A simple wrapper around @mseitzer's great pytorch-fid work.
The goal is to compute the Fréchet Inception Distance between two sets of images in-memory using PyTorch.
pip install pytorch-fid-wrapper
Requires (and will install) (as pytorch-fid):
- Python >= 3.5
- Pillow
- Numpy
- Scipy
- Torch
- Torchvision
import pytorch_fid_wrapper as pfw
# ---------------------------
# ----- Initial Setup -----
# ---------------------------
# Optional: set pfw's configuration with your parameters once and for all
pfw.set_config(batch_size=BATCH_SIZE, dims=DIMS, device=DEVICE)
# Optional: compute real_m and real_s only once, they will not change during training
real_m, real_s = pfw.get_stats(real_images)
...
# -------------------------------------
# ----- Computing the FID Score -----
# -------------------------------------
val_fid = pfw.fid(fake_images, real_m=real_m, real_s=real_s) # (1)
# OR
val_fid = pfw.fid(fake_images, real_images=new_real_images) # (2)All _images variables in the example above are torch.Tensor instances with shape N x C x H x W. They will be sent to the appropriate device depending on what you ask for (see Config).
To compute the FID score between your fake images and some real dataset, you can either re-use pre-computed stats real_m, real_s at each validation stage (1), or provide another dataset for which the stats will be computed (in addition to your fake images' which are computed in both scenarios) (2). Score is computed in pfw.fid_score.calculate_frechet_distance(...), following pytorch-fid's implementation.
Please refer to pytorch-fid for any documentation on the InceptionV3 implementation or FID calculations.
pfw.get_stats(...) and pfw.fid(...) need to know what block of the InceptionV3 model to use (dims), on what device to compute inference (device) and with what batch size (batch_size).
Default values are in pfw.params: batch_size = 50, dims = 2048 and device = "cpu". If you want to override those, you have two options:
1/ override any of these parameters in the function calls. For instance:
pfw.fid(fake_images, new_real_data, device="cuda:0")2/ override the params globally with pfw.set_config and set them for all future calls without passing parameters again. For instance:
pfw.set_config(batch_size=100, dims=768, device="cuda:0")
...
pfw.fid(fake_images, new_real_data)Remember to cite their work if using pytorch-fid-wrapper or pytorch-fid:
@misc{Seitzer2020FID,
author={Maximilian Seitzer},
title={{pytorch-fid: FID Score for PyTorch}},
month={August},
year={2020},
note={Version 0.1.1},
howpublished={\url{https://github.com/mseitzer/pytorch-fid}},
}
This implementation is licensed under the Apache License 2.0.
FID was introduced by Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler and Sepp Hochreiter in "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium", see https://arxiv.org/abs/1706.08500
The original implementation is by the Institute of Bioinformatics, JKU Linz, licensed under the Apache License 2.0. See https://github.com/bioinf-jku/TTUR.