# pytorch-caney

Python package for lots of Pytorch tools.

[![DOI](https://zenodo.org/badge/472450059.svg)](https://zenodo.org/badge/latestdoi/472450059)
![CI Workflow](https://github.com/nasa-nccs-hpda/pytorch-caney/actions/workflows/ci.yml/badge.svg)
![CI to DockerHub ](https://github.com/nasa-nccs-hpda/pytorch-caney/actions/workflows/dockerhub.yml/badge.svg)
![Code style: PEP8](https://github.com/nasa-nccs-hpda/pytorch-caney/actions/workflows/lint.yml/badge.svg)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Coverage Status](https://coveralls.io/repos/github/nasa-nccs-hpda/pytorch-caney/badge.svg?branch=main)](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, jordan.a.caraballo-vega@nasa.gov
- Caleb Spradlin, caleb.s.spradlin@nasa.gov

## 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 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

```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 <mounts> /path/to/container/pytorch-caney-container
cd pytorch-caney; python -m unittest discover pytorch_caney/tests
```

```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](https://github.com/Lightning-AI/lightning)
- [Swin Transformer](https://github.com/microsoft/Swin-Transformer)
- [SimMIM](https://github.com/microsoft/SimMIM)