File size: 1,928 Bytes
989117c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import argparse
import glob
import json
import os
import monai
from sklearn.model_selection import train_test_split
def produce_sample_dict(line: str):
return {"label": line, "image": line.replace("labelsTr", "imagesTr")}
def produce_datalist(dataset_dir: str, train_size: int = 196):
"""
This function is used to split the dataset.
It will produce "train_size" number of samples for training.
"""
samples = sorted(glob.glob(os.path.join(dataset_dir, "labelsTr", "*"), recursive=True))
samples = [_item.replace(os.path.join(dataset_dir, "labelsTr"), "labelsTr") for _item in samples]
datalist = []
for line in samples:
datalist.append(produce_sample_dict(line))
train_list, other_list = train_test_split(datalist, train_size=train_size)
val_list, test_list = train_test_split(other_list, train_size=0.66)
return {"training": train_list, "validation": val_list, "testing": test_list}
def main(args):
"""
split the dataset and output the data list into a json file.
"""
data_file_base_dir = args.path
output_json = args.output
# produce deterministic data splits
monai.utils.set_determinism(seed=123)
datalist = produce_datalist(dataset_dir=data_file_base_dir, train_size=args.train_size)
with open(output_json, "w") as f:
json.dump(datalist, f, ensure_ascii=True, indent=4)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="")
parser.add_argument(
"--path",
type=str,
default="/workspace/data/msd/Task07_Pancreas",
help="root path of MSD Task07_Pancreas dataset.",
)
parser.add_argument(
"--output", type=str, default="dataset_0.json", help="relative path of output datalist json file."
)
parser.add_argument("--train_size", type=int, default=196, help="number of training samples.")
args = parser.parse_args()
main(args)
|