text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import logging
  
  import torch
  import torch.nn as nn
  from collections import OrderedDict
  import numpy as np
  import math
  import torch.nn.functional as F
  
  
  BN_MOMENTUM = 0.1
  logger = logging.getLogger(__name__)
  
  
  class SELayer(nn.Module):
      def __init__(self, channel):
          super(SELayer, self).__init__()
          self.avg_pool = nn.AdaptiveAvgPool2d(1)
          self.fc = nn.Sequential(
              nn.Conv2d(channel, channel, 1),
              nn.BatchNorm2d(channel),
              nn.ReLU(inplace=True),
              nn.Conv2d(channel, channel, 1),
              nn.Sigmoid()
          )
  
      def forward(self, x):
          y = self.avg_pool(x)
          y = self.fc(y)
          return y
  
  
  class GFF(nn.Module):
      def __init__(self, channel):
          super(GFF, self).__init__()
          self.conv = nn.Sequential(
              nn.Conv2d(channel, channel, 1),
              nn.Sigmoid()
          )
  
      def forward(self, x):
          y = self.conv(x)
          return y
  
  
  class ScaledDotProductAttention(nn.Module):
      ''' Scaled Dot-Product Attention '''
      def __init__(self, temperature):
          super().__init__()
          self.temperature = temperature**0.5
  
      def forward(self, q, k, v):
  
          residual = q
  
          attn = torch.matmul(q / self.temperature, k.transpose(1, 2))  #(b,16,4096) matmul (b,4096,16) = (b,16,16)
  
          attn = F.softmax(attn, dim=-1)
  
          output = torch.matmul(attn, v) #(b,16,16) matmul (b,16,4096) = (b,16,4096)
  
          output = output+q
  
          return output
  
  
  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(True)
         
          if self.num_branches == 2:
              self.senet1_1 = SELayer(channel=32)
              self.gff1_1 = GFF(channel=32)
  
              self.senet1_2 = SELayer(channel=32)
              self.gff1_2 = GFF(channel=32)
  
          elif self.num_branches == 3:
              self.senet2_1 = SELayer(channel=32)
              self.gff2_1 = GFF(channel=32)
  
              self.senet2_2 = SELayer(channel=32)
              self.gff2_2 = GFF(channel=32)
  
              self.senet2_3 = SELayer(channel=32)
              self.gff2_3 = GFF(channel=32)
  
              self.senet2_4 = SELayer(channel=64)
              self.gff2_4 = GFF(channel=64)
  
              self.senet2_5 = SELayer(channel=64)
              self.gff2_5 = GFF(channel=64)
          else:
              self.senet3_1 = SELayer(channel=32)
              self.gff3_1 = GFF(channel=32)
  
              self.senet3_2 = SELayer(channel=32)
              self.gff3_2 = GFF(channel=32)
  
              self.senet3_3 = SELayer(channel=32)
              self.gff3_3 = GFF(channel=32)
  
              self.senet3_4 = SELayer(channel=32)
              self.gff3_4 = GFF(channel=32)
  
              self.senet3_5 = SELayer(channel=64)
              self.gff3_5 = GFF(channel=64)
  
              self.senet3_6 = SELayer(channel=64)
              self.gff3_6 = GFF(channel=64)
  
              self.senet3_7 = SELayer(channel=64)
              self.gff3_7 = GFF(channel=64)
  
              self.senet3_8 = SELayer(channel=128)
              self.gff3_8 = GFF(channel=128)
  
              self.senet3_9 = SELayer(channel=128)
              self.gff3_9 = GFF(channel=128)
  
  
      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  #2/3/4
          num_inchannels = self.num_inchannels
          fuse_layers = []
          for i in range(num_branches if self.multi_scale_output else 1):  #2/3/4
              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]),
                              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)
                                  )
                              )
                          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),
                                      nn.ReLU(True)
                                  )
                              )
                      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])  
              #[(b,32,64,64),(b,64,32,32)] / [(b,32,64,64),(b,64,32,32),(b,128,16,16)] / [(b,32,64,64),(b,64,32,32),(b,128,16,16),(b,256,8,8)]
  
          x_fuse = []
  
          for i in range(len(self.fuse_layers)):  #len(self.fuse_layers) == 2/3/4
              if len(x) == 2:
                  y = (1+self.senet1_1(x[0])*self.gff1_1(x[0]))*x[0] if i == 0 else self.fuse_layers[i][0](x[0])
                  for j in range(1, self.num_branches): #2
                      if i == j:
                          y = y + x[j]
                      else: 
                          y = y + (1+self.senet1_1(x[0])*self.gff1_1(x[0])*self.senet1_2(self.fuse_layers[i][j](x[j]))*self.gff1_2(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])  
                          #self.fuse_layers[i][j](x[j]) is the feature after the 1x1 and upsample (b,32,64,64)
                  x_fuse.append(self.relu(y))
  
