text: import sys
  sys.path.append("..")
  import torch
  from torch import nn
  from torchvision import models
  from .vgg_sphe import *
  
  from utils import get_upsampling_weight
  
  # This is implemented in full accordance with the original one (https://github.com/shelhamer/fcn.berkeleyvision.org)
  class FCN8s_sphe(nn.Module):
      def __init__(self, num_classes, pretrained=True, feat=64, **kwargs):
          super(FCN8s_sphe, self).__init__()
          vgg = vgg16(pretrained=pretrained, feat=feat, **kwargs)
          features, classifier = list(vgg.features.children()), list(vgg.classifier.children())
          '''
          100 padding for 2 reasons:
              1) support very small input size
              2) allow cropping in order to match size of different layers' feature maps
          Note that the cropped part corresponds to a part of the 100 padding
          Spatial information of different layers' feature maps cannot be align exactly because of cropping, which is bad
          '''
          features[0].padding = (100, 100)
          ft = feat
  
          for f in features:
              if 'MaxPool' in f.__class__.__name__:
                  f.ceil_mode = True
              elif 'ReLU' in f.__class__.__name__:
                  f.inplace = True
  
          self.features3 = nn.Sequential(*features[: 17])
          self.features4 = nn.Sequential(*features[17: 24])
          self.features5 = nn.Sequential(*features[24:])
  
          self.score_pool3 = nn.Conv2d(ft*4, num_classes, kernel_size=1)
          self.score_pool4 = nn.Conv2d(ft*8, num_classes, kernel_size=1)
          self.score_pool3.weight.data.zero_()
          self.score_pool3.bias.data.zero_()
          self.score_pool4.weight.data.zero_()
          self.score_pool4.bias.data.zero_()
  
          fc6 = nn.Conv2d(ft*8, ft*64, kernel_size=7)
          fc6.weight.data.copy_(classifier[0].weight.data.view(ft*64, ft*8, 7, 7))
          fc6.bias.data.copy_(classifier[0].bias.data)
          fc7 = nn.Conv2d(ft*64, ft*64, kernel_size=1)
          fc7.weight.data.copy_(classifier[3].weight.data.view(ft*64, ft*64, 1, 1))
          fc7.bias.data.copy_(classifier[3].bias.data)
          score_fr = nn.Conv2d(ft*64, num_classes, kernel_size=1)
          score_fr.weight.data.zero_()
          score_fr.bias.data.zero_()
          self.score_fr = nn.Sequential(
              fc6, nn.ReLU(inplace=True), nn.Dropout(), fc7, nn.ReLU(inplace=True), nn.Dropout(), score_fr
          )
  
          self.upscore2 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2, bias=False)
          self.upscore_pool4 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2, bias=False)
          self.upscore8 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=16, stride=8, bias=False)
          self.upscore2.weight.data.copy_(get_upsampling_weight(num_classes, num_classes, 4))
          self.upscore_pool4.weight.data.copy_(get_upsampling_weight(num_classes, num_classes, 4))
          self.upscore8.weight.data.copy_(get_upsampling_weight(num_classes, num_classes, 16))
  
      def forward(self, x):
          x_size = x.size()
          pool3 = self.features3(x)
          pool4 = self.features4(pool3)
          pool5 = self.features5(pool4)
  
          score_fr = self.score_fr(pool5)
          upscore2 = self.upscore2(score_fr)
  
          score_pool4 = self.score_pool4(0.01 * pool4)
          upscore_pool4 = self.upscore_pool4(score_pool4[:, :, 5: (5 + upscore2.size()[2]), 5: (5 + upscore2.size()[3])]
                                             + upscore2)
  
          score_pool3 = self.score_pool3(0.0001 * pool3)
          upscore8 = self.upscore8(score_pool3[:, :, 9: (9 + upscore_pool4.size()[2]), 9: (9 + upscore_pool4.size()[3])]
                                   + upscore_pool4)
          return upscore8[:, :, 31: (31 + x_size[2]), 31: (31 + x_size[3])].contiguous()