a  d!@sddlmZmZmZmZmZddlmZmZddl m Z ddl m Z m Z mZmZddlmZddlmZesrdgZGdddeZd S) )AnyLiteralOptionalTupleUnion)Tensornn)_LPIPS) GeneratorType*_perceptual_path_length_validate_arguments_validate_generator_modelperceptual_path_length)Metric)_TORCHVISION_AVAILABLEPerceptualPathLengthc seZdZUdZdZeed<dZeeed<dZ eed<de ee e de ee ee ee e eje dfedd fdd ZeddddZeeeefdddZZS)raComputes the perceptual path length (`PPL`_) of a generator model. The perceptual path length can be used to measure the consistency of interpolation in latent-space models. It is defined as .. math:: PPL = \mathbb{E}\left[\frac{1}{\epsilon^2} D(G(I(z_1, z_2, t)), G(I(z_1, z_2, t+\epsilon)))\right] where :math:`G` is the generator, :math:`I` is the interpolation function, :math:`D` is a similarity metric, :math:`z_1` and :math:`z_2` are two sets of latent points, and :math:`t` is a parameter between 0 and 1. The metric thus works by interpolating between two sets of latent points, and measuring the similarity between the generated images. The expectation is approximated by sampling :math:`z_1` and :math:`z_2` from the generator, and averaging the calculated distanced. The similarity metric :math:`D` is by default the `LPIPS`_ metric, but can be changed by setting the `sim_net` argument. The provided generator model must have a `sample` method with signature `sample(num_samples: int) -> Tensor` where the returned tensor has shape `(num_samples, z_size)`. If the generator is conditional, it must also have a `num_classes` attribute. The `forward` method of the generator must have signature `forward(z: Tensor) -> Tensor` if `conditional=False`, and `forward(z: Tensor, labels: Tensor) -> Tensor` if `conditional=True`. The returned tensor should have shape `(num_samples, C, H, W)` and be scaled to the range [0, 255]. .. note:: using this metric with the default feature extractor requires that ``torchvision`` is installed. Either install as ``pip install torchmetrics[image]`` or ``pip install torchvision`` As input to ``forward`` and ``update`` the metric accepts the following input - ``generator`` (:class:`~torch.nn.Module`): Generator model, with specific requirements. See above. As output of `forward` and `compute` the metric returns the following output - ``ppl_mean`` (:class:`~torch.Tensor`): float scalar tensor with mean PPL value over distances - ``ppl_std`` (:class:`~torch.Tensor`): float scalar tensor with std PPL value over distances - ``ppl_raw`` (:class:`~torch.Tensor`): float scalar tensor with raw PPL distances Args: num_samples: Number of samples to use for the PPL computation. conditional: Whether the generator is conditional or not (i.e. whether it takes labels as input). batch_size: Batch size to use for the PPL computation. interpolation_method: Interpolation method to use. Choose from 'lerp', 'slerp_any', 'slerp_unit'. epsilon: Spacing between the points on the path between latent points. resize: Resize images to this size before computing the similarity between generated images. lower_discard: Lower quantile to discard from the distances, before computing the mean and standard deviation. upper_discard: Upper quantile to discard from the distances, before computing the mean and standard deviation. sim_net: Similarity network to use. Can be a `nn.Module` or one of 'alex', 'vgg', 'squeeze', where the three latter options correspond to the pretrained networks from the `LPIPS`_ paper. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ModuleNotFoundError: If ``torch-fidelity`` is not installed. ValueError: If ``num_samples`` is not a positive integer. ValueError: If `conditional` is not a boolean. ValueError: If ``batch_size`` is not a positive integer. ValueError: If ``interpolation_method`` is not one of 'lerp', 'slerp_any', 'slerp_unit'. ValueError: If ``epsilon`` is not a positive float. ValueError: If ``resize`` is not a positive integer. ValueError: If ``lower_discard`` is not a float between 0 and 1 or None. ValueError: If ``upper_discard`` is not a float between 0 and 1 or None. Example:: >>> from torchmetrics.image import PerceptualPathLength >>> import torch >>> _ = torch.manual_seed(42) >>> class DummyGenerator(torch.nn.Module): ... def __init__(self, z_size) -> None: ... super().__init__() ... self.z_size = z_size ... self.model = torch.nn.Sequential(torch.nn.Linear(z_size, 3*128*128), torch.nn.Sigmoid()) ... def forward(self, z): ... return 255 * (self.model(z).reshape(-1, 3, 128, 128) + 1) ... def sample(self, num_samples): ... return torch.randn(num_samples, self.z_size) >>> generator = DummyGenerator(2) >>> ppl = PerceptualPathLength(num_samples=10) >>> ppl(generator) # doctest: +SKIP (tensor(0.2371), tensor(0.1763), tensor([0.3502, 0.1362, 0.2535, 0.0902, 0.1784, 0.0769, 0.5871, 0.0691, 0.3921])) Fis_differentiableThigher_is_betterfull_state_update'lerp-C6?@{Gz?Gz?vgg)rZ slerp_anyZ slerp_unitZalexrZsqueezeN) num_samples conditional batch_sizeinterpolation_methodepsilonresize lower_discard upper_discardsim_netkwargsreturnc stjfi| tstdt||||||||||_||_||_||_||_ ||_ ||_ ||_ t | tjrx| |_n(| dvrtd| |d|_ntd| dS)NzMetric `PerceptualPathLength` requires torchvision which is not installed.Install with `pip install torchvision` or `pip install torchmetrics[image]`rT)Z pretrainednetr"zDsim_net must be a nn.Module or one of 'alex', 'vgg', 'squeeze', got )super__init__rModuleNotFoundErrorr rrrr r!r"r#r$ isinstancerModuler(r ValueError) selfrrrr r!r"r#r$r%r& __class__r/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/image/perceptual_path_length.pyr*}s*  zPerceptualPathLength.__init__) generatorr'cCst||j||_dS)zUpdate the generator model.N)r rr4)r/r4r2r2r3updates zPerceptualPathLength.update)r'c Cs0t|j|j|j|j|j|j|j|j|j |j d S)z#Compute the perceptual path length.) r4rrr r!r"r#r$r%device) r r4rrr r!r"r#r$r(r6)r/r2r2r3computeszPerceptualPathLength.compute) rFrrrrrrr)__name__ __module__ __qualname____doc__rbool__annotations__rrrintrfloatrrr-rr*r r5rrr7 __classcell__r2r2r0r3r s8 X  &N)typingrrrrrZtorchrrZ#torchmetrics.functional.image.lpipsr Z4torchmetrics.functional.image.perceptual_path_lengthr r r r Ztorchmetrics.metricrZtorchmetrics.utilities.importsrZ__doctest_skip__rr2r2r2r3s