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Zero
| #!/usr/bin/env python3 | |
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| PointRend Training Script. | |
| This script is a simplified version of the training script in detectron2/tools. | |
| """ | |
| import os | |
| import torch | |
| 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 ( | |
| CityscapesInstanceEvaluator, | |
| CityscapesSemSegEvaluator, | |
| COCOEvaluator, | |
| DatasetEvaluators, | |
| LVISEvaluator, | |
| SemSegEvaluator, | |
| verify_results, | |
| ) | |
| from point_rend import SemSegDatasetMapper, add_pointrend_config | |
| class Trainer(DefaultTrainer): | |
| """ | |
| We use the "DefaultTrainer" which contains a number pre-defined 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 use the cleaner | |
| "SimpleTrainer", or write your own training loop. | |
| """ | |
| 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 == "lvis": | |
| return LVISEvaluator(dataset_name, cfg, True, output_folder) | |
| if evaluator_type == "coco": | |
| return COCOEvaluator(dataset_name, cfg, True, output_folder) | |
| if evaluator_type == "sem_seg": | |
| return SemSegEvaluator( | |
| dataset_name, | |
| distributed=True, | |
| num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, | |
| ignore_label=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, | |
| output_dir=output_folder, | |
| ) | |
| if evaluator_type == "cityscapes_instance": | |
| assert ( | |
| torch.cuda.device_count() >= comm.get_rank() | |
| ), "CityscapesEvaluator currently do not work with multiple machines." | |
| return CityscapesInstanceEvaluator(dataset_name) | |
| if evaluator_type == "cityscapes_sem_seg": | |
| assert ( | |
| torch.cuda.device_count() >= comm.get_rank() | |
| ), "CityscapesEvaluator currently do not work with multiple machines." | |
| return CityscapesSemSegEvaluator(dataset_name) | |
| if len(evaluator_list) == 0: | |
| raise NotImplementedError( | |
| "no Evaluator for the dataset {} with the type {}".format( | |
| dataset_name, evaluator_type | |
| ) | |
| ) | |
| if len(evaluator_list) == 1: | |
| return evaluator_list[0] | |
| return DatasetEvaluators(evaluator_list) | |
| def build_train_loader(cls, cfg): | |
| if "SemanticSegmentor" in cfg.MODEL.META_ARCHITECTURE: | |
| mapper = SemSegDatasetMapper(cfg, 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) | |
| 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,), | |
| ) | |