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Source code for mmengine.runner.runner

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
import os
import os.path as osp
import pickle
import platform
import time
import warnings
from collections import OrderedDict
from functools import partial
from typing import Callable, Dict, List, Optional, Sequence, Union

import torch
import torch.nn as nn
from torch.nn.parallel.distributed import DistributedDataParallel
from torch.optim import Optimizer
from torch.utils.data import DataLoader

import mmengine
from mmengine.config import Config, ConfigDict
from mmengine.dataset import worker_init_fn as default_worker_init_fn
from mmengine.device import get_device
from mmengine.dist import (broadcast, get_dist_info, get_rank, init_dist,
                           is_distributed, master_only)
from mmengine.evaluator import Evaluator
from mmengine.fileio import FileClient, join_path
from mmengine.hooks import Hook
from mmengine.logging import MessageHub, MMLogger, print_log
from mmengine.model import (MMDistributedDataParallel, convert_sync_batchnorm,
                            is_model_wrapper, revert_sync_batchnorm)
from mmengine.optim import (OptimWrapper, OptimWrapperDict, _ParamScheduler,
                            build_optim_wrapper)
from mmengine.registry import (DATA_SAMPLERS, DATASETS, EVALUATOR, FUNCTIONS,
                               HOOKS, LOG_PROCESSORS, LOOPS, MODEL_WRAPPERS,
                               MODELS, OPTIM_WRAPPERS, PARAM_SCHEDULERS,
                               RUNNERS, VISUALIZERS, DefaultScope)
from mmengine.utils import apply_to, digit_version, get_git_hash, is_seq_of
from mmengine.utils.dl_utils import (TORCH_VERSION, collect_env,
                                     set_multi_processing)
from mmengine.visualization import Visualizer
from .base_loop import BaseLoop
from .checkpoint import (_load_checkpoint, _load_checkpoint_to_model,
                         find_latest_checkpoint, save_checkpoint,
                         weights_to_cpu)
from .log_processor import LogProcessor
from .loops import EpochBasedTrainLoop, IterBasedTrainLoop, TestLoop, ValLoop
from .priority import Priority, get_priority
from .utils import set_random_seed

ConfigType = Union[Dict, Config, ConfigDict]
ParamSchedulerType = Union[List[_ParamScheduler], Dict[str,
                                                       List[_ParamScheduler]]]
OptimWrapperType = Union[OptimWrapper, OptimWrapperDict]


[docs]@RUNNERS.register_module() class Runner: """A training helper for PyTorch. Runner object can be built from config by ``runner = Runner.from_cfg(cfg)`` where the ``cfg`` usually contains training, validation, and test-related configurations to build corresponding components. We usually use the same config to launch training, testing, and validation tasks. However, only some of these components are necessary at the same time, e.g., testing a model does not need training or validation-related components. To avoid repeatedly modifying config, the construction of ``Runner`` adopts lazy initialization to only initialize components when they are going to be used. Therefore, the model is always initialized at the beginning, and training, validation, and, testing related components are only initialized when calling ``runner.train()``, ``runner.val()``, and ``runner.test()``, respectively. Args: model (:obj:`torch.nn.Module` or dict): The model to be run. It can be a dict used for build a model. work_dir (str): The working directory to save checkpoints. The logs will be saved in the subdirectory of `work_dir` named :attr:`timestamp`. train_dataloader (Dataloader or dict, optional): A dataloader object or a dict to build a dataloader. If ``None`` is given, it means skipping training steps. Defaults to None. See :meth:`build_dataloader` for more details. val_dataloader (Dataloader or dict, optional): A dataloader object or a dict to build a dataloader. If ``None`` is given, it means skipping validation steps. Defaults to None. See :meth:`build_dataloader` for more details. test_dataloader (Dataloader or dict, optional): A dataloader object or a dict to build a dataloader. If ``None`` is given, it means skipping test steps. Defaults to None. See :meth:`build_dataloader` for more details. train_cfg (dict, optional): A dict to build a training loop. If it does not provide "type" key, it should contain "by_epoch" to decide which type of training loop :class:`EpochBasedTrainLoop` or :class:`IterBasedTrainLoop` should be used. If ``train_cfg`` specified, :attr:`train_dataloader` should also be specified. Defaults to None. See :meth:`build_train_loop` for more details. val_cfg (dict, optional): A dict to build a validation loop. If it does not provide "type" key, :class:`ValLoop` will be used by default. If ``val_cfg`` specified, :attr:`val_dataloader` should also be specified. If ``ValLoop`` is built with `fp16=True``, ``runner.val()`` will be performed under fp16 precision. Defaults to None. See :meth:`build_val_loop` for more details. test_cfg (dict, optional): A dict to build a test loop. If it does not provide "type" key, :class:`TestLoop` will be used by default. If ``test_cfg`` specified, :attr:`test_dataloader` should also be specified. If ``ValLoop`` is built with `fp16=True``, ``runner.val()`` will be performed under fp16 precision. Defaults to None. See :meth:`build_test_loop` for more details. auto_scale_lr (dict, Optional): Config to scale the learning rate automatically. It includes ``base_batch_size`` and ``enable``. ``base_batch_size`` is the batch size that the optimizer lr is based on. ``enable`` is the switch to turn on and off the feature. optim_wrapper (OptimWrapper or dict, optional): Computing gradient of model parameters. If specified, :attr:`train_dataloader` should also be specified. If automatic mixed precision or gradient accmulation training is required. The type of ``optim_wrapper`` should be AmpOptimizerWrapper. See :meth:`build_optim_wrapper` for examples. Defaults to None. param_scheduler (_ParamScheduler or dict or list, optional): Parameter scheduler for updating optimizer parameters. If specified, :attr:`optimizer` should also be specified. Defaults to None. See :meth:`build_param_scheduler` for examples. val_evaluator (Evaluator or dict or list, optional): A evaluator object used for computing metrics for validation. It can be a dict or a list of dict to build a evaluator. If specified, :attr:`val_dataloader` should also be specified. Defaults to None. test_evaluator (Evaluator or dict or list, optional): A evaluator object used for computing metrics for test