#!/usr/bin/env bash # Copyright (c) OpenMMLab. All rights reserved. """This file is for benchmark data loading process. It can also be used to refresh the memcached cache. The command line to run this file is: $ python -m cProfile -o program.prof tools/analysis/benchmark_processing.py configs/task/method/[config filename] Note: When debugging, the `workers_per_gpu` in the config should be set to 0 during benchmark. It use cProfile to record cpu running time and output to program.prof To visualize cProfile output program.prof, use Snakeviz and run: $ snakeviz program.prof """ import argparse import mmcv from mmcv import Config from mmpose import __version__ from mmpose.datasets import build_dataloader, build_dataset from mmpose.utils import get_root_logger def main(): parser = argparse.ArgumentParser(description='Benchmark data loading') parser.add_argument('config', help='train config file path') args = parser.parse_args() cfg = Config.fromfile(args.config) # init logger before other steps logger = get_root_logger() logger.info(f'MMPose Version: {__version__}') logger.info(f'Config: {cfg.text}') dataset = build_dataset(cfg.data.train) data_loader = build_dataloader( dataset, samples_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=False, shuffle=False) # Start progress bar after first 5 batches prog_bar = mmcv.ProgressBar( len(dataset) - 5 * cfg.data.samples_per_gpu, start=False) for i, data in enumerate(data_loader): if i == 5: prog_bar.start() for _ in data['img']: if i < 5: continue prog_bar.update() if __name__ == '__main__': main()