text: '''
  MIT License
  
  Copyright (c) 2019 Shunsuke Saito, Zeng Huang, and Ryota Natsume
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
  '''
  import torch
  import torch.nn as nn 
  import torch.nn.functional as F 
  from .net_util import conv3x3
  
  # Residual Block
  class ConvBlock(nn.Module):
      def __init__(self, in_planes, out_planes, norm='batch'):
          super(ConvBlock, self).__init__()
          self.conv1 = conv3x3(in_planes, int(out_planes / 2))
          self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
          self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
  
          if norm == 'batch':
              self.bn1 = nn.BatchNorm2d(in_planes)
              self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
              self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
              self.bn4 = nn.BatchNorm2d(in_planes)
          elif norm == 'group':
              self.bn1 = nn.GroupNorm(32, in_planes)
              self.bn2 = nn.GroupNorm(32, int(out_planes / 2))
              self.bn3 = nn.GroupNorm(32, int(out_planes / 4))
              self.bn4 = nn.GroupNorm(32, in_planes)
          
          if in_planes != out_planes:
              self.downsample = nn.Sequential(
                  self.bn4,
                  nn.ReLU(True),
                  nn.Conv2d(in_planes, out_planes,
                            kernel_size=1, stride=1, bias=False),
              )
          else:
              self.downsample = None
      
      def forward(self, x):
          residual = x
  
          out1 = self.conv1(F.relu(self.bn1(x), True))
          out2 = self.conv2(F.relu(self.bn2(out1), True))
          out3 = self.conv3(F.relu(self.bn3(out2), True))
  
          out3 = torch.cat([out1, out2, out3], 1)
  
          if self.downsample is not None:
              residual = self.downsample(residual)
          
          out3 += residual
  
          return out3
  
  class HourGlass(nn.Module):
      def __init__(self, depth, n_features, norm='batch'):
          super(HourGlass, self).__init__()
          self.depth = depth
          self.features = n_features
          self.norm = norm
  
          self._generate_network(self.depth)
  
      def _generate_network(self, level):
          self.add_module('b1_' + str(level), ConvBlock(self.features, self.features, norm=self.norm)) # add a residual block
          self.add_module('b2_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
  
          if level > 1:
              self._generate_network(level - 1)
          else:
              self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
  
          self.add_module('b3_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
  
      def _forward(self, level, inp):
          # upper branch
          up1 = inp 
          up1 = self._modules['b1_' + str(level)](up1)
  
          # lower branch
          low1 = F.avg_pool2d(inp, 2, stride=2)
          low1 = self._modules['b2_' + str(level)](low1)
  
          if level > 1:
              low2 = self._forward(level - 1, low1)
          else:
              low2 = low1
              low2 = self._modules['b2_plus_' + str(level)](low2)
  
          low3 = low2
          low3 = self._modules['b3_' + str(level)](low3)
  
          up2 = F.interpolate(low3, scale_factor=2, mode='bicubic', align_corners=True)
          # up2 = F.interpolate(low3, scale_factor=2, mode='bilinear')
  
          return up1 + up2
      
      def forward(self, x):
          return self._forward(self.depth, x)
          
  
  class HGFilter(nn.Module):
      def __init__(self, stack, depth, in_ch, last_ch, norm='batch', down_type='conv64', use_sigmoid=True):
          super(HGFilter, self).__init__()
          self.n_stack = stack
          self.use_sigmoid = use_sigmoid
          self.depth = depth
          self.last_ch = last_ch
          self.norm = norm
          self.down_type = down_type
  
          self.conv1 = nn.Conv2d(in_ch, 64, kernel_size=7, stride=2, padding=3)
  
          last_ch = self.last_ch
  
          if self.norm == 'batch':
              self.bn1 = nn.BatchNorm2d(64)
          elif self.norm == 'group':
              self.bn1 = nn.GroupNorm(32, 64)
  
          if self.down_type == 'conv64':
              self.conv2 = ConvBlock(64, 64, self.norm)
              self.down_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
          elif self.down_type == 'conv128':
              self.conv2 = ConvBlock(128, 128, self.norm)
              self.down_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)
          elif self.down_type == 'ave_pool' or self.down_type == 'no_down':
              self.conv2 = ConvBlock(64, 128, self.norm)
          
          self.conv3 = ConvBlock(128, 128, self.norm)
          self.conv4 = ConvBlock(128, 256, self.norm)
          
          # start stacking
          for stack in range(self.n_stack):
              self.add_module('m' + str(stack), HourGlass(self.depth, 256, self.norm))
  
              self.add_module('top_m_' + str(stack), ConvBlock(256, 256, self.norm))
              self.add_module('conv_last' + str(stack),
                              nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
              if self.norm == 'batch':
                  self.add_module('bn_end' + str(stack), nn.BatchNorm2d(256))
              elif self.norm == 'group':
                  self.add_module('bn_end' + str(stack), nn.GroupNorm(32, 256))
              
              self.add_module('l' + str(stack),
                              nn.Conv2d(256, last_ch, 
                              kernel_size=1, stride=1, padding=0))
              
              if stack < self.n_stack - 1:
                  self.add_module(
                      'bl' + str(stack), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
                  self.add_module(
                      'al' + str(stack), nn.Conv2d(last_ch, 256, kernel_size=1, stride=1, padding=0))
  
      def forward(self, x):
          x = F.relu(self.bn1(self.conv1(x)), True)
  
          if self.down_type == 'ave_pool':
              x = F.avg_pool2d(self.conv2(x), 2, stride=2)
          elif self.down_type in ['conv64', 'conv128']:
              x = self.conv2(x)
              x = self.down_conv2(x)
          elif self.down_type == 'no_down':
              x = self.conv2(x)
          else:
              raise NameError('unknown downsampling type')
      
          normx = x
  
          x = self.conv3(x)
          x = self.conv4(x)
  
          previous = x
  
          outputs = []
          for i in range(self.n_stack):
              hg = self._modules['m' + str(i)](previous)
  
              ll = hg
              ll = self._modules['top_m_' + str(i)](ll)
  
              ll = F.relu(self._modules['bn_end' + str(i)]
                         (self._modules['conv_last' + str(i)](ll)), True)
  
              tmp_out = self._modules['l' + str(i)](ll)
  
              if self.use_sigmoid:
                  outputs.append(nn.Tanh()(tmp_out))
              else:
                  outputs.append(tmp_out)
              
              if i < self.n_stack - 1:
                  ll = self._modules['bl' + str(i)](ll)
                  tmp_out_ = self._modules['al' + str(i)](tmp_out)
                  previous = previous + ll + tmp_out_
              
          return outputs, normx
      