================ pytorch-caney ================ Python package for lots of Pytorch tools for geospatial science problems. .. image:: https://zenodo.org/badge/472450059.svg :target: https://zenodo.org/badge/latestdoi/472450059 Objectives ------------ - Library to process remote sensing imagery using GPU and CPU parallelization. - Machine Learning and Deep Learning image classification and regression. - Agnostic array and vector-like data structures. - User interface environments via Notebooks for easy to use AI/ML projects. - Example notebooks for quick AI/ML start with your own data. Installation ---------------- The following library is intended to be used to accelerate the development of data science products for remote sensing satellite imagery, or any other applications. pytorch-caney can be installed by itself, but instructions for installing the full environments are listed under the requirements directory so projects, examples, and notebooks can be run. Note: PIP installations do not include CUDA libraries for GPU support. Make sure NVIDIA libraries are installed locally in the system if not using conda/mamba. .. code-block:: bash module load singularity # if a module needs to be loaded singularity build --sandbox pytorch-caney-container docker://nasanccs/pytorch-caney:latest Why Caney? --------------- "Caney" means longhouse in Taíno. Contributors ------------- - Jordan Alexis Caraballo-Vega, jordan.a.caraballo-vega@nasa.gov - Caleb Spradlin, caleb.s.spradlin@nasa.gov - Jian Li, jian.li@nasa.gov Contributing ------------- Please see our `guide for contributing to pytorch-caney `_. SatVision ------------ +---------------+--------------+------------+------------+ | Name | Pretrain | Resolution | Parameters | +===============+==============+============+============+ | SatVision-B | MODIS-1.9-M | 192x192 | 84.5M | +---------------+--------------+------------+------------+ SatVision Datasets ----------------------- +---------------+-----------+------------+-------------+ | Name | Bands | Resolution | Image Chips | +===============+===========+============+=============+ | MODIS-Small | 7 | 128x128 | 1,994,131 | +---------------+-----------+------------+-------------+ MODIS Surface Reflectance (MOD09GA) Band Details ------------------------------------------------------ +-----------------+---------------+ | Band Name | Bandwidth | +=================+===============+ | sur_refl_b01_1 | 0.620 - 0.670 | +-----------------+---------------+ | sur_refl_b02_1 | 0.841 - 0.876 | +-----------------+---------------+ | sur_refl_b03_1 | 0.459 - 0.479 | +-----------------+---------------+ | sur_refl_b04_1 | 0.545 - 0.565 | +-----------------+---------------+ | sur_refl_b05_1 | 1.230 - 1.250 | +-----------------+---------------+ | sur_refl_b06_1 | 1.628 - 1.652 | +-----------------+---------------+ | sur_refl_b07_1 | 2.105 - 2.155 | +-----------------+---------------+ Pre-training with Masked Image Modeling ----------------------------------------- To pre-train the swinv2 base model with masked image modeling pre-training, run: .. code-block:: bash torchrun --nproc_per_node pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg --dataset --data-paths --batch-size --output --enable-amp For example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run: .. code-block:: bash singularity shell --nv -B /path/to/container/pytorch-caney-container Singularity> export PYTHONPATH=$PWD:$PWD/pytorch-caney Singularity> torchrun --nproc_per_node 4 pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg pytorch-caney/examples/satvision/mim_pretrain_swinv2_satvision_base_192_window12_800ep.yaml --dataset MODIS --data-paths /explore/nobackup/projects/ilab/data/satvision/pretraining/training_* --batch-size 128 --output . --enable-amp This example script runs the exact configuration used to make the SatVision-base model pre-training with MiM and the MODIS pre-training dataset. .. code-block:: bash singularity shell --nv -B /path/to/container/pytorch-caney-container Singularity> cd pytorch-caney/examples/satvision Singularity> ./run_satvision_pretrain.sh Fine-tuning Satvision-base ----------------------------- To fine-tune the satvision-base pre-trained model, run: .. code-block:: bash torchrun --nproc_per_node pytorch-caney/pytorch_caney/pipelines/finetuning/finetune.py --cfg --pretrained --dataset --data-paths --batch-size --output --enable-amp See example config files pytorch-caney/examples/satvision/finetune_satvision_base_*.yaml to see how to structure your config file for fine-tuning. Testing ------------ For unittests, run this bash command to run linting and unit test runs. This will execute unit tests and linting in a temporary venv environment only used for testing. .. code-block:: bash git clone git@github.com:nasa-nccs-hpda/pytorch-caney.git cd pytorch-caney; bash test.sh or run unit tests directly with container or anaconda env .. code-block:: bash git clone git@github.com:nasa-nccs-hpda/pytorch-caney.git singularity build --sandbox pytorch-caney-container docker://nasanccs/pytorch-caney:latest singularity shell --nv -B /path/to/container/pytorch-caney-container cd pytorch-caney; python -m unittest discover pytorch_caney/tests .. code-block:: bash git clone git@github.com:nasa-nccs-hpda/pytorch-caney.git cd pytorch-caney; conda env create -f requirements/environment_gpu.yml; conda activate pytorch-caney python -m unittest discover pytorch_caney/tests References ------------ - `Pytorch Lightning `_ - `Swin Transformer `_ - `SimMIM `_