GaNDLF

For any new feature, please ensure the corresponding option in the sample configuration is added, so that others can review/use/extend it as needed.

Environment

Before starting to work on the code-level on GaNDLF, please follow the installation instructions to get the environment ready. Once that’s done, please install GaNDLF from the source code using the following commands (this can be skipped if you have installed from sources):

# continue from previous shell
(venv_gandlf) $> git clone https://github.com/mlcommons/GaNDLF.git
cd GaNDLF
pip install -e .

Overall Architecture

Command-line parsing: gandlf_run
Parameters from training configuration get passed as a dict via parameter parser
Training Manager:
- Handles k-fold training
- Main entry point from CLI
Training Function:
- Performs actual training
Inference Manager:
- Handles inference functionality
- Main entry point from CLI
Inference Function:
- Performs actual inference

Dependency Management

To update/change/add a dependency in setup, please ensure at least the following conditions are met:

The package is being actively maintained.
The new dependency is being testing against the minimum python version supported by GaNDLF (see the python_requires variable in setup).
It does not clash with any existing dependencies.

Adding Models

For details, please see README for GANDLF.models submodule.
Update Tests

Adding Augmentation Transformations

Update or add dependency in setup, if appropriate.
Add transformation to global_augs_dict, defined in GANDLF/data/augmentation/__init__.py
Ensure probability is used as input; probability is not used for any preprocessing operations
For details, please see README for GANDLF.data.augmentation submodule.
Update Tests

Adding Preprocessing functionality

Update or add dependency in setup, if appropriate; see section on Dependency Management for details.
All transforms should be defined by inheriting from torchio.transforms.intensity_transform.IntensityTransform. For example, please see the threshold/clip functionality in the GANDLF/data/preprocessing/threshold_and_clip.py file.
Define each option in the configuration file under the correct key (again, see threshold/clip as examples)
Add transformation to global_preprocessing_dict, defined in GANDLF/data/preprocessing/__init__.py
For details, please see README for GANDLF.data.preprocessing submodule.
Update Tests

Adding Training Functionality

Update Training Function
Update Training Manager, if any training API has changed
Update Tests

Adding Inference Functionality

Update Inference Function
Update Inference Manager, if any inference API has changed
Update Tests

Update Tests

Once you have made changes to functionality, it is imperative that the unit tests be updated to cover the new code. Please see the full testing suite for details and examples.

Run Tests

Once you have the virtual environment set up, tests can be run using the following command:

pytest --device cuda # can be cuda or cpu, defaults to cpu

Any failures will be reported in the file ${GaNDLF_HOME}/testing/failures.log.

Code coverage

The code coverage for the tests can be obtained by the following command:

coverage run -m pytest --device cuda; coverage report -m

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