High-fidelity performance metrics for generative models in PyTorch

Overview

High-fidelity performance metrics for generative models in PyTorch

Documentation Status TestStatus PyPiVersion PyPiDownloads Twitter Follow

This repository provides precise, efficient, and extensible implementations of the popular metrics for generative model evaluation, including:

  • Inception Score (ISC)
  • Fréchet Inception Distance (FID)
  • Kernel Inception Distance (KID)
  • Perceptual Path Length (PPL)

Precision: Unlike many other reimplementations, the values produced by torch-fidelity match reference implementations up to machine precision. This allows using torch-fidelity for reporting metrics in papers instead of scattered and slow reference implementations. Read more about precision

Efficiency: Feature sharing between different metrics saves recomputation time, and an additional caching level avoids recomputing features and statistics whenever possible. High efficiency allows using torch-fidelity in the training loop, for example at the end of every epoch. Read more about efficiency

Extensibility: Going beyond 2D image generation is easy due to high modularity and abstraction of the metrics from input data, models, and feature extractors. For example, one can swap out InceptionV3 feature extractor for a one accepting 3D scan volumes, such as used in MRI. Read more about extensibility

TLDR; fast and reliable GAN evaluation in PyTorch

Installation

pip install torch-fidelity

See also: Installing the latest GitHub code

Usage Examples with Command Line

Below are three examples of using torch-fidelity to evaluate metrics from the command line. See more examples in the documentation.

Simple

Inception Score of CIFAR-10 training split:

> fidelity --gpu 0 --isc --input1 cifar10-train

inception_score_mean: 11.23678
inception_score_std: 0.09514061

Medium

Inception Score of a directory of images stored in ~/images/:

> fidelity --gpu 0 --isc --input1 ~/images/

Pro

Efficient computation of ISC and PPL for input1, and FID and KID between a generative model stored in ~/generator.onnx and CIFAR-10 training split:

> fidelity \
  --gpu 0 \
  --isc \
  --fid \
  --kid \
  --ppl \
  --input1 ~/generator.onnx \ 
  --input1-model-z-type normal \
  --input1-model-z-size 128 \
  --input1-model-num-samples 50000 \ 
  --input2 cifar10-train 

See also: Other usage examples

Quick Start with Python API

When it comes to tracking the performance of generative models as they train, evaluating metrics after every epoch becomes prohibitively expensive due to long computation times. torch_fidelity tackles this problem by making full use of caching to avoid recomputing common features and per-metric statistics whenever possible. Computing all metrics for 50000 32x32 generated images and cifar10-train takes only 2 min 26 seconds on NVIDIA P100 GPU, compared to >10 min if using original codebases. Thus, computing metrics 20 times over the whole training cycle makes overall training time just one hour longer.

In the following example, assume unconditional image generation setting with CIFAR-10, and the generative model generator, which takes a 128-dimensional standard normal noise vector.

First, import the module:

import torch_fidelity

Add the following lines at the end of epoch evaluation:

wrapped_generator = torch_fidelity.GenerativeModelModuleWrapper(generator, 128, 'normal', 0)

metrics_dict = torch_fidelity.calculate_metrics(
    input1=wrapped_generator, 
    input2='cifar10-train', 
    cuda=True, 
    isc=True, 
    fid=True, 
    kid=True, 
    verbose=False,
)

The resulting dictionary with computed metrics can logged directly to tensorboard, wandb, or console:

print(metrics_dict)

Output:

{
    'inception_score_mean': 11.23678, 
    'inception_score_std': 0.09514061, 
    'frechet_inception_distance': 18.12198,
    'kernel_inception_distance_mean': 0.01369556, 
    'kernel_inception_distance_std': 0.001310059
}

See also: Full API reference

Example of Integration with the Training Loop

Refer to sngan_cifar10.py for a complete training example.

Evolution of fixed generator latents in the example:

Evolution of fixed generator latents

A generator checkpoint resulting from training the example can be downloaded here.

