This repository contains the supporting code and experiments for the paper "Senescence Reversion in Plant Images Using Perception and Unpaired Data".
Install dependencies
# clone project
git clone https://github.com/eadan97/lat-6765
cd lat-6765
# [OPTIONAL] create conda environment
conda env create -f conda_env_gpu.yaml -n myenv
conda activate myenv
# install requirements
pip install -r requirements.txtTrain model with default configuration
# default
python run.py
# train on CPU
python run.py trainer.gpus=0
# train on GPU
python run.py trainer.gpus=1Train model with chosen experiment configuration from configs/experiment/
python run.py experiment=experiment_nameYou can override any parameter from command line like this
python run.py trainer.max_epochs=20 datamodule.batch_size=64The directory structure of new project looks like this:
├── configs <- Hydra configuration files
│ ├── callbacks <- Callbacks configs
│ ├── datamodule <- Datamodule configs
│ ├── experiment <- Experiment configs
│ ├── hparams_search <- Hyperparameter search configs
│ ├── hydra <- Hydra related configs
│ ├── logger <- Logger configs
│ ├── model <- Model configs
│ ├── trainer <- Trainer configs
│ │
│ └── config.yaml <- Main project configuration file
│
├── data <- Project data
│
├── logs <- Logs generated by Hydra and PyTorch Lightning loggers
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration.ipynb`.
│
├── tests <- Tests of any kind
│ ├── smoke
│ └── unit
│
├── src
│ ├── callbacks <- Lightning callbacks
│ ├── datamodules <- Lightning datamodules
│ ├── models <- Lightning models
│ ├── utils <- Utility scripts
│ │
│ └── train.py <- Training pipeline
│
├── run.py <- Run any pipeline with chosen experiment configuration
│
├── .env.example <- Template of the file for storing private environment variables
├── .gitignore <- List of files/folders ignored by git
├── .pre-commit-config.yaml <- Configuration of automatic code formatting
├── conda_env_gpu.yaml <- File for installing conda environment
├── Dockerfile <- File for building docker container
├── requirements.txt <- File for installing python dependencies
├── setup.cgf <- Configurations of linters and pytest
├── LICENSE
└── README.md