steps. It can be a dict or a list of dict to build a evaluator. If specified, :attr:`test_dataloader` should also be specified. Defaults to None. default_hooks (dict[str, dict] or dict[str, Hook], optional): Hooks to execute default actions like updating model parameters and saving checkpoints. Default hooks are ``OptimizerHook``, ``IterTimerHook``, ``LoggerHook``, ``ParamSchedulerHook`` and ``CheckpointHook``. Defaults to None. See :meth:`register_default_hooks` for more details. custom_hooks (list[dict] or list[Hook], optional): Hooks to execute custom actions like visualizing images processed by pipeline. Defaults to None. data_preprocessor (dict, optional): The pre-process config of :class:`BaseDataPreprocessor`. If the ``model`` argument is a dict and doesn't contain the key ``data_preprocessor``, set the argument as the ``data_preprocessor`` of the ``model`` dict. Defaults to None. load_from (str, optional): The checkpoint file to load from. Defaults to None. resume (bool): Whether to resume training. Defaults to False. If ``resume`` is True and ``load_from`` is None, automatically to find latest checkpoint from ``work_dir``. If not found, resuming does nothing. launcher (str): Way to launcher multi-process. Supported launchers are 'pytorch', 'mpi', 'slurm' and 'none'. If 'none' is provided, non-distributed environment will be launched. env_cfg (dict): A dict used for setting environment. Defaults to dict(dist_cfg=dict(backend='nccl')). log_processor (dict, optional): A processor to format logs. Defaults to None. log_level (int or str): The log level of MMLogger handlers. Defaults to 'INFO'. visualizer (Visualizer or dict, optional): A Visualizer object or a dict build Visualizer object. Defaults to None. If not specified, default config will be used. default_scope (str): Used to reset registries location. Defaults to "mmengine". randomness (dict): Some settings to make the experiment as reproducible as possible like seed and deterministic. Defaults to ``dict(seed=None)``. If seed is None, a random number will be generated and it will be broadcasted to all other processes if in distributed environment. If ``cudnn_benchmark`` is ``True`` in ``env_cfg`` but ``deterministic`` is ``True`` in ``randomness``, the value of ``torch.backends.cudnn.benchmark`` will be ``False`` finally. experiment_name (str, optional): Name of current experiment. If not specified, timestamp will be used as ``experiment_name``. Defaults to None. cfg (dict or Configdict or :obj:`Config`, optional): Full config. Defaults to None. Note: Since PyTorch 2.0.0, you can enable ``torch.compile`` by passing in `cfg.compile = True`. If you want to control compile options, you can pass a dict, e.g. ``cfg.compile = dict(backend='eager')``. Refer to `PyTorch API Documentation <https://pytorch.org/docs/ master/generated/torch.compile.html#torch.compile>`_ for more valid options. Examples: >>> from mmengine.runner import Runner >>> cfg = dict( >>> model=dict(type='ToyModel'), >>> work_dir='path/of/work_dir', >>> train_dataloader=dict( >>> dataset=dict(type='ToyDataset'), >>> sampler=dict(type='DefaultSampler', shuffle=True), >>> batch_size=1, >>> num_workers=0), >>> val_dataloader=dict( >>> dataset=dict(type='ToyDataset'), >>> sampler=dict(type='DefaultSampler', shuffle=False), >>> batch_size=1, >>> num_workers=0), >>> test_dataloader=dict( >>> dataset=dict(type='ToyDataset'), >>> sampler=dict(type='DefaultSampler', shuffle=False), >>> batch_size=1, >>> num_workers=0), >>> auto_scale_lr=dict(base_batch_size=16, enable=False), >>> optim_wrapper=dict(type='OptimizerWrapper', optimizer=dict( >>> type='SGD', lr=0.01)), >>> param_scheduler=dict(type='MultiStepLR', milestones=[1, 2]), >>> val_evaluator=dict(type='ToyEvaluator'), >>> test_evaluator=dict(type='ToyEvaluator'), >>> train_cfg=dict(by_epoch=True, max_epochs=3, val_interval=1), >>> val_cfg=dict(), >>> test_cfg=dict(), >>> custom_hooks=[], >>> default_hooks=dict( >>> timer=dict(type='IterTimerHook'), >>> checkpoint=dict(type='CheckpointHook', interval=1), >>> logger=dict(type='LoggerHook'), >>> optimizer=dict(type='OptimizerHook', grad_clip=False), >>> param_scheduler=dict(type='ParamSchedulerHook')), >>> launcher='none', >>> env_cfg=dict(dist_cfg=dict(backend='nccl')), >>> log_processor=dict(window_size=20), >>> visualizer=dict(type='Visualizer', >>> vis_backends=[dict(type='LocalVisBackend', >>> save_dir='temp_dir')]) >>> ) >>> runner = Runner.from_cfg(cfg) >>> runner.train() >>> runner.test() """ cfg: Config _train_loop: Optional[Union[BaseLoop, Dict]] _val_loop: Optional[Union[BaseLoop, Dict]] _test_loop: Optional[Union[BaseLoop, Dict]] def __init__( self, model: Union[nn.Module, Dict], work_dir: str, train_dataloader: Optional[Union[DataLoader, Dict]] = None, val_dataloader: Optional[Union[DataLoader, Dict]] = None, test_dataloader: Optional[Union[DataLoader, Dict]] = None, train_cfg: Optional[Dict] = None, val_cfg: Optional[Dict] = None, test_cfg: Optional[Dict] = None, auto_scale_lr: Optional[Dict] = None, optim_wrapper: Optional[Union[OptimWrapper, Dict]] = None, param_scheduler: Optional[Union[_ParamScheduler, Dict, List]] = None, val_evaluator: Optional[Union[Evaluator, Dict, List]] = None, test_evaluator: Optional[Union[Evaluator, Dict, List]] = None, default_hooks: Optional[Dict[str, Union[Hook, Dict]]] = None, custom_hooks: Optional[List[Union[Hook, Dict]]] = None, data_preprocessor: Union[nn.Module, Dict, None] = None, load_from: Optional[str] = None, resume: bool = False, launcher: str = 'none', env_cfg: Dict = dict(dist_cfg=dict(backend='nccl')), log_processor: Optional[Dict] = None, log_level: str = 'INFO', visualizer: Optional[Union[Visualizer, Dict]] = None, default_scope: str = 'mmengine', randomness: Dict = dict(seed=None), experiment_name: Optional[str] = None, cfg: Optional[ConfigType] = None, ): self._work_dir = osp.abspath(work_dir) mmengine.mkdir_or_exist(self._work_dir) # recursively copy the `cfg` because `self.cfg` will be modified # everywhere. if cfg is not None: if isinstance(cfg, Config): self.cfg = copy.deepcopy(cfg) elif isinstance(cfg, dict): self.cfg = Config(cfg) else: self.cfg = Config(dict()) # lazy initialization training_related = [train_dataloader, train_cfg, optim_wrapper] if not (all(item is None for item in training_related) or all(item is not None for item in training_related)): raise ValueError( 'train_dataloader, train_cfg, and optim_wrapper should be ' 'either all None or not None, but got ' f'train_dataloader={train_dataloader}, ' f'train_cfg={train_cfg}, ' f'optim_wrapper={optim_wrapper}.') self._train_dataloader = train_dataloader self._train_loop = train_cfg self.optim_wrapper: