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# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
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
workers_per_gpu = cfg.data.get('workers_per_gpu', 1)
if 'train_dataloader' in cfg.data:
workers_per_gpu = \
max(cfg.data.train_dataloader.get('workers_per_gpu', 1),
workers_per_gpu)
if 'OMP_NUM_THREADS' not in os.environ and 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 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 mmdet into the registries.
Args:
init_default_scope (bool): Whether initialize the mmdet default scope.
When `init_default_scope=True`, the global default scope will be
set to `mmdet`, and all registries will build modules from mmdet'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 mmdet.datasets # noqa: F401,F403
import mmdet.engine # noqa: F401,F403
import mmdet.evaluation # noqa: F401,F403
import mmdet.models # noqa: F401,F403
import mmdet.visualization # noqa: F401,F403
if init_default_scope:
never_created = DefaultScope.get_current_instance() is None \
or not DefaultScope.check_instance_created('mmdet')
if never_created:
DefaultScope.get_instance('mmdet', scope_name='mmdet')
return
current_scope = DefaultScope.get_current_instance()
if current_scope.scope_name != 'mmdet':
warnings.warn('The current default scope '
f'"{current_scope.scope_name}" is not "mmdet", '
'`register_all_modules` will force the current'
'default scope to be "mmdet". If this is not '
'expected, please set `init_default_scope=False`.')
# avoid name conflict
new_instance_name = f'mmdet-{datetime.datetime.now()}'
DefaultScope.get_instance(new_instance_name, scope_name='mmdet')
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