# 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, List, Tuple, Union import torch from torch import Tensor from torch.nn import Module from torchmetrics.image.fid import NoTrainInceptionV3 from torchmetrics.metric import Metric from torchmetrics.utilities import rank_zero_warn from torchmetrics.utilities.data import dim_zero_cat from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE __doctest_requires__ = {("InceptionScore", "IS"): ["torch_fidelity"]} class InceptionScore(Metric): r"""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 ``(3 x H x W)``. 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)) """ is_differentiable: bool = False higher_is_better: bool = True full_state_update: bool = False features: List def __init__( self, feature: Union[str, int, Module] = "logits_unbiased", splits: int = 10, normalize: bool = False, **kwargs: Any, ) -> None: super().__init__(**kwargs) rank_zero_warn( "Metric `InceptionScore` will save all extracted features in buffer." " For large datasets this may lead to large memory footprint.", UserWarning, ) if isinstance(feature, (str, int)): if not _TORCH_FIDELITY_AVAILABLE: raise ModuleNotFoundError( "InceptionScore metric requires that `Torch-fidelity` is installed." " Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`." ) valid_int_input = ("logits_unbiased", 64, 192, 768, 2048) if feature not in valid_int_input: raise ValueError( f"Integer input to argument `feature` must be one of {valid_int_input}," f" but got {feature}." ) self.inception = NoTrainInceptionV3(name="inception-v3-compat", features_list=[str(feature)]) elif isinstance(feature, Module): self.inception = feature else: raise TypeError("Got unknown input to argument `feature`") if not isinstance(normalize, bool): raise ValueError("Argument `normalize` expected to be a bool") self.normalize = normalize self.splits = splits self.add_state("features", [], dist_reduce_fx=None) def update(self, imgs: Tensor) -> None: # type: ignore """Update the state with extracted features.""" imgs = (imgs * 255).byte() if self.normalize else imgs features = self.inception(imgs) self.features.append(features) def compute(self) -> Tuple[Tensor, Tensor]: features = dim_zero_cat(self.features) # random permute the features idx = torch.randperm(features.shape[0]) features = features[idx] # calculate probs and logits prob = features.softmax(dim=1) log_prob = features.log_softmax(dim=1) # split into groups prob = prob.chunk(self.splits, dim=0) log_prob = log_prob.chunk(self.splits, dim=0) # calculate score per split mean_prob = [p.mean(dim=0, keepdim=True) for p in prob] kl_ = [p * (log_p - m_p.log()) for p, log_p, m_p in zip(prob, log_prob, mean_prob)] kl_ = [k.sum(dim=1).mean().exp() for k in kl_] kl = torch.stack(kl_) # return mean and std return kl.mean(), kl.std()