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nnUNet_calvingfront_detection / nnunet /run /run_training_DP.py
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# 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
from batchgenerators.utilities.file_and_folder_operations import *
from nnunet.run.default_configuration import get_default_configuration
from nnunet.paths import default_plans_identifier
from nnunet.run.load_pretrained_weights import load_pretrained_weights
from nnunet.training.cascade_stuff.predict_next_stage import predict_next_stage
from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer
from nnunet.training.network_training.nnUNetTrainerCascadeFullRes import nnUNetTrainerCascadeFullRes
from nnunet.utilities.task_name_id_conversion import convert_id_to_task_name
def main():
parser = argparse.ArgumentParser()
parser.add_argument("network")
parser.add_argument("network_trainer")
parser.add_argument("task", help="can be task name or task id")
parser.add_argument("fold", help='0, 1, ..., 5 or \'all\'')
parser.add_argument("-val", "--validation_only", help="use this if you want to only run the validation",
action="store_true")
parser.add_argument("-c", "--continue_training", help="use this if you want to continue a training",
action="store_true")
parser.add_argument("-p", help="plans identifier. Only change this if you created a custom experiment planner",
default=default_plans_identifier, required=False)
parser.add_argument("--use_compressed_data", default=False, action="store_true",
help="If you set use_compressed_data, the training cases will not be decompressed. Reading compressed data "
"is much more CPU and RAM intensive and should only be used if you know what you are "
"doing", required=False)
parser.add_argument("--deterministic",
help="Makes training deterministic, but reduces training speed substantially. I (Fabian) think "
"this is not necessary. Deterministic training will make you overfit to some random seed. "
"Don't use that.",
required=False, default=False, action="store_true")
parser.add_argument("-gpus", help="number of gpus", required=True,type=int)
parser.add_argument("--dbs", required=False, default=False, action="store_true", help="distribute batch size. If "
"True then whatever "
"batch_size is in plans will "
"be distributed over DDP "
"models, if False then each "
"model will have batch_size "
"for a total of "
"GPUs*batch_size")
parser.add_argument("--npz", required=False, default=False, action="store_true", help="if set then nnUNet will "
"export npz files of "
"predicted segmentations "
"in the vlaidation as well. "
"This is needed to run the "
"ensembling step so unless "
"you are developing nnUNet "
"you should enable this")
parser.add_argument("--valbest", required=False, default=False, action="store_true", help="")
parser.add_argument("--find_lr", required=False, default=False, action="store_true", help="")
parser.add_argument("--fp32", required=False, default=False, action="store_true",
help="disable mixed precision training and run old school fp32")
parser.add_argument("--val_folder", required=False, default="validation_raw",
help="name of the validation folder. No need to use this for most people")
parser.add_argument("--disable_saving", required=False, action='store_true',
help="If set nnU-Net will not save any parameter files. Useful for development when you are "
"only interested in the results and want to save some disk space")
parser.add_argument("--disable_postprocessing_on_folds", required=False, action='store_true',
help="Running postprocessing on each fold only makes sense when developing with nnU-Net and "
"closely observing the model performance on specific configurations. You do not need it "
"when applying nnU-Net because the postprocessing for this will be determined only once "
"all five folds have been trained and nnUNet_find_best_configuration is called. Usually "
"running postprocessing on each fold is computationally cheap, but some users have "
"reported issues with very large images. If your images are large (>600x600x600 voxels) "
"you should consider setting this flag.")
# parser.add_argument("--interp_order", required=False, default=3, type=int,
# help="order of interpolation for segmentations. Testing purpose only. Hands off")
# parser.add_argument("--interp_order_z", required=False, default=0, type=int,
# help="order of interpolation along z if z is resampled separately. Testing purpose only. "
# "Hands off")
# parser.add_argument("--force_separate_z", required=False, default="None", type=str,
# help="force_separate_z resampling. Can be None, True or False. Testing purpose only. Hands off")
parser.add_argument('-pretrained_weights', type=str, required=False, default=None,
help='path to nnU-Net checkpoint file to be used as pretrained model (use .model '
'file, for example model_final_checkpoint.model). Will only be used when actually training. '
'Optional. Beta. Use with caution.')
args = parser.parse_args()
task = args.task
fold = args.fold
network = args.network
network_trainer = args.network_trainer
validation_only = args.validation_only
plans_identifier = args.p
disable_postprocessing_on_folds = args.disable_postprocessing_on_folds
use_compressed_data = args.use_compressed_data
decompress_data = not use_compressed_data
deterministic = args.deterministic
valbest = args.valbest
find_lr = args.find_lr
num_gpus = args.gpus
fp32 = args.fp32
val_folder = args.val_folder
# interp_order = args.interp_order
# interp_order_z = args.interp_order_z
# force_separate_z = args.force_separate_z
if not task.startswith("Task"):
task_id = int(task)
task = convert_id_to_task_name(task_id)
if fold == 'all':
pass
else:
fold = int(fold)
# 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)
plans_file, output_folder_name, dataset_directory, batch_dice, stage, \
trainer_class = get_default_configuration(network, task, network_trainer, plans_identifier)
if trainer_class is None:
raise RuntimeError("Could not find trainer class")
if network == "3d_cascade_fullres":
assert issubclass(trainer_class, nnUNetTrainerCascadeFullRes), "If running 3d_cascade_fullres then your " \
"trainer class must be derived from " \
"nnUNetTrainerCascadeFullRes"
else:
assert issubclass(trainer_class, nnUNetTrainer), "network_trainer was found but is not derived from " \
"nnUNetTrainer"
trainer = trainer_class(plans_file, fold, output_folder=output_folder_name,
dataset_directory=dataset_directory, batch_dice=batch_dice, stage=stage,
unpack_data=decompress_data, deterministic=deterministic,
distribute_batch_size=args.dbs, num_gpus=num_gpus, fp16=not fp32)
if args.disable_saving:
trainer.save_latest_only = False # if false it will not store/overwrite _latest but separate files each
trainer.save_intermediate_checkpoints = False # whether or not to save checkpoint_latest
trainer.save_best_checkpoint = False # whether or not to save the best checkpoint according to self.best_val_eval_criterion_MA
trainer.save_final_checkpoint = False # whether or not to save the final checkpoint
trainer.initialize(not validation_only)
if find_lr:
trainer.find_lr()
else:
if not validation_only:
if args.continue_training:
# -c was set, continue a previous training and ignore pretrained weights
trainer.load_latest_checkpoint()
elif (not args.continue_training) and (args.pretrained_weights is not None):
# we start a new training. If pretrained_weights are set, use them
load_pretrained_weights(trainer.network, args.pretrained_weights)
else:
# new training without pretraine weights, do nothing
pass
trainer.run_training()
else:
if valbest:
trainer.load_best_checkpoint(train=False)
else:
trainer.load_final_checkpoint(train=False)
trainer.network.eval()
# predict validation
trainer.validate(save_softmax=args.npz, validation_folder_name=val_folder,
run_postprocessing_on_folds=not disable_postprocessing_on_folds)
if network == '3d_lowres':
print("predicting segmentations for the next stage of the cascade")
predict_next_stage(trainer, join(dataset_directory, trainer.plans['data_identifier'] + "_stage%d" % 1))
if __name__ == "__main__":
main()