Setup

Downloading Data

Before running the script, download the ORIGA dataset and unzip it using the following commands:

wget -O data/data.zip https://drive.google.com/uc\?export\=download\&id\=1svVhcxeeEFaEU-MlQSYH-madg6aYx2h6
unzip -o data/data.zip -d data/

Environment Setup

Create and activate virtual environment (optional but recommended):

python -m venv .env
source .env/bin/activate

Install Python packages from the requirements.txt file:

pip install -r requirements.txt

Run

Preprocessing

preprocess.py is used to process the the raw images and their corresponding raw masks. As mentionned in the artice.

Arguments

• --no_polar_transform: If specified, the script will skip the polar transformation step, preserving the data in the Cartesian coordinate system.
• --n_data: Number of data elements (image, and mask) to process, (default is 650, the total number in the ORIGA dataset).

Running the Script

python preprocess.py [--no_polar_transform] [--n_data]

Training

train.py is used to sequentially train, validate, and test the model on the training, validation, and testing datasets after partitioning the entire dataset.

Running the Script

python train.py [--image_dir] [--mask_dir][--weights_path][--train_batch_size][--val_batch_size] [--test_batch_size] [--train_size][--val_size] [--test_size] [--epochs] [--learning_rate][--momentum][--include_background] [--to_onehot_y] [--reduction] [--device] [--test_only]

Arguments

• --image_dir: Path to the directory containing image data (default is ./data/processed_images/).
• --mask_dir: Path to the directory containing mask data (default is ./data/processed_masks/).
• --weights_path: Path to the directory containing initial weights for the model.
• --train_batch_size: Batch size for training (default is 4).
• --val_batch_size: Batch size for validation (default is 4).
• --test_batch_size: Batch size for testing (default is 4).
• --train_size: Size of the training set (default is 325).
• --val_size: Size of the validation set (default is 75).
• --test_size: Size of the test set (default is 250).
• --epochs: Number of epochs for training (default is 10).
• --learning_rate: Learning rate for the optimizer (0.0001).
• --momentum: Momentum for the optimizer (default is 0.9).
• --include_background: Include background in the loss computation (default is False; If not specified, background is not included).
• --to_onehot_y: Convert target masks to one-hot encoding (defualt is False; if not specified, target masks are not converted to one-hot encoding).
• --reduction: Reduction method for the loss (default is 'mean').
• --device: Specify device: 'cuda' or 'cpu' (default is 'cpu').
• --test_only: Run only the test loop (default is False; if not specified, training and validation loops will be executed in addition to the test loop).

Testing

train.py is used not only for training the model but also for testing it. This is performed after dividing the entire dataset into training, validation, and test datasets.

Running the Script

To conduct testing using a pre-trained model with specified weights, execute the train.py script as demonstrated in the Training part above. However, ensure to include the --test_only and --weights_path options in the command.

Arguments

• --weights_path: Path to the pre-trained weights for the model to test (specify the path to the model's pre-trained weights. This allows you to test the model without retraining).
• --test_only: Flag to run only the test loop (when this flag is specified, the script will only execute the testing loop, skipping training and validation).