def save_tensor(
maps_dir,
data_group,
caps_directory,
tsv_path,
gpu=True,
n_proc=0,
batch_size=1,
# prepare_dl=True,
selection_metrics=None,
diagnoses=None,
multi_cohort=False,
overwrite=False,
):
"""TO CHANGE
This function loads a MAPS and predicts the global metrics and individual values
for all the models selected using a metric in selection_metrics.
Args:
maps_dir (str): file with the model (pth format).
data_group: prefix of all classification outputs.
caps_directory (str): path to the CAPS folder. For more information please refer to
[clinica documentation](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/).
tsv_path (str): path to a TSV file containing the list of participants and sessions to interpret.
labels: by default is True. If False no metrics tsv files will be written.
gpu: if true, it uses gpu.
n_proc: num_workers used in DataLoader
batch_size: batch size of the DataLoader
prepare_dl: if true, uses extracted patches/slices otherwise extract them
on-the-fly.
selection_metrics: list of metrics to find best models to be evaluated.
diagnoses: list of diagnoses to be tested if tsv_path is a folder.
verbose: level of verbosity.
multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths.
overwrite (bool): If True former definition of data group is erased
"""
verbose_list = ["warning", "info", "debug"]
maps_manager = MapsManager(maps_dir, verbose=verbose_list[0])
maps_manager.save_tensors(
data_group,
caps_directory=caps_directory,
tsv_path=tsv_path,
selection_metrics=selection_metrics,
multi_cohort=multi_cohort,
diagnoses=diagnoses,
# prepare_dl=prepare_dl,
# batch_size=batch_size,
# n_proc=n_proc,
gpu=gpu,
overwrite=overwrite,
)
def save_tensor(
maps_dir,
data_group,
caps_directory,
tsv_path,
gpu=True,
selection_metrics=None,
diagnoses=None,
multi_cohort=False,
nifti=False,
overwrite=False,
):
"""
This function loads a MAPS and compute reconstruction outputs and will save them in the MAPS
for all the models selected.
Args:
maps_dir (str): file with the model (pth format).
data_group: prefix of all classification outputs.
caps_directory (str): path to the CAPS folder. For more information please refer to
[clinica documentation](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/).
tsv_path (str): path to a TSV file containing the list of participants and sessions to interpret.
gpu: if true, it uses gpu.
selection_metrics: list of metrics to find best models to be evaluated.
diagnoses: list of diagnoses to be tested if tsv_path is a folder.
verbose: level of verbosity.
multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths.
nifti (bool): If True will save the outputs as nifti files instead of Pytorch tensors.
overwrite (bool): If True former definition of data group is erased
"""
verbose_list = ["warning", "info", "debug"]
maps_manager = MapsManager(maps_dir, verbose=verbose_list[0])
maps_manager.save_tensors(
data_group,
caps_directory=caps_directory,
tsv_path=tsv_path,
selection_metrics=selection_metrics,
multi_cohort=multi_cohort,
diagnoses=diagnoses,
gpu=gpu,
nifti=nifti,
overwrite=overwrite,
)