  
              elif len(x) == 3:
                  y = (1+self.senet2_1(x[0])*self.gff2_1(x[0]))*x[0] if i == 0 else self.fuse_layers[i][0](x[0])
                  for j in range(1, self.num_branches): #3
                      if i==0 and j==1:
                          y = y + (1+self.senet2_1(x[0])*self.gff2_1(x[0])*self.senet2_2(self.fuse_layers[i][j](x[j]))*self.gff2_2(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
                      elif i==0 and j==2:
                          y = y + (1+self.senet2_1(x[0])*self.gff2_1(x[0])*self.senet2_3(self.fuse_layers[i][j](x[j]))*self.gff2_3(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
  
                      elif i==1 and j==1:
                          y = y + (1+self.senet2_4(x[j])*self.gff2_4(x[j]))*x[j]
                      elif i==1 and j==2:
                          y = y + (1+self.senet2_4(x[1])*self.gff2_4(x[1])*self.senet2_5(self.fuse_layers[i][j](x[j]))*self.gff2_5(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
  
                      else: #i==2
                          if i == j:
                              y = y + x[j]
                          else:
                              y = y + self.fuse_layers[i][j](x[j])
  
                  x_fuse.append(self.relu(y))
  
  
              else:
                  y = (1+self.senet3_1(x[0])*self.gff3_1(x[0]))*x[0] if i == 0 else self.fuse_layers[i][0](x[0])
                  for j in range(1, self.num_branches): #4
                      if i==0 and j==1:
                          y = y + (1+self.senet3_1(x[0])*self.gff3_1(x[0])*self.senet3_2(self.fuse_layers[i][j](x[j]))*self.gff3_2(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
                      elif i==0 and j==2:
                          y = y + (1+self.senet3_1(x[0])*self.gff3_1(x[0])*self.senet3_3(self.fuse_layers[i][j](x[j]))*self.gff3_3(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
                      elif i==0 and j==3:
                          y = y + (1+self.senet3_1(x[0])*self.gff3_1(x[0])*self.senet3_4(self.fuse_layers[i][j](x[j]))*self.gff3_4(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
  
                      elif i==1 and j==1:
                          y = y + (1+self.senet3_5(x[j])*self.gff3_5(x[j]))*x[j]
                      elif i==1 and j==2:
                          y = y + (1+self.senet3_5(x[1])*self.gff3_5(x[1])*self.senet3_6(self.fuse_layers[i][j](x[j]))*self.gff3_6(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
                      elif i==1 and j==3:
                          y = y + (1+self.senet3_5(x[1])*self.gff3_5(x[1])*self.senet3_7(self.fuse_layers[i][j](x[j]))*self.gff3_7(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
  
                      elif i==2 and j==1:
                          y = y + self.fuse_layers[i][j](x[j])
                      elif i==2 and j==2:
                          y = y + (1+self.senet3_8(x[j])*self.gff3_8(x[j]))*x[j]
                      elif i==2 and j==3:
                          y = y + (1+self.senet3_8(x[2])*self.gff3_8(x[2])*self.senet3_9(self.fuse_layers[i][j](x[j]))*self.gff3_9(self.fuse_layers[i][j](x[j])))*self.fuse_layers[i][j](x[j])
                      else: #i==3
                          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 PoseHighResolutionNet(nn.Module):
  
      def __init__(self, cfg, **kwargs):
          self.inplanes = 64
          extra = cfg['MODEL']['EXTRA']
          super(PoseHighResolutionNet, self).__init__()
  
          # stem net
          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, 4)
  
          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([256], 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=False)
  
          self.final_layer = nn.Conv2d(
              in_channels=pre_stage_channels[0],
              out_channels=cfg['MODEL']['NUM_JOINTS'],
              kernel_size=extra['FINAL_CONV_KERNEL'],
              stride=1,
              padding=1 if extra['FINAL_CONV_KERNEL'] == 3 else 0
          )
  
          self.pretrained_layers = extra['PRETRAINED_LAYERS']
  
          self.sha1 = ScaledDotProductAttention(temperature=4096)
  
      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]),
                              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),
                              nn.ReLU(inplace=True)
                          )
                      )
                  transition_layers.append(nn.Sequential(*conv3x3s))
  
          return nn.ModuleList(transition_layers)
  
      def _make_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(
                      self.inplanes, planes * block.expansion,
                      kernel_size=1, stride=stride, bias=False
                  ),
                  nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.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):
          #a1,a2,a3,a4,a5=[],[],[],[],[]
          #c=[]
  #x(b,3,256,256)
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  #(b,64,128,128)
          x = self.conv2(x)
          x = self.bn2(x)
          x = self.relu(x)
  #(b,64,64,64)
          x = self.layer1(x)
  #(b,256,64,64)
  
          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)
  #x_list = [(b,32,64,64), (b,64,32,32)]
          y_list = self.stage2(x_list)
  #y_list = [(b,32,64,64), (b,64,32,32)]
  
          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])
  #x_list = [(b,32,64,64), (b,64,32,32), (b,128,16,16)]
          y_list = self.stage3(x_list)
  #y_list = [(b,32,64,64), (b,64,32,32), (b,128,16,16)]
  
