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| import hashlib | |
| import os | |
| from collections import namedtuple | |
| import requests | |
| import torch | |
| import torch.nn as nn | |
| from torchvision import models | |
| from tqdm import tqdm | |
| URL_MAP = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"} | |
| CKPT_MAP = {"vgg_lpips": "vgg.pth"} | |
| MD5_MAP = {"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"} | |
| def md5_hash(path): | |
| with open(path, "rb") as f: | |
| content = f.read() | |
| return hashlib.md5(content).hexdigest() | |
| def download(url, local_path, chunk_size=1024): | |
| os.makedirs(os.path.split(local_path)[0], exist_ok=True) | |
| with requests.get(url, stream=True) as r: | |
| total_size = int(r.headers.get("content-length", 0)) | |
| with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: | |
| with open(local_path, "wb") as f: | |
| for data in r.iter_content(chunk_size=chunk_size): | |
| if data: | |
| f.write(data) | |
| pbar.update(chunk_size) | |
| def get_ckpt_path(name, root, check=False): | |
| assert name in URL_MAP | |
| path = os.path.join(root, CKPT_MAP[name]) | |
| if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): | |
| print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) | |
| download(URL_MAP[name], path) | |
| md5 = md5_hash(path) | |
| assert md5 == MD5_MAP[name], md5 | |
| return 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, "model_zoo/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)) | |
| 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, 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([-0.030, -0.088, -0.188])[None, :, None, None]) | |
| self.register_buffer("scale", torch.Tensor([0.458, 0.448, 0.450])[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) | |