Citation

Citation is recommended to reinforce the evaluation protocol in works relying on torch-fidelity. To ensure reproducibility when citing this repository, use the following BibTeX:

@misc{obukhov2020torchfidelity,
  author={Anton Obukhov and Maximilian Seitzer and Po-Wei Wu and Semen Zhydenko and Jonathan Kyl and Elvis Yu-Jing Lin},
  year=2020,
  title={High-fidelity performance metrics for generative models in PyTorch},
  url={https://github.com/toshas/torch-fidelity},
  publisher={Zenodo},
  version={v0.3.0},
  doi={10.5281/zenodo.4957738},
  note={Version: 0.3.0, DOI: 10.5281/zenodo.4957738}
}
Owner
Vikram Voleti
PhD student at Mila, University of Montreal
Vikram Voleti
Distiller is an open-source Python package for neural network compression research.

Wiki and tutorials | Documentation | Getting Started | Algorithms | Design | FAQ Distiller is an open-source Python package for neural network compres

Intel Labs 4.1k Dec 28, 2022
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

README TabNet : Attentive Interpretable Tabular Learning This is a pyTorch implementation of Tabnet (Arik, S. O., & Pfister, T. (2019). TabNet: Attent

DreamQuark 2k Dec 27, 2022
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

PyTorch Implementation of Differentiable ODE Solvers This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpr

Ricky Chen 4.4k Jan 04, 2023
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

higher is a library providing support for higher-order optimization, e.g. through unrolled first-order optimization loops, of "meta" aspects of these

Facebook Research 1.5k Jan 03, 2023
pip install antialiased-cnns to improve stability and accuracy

Antialiased CNNs [Project Page] [Paper] [Talk] Making Convolutional Networks Shift-Invariant Again Richard Zhang. In ICML, 2019. Quick & easy start Ru

Adobe, Inc. 1.6k Dec 28, 2022
Pytorch bindings for Fortran

Pytorch bindings for Fortran

Dmitry Alexeev 46 Dec 29, 2022
PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

glow-pytorch PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

Kim Seonghyeon 433 Dec 27, 2022
OptNet: Differentiable Optimization as a Layer in Neural Networks

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

CMU Locus Lab 428 Dec 24, 2022
Code snippets created for the PyTorch discussion board

PyTorch misc Collection of code snippets I've written for the PyTorch discussion board. All scripts were testes using the PyTorch 1.0 preview and torc

461 Dec 26, 2022
A tiny package to compare two neural networks in PyTorch

Compare neural networks by their feature similarity

Anand Krishnamoorthy 180 Dec 30, 2022
Implements pytorch code for the Accelerated SGD algorithm.

AccSGD This is the code associated with Accelerated SGD algorithm used in the paper On the insufficiency of existing momentum schemes for Stochastic O

205 Jan 02, 2023
High-fidelity performance metrics for generative models in PyTorch

High-fidelity performance metrics for generative models in PyTorch

Vikram Voleti 5 Oct 24, 2021
270 Dec 24, 2022
PyTorch framework A simple and complete framework for PyTorch, providing a variety of data loading and simple task solutions that are easy to extend and migrate

PyTorch framework A simple and complete framework for PyTorch, providing a variety of data loading and simple task solutions that are easy to extend and migrate

Cong Cai 12 Dec 19, 2021
Use Jax functions in Pytorch with DLPack

Use Jax functions in Pytorch with DLPack

Phil Wang 106 Dec 17, 2022
Learning Sparse Neural Networks through L0 regularization

Example implementation of the L0 regularization method described at Learning Sparse Neural Networks through L0 regularization, Christos Louizos, Max W

AMLAB 202 Nov 10, 2022
Fast Discounted Cumulative Sums in PyTorch

TODO: update this README! Fast Discounted Cumulative Sums in PyTorch This repository implements an efficient parallel algorithm for the computation of

Daniel Povey 7 Feb 17, 2022
torch-optimizer -- collection of optimizers for Pytorch

torch-optimizer torch-optimizer -- collection of optimizers for PyTorch compatible with optim module. Simple example import torch_optimizer as optim

Nikolay Novik 2.6k Jan 03, 2023
PyTorch wrappers for using your model in audacity!

PyTorch wrappers for using your model in audacity!

130 Dec 14, 2022