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
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
Official code repository for the EMNLP 2021 paper

Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization PyTorch code for the EMNLP 2021 paper "Integrating Visuospatia

Adyasha Maharana 23 Dec 19, 2022
PyTorch implementation of: Michieli U. and Zanuttigh P., "Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations", CVPR 2021.

Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations This is the official PyTorch implementation

Multimedia Technology and Telecommunication Lab 42 Nov 09, 2022
Reference PyTorch implementation of "End-to-end optimized image compression with competition of prior distributions"

PyTorch reference implementation of "End-to-end optimized image compression with competition of prior distributions" by Benoit Brummer and Christophe

Benoit Brummer 6 Jun 16, 2022
NeRF visualization library under construction

NeRF visualization library using PlenOctrees, under construction pip install nerfvis Docs will be at: https://nerfvis.readthedocs.org import nerfvis s

Alex Yu 196 Jan 04, 2023
Denoising Normalizing Flow

Denoising Normalizing Flow Christian Horvat and Jean-Pascal Pfister 2021 We combine Normalizing Flows (NFs) and Denoising Auto Encoder (DAE) by introd

CHrvt 17 Oct 15, 2022
End-To-End Optimization of LiDAR Beam Configuration

End-To-End Optimization of LiDAR Beam Configuration arXiv | IEEE Xplore This repository is the official implementation of the paper: End-To-End Optimi

Niclas 30 Nov 28, 2022
Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.

tflite2tensorflow Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite. 1. Supported Layers No. TFLite Layer TF

Katsuya Hyodo 214 Dec 29, 2022
This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"

Stock Market Buy/Sell/Hold prediction Using convolutional Neural Network This repo is an attempt to implement the research paper titled "Algorithmic F

Asutosh Nayak 136 Dec 28, 2022
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering Authors: Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou and

Salesforce 72 Dec 05, 2022
NLU Dataset Diagnostics

NLU Dataset Diagnostics This repository contains data and scripts to reproduce the results from our paper: Aarne Talman, Marianna Apidianaki, Stergios

Language Technology at the University of Helsinki 1 Jul 20, 2022
The code for our paper Semi-Supervised Learning with Multi-Head Co-Training

Semi-Supervised Learning with Multi-Head Co-Training (PyTorch) Abstract Co-training, extended from self-training, is one of the frameworks for semi-su

cmc 6 Dec 04, 2022
Benchmarking the robustness of Spatial-Temporal Models

Benchmarking the robustness of Spatial-Temporal Models This repositery contains the code for the paper Benchmarking the Robustness of Spatial-Temporal

Yi Chenyu Ian 15 Dec 16, 2022
Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Adaptive wavelets Wavelets which adapt given data (and optionally a pre-trained model). This yields models which are faster, more compressible, and mo

Yu Group 50 Dec 16, 2022
Pytorch implementation of PCT: Point Cloud Transformer

PCT: Point Cloud Transformer This is a Pytorch implementation of PCT: Point Cloud Transformer.

Yi_Zhang 265 Dec 22, 2022
Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Joint-task Self-supervised Learning for Temporal Correspondence Project | Paper Overview Joint-task Self-supervised Learning for Temporal Corresponden

Sifei Liu 167 Dec 14, 2022
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering Jang Hyun Cho1, Utkarsh Mall2, Kavita Bala2, Bharath Harihar

Jang Hyun Cho 164 Dec 30, 2022
Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

Variance-Aware-MT-Test-Sets Variance-Aware Machine Translation Test Sets License See LICENSE. We follow the data licensing plan as the same as the WMT

NLP2CT Lab, University of Macau 5 Dec 21, 2021
PPO Lagrangian in JAX

PPO Lagrangian in JAX This repository implements PPO in JAX. Implementation is tested on the safety-gym benchmark. Usage Install dependencies using th

Karush Suri 2 Sep 14, 2022
Revealing and Protecting Labels in Distributed Training

Revealing and Protecting Labels in Distributed Training

Google Interns 0 Nov 09, 2022