File size: 16,632 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 |
# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Content copied from
# https://github.com/richzhang/PerceptualSimilarity/blob/master/lpips/lpips.py
# and
# https://github.com/richzhang/PerceptualSimilarity/blob/master/lpips/pretrained_networks.py
# and with adjustments from
# https://github.com/richzhang/PerceptualSimilarity/pull/114/files
# due to package no longer being maintained
# Copyright (c) 2018, Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
# All rights reserved.
# License under BSD 2-clause
import inspect
import os
from typing import List, NamedTuple, Optional, Union
import torch
from torch import Tensor, nn
from typing_extensions import Literal
from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE
_weight_map = {
"squeezenet1_1": "SqueezeNet1_1_Weights",
"alexnet": "AlexNet_Weights",
"vgg16": "VGG16_Weights",
}
if not _TORCHVISION_AVAILABLE:
__doctest_skip__ = ["learned_perceptual_image_patch_similarity"]
def _get_net(net: str, pretrained: bool) -> nn.modules.container.Sequential:
"""Get torchvision network.
Args:
net: Name of network
pretrained: If pretrained weights should be used
"""
from torchvision import models as tv
if _TORCHVISION_AVAILABLE:
if pretrained:
pretrained_features = getattr(tv, net)(weights=getattr(tv, _weight_map[net]).IMAGENET1K_V1).features
else:
pretrained_features = getattr(tv, net)(weights=None).features
return pretrained_features
class SqueezeNet(torch.nn.Module):
"""SqueezeNet implementation."""
def __init__(self, requires_grad: bool = False, pretrained: bool = True) -> None:
super().__init__()
pretrained_features = _get_net("squeezenet1_1", pretrained)
self.N_slices = 7
slices = []
feature_ranges = [range(2), range(2, 5), range(5, 8), range(8, 10), range(10, 11), range(11, 12), range(12, 13)]
for feature_range in feature_ranges:
seq = torch.nn.Sequential()
for i in feature_range:
seq.add_module(str(i), pretrained_features[i])
slices.append(seq)
self.slices = nn.ModuleList(slices)
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, x: Tensor) -> NamedTuple:
"""Process input."""
class _SqueezeOutput(NamedTuple):
relu1: Tensor
relu2: Tensor
relu3: Tensor
relu4: Tensor
relu5: Tensor
relu6: Tensor
relu7: Tensor
relus = []
for slice_ in self.slices:
x = slice_(x)
relus.append(x)
return _SqueezeOutput(*relus)
class Alexnet(torch.nn.Module):
"""Alexnet implementation."""
def __init__(self, requires_grad: bool = False, pretrained: bool = True) -> None:
super().__init__()
alexnet_pretrained_features = _get_net("alexnet", pretrained)
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(2):
self.slice1.add_module(str(x), alexnet_pretrained_features[x])
for x in range(2, 5):
self.slice2.add_module(str(x), alexnet_pretrained_features[x])
for x in range(5, 8):
self.slice3.add_module(str(x), alexnet_pretrained_features[x])
for x in range(8, 10):
self.slice4.add_module(str(x), alexnet_pretrained_features[x])
for x in range(10, 12):
self.slice5.add_module(str(x), alexnet_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, x: Tensor) -> NamedTuple:
"""Process input."""
h = self.slice1(x)
h_relu1 = h
h = self.slice2(h)
h_relu2 = h
h = self.slice3(h)
h_relu3 = h
h = self.slice4(h)
h_relu4 = h
h = self.slice5(h)
h_relu5 = h
class _AlexnetOutputs(NamedTuple):
relu1: Tensor
relu2: Tensor
relu3: Tensor
relu4: Tensor
relu5: Tensor
return _AlexnetOutputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
class Vgg16(torch.nn.Module):
"""Vgg16 implementation."""
def __init__(self, requires_grad: bool = False, pretrained: bool = True) -> None:
super().__init__()
vgg_pretrained_features = _get_net("vgg16", pretrained)
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: Tensor) -> NamedTuple:
"""Process input."""
