Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Reworking nnunetv2 predictor #7069

Draft
wants to merge 6 commits into
base: dev
Choose a base branch
from
Draft

Reworking nnunetv2 predictor #7069

Changes from 3 commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 9 additions & 8 deletions monai/apps/nnunet/nnunetv2_runner.py
View file
Open in desktop
Original file line number Diff line number Diff line change
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.initialise_from_trained_model_folder(
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

thanks for lookint into the issue, this currently generates an error AttributeError: 'nnUNetPredictor' object has no attribute 'initialise_from_trained_model_folder'. Did you mean: 'initialize_from_trained_model_folder'?

I think you can test within a docker container (docker run --ipc=host --net=host --gpus all -ti --rm projectmonai/monai):

cd /tmp
git clone --depth 1 --branch 7013nnunetv2-predict --single-branch https://github.com/JupiLogy/MONAI.git
python -m pip install git+https://github.com/julien-blanchon/hiddenlayer.git
python -m pip install nnunetv2
cd MONAI/
python -m tests.test_integration_nnunetv2_runner

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