inception.py

# Copyright The 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# 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, Optional, Sequence, 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 _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE

if not _MATPLOTLIB_AVAILABLE:
    __doctest_skip__ = ["InceptionScore.plot"]


__doctest_requires__ = {("InceptionScore", "InceptionScore.plot"): ["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 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.

    .. 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

    - ``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:
        >>> 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
    plot_lower_bound: float = 0.0

    features: List
    inception: Module
    feature_network: str = "inception"

    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}, 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:
        """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]:
        """Compute metric."""
        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()

    def plot(
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
    ) -> _PLOT_OUT_TYPE:
        """Plot 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)

        """
        val = val or self.compute()[0]  # by default we select the mean to plot
        return self._plot(val, ax)