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Sequential Inference (Non-MP) #2705

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Sequential Inference (Non-MP) #2705

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5787a9a
add nnUNet_npp and nnUNet_nps environment variables
LennyN95 Feb 17, 2025
3f32df8
use predict_from_files_sequential when nps and npp are zero
LennyN95 Feb 17, 2025
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37 changes: 28 additions & 9 deletions nnunetv2/inference/predict_from_raw_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -748,6 +748,12 @@ def predict_from_files_sequential(self,
empty_cache(self.device)
return ret

def _getDefaultValue(env: str, dtype: type, default: any,) -> any:
try:
val = dtype(os.environ.get(env) or default)
except:
val = default
return val

def predict_entry_point_modelfolder():
import argparse
Expand Down Expand Up @@ -883,10 +889,10 @@ def predict_entry_point():
help='Continue an aborted previous prediction (will not overwrite existing files)')
parser.add_argument('-chk', type=str, required=False, default='checkpoint_final.pth',
help='Name of the checkpoint you want to use. Default: checkpoint_final.pth')
parser.add_argument('-npp', type=int, required=False, default=3,
parser.add_argument('-npp', type=int, required=False, default=_getDefaultValue('nnUNet_npp', int, 3),
help='Number of processes used for preprocessing. More is not always better. Beware of '
'out-of-RAM issues. Default: 3')
parser.add_argument('-nps', type=int, required=False, default=3,
parser.add_argument('-nps', type=int, required=False, default=_getDefaultValue('nnUNet_nps', int, 3),
help='Number of processes used for segmentation export. More is not always better. Beware of '
'out-of-RAM issues. Default: 3')
parser.add_argument('-prev_stage_predictions', type=str, required=False, default=None,
Expand Down Expand Up @@ -953,13 +959,26 @@ def predict_entry_point():
args.f,
checkpoint_name=args.chk
)
predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities,
overwrite=not args.continue_prediction,
num_processes_preprocessing=args.npp,
num_processes_segmentation_export=args.nps,
folder_with_segs_from_prev_stage=args.prev_stage_predictions,
num_parts=args.num_parts,
part_id=args.part_id)

run_sequential = args.nps == 0 and args.npp == 0

if run_sequential:

print("Running in non-multiprocessing mode")
predictor.predict_from_files_sequential(args.i, args.o, save_probabilities=args.save_probabilities,
overwrite=not args.continue_prediction,
folder_with_segs_from_prev_stage=args.prev_stage_predictions)

else:

predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities,
overwrite=not args.continue_prediction,
num_processes_preprocessing=args.npp,
num_processes_segmentation_export=args.nps,
folder_with_segs_from_prev_stage=args.prev_stage_predictions,
num_parts=args.num_parts,
part_id=args.part_id)

# r = predict_from_raw_data(args.i,
# args.o,
# model_folder,
Expand Down