),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w18 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(4,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4),
              NUM_CHANNELS=(18, 36),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=4,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4),
              NUM_CHANNELS=(18, 36, 72),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4, 4),
              NUM_CHANNELS=(18, 36, 72, 144),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w30 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(4,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4),
              NUM_CHANNELS=(30, 60),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=4,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4),
              NUM_CHANNELS=(30, 60, 120),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4, 4),
              NUM_CHANNELS=(30, 60, 120, 240),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w32 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(4,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4),
              NUM_CHANNELS=(32, 64),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=4,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4),
              NUM_CHANNELS=(32, 64, 128),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4, 4),
              NUM_CHANNELS=(32, 64, 128, 256),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w40 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(4,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4),
              NUM_CHANNELS=(40, 80),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=4,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4),
              NUM_CHANNELS=(40, 80, 160),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4, 4),
              NUM_CHANNELS=(40, 80, 160, 320),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w44 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(4,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4),
              NUM_CHANNELS=(44, 88),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=4,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4),
              NUM_CHANNELS=(44, 88, 176),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4, 4),
              NUM_CHANNELS=(44, 88, 176, 352),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w48 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(4,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4),
              NUM_CHANNELS=(48, 96),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=4,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4),
              NUM_CHANNELS=(48, 96, 192),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4, 4),
              NUM_CHANNELS=(48, 96, 192, 384),
              FUSE_METHOD='SUM',
          ),
      ),
  
      hrnet_w64 = dict(
          STEM_WIDTH=64,
          STAGE1=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=1,
              BLOCK='BOTTLENECK',
              NUM_BLOCKS=(4,),
              NUM_CHANNELS=(64,),
              FUSE_METHOD='SUM',
          ),
          STAGE2=dict(
              NUM_MODULES=1,
              NUM_BRANCHES=2,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4),
              NUM_CHANNELS=(64, 128),
              FUSE_METHOD='SUM'
          ),
          STAGE3=dict(
              NUM_MODULES=4,
              NUM_BRANCHES=3,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4),
              NUM_CHANNELS=(64, 128, 256),
              FUSE_METHOD='SUM'
          ),
          STAGE4=dict(
              NUM_MODULES=3,
              NUM_BRANCHES=4,
              BLOCK='BASIC',
              NUM_BLOCKS=(4, 4, 4, 4),
              NUM_CHANNELS=(64, 128, 256, 512),
              FUSE_METHOD='SUM',
          ),
      )
  )
  
  
  class HighResolutionModule(nn.Module):
      def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
                   num_channels, fuse_method, multi_scale_output=True):
          super(HighResolutionModule, self).__init__()
          self._check_branches(
              num_branches, blocks, num_blocks, num_inchannels, num_channels)
  
          self.num_inchannels = num_inchannels
          self.fuse_method = fuse_method
          self.num_branches = num_branches
  
          self.multi_scale_output = multi_scale_output
  
          self.branches = self._make_branches(
              num_branches, blocks, num_blocks, num_channels)
          self.fuse_layers = self._make_fuse_layers()
          self.relu = nn.ReLU(False)
  
      def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels):
          if num_branches != len(num_blocks):
              error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
                  num_branches, len(num_blocks))
              logger.error(error_msg)
              raise ValueError(error_msg)
  
          if num_branches != len(num_channels):
              error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
                  num_branches, len(num_channels))
              logger.error(error_msg)
              raise ValueError(error_msg)
  
          if num_branches != len(num_inchannels):
              error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
                  num_branches, len(num_inchannels))
              logger.error(error_msg)
              raise ValueError(error_msg)
  
      def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
                           stride=1):
          downsample = None
          if stride != 1 or \
                  self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(
                      self.num_inchannels[branch_index], num_channels[branch_index] * block.expansion,
                      kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(num_channels[branch_index] * block.expansion, momentum=_BN_MOMENTUM),
              )
  
          layers = [block(self.num_inchannels[branch_index], num_channels[branch_index], stride, downsample)]
          self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion
          for i in range(1, num_blocks[branch_index]):
              layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index]))
  
          return nn.Sequential(*layers)
  
      def _make_branches(self, num_branches, block, num_blocks, num_channels):
          branches = []
          for i in range(num_branches):
              branches.append(self._make_one_branch(i, block, num_blocks, num_channels))
  
          return nn.ModuleList(branches)
  
      def _make_fuse_layers(self):
          if self.num_branches == 1:
              return None
  
