The base library can be found here: https://github.com/fizyr/keras-retinanet. See section below for major changes and modifications.
You must have numpy version 1.15.4 or greater to pip install this package.
Using pip3 version 19 or greater: pip3 install -e git+https://github.com/NewKnowledge/object-detection-retinanet#egg=object_detection_retinanet.
The D3M primitive is essentially a wrapper on the entire Keras-RetinaNet codebase to fit into D3M specifications. The primitive itself is found in the object-detection-d3m-wrapper repo.
Since the Keras-RetinaNet codebase is a command-line tool, these details had to be stripped out and the arguments exposed as primitive hyperparameters. Most of the train.py script was inserted into the fit() and the other methods it calls were inserted into the primitive class. The only major modifications were to the Generator class which has to be modified slightly to parse the datasets as they are input in D3M format.
convert_model.py and evaluate.py were inserted into the produce() method. evaluate.py has a --convert-model CLI argument which is mostly the same as the content in convert_model.py. Therefore, convert_model.py was removed and the contents of evaluate.py that convert the model were retained to be inserted into produce(). The modifications to evaluate.py() were to output a data frame that contains the list of bounding boxes in the expected format for the metric.py D3M evaluation using average precision.