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Update test_nnunetv2runner #7483

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merged 3 commits into from
Feb 22, 2024
Merged

Update test_nnunetv2runner #7483

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004c7a3
fix #7013
KumoLiu Feb 21, 2024
ff35c37
address comments
KumoLiu Feb 21, 2024
b378a37
Merge branch 'dev' into nnunetv2
KumoLiu Feb 21, 2024
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22 changes: 12 additions & 10 deletions monai/apps/nnunet/nnunetv2_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,7 @@ class nnUNetV2Runner: # noqa: N801
"""
``nnUNetV2Runner`` provides an interface in MONAI to use `nnU-Net` V2 library to analyze, train, and evaluate
neural networks for medical image segmentation tasks.
A version of nnunetv2 higher than 2.2 is needed for this class.

``nnUNetV2Runner`` can be used in two ways:

Expand Down Expand Up @@ -770,7 +771,7 @@ def find_best_configuration(
def predict(
self,
list_of_lists_or_source_folder: str | list[list[str]],
output_folder: str,
output_folder: str | None | list[str],
model_training_output_dir: str,
use_folds: tuple[int, ...] | str | None = None,
tile_step_size: float = 0.5,
Expand Down Expand Up @@ -824,28 +825,29 @@ def predict(
"""
os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_id}"

from nnunetv2.inference.predict_from_raw_data import predict_from_raw_data
from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor

n_processes_preprocessing = (
self.default_num_processes if num_processes_preprocessing < 0 else num_processes_preprocessing
)
n_processes_segmentation_export = (
self.default_num_processes if num_processes_segmentation_export < 0 else num_processes_segmentation_export
)

predict_from_raw_data(
list_of_lists_or_source_folder=list_of_lists_or_source_folder,
output_folder=output_folder,
model_training_output_dir=model_training_output_dir,
use_folds=use_folds,
predictor = nnUNetPredictor(
tile_step_size=tile_step_size,
use_gaussian=use_gaussian,
use_mirroring=use_mirroring,
perform_everything_on_gpu=perform_everything_on_gpu,
perform_everything_on_device=perform_everything_on_gpu,
verbose=verbose,
)
predictor.initialize_from_trained_model_folder(
model_training_output_dir=model_training_output_dir, use_folds=use_folds, checkpoint_name=checkpoint_name
)
predictor.predict_from_files(
list_of_lists_or_source_folder=list_of_lists_or_source_folder,
output_folder_or_list_of_truncated_output_files=output_folder,
save_probabilities=save_probabilities,
overwrite=overwrite,
checkpoint_name=checkpoint_name,
num_processes_preprocessing=n_processes_preprocessing,
num_processes_segmentation_export=n_processes_segmentation_export,
folder_with_segs_from_prev_stage=folder_with_segs_from_prev_stage,
Expand Down
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