Optional[Union[OptimWrapper, dict]] self.optim_wrapper = optim_wrapper self.auto_scale_lr = auto_scale_lr # If there is no need to adjust learning rate, momentum or other # parameters of optimizer, param_scheduler can be None if param_scheduler is not None and self.optim_wrapper is None: raise ValueError( 'param_scheduler should be None when optim_wrapper is None, ' f'but got {param_scheduler}') # Parse `param_scheduler` to a list or a dict. If `optim_wrapper` is a # `dict` with single optimizer, parsed param_scheduler will be a # list of parameter schedulers. If `optim_wrapper` is # a `dict` with multiple optimizers, parsed `param_scheduler` will be # dict with multiple list of parameter schedulers. self._check_scheduler_cfg(param_scheduler) self.param_schedulers = param_scheduler val_related = [val_dataloader, val_cfg, val_evaluator] if not (all(item is None for item in val_related) or all(item is not None for item in val_related)): raise ValueError( 'val_dataloader, val_cfg, and val_evaluator should be either ' 'all None or not None, but got ' f'val_dataloader={val_dataloader}, val_cfg={val_cfg}, ' f'val_evaluator={val_evaluator}') self._val_dataloader = val_dataloader self._val_loop = val_cfg self._val_evaluator = val_evaluator test_related = [test_dataloader, test_cfg, test_evaluator] if not (all(item is None for item in test_related) or all(item is not None for item in test_related)): raise ValueError( 'test_dataloader, test_cfg, and test_evaluator should be ' 'either all None or not None, but got ' f'test_dataloader={test_dataloader}, test_cfg={test_cfg}, ' f'test_evaluator={test_evaluator}') self._test_dataloader = test_dataloader self._test_loop = test_cfg self._test_evaluator = test_evaluator self._launcher = launcher if self._launcher == 'none': self._distributed = False else: self._distributed = True # self._timestamp will be set in the `setup_env` method. Besides, # it also will initialize multi-process and (or) distributed # environment. self.setup_env(env_cfg) # self._deterministic and self._seed will be set in the # `set_randomness`` method self._randomness_cfg = randomness self.set_randomness(**randomness) if experiment_name is not None: self._experiment_name = f'{experiment_name}_{self._timestamp}' elif self.cfg.filename is not None: filename_no_ext = osp.splitext(osp.basename(self.cfg.filename))[0] self._experiment_name = f'{filename_no_ext}_{self._timestamp}' else: self._experiment_name = self.timestamp self._log_dir = osp.join(self.work_dir, self.timestamp) mmengine.mkdir_or_exist(self._log_dir) # Used to reset registries location. See :meth:`Registry.build` for # more details. if default_scope is not None: default_scope = DefaultScope.get_instance( # type: ignore self._experiment_name, scope_name=default_scope) self.default_scope = default_scope # Build log processor to format message. log_processor = dict() if log_processor is None else log_processor self.log_processor = self.build_log_processor(log_processor) # Since `get_instance` could return any subclass of ManagerMixin. The # corresponding attribute needs a type hint. self.logger = self.build_logger(log_level=log_level) # Collect and log environment information. self._log_env(env_cfg) # Build `message_hub` for communication among components. # `message_hub` can store log scalars (loss, learning rate) and # runtime information (iter and epoch). Those components that do not # have access to the runner can get iteration or epoch information # from `message_hub`. For example, models can get the latest created # `message_hub` by # `self.message_hub=MessageHub.get_current_instance()` and then get # current epoch by `cur_epoch = self.message_hub.get_info('epoch')`. # See `MessageHub` and `ManagerMixin` for more details. self.message_hub = self.build_message_hub() # visualizer used for writing log or visualizing all kinds of data self.visualizer = self.build_visualizer(visualizer) if self.cfg: self.visualizer.add_config(self.cfg) self._load_from = load_from self._resume = resume # flag to mark whether checkpoint has been loaded or resumed self._has_loaded = False # build a model if isinstance(model, dict) and data_preprocessor is not None: # Merge the data_preprocessor to model config. model.setdefault('data_preprocessor', data_preprocessor) self.model = self.build_model(model) # wrap model self.model = self.wrap_model( self.cfg.get('model_wrapper_cfg'), self.model) # get model name from the model class if hasattr(self.model, 'module'): self._model_name = self.model.module.__class__.__name__ else: self._model_name = self.model.__class__.__name__ self._hooks: List[Hook] = [] # register hooks to `self._hooks` self.register_hooks(default_hooks, custom_hooks) # log hooks information self.logger.info(f'Hooks will be executed in the following ' f'order:\n{self.get_hooks_info()}') # dump `cfg` to `work_dir` self.dump_config()
[docs] @classmethod def from_cfg(cls, cfg: ConfigType) -> 'Runner': """Build a runner from config. Args: cfg (ConfigType): A config used for building runner. Keys of ``cfg`` can see :meth:`__init__`. Returns: Runner: A runner build from ``cfg``. """ cfg = copy.deepcopy(cfg) runner = cls( model=cfg['model'], work_dir=cfg['work_dir'], train_dataloader=cfg.get('train_dataloader'), val_dataloader=cfg.get('val_dataloader'), test_dataloader=cfg.get('test_dataloader'), train_cfg=cfg.get('train_cfg'), val_cfg=cfg.get('val_cfg'), test_cfg=cfg.get('test_cfg'), auto_scale_lr=cfg.get('auto_scale_lr'), optim_wrapper=cfg.get('optim_wrapper'), param_scheduler=cfg.get('param_scheduler'), val_evaluator=cfg.get('val_evaluator'), test_evaluator=cfg.get('test_evaluator'), default_hooks=cfg.get('default_hooks'), custom_hooks=cfg.get('custom_hooks'), data_preprocessor=cfg.get('data_preprocessor'), load_from=cfg.get('load_from'), resume=cfg.get('resume', False), launcher=cfg.get('launcher', 'none'), env_cfg=cfg.get('env_cfg'), # type: ignore log_processor=cfg.get('log_processor'), log_level=cfg.get('log_level', 'INFO'), visualizer=cfg.get('visualizer'), default_scope=cfg.get('default_scope', 'mmengine'), randomness=cfg.get('randomness', dict(seed=None)), experiment_name=cfg.get('experiment_name'), cfg=cfg, ) return runner
