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https://github.com/vict0rsch/pytorch-fid-wrapper
A simple wrapper around @mseitzer's great pytorch-fid work to compute Fréchet Inception Distance in-memory from batches of images, using PyTorch
https://github.com/vict0rsch/pytorch-fid-wrapper
fid generative-adversarial-network python pytorch
Last synced: 3 months ago
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A simple wrapper around @mseitzer's great pytorch-fid work to compute Fréchet Inception Distance in-memory from batches of images, using PyTorch
- Host: GitHub
- URL: https://github.com/vict0rsch/pytorch-fid-wrapper
- Owner: vict0rsch
- License: apache-2.0
- Created: 2020-10-31T19:12:11.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2020-11-08T17:08:28.000Z (about 4 years ago)
- Last Synced: 2024-10-08T12:10:25.639Z (4 months ago)
- Topics: fid, generative-adversarial-network, python, pytorch
- Language: Python
- Homepage:
- Size: 31.3 KB
- Stars: 16
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pytorch-fid-wrapper
A simple wrapper around [@mseitzer](https://github.com/mseitzer)'s great [**pytorch-fid**](https://github.com/mseitzer/pytorch-fid) work.
The goal is to compute the Fréchet Inception Distance between two sets of images *in-memory* using PyTorch.
## Installation
[](https://pypi.org/project/pytorch-fid/)
```
pip install pytorch-fid-wrapper
```
Requires (and will install) (as `pytorch-fid`):
* Python >= 3.5
* Pillow
* Numpy
* Scipy
* Torch
* Torchvision
## Usage
```python
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](#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`](https://github.com/mseitzer/pytorch-fid)'s implementation.
Please refer to [**pytorch-fid**](https://github.com/mseitzer/pytorch-fid) for any documentation on the InceptionV3 implementation or FID calculations.
## Config
`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:
```python
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:
```python
pfw.set_config(batch_size=100, dims=768, device="cuda:0")
...
pfw.fid(fake_images, new_real_data)
```
## Recognition
Remember to cite their work if using [`pytorch-fid-wrapper`](https://github.com/vict0rsch/pytorch-fid-wrapper) or [`pytorch-fid`](https://github.com/mseitzer/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}},
}
```
## License
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](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](https://github.com/bioinf-jku/TTUR).