| |
| |
| """ |
| Point supervision Training Script. |
| |
| This script is a simplified version of the training script in detectron2/tools. |
| """ |
|
|
| import os |
|
|
| import detectron2.utils.comm as comm |
| from detectron2.checkpoint import DetectionCheckpointer |
| from detectron2.config import get_cfg |
| from detectron2.data import MetadataCatalog, build_detection_train_loader |
| from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch |
| from detectron2.evaluation import COCOEvaluator, DatasetEvaluators, verify_results |
| from detectron2.projects.point_rend import add_pointrend_config |
| from detectron2.utils.logger import setup_logger |
|
|
| from point_sup import PointSupDatasetMapper, add_point_sup_config |
|
|
|
|
| class Trainer(DefaultTrainer): |
| """ |
| We use the "DefaultTrainer" which contains pre-defined default logic for |
| standard training workflow. They may not work for you, especially if you |
| are working on a new research project. In that case you can write your |
| own training loop. You can use "tools/plain_train_net.py" as an example. |
| """ |
|
|
| @classmethod |
| def build_evaluator(cls, cfg, dataset_name, output_folder=None): |
| """ |
| Create evaluator(s) for a given dataset. |
| This uses the special metadata "evaluator_type" associated with each builtin dataset. |
| For your own dataset, you can simply create an evaluator manually in your |
| script and do not have to worry about the hacky if-else logic here. |
| """ |
| if output_folder is None: |
| output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") |
| evaluator_list = [] |
| evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type |
| if evaluator_type == "coco": |
| evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) |
| if len(evaluator_list) == 0: |
| raise NotImplementedError( |
| "no Evaluator for the dataset {} with the type {}".format( |
| dataset_name, evaluator_type |
| ) |
| ) |
| elif len(evaluator_list) == 1: |
| return evaluator_list[0] |
| return DatasetEvaluators(evaluator_list) |
|
|
| @classmethod |
| def build_train_loader(cls, cfg): |
| if cfg.INPUT.POINT_SUP: |
| mapper = PointSupDatasetMapper(cfg, is_train=True) |
| else: |
| mapper = None |
| return build_detection_train_loader(cfg, mapper=mapper) |
|
|
|
|
| def setup(args): |
| """ |
| Create configs and perform basic setups. |
| """ |
| cfg = get_cfg() |
| add_pointrend_config(cfg) |
| add_point_sup_config(cfg) |
| cfg.merge_from_file(args.config_file) |
| cfg.merge_from_list(args.opts) |
| cfg.freeze() |
| default_setup(cfg, args) |
| |
| setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="point_sup") |
| 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) |
| if cfg.TEST.AUG.ENABLED: |
| res.update(Trainer.test_with_TTA(cfg, model)) |
| if comm.is_main_process(): |
| verify_results(cfg, res) |
| return res |
|
|
| """ |
| If you'd like to do anything fancier than the standard training logic, |
| consider writing your own training loop (see plain_train_net.py) or |
| subclassing the trainer. |
| """ |
| 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,), |
| ) |
|
|
