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train.py
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train.py
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#!/usr/bin/python
import os
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
from detectron2.config import *
from utils.custom_trainers import Trainer_bbox, Trainer_mask
from detectron2.data.datasets import register_coco_instances
from detectron2.engine import default_setup
import logging
logger = logging.getLogger("detectron2")
def main(args):
TRAIN_CONFIG = args.config_file
OUTPUT_PATHS = args.output_folder
TRAIN_IMG_DIR = args.train_img_dir
TRAIN_COCO_JSON = args.train_coco_json
VAL_IMG_DIR= args.val_img_dir
VAL_COCO_JSON = args.val_coco_json
register_coco_instances(f"custom_dataset_train", {},TRAIN_COCO_JSON , TRAIN_IMG_DIR)
register_coco_instances(f"custom_dataset_val", {},VAL_COCO_JSON , VAL_IMG_DIR,)
cfg = get_cfg()
cfg.merge_from_file(TRAIN_CONFIG) #TRAINING_MODEL_YAML
cfg.OUTPUT_DIR= OUTPUT_PATHS
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True) #lets just check our output dir exists
cfg.freeze() # make the configuration unchangeable during the training process
default_setup(cfg, args)
cfg.dump()
if args.train_method == 'bbox':
trainer = Trainer_bbox(cfg)
elif args.train_method == 'segm':
trainer = Trainer_mask(cfg)
## TODO: add rotated and obb
else:
print("define train_method in arugments")
exit()
trainer.resume_or_load()
trainer.train()
if __name__ == "__main__":
from utils.args_lib import local_train_args
args = local_train_args().parse_args()
main(args)