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inception.py
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inception.py
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# 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, Callable, List, Optional, Tuple, Union
import torch
from torch import Tensor
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
class IS(Metric):
r"""
Calculates the Inception Score (IS) which is used to access how realistic generated images are.
It is defined as
.. 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 [1].
Using the default feature extraction (Inception v3 using the original weights from [2]), the input is
expected to be mini-batches of 3-channel RGB images of shape (3 x H x W) with dtype uint8. 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``
.. note:: the ``forward`` method can be used but ``compute_on_step`` is disabled by default (oppesit of
all other metrics) as this metric does not really make sense to calculate on a single batch. This
means that by default ``forward`` will just call ``update`` underneat.
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
compute_on_step:
Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step
process_group:
Specify the process group on which synchronization is called.
default: ``None`` (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the allgather
References:
[1] Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
https://arxiv.org/abs/1606.03498
[2] GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium,
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter
https://arxiv.org/abs/1706.08500
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 import IS
>>> inception = IS() # doctest: +SKIP
>>> # generate some images
>>> imgs = torch.randint(0, 255, (100, 3, 299, 299), dtype=torch.uint8) # doctest: +SKIP
>>> inception.update(imgs) # doctest: +SKIP
>>> inception.compute() # doctest: +SKIP
(tensor(1.0569), tensor(0.0113))
"""
features: List
def __init__(
self,
feature: Union[str, int, torch.nn.Module] = "logits_unbiased",
splits: int = 10,
compute_on_step: bool = False,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable[[Tensor], List[Tensor]] = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
rank_zero_warn(
"Metric `IS` 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 ValueError(
"IS 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, torch.nn.Module):
self.inception = feature
else:
raise TypeError("Got unknown input to argument `feature`")
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.
Args:
imgs: tensor with images feed to the feature extractor
"""
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()