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lpip.py
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lpip.py
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# Copyright The PyTorch 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.
from typing import Any, Dict, List, Optional
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _LPIPS_AVAILABLE
if _LPIPS_AVAILABLE:
from lpips import LPIPS as _LPIPS
else:
class _LPIPS(torch.nn.Module): # type: ignore
pass
__doctest_skip__ = ["LearnedPerceptualImagePatchSimilarity", "LPIPS"]
class NoTrainLpips(_LPIPS):
def train(self, mode: bool) -> "NoTrainLpips":
"""the network should not be able to be switched away from evaluation mode."""
return super().train(False)
def _valid_img(img: Tensor) -> bool:
"""check that input is a valid image to the network."""
return img.ndim == 4 and img.shape[1] == 3 and img.min() >= -1.0 and img.max() <= 1.0
class LearnedPerceptualImagePatchSimilarity(Metric):
"""The Learned Perceptual Image Patch Similarity (`LPIPS_`) is used to judge the 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 perseption 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]` and be normalized to the [-1,1]
range. The minimum size of `H, W` depends on the chosen backbone (see `net_type` arg).
.. note:: using this metrics requires you to have ``lpips`` package installed. Either install
as ``pip install torchmetrics[image]`` or ``pip install lpips``
.. note:: this metric is not scriptable when using ``torch<1.8``. Please update your pytorch installation
if this is a issue.
Args:
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'`.
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False.
.. deprecated:: v0.8
Argument has no use anymore and will be removed v0.9.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ModuleNotFoundError:
If ``lpips`` package is not installed
ValueError:
If ``net_type`` is not one of ``"vgg"``, ``"alex"`` or ``"squeeze"``
ValueError:
If ``reduction`` is not one of ``"mean"`` or ``"sum"``
Example:
>>> import torch
>>> _ = torch.manual_seed(123)
>>> from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
>>> lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg')
>>> img1 = torch.rand(10, 3, 100, 100)
>>> img2 = torch.rand(10, 3, 100, 100)
>>> lpips(img1, img2)
tensor(0.3566, grad_fn=<SqueezeBackward0>)
"""
is_differentiable = True
higher_is_better = False
real_features: List[Tensor]
fake_features: List[Tensor]
# due to the use of named tuple in the backbone the net variable cannot be scripted
__jit_ignored_attributes__ = ["net"]
def __init__(
self,
net_type: str = "alex",
reduction: Literal["sum", "mean"] = "mean",
compute_on_step: Optional[bool] = None,
**kwargs: Dict[str, Any],
) -> None:
super().__init__(compute_on_step=compute_on_step, **kwargs)
if not _LPIPS_AVAILABLE:
raise ModuleNotFoundError(
"LPIPS metric requires that lpips is installed."
" Either install as `pip install torchmetrics[image]` or `pip install lpips`."
)
valid_net_type = ("vgg", "alex", "squeeze")
if net_type not in valid_net_type:
raise ValueError(f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}.")
self.net = NoTrainLpips(net=net_type, verbose=False)
valid_reduction = ("mean", "sum")
if reduction not in valid_reduction:
raise ValueError(f"Argument `reduction` must be one of {valid_reduction}, but got {reduction}")
self.reduction = reduction
self.add_state("sum_scores", torch.tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", torch.tensor(0.0), dist_reduce_fx="sum")
def update(self, img1: Tensor, img2: Tensor) -> None: # type: ignore
"""Update internal states with lpips score.
Args:
img1: tensor with images of shape ``[N, 3, H, W]``
img2: tensor with images of shape ``[N, 3, H, W]``
"""
if not (_valid_img(img1) and _valid_img(img2)):
raise ValueError(
"Expected both input arguments to be normalized tensors (all values in range [-1,1])"
f" and to have shape [N, 3, H, W] but `img1` have shape {img1.shape} with values in"
f" range {[img1.min(), img1.max()]} and `img2` have shape {img2.shape} with value"
f" in range {[img2.min(), img2.max()]}"
)
loss = self.net(img1, img2).squeeze()
self.sum_scores += loss.sum()
self.total += img1.shape[0]
def compute(self) -> Tensor:
"""Compute final perceptual similarity metric."""
if self.reduction == "mean":
return self.sum_scores / self.total
if self.reduction == "sum":
return self.sum_scores