CompressLab

A PyTorch-based framework for deep-learning-based data compression research.

Installation

conda create -n compresslab python=3.9
conda activate compresslab
pip install -e .

How to use

Run

python run.py --list

to check all registered modules.

Run

python run.py --config config/your_config.yaml

to train a model with the specified configuration file.

Run

tensorboard --logdir output

to monitor the training process.

Dataset preparation

download.py can be used to download and extract the datasets automatically. For example, to download the CLIC2020 Professional Validation dataset, run

python dataset/download.py --dataset CLIC2020_professional_val

Current supported datasets:

• Vimeo90k
• CLIC2020
• DIV2K
• Tecnick
• UVG
• Kodak (Already included in the repository)

Run

python dataset/download.py --list

to check all supported datasets.

Available models

VAE-based lossy image compression

• All models implemented in CompressAI:
  • FactorizedPrior
  • ScaleHyperprior
  • MeanScaleHyperprior, JointAutoregressiveandHierarchicalPriors
  • Cheng2020
  • Cheng2020Checkerboard
  • ELIC
• ChARM
• TCM
• MLIC

Generative image compression (Only support evaluation of the pre-trained models now)

• DiffEIC
• StableCodec

Related links

• PyTorch Lightning
• CompressAI
• ChARM-PyTorch
• LIC-TCM
• MLIC
• DVC-PyTorch
• PyTorchVideoCompression
• DiffEIC
• StableCodec
• CLIC
• UVG
• Kodak