| |
| |
|
|
| """ |
| TensorMask 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.engine import DefaultTrainer, default_argument_parser, default_setup, launch |
| from detectron2.evaluation import COCOEvaluator, verify_results |
|
|
| from tensormask import add_tensormask_config |
|
|
|
|
| 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) |
|
|
|
|
| def setup(args): |
| """ |
| Create configs and perform basic setups. |
| """ |
| cfg = get_cfg() |
| add_tensormask_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) |
| if comm.is_main_process(): |
| verify_results(cfg, res) |
| 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,), |
| ) |
|
|
