mirror of https://github.com/JDAI-CV/fast-reid.git
128 lines
3.6 KiB
Python
128 lines
3.6 KiB
Python
#!/usr/bin/env python
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# encoding: utf-8
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"""
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@author: sherlock
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@contact: sherlockliao01@gmail.com
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"""
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import json
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import logging
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import os
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import sys
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sys.path.append('.')
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from fastreid.config import get_cfg
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from fastreid.engine import default_argument_parser, default_setup, launch
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from fastreid.data.build import build_reid_train_loader, build_reid_test_loader
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from fastreid.evaluation.clas_evaluator import ClasEvaluator
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from fastreid.utils.checkpoint import Checkpointer, PathManager
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from fastreid.utils import comm
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from fastreid.engine import DefaultTrainer
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from fastreid.data.datasets import DATASET_REGISTRY
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from fastreid.data.transforms import build_transforms
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from fastreid.data.build import _root
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from fastclas import *
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class ClasTrainer(DefaultTrainer):
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@classmethod
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def build_train_loader(cls, cfg):
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"""
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Returns:
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iterable
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It now calls :func:`fastreid.data.build_reid_train_loader`.
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Overwrite it if you'd like a different data loader.
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"""
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logger = logging.getLogger("fastreid.clas_dataset")
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logger.info("Prepare training set")
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train_items = list()
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for d in cfg.DATASETS.NAMES:
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data = DATASET_REGISTRY.get(d)(root=_root)
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if comm.is_main_process():
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data.show_train()
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train_items.extend(data.train)
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transforms = build_transforms(cfg, is_train=True)
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train_set = ClasDataset(train_items, transforms)
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data_loader = build_reid_train_loader(cfg, train_set=train_set)
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# Save index to class dictionary
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output_dir = cfg.OUTPUT_DIR
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if comm.is_main_process() and output_dir:
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path = os.path.join(output_dir, "idx2class.json")
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with PathManager.open(path, "w") as f:
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json.dump(train_set.idx_to_class, f)
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return data_loader
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@classmethod
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def build_test_loader(cls, cfg, dataset_name):
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"""
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Returns:
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iterable
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It now calls :func:`fastreid.data.build_reid_test_loader`.
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Overwrite it if you'd like a different data loader.
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"""
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data = DATASET_REGISTRY.get(dataset_name)(root=_root)
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if comm.is_main_process():
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data.show_test()
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transforms = build_transforms(cfg, is_train=False)
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test_set = ClasDataset(data.query, transforms)
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data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)
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return data_loader
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@classmethod
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def build_evaluator(cls, cfg, dataset_name, output_dir=None):
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data_loader = cls.build_test_loader(cfg, dataset_name)
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return data_loader, ClasEvaluator(cfg, output_dir)
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def setup(args):
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"""
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Create configs and perform basic setups.
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"""
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cfg = get_cfg()
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cfg.merge_from_file(args.config_file)
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cfg.merge_from_list(args.opts)
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cfg.freeze()
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default_setup(cfg, args)
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return cfg
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def main(args):
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cfg = setup(args)
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if args.eval_only:
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cfg.defrost()
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cfg.MODEL.BACKBONE.PRETRAIN = False
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model = ClasTrainer.build_model(cfg)
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Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
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res = ClasTrainer.test(cfg, model)
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return res
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trainer = ClasTrainer(cfg)
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trainer.resume_or_load(resume=args.resume)
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return trainer.train()
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if __name__ == "__main__":
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args = default_argument_parser().parse_args()
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print("Command Line Args:", args)
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launch(
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main,
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args.num_gpus,
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num_machines=args.num_machines,
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machine_rank=args.machine_rank,
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dist_url=args.dist_url,
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args=(args,),
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)
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