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import argparse |
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import os |
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from mmengine import MMLogger |
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from mmengine.config import Config, DictAction |
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from mmengine.dist import init_dist |
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from mmengine.registry import init_default_scope |
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from mmengine.utils import mkdir_or_exist |
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from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark, |
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InferenceBenchmark) |
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def parse_args(): |
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parser = argparse.ArgumentParser(description='MMDet benchmark') |
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parser.add_argument('config', help='test config file path') |
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parser.add_argument('--checkpoint', help='checkpoint file') |
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parser.add_argument( |
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'--task', |
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choices=['inference', 'dataloader', 'dataset'], |
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default='dataloader', |
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help='Which task do you want to go to benchmark') |
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parser.add_argument( |
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'--repeat-num', |
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type=int, |
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default=1, |
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help='number of repeat times of measurement for averaging the results') |
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parser.add_argument( |
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'--max-iter', type=int, default=2000, help='num of max iter') |
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parser.add_argument( |
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'--log-interval', type=int, default=50, help='interval of logging') |
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parser.add_argument( |
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'--num-warmup', type=int, default=5, help='Number of warmup') |
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parser.add_argument( |
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'--fuse-conv-bn', |
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action='store_true', |
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help='Whether to fuse conv and bn, this will slightly increase' |
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'the inference speed') |
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parser.add_argument( |
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'--dataset-type', |
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choices=['train', 'val', 'test'], |
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default='test', |
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help='Benchmark dataset type. only supports train, val and test') |
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parser.add_argument( |
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'--work-dir', |
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help='the directory to save the file containing ' |
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'benchmark metrics') |
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parser.add_argument( |
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'--cfg-options', |
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nargs='+', |
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action=DictAction, |
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help='override some settings in the used config, the key-value pair ' |
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'in xxx=yyy format will be merged into config file. If the value to ' |
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' |
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' |
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'Note that the quotation marks are necessary and that no white space ' |
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'is allowed.') |
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parser.add_argument( |
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'--launcher', |
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choices=['none', 'pytorch', 'slurm', 'mpi'], |
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default='none', |
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help='job launcher') |
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parser.add_argument('--local_rank', type=int, default=0) |
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args = parser.parse_args() |
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if 'LOCAL_RANK' not in os.environ: |
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os.environ['LOCAL_RANK'] = str(args.local_rank) |
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return args |
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def inference_benchmark(args, cfg, distributed, logger): |
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benchmark = InferenceBenchmark( |
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cfg, |
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args.checkpoint, |
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distributed, |
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args.fuse_conv_bn, |
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args.max_iter, |
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args.log_interval, |
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args.num_warmup, |
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logger=logger) |
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return benchmark |
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def dataloader_benchmark(args, cfg, distributed, logger): |
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benchmark = DataLoaderBenchmark( |
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cfg, |
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distributed, |
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args.dataset_type, |
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args.max_iter, |
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args.log_interval, |
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args.num_warmup, |
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logger=logger) |
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return benchmark |
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def dataset_benchmark(args, cfg, distributed, logger): |
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benchmark = DatasetBenchmark( |
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cfg, |
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args.dataset_type, |
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args.max_iter, |
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args.log_interval, |
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args.num_warmup, |
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logger=logger) |
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return benchmark |
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def main(): |
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args = parse_args() |
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cfg = Config.fromfile(args.config) |
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if args.cfg_options is not None: |
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cfg.merge_from_dict(args.cfg_options) |
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init_default_scope(cfg.get('default_scope', 'mmdet')) |
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distributed = False |
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if args.launcher != 'none': |
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init_dist(args.launcher, **cfg.get('env_cfg', {}).get('dist_cfg', {})) |
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distributed = True |
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log_file = None |
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if args.work_dir: |
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log_file = os.path.join(args.work_dir, 'benchmark.log') |
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mkdir_or_exist(args.work_dir) |
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logger = MMLogger.get_instance( |
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'mmdet', log_file=log_file, log_level='INFO') |
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benchmark = eval(f'{args.task}_benchmark')(args, cfg, distributed, logger) |
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benchmark.run(args.repeat_num) |
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if __name__ == '__main__': |
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main() |
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