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from collections import defaultdict
from pathlib import Path
from typing import Union
import torch
import torch.nn as nn
from PIL import Image
from torchvision import transforms
ImageInput = Union[str, os.PathLike, Image.Image]
IMAGE_EXTENSIONS = {".bmp", ".jpg", ".jpeg", ".pgm", ".png", ".ppm", ".tif", ".tiff", ".webp"}
LPIPS_NET_CHOICES = ("alex", "vgg", "squeeze")
def _list_image_files(path: Union[str, os.PathLike]):
path = os.fspath(path)
if not os.path.isdir(path):
raise ValueError(f"Expected a directory for LPIPS, got: {path}")
files = []
for entry in sorted(os.listdir(path)):
full = os.path.join(path, entry)
if os.path.isfile(full) and os.path.splitext(entry)[1].lower() in IMAGE_EXTENSIONS:
files.append(full)
if not files:
raise ValueError(f"No images found under {path}.")
return files
def _pair_directories_by_stem(dir_a, dir_b):
files_a = _list_image_files(dir_a)
files_b = _list_image_files(dir_b)
by_stem_a = defaultdict(list)
for f in files_a:
by_stem_a[Path(f).stem].append(f)
by_stem_b = defaultdict(list)
for f in files_b:
by_stem_b[Path(f).stem].append(f)
common = sorted(set(by_stem_a.keys()) & set(by_stem_b.keys()))
if not common:
raise ValueError(f"No matching filename stems between {dir_a} and {dir_b}.")
pairs = []
for stem in common:
pairs.append((sorted(by_stem_a[stem])[0], sorted(by_stem_b[stem])[0]))
return pairs
def _open_rgb(image: ImageInput) -> Image.Image:
if isinstance(image, (str, os.PathLike)):
image = Image.open(image)
if not isinstance(image, Image.Image):
raise TypeError(f"LPIPS expects PIL images or image paths, got {type(image)}.")
return image.convert("RGB")
class _AlexFeatures(nn.Module):
def __init__(self):
super().__init__()
self.slice1 = nn.Sequential()
self.slice2 = nn.Sequential()
self.slice3 = nn.Sequential()
self.slice4 = nn.Sequential()
self.slice5 = nn.Sequential()
self.slice1.add_module("0", nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2))
self.slice1.add_module("1", nn.ReLU(inplace=True))
self.slice2.add_module("2", nn.MaxPool2d(kernel_size=3, stride=2))
self.slice2.add_module("3", nn.Conv2d(64, 192, kernel_size=5, padding=2))
self.slice2.add_module("4", nn.ReLU(inplace=True))
self.slice3.add_module("5", nn.MaxPool2d(kernel_size=3, stride=2))
self.slice3.add_module("6", nn.Conv2d(192, 384, kernel_size=3, padding=1))
self.slice3.add_module("7", nn.ReLU(inplace=True))
self.slice4.add_module("8", nn.Conv2d(384, 256, kernel_size=3, padding=1))
self.slice4.add_module("9", nn.ReLU(inplace=True))
self.slice5.add_module("10", nn.Conv2d(256, 256, kernel_size=3, padding=1))
self.slice5.add_module("11", nn.ReLU(inplace=True))
def forward(self, x):
h1 = self.slice1(x)
h2 = self.slice2(h1)
h3 = self.slice3(h2)
h4 = self.slice4(h3)
h5 = self.slice5(h4)
return [h1, h2, h3, h4, h5]
class _VGG16Features(nn.Module):
def __init__(self):
super().__init__()
self.slice1 = nn.Sequential()
self.slice2 = nn.Sequential()
self.slice3 = nn.Sequential()
self.slice4 = nn.Sequential()
self.slice5 = nn.Sequential()
cfg = [
(1, 0, nn.Conv2d(3, 64, 3, padding=1)),
(1, 1, nn.ReLU(inplace=True)),
(1, 2, nn.Conv2d(64, 64, 3, padding=1)),
(1, 3, nn.ReLU(inplace=True)),
(2, 4, nn.MaxPool2d(2, 2)),
(2, 5, nn.Conv2d(64, 128, 3, padding=1)),
(2, 6, nn.ReLU(inplace=True)),
(2, 7, nn.Conv2d(128, 128, 3, padding=1)),
(2, 8, nn.ReLU(inplace=True)),
(3, 9, nn.MaxPool2d(2, 2)),
(3, 10, nn.Conv2d(128, 256, 3, padding=1)),
(3, 11, nn.ReLU(inplace=True)),
(3, 12, nn.Conv2d(256, 256, 3, padding=1)),
(3, 13, nn.ReLU(inplace=True)),
(3, 14, nn.Conv2d(256, 256, 3, padding=1)),
(3, 15, nn.ReLU(inplace=True)),
(4, 16, nn.MaxPool2d(2, 2)),
(4, 17, nn.Conv2d(256, 512, 3, padding=1)),
(4, 18, nn.ReLU(inplace=True)),