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
class _VGGOutputs(NamedTuple):
relu1_2: Tensor
relu2_2: Tensor
relu3_3: Tensor
relu4_3: Tensor
relu5_3: Tensor
return _VGGOutputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
def _spatial_average(in_tens: Tensor, keep_dim: bool = True) -> Tensor:
"""Spatial averaging over height and width of images."""
return in_tens.mean([2, 3], keepdim=keep_dim)
def _upsample(in_tens: Tensor, out_hw: tuple[int, ...] = (64, 64)) -> Tensor:
"""Upsample input with bilinear interpolation."""
return nn.Upsample(size=out_hw, mode="bilinear", align_corners=False)(in_tens)
def _normalize_tensor(in_feat: Tensor, eps: float = 1e-8) -> Tensor:
"""Normalize input tensor."""
norm_factor = torch.sqrt(eps + torch.sum(in_feat**2, dim=1, keepdim=True))
return in_feat / norm_factor
def _resize_tensor(x: Tensor, size: int = 64) -> Tensor:
"""https://github.com/toshas/torch-fidelity/blob/master/torch_fidelity/sample_similarity_lpips.py#L127C22-L132."""
if x.shape[-1] > size and x.shape[-2] > size:
return torch.nn.functional.interpolate(x, (size, size), mode="area")
return torch.nn.functional.interpolate(x, (size, size), mode="bilinear", align_corners=False)
class ScalingLayer(nn.Module):
"""Scaling layer."""
shift: Tensor
scale: Tensor
def __init__(self) -> None:
super().__init__()
self.register_buffer("shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None], persistent=False)
self.register_buffer("scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None], persistent=False)
def forward(self, inp: Tensor) -> Tensor:
"""Process input."""
return (inp - self.shift) / self.scale
class NetLinLayer(nn.Module):
"""A single linear layer which does a 1x1 conv."""
def __init__(self, chn_in: int, chn_out: int = 1, use_dropout: bool = False) -> None:
super().__init__()
layers = [nn.Dropout()] if use_dropout else []
layers += [
nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), # type: ignore[list-item]
]
self.model = nn.Sequential(*layers)
def forward(self, x: Tensor) -> Tensor:
"""Process input."""
return self.model(x)
class _LPIPS(nn.Module):
def __init__(
self,
pretrained: bool = True,
net: Literal["alex", "vgg", "squeeze"] = "alex",
spatial: bool = False,
pnet_rand: bool = False,
pnet_tune: bool = False,
use_dropout: bool = True,
model_path: Optional[str] = None,
eval_mode: bool = True,
resize: Optional[int] = None,
) -> None:
"""Initializes a perceptual loss torch.nn.Module.
Args:
pretrained: This flag controls the linear layers should be pretrained version or random
net: Indicate backbone to use, choose between ['alex','vgg','squeeze']
spatial: If input should be spatial averaged
pnet_rand: If backbone should be random or use imagenet pre-trained weights
pnet_tune: If backprop should be enabled for both backbone and linear layers
use_dropout: If dropout layers should be added
model_path: Model path to load pretained models from
eval_mode: If network should be in evaluation mode
resize: If input should be resized to this size
"""
super().__init__()
self.pnet_type = net
self.pnet_tune = pnet_tune
self.pnet_rand = pnet_rand
self.spatial = spatial
self.resize = resize
self.scaling_layer = ScalingLayer()
if self.pnet_type in ["vgg", "vgg16"]:
net_type = Vgg16
self.chns = [64, 128, 256, 512, 512]
elif self.pnet_type == "alex":
net_type = Alexnet # type: ignore[assignment]
self.chns = [64, 192, 384, 256, 256]
elif self.pnet_type == "squeeze":
net_type = SqueezeNet # type: ignore[assignment]
self.chns = [64, 128, 256, 384, 384, 512, 512]
self.L = len(self.chns)
self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)
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.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
if self.pnet_type == "squeeze": # 7 layers for squeezenet
self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)
self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)
self.lins += [self.lin5, self.lin6]
self.lins = nn.ModuleList(self.lins) # type: ignore[assignment]
if pretrained:
if model_path is None:
model_path = os.path.abspath(
os.path.join(inspect.getfile(self.__init__), "..", f"lpips_models/{net}.pth") # type: ignore[misc]
)
self.load_state_dict(torch.load(model_path, map_location="cpu"), strict=False)
if eval_mode:
self.eval()
if not self.pnet_tune:
for param in self.parameters():
param.requires_grad = False
def forward(
self, in0: Tensor, in1: Tensor, retperlayer: bool = False, normalize: bool = False
) -> Union[Tensor, tuple[Tensor, List[Tensor]]]:
if normalize: # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1]
in0 = 2 * in0 - 1
in1 = 2 * in1 - 1
# normalize input
in0_input, in1_input = self.scaling_layer(in0), self.scaling_layer(in1)
# resize input if needed
if self.resize is not None:
in0_input = _resize_tensor(in0_input, size=self.resize)
in1_input = _resize_tensor(in1_input, size=self.resize)
outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input)
feats0, feats1, diffs = {}, {}, {}
for kk in range(self.L):
feats0[kk], feats1[kk] = _normalize_tensor(outs0[kk]), _normalize_tensor(outs1[kk])
diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
res = []
for kk in range(self.L):
if self.spatial:
res.append(_upsample(self.lins[kk](diffs[kk]), out_hw=tuple(in0.shape[2:])))
else:
res.append(_spatial_average(self.lins[kk](diffs[kk]), keep_dim=True))
val: Tensor = sum(res) # type: ignore[assignment]
if retperlayer:
return (val, res)
return val
class _NoTrainLpips(_LPIPS):
"""Wrapper to make sure LPIPS never leaves evaluation mode."""
