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@ -1,8 +1,10 @@ |
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# Copyright (c) OpenMMLab. All rights reserved. |
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import argparse |
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import copy |
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import multiprocessing as mp |
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import os |
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import os.path as osp |
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import platform |
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import time |
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import warnings |
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@ -19,8 +21,6 @@ from mmdet.datasets import build_dataset |
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from mmdet.models import build_detector |
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from mmdet.utils import collect_env, get_root_logger |
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cv2.setNumThreads(0) |
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def parse_args(): |
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parser = argparse.ArgumentParser(description='Train a detector') |
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@ -91,12 +91,48 @@ def parse_args(): |
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return args |
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def setup_multi_processes(cfg): |
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# set multi-process start method as `fork` to speed up the training |
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if platform.system() != 'Windows': |
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mp_start_method = cfg.get('mp_start_method', 'fork') |
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mp.set_start_method(mp_start_method) |
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# disable opencv multithreading to avoid system being overloaded |
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opencv_num_threads = cfg.get('opencv_num_threads', 0) |
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cv2.setNumThreads(opencv_num_threads) |
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# setup OMP threads |
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# This code is referred from https://github.com/pytorch/pytorch/blob/master/torch/distributed/run.py # noqa |
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if ('OMP_NUM_THREADS' not in os.environ and cfg.data.workers_per_gpu > 1): |
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omp_num_threads = 1 |
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warnings.warn( |
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f'Setting OMP_NUM_THREADS environment variable for each process ' |
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f'to be {omp_num_threads} in default, to avoid your system being ' |
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f'overloaded, please further tune the variable for optimal ' |
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f'performance in your application as needed.') |
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os.environ['OMP_NUM_THREADS'] = str(omp_num_threads) |
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# setup MKL threads |
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if 'MKL_NUM_THREADS' not in os.environ and cfg.data.workers_per_gpu > 1: |
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mkl_num_threads = 1 |
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warnings.warn( |
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f'Setting MKL_NUM_THREADS environment variable for each process ' |
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f'to be {mkl_num_threads} in default, to avoid your system being ' |
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f'overloaded, please further tune the variable for optimal ' |
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f'performance in your application as needed.') |
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os.environ['MKL_NUM_THREADS'] = str(mkl_num_threads) |
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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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# set multi-process settings |
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setup_multi_processes(cfg) |
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# set cudnn_benchmark |
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if cfg.get('cudnn_benchmark', False): |
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torch.backends.cudnn.benchmark = True |
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