@property def experiment_name(self): """str: Name of experiment.""" return self._experiment_name @property def model_name(self): """str: Name of the model, usually the module class name.""" return self._model_name @property def work_dir(self): """str: The working directory to save checkpoints and logs.""" return self._work_dir @property def log_dir(self): return self._log_dir @property def max_epochs(self): """int: Total epochs to train model.""" if isinstance(self.train_loop, BaseLoop): return self.train_loop.max_epochs else: return 0 @property def max_iters(self): """int: Total iterations to train model.""" if isinstance(self.train_loop, BaseLoop): return self.train_loop.max_iters else: return 0 @property def epoch(self): """int: Current epoch.""" if isinstance(self.train_loop, BaseLoop): return self.train_loop.epoch else: return 0 @property def iter(self): """int: Current iteration.""" if isinstance(self.train_loop, BaseLoop): return self.train_loop.iter else: return 0 @property def launcher(self): """str: Way to launcher multi processes.""" return self._launcher @property def distributed(self): """bool: Whether current environment is distributed.""" return self._distributed @property def rank(self): """int: Rank of current process.""" return self._rank @property def world_size(self): """int: Number of processes participating in the job.""" return self._world_size @property def deterministic(self): """int: Whether cudnn to select deterministic algorithms.""" return self._deterministic @property def seed(self): """int: A number to set random modules.""" return self._seed @property def timestamp(self): """str: Timestamp when creating experiment.""" return self._timestamp @property def hooks(self): """list[:obj:`Hook`]: A list of registered hooks.""" return self._hooks @property def train_loop(self): """:obj:`BaseLoop`: A loop to run training.""" if isinstance(self._train_loop, BaseLoop) or self._train_loop is None: return self._train_loop else: self._train_loop = self.build_train_loop(self._train_loop) return self._train_loop @property def val_loop(self): """:obj:`BaseLoop`: A loop to run validation.""" if isinstance(self._val_loop, BaseLoop) or self._val_loop is None: return self._val_loop else: self._val_loop = self.build_val_loop(self._val_loop) return self._val_loop @property def test_loop(self): """:obj:`BaseLoop`: A loop to run testing.""" if isinstance(self._test_loop, BaseLoop) or self._test_loop is None: return self._test_loop else: self._test_loop = self.build_test_loop(self._test_loop) return self._test_loop @property def train_dataloader(self): """The data loader for training.""" return self.train_loop.dataloader @property def val_dataloader(self): """The data loader for validation.""" return self.val_loop.dataloader @property def test_dataloader(self): """The data loader for testing.""" return self.test_loop.dataloader @property def val_evaluator(self): """:obj:`Evaluator`: An evaluator for validation.""" return self.val_loop.evaluator @property def test_evaluator(self): """:obj:`Evaluator`: An evaluator for testing.""" return self.test_loop.evaluator @property def val_interval(self): """int: Interval to run validation during training.""" return self.train_loop.val_interval @property def val_begin(self): """int: The epoch/iteration to start running validation during training.""" return self.train_loop.val_begin
[docs] def setup_env(self, env_cfg: Dict) -> None: """Setup environment. An example of ``env_cfg``:: env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict( mp_start_method='fork', opencv_num_threads=0 ), dist_cfg=dict(backend='nccl', timeout=1800), resource_limit=4096 ) Args: env_cfg (dict): Config for setting environment. """ if env_cfg.get('cudnn_benchmark'): torch.backends.cudnn.benchmark = True mp_cfg: dict = env_cfg.get('mp_cfg', {}) set_multi_processing(**mp_cfg, distributed=self.distributed) # init distributed env first, since logger depends on the dist info. if self.distributed and not is_distributed(): dist_cfg: dict = env_cfg.get('dist_cfg', {}) init_dist(self.launcher, **dist_cfg) self._rank, self._world_size = get_dist_info() timestamp = torch.tensor(time.time(), dtype=torch.float64) # broadcast timestamp from 0 process to other processes broadcast(timestamp) self._timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(timestamp.item())) # https://github.com/pytorch/pytorch/issues/973 # set resource limit if platform.system() != 'Windows': import resource rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) base_soft_limit = rlimit[0] hard_limit = rlimit[1] soft_limit = min( max(env_cfg.get('resource_limit', 4096), base_soft_limit), hard_limit) resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
[docs] def set_randomness(self, seed, diff_rank_seed: bool = False, deterministic: bool = False) -> None: """Set random seed to guarantee reproducible results. Args: seed (int): A number to set random modules. diff_rank_seed (bool): Whether or not set different seeds according to global rank. Defaults to False. deterministic (bool): Whether to set the deterministic option for CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` to True and `torch.backends.cudnn.benchmark` to False. Defaults to False. See https://pytorch.org/docs/stable/notes/randomness.html for more details. """ self._deterministic = deterministic self._seed = set_random_seed( seed=seed, deterministic=deterministic, diff_rank_seed=diff_rank_seed)
[docs] def build_logger(self, log_level: Union[int, str] = 'INFO', log_file: str = None, **kwargs) -> MMLogger: """Build a global asscessable MMLogger. Args: log_level (int or str): The log level of MMLogger handlers. Defaults to 'INFO'. log_file (str, optional): Path of filename to save log. Defaults to None. **kwargs: Remaining parameters passed to ``MMLogger``. Returns: MMLogger: A MMLogger object build from ``logger``. """ if log_file is None: log_file = osp.join(self._log_dir, f'{self.timestamp}.log') log_cfg = dict(log_level=log_level, log_file=log_file, **kwargs) log_cfg.setdefault('name', self._experiment_name) # `torch.compile` in PyTorch 2.0 could close all user defined handlers # unexpectedly. Using file mode 'a' can help prevent abnormal # termination of the FileHandler and ensure that the log file could # be continuously updated during the lifespan of the runner. log_cfg.setdefault('file_mode', 'a') return MMLogger.get_instance(**log_cfg) # type: ignore
[docs] def build_message_hub(self, message_hub: Optional[Dict] = None) -> MessageHub: """Build a global asscessable MessageHub. Args: message_hub (dict, optional): A dict to build MessageHub object. If not specified, default config will be used to build MessageHub object. Defaults to None. Returns: MessageHub: A MessageHub object build from ``message_hub``. """ if message_hub is None: message_hub = dict(name=self._experiment_name) elif isinstance(message_hub, dict): # ensure message_hub containing name key message_hub.setdefault('name', self._experiment_name) else: raise TypeError( f'message_hub should be dict or None, but got {message_hub}') return MessageHub.get_instance(**message_hub)