def train(self, mode: bool) -> "_NoTrainLpips": # type: ignore[override]
"""Force network to always be in evaluation mode."""
return super().train(False)
def _valid_img(img: Tensor, normalize: bool) -> bool:
"""Check that input is a valid image to the network."""
value_check = img.max() <= 1.0 and img.min() >= 0.0 if normalize else img.min() >= -1
return img.ndim == 4 and img.shape[1] == 3 and value_check # type: ignore[return-value]
def _lpips_update(img1: Tensor, img2: Tensor, net: nn.Module, normalize: bool) -> tuple[Tensor, Union[int, Tensor]]:
if not (_valid_img(img1, normalize) and _valid_img(img2, normalize)):
raise ValueError(
"Expected both input arguments to be normalized tensors with shape [N, 3, H, W]."
f" Got input with shape {img1.shape} and {img2.shape} and values in range"
f" {[img1.min(), img1.max()]} and {[img2.min(), img2.max()]} when all values are"
f" expected to be in the {[0, 1] if normalize else [-1, 1]} range."
)
loss = net(img1, img2, normalize=normalize).squeeze()
return loss, img1.shape[0]
def _lpips_compute(sum_scores: Tensor, total: Union[Tensor, int], reduction: Literal["sum", "mean"] = "mean") -> Tensor:
return sum_scores / total if reduction == "mean" else sum_scores
def learned_perceptual_image_patch_similarity(
img1: Tensor,
img2: Tensor,
net_type: Literal["alex", "vgg", "squeeze"] = "alex",
reduction: Literal["sum", "mean"] = "mean",
normalize: bool = False,
) -> Tensor:
"""The Learned Perceptual Image Patch Similarity (`LPIPS_`) calculates perceptual similarity between two images.
LPIPS essentially computes the similarity between the activations of two image patches for some pre-defined network.
This measure has been shown to match human perception well. A low LPIPS score means that image patches are
perceptual similar.
Both input image patches are expected to have shape ``(N, 3, H, W)``. The minimum size of `H, W` depends on the
chosen backbone (see `net_type` arg).
Args:
img1: first set of images
img2: second set of images
net_type: str indicating backbone network type to use. Choose between `'alex'`, `'vgg'` or `'squeeze'`
reduction: str indicating how to reduce over the batch dimension. Choose between `'sum'` or `'mean'`.
normalize: by default this is ``False`` meaning that the input is expected to be in the [-1,1] range. If set
to ``True`` will instead expect input to be in the ``[0,1]`` range.
Example:
>>> from torch import rand
>>> from torchmetrics.functional.image.lpips import learned_perceptual_image_patch_similarity
>>> img1 = (rand(10, 3, 100, 100) * 2) - 1
>>> img2 = (rand(10, 3, 100, 100) * 2) - 1
>>> learned_perceptual_image_patch_similarity(img1, img2, net_type='squeeze')
tensor(0.1005)
"""
net = _NoTrainLpips(net=net_type).to(device=img1.device, dtype=img1.dtype)
loss, total = _lpips_update(img1, img2, net, normalize)
return _lpips_compute(loss.sum(), total, reduction)
|