a  d]#@sddlmZmZmZmZmZmZddlZddlmZddl m Z ddl m Z ddl mZddlmZddlmZdd lmZmZdd lmZmZesd gZd d giZGdddeZdS))AnyListOptionalSequenceTupleUnionN)Tensor)Module)NoTrainInceptionV3)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE_TORCH_FIDELITY_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEInceptionScore.plot)InceptionScorerZtorch_fidelitycseZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <de eee feeed dfdd Zed dddZeeefdddZdee eeefeeedddZZS)ra\ Calculate the Inception Score (IS) which is used to access how realistic generated images are. .. math:: IS = exp(\mathbb{E}_x KL(p(y | x ) || p(y))) where :math:`KL(p(y | x) || p(y))` is the KL divergence between the conditional distribution :math:`p(y|x)` and the margianl distribution :math:`p(y)`. Both the conditional and marginal distribution is calculated from features extracted from the images. The score is calculated on random splits of the images such that both a mean and standard deviation of the score are returned. The metric was originally proposed in `inception ref1`_. Using the default feature extraction (Inception v3 using the original weights from `inception ref2`_), the input is expected to be mini-batches of 3-channel RGB images of shape ``(3xHxW)``. If argument ``normalize`` is ``True`` images are expected to be dtype ``float`` and have values in the ``[0,1]`` range, else if ``normalize`` is set to ``False`` images are expected to have dtype uint8 and take values in the ``[0, 255]`` range. All images will be resized to 299 x 299 which is the size of the original training data. .. note:: using this metric with the default feature extractor requires that ``torch-fidelity`` is installed. Either install as ``pip install torchmetrics[image]`` or ``pip install torch-fidelity`` As input to ``forward`` and ``update`` the metric accepts the following input - ``imgs`` (:class:`~torch.Tensor`): tensor with images feed to the feature extractor As output of `forward` and `compute` the metric returns the following output - ``fid`` (:class:`~torch.Tensor`): float scalar tensor with mean FID value over samples Args: feature: Either an str, integer or ``nn.Module``: - an str or integer will indicate the inceptionv3 feature layer to choose. Can be one of the following: 'logits_unbiased', 64, 192, 768, 2048 - an ``nn.Module`` for using a custom feature extractor. Expects that its forward method returns an ``(N,d)`` matrix where ``N`` is the batch size and ``d`` is the feature size. splits: integer determining how many splits the inception score calculation should be split among kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``feature`` is set to an ``str`` or ``int`` and ``torch-fidelity`` is not installed ValueError: If ``feature`` is set to an ``str`` or ``int`` and not one of ``('logits_unbiased', 64, 192, 768, 2048)`` TypeError: If ``feature`` is not an ``str``, ``int`` or ``torch.nn.Module`` Example: >>> import torch >>> _ = torch.manual_seed(123) >>> from torchmetrics.image.inception import InceptionScore >>> inception = InceptionScore() >>> # generate some images >>> imgs = torch.randint(0, 255, (100, 3, 299, 299), dtype=torch.uint8) >>> inception.update(imgs) >>> inception.compute() (tensor(1.0544), tensor(0.0117)) Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundfeatures inceptionlogits_unbiased N)featuresplits normalizekwargsreturnc stjfi|tdtt|ttfrnts6tdd}||vrXt d|d|dt dt|gd|_ nt|t r||_ nt d t|tst d ||_||_|jd gdd dS) NzMetric `InceptionScore` will save all extracted features in buffer. For large datasets this may lead to large memory footprint.zInceptionScore metric requires that `Torch-fidelity` is installed. Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`.)r@iiz3Integer input to argument `feature` must be one of z , but got .zinception-v3-compat)nameZ features_listz'Got unknown input to argument `feature`z*Argument `normalize` expected to be a boolr)Zdist_reduce_fx)super__init__r UserWarning isinstancestrintrModuleNotFoundError ValueErrorr rr TypeErrorboolrrZ add_state)selfrrrrZvalid_int_input __class__e/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/image/inception.pyr&hs0  zInceptionScore.__init__)imgsr cCs0|jr|dn|}||}|j|dS)z)Update the state with extracted features.N)rbyterrappend)r/r4rr2r2r3updates zInceptionScore.update)r cCst|j}t|jd}||}|jdd}|jdd}|j|jdd}|j|jdd}dd|D}ddt |||D}dd|D}t |}| | fS)zCompute metric.rdimcSsg|]}|jdddqS)rT)r;Zkeepdim)mean).0pr2r2r3 z*InceptionScore.compute..cSs"g|]\}}}|||qSr2)log)r=r>Zlog_pZm_pr2r2r3r?r@cSs g|]}|jddqS)r9r:)sumr<exp)r=kr2r2r3r?r@) r rtorchZrandpermshapeZsoftmaxZ log_softmaxchunkrzipstackr<Zstd)r/ridxZprobZlog_probZ mean_probZkl_klr2r2r3computes    zInceptionScore.compute)valaxr cCs|p|d}|||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.inception import InceptionScore >>> metric = InceptionScore() >>> metric.update(torch.randint(0, 255, (50, 3, 299, 299), dtype=torch.uint8)) >>> fig_, ax_ = metric.plot() # the returned plot only shows the mean value by default .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image.inception import InceptionScore >>> metric = InceptionScore() >>> values = [ ] >>> for _ in range(3): ... # we index by 0 such that only the mean value is plotted ... values.append(metric(torch.randint(0, 255, (50, 3, 299, 299), dtype=torch.uint8))[0]) >>> fig_, ax_ = metric.plot(values) r)rLZ_plot)r/rMrNr2r2r3plots)r)rrF)NN)__name__ __module__ __qualname____doc__rr.__annotations__rrrfloatrr rr)r*rr&rr8rrLrrrrrO __classcell__r2r2r0r3r"s0 =     (r)typingrrrrrrrErZtorch.nnr Ztorchmetrics.image.fidr Ztorchmetrics.metricr Ztorchmetrics.utilitiesr Ztorchmetrics.utilities.datar Ztorchmetrics.utilities.importsrrZtorchmetrics.utilities.plotrrZ__doctest_skip__Z__doctest_requires__rr2r2r2r3s