text: # ------------------------------------------------------------------------------
  # 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)
  
  # https://github.com/HRNet/HRNet-Image-Classification
  # ------------------------------------------------------------------------------
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import logging
  import functools
  import numpy as np
  
  import torch
  import torch.nn as nn
  import torch._utils
  import torch.nn.functional as F
  import xtils
  import os.path as osp
  
  BN_MOMENTUM = 0.1
  logger = logging.getLogger(__name__)
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      """3x3 convolution with padding"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=1, bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
          self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
                                 bias=False)
          self.bn3 = nn.BatchNorm2d(planes * self.expansion,
                                    momentum=BN_MOMENTUM)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  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 = []
          layers.append(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, **kwargs):
          super(HighResolutionNet, self).__init__()
  
          self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
          self.conv2 = nn.Conv2d(64, 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.layer1 = self._make_layer(Bottleneck, 64, 64, 4)
  
          self.stage2_cfg = cfg['MODEL']['EXTRA']['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(
              [256], num_channels)
          self.stage2, pre_stage_channels = self._make_stage(
              self.stage2_cfg, num_channels)
  
          self.stage3_cfg = cfg['MODEL']['EXTRA']['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['MODEL']['EXTRA']['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.incre_modules, self.downsamp_modules, \
          self.final_layer = self._make_head(pre_stage_channels)
  
          self.classifier = nn.Linear(2048, cfg['MODEL']['NUM_CLASSES'])
  
          self.init_weights(pretrained=cfg['MODEL']['PRETRAINED'])
  
      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_module = self._make_layer(head_block,
                                              channels,
                                              head_channels[i],
                                              1,
                                              stride=1)
              incre_modules.append(incre_module)
          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=2048,
                  kernel_size=1,
                  stride=1,
                  padding=0
              ),
              nn.BatchNorm2d(2048, 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 = []
          layers.append(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 forward(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)
  
          y = F.avg_pool2d(y, kernel_size=y.size()
          [2:]).view(y.size(0), -1)
  
          y = self.classifier(y)
  
          return y
  
      def init_weights(self, pretrained='', ):
          print('\n=> init weights from normal distribution')
          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)
  
          if os.path.isfile(pretrained):
              print('\n=> loading pretrained model from {}'.format(pretrained))
              pretrained_dict = torch.load(pretrained)
              model_dict = self.state_dict()
              pretrained_dict = {k: v for k, v in pretrained_dict.items()
                                 if k in model_dict.keys()}
              for k, _ in pretrained_dict.items():
                  logger.info('=> loading {} pretrained model {}'.format(k, pretrained))
              model_dict.update(pretrained_dict)
              self.load_state_dict(model_dict)
              print('\n=> loaded pretrained model from {}'.format(pretrained))
  
  
  def get_cls_hrnet(config, **kwargs):
      model = HighResolutionNet(config, **kwargs)
      # model.init_weights()
      return model
  
  
  # 统一接口 interface for model_factory ##########################################
  
  
  def HRNets(MODEL):
      # 自定义接口 for model_factory
      # MODEL is net_cfg or model_kwargs
      cfg = {'MODEL': MODEL}
      return HighResolutionNet(**{'cfg': cfg})
  
  
  model_dir = xtils.get_pretrained_models()
  
  # PRETRAINED => '': load empty-model ; 'ckpt-model.pth': load pretrained-model
  
  hrw18 = {'MODEL': {'NAME': 'hrnet_w18', 'IMAGE_SIZE': [224, 224], 'NUM_CLASSES': 1000,
                     'PRETRAINED': osp.join(model_dir, 'hrnetv2_w18_imagenet_pretrained.pth') and '',
                     'EXTRA':
                         {'STAGE2': {'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4],
                                     'NUM_CHANNELS': [18, 36], 'FUSE_METHOD': 'SUM'},
                          'STAGE3': {'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4],
                                     'NUM_CHANNELS': [18, 36, 72], 'FUSE_METHOD': 'SUM'},
                          'STAGE4': {'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4, 4],
                                     'NUM_CHANNELS': [18, 36, 72, 144], 'FUSE_METHOD': 'SUM'}}}}
  