[docs] def build_visualizer( self, visualizer: Optional[Union[Visualizer, Dict]] = None) -> Visualizer: """Build a global asscessable Visualizer. Args: visualizer (Visualizer or dict, optional): A Visualizer object or a dict to build Visualizer object. If ``visualizer`` is a Visualizer object, just returns itself. If not specified, default config will be used to build Visualizer object. Defaults to None. Returns: Visualizer: A Visualizer object build from ``visualizer``. """ if visualizer is None: visualizer = dict( name=self._experiment_name, vis_backends=[dict(type='LocalVisBackend')], save_dir=self._log_dir) return Visualizer.get_instance(**visualizer) if isinstance(visualizer, Visualizer): return visualizer if isinstance(visualizer, dict): # ensure visualizer containing name key visualizer.setdefault('name', self._experiment_name) visualizer.setdefault('save_dir', self._log_dir) return VISUALIZERS.build(visualizer) else: raise TypeError( 'visualizer should be Visualizer object, a dict or None, ' f'but got {visualizer}')
[docs] def build_model(self, model: Union[nn.Module, Dict]) -> nn.Module: """Build model. If ``model`` is a dict, it will be used to build a nn.Module object. Else, if ``model`` is a nn.Module object it will be returned directly. An example of ``model``:: model = dict(type='ResNet') Args: model (nn.Module or dict): A ``nn.Module`` object or a dict to build nn.Module object. If ``model`` is a nn.Module object, just returns itself. Note: The returned model must implement ``train_step``, ``test_step`` if ``runner.train`` or ``runner.test`` will be called. If ``runner.val`` will be called or ``val_cfg`` is configured, model must implement `val_step`. Returns: nn.Module: Model build from ``model``. """ if isinstance(model, nn.Module): return model elif isinstance(model, dict): model = MODELS.build(model) return model # type: ignore else: raise TypeError('model should be a nn.Module object or dict, ' f'but got {model}')
[docs] def wrap_model( self, model_wrapper_cfg: Optional[Dict], model: nn.Module) -> Union[DistributedDataParallel, nn.Module]: """Wrap the model to :obj:`MMDistributedDataParallel` or other custom distributed data-parallel module wrappers. An example of ``model_wrapper_cfg``:: model_wrapper_cfg = dict( broadcast_buffers=False, find_unused_parameters=False ) Args: model_wrapper_cfg (dict, optional): Config to wrap model. If not specified, ``DistributedDataParallel`` will be used in distributed environment. Defaults to None. model (nn.Module): Model to be wrapped. Returns: nn.Module or DistributedDataParallel: nn.Module or subclass of ``DistributedDataParallel``. """ if is_model_wrapper(model): if model_wrapper_cfg is not None: raise TypeError( 'model has been wrapped and "model_wrapper_cfg" should be ' f'None, but got {model_wrapper_cfg}') return model # Set `export CUDA_VISIBLE_DEVICES=-1` to enable CPU training. model = model.to(get_device()) if not self.distributed: self.logger.info( 'Distributed training is not used, all SyncBatchNorm (SyncBN) ' 'layers in the model will be automatically reverted to ' 'BatchNormXd layers if they are used.') model = revert_sync_batchnorm(model) return model # type: ignore else: sync_bn = self.cfg.get('sync_bn', None) if sync_bn is not None: try: model = convert_sync_batchnorm(model, sync_bn) except ValueError as e: self.logger.error('cfg.sync_bn should be "torch" or ' f'"mmcv", but got {sync_bn}') raise e if model_wrapper_cfg is None: find_unused_parameters = self.cfg.get('find_unused_parameters', False) # Sets the `find_unused_parameters` parameter in # torch.nn.parallel.DistributedDataParallel # TODO: may use a more elegant way to get local device ID. model = MMDistributedDataParallel( module=model, device_ids=[int(os.environ['LOCAL_RANK'])], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) else: model_wrapper_cfg.setdefault('type', 'MMDistributedDataParallel') model_wrapper_type = MODEL_WRAPPERS.get( model_wrapper_cfg.get('type')) # type: ignore default_args: dict = dict() if issubclass( model_wrapper_type, # type: ignore DistributedDataParallel): default_args['device_ids'] = [int(os.environ['LOCAL_RANK'])] default_args['module'] = model model = MODEL_WRAPPERS.build( model_wrapper_cfg, default_args=default_args) return model
def _init_model_weights(self) -> None: """Initialize the model weights if the model has :meth:`init_weights`""" model = self.model.module if is_model_wrapper( self.model) else self.model if hasattr(model, 'init_weights'): model.init_weights() # sync params and buffers for name, params in model.state_dict().items(): broadcast(params)
[docs] def scale_lr(self, optim_wrapper: OptimWrapper, auto_scale_lr: Optional[Dict] = None) -> None: """Automatically scaling learning rate in training according to the ratio of ``base_batch_size`` in ``autoscalelr_cfg`` and real batch size. It scales the learning rate linearly according to the `paper <https://arxiv.org/abs/1706.02677>`_. Note: ``scale_lr`` must be called after building optimizer wrappers and before building parameter schedulers. Args: optim_wrapper (OptimWrapper): An OptimWrapper object whose parameter groups' learning rate need to be scaled. auto_scale_lr (Dict, Optional): Config to scale the learning rate automatically. It includes ``base_batch_size`` and ``enable``. ``base_batch_size`` is the batch size that the optimizer lr is based on. ``enable`` is the switch to turn on and off the feature. """ if (auto_scale_lr is None or not auto_scale_lr.get('enable', False)): return None assert 'base_batch_size' in auto_scale_lr, \ 'Lack of `base_batch_size` in `auto_scale_lr`.' dataloader: Union[DataLoader, Dict] = self._train_dataloader bs = dataloader.batch_size if isinstance( dataloader, DataLoader) else dataloader['batch_size'] real_bs = self.world_size * bs base_bs = auto_scale_lr['base_batch_size'] ratio = float(real_bs) / float(base_bs) self.logger.info(f'LR is set based on batch size of {base_bs} ' f'and the current batch size is {real_bs}. ' f'Scaling the original LR by {ratio}.') def _is_built(schedulers): if isinstance(schedulers, dict): return False if 'type' in schedulers else any( _is_built(s) for s in schedulers.values()) if isinstance(schedulers, list): return any(_is_built(s) for s in schedulers) return isinstance(schedulers, _ParamScheduler) if _is_built(self.param_schedulers): raise RuntimeError('`scale_lr` should be called before building ' 'ParamScheduler because ParamScheduler will ' 'store initial lr from optimizer wrappers') assert isinstance(optim_wrapper, OptimWrapper), \ '`scale_lr should be called after building OptimWrapper' wrappers = list(optim_wrapper.values()) if isinstance( optim_wrapper, OptimWrapperDict) else [optim_wrapper] for wrapper in wrappers: for group in wrapper.optimizer.param_groups: group['lr'] = group['lr'] * ratio
