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| # Copyright (c) OpenMMLab. All rights reserved. | |
| 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') | |