DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). Prerequisites Python 3 PyTorch 1.0 or higher, with NVIDIA CUDA Support Other required python packages specified by requirements.txt. See the Installation. Installation Install Jacinle: Clone the package, and add the bin path to your global PATH environment variable: git clone https://github.com/vacancy/Jacinle –recursive export PATH=/bin:$PATH Clone this repository: git clone https://github.com/zfchenUnique/DCL-Release.git –recursive Create a conda environment for NS-CL, and install the requirements. This includes the required python packagesfrom both Jacinle NS-CL. Most […]

DeepMetaHandles is a shape deformation technique. It learns a set of meta-handles for each given shape. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation direction. A new deformation can then be generated by the “linear combination” of the meta-handles. Although the approach is learned in an unsupervised manner, the learned meta-handles possess strong interpretability and consistency. Environment setup Create a conda environment by conda env create -f environment.yml. […]

DA2Lite DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. Install 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 Use Run 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 […]