Reference PyTorch implementation of "End-to-end optimized image compression with competition of prior distributions"

Related tags

Deep Learningneural-networksimage-compressioncdf
Overview

PyTorch reference implementation of "End-to-end optimized image compression with competition of prior distributions" by Benoit Brummer and Christophe De Vleeschouwer ( https://github.com/trougnouf/Manypriors )

Forked from PyTorch implementation of "Variational image compression with a scale hyperprior" by Jiaheng Liu ( https://github.com/liujiaheng/compression )

This code is experimental.

Requirements

TODO torchac should be switched to the standalone release on https://github.com/fab-jul/torchac (which was not yet released at the time of writing this code)

Arch

pacaur -S python-tqdm python-pytorch-torchac python-configargparse python-yaml python-ptflops python-colorspacious python-pypng python-pytorch-piqa-git

Ubuntu / Slurm cluster / misc:

TMPDIR=tmp pip3 install --user torch==1.7.0+cu92 torchvision==0.8.1+cu92 -f https://download.pytorch.org/whl/torch_stable.html
TMPDIR=tmp pip3 install --user tqdm matplotlib tensorboardX scipy scikit-image scikit-video ConfigArgParse pyyaml h5py ptflops colorspacious pypng piqa

torchac must be compiled and installed per https://github.com/trougnouf/L3C-PyTorch/tree/master/src/torchac

torchac $ COMPILE_CUDA=auto python3 setup.py build
torchac $ python3 setup.py install --optimize=1 --skip-build

or (untested)

torchac $ pip install .

Once Ubuntu updates PyTorch then tensorboardX won't be required

Dataset gathering

Copy the kodak dataset into datasets/test/kodak

cd ../common
python tools/wikidownloader.py --category "Category:Featured pictures on Wikimedia Commons"
python tools/wikidownloader.py --category "Category:Formerly featured pictures on Wikimedia Commons"
python tools/wikidownloader.py --category "Category:Photographs taken on Ektachrome and Elite Chrome film"
mv "../../datasets/Category:Featured pictures on Wikimedia Commons" ../../datasets/FeaturedPictures
mv "../../datasets/Category:Formerly featured pictures on Wikimedia Commons" ../../datasets/Formerly_featured_pictures_on_Wikimedia_Commons
mv "../../datasets/Category:Photographs taken on Ektachrome and Elite Chrome film" ../../datasets/Photographs_taken_on_Ektachrome_and_Elite_Chrome_film
python tools/verify_images.py ../../datasets/FeaturedPictures/
python tools/verify_images.py ../../datasets/Formerly_featured_pictures_on_Wikimedia_Commons/
python tools/verify_images.py ../../datasets/Photographs_taken_on_Ektachrome_and_Elite_Chrome_film/

# TODO make a list of train/test img automatically s.t. images don't have to be copied over the network

Crop images to 1024*1024. from src/common: (in python)

import os
from libs import libdsops
for ads in ['Formerly_featured_pictures_on_Wikimedia_Commons', 'Photographs_taken_on_Ektachrome_and_Elite_Chrome_film', 'FeaturedPictures']:
    libdsops.split_traintest(ads)
    libdsops.crop_ds_dpath(ads, 1024, root_ds_dpath=os.path.join(libdsops.ROOT_DS_DPATH, 'train'), num_threads=os.cpu_count()//2)

#verify crops
python3 tools/verify_images.py ../../datasets/train/resized/1024/FeaturedPictures/
python3 tools/verify_images.py ../../datasets/train/resized/1024/Formerly_featured_pictures_on_Wikimedia_Commons/
python3 tools/verify_images.py ../../datasets/train/resized/1024/Photographs_taken_on_Ektachrome_and_Elite_Chrome_film/
# use the --save_img flag at the end of verify_images.py commands if training fails after the simple verification

Move a small subset of the training cropped images to a matching test directory and use it as args.val_dpath

JPEG/BPG compression of the Commons Test Images is done with common/tools/bpg_jpeg_compress_commons.py and comp/tools/bpg_jpeg_test_commons.py

Loading

Loading a model: provide all necessary (non-default) parameters s.a. arch, num_distributions, etc. Saved yaml can be used iff the ConfigArgParse patch from https://github.com/trougnouf/ConfigArgParse is applied, otherwise unset values are overwritten with the "None" string.

Training

Train a base model (given arch and num_distributions) for 6M steps at train_lambda=4096, fine-tune for 4M steps with lower train_lambda and/or msssim lossf Set arch to Manypriors for this work, use num_distributions 1 for Balle2017, or set arch to Balle2018PTTFExp for Balle2018 (hyperprior) egrun:

python train.py --num_distributions 64 --arch ManyPriors --train_lambda 4096 --expname mse_4096_manypriors_64_CLI
# and/or
python train.py --config configs/mse_4096_manypriors_64pr.yaml
# and/or
python train.py --config configs/mse_2048_manypriors_64pr.yaml --pretrain mse_4096_manypriors_64pr --reset_lr --reset_global_step # --reset_optimizer
# and/or
python train.py --config configs/mse_4096_hyperprior.yaml

--passthrough_ae is now activated by default. It was not used in the paper, but should result in better rate-distortion. To turn it off, change config/defaults.yaml or use --no_passthrough_ae

Tests

egruns: Test complexity:

python tests.py --complexity --pretrain mse_4096_manypriors_64pr --arch ManyPriors --num_distributions 64