[docs] def build_optim_wrapper( self, optim_wrapper: Union[Optimizer, OptimWrapper, Dict] ) -> Union[OptimWrapper, OptimWrapperDict]: """Build optimizer wrapper. If ``optim_wrapper`` is a config dict for only one optimizer, the keys must contain ``optimizer``, and ``type`` is optional. It will build a :obj:`OptimWrapper` by default. If ``optim_wrapper`` is a config dict for multiple optimizers, i.e., it has multiple keys and each key is for an optimizer wrapper. The constructor must be specified since :obj:`DefaultOptimizerConstructor` cannot handle the building of training with multiple optimizers. If ``optim_wrapper`` is a dict of pre-built optimizer wrappers, i.e., each value of ``optim_wrapper`` represents an ``OptimWrapper`` instance. ``build_optim_wrapper`` will directly build the :obj:`OptimWrapperDict` instance from ``optim_wrapper``. Args: optim_wrapper (OptimWrapper or dict): An OptimWrapper object or a dict to build OptimWrapper objects. If ``optim_wrapper`` is an OptimWrapper, just return an ``OptimizeWrapper`` instance. Note: For single optimizer training, if `optim_wrapper` is a config dict, `type` is optional(defaults to :obj:`OptimWrapper`) and it must contain `optimizer` to build the corresponding optimizer. Examples: >>> # build an optimizer >>> optim_wrapper_cfg = dict(type='OptimWrapper', optimizer=dict( ... type='SGD', lr=0.01)) >>> # optim_wrapper_cfg = dict(optimizer=dict(type='SGD', lr=0.01)) >>> # is also valid. >>> optim_wrapper = runner.build_optim_wrapper(optim_wrapper_cfg) >>> optim_wrapper Type: OptimWrapper accumulative_counts: 1 optimizer: SGD ( Parameter Group 0 dampening: 0 lr: 0.01 momentum: 0 nesterov: False weight_decay: 0 ) >>> # build optimizer without `type` >>> optim_wrapper_cfg = dict(optimizer=dict(type='SGD', lr=0.01)) >>> optim_wrapper = runner.build_optim_wrapper(optim_wrapper_cfg) >>> optim_wrapper Type: OptimWrapper accumulative_counts: 1 optimizer: SGD ( Parameter Group 0 dampening: 0 lr: 0.01 maximize: False momentum: 0 nesterov: False weight_decay: 0 ) >>> # build multiple optimizers >>> optim_wrapper_cfg = dict( ... generator=dict(type='OptimWrapper', optimizer=dict( ... type='SGD', lr=0.01)), ... discriminator=dict(type='OptimWrapper', optimizer=dict( ... type='Adam', lr=0.001)) ... # need to customize a multiple optimizer constructor ... constructor='CustomMultiOptimizerConstructor', ...) >>> optim_wrapper = runner.optim_wrapper(optim_wrapper_cfg) >>> optim_wrapper name: generator Type: OptimWrapper accumulative_counts: 1 optimizer: SGD ( Parameter Group 0 dampening: 0 lr: 0.1 momentum: 0 nesterov: False weight_decay: 0 ) name: discriminator Type: OptimWrapper accumulative_counts: 1 optimizer: 'discriminator': Adam ( Parameter Group 0 dampening: 0 lr: 0.02 momentum: 0 nesterov: False weight_decay: 0 ) Important: If you need to build multiple optimizers, you should implement a MultiOptimWrapperConstructor which gets parameters passed to corresponding optimizers and compose the ``OptimWrapperDict``. More details about how to customize OptimizerConstructor can be found at `optimizer-docs`_. Returns: OptimWrapper: Optimizer wrapper build from ``optimizer_cfg``. """ # noqa: E501 if isinstance(optim_wrapper, OptimWrapper): return optim_wrapper if isinstance(optim_wrapper, (dict, ConfigDict, Config)): # optimizer must be defined for single optimizer training. optimizer = optim_wrapper.get('optimizer', None) # If optimizer is a built `Optimizer` instance, the optimizer # wrapper should be built by `OPTIM_WRAPPERS` registry. if isinstance(optimizer, Optimizer): optim_wrapper.setdefault('type', 'OptimWrapper') return OPTIM_WRAPPERS.build(optim_wrapper) # type: ignore # If `optimizer` is not None or `constructor` is defined, it means, # optimizer wrapper will be built by optimizer wrapper # constructor. Therefore, `build_optim_wrapper` should be called. if optimizer is not None or 'constructor' in optim_wrapper: return build_optim_wrapper(self.model, optim_wrapper) else: # if `optimizer` is not defined, it should be the case of # training with multiple optimizers. If `constructor` is not # defined either, each value of `optim_wrapper` must be an # `OptimWrapper` instance since `DefaultOptimizerConstructor` # will not handle the case of training with multiple # optimizers. `build_optim_wrapper` will directly build the # `OptimWrapperDict` instance from `optim_wrapper.` optim_wrappers = OrderedDict() for name, optim in optim_wrapper.items(): if not isinstance(optim, OptimWrapper): raise ValueError( 'each item mush be an optimizer object when ' '"type" and "constructor" are not in ' f'optimizer, but got {name}={optim}') optim_wrappers[name] = optim return OptimWrapperDict(**optim_wrappers) else: raise TypeError('optimizer wrapper should be an OptimWrapper ' f'object or dict, but got {optim_wrapper}')
def _build_param_scheduler( self, scheduler: Union[_ParamScheduler, Dict, List], optim_wrapper: OptimWrapper) -> List[_ParamScheduler]: """Build parameter schedulers for a single optimizer. Args: scheduler (_ParamScheduler or dict or list): A Param Scheduler object or a dict or list of dict to build parameter schedulers. optim_wrapper (OptimWrapper): An optimizer wrapper object is passed to construct ParamScheduler object. Returns: list[_ParamScheduler]: List of parameter schedulers build from ``scheduler``. Note: If the train loop is built, when building parameter schedulers, it supports setting the max epochs/iters as the default ``end`` of schedulers, and supports converting epoch-based schedulers to iter-based according to the ``convert_to_iter_based`` key. """ if not isinstance(scheduler, Sequence): schedulers = [scheduler] else: schedulers = scheduler param_schedulers = [] for scheduler in schedulers: if isinstance(scheduler, _ParamScheduler): param_schedulers.append(scheduler) elif isinstance(scheduler, dict): _scheduler = copy.deepcopy(scheduler) # Set default end if isinstance(self._train_loop, BaseLoop): default_end = self.max_epochs if _scheduler.get( 'by_epoch', True) else self.max_iters _scheduler.setdefault('end', default_end) self.logger.debug( f'The `end` of {_scheduler["type"]} is not set. ' 'Use the max epochs/iters of train loop as default.') param_schedulers.append( PARAM_SCHEDULERS.build( _scheduler, default_args=dict( optimizer=optim_wrapper, epoch_length=len(self.train_dataloader)))) else: raise TypeError( 'scheduler should be a _ParamScheduler object or dict, ' f'but got {scheduler}') return param_schedulers
