text: """Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
  
  import torch
  import torch.nn as nn
  from torchvision import models
  from collections import namedtuple
  
  from vqgan.util import get_ckpt_path
  
  
  class LPIPS(nn.Module):
      # Learned perceptual metric
      def __init__(self, use_dropout=True):
          super().__init__()
          self.scaling_layer = ScalingLayer()
          self.chns = [64, 128, 256, 512, 512]  # vg16 features
          self.net = vgg16(pretrained=True, requires_grad=False)
          self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
          self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
          self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
          self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
          self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
          self.load_from_pretrained()
          for param in self.parameters():
              param.requires_grad = False
  
      def load_from_pretrained(self, name="vgg_lpips"):
          ckpt = get_ckpt_path(name, "taming/modules/autoencoder/lpips")
          self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
          print("loaded pretrained LPIPS loss from {}".format(ckpt))
  
      @classmethod
      def from_pretrained(cls, name="vgg_lpips"):
          if name is not "vgg_lpips":
              raise NotImplementedError
          model = cls()
          ckpt = get_ckpt_path(name)
          model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
          return model
  
      def forward(self, input, target):
          in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
          outs0, outs1 = self.net(in0_input), self.net(in1_input)
          feats0, feats1, diffs = {}, {}, {}
          lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
          for kk in range(len(self.chns)):
              feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
              diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
  
          res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
          val = res[0]
          for l in range(1, len(self.chns)):
              val += res[l]
          return val
  
  
  class ScalingLayer(nn.Module):
      def __init__(self):
          super(ScalingLayer, self).__init__()
          self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
          self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])
  
      def forward(self, inp):
          return (inp - self.shift) / self.scale
  
  
  class NetLinLayer(nn.Module):
      """ A single linear layer which does a 1x1 conv """
      def __init__(self, chn_in, chn_out=1, use_dropout=False):
          super(NetLinLayer, self).__init__()
          layers = [nn.Dropout(), ] if (use_dropout) else []
          layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
          self.model = nn.Sequential(*layers)
  
  
  class vgg16(torch.nn.Module):
      def __init__(self, requires_grad=False, pretrained=True):
          super(vgg16, self).__init__()
          vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
          self.slice1 = torch.nn.Sequential()
          self.slice2 = torch.nn.Sequential()
          self.slice3 = torch.nn.Sequential()
          self.slice4 = torch.nn.Sequential()
          self.slice5 = torch.nn.Sequential()
          self.N_slices = 5
          for x in range(4):
              self.slice1.add_module(str(x), vgg_pretrained_features[x])
          for x in range(4, 9):
              self.slice2.add_module(str(x), vgg_pretrained_features[x])
          for x in range(9, 16):
              self.slice3.add_module(str(x), vgg_pretrained_features[x])
          for x in range(16, 23):
              self.slice4.add_module(str(x), vgg_pretrained_features[x])
          for x in range(23, 30):
              self.slice5.add_module(str(x), vgg_pretrained_features[x])
          if not requires_grad:
              for param in self.parameters():
                  param.requires_grad = False
  
      def forward(self, X):
          h = self.slice1(X)
          h_relu1_2 = h
          h = self.slice2(h)
          h_relu2_2 = h
          h = self.slice3(h)
          h_relu3_3 = h
          h = self.slice4(h)
          h_relu4_3 = h
          h = self.slice5(h)
          h_relu5_3 = h
          vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
          out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
          return out
  
  
  def normalize_tensor(x,eps=1e-10):
      norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True))
      return x/(norm_factor+eps)
  
  
  def spatial_average(x, keepdim=True):
      return x.mean([2,3],keepdim=keepdim)
  
  