Test timing:

python tests.py --timing "../../datasets/test/Commons_Test_Photographs" --pretrain mse_4096_manypriors_64pr --arch ManyPriors --num_distributions 64

Segment the images in commons_test_dpath by distribution index:

python tests.py --segmentation --commons_test_dpath "../../datasets/test/Commons_Test_Photographs" --pretrain mse_4096_manypriors_64pr --arch ManyPriors --num_distributions 64

Visualize cumulative distribution functions:

python tests.py --plot --pretrain mse_4096_manypriors_64pr --arch ManyPriors --num_distributions 64

Test on kodak images:

python tests.py --encdec_kodak --test_dpath "../../datasets/test/kodak/" --pretrain mse_4096_manypriors_64pr --arch ManyPriors --num_distributions 64

Test on commons images (larger, uses CPU):

python tests.py --encdec_commons --test_commons_dpath "../../datasets/test/Commons_Test_Photographs/" --pretrain checkpoints/mse_4096_manypriors_64pr/saved_models/checkpoint.pth --arch ManyPriors --num_distributions 64

Encode an image:

python tests.py --encode "../../datasets/test/Commons_Test_Photographs/Garden_snail_moving_down_the_Vennbahn_in_disputed_territory_(DSCF5879).png" --pretrain mse_4096_manypriors_64pr --arch ManyPriors --num_distributions 64 --device -1

Decode that image:

python tests.py --decode "checkpoints/mse_4096_manypriors_64pr/encoded/Garden_snail_moving_down_the_Vennbahn_in_disputed_territory_(DSCF5879).png" --pretrain mse_4096_manypriors_64pr --arch ManyPriors --num_distributions 64 --device -1
Owner
Benoit Brummer
BS CpE at @UCF (2016), MS CS (AI) @uclouvain (2019), PhD student @uclouvain w/ intoPIX
Benoit Brummer
GitHub Repository
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations

💎A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily instal

DefTruth 142 Dec 25, 2022
Deploy optimized transformer based models on Nvidia Triton server

Deploy optimized transformer based models on Nvidia Triton server

Lefebvre Sarrut Services 1.2k Jan 05, 2023
Unofficial implementation (replicates paper results!) of MINER: Multiscale Implicit Neural Representations in pytorch-lightning

MINER_pl Unofficial implementation of MINER: Multiscale Implicit Neural Representations in pytorch-lightning. 📖 Ref readings Laplacian pyramid explan

AI葵 51 Nov 28, 2022
Multispectral Object Detection with Yolov5

Multispectral-Object-Detection Intro Official Code for Cross-Modality Fusion Transformer for Multispectral Object Detection. Multispectral Object Dete

Richard Fang 121 Jan 01, 2023
Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network

Introduction This repository is about paper SpeakerGAN , and is unofficially implemented by Mingming Huang ( 7 Jan 03, 2023

Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.

DECOR-GAN PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement, Zhiqin Chen, Vladimir G. Kim, Matthew Fish

Zhiqin Chen 72 Dec 31, 2022
This is a classifier which basically predicts whether there is a gun law in a state or not, depending on various things like murder rates etc.

Gun-Laws-Classifier This is a classifier which basically predicts whether there is a gun law in a state or not, depending on various things like murde

Awais Saleem 1 Jan 20, 2022
Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion

Feature-Style Encoder for Style-Based GAN Inversion Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion. Code will

InterDigital 63 Jan 03, 2023
Reinforcement learning for self-driving in a 3D simulation

SelfDrive_AI Reinforcement learning for self-driving in a 3D simulation (Created using UNITY-3D) 1. Requirements for the SelfDrive_AI Gym You need Pyt

Surajit Saikia 17 Dec 14, 2021
Pytorch implementation of Hinton's Dynamic Routing Between Capsules

pytorch-capsule A Pytorch implementation of Hinton's "Dynamic Routing Between Capsules". https://arxiv.org/pdf/1710.09829.pdf Thanks to @naturomics fo

Tim Omernick 625 Oct 27, 2022
This is the code of using DQN to play Sekiro .

Update for using DQN to play sekiro 2021.2.2（English Version） This is the code of using DQN to play Sekiro . I am very glad to tell that I have writen

144 Dec 25, 2022
Generate pixel-style avatars with python.

face2pixel Generate pixel-style avatars with python. Run: Clone the project: git clone https://github.com/theodorecooper/face2pixel install requiremen

Theodore Cooper 2 May 11, 2022
Doods2 - API for detecting objects in images and video streams using Tensorflow

DOODS2 - Return of DOODS Dedicated Open Object Detection Service - Yes, it's a b

Zach 101 Jan 04, 2023
Resources complimenting the Machine Learning Course led in the Faculty of mathematics and informatics part of Sofia University.

Machine Learning and Data Mining, Summer 2021-2022 How to learn data science and machine learning? Programming. Learn Python. Basic Statistics. Take a

Simeon Hristov 8 Oct 04, 2022
The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

DV Lab 71 Jan 06, 2023
To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

Kunal Wadhwa 2 Jan 05, 2022
An implementation of the BADGE batch active learning algorithm.

Batch Active learning by Diverse Gradient Embeddings (BADGE) An implementation of the BADGE batch active learning algorithm. Details are provided in o

125 Dec 24, 2022
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

Who has never met a forged picture on the web ? No one ! Everyday we are constantly facing fake pictures touched up in Photoshop but it is not always

Rony Abecidan 77 Dec 16, 2022
EssentialMC2 Video Understanding

EssentialMC2 Introduction EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2( relatio

Alibaba 106 Dec 11, 2022