          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)
  #y_list = [(b,32,64,64), (b,64,32,32), (b,128,16,16), (b,256,8,8)]
  
          x = self.final_layer(y_list[0])
  #(b,16,64,64)  heatmap
  
          #(b,16,4096)
          f_a = x.view(x.shape[0],16,-1)
  
          '''#(b,4,4096)
          for i in range(x.shape[0]):
              a1.append(f_a[i][0:1,:])
              a1.append(f_a[i][1:2,:])
              a1.append(f_a[i][4:5,:])
              a1.append(f_a[i][5:6,:])             
          f_a_1=torch.cat(a1,dim=0)
          f_a_1=f_a_1.view(x.shape[0],-1,f_a_1.shape[-1])
          #(b,3,4096)
          for i in range(x.shape[0]):
              a2.append(f_a[i][2:3,:])
              a2.append(f_a[i][3:4,:])
              a2.append(f_a[i][6:7,:])           
          f_a_2 = torch.cat(a2,dim=0)
          f_a_2=f_a_2.view(x.shape[0],-1,f_a_2.shape[-1])
          #(b,5,4096)
          for i in range(x.shape[0]):
              a3.append(f_a[i][7:8,:])
              a3.append(f_a[i][8:9,:])
              a3.append(f_a[i][9:10,:])
              a3.append(f_a[i][12:13,:])
              a3.append(f_a[i][13:14,:])             
          f_a_3 = torch.cat(a3,dim=0)
          f_a_3=f_a_3.view(x.shape[0],-1,f_a_3.shape[-1])
          #(b,2,4096)
          for i in range(x.shape[0]):
              a4.append(f_a[i][10:11,:])
              a4.append(f_a[i][11:12,:])           
          f_a_4 = torch.cat(a4,dim=0)
          f_a_4=f_a_4.view(x.shape[0],-1,f_a_4.shape[-1])
          #(b,2,4096)
          for i in range(x.shape[0]):
              a5.append(f_a[i][14:15,:])
              a5.append(f_a[i][15:16,:])           
          f_a_5 = torch.cat(a5,dim=0)
          f_a_5=f_a_5.view(x.shape[0],-1,f_a_5.shape[-1])'''
  
          #(b,16,4096)
          enhanced_f_a_1 = self.sha1(q=f_a,k=f_a,v=f_a)
          '''#(b,3,4096)
          enhanced_f_a_2 = self.mha2(q=f_a_2,k=f_a_2,v=f_a_2)
          #(b,5,4096)
          enhanced_f_a_3 = self.mha3(q=f_a_3,k=f_a_3,v=f_a_3)
          #(b,2,4096)
          enhanced_f_a_4 = self.mha4(q=f_a_4,k=f_a_4,v=f_a_4)
          #(b,2,4096)
          enhanced_f_a_5 = self.mha5(q=f_a_5,k=f_a_5,v=f_a_5)
  
          #c(b,16,4096)
          for i in range(x.shape[0]):
              c.append(enhanced_f_a_1[i][0:2,:])
              c.append(enhanced_f_a_2[i][0:2,:])
              c.append(enhanced_f_a_1[i][2:4,:])
              c.append(enhanced_f_a_2[i][2:3,:])
              c.append(enhanced_f_a_3[i][0:3,:])
              c.append(enhanced_f_a_4[i][0:2,:])
              c.append(enhanced_f_a_3[i][3:5,:])
              c.append(enhanced_f_a_5[i][0:2,:])
          #(b,16,4096)
          out = torch.cat(c,dim=0)
          out=out.view(x.shape[0],-1,out.shape[-1])'''
          #(b,16,64,64)
          out = enhanced_f_a_1.view(enhanced_f_a_1.shape[0],enhanced_f_a_1.shape[1],64,-1)
          return out
          #return x
  
      def init_weights(self, pretrained=''):
          logger.info('=> 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')
                  nn.init.normal_(m.weight, std=0.001)
                  for name, _ in m.named_parameters():
                      if name in ['bias']:
                          nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.ConvTranspose2d):
                  nn.init.normal_(m.weight, std=0.001)
                  for name, _ in m.named_parameters():
                      if name in ['bias']:
                          nn.init.constant_(m.bias, 0)
  
          if os.path.isfile(pretrained):
              pretrained_state_dict = torch.load(pretrained)
              logger.info('=> loading pretrained model {}'.format(pretrained))
  
              need_init_state_dict = {}
              for name, m in pretrained_state_dict.items():
                  if name.split('.')[0] in self.pretrained_layers \
                     or self.pretrained_layers[0] is '*':
                      need_init_state_dict[name] = m
              self.load_state_dict(need_init_state_dict, strict=False)
          elif pretrained:
              logger.error('=> please download pre-trained models first!')
              raise ValueError('{} is not exist!'.format(pretrained))
  
  
  def get_pose_net(cfg, is_train, **kwargs):
      model = PoseHighResolutionNet(cfg, **kwargs)
  
      if is_train and cfg['MODEL']['INIT_WEIGHTS']:
          model.init_weights(cfg['MODEL']['PRETRAINED'])
  
      return model
  