File size: 3,174 Bytes
505117b | 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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | from __future__ import annotations
import argparse
import os
from pathlib import Path
import torch
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.data.datasets import register_coco_instances
from detectron2.engine import DefaultTrainer
from detectron2.evaluation import COCOEvaluator
class BubbleTrainer(DefaultTrainer):
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "eval")
return COCOEvaluator(dataset_name, output_dir=output_folder)
def main() -> None:
parser = argparse.ArgumentParser(description="Fine-tune Detectron2 Mask R-CNN for flow bubbles.")
parser.add_argument("--train-json", required=True)
parser.add_argument("--val-json", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--max-iter", type=int, default=600)
parser.add_argument("--eval-period", type=int, default=100)
parser.add_argument("--base-lr", type=float, default=2.5e-4)
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
args = parser.parse_args()
train_name = "bubbleid_flow_train"
val_name = "bubbleid_flow_val"
register_coco_instances(train_name, {}, args.train_json, "")
register_coco_instances(val_name, {}, args.val_json, "")
MetadataCatalog.get(train_name).thing_classes = ["bubble"]
MetadataCatalog.get(val_name).thing_classes = ["bubble"]
cfg = get_cfg()
cfg.merge_from_file(
model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
)
cfg.DATASETS.TRAIN = (train_name,)
cfg.DATASETS.TEST = (val_name,)
cfg.DATALOADER.NUM_WORKERS = 0
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
)
cfg.MODEL.DEVICE = args.device
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128
# Preserve all predictions needed for standard COCO evaluation. The
# engineering vapor-area-fraction endpoint is evaluated separately at 0.30.
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
cfg.SOLVER.BASE_LR = args.base_lr
cfg.SOLVER.MAX_ITER = args.max_iter
cfg.SOLVER.STEPS = []
cfg.TEST.EVAL_PERIOD = args.eval_period
cfg.INPUT.MIN_SIZE_TRAIN = (512, 640)
cfg.INPUT.MAX_SIZE_TRAIN = 900
cfg.INPUT.MIN_SIZE_TEST = 640
cfg.INPUT.MAX_SIZE_TEST = 900
cfg.OUTPUT_DIR = args.output_dir
output_dir = Path(cfg.OUTPUT_DIR)
output_dir.mkdir(parents=True, exist_ok=True)
(output_dir / "config.yaml").write_text(cfg.dump(), encoding="utf-8")
trainer = BubbleTrainer(cfg)
trainer.resume_or_load(resume=False)
trainer.train()
if __name__ == "__main__":
main()
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