[docs] def build_param_scheduler( self, scheduler: Union[_ParamScheduler, Dict, List]) -> ParamSchedulerType: """Build parameter schedulers. ``build_param_scheduler`` should be called after ``build_optim_wrapper`` because the building logic will change according to the number of optimizers built by the runner. The cases are as below: - Single optimizer: When only one optimizer is built and used in the runner, ``build_param_scheduler`` will return a list of parameter schedulers. - Multiple optimizers: When two or more optimizers are built and used in runner, ``build_param_scheduler`` will return a dict containing the same keys with multiple optimizers and each value is a list of parameter schedulers. Note that, if you want different optimizers to use different parameter schedulers to update optimizer's hyper-parameters, the input parameter ``scheduler`` also needs to be a dict and its key are consistent with multiple optimizers. Otherwise, the same parameter schedulers will be used to update optimizer's hyper-parameters. Args: scheduler (_ParamScheduler or dict or list): A Param Scheduler object or a dict or list of dict to build parameter schedulers. Examples: >>> # build one scheduler >>> optim_cfg = dict(dict(type='SGD', lr=0.01)) >>> runner.optim_wrapper = runner.build_optim_wrapper( >>> optim_cfg) >>> scheduler_cfg = dict(type='MultiStepLR', milestones=[1, 2]) >>> schedulers = runner.build_param_scheduler(scheduler_cfg) >>> schedulers [<mmengine.optim.scheduler.lr_scheduler.MultiStepLR at 0x7f70f6966290>] # noqa: E501 >>> # build multiple schedulers >>> scheduler_cfg = [ ... dict(type='MultiStepLR', milestones=[1, 2]), ... dict(type='StepLR', step_size=1) ... ] >>> schedulers = runner.build_param_scheduler(scheduler_cfg) >>> schedulers [<mmengine.optim.scheduler.lr_scheduler.MultiStepLR at 0x7f70f60dd3d0>, # noqa: E501 <mmengine.optim.scheduler.lr_scheduler.StepLR at 0x7f70f6eb6150>] Above examples only provide the case of one optimizer and one scheduler or multiple schedulers. If you want to know how to set parameter scheduler when using multiple optimizers, you can find more examples `optimizer-docs`_. Returns: list[_ParamScheduler] or dict[str, list[_ParamScheduler]]: List of parameter schedulers or a dictionary contains list of parameter schedulers build from ``scheduler``. .. _optimizer-docs: https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html """ param_schedulers: ParamSchedulerType if not isinstance(self.optim_wrapper, OptimWrapperDict): # Since `OptimWrapperDict` inherits from `OptimWrapper`, # `isinstance(self.optim_wrapper, OptimWrapper)` cannot tell # whether `self.optim_wrapper` is an `OptimizerWrapper` or # `OptimWrapperDict` instance. Therefore, here we simply check # self.optim_wrapper is not an `OptimWrapperDict` instance and # then assert it is an OptimWrapper instance. assert isinstance(self.optim_wrapper, OptimWrapper), ( '`build_optimizer` should be called before' '`build_param_scheduler` because the latter depends ' 'on the former') param_schedulers = self._build_param_scheduler( scheduler, self.optim_wrapper) # type: ignore return param_schedulers else: param_schedulers = dict() for name, optimizer in self.optim_wrapper.items(): if isinstance(scheduler, dict) and 'type' not in scheduler: # scheduler is a dict and each item is a ParamScheduler # object or a config to build ParamScheduler objects param_schedulers[name] = self._build_param_scheduler( scheduler[name], optimizer) else: param_schedulers[name] = self._build_param_scheduler( scheduler, optimizer) return param_schedulers
[docs] def build_evaluator(self, evaluator: Union[Dict, List, Evaluator]) -> Evaluator: """Build evaluator. Examples of ``evaluator``:: # evaluator could be a built Evaluator instance evaluator = Evaluator(metrics=[ToyMetric()]) # evaluator can also be a list of dict evaluator = [ dict(type='ToyMetric1'), dict(type='ToyEvaluator2') ] # evaluator can also be a list of built metric evaluator = [ToyMetric1(), ToyMetric2()] # evaluator can also be a dict with key metrics evaluator = dict(metrics=ToyMetric()) # metric is a list evaluator = dict(metrics=[ToyMetric()]) Args: evaluator (Evaluator or dict or list): An Evaluator object or a config dict or list of config dict used to build an Evaluator. Returns: Evaluator: Evaluator build from ``evaluator``. """ if isinstance(evaluator, Evaluator): return evaluator elif isinstance(evaluator, dict): # if `metrics` in dict keys, it means to build customized evalutor if 'metrics' in evaluator: evaluator.setdefault('type', 'Evaluator') return EVALUATOR.build(evaluator) # otherwise, default evalutor will be built else: return Evaluator(evaluator) # type: ignore elif isinstance(evaluator, list): # use the default `Evaluator` return Evaluator(evaluator) # type: ignore else: raise TypeError( 'evaluator should be one of dict, list of dict, and Evaluator' f', but got {evaluator}')
[docs] @staticmethod def build_dataloader(dataloader: Union[DataLoader, Dict], seed: Optional[int] = None, diff_rank_seed: bool = False) -> DataLoader: """Build dataloader. The method builds three components: - Dataset - Sampler - Dataloader An example of ``dataloader``:: dataloader = dict( dataset=dict(type='ToyDataset'), sampler=dict(type='DefaultSampler', shuffle=True), batch_size=1, num_workers=9 ) Args: dataloader (DataLoader or dict): A Dataloader object or a dict to build Dataloader object. If ``dataloader`` is a Dataloader object, just returns itself. seed (int, optional): Random seed. Defaults to None. diff_rank_seed (bool): Whether or not set different seeds to different ranks. If True, the seed passed to sampler is set to None, in order to synchronize the seeds used in samplers across different ranks. Returns: Dataloader: DataLoader build from ``dataloader_cfg``. """ if isinstance(dataloader, DataLoader): return dataloader dataloader_cfg = copy.deepcopy(dataloader) # build dataset dataset_cfg = dataloader_cfg.pop('dataset') if isinstance(dataset_cfg, dict): dataset = DATASETS.build(dataset_cfg) if hasattr(dataset, 'full_init'): dataset.full_init() else: # fallback to raise error in dataloader # if `dataset_cfg` is not a valid type dataset = dataset_cfg # build sampler sampler_cfg = dataloader_cfg.pop('sampler') if isinstance(sampler_cfg, dict): sampler_seed = None if diff_rank_seed else seed sampler = DATA_SAMPLERS.build( sampler_cfg, default_args=dict(dataset=dataset, seed=sampler_seed)) else: # fallback to raise error in dataloader # if `sampler_cfg` is not a valid type sampler = sampler_cfg # build batch sampler batch_sampler_cfg = dataloader_cfg.pop('batch_sampler', None) if batch_sampler_cfg is None: