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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import datetime
import logging
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
from mmengine.logging import print_log
from mmengine.utils import digit_version
def setup_cache_size_limit_of_dynamo():
"""Setup cache size limit of dynamo.
Note: Due to the dynamic shape of the loss calculation and
post-processing parts in the object detection algorithm, these
functions must be compiled every time they are run.
Setting a large value for torch._dynamo.config.cache_size_limit
may result in repeated compilation, which can slow down training
and testing speed. Therefore, we need to set the default value of
cache_size_limit smaller. An empirical value is 4.
"""
import torch
if digit_version(torch.__version__) >= digit_version('2.0.0'):
if 'DYNAMO_CACHE_SIZE_LIMIT' in os.environ:
import torch._dynamo
cache_size_limit = int(os.environ['DYNAMO_CACHE_SIZE_LIMIT'])
torch._dynamo.config.cache_size_limit = cache_size_limit
print_log(
f'torch._dynamo.config.cache_size_limit is force '
f'set to {cache_size_limit}.',
logger='current',
level=logging.WARNING)
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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