Source code for ignite.metrics.gan.fid

import warnings
from typing import Callable, Optional, Sequence, Union

import torch
from packaging.version import Version

from ignite.metrics.gan.utils import _BaseInceptionMetric, InceptionModel
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce

__all__ = [

if Version(torch.__version__) <= Version("1.7.0"):
    torch_outer = torch.ger
    torch_outer = torch.outer

def fid_score(
    mu1: torch.Tensor, mu2: torch.Tensor, sigma1: torch.Tensor, sigma2: torch.Tensor, eps: float = 1e-6
) -> float:
        import numpy as np
    except ImportError:
        raise ModuleNotFoundError("fid_score requires numpy to be installed.")

        import scipy.linalg
    except ImportError:
        raise ModuleNotFoundError("fid_score requires scipy to be installed.")

    mu1, mu2 = mu1.cpu(), mu2.cpu()
    sigma1, sigma2 = sigma1.cpu(), sigma2.cpu()

    diff = mu1 - mu2

    # Product might be almost singular
    covmean, _ = scipy.linalg.sqrtm(, disp=False)
    # Numerical error might give slight imaginary component
    if np.iscomplexobj(covmean):
        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
            m = np.max(np.abs(covmean.imag))
            raise ValueError("Imaginary component {}".format(m))
        covmean = covmean.real

    tr_covmean = np.trace(covmean)

    if not np.isfinite(covmean).all():
        tr_covmean = np.sum(np.sqrt(((np.diag(sigma1) * eps) * (np.diag(sigma2) * eps)) / (eps * eps)))

    return float( + torch.trace(sigma1) + torch.trace(sigma2) - 2 * tr_covmean)

[docs]class FID(_BaseInceptionMetric): r"""Calculates Frechet Inception Distance. .. math:: \text{FID} = |\mu_{1} - \mu_{2}| + \text{Tr}(\sigma_{1} + \sigma_{2} - {2}\sqrt{\sigma_1*\sigma_2}) where :math:`\mu_1` and :math:`\sigma_1` refer to the mean and covariance of the train data and :math:`\mu_2` and :math:`\sigma_2` refer to the mean and covariance of the test data. More details can be found in `Heusel et al. 2017`__ __ In addition, a faster and online computation approach can be found in `Mathiasen et al. 2020`__ __ Remark: This implementation is inspired by `pytorch_fid` package which can be found `here`__ __ .. note:: The default Inception model requires the `torchvision` module to be installed. FID also requires `scipy` library for matrix square root calculations. Args: num_features: number of features predicted by the model or the reduced feature vector of the image. Default value is 1000. feature_extractor: a torch Module for extracting the features from the input data. It returns a tensor of shape (batch_size, num_features). If neither ``num_features`` nor ``feature_extractor`` are defined, by default we use an ImageNet pretrained Inception Model and use model's output logits as features. If only ``num_features`` is defined but ``feature_extractor`` is not defined, ``feature_extractor`` is assigned Identity Function. Please note that the model will be implicitly converted to device mentioned in the ``device`` argument. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = FID(num_features=1, feature_extractor=default_model) metric.attach(default_evaluator, "fid") y_true = torch.ones(10, 4) y_pred = torch.ones(10, 4) state =[[y_pred, y_true]]) print(state.metrics["fid"]) .. testoutput:: 0.0 .. note:: The default `torchvision` model used is InceptionV3 pretrained on ImageNet. This can lead to differences in results with `pytorch_fid`. To find comparable results, the following model wrapper should be used: .. code:: import torch.nn as nn # wrapper class as feature_extractor class WrapperInceptionV3(nn.Module): def __init__(self, fid_incv3): super().__init__() self.fid_incv3 = fid_incv3 @torch.no_grad() def forward(self, x): y = self.fid_incv3(x) y = y[0] y = y[:, :, 0, 0] return y # use cpu rather than cuda to get comparable results device = "cpu" # pytorch_fid model dims = 2048 block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) # wrapper model to pytorch_fid model wrapper_model = WrapperInceptionV3(model) wrapper_model.eval(); # comparable metric pytorch_fid_metric = FID(num_features=dims, feature_extractor=wrapper_model) Important, `pytorch_fid` results depend on the batch size if the device is `cuda`. .. versionadded:: 0.4.6 """ _state_dict_all_req_keys = ("_num_examples", "_train_total", "_test_total", "_train_sigma", "_test_sigma") def __init__( self, num_features: Optional[int] = None, feature_extractor: Optional[torch.nn.Module] = None, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: try: import numpy as np # noqa: F401 except ImportError: raise ModuleNotFoundError("This module requires numpy to be installed.") try: import scipy # noqa: F401 except ImportError: raise ModuleNotFoundError("This module requires scipy to be installed.") if num_features is None and feature_extractor is None: num_features = 1000 feature_extractor = InceptionModel(return_features=False, device=device) self._eps = 1e-6 super(FID, self).__init__( num_features=num_features, feature_extractor=feature_extractor, output_transform=output_transform, device=device, ) @staticmethod def _online_update(features: torch.Tensor, total: torch.Tensor, sigma: torch.Tensor) -> None: total += features sigma += torch_outer(features, features) def _get_covariance(self, sigma: torch.Tensor, total: torch.Tensor) -> torch.Tensor: r""" Calculates covariance from mean and sum of products of variables """ sub_matrix = torch_outer(total, total) sub_matrix = sub_matrix / self._num_examples return (sigma - sub_matrix) / (self._num_examples - 1)
[docs] @reinit__is_reduced def reset(self) -> None: self._train_sigma = torch.zeros( (self._num_features, self._num_features), dtype=torch.float64, device=self._device ) self._train_total = torch.zeros(self._num_features, dtype=torch.float64, device=self._device) self._test_sigma = torch.zeros( (self._num_features, self._num_features), dtype=torch.float64, device=self._device ) self._test_total = torch.zeros(self._num_features, dtype=torch.float64, device=self._device) self._num_examples: int = 0 super(FID, self).reset()
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: train, test = output train_features = self._extract_features(train) test_features = self._extract_features(test) if train_features.shape[0] != test_features.shape[0] or train_features.shape[1] != test_features.shape[1]: raise ValueError( f""" Number of Training Features and Testing Features should be equal ({train_features.shape} != {test_features.shape}) """ ) # Updates the mean and covariance for the train features for features in train_features: self._online_update(features, self._train_total, self._train_sigma) # Updates the mean and covariance for the test features for features in test_features: self._online_update(features, self._test_total, self._test_sigma) self._num_examples += train_features.shape[0]
[docs] @sync_all_reduce("_num_examples", "_train_total", "_test_total", "_train_sigma", "_test_sigma") def compute(self) -> float: fid = fid_score( mu1=self._train_total / self._num_examples, mu2=self._test_total / self._num_examples, sigma1=self._get_covariance(self._train_sigma, self._train_total), sigma2=self._get_covariance(self._test_sigma, self._test_total), eps=self._eps, ) if torch.isnan(torch.tensor(fid)) or torch.isinf(torch.tensor(fid)): warnings.warn("The product of covariance of train and test features is out of bounds.") return fid

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