This is a demo for the synthetic data experiments of the NeurIPS 2019 paper on Region-specific Diffeomorphic Metric Mapping.
To start off we first show how to generate the synthetic data.
cd demos/rdmm_synth_data_generation
# by default uses 20 threads for data generation
python demo_for_generation.py
The optional settings in demo_for_generation.py are as follows:
Creates synthetic registration examples for RDMM related experiments
optional arguments:
-h, --help show this help message and exit
-dp DATA_SAVING_PATH, --data_saving_path DATA_SAVING_PATH
path of the folder saving synthesis data
-di DATA_TASK_PATH, --data_task_path DATA_TASK_PATH
path of the folder recording data info for
registration tasks
The data generation may take minutes. Once the data are prepared, we can run RDMM registration by
cd ..
python example_2d_synth.py
The optional settings in example_2d_synth.py are as follows:
Registration demo for 2d synthetic data
optional arguments:
-h, --help show this help message and exit
--expr_name EXPR_NAME
the name of the experiment
--data_task_path DATA_TASK_PATH
the path of data task folder
--model_name MODEL_NAME
non-parametric method, vsvf/lddmm/rdmm are currently
supported in this demo
--use_predefined_weight_in_rdmm
this flag is only for RDMM model, if set true, the
predefined regularizer mask will be loaded and only
the momentum will be optimized; if set false, both
weight and momenutm will be jointly optimized
--mermaid_setting_path MERMAID_SETTING_PATH
path of mermaid setting json
Once the registrations are done, we can check the results at the default data_task_path ./rdmm_synth_data_generation/data_task.