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
OpenMMLab Computer Vision Foundation

English | 简体中文 Introduction MMCV is a foundational library for computer vision research and supports many research projects as below: MMCV: OpenMMLab

OpenMMLab 4.6k Jan 09, 2023
MoveNet Single Pose on DepthAI

MoveNet Single Pose tracking on DepthAI Running Google MoveNet Single Pose models on DepthAI hardware (OAK-1, OAK-D,...). A convolutional neural netwo

64 Dec 29, 2022
SiT: Self-supervised vIsion Transformer

This repository contains the official PyTorch self-supervised pretraining, finetuning, and evaluation codes for SiT (Self-supervised image Transformer).

Sara Ahmed 275 Dec 28, 2022
Plotting points that lie on the intersection of the given curves using gradient descent.

Plotting intersection of curves using gradient descent Webapp Link --- What's the app about Why this app Plotting functions and their intersection. A

Divakar Verma 2 Jan 09, 2022
Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

Why, hello there! This is the supporting notebook for the research paper — Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomal

2 Dec 14, 2021
Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

mmc installation git clone https://github.com/dmarx/Multi-Modal-Comparators cd 'Multi-Modal-Comparators' pip install poetry poetry build pip install d

David Marx 37 Nov 25, 2022
Zalo AI challenge 2021 task hum to song

Zalo AI challenge 2021 task Hum to Song pipeline: Chuẩn bị dữ liệu cho quá trình train: Sửa các file đường dẫn trong config/preprocess.yaml raw_path:

Vo Van Phuc 105 Dec 16, 2022
Cross-modal Deep Face Normals with Deactivable Skip Connections

Cross-modal Deep Face Normals with Deactivable Skip Connections Victoria Fernández Abrevaya*, Adnane Boukhayma*, Philip H. S. Torr, Edmond Boyer (*Equ

72 Nov 27, 2022
Train SN-GAN with AdaBelief

SNGAN-AdaBelief Train a state-of-the-art spectral normalization GAN with AdaBelief https://github.com/juntang-zhuang/Adabelief-Optimizer Acknowledgeme

Juntang Zhuang 10 Jun 11, 2022
Multi-angle c(q)uestion answering

Macaw Introduction Macaw (Multi-angle c(q)uestion answering) is a ready-to-use model capable of general question answering, showing robustness outside

AI2 430 Jan 04, 2023
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
A simple AI that will give you si ple task and this is made with python

Crystal-AI A simple AI that will give you si ple task and this is made with python Prerequsites: Python3.6.2 pyttsx3 pip install pyttsx3 pyaudio pip i

CrystalAnd 1 Dec 25, 2021
PyTorch implementation of TSception V2 using DEAP dataset

TSception This is the PyTorch implementation of TSception V2 using DEAP dataset in our paper: Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai

Yi Ding 27 Dec 15, 2022
Implementation of Online Label Smoothing in PyTorch

Online Label Smoothing Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing. Introduction As the abst

83 Dec 14, 2022
Deep learning for Engineers - Physics Informed Deep Learning

SciANN: Neural Networks for Scientific Computations SciANN is a Keras wrapper for scientific computations and physics-informed deep learning. New to S

SciANN 195 Jan 03, 2023
Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Graph Transformer - Pytorch Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by bot

Phil Wang 97 Dec 28, 2022
Unofficial Implement PU-Transformer

PU-Transformer-pytorch Pytorch unofficial implementation of PU-Transformer (PU-Transformer: Point Cloud Upsampling Transformer) https://arxiv.org/abs/

Lee Hyung Jun 7 Sep 21, 2022
An official repository for Paper "Uformer: A General U-Shaped Transformer for Image Restoration".

Uformer: A General U-Shaped Transformer for Image Restoration Zhendong Wang, Xiaodong Cun, Jianmin Bao and Jianzhuang Liu Paper: https://arxiv.org/abs

Zhendong Wang 497 Dec 22, 2022
PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

Convolutional Networks with Adaptive Inference Graphs (ConvNet-AIG) This repository contains a PyTorch implementation of the paper Convolutional Netwo

Andreas Veit 176 Dec 07, 2022
Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"

ElasticGNN This repository includes the official implementation of ElasticGNN in the paper "Elastic Graph Neural Networks" [ICML 2021]. Xiaorui Liu, W

liuxiaorui 34 Dec 04, 2022