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# Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.system() != 'Windows':
mp_start_method = cfg.get('mp_start_method', 'fork')
current_method = mp.get_start_method(allow_none=True)
if current_method is not None and current_method != mp_start_method:
warnings.warn(
f'Multi-processing start method `{mp_start_method}` is '
f'different from the previous setting `{current_method}`.'
f'It will be force set to `{mp_start_method}`. You can change '
f'this behavior by changing `mp_start_method` in your config.')
mp.set_start_method(mp_start_method, force=True)
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = cfg.get('opencv_num_threads', 0)
cv2.setNumThreads(opencv_num_threads)
# setup OMP threads
# This code is referred from https://github.com/pytorch/pytorch/blob/master/torch/distributed/run.py # noqa
if 'OMP_NUM_THREADS' not in os.environ and cfg.data.workers_per_gpu > 1:
omp_num_threads = 1
warnings.warn(
f'Setting OMP_NUM_THREADS environment variable for each process '
f'to be {omp_num_threads} in default, to avoid your system being '
f'overloaded, please further tune the variable for optimal '
f'performance in your application as needed.')
os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
# setup MKL threads
if 'MKL_NUM_THREADS' not in os.environ and cfg.data.workers_per_gpu > 1:
mkl_num_threads = 1
warnings.warn(
f'Setting MKL_NUM_THREADS environment variable for each process '
f'to be {mkl_num_threads} in default, to avoid your system being '
f'overloaded, please further tune the variable for optimal '
f'performance in your application as needed.')
os.environ['MKL_NUM_THREADS'] = str(mkl_num_threads)
# def register_all_modules(init_default_scope: bool = True) -> None:
# """Register all modules in mmpose into the registries.
# Args:
# init_default_scope (bool): Whether initialize the mmpose default scope.
# When `init_default_scope=True`, the global default scope will be
# set to `mmpose`, and all registries will build modules from mmpose's
# registry node. To understand more about the registry, please refer
# to https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/registry.md
# Defaults to True.
# """ # noqa
# import mmpose.models # noqa: F401,F403
# if init_default_scope:
# never_created = DefaultScope.get_current_instance() is None \
# or not DefaultScope.check_instance_created('mmpose')
# if never_created:
# DefaultScope.get_instance('mmpose', scope_name='mmpose')
# return
# current_scope = DefaultScope.get_current_instance()
# if current_scope.scope_name != 'mmpose':
# warnings.warn('The current default scope '
# f'"{current_scope.scope_name}" is not "mmpose", '
# '`register_all_modules` will force the current'
# 'default scope to be "mmpose". If this is not '
# 'expected, please set `init_default_scope=False`.')
# # avoid name conflict
# new_instance_name = f'mmpose-{datetime.datetime.now()}'
# DefaultScope.get_instance(new_instance_name, scope_name='mmpose') |