o .wi$@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z ddlmZdd lmZdd lmZmZdd lmZmZesMd gZd dgiZGddde ZdS))Sequence)AnyOptionalUnionN)Tensor)Module)NoTrainInceptionV3)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE_TORCH_FIDELITY_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEInceptionScore.plot)InceptionScorertorch_fidelityc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <d Z eed < ddeeee fdedededdf fdd ZdeddfddZdeeeffddZ d deeeeefdeedefddZZS)!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 marginal 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. .. hint:: 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 - ``inception_mean`` (:class:`~torch.Tensor`): float scalar tensor with mean inception score over subsets - ``inception_std`` (:class:`~torch.Tensor`): float scalar tensor with standard deviation of inception score over subsets 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: >>> from torch import rand >>> 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(1.0549), tensor(0.0121)) Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundfeatures inceptionfeature_networklogits_unbiased featuresplits normalizekwargsreturnNc stjd i|tdtt|ttfr7tstdd}||vr,t d|d|dt dt|gd|_ n t|t r@||_ nt d t|tsMt 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)name features_listz'Got unknown input to argument `feature`z*Argument `normalize` expected to be a boolr)dist_reduce_fx)super__init__r UserWarning isinstancestrintr ModuleNotFoundError ValueErrorrrr TypeErrorboolrr add_state)selfrrrrvalid_int_input __class__r'Y/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/image/inception.pyr)ms0  zInceptionScore.__init__imgscCs0|jr |dn|}||}|j|dS)z)Update the state with extracted features.N)rbyterrappend)r3r8rr'r'r7updates zInceptionScore.updatecCst|j}t|jd}||}|jdd}|jdd}|j|jdd}|j|jdd}dd|D}ddt |||D}dd|D}t |}| | fS)zCompute metric.rdimcSsg|] }|jdddqS)rT)r?keepdim)mean).0pr'r'r7 sz*InceptionScore.compute..cSs"g|] \}}}|||qSr')log)rBrClog_pm_pr'r'r7rDs"cSs g|] }|jddqS)r=r>)sumrAexp)rBkr'r'r7rDs ) r rtorchrandpermshapesoftmax log_softmaxchunkrzipstackrAstd)r3ridxproblog_prob mean_probkl_klr'r'r7computes    zInceptionScore.computevalaxcCs|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)rZ_plot)r3r[r\r'r'r7plots) r)rrF)NN)__name__ __module__ __qualname____doc__rr1__annotations__rrrfloatlistrrr,rr-rr)rr<tuplerZrrrrr^ __classcell__r'r'r5r7r#sB  @     (r)collections.abcrtypingrrrrKrtorch.nnrtorchmetrics.image.fidrtorchmetrics.metricr torchmetrics.utilitiesr torchmetrics.utilities.datar torchmetrics.utilities.importsr r torchmetrics.utilities.plotrr__doctest_skip____doctest_requires__rr'r'r'r7s