| from __future__ import annotations
|
|
|
| import argparse
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| import os
|
| from pathlib import Path
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|
|
| import torch
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| from detectron2 import model_zoo
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| from detectron2.config import get_cfg
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| from detectron2.data import MetadataCatalog
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| from detectron2.data.datasets import register_coco_instances
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| from detectron2.engine import DefaultTrainer
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| from detectron2.evaluation import COCOEvaluator
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|
|
|
|
| class BubbleTrainer(DefaultTrainer):
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| @classmethod
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| def build_evaluator(cls, cfg, dataset_name, output_folder=None):
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| if output_folder is None:
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| output_folder = os.path.join(cfg.OUTPUT_DIR, "eval")
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| return COCOEvaluator(dataset_name, output_dir=output_folder)
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|
|
|
|
| def main() -> None:
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| parser = argparse.ArgumentParser(description="Fine-tune Detectron2 Mask R-CNN for flow bubbles.")
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| parser.add_argument("--train-json", required=True)
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| parser.add_argument("--val-json", required=True)
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| parser.add_argument("--output-dir", required=True)
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| parser.add_argument("--max-iter", type=int, default=600)
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| parser.add_argument("--eval-period", type=int, default=100)
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| parser.add_argument("--base-lr", type=float, default=2.5e-4)
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| parser.add_argument("--batch-size", type=int, default=1)
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| parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
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| args = parser.parse_args()
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|
|
| train_name = "bubbleid_flow_train"
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| val_name = "bubbleid_flow_val"
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| register_coco_instances(train_name, {}, args.train_json, "")
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| register_coco_instances(val_name, {}, args.val_json, "")
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| MetadataCatalog.get(train_name).thing_classes = ["bubble"]
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| MetadataCatalog.get(val_name).thing_classes = ["bubble"]
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|
|
| cfg = get_cfg()
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| cfg.merge_from_file(
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| model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
|
| )
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| cfg.DATASETS.TRAIN = (train_name,)
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| cfg.DATASETS.TEST = (val_name,)
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| cfg.DATALOADER.NUM_WORKERS = 0
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| cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
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| "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
|
| )
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| cfg.MODEL.DEVICE = args.device
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| cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
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| cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128
|
| |
| |
| cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05 |
| cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[8], [16], [32], [64], [128]]
|
| cfg.SOLVER.IMS_PER_BATCH = args.batch_size
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| cfg.SOLVER.BASE_LR = args.base_lr
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| cfg.SOLVER.MAX_ITER = args.max_iter
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| cfg.SOLVER.STEPS = []
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| cfg.TEST.EVAL_PERIOD = args.eval_period
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| cfg.INPUT.MIN_SIZE_TRAIN = (512, 640)
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| cfg.INPUT.MAX_SIZE_TRAIN = 900
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| cfg.INPUT.MIN_SIZE_TEST = 640
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| cfg.INPUT.MAX_SIZE_TEST = 900
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| cfg.OUTPUT_DIR = args.output_dir
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|
|
| output_dir = Path(cfg.OUTPUT_DIR)
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| output_dir.mkdir(parents=True, exist_ok=True)
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| (output_dir / "config.yaml").write_text(cfg.dump(), encoding="utf-8")
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|
|
| trainer = BubbleTrainer(cfg)
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| trainer.resume_or_load(resume=False)
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| trainer.train()
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|
|
|
|
| if __name__ == "__main__":
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| main()
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|
|
