a  dw@sddlmZmZmZmZmZmZddlZddlmZddl m Z ddl m Z m Z mZmZddlmZddlmZmZddlmZmZdd lmZmZesd gZerdd d d Zereesdd gZndd gZGdddeZdS))AnyClassVarListOptionalSequenceUnionN)Tensor)Literal)_LPIPS_lpips_compute _lpips_update _NoTrainLpips)Metric)_SKIP_SLOW_DOCTEST_try_proceed_with_timeout)_MATPLOTLIB_AVAILABLE_TORCHVISION_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE*LearnedPerceptualImagePatchSimilarity.plotreturncCstddddS)NTvgg)Z pretrainednet)r rr`/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/image/lpip.py_download_lpipssr%LearnedPerceptualImagePatchSimilaritycseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <e ed <d gZ eeeed<d ededeeddfdd Ze e ddddZe dddZd!eee ee feeedddZZS)"ra The Learned Perceptual Image Patch Similarity (`LPIPS_`) calculates 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 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). .. 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. As input to ``forward`` and ``update`` the metric accepts the following input - ``img1`` (:class:`~torch.Tensor`): tensor with images of shape ``(N, 3, H, W)`` - ``img2`` (:class:`~torch.Tensor`): tensor with images of shape ``(N, 3, H, W)`` As output of `forward` and `compute` the metric returns the following output - ``lpips`` (:class:`~torch.Tensor`): returns float scalar tensor with average LPIPS value over samples 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'`. 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. 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='squeeze') >>> # LPIPS needs the images to be in the [-1, 1] range. >>> img1 = (torch.rand(10, 3, 100, 100) * 2) - 1 >>> img2 = (torch.rand(10, 3, 100, 100) * 2) - 1 >>> lpips(img1, img2) tensor(0.1046, grad_fn=) Tis_differentiableFhigher_is_betterfull_state_updateplot_lower_boundg?plot_upper_bound sum_scorestotalr__jit_ignored_attributes__alexmeanrr'Zsqueeze)sumr(N)net_type reduction normalizekwargsrc stjfi|tstdd}||vr@td|d|dt|d|_d}||vrltd|d|||_t|t std |||_ |j d t d d d |j dt d d d dS)NzLPIPS metric requires that torchvision is installed. Either install as `pip install torchmetrics[image]` or `pip install torchvision`.r)z#Argument `net_type` must be one of z , but got .)r)r(r*z$Argument `reduction` must be one of z/Argument `normalize` should be an bool but got r$r!r*)Zdist_reduce_fxr%)super__init__rModuleNotFoundError ValueErrorr rr, isinstanceboolr-Z add_statetorchZtensor)selfr+r,r-r.Zvalid_net_typeZvalid_reduction __class__rrr1is$  z.LearnedPerceptualImagePatchSimilarity.__init__)img1img2rcCs<t|||j|jd\}}|j|7_|j|7_dS)z(Update internal states with lpips score.)rr-N)r rr-r$r*r%)r7r:r;Zlossr%rrrupdatesz,LearnedPerceptualImagePatchSimilarity.updatercCst|j|j|jS)z+Compute final perceptual similarity metric.)r r$r%r,)r7rrrcomputesz-LearnedPerceptualImagePatchSimilarity.compute)valaxrcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity >>> metric = LearnedPerceptualImagePatchSimilarity(net_type='squeeze') >>> metric.update(torch.rand(10, 3, 100, 100), torch.rand(10, 3, 100, 100)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity >>> metric = LearnedPerceptualImagePatchSimilarity(net_type='squeeze') >>> values = [ ] >>> for _ in range(3): ... values.append(metric(torch.rand(10, 3, 100, 100), torch.rand(10, 3, 100, 100))) >>> fig_, ax_ = metric.plot(values) )Z_plot)r7r>r?rrrplots(r)r'r(F)NN)__name__ __module__ __qualname____doc__rr5__annotations__rr r"floatr#rr&rrstrr rr1r<r=rrrrrr@ __classcell__rrr8rr(s4 4      )typingrrrrrrr6rZtyping_extensionsr Z#torchmetrics.functional.image.lpipsr r r r Ztorchmetrics.metricrZtorchmetrics.utilities.checksrrZtorchmetrics.utilities.importsrrZtorchmetrics.utilities.plotrrZ__doctest_skip__rrrrrrs