Torchlight provides an light-weight PyTorch trainer, as well as many useful utils, including network components, transforms, metrics, etal. for fast prototyping of computer vision models.
✨ All top level packages are self-contained and independent. Feel free to steal any part into your own project.
- Install via PyPI.
pip install torchlights- Install the latest version from source.
git clone https://github.com/Zeqiang-Lai/torchlight.git
cd torchlight
pip install .
pip install -e . # editable installation
# or simply if you don't need editable installation
pip install git+https://github.com/Zeqiang-Lai/torchlight.git- Most modules are self-contained.
- Debug Mode.
- User friendly progress bar .
- Save latest checkpoint if interrupted by Ctrl-C.
- Override any option in configuration file with cmd args.
- kornia: Open Source Differentiable Computer Vision Library.
- huggingface/datasets: original design for NLP, but also include some vision datasets.
- huggingface/accelerate: A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
- einops: Flexible and powerful tensor operations for readable and reliable code.
- torch-fidelity: High-fidelity performance metrics for generative models in PyTorch.
- piq: Measures and metrics for image2image tasks. PyTorch.
- image-similarity-measures: Numpy implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.
- ResizeRight: The correct way to resize images or tensors. For Numpy or Pytorch (differentiable).
- omegaconf: Flexible Python configuration system. The last one you will ever need.