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 taming.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 != "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, content_input, target, return_feats=False):
          input_c, input_t = self.scaling_layer(content_input), self.scaling_layer(target)
          outs_c, outs_t = self.net(input_c), self.net(input_t)
          feats_c, feats_t, diffs = list(), list(), list()
          for kk in range(len(self.chns)):
              feats_c.append(normalize_tensor(outs_c[kk]))
              feats_t.append(normalize_tensor(outs_t[kk]))
              diffs.append((feats_c[-1] - feats_t[-1]) ** 2)
  
          lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
          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]
  
          if return_feats:
              return val.mean(), outs_c, outs_t
          return val.mean()
  
  
  class LPIPSWithStyle(LPIPS):
      def __init__(self, use_dropout=True):
          super(LPIPSWithStyle, self).__init__(use_dropout=use_dropout)
          self.style_loss = nn.MSELoss(reduce=False)
          self.smooth = nn.Softmax2d()
  
      def forward(self, content_input, target, style_input=None):
          loss_c, outs_c, outs_t = super().forward(content_input, target, return_feats=True)
  
          outs_s = self.net(self.scaling_layer(style_input)) if style_input is not None else outs_c
          diffs = list()
          for kk in range(len(self.chns)):
              smooth_out_s = self.smooth(outs_s[kk])
              smooth_out_t = self.smooth(outs_t[kk])
              std_s, mean_s = self.calc_mean_std(smooth_out_s)
              std_t, mean_t = self.calc_mean_std(smooth_out_t)
              diff = self.style_loss(std_s, std_t) + self.style_loss(mean_s, mean_t)
              diff = diff / self.calc_balanced_loss_scale(smooth_out_s, smooth_out_t)
              diffs.append(diff.mean())
  
          val = diffs[0]
          for l in range(1, len(self.chns)):
              val += diffs[l]
          return loss_c, val
  
      @staticmethod
      def calc_mean_std(feat, eps=1e-5):
          N, *_ = feat.shape
          feat_var = feat.view(N, -1).var(dim=-1) + eps
          feat_std = feat_var.sqrt().view(N, 1, 1, 1)
          feat_mean = feat.view(N, -1).mean(dim=-1).view(N, 1, 1, 1)
          return feat_mean, feat_std
  
      @staticmethod
      def calc_balanced_loss_scale(feat, target_feat):
          return torch.mean(feat ** 2, dim=(1, 2, 3)) + torch.mean(target_feat ** 2, dim=(1, 2, 3))
  
  
  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)
  