text: """  
  Copyright (c) 2019-present NAVER Corp.
  MIT License
  """
  
  # -*- coding: utf-8 -*-
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  from application.components.prediction.basenet.vgg16_bn import init_weights
  
  
  class RefineNet(nn.Module):
      def __init__(self):
          super(RefineNet, self).__init__()
  
          self.last_conv = nn.Sequential(
              nn.Conv2d(34, 64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True),
              nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True),
              nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True)
          )
  
          self.aspp1 = nn.Sequential(
              nn.Conv2d(64, 128, kernel_size=3, dilation=6, padding=6), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 128, kernel_size=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 1, kernel_size=1)
          )
  
          self.aspp2 = nn.Sequential(
              nn.Conv2d(64, 128, kernel_size=3, dilation=12, padding=12), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 128, kernel_size=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 1, kernel_size=1)
          )
  
          self.aspp3 = nn.Sequential(
              nn.Conv2d(64, 128, kernel_size=3, dilation=18, padding=18), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 128, kernel_size=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 1, kernel_size=1)
          )
  
          self.aspp4 = nn.Sequential(
              nn.Conv2d(64, 128, kernel_size=3, dilation=24, padding=24), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 128, kernel_size=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True),
              nn.Conv2d(128, 1, kernel_size=1)
          )
  
          init_weights(self.last_conv.modules())
          init_weights(self.aspp1.modules())
          init_weights(self.aspp2.modules())
          init_weights(self.aspp3.modules())
          init_weights(self.aspp4.modules())
  
      def forward(self, y, upconv4):
          refine = torch.cat([y.permute(0,3,1,2), upconv4], dim=1)
          refine = self.last_conv(refine)
  
          aspp1 = self.aspp1(refine)
          aspp2 = self.aspp2(refine)
          aspp3 = self.aspp3(refine)
          aspp4 = self.aspp4(refine)
  
          #out = torch.add([aspp1, aspp2, aspp3, aspp4], dim=1)
          out = aspp1 + aspp2 + aspp3 + aspp4
          return out.permute(0, 2, 3, 1)  # , refine.permute(0,2,3,1)
  