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Reworking nnunetv2 predictor #7069

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11648f4
Reworking nnunetv2 predictor
JupiLogy Sep 30, 2023
ffcf5e3
typo and formatting
JupiLogy Sep 30, 2023
16c95b4
Merge branch 'dev' into 7013nnunetv2-predict
JupiLogy Sep 30, 2023
e3b4022
typo
JupiLogy Oct 2, 2023
f59a7f1
changing folder names
JupiLogy Oct 2, 2023
72248d4
Merge branch 'Project-MONAI:dev' into 7013nnunetv2-predict
JupiLogy Dec 5, 2023
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23 changes: 12 additions & 11 deletions monai/apps/nnunet/nnunetv2_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -167,12 +167,12 @@ def __init__(
else:
raise ValueError(f"{input_config} is not a valid file or dict")

self.nnunet_raw = self.input_info.pop("nnunet_raw", os.path.join(".", self.work_dir, "nnUNet_raw_data_base"))
self.nnunet_raw = self.input_info.pop("nnunet_raw", os.path.join(".", self.work_dir, "nnUNetv2_raw_data_base"))
self.nnunet_preprocessed = self.input_info.pop(
"nnunet_preprocessed", os.path.join(".", self.work_dir, "nnUNet_preprocessed")
"nnunet_preprocessed", os.path.join(".", self.work_dir, "nnUNetv2_preprocessed")
)
self.nnunet_results = self.input_info.pop(
"nnunet_results", os.path.join(".", self.work_dir, "nnUNet_trained_models")
"nnunet_results", os.path.join(".", self.work_dir, "nnUNetv2_trained_models")
)

if not os.path.exists(self.nnunet_raw):
Expand Down Expand Up @@ -824,7 +824,7 @@ 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
Expand All @@ -833,19 +833,20 @@ def predict(
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,
tile_step_size=tile_step_size,
predictor = nnUNetPredictor(
use_gaussian=use_gaussian,
use_mirroring=use_mirroring,
perform_everything_on_gpu=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=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