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| import argparse |
| import torch |
|
|
| from nnunet.inference.predict import predict_from_folder |
| from nnunet.paths import default_plans_identifier, network_training_output_dir, default_cascade_trainer, default_trainer |
| from batchgenerators.utilities.file_and_folder_operations import join, isdir |
| from nnunet.utilities.task_name_id_conversion import convert_id_to_task_name |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("-i", '--input_folder', help="Must contain all modalities for each patient in the correct" |
| " order (same as training). Files must be named " |
| "CASENAME_XXXX.nii.gz where XXXX is the modality " |
| "identifier (0000, 0001, etc)", required=True) |
| parser.add_argument('-o', "--output_folder", required=True, help="folder for saving predictions") |
| parser.add_argument('-t', '--task_name', help='task name or task ID, required.', |
| default=default_plans_identifier, required=True) |
|
|
| parser.add_argument('-tr', '--trainer_class_name', |
| help='Name of the nnUNetTrainer used for 2D U-Net, full resolution 3D U-Net and low resolution ' |
| 'U-Net. The default is %s. If you are running inference with the cascade and the folder ' |
| 'pointed to by --lowres_segmentations does not contain the segmentation maps generated by ' |
| 'the low resolution U-Net then the low resolution segmentation maps will be automatically ' |
| 'generated. For this case, make sure to set the trainer class here that matches your ' |
| '--cascade_trainer_class_name (this part can be ignored if defaults are used).' |
| % default_trainer, |
| required=False, |
| default=default_trainer) |
| parser.add_argument('-ctr', '--cascade_trainer_class_name', |
| help="Trainer class name used for predicting the 3D full resolution U-Net part of the cascade." |
| "Default is %s" % default_cascade_trainer, required=False, |
| default=default_cascade_trainer) |
|
|
| parser.add_argument('-m', '--model', help="2d, 3d_lowres, 3d_fullres or 3d_cascade_fullres. Default: 3d_fullres", |
| default="3d_fullres", required=False) |
|
|
| parser.add_argument('-p', '--plans_identifier', help='do not touch this unless you know what you are doing', |
| default=default_plans_identifier, required=False) |
|
|
| parser.add_argument('-f', '--folds', nargs='+', default='None', |
| help="folds to use for prediction. Default is None which means that folds will be detected " |
| "automatically in the model output folder") |
|
|
| parser.add_argument('-z', '--save_npz', required=False, action='store_true', |
| help="use this if you want to ensemble these predictions with those of other models. Softmax " |
| "probabilities will be saved as compressed numpy arrays in output_folder and can be " |
| "merged between output_folders with nnUNet_ensemble_predictions") |
|
|
| parser.add_argument('-l', '--lowres_segmentations', required=False, default='None', |
| help="if model is the highres stage of the cascade then you can use this folder to provide " |
| "predictions from the low resolution 3D U-Net. If this is left at default, the " |
| "predictions will be generated automatically (provided that the 3D low resolution U-Net " |
| "network weights are present") |
|
|
| parser.add_argument("--part_id", type=int, required=False, default=0, help="Used to parallelize the prediction of " |
| "the folder over several GPUs. If you " |
| "want to use n GPUs to predict this " |
| "folder you need to run this command " |
| "n times with --part_id=0, ... n-1 and " |
| "--num_parts=n (each with a different " |
| "GPU (for example via " |
| "CUDA_VISIBLE_DEVICES=X)") |
|
|
| parser.add_argument("--num_parts", type=int, required=False, default=1, |
| help="Used to parallelize the prediction of " |
| "the folder over several GPUs. If you " |
| "want to use n GPUs to predict this " |
| "folder you need to run this command " |
| "n times with --part_id=0, ... n-1 and " |
| "--num_parts=n (each with a different " |
| "GPU (via " |
| "CUDA_VISIBLE_DEVICES=X)") |
|
|
| parser.add_argument("--num_threads_preprocessing", required=False, default=6, type=int, help= |
| "Determines many background processes will be used for data preprocessing. Reduce this if you " |
| "run into out of memory (RAM) problems. Default: 6") |
|
|
| parser.add_argument("--num_threads_nifti_save", required=False, default=2, type=int, help= |
| "Determines many background processes will be used for segmentation export. Reduce this if you " |
| "run into out of memory (RAM) problems. Default: 2") |
|
|
| parser.add_argument("--disable_tta", required=False, default=False, action="store_true", |
| help="set this flag to disable test time data augmentation via mirroring. Speeds up inference " |
| "by roughly factor 4 (2D) or 8 (3D)") |
|
|