  hrw30 = {'MODEL': {'NAME': 'hrnet_w30', 'IMAGE_SIZE': [224, 224], 'NUM_CLASSES': 1000,
                     'PRETRAINED': osp.join(model_dir, 'hrnetv2_w30_imagenet_pretrained.pth') and '',
                     'EXTRA': {
                         'STAGE2': {'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4],
                                    'NUM_CHANNELS': [30, 60], 'FUSE_METHOD': 'SUM'},
                         'STAGE3': {'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4],
                                    'NUM_CHANNELS': [30, 60, 120], 'FUSE_METHOD': 'SUM'},
                         'STAGE4': {'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4, 4],
                                    'NUM_CHANNELS': [30, 60, 120, 240], 'FUSE_METHOD': 'SUM'}}}}
  
  hrw32 = {'MODEL': {'NAME': 'hrnet_w32', 'IMAGE_SIZE': [224, 224], 'NUM_CLASSES': 1000,
                     'PRETRAINED': osp.join(model_dir, 'hrnetv2_w32_imagenet_pretrained.pth') and '',
                     'EXTRA': {
                         'STAGE2': {'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4],
                                    'NUM_CHANNELS': [32, 64], 'FUSE_METHOD': 'SUM'},
                         'STAGE3': {'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4],
                                    'NUM_CHANNELS': [32, 64, 128], 'FUSE_METHOD': 'SUM'},
                         'STAGE4': {'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4, 4],
                                    'NUM_CHANNELS': [32, 64, 128, 256], 'FUSE_METHOD': 'SUM'}}}}
  
  hrw40 = {'MODEL': {'NAME': 'hrnet_w40', 'IMAGE_SIZE': [224, 224], 'NUM_CLASSES': 1000,
                     'PRETRAINED': osp.join(model_dir, 'hrnetv2_w40_imagenet_pretrained.pth') and '',
                     'EXTRA': {
                         'STAGE2': {'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4],
                                    'NUM_CHANNELS': [40, 80], 'FUSE_METHOD': 'SUM'},
                         'STAGE3': {'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4],
                                    'NUM_CHANNELS': [40, 80, 160], 'FUSE_METHOD': 'SUM'},
                         'STAGE4': {'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4, 4],
                                    'NUM_CHANNELS': [40, 80, 160, 320], 'FUSE_METHOD': 'SUM'}}}}
  
  hrw44 = {'MODEL': {'NAME': 'hrnet_w44', 'IMAGE_SIZE': [224, 224], 'NUM_CLASSES': 1000,
                     'PRETRAINED': osp.join(model_dir, 'hrnetv2_w44_imagenet_pretrained.pth') and '',
                     'EXTRA': {'STAGE2': {'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4],
                                          'NUM_CHANNELS': [44, 88], 'FUSE_METHOD': 'SUM'},
                               'STAGE3': {'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4],
                                          'NUM_CHANNELS': [44, 88, 176], 'FUSE_METHOD': 'SUM'},
                               'STAGE4': {'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC',
                                          'NUM_BLOCKS': [4, 4, 4, 4], 'NUM_CHANNELS': [44, 88, 176, 352],
                                          'FUSE_METHOD': 'SUM'}}}}
  
  hrw48 = {'MODEL': {'NAME': 'hrnet_w48', 'IMAGE_SIZE': [224, 224], 'NUM_CLASSES': 1000,
                     'PRETRAINED': osp.join(model_dir, 'hrnetv2_w48_imagenet_pretrained.pth') and '',
                     'EXTRA': {
                         'STAGE2': {'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4],
                                    'NUM_CHANNELS': [48, 96], 'FUSE_METHOD': 'SUM'},
                         'STAGE3': {'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4],
                                    'NUM_CHANNELS': [48, 96, 192], 'FUSE_METHOD': 'SUM'},
                         'STAGE4': {'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': [4, 4, 4, 4],
                                    'NUM_CHANNELS': [48, 96, 192, 384], 'FUSE_METHOD': 'SUM'}}}}
  
  # 测试结果
  # hrw18  Prec@1-76.756  Prec@5-93.442
  
  if __name__ == '__main__':
      import xtils
  
      hrarch = [hrw18, hrw30, hrw32, hrw40, hrw44, hrw48][0]
      # model = get_cls_hrnet(hrarch)
      # model = HighResolutionNet(**{'cfg': hrarch})
      model = HRNets(**hrarch)
      print(model)
  
      xtils.calculate_layers_num(model, layers=('conv2d', 'linear'))
      xtils.calculate_FLOPs_scale(model, input_size=224, multiply_adds=False)
      xtils.calculate_params_scale(model, format='million')
      xtils.calculate_time_cost(model, insize=224, use_gpu=False, toc=1, pritout=True)
  