          num_branches = self.num_branches
          num_inchannels = self.num_inchannels
          fuse_layers = []
          for i in range(num_branches if self.multi_scale_output else 1):
              fuse_layer = []
              for j in range(num_branches):
                  if j > i:
                      fuse_layer.append(nn.Sequential(
                          nn.Conv2d(num_inchannels[j], num_inchannels[i], 1, 1, 0, bias=False),
                          nn.BatchNorm2d(num_inchannels[i], momentum=_BN_MOMENTUM),
                          nn.Upsample(scale_factor=2 ** (j - i), mode='nearest')))
                  elif j == i:
                      fuse_layer.append(None)
                  else:
                      conv3x3s = []
                      for k in range(i - j):
                          if k == i - j - 1:
                              num_outchannels_conv3x3 = num_inchannels[i]
                              conv3x3s.append(nn.Sequential(
                                  nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False),
                                  nn.BatchNorm2d(num_outchannels_conv3x3, momentum=_BN_MOMENTUM)))
                          else:
                              num_outchannels_conv3x3 = num_inchannels[j]
                              conv3x3s.append(nn.Sequential(
                                  nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False),
                                  nn.BatchNorm2d(num_outchannels_conv3x3, momentum=_BN_MOMENTUM),
                                  nn.ReLU(False)))
                      fuse_layer.append(nn.Sequential(*conv3x3s))
              fuse_layers.append(nn.ModuleList(fuse_layer))
  
          return nn.ModuleList(fuse_layers)
  
      def get_num_inchannels(self):
          return self.num_inchannels
  
      def forward(self, x):
          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 = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
              for j in range(1, self.num_branches):
                  if i == j:
                      y = y + x[j]
                  else:
                      y = y + self.fuse_layers[i][j](x[j])
              x_fuse.append(self.relu(y))
  
          return x_fuse
  
  
  blocks_dict = {
      'BASIC': BasicBlock,
      'BOTTLENECK': Bottleneck
  }
  
  
  class HighResolutionNet(nn.Module):
  
      def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0.0):
          super(HighResolutionNet, self).__init__()
          self.num_classes = num_classes
          self.drop_rate = drop_rate
  
          stem_width = cfg['STEM_WIDTH']
          self.conv1 = nn.Conv2d(in_chans, stem_width, kernel_size=3, stride=2, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(stem_width, momentum=_BN_MOMENTUM)
          self.conv2 = nn.Conv2d(stem_width, 64, kernel_size=3, stride=2, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(64, momentum=_BN_MOMENTUM)
          self.relu = nn.ReLU(inplace=True)
  
          self.stage1_cfg = cfg['STAGE1']
          num_channels = self.stage1_cfg['NUM_CHANNELS'][0]
          block = blocks_dict[self.stage1_cfg['BLOCK']]
          num_blocks = self.stage1_cfg['NUM_BLOCKS'][0]
          self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
          stage1_out_channel = block.expansion * num_channels
  
          self.stage2_cfg = cfg['STAGE2']
          num_channels = self.stage2_cfg['NUM_CHANNELS']
          block = blocks_dict[self.stage2_cfg['BLOCK']]
          num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))]
          self.transition1 = self._make_transition_layer([stage1_out_channel], num_channels)
          self.stage2, pre_stage_channels = self._make_stage(self.stage2_cfg, num_channels)
  
          self.stage3_cfg = cfg['STAGE3']
          num_channels = self.stage3_cfg['NUM_CHANNELS']
          block = blocks_dict[self.stage3_cfg['BLOCK']]
          num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))]
          self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels)
          self.stage3, pre_stage_channels = self._make_stage(self.stage3_cfg, num_channels)
  
          self.stage4_cfg = cfg['STAGE4']
          num_channels = self.stage4_cfg['NUM_CHANNELS']
          block = blocks_dict[self.stage4_cfg['BLOCK']]
          num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))]
          self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels)
          self.stage4, pre_stage_channels = self._make_stage(self.stage4_cfg, num_channels, multi_scale_output=True)
  
          # Classification Head
          self.num_features = 2048
          self.incre_modules, self.downsamp_modules, self.final_layer = self._make_head(pre_stage_channels)
          self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
          self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
  
          self.init_weights()
  
      def _make_head(self, pre_stage_channels):
          head_block = Bottleneck
          head_channels = [32, 64, 128, 256]
  
          # Increasing the #channels on each resolution
          # from C, 2C, 4C, 8C to 128, 256, 512, 1024
          incre_modules = []
          for i, channels in enumerate(pre_stage_channels):
              incre_modules.append(
                  self._make_layer(head_block, channels, head_channels[i], 1, stride=1))
          incre_modules = nn.ModuleList(incre_modules)
  
          # downsampling modules
          downsamp_modules = []
          for i in range(len(pre_stage_channels) - 1):
              in_channels = head_channels[i] * head_block.expansion
              out_channels = head_channels[i + 1] * head_block.expansion
              downsamp_module = nn.Sequential(
                  nn.Conv2d(
                      in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1),
                  nn.BatchNorm2d(out_channels, momentum=_BN_MOMENTUM),
                  nn.ReLU(inplace=True)
              )
              downsamp_modules.append(downsamp_module)
          downsamp_modules = nn.ModuleList(downsamp_modules)
  
          final_layer = nn.Sequential(
              nn.Conv2d(
                  in_channels=head_channels[3] * head_block.expansion,
                  out_channels=self.num_features, kernel_size=1, stride=1, padding=0
              ),
              nn.BatchNorm2d(self.num_features, momentum=_BN_MOMENTUM),
              nn.ReLU(inplace=True)
          )
  
