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# 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
#
# 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 collections.abc import Sequence
from typing import Any, Optional, 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.
.. 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))
"""
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)
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