# 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, List, Optional, Union import torch from torch import Tensor from torch.nn import Module from torchmetrics.image.fid import NoTrainInceptionV3, _compute_fid from torchmetrics.metric import Metric 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 __doctest_requires__ = { ("MemorizationInformedFrechetInceptionDistance", "MemorizationInformedFrechetInceptionDistance.plot"): [ "torch_fidelity" ] } if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["MemorizationInformedFrechetInceptionDistance.plot"] def _compute_cosine_distance(features1: Tensor, features2: Tensor, cosine_distance_eps: float = 0.1) -> Tensor: """Compute the cosine distance between two sets of features.""" features1_nozero = features1[torch.sum(features1, dim=1) != 0] features2_nozero = features2[torch.sum(features2, dim=1) != 0] # normalize norm_f1 = features1_nozero / torch.norm(features1_nozero, dim=1, keepdim=True) norm_f2 = features2_nozero / torch.norm(features2_nozero, dim=1, keepdim=True) d = 1.0 - torch.abs(torch.matmul(norm_f1, norm_f2.t())) mean_min_d = torch.mean(d.min(dim=1).values) return mean_min_d if mean_min_d < cosine_distance_eps else torch.ones_like(mean_min_d) def _mifid_compute( mu1: Tensor, sigma1: Tensor, features1: Tensor, mu2: Tensor, sigma2: Tensor, features2: Tensor, cosine_distance_eps: float = 0.1, ) -> Tensor: """Compute MIFID score given two sets of features and their statistics.""" fid_value = _compute_fid(mu1, sigma1, mu2, sigma2) distance = _compute_cosine_distance(features1, features2, cosine_distance_eps) # secure that very small fid values does not explode the mifid return fid_value / (distance + 10e-15) if fid_value > 1e-8 else torch.zeros_like(fid_value) class MemorizationInformedFrechetInceptionDistance(Metric): r"""Calculate Memorization-Informed Frechet Inception Distance (MIFID_). MIFID is a improved variation of the Frechet Inception Distance (FID_) that penalizes memorization of the training set by the generator. It is calculated as .. math:: MIFID = \frac{FID(F_{real}, F_{fake})}{M(F_{real}, F_{fake})} where :math:`FID` is the normal FID score and :math:`M` is the memorization penalty. The memorization penalty essentially corresponds to the average minimum cosine distance between the features of the real and fake distribution. Using the default feature extraction (Inception v3 using the original weights from `fid 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. The boolian flag ``real`` determines if the images should update the statistics of the real distribution or the fake distribution. .. hint:: Using this metrics requires you to have ``scipy`` install. Either install as ``pip install torchmetrics[image]`` or ``pip install scipy`` .. 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 with - ``real`` (:class:`~bool`): bool indicating if ``imgs`` belong to the real or the fake distribution As output of `forward` and `compute` the metric returns the following output - ``mifid`` (:class:`~torch.Tensor`): float scalar tensor with mean MIFID value over samples Args: feature: Either an integer or ``nn.Module``: - an integer will indicate the inceptionv3 feature layer to choose. Can be one of the following: 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. reset_real_features: Whether to also reset the real features. Since in many cases the real dataset does not change, the features can be cached them to avoid recomputing them which is costly. Set this to ``False`` if your dataset does not change. normalize: Whether to normalize the input images. If ``True`` the input is expected to be in the range [0, 1] and converted to ``uint8``. If ``False`` the input is expected to already be in the range [0, 255] and of type ``uint8``. If a custom feature extractor is used, this argument is ignored. cosine_distance_eps: Epsilon value for the cosine distance. If the cosine distance is larger than this value it is set to 1 and thus ignored in the MIFID calculation. