mirror of https://github.com/open-mmlab/mmocr.git
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
#!/usr/bin/env python
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"""This file is for benchmark data loading process. It can also be used to
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refresh the memcached cache. The command line to run this file is:
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$ python -m cProfile -o program.prof tools/analysis/benchmark_processing.py
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configs/task/method/[config filename]
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Note: When debugging, the `workers_per_gpu` in the config should be set to 0
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during benchmark.
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It use cProfile to record cpu running time and output to program.prof
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To visualize cProfile output program.prof, use Snakeviz and run:
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$ snakeviz program.prof
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"""
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import argparse
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import mmcv
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from mmcv import Config
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from mmdet.datasets import build_dataloader
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from mmocr.datasets import build_dataset
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assert build_dataset is not None
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def main():
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parser = argparse.ArgumentParser(description='Benchmark data loading')
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parser.add_argument('config', help='Train config file path.')
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args = parser.parse_args()
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cfg = Config.fromfile(args.config)
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dataset = build_dataset(cfg.data.train)
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# prepare data loaders
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if 'imgs_per_gpu' in cfg.data:
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cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
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data_loader = build_dataloader(
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dataset,
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cfg.data.samples_per_gpu,
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cfg.data.workers_per_gpu,
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1,
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dist=False,
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seed=None)
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# Start progress bar after first 5 batches
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prog_bar = mmcv.ProgressBar(
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len(dataset) - 5 * cfg.data.samples_per_gpu, start=False)
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for i, data in enumerate(data_loader):
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if i == 5:
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prog_bar.start()
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for _ in range(len(data['img'])):
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if i < 5:
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continue
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prog_bar.update()
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if __name__ == '__main__':
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main()
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