mednet.engine.segment.predictor¶
Functions
|
Run inference on input data, output predictions. |
- mednet.engine.segment.predictor.run(model, datamodule, device_manager, output_folder)[source]¶
Run inference on input data, output predictions.
- Parameters:
model (
LightningModule
) – Neural network model (e.g. lwnet).datamodule (
LightningDataModule
) – The lightning DataModule to run predictions on.device_manager (
DeviceManager
) – An internal device representation, to be used for prediction. This representation can be converted into a pytorch device or a lightning accelerator setup.output_folder (
Path
) – Folder where to store HDF5 representations of probability maps.
- Return type:
dict
[str
,list
[tuple
[str
,str
]]] |list
[list
[tuple
[str
,str
]]] |list
[tuple
[str
,str
]] |None
- Returns:
A JSON-able representation of sample data stored at
output_folder
. For every split (dataloader), a list of samples in the form[sample-name, hdf5-path]
is returned. In the cases where thepredict_dataloader()
returns a single loader, we then return a list. A dictionary is returned in casepredict_dataloader()
also returns a dictionary.- Raises:
TypeError – If the DataModule’s
predict_dataloader()
method does not return any of the types described above.