# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. 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") # parser.add_argument("--interp_order", required=False, default=3, type=int, # help="order of interpolation for segmentations, has no effect if mode=fastest. Do not touch this.") # parser.add_argument("--interp_order_z", required=False, default=0, type=int, # help="order of interpolation along z is z is done differently. Do not touch this.") # parser.add_argument("--force_separate_z", required=False, default="None", type=str, # help="force_separate_z resampling. Can be None, True or False, has no effect if mode=fastest. " # "Do not touch this.") 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 yhis is not recommended (mixed precision is ~2x faster!)') parser.add_argument('-model_folder_name', default=False, required=False, help='Path to the pretrained model.') 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 # interp_order = args.interp_order # interp_order_z = args.interp_order_z # force_separate_z = args.force_separate_z 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 model_folder_name = args.model_folder_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" # if force_separate_z == "None": # force_separate_z = None # elif force_separate_z == "False": # force_separate_z = False # elif force_separate_z == "True": # force_separate_z = True # else: # raise ValueError("force_separate_z must be None, True or False. Given: %s" % force_separate_z) 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 # we need to catch the case where model is 3d cascade fullres and the low resolution folder has not been set. # In that case we need to try and predict with 3d low res first 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 if model_folder_name == False: 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 print(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()