tools/benchmark_processing.py

#!/usr/bin/env python
# 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 mmdet.datasets import build_dataloader

from mmocr.datasets import build_dataset

assert build_dataset is not None


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)

    dataset = build_dataset(cfg.data.train)

    # prepare data loaders
    if 'imgs_per_gpu' in cfg.data:
        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu

    data_loader = build_dataloader(
        dataset,
        cfg.data.samples_per_gpu,
        cfg.data.workers_per_gpu,
        1,
        dist=False,
        seed=None)

    # 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 range(len(data['img'])):
            if i < 5:
                continue
            prog_bar.update()


if __name__ == '__main__':
    main()