pip install pytorch-image-generation-metrics
from pytorch_image_generation_metrics import get_inception_score, get_fid
images = ... # [N, 3, H, W] normalized to [0, 1]
IS, IS_std = get_inception_score(images) # Inception Score
FID = get_fid(images, 'path/to/fid_ref.npz') # Frechet Inception Distance
The file path/to/fid_ref.npz
is compatiable with the official FID implementation.
The FID implementation is inspired by pytorch-fid.
This repository is developed for personal research. If you find this package useful, please feel free to open issues.
np.cov
and scipy.linalg.sqrtm
.Train IS | Test IS | Train(50k) vs Test(10k) FID |
|
---|---|---|---|
Official | 11.24±0.20 | 10.98±0.22 | 3.1508 |
ours | 11.26±0.13 | 10.97±0.19 | 3.1525 |
ours use_torch=True |
11.26±0.15 | 10.97±0.20 | 3.1457 |
The results differ slightly from the official implementations due to the framework differences between PyTorch and TensorFlow.
python -m pytorch_image_generation_metrics.fid_ref \
--path path/to/images \
--output path/to/fid_ref.npz
See fid_ref.py for details.
InceptionV3
model will be loaded into torch.device('cuda:0')
by default.device
argument in the get_*
functions to set the torch device.torch.Tensor
as imagesPrepare images as torch.float32
tensors with shape [N, 3, H, W]
, normalized to [0,1]
.
from pytorch_image_generation_metrics import (
get_inception_score,
get_fid,
get_inception_score_and_fid
)
images = ... # [N, 3, H, W]
assert 0 <= images.min() and images.max() <= 1
# Inception Score
IS, IS_std = get_inception_score(
images)
# Frechet Inception Distance
FID = get_fid(
images, 'path/to/fid_ref.npz')
# Inception Score & Frechet Inception Distance
(IS, IS_std), FID = get_inception_score_and_fid(
images, 'path/to/fid_ref.npz')
Use pytorch_image_generation_metrics.ImageDataset
to collect images from your storage or use your custom torch.utils.data.Dataset
.
from pytorch_image_generation_metrics import ImageDataset
from torch.utils.data import DataLoader
dataset = ImageDataset(path_to_dir, exts=['png', 'jpg'])
loader = DataLoader(dataset, batch_size=50, num_workers=4)
You can wrap a generative model in a dataset to support generating images on the fly.
class GeneratorDataset(Dataset):
def __init__(self, G, noise_dim):
self.G = G
self.noise_dim = noise_dim
def __len__(self):
return 50000
def __getitem__(self, index):
return self.G(torch.randn(1, self.noise_dim))
dataset = GeneratorDataset(G, noise_dim=128)
loader = DataLoader(dataset, batch_size=50, num_workers=0)
Calculate metrics
from pytorch_image_generation_metrics import (
get_inception_score,
get_fid,
get_inception_score_and_fid
)
# Inception Score
IS, IS_std = get_inception_score(
loader)
# Frechet Inception Distance
FID = get_fid(
loader, 'path/to/fid_ref.npz')
# Inception Score & Frechet Inception Distance
(IS, IS_std), FID = get_inception_score_and_fid(
loader, 'path/to/fid_ref.npz')
Calculate metrics for images in a directory and its subfolders.
from pytorch_image_generation_metrics import (
get_inception_score_from_directory,
get_fid_from_directory,
get_inception_score_and_fid_from_directory)
IS, IS_std = get_inception_score_from_directory(
'path/to/images')
FID = get_fid_from_directory(
'path/to/images', 'path/to/fid_ref.npz')
(IS, IS_std), FID = get_inception_score_and_fid_from_directory(
'path/to/images', 'path/to/fid_ref.npz')
Set use_torch=True
when calling functions like get_inception_score
, get_fid
, etc.
WARNING: when use_torch=True
is used, the FID might be nan
due to the unstable implementation of matrix sqrt root.
python 3.9 + torch 1.13.1 + CUDA 11.7
python 3.9 + torch 2.3.0 + CUDA 12.1
This implementation is licensed under the Apache License 2.0.
This implementation is derived from pytorch-fid, 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 of FID is by the Institute of Bioinformatics, JKU Linz, licensed under the Apache License 2.0. See https://github.com/bioinf-jku/TTUR.