https://github.com/tsc2017/Frechet-Inception-Distance

CPU/GPU/TPU implementation of the Fréchet Inception Distance
https://github.com/tsc2017/Frechet-Inception-Distance

deep-learning distance-measures frechet-distance gan generative-model

Last synced: 16 days ago
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CPU/GPU/TPU implementation of the Fréchet Inception Distance

• Host: GitHub
• URL: https://github.com/tsc2017/Frechet-Inception-Distance
• Owner: tsc2017
• Created: 2018-11-18T08:05:57.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-06-20T07:38:07.000Z (over 5 years ago)
• Last Synced: 2023-10-20T09:32:32.015Z (almost 2 years ago)
• Topics: deep-learning, distance-measures, frechet-distance, gan, generative-model
• Language: Python
• Homepage:
• Size: 54.7 KB
• Stars: 81
• Watchers: 3
• Forks: 14
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# Fréchet Inception Distance

Tensorflow implementation of the "Fréchet Inception Distance" (FID) between two image distributions, along with a numpy interface. The FID can be used to evaluate generative models by calculating the FID between real and fake data distributions (lower is better).

## Major Dependencies

- `tensorflow==1.14` or (`tensorflow==1.15` and `tensorflow-gan==1.0.0.dev0`) or (`tensorflow>=2` and `tensorflow-gan>=2.0.0`)

## Features

- Fast, easy-to-use and memory-efficient

- No prior knowledge about Tensorflow is necessary if your are using CPUs or GPUs

- Makes use of [TF-GAN](https://github.com/tensorflow/gan)

- Downloads InceptionV1 automatically

- Compatible with both Python 2 and Python 3

## Usage

- If you are working with GPUs, use `fid.py`; if you are working with TPUs, use `fid_tpu.py` and pass a Tensorflow Session and a [TPUStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/TPUStrategy) as additional arguments.

- Call `get_fid(images1, images2)`, where `images1`, `images2` are numpy arrays with values ranging from 0 to 255 and shape in the form `[N, 3, HEIGHT, WIDTH]` where `N`, `HEIGHT` and `WIDTH` can be arbitrary. `dtype` of the images is recommended to be `np.uint8` to save CPU memory.

- A smaller `BATCH_SIZE` reduces GPU/TPU memory usage, but at the cost of a slight slowdown.

- If you want to compute a general "Fréchet Classifier Distance" with activations (e.g., outputs of the last pooling layer) `act1` and `act2` from another classifier, call `activations2distance(act1, act2)`. `act1` and `act2` can be numpy arrays of a same arbitrary shape `[N, d]`.

## Examples

GPU: [![Example In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hgJJI5wuILxcHsmrkZMkHJtk6uDlKOwr?usp=sharing)

TPU and TF1: [![Example In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1F0fXOKlzIkOSEAdIRa9oyacW34SUX2_v?usp=sharing)

TPU and TF2: [![Example In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Cb8erVc-v6zCG-cLfOWCIjFZPl5zQ4jl?usp=sharing) 

## Links

- The Fréchet Inception Distance was proposed in the paper [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium ](https://arxiv.org/abs/1706.08500)

- Code for the [Inception Score](https://github.com/tsc2017/Inception-Score)