This example mainly shows a typical use case that brings customized python components (such as transform, network, metrics) in a configuration-based workflow.
Please note that this example depends on the spleen_ct_segmentation bundle example and executes via overriding the config file of it.
Download the spleen_ct_segmentation bundle for this example.
python -m monai.bundle download --name spleen_ct_segmentation --bundle_dir "./"To run the workflow with customized components, PYTHONPATH should be revised to include the path to the customized component:
export PYTHONPATH=$PYTHONPATH:"<path to 'custom_component/scripts'>"
And please make sure the folder custom_component/scripts is a valid python module (it has a __init__.py file in the folder).
Override the train config with the customized transform and execute training:
python -m monai.bundle run --meta_file <spleen_configs_path>/metadata.json \
--config_file "['<spleen_configs_path>/train.json','configs/custom_train.json']" \
--logging_file <spleen_configs_path>/logging.conf