(4, 19, nn.Conv2d(512, 512, 3, padding=1)),
(4, 20, nn.ReLU(inplace=True)),
(4, 21, nn.Conv2d(512, 512, 3, padding=1)),
(4, 22, nn.ReLU(inplace=True)),
(5, 23, nn.MaxPool2d(2, 2)),
(5, 24, nn.Conv2d(512, 512, 3, padding=1)),
(5, 25, nn.ReLU(inplace=True)),
(5, 26, nn.Conv2d(512, 512, 3, padding=1)),
(5, 27, nn.ReLU(inplace=True)),
(5, 28, nn.Conv2d(512, 512, 3, padding=1)),
(5, 29, nn.ReLU(inplace=True)),
]
for slice_idx, orig_idx, module in cfg:
getattr(self, f"slice{slice_idx}").add_module(str(orig_idx), module)
def forward(self, x):
h1 = self.slice1(x)
h2 = self.slice2(h1)
h3 = self.slice3(h2)
h4 = self.slice4(h3)
h5 = self.slice5(h4)
return [h1, h2, h3, h4, h5]
class _Fire(nn.Module):
def __init__(self, in_channels, squeeze_channels, expand1x1_channels, expand3x3_channels):
super().__init__()
self.squeeze = nn.Conv2d(in_channels, squeeze_channels, kernel_size=1)
self.squeeze_activation = nn.ReLU(inplace=True)
self.expand1x1 = nn.Conv2d(squeeze_channels, expand1x1_channels, kernel_size=1)
self.expand1x1_activation = nn.ReLU(inplace=True)
self.expand3x3 = nn.Conv2d(squeeze_channels, expand3x3_channels, kernel_size=3, padding=1)
self.expand3x3_activation = nn.ReLU(inplace=True)
def forward(self, x):
x = self.squeeze_activation(self.squeeze(x))
return torch.cat(
[
self.expand1x1_activation(self.expand1x1(x)),
self.expand3x3_activation(self.expand3x3(x)),
],
dim=1,
)
class _SqueezeNet11Features(nn.Module):
def __init__(self):
super().__init__()
self.slice1 = nn.Sequential()
self.slice2 = nn.Sequential()
self.slice3 = nn.Sequential()
self.slice4 = nn.Sequential()
self.slice5 = nn.Sequential()
self.slice6 = nn.Sequential()
self.slice7 = nn.Sequential()
self.slice1.add_module("0", nn.Conv2d(3, 64, kernel_size=3, stride=2))
self.slice1.add_module("1", nn.ReLU(inplace=True))
self.slice2.add_module("2", nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True))
self.slice2.add_module("3", _Fire(64, 16, 64, 64))
self.slice2.add_module("4", _Fire(128, 16, 64, 64))
self.slice3.add_module("5", nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True))
self.slice3.add_module("6", _Fire(128, 32, 128, 128))
self.slice3.add_module("7", _Fire(256, 32, 128, 128))
self.slice4.add_module("8", nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True))
self.slice4.add_module("9", _Fire(256, 48, 192, 192))
self.slice5.add_module("10", _Fire(384, 48, 192, 192))
self.slice6.add_module("11", _Fire(384, 64, 256, 256))
self.slice7.add_module("12", _Fire(512, 64, 256, 256))
def forward(self, x):
h1 = self.slice1(x)
h2 = self.slice2(h1)
h3 = self.slice3(h2)
h4 = self.slice4(h3)
h5 = self.slice5(h4)
h6 = self.slice6(h5)
h7 = self.slice7(h6)
return [h1, h2, h3, h4, h5, h6, h7]
_NET_CONFIG = {
"alex": {"factory": _AlexFeatures, "channels": (64, 192, 384, 256, 256)},
"vgg": {"factory": _VGG16Features, "channels": (64, 128, 256, 512, 512)},
"squeeze": {"factory": _SqueezeNet11Features, "channels": (64, 128, 256, 384, 384, 512, 512)},
}
class _ScalingLayer(nn.Module):
def __init__(self):
super().__init__()
self.register_buffer("shift", torch.tensor([-0.030, -0.088, -0.188]).view(1, 3, 1, 1))
self.register_buffer("scale", torch.tensor([0.458, 0.448, 0.450]).view(1, 3, 1, 1))
def forward(self, x):
return (x - self.shift) / self.scale
class _NetLinLayer(nn.Module):
def __init__(self, chn_in, use_dropout=True):
super().__init__()
layers = []
if use_dropout:
layers.append(nn.Dropout())
layers.append(nn.Conv2d(chn_in, 1, kernel_size=1, stride=1, padding=0, bias=False))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
def _normalize_tensor(x, eps=1e-10):
norm = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
return x / (norm + eps)
def _spatial_average(x):
return x.mean(dim=(2, 3), keepdim=True)
class LPIPSModel(nn.Module):
