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