mirror of https://github.com/JDAI-CV/fast-reid.git
79 lines
2.2 KiB
Python
79 lines
2.2 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 os
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import logging
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import sys
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sys.path.append('.')
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from torch import nn
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from fastreid.config import get_cfg
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from fastreid.engine import DefaultTrainer, default_argument_parser, default_setup
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from fastreid.utils.checkpoint import Checkpointer
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from fastreid.engine import hooks
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from fastreid.evaluation import ReidEvaluator
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class Trainer(DefaultTrainer):
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@classmethod
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def build_evaluator(cls, cfg, num_query, output_folder=None):
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if output_folder is None:
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output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
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return ReidEvaluator(cfg, num_query)
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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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logger = logging.getLogger('fastreid.' + __name__)
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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 = Trainer.build_model(cfg)
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model = nn.DataParallel(model)
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model = model.cuda()
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Checkpointer(model, save_dir=cfg.OUTPUT_DIR).load(cfg.MODEL.WEIGHTS) # load trained model
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if cfg.TEST.PRECISE_BN.ENABLED and hooks.get_bn_modules(model):
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prebn_cfg = cfg.clone()
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prebn_cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
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prebn_cfg.DATASETS.NAMES = tuple([cfg.TEST.PRECISE_BN.DATASET]) # set dataset name for PreciseBN
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logger.info("Prepare precise BN dataset")
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hooks.PreciseBN(
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# Run at the same freq as (but before) evaluation.
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model,
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# Build a new data loader to not affect training
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Trainer.build_train_loader(prebn_cfg),
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cfg.TEST.PRECISE_BN.NUM_ITER,
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).update_stats()
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res = Trainer.test(cfg, model)
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return res
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trainer = Trainer(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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main(args)
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