| # pytorch-caney | |
| Python package for lots of Pytorch tools. | |
| [](https://zenodo.org/badge/latestdoi/472450059) | |
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| [](https://github.com/psf/black) | |
| [](https://coveralls.io/github/nasa-nccs-hpda/pytorch-caney?branch=main) | |
| ## Documentation | |
| - Latest: https://nasa-nccs-hpda.github.io/pytorch-caney/latest | |
| ## 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. | |
| ```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, [email protected] | |
| - Caleb Spradlin, [email protected] | |
| ## Contributing | |
| Please see our [guide for contributing to pytorch-caney](CONTRIBUTING.md). | |
| ## SatVision | |
| | name | pretrain | resolution | #params | | |
| | :---: | :---: | :---: | :---: | | |
| | SatVision-B | MODIS-1.9-M | 192x192 | 84.5M | | |
| ## SatVision Datasets | |
| | name | bands | resolution | #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: | |
| ```bash | |
| torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg <config-file> --dataset <dataset-name> --data-paths <path-to-data-subfolder-1> --batch-size <batch-size> --output <output-dir> --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: | |
| ```bash | |
| singularity shell --nv -B <mounts> /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. | |
| ```bash | |
| singularity shell --nv -B <mounts> /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: | |
| ```bash | |
| torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/finetuning/finetune.py --cfg <config-file> --pretrained <path-to-pretrained> --dataset <dataset-name> --data-paths <path-to-data-subfolder-1> --batch-size <batch-size> --output <output-dir> --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. | |
| ```bash | |
| git clone [email protected]:nasa-nccs-hpda/pytorch-caney.git | |
| cd pytorch-caney; bash test.sh | |
| ``` | |
| or run unit tests directly with container or anaconda env | |
| ```bash | |
| git clone [email protected]:nasa-nccs-hpda/pytorch-caney.git | |
| singularity build --sandbox pytorch-caney-container docker://nasanccs/pytorch-caney:latest | |
| singularity shell --nv -B <mounts> /path/to/container/pytorch-caney-container | |
| cd pytorch-caney; python -m unittest discover pytorch_caney/tests | |
| ``` | |
| ```bash | |
| git clone [email protected]: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](https://github.com/Lightning-AI/lightning) | |
| - [Swin Transformer](https://github.com/microsoft/Swin-Transformer) | |
| - [SimMIM](https://github.com/microsoft/SimMIM) | |