          return incre_modules, downsamp_modules, final_layer
  
      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:
                  if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                      transition_layers.append(nn.Sequential(
                          nn.Conv2d(num_channels_pre_layer[i], num_channels_cur_layer[i], 3, 1, 1, bias=False),
                          nn.BatchNorm2d(num_channels_cur_layer[i], momentum=_BN_MOMENTUM),
                          nn.ReLU(inplace=True)))
                  else:
                      transition_layers.append(None)
              else:
                  conv3x3s = []
                  for j in range(i + 1 - num_branches_pre):
                      inchannels = num_channels_pre_layer[-1]
                      outchannels = num_channels_cur_layer[i] if j == i - num_branches_pre else inchannels
                      conv3x3s.append(nn.Sequential(
                          nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
                          nn.BatchNorm2d(outchannels, momentum=_BN_MOMENTUM),
                          nn.ReLU(inplace=True)))
                  transition_layers.append(nn.Sequential(*conv3x3s))
  
          return nn.ModuleList(transition_layers)
  
      def _make_layer(self, block, inplanes, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(planes * block.expansion, momentum=_BN_MOMENTUM),
              )
  
          layers = [block(inplanes, planes, stride, downsample)]
          inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def _make_stage(self, layer_config, num_inchannels, multi_scale_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 = blocks_dict[layer_config['BLOCK']]
          fuse_method = layer_config['FUSE_METHOD']
  
          modules = []
          for i in range(num_modules):
              # multi_scale_output is only used last module
              if not multi_scale_output and i == num_modules - 1:
                  reset_multi_scale_output = False
              else:
                  reset_multi_scale_output = True
  
              modules.append(HighResolutionModule(
                  num_branches, block, num_blocks, num_inchannels, num_channels, fuse_method, reset_multi_scale_output)
              )
              num_inchannels = modules[-1].get_num_inchannels()
  
          return nn.Sequential(*modules), num_inchannels
  
      def init_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(
                      m.weight, mode='fan_out', nonlinearity='relu')
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
      def get_classifier(self):
          return self.classifier
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.num_classes = num_classes
          self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
          self.classifier = nn.Linear(
              self.num_features * self.global_pool.feat_mult(), num_classes) if num_classes else None
  
      def forward_features(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.conv2(x)
          x = self.bn2(x)
          x = self.relu(x)
          x = self.layer1(x)
  
          x_list = []
          for i in range(self.stage2_cfg['NUM_BRANCHES']):
              if self.transition1[i] is not None:
                  x_list.append(self.transition1[i](x))
              else:
                  x_list.append(x)
          y_list = self.stage2(x_list)
  
          x_list = []
          for i in range(self.stage3_cfg['NUM_BRANCHES']):
              if self.transition2[i] is not None:
                  x_list.append(self.transition2[i](y_list[-1]))
              else:
                  x_list.append(y_list[i])
          y_list = self.stage3(x_list)
  
          x_list = []
          for i in range(self.stage4_cfg['NUM_BRANCHES']):
              if self.transition3[i] is not None:
                  x_list.append(self.transition3[i](y_list[-1]))
              else:
                  x_list.append(y_list[i])
          y_list = self.stage4(x_list)
  
          # Classification Head
          y = self.incre_modules[0](y_list[0])
          for i in range(len(self.downsamp_modules)):
              y = self.incre_modules[i + 1](y_list[i + 1]) + self.downsamp_modules[i](y)
          y = self.final_layer(y)
          return y
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.global_pool(x).flatten(1)
          if self.drop_rate > 0.:
              x = F.dropout(x, p=self.drop_rate, training=self.training)
          x = self.classifier(x)
          return x
  
  
  def _create_model(variant, pretrained, model_kwargs):
      if model_kwargs.pop('features_only', False):
          assert False, 'Not Implemented'  # TODO
          load_strict = False
          model_kwargs.pop('num_classes', 0)
          model_class = HighResolutionNet
      else:
          load_strict = True
          model_class = HighResolutionNet
  
      model = model_class(cfg_cls[variant], **model_kwargs)
      model.default_cfg = default_cfgs[variant]
      if pretrained:
          load_pretrained(
              model,
              num_classes=model_kwargs.get('num_classes', 0),
              in_chans=model_kwargs.get('in_chans', 3),
              strict=load_strict)
      return model
  
  
  @register_model
  def hrnet_w18_small(pretrained=True, **kwargs):
      return _create_model('hrnet_w18_small', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w18_small_v2(pretrained=True, **kwargs):
      return _create_model('hrnet_w18_small_v2', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w18(pretrained=True, **kwargs):
      return _create_model('hrnet_w18', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w30(pretrained=True, **kwargs):
      return _create_model('hrnet_w30', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w32(pretrained=True, **kwargs):
      return _create_model('hrnet_w32', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w40(pretrained=True, **kwargs):
      return _create_model('hrnet_w40', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w44(pretrained=True, **kwargs):
      return _create_model('hrnet_w44', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w48(pretrained=True, **kwargs):
      return _create_model('hrnet_w48', pretrained, kwargs)
  
  
  @register_model
  def hrnet_w64(pretrained=True, **kwargs):
      return _create_model('hrnet_w64', pretrained, kwargs)
  