DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models.


git clone https://github.com/da2so/DA2Lite.git

You will need a machine with a GPU and CUDA installed.
Then, you prepare runtime environment:

pip install -r requirements.txt



main.py(DA2Lite) runs with two main configurations like as follows:

CUDA_VISIBLE_DEVICES=0 python main.py --train_config_file=./configs/train/cifar10/cifar10/vgg16.yaml --compress_config_file=./configs/compress/tucker.yaml

The first one is train_config_file, which indicates training configurations and the other is compress_config_file, which represents compress configurations.
The details of available configurations are described in Here.

After you run DA2Lite to compress a DNN model, logging and compressed model are saved in ./log directory.

The following shows the format of saving:

  • YYYY-MM-DD.HH.MM.SS : format of saved directory for an instance.
    • models
      • origin_{dataset}_{model}.pt : The original model is saved.
      • compress_1_{dataset}_{model}.pt : The first compressed model is saved.
      • ...
    • process.log : The inevitable log is only logged.
    • specific_process.log : The training procedure log is added with process.log


  • Run the CIFAR10 example with resnet18 using tucker decomposition.


  • [ ] Multi-GPU training
  • [ ] PyTorchMobile conversion
  • [ ] Train a model based on a custom dataset
  • [ ] Rand-augmentation for improving accuracy
  • [x] Make model zoo
  • [ ] Up-to-date model architectures.
  • [ ] Train a model for object detection (further future...)
  • [ ] Compression methods for object detection (further future...)