| parser.add_argument("--overwrite_existing", required=False, default=False, action="store_true", |
| help="Set this flag if the target folder contains predictions that you would like to overwrite") |
|
|
| parser.add_argument("--mode", type=str, default="normal", required=False, help="Hands off!") |
| parser.add_argument("--all_in_gpu", type=str, default="None", required=False, help="can be None, False or True. " |
| "Do not touch.") |
| parser.add_argument("--step_size", type=float, default=0.5, required=False, help="don't touch") |
| |
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| |
| parser.add_argument('-chk', |
| help='checkpoint name, default: model_final_checkpoint', |
| required=False, |
| default='model_final_checkpoint') |
| parser.add_argument('--disable_mixed_precision', default=False, action='store_true', required=False, |
| help='Predictions are done with mixed precision by default. This improves speed and reduces ' |
| 'the required vram. If you want to disable mixed precision you can set this flag. Note ' |
| 'that this is not recommended (mixed precision is ~2x faster!)') |
|
|
| args = parser.parse_args() |
| input_folder = args.input_folder |
| output_folder = args.output_folder |
| part_id = args.part_id |
| num_parts = args.num_parts |
| folds = args.folds |
| save_npz = args.save_npz |
| lowres_segmentations = args.lowres_segmentations |
| num_threads_preprocessing = args.num_threads_preprocessing |
| num_threads_nifti_save = args.num_threads_nifti_save |
| disable_tta = args.disable_tta |
| step_size = args.step_size |
| |
| |
| |
| overwrite_existing = args.overwrite_existing |
| mode = args.mode |
| all_in_gpu = args.all_in_gpu |
| model = args.model |
| trainer_class_name = args.trainer_class_name |
| cascade_trainer_class_name = args.cascade_trainer_class_name |
|
|
| task_name = args.task_name |
|
|
| if not task_name.startswith("Task"): |
| task_id = int(task_name) |
| task_name = convert_id_to_task_name(task_id) |
|
|
| assert model in ["2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"], "-m must be 2d, 3d_lowres, 3d_fullres or " \ |
| "3d_cascade_fullres" |
|
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|
| if lowres_segmentations == "None": |
| lowres_segmentations = None |
|
|
| if isinstance(folds, list): |
| if folds[0] == 'all' and len(folds) == 1: |
| pass |
| else: |
| folds = [int(i) for i in folds] |
| elif folds == "None": |
| folds = None |
| else: |
| raise ValueError("Unexpected value for argument folds") |
|
|
| assert all_in_gpu in ['None', 'False', 'True'] |
| if all_in_gpu == "None": |
| all_in_gpu = None |
| elif all_in_gpu == "True": |
| all_in_gpu = True |
| elif all_in_gpu == "False": |
| all_in_gpu = False |
|
|
| |
| |
| if model == "3d_cascade_fullres" and lowres_segmentations is None: |
| print("lowres_segmentations is None. Attempting to predict 3d_lowres first...") |
| assert part_id == 0 and num_parts == 1, "if you don't specify a --lowres_segmentations folder for the " \ |
| "inference of the cascade, custom values for part_id and num_parts " \ |
| "are not supported. If you wish to have multiple parts, please " \ |
| "run the 3d_lowres inference first (separately)" |
| model_folder_name = join(network_training_output_dir, "3d_lowres", task_name, trainer_class_name + "__" + |
| args.plans_identifier) |
| assert isdir(model_folder_name), "model output folder not found. Expected: %s" % model_folder_name |
| lowres_output_folder = join(output_folder, "3d_lowres_predictions") |
| predict_from_folder(model_folder_name, input_folder, lowres_output_folder, folds, False, |
| num_threads_preprocessing, num_threads_nifti_save, None, part_id, num_parts, not disable_tta, |
| overwrite_existing=overwrite_existing, mode=mode, overwrite_all_in_gpu=all_in_gpu, |
| mixed_precision=not args.disable_mixed_precision, |
| step_size=step_size) |
| lowres_segmentations = lowres_output_folder |
| torch.cuda.empty_cache() |
| print("3d_lowres done") |
|
|
| if model == "3d_cascade_fullres": |
| trainer = cascade_trainer_class_name |
| else: |
| trainer = trainer_class_name |
|
|
| model_folder_name = join(network_training_output_dir, model, task_name, trainer + "__" + |
| args.plans_identifier) |
| print("using model stored in ", model_folder_name) |
| assert isdir(model_folder_name), "model output folder not found. Expected: %s" % model_folder_name |
|
|
| predict_from_folder(model_folder_name, input_folder, output_folder, folds, save_npz, num_threads_preprocessing, |
| num_threads_nifti_save, lowres_segmentations, part_id, num_parts, not disable_tta, |
| overwrite_existing=overwrite_existing, mode=mode, overwrite_all_in_gpu=all_in_gpu, |
| mixed_precision=not args.disable_mixed_precision, |
| step_size=step_size, checkpoint_name=args.chk) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
