train_custom.py

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.

"""

Training script using custom coco format dataset

what you need to do is simply change the img_dir and annotation path here
Also define your own categories.

"""

import os
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
from detectron2.evaluation import COCOEvaluator
from detectron2.data import MetadataCatalog, build_detection_train_loader, DatasetCatalog
from detectron2.data.datasets.coco import load_coco_json
from detectron2.data.dataset_mapper import DatasetMapper

from yolov7.config import add_yolo_config
from yolov7.data.dataset_mapper import MyDatasetMapper


# here is your dataset config
CLASS_NAMES = ["face", 'face_mask']
DATASET_ROOT = './datasets/facemask'
ANN_ROOT = os.path.join(DATASET_ROOT, 'annotations')
TRAIN_PATH = os.path.join(DATASET_ROOT, 'train')
VAL_PATH = os.path.join(DATASET_ROOT, 'val')
TRAIN_JSON = os.path.join(ANN_ROOT, 'instances_train2017.json')
VAL_JSON = os.path.join(ANN_ROOT, 'instances_val2017.json')
PREDEFINED_SPLITS_DATASET = {
    "facemask_train": (TRAIN_PATH, TRAIN_JSON),
    "facemask_val": (VAL_PATH, VAL_JSON),
}


def plain_register_dataset():
    for k, v in PREDEFINED_SPLITS_DATASET.items():
        DatasetCatalog.register(
            k, lambda: load_coco_json(v[1], v[0]))
        MetadataCatalog.get(k).set(thing_classes=CLASS_NAMES,
                                   evaluator_type='coco',
                                   json_file=v[1],
                                   image_root=v[0])


plain_register_dataset()


class Trainer(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, "inference")
        return COCOEvaluator(dataset_name, output_dir=output_folder)

    @classmethod
    def build_train_loader(cls, cfg):
        # return build_detection_train_loader(cfg, mapper=DatasetMapper(cfg, True))
        # test our own dataset mapper to add more augmentations
        return build_detection_train_loader(cfg, mapper=MyDatasetMapper(cfg, True))


def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    add_yolo_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    return cfg


def main(args):
    cfg = setup(args)

    if args.eval_only:
        model = Trainer.build_model(cfg)
        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )
        res = Trainer.test(cfg, model)
        return res

    trainer = Trainer(cfg)
    trainer.resume_or_load(resume=args.resume)
    return trainer.train()


if __name__ == "__main__":
    args = default_argument_parser().parse_args()
    print("Command Line Args:", args)
    launch(
        main,
        args.num_gpus,
        num_machines=args.num_machines,
        machine_rank=args.machine_rank,
        dist_url=args.dist_url,
        args=(args,),
    )