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: RuntimeError: If ``torch`` is version less than 1.10 ValueError: If ``feature`` is set to an ``int`` and ``torch-fidelity`` is not installed ValueError: If ``feature`` is set to an ``int`` not in [64, 192, 768, 2048] TypeError: If ``feature`` is not an ``str``, ``int`` or ``torch.nn.Module`` ValueError: If ``reset_real_features`` is not an ``bool`` Example:: >>> from torch import randint >>> from torchmetrics.image.mifid import MemorizationInformedFrechetInceptionDistance >>> mifid = MemorizationInformedFrechetInceptionDistance(feature=64) >>> # generate two slightly overlapping image intensity distributions >>> imgs_dist1 = randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8) >>> imgs_dist2 = randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8) >>> mifid.update(imgs_dist1, real=True) >>> mifid.update(imgs_dist2, real=False) >>> mifid.compute() tensor(3003.3691) """ higher_is_better: bool = False is_differentiable: bool = False full_state_update: bool = False real_features: List[Tensor] fake_features: List[Tensor] inception: Module feature_network: str = "inception" def __init__( self, feature: Union[int, Module] = 2048, reset_real_features: bool = True, normalize: bool = False, cosine_distance_eps: float = 0.1, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.used_custom_model = False if isinstance(feature, int): if not _TORCH_FIDELITY_AVAILABLE: raise ModuleNotFoundError( "MemorizationInformedFrechetInceptionDistance metric requires that `Torch-fidelity` is installed." " Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`." ) valid_int_input = [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 self.used_custom_model = True else: raise TypeError("Got unknown input to argument `feature`") if not isinstance(reset_real_features, bool): raise ValueError("Argument `reset_real_features` expected to be a bool") self.reset_real_features = reset_real_features if not isinstance(normalize, bool): raise ValueError("Argument `normalize` expected to be a bool") self.normalize = normalize if not (isinstance(cosine_distance_eps, float) and 1 >= cosine_distance_eps > 0): raise ValueError("Argument `cosine_distance_eps` expected to be a float greater than 0 and less than 1") self.cosine_distance_eps = cosine_distance_eps # states for extracted features self.add_state("real_features", [], dist_reduce_fx=None) self.add_state("fake_features", [], dist_reduce_fx=None) def update(self, imgs: Tensor, real: bool) -> None: """Update the state with extracted features.""" imgs = (imgs * 255).byte() if self.normalize and not self.used_custom_model else imgs features = self.inception(imgs) self.orig_dtype = features.dtype features = features.double() if real: self.real_features.append(features) else: self.fake_features.append(features) def compute(self) -> Tensor: """Calculate FID score based on accumulated extracted features from the two distributions.""" real_features = dim_zero_cat(self.real_features) fake_features = dim_zero_cat(self.fake_features) mean_real, mean_fake = torch.mean(real_features, dim=0), torch.mean(fake_features, dim=0) cov_real, cov_fake = torch.cov(real_features.t()), torch.cov(fake_features.t()) return _mifid_compute( mean_real, cov_real, real_features, mean_fake, cov_fake, fake_features, cosine_distance_eps=self.cosine_distance_eps, ).to(self.orig_dtype) def reset(self) -> None: """Reset metric states.""" if not self.reset_real_features: # remove temporarily to avoid resetting value = self._defaults.pop("real_features") super().reset() self._defaults["real_features"] = value else: super().reset() 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.mifid import MemorizationInformedFrechetInceptionDistance >>> imgs_dist1 = torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8) >>> imgs_dist2 = torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8) >>> metric = MemorizationInformedFrechetInceptionDistance(feature=64) >>> metric.update(imgs_dist1, real=True) >>> metric.update(imgs_dist2, real=False) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image.mifid import MemorizationInformedFrechetInceptionDistance >>> imgs_dist1 = lambda: torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8) >>> imgs_dist2 = lambda: torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8) >>> metric = MemorizationInformedFrechetInceptionDistance(feature=64) >>> values = [ ] >>> for _ in range(3): ... metric.update(imgs_dist1(), real=True) ... metric.update(imgs_dist2(), real=False) ... values.append(metric.compute()) ... metric.reset() >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)