def __init__(self, net: str = "alex", use_dropout: bool = True):
super().__init__()
if net not in _NET_CONFIG:
raise ValueError(f"net must be one of {LPIPS_NET_CHOICES}, got {net!r}")
self.net_name = net
self.scaling_layer = _ScalingLayer()
self.net = _NET_CONFIG[net]["factory"]()
chns = _NET_CONFIG[net]["channels"]
for i, chn in enumerate(chns):
setattr(self, f"lin{i}", _NetLinLayer(chn, use_dropout=use_dropout))
self.num_layers = len(chns)
for p in self.parameters():
p.requires_grad = False
def forward(self, in0, in1):
in0 = self.scaling_layer(in0)
in1 = self.scaling_layer(in1)
feats0 = self.net(in0)
feats1 = self.net(in1)
val = 0
for i in range(self.num_layers):
diff = (_normalize_tensor(feats0[i]) - _normalize_tensor(feats1[i])) ** 2
lin = getattr(self, f"lin{i}")
val = val + _spatial_average(lin(diff))
return val.view(-1)
class LPIPSCompute(nn.Module):
def __init__(
self,
model: LPIPSModel,
device: Union[str, torch.device] = "cpu",
batch_size: int = 16,
target_size: int = 512,
):
super().__init__()
self.model = model
self.batch_size = batch_size
self.target_size = target_size
self._resize_transform = transforms.Compose(
[
transforms.Resize(target_size, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(target_size),
transforms.ToTensor(),
]
)
self._raw_transform = transforms.ToTensor()
self.to(device)
@property
def device(self):
try:
return next(self.model.parameters()).device
except StopIteration:
return torch.device("cpu")
def _to_tensor(self, image: Image.Image, do_resize: bool) -> torch.Tensor:
transform = self._resize_transform if do_resize else self._raw_transform
x = transform(image).clamp(0.0, 1.0) * 2.0 - 1.0
return x
@torch.no_grad()
def _compute_pair(self, img_a: Image.Image, img_b: Image.Image, do_resize: bool) -> float:
x0 = self._to_tensor(img_a, do_resize).unsqueeze(0).to(self.device)
x1 = self._to_tensor(img_b, do_resize).unsqueeze(0).to(self.device)
return float(self.model(x0, x1).item())
@torch.no_grad()
def _compute_pairs(self, pairs, do_resize: bool) -> float:
scores = []
batch_size = max(1, self.batch_size)
for start in range(0, len(pairs), batch_size):
chunk = pairs[start : start + batch_size]
xs0 = torch.stack([self._to_tensor(_open_rgb(a), do_resize) for a, _ in chunk]).to(self.device)
xs1 = torch.stack([self._to_tensor(_open_rgb(b), do_resize) for _, b in chunk]).to(self.device)
scores.append(self.model(xs0, xs1).detach().cpu())
merged = torch.cat(scores, dim=0)
return float(merged.mean().item())
@staticmethod
def _is_dir(value) -> bool:
return isinstance(value, (str, os.PathLike)) and os.path.isdir(os.fspath(value))
@staticmethod
def _is_image_input(value) -> bool:
if isinstance(value, Image.Image):
return True
if isinstance(value, (str, os.PathLike)):
return os.path.isfile(os.fspath(value))
return False
def compute(self, image_a, image_b) -> float:
a_is_dir = self._is_dir(image_a)
b_is_dir = self._is_dir(image_b)
if a_is_dir != b_is_dir:
raise ValueError("LPIPS.compute requires both inputs to be directories or both to be single images.")
if a_is_dir:
pairs = _pair_directories_by_stem(image_a, image_b)
sizes = set()
for path_a, path_b in pairs:
with Image.open(path_a) as ia, Image.open(path_b) as ib:
sizes.add(ia.size)
sizes.add(ib.size)
do_resize = len(sizes) > 1
return self._compute_pairs(pairs, do_resize=do_resize)
if not (self._is_image_input(image_a) and self._is_image_input(image_b)):
raise ValueError("LPIPS.compute inputs must be image paths, PIL images, or directories.")
img_a = _open_rgb(image_a)
img_b = _open_rgb(image_b)
do_resize = img_a.size != img_b.size
return self._compute_pair(img_a, img_b, do_resize=do_resize)
def forward(self, image_a, image_b):
return self.compute(image_a, image_b)
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