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Frechet Inception Distance (FID)

Module Interface

class torchmetrics.image.fid.FrechetInceptionDistance(feature=2048, reset_real_features=True, normalize=False, **kwargs)[source]

Calculate Fréchet inception distance (FID) which is used to access the quality of generated images. Given by.

FID = |\mu - \mu_w| + tr(\Sigma + \Sigma_w - 2(\Sigma \Sigma_w)^{\frac{1}{2}})

where \mathcal{N}(\mu, \Sigma) is the multivariate normal distribution estimated from Inception v3 (fid ref1) features calculated on real life images and \mathcal{N}(\mu_w, \Sigma_w) is the multivariate normal distribution estimated from Inception v3 features calculated on generated (fake) images. The metric was originally proposed in fid ref1.

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.

Note

using this metrics requires you to have scipy install. Either install as pip install torchmetrics[image] or pip install scipy

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 (Tensor): tensor with images feed to the feature extractor with

  • real (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

  • fid (Tensor): float scalar tensor with mean FID value over samples

Parameters
  • feature (Union[int, Module]) –

    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 (bool) – 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.

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises
  • ValueError – If feature is set to an int (default settings) 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

>>> import torch
>>> _ = torch.manual_seed(123)
>>> from torchmetrics.image.fid import FrechetInceptionDistance
>>> fid = FrechetInceptionDistance(feature=64)
>>> # generate two slightly overlapping image intensity distributions
>>> 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)
>>> fid.update(imgs_dist1, real=True)
>>> fid.update(imgs_dist2, real=False)
>>> fid.compute()
tensor(12.7202)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

reset()[source]

This method automatically resets the metric state variables to their default value.

Return type

None

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