text: # Most of the code below is from the following repo:
  #  https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/HRNet-OCR
  #
  # ------------------------------------------------------------------------------
  # Copyright (c) Microsoft
  # Licensed under the MIT License.
  # Written by Ke Sun (sunk@mail.ustc.edu.cn), Jingyi Xie (hsfzxjy@gmail.com)
  # ------------------------------------------------------------------------------
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import numpy as np
  
  import torch
  import torch.nn as nn
  import torch._utils
  import torch.nn.functional as F
  
  from network.mynn import Norm2d
  from runx.logx import logx
  from config import cfg
  
  
  BN_MOMENTUM = 0.1
  align_corners = cfg.MODEL.ALIGN_CORNERS
  relu_inplace = True
  
  
  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 = Norm2d(planes, momentum=BN_MOMENTUM)
          self.relu = nn.ReLU(inplace=relu_inplace)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = Norm2d(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 = 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 = Norm2d(planes, momentum=BN_MOMENTUM)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, bias=False)
          self.bn2 = Norm2d(planes, momentum=BN_MOMENTUM)
          self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
                                 bias=False)
          self.bn3 = Norm2d(planes * self.expansion, momentum=BN_MOMENTUM)
          self.relu = nn.ReLU(inplace=relu_inplace)
          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 = 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(inplace=relu_inplace)
  
      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))
              logx.msg(error_msg)
              raise ValueError(error_msg)
  
          if num_branches != len(num_channels):
              error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
                  num_branches, len(num_channels))
              logx.msg(error_msg)
              raise ValueError(error_msg)
  
          if num_branches != len(num_inchannels):
              error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
                  num_branches, len(num_inchannels))
              logx.msg(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),
                  Norm2d(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),
                          Norm2d(num_inchannels[i], momentum=BN_MOMENTUM)))
                  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),
                                  Norm2d(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),
                                  Norm2d(num_outchannels_conv3x3,
                                         momentum=BN_MOMENTUM),
                                  nn.ReLU(inplace=relu_inplace)))
                      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]
                  elif j > i:
                      width_output = x[i].shape[-1]
                      height_output = x[i].shape[-2]
                      y = y + F.interpolate(
                          self.fuse_layers[i][j](x[j]),
                          size=[height_output, width_output],
                          mode='bilinear', align_corners=align_corners)
                  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, **kwargs):
          extra = cfg.MODEL.OCR_EXTRA
          super(HighResolutionNet, self).__init__()
  
          # stem net
          self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
                                 bias=False)
          self.bn1 = Norm2d(64, momentum=BN_MOMENTUM)
          self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
                                 bias=False)
          self.bn2 = Norm2d(64, momentum=BN_MOMENTUM)
          self.relu = nn.ReLU(inplace=relu_inplace)
  
          self.stage1_cfg = extra['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 = 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(
              [stage1_out_channel], num_channels)
          self.stage2, pre_stage_channels = self._make_stage(
              self.stage2_cfg, num_channels)
  
          self.stage3_cfg = 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 = 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)
  
          self.high_level_ch = np.int(np.sum(pre_stage_channels))
  
      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),
                          Norm2d(
                              num_channels_cur_layer[i], momentum=BN_MOMENTUM),
                          nn.ReLU(inplace=relu_inplace)))
                  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),
                          Norm2d(outchannels, momentum=BN_MOMENTUM),
                          nn.ReLU(inplace=relu_inplace)))
                  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),
                  Norm2d(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_in):
          x = self.conv1(x_in)
          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:
                  if i < self.stage2_cfg['NUM_BRANCHES']:
                      x_list.append(self.transition2[i](y_list[i]))
                  else:
                      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:
                  if i < self.stage3_cfg['NUM_BRANCHES']:
                      x_list.append(self.transition3[i](y_list[i]))
                  else:
                      x_list.append(self.transition3[i](y_list[-1]))
              else:
                  x_list.append(y_list[i])
          x = self.stage4(x_list)
  
          # Upsampling
          x0_h, x0_w = x[0].size(2), x[0].size(3)
          x1 = F.interpolate(x[1], size=(x0_h, x0_w),
                             mode='bilinear', align_corners=align_corners)
          x2 = F.interpolate(x[2], size=(x0_h, x0_w),
                             mode='bilinear', align_corners=align_corners)
          x3 = F.interpolate(x[3], size=(x0_h, x0_w),
                             mode='bilinear', align_corners=align_corners)
  
          feats = torch.cat([x[0], x1, x2, x3], 1)
  
          return None, None, feats
  
      def init_weights(self, pretrained=cfg.MODEL.HRNET_CHECKPOINT):
          # logx.msg('=> init weights from normal distribution')
          for name, m in self.named_modules():
              if any(part in name for part in {'cls', 'aux', 'ocr'}):
                  # print('skipped', name)
                  continue
              if isinstance(m, nn.Conv2d):
                  nn.init.normal_(m.weight, std=0.001)
              elif isinstance(m, cfg.MODEL.BNFUNC):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
          if os.path.isfile(pretrained):
              pretrained_dict = torch.load(pretrained,
                                           map_location={'cuda:0': 'cpu'})
              logx.msg('=> loading pretrained model {}'.format(pretrained))
              model_dict = self.state_dict()
              pretrained_dict = {k.replace('last_layer',
                                           'aux_head').replace('model.', ''): v
                                 for k, v in pretrained_dict.items()}
              #print(set(model_dict) - set(pretrained_dict))
              #print(set(pretrained_dict) - set(model_dict))
              pretrained_dict = {k: v for k, v in pretrained_dict.items()
                                 if k in model_dict.keys()}
              model_dict.update(pretrained_dict)
              self.load_state_dict(model_dict)
          elif pretrained:
              raise RuntimeError('No such file {}'.format(pretrained))
  
  
  def get_seg_model():
      model = HighResolutionNet()
      model.init_weights()
  
      return model
  
  def get_seg_model_onnx(hrnet_path):
      model = HighResolutionNet()
      model.init_weights(hrnet_path)
  
      return model