batch_sampler = None elif isinstance(batch_sampler_cfg, dict): batch_sampler = DATA_SAMPLERS.build( batch_sampler_cfg, default_args=dict( sampler=sampler, batch_size=dataloader_cfg.pop('batch_size'))) else: # fallback to raise error in dataloader # if `batch_sampler_cfg` is not a valid type batch_sampler = batch_sampler_cfg # build dataloader init_fn: Optional[partial] if 'worker_init_fn' in dataloader_cfg: worker_init_fn_cfg = dataloader_cfg.pop('worker_init_fn') worker_init_fn_type = worker_init_fn_cfg.pop('type') if isinstance(worker_init_fn_type, str): worker_init_fn = FUNCTIONS.get(worker_init_fn_type) elif callable(worker_init_fn_type): worker_init_fn = worker_init_fn_type else: raise TypeError( 'type of worker_init_fn should be string or callable ' f'object, but got {type(worker_init_fn_type)}') assert callable(worker_init_fn) init_fn = partial(worker_init_fn, **worker_init_fn_cfg) # type: ignore else: if seed is not None: disable_subprocess_warning = dataloader_cfg.pop( 'disable_subprocess_warning', False) assert isinstance(disable_subprocess_warning, bool), ( 'disable_subprocess_warning should be a bool, but got ' f'{type(disable_subprocess_warning)}') init_fn = partial( default_worker_init_fn, num_workers=dataloader_cfg.get('num_workers'), rank=get_rank(), seed=seed, disable_subprocess_warning=disable_subprocess_warning) else: init_fn = None # `persistent_workers` requires pytorch version >= 1.7 if ('persistent_workers' in dataloader_cfg and digit_version(TORCH_VERSION) < digit_version('1.7.0')): print_log( '`persistent_workers` is only available when ' 'pytorch version >= 1.7', logger='current', level=logging.WARNING) dataloader_cfg.pop('persistent_workers') # The default behavior of `collat_fn` in dataloader is to # merge a list of samples to form a mini-batch of Tensor(s). # However, in mmengine, if `collate_fn` is not defined in # dataloader_cfg, `pseudo_collate` will only convert the list of # samples into a dict without stacking the batch tensor. collate_fn_cfg = dataloader_cfg.pop('collate_fn', dict(type='pseudo_collate')) if isinstance(collate_fn_cfg, dict): collate_fn_type = collate_fn_cfg.pop('type') if isinstance(collate_fn_type, str): collate_fn = FUNCTIONS.get(collate_fn_type) else: collate_fn = collate_fn_type collate_fn = partial(collate_fn, **collate_fn_cfg) # type: ignore elif callable(collate_fn_cfg): collate_fn = collate_fn_cfg else: raise TypeError( 'collate_fn should be a dict or callable object, but got ' f'{collate_fn_cfg}') data_loader = DataLoader( dataset=dataset, sampler=sampler if batch_sampler is None else None, batch_sampler=batch_sampler, collate_fn=collate_fn, worker_init_fn=init_fn, **dataloader_cfg) return data_loader
[docs] def build_train_loop(self, loop: Union[BaseLoop, Dict]) -> BaseLoop: """Build training loop. Examples of ``loop``:: # `EpochBasedTrainLoop` will be used loop = dict(by_epoch=True, max_epochs=3) # `IterBasedTrainLoop` will be used loop = dict(by_epoch=False, max_epochs=3) # custom training loop loop = dict(type='CustomTrainLoop', max_epochs=3) Args: loop (BaseLoop or dict): A training loop or a dict to build training loop. If ``loop`` is a training loop object, just returns itself. Returns: :obj:`BaseLoop`: Training loop object build from ``loop``. """ if isinstance(loop, BaseLoop): return loop elif not isinstance(loop, dict): raise TypeError( f'train_loop should be a Loop object or dict, but got {loop}') loop_cfg = copy.deepcopy(loop) if 'type' in loop_cfg and 'by_epoch' in loop_cfg: raise RuntimeError( 'Only one of `type` or `by_epoch` can exist in `loop_cfg`.') if 'type' in loop_cfg: loop = LOOPS.build( loop_cfg, default_args=dict( runner=self, dataloader=self._train_dataloader)) else: by_epoch = loop_cfg.pop('by_epoch') if by_epoch: loop = EpochBasedTrainLoop( **loop_cfg, runner=self, dataloader=self._train_dataloader) else: loop = IterBasedTrainLoop( **loop_cfg, runner=self, dataloader=self._train_dataloader) return loop # type: ignore
[docs] def build_val_loop(self, loop: Union[BaseLoop, Dict]) -> BaseLoop: """Build validation loop. Examples of ``loop``: # `ValLoop` will be used loop = dict() # custom validation loop loop = dict(type='CustomValLoop') Args: loop (BaseLoop or dict): A validation loop or a dict to build validation loop. If ``loop`` is a validation loop object, just returns itself. Returns: :obj:`BaseLoop`: Validation loop object build from ``loop``. """ if isinstance(loop, BaseLoop): return loop elif not isinstance(loop, dict): raise TypeError( f'val_loop should be a Loop object or dict, but got {loop}') loop_cfg = copy.deepcopy(loop) if 'type' in loop_cfg: loop = LOOPS.build( loop_cfg, default_args=dict( runner=self, dataloader=self._val_dataloader, evaluator=self._val_evaluator)) else: loop = ValLoop( **loop_cfg, runner=self, dataloader=self._val_dataloader, evaluator=self._val_evaluator) # type: ignore return loop # type: ignore
[docs] def build_test_loop(self, loop: Union[BaseLoop, Dict]) -> BaseLoop: """Build test loop. Examples of ``loop``:: # `TestLoop` will be used loop = dict() # custom test loop loop = dict(type='CustomTestLoop') Args: loop (BaseLoop or dict): A test loop or a dict to build test loop. If ``loop`` is a test loop object, just returns itself. Returns: :obj:`BaseLoop`: Test loop object build from ``loop_cfg``. """ if isinstance(loop, BaseLoop): return loop elif not isinstance(loop, dict): raise TypeError( f'test_loop should be a Loop object or dict, but got {loop}') loop_cfg = copy.deepcopy(loop) # type: ignore if 'type' in loop_cfg: loop = LOOPS.build( loop_cfg, default_args=dict( runner=self, dataloader=self._test_dataloader, evaluator=self._test_evaluator)) else: loop = TestLoop( **loop_cfg, runner=self, dataloader=self._test_dataloader, evaluator=self._test_evaluator) # type: ignore return loop # type: ignore
[docs] def build_log_processor( self, log_processor: Union[LogProcessor, Dict]) -> LogProcessor: """Build test log_processor. Examples of ``log_processor``: # `LogProcessor` will be used log_processor = dict() # custom log_processor log_processor = dict(type='CustomLogProcessor') Args: log_processor (LogProcessor or dict): A log processor or a dict to build log processor. If ``log_processor`` is a log processor object, just returns itself. Returns: :obj:`LogProcessor`: Log processor object build from ``log_processor_cfg``. """ if isinstance(log_processor, LogProcessor): return log_processor elif not isinstance(log_processor, dict): raise TypeError( 'log processor should be a LogProcessor object or dict, but' f'got {log_processor}') log_processor_cfg = copy.deepcopy(log_processor) # type: ignore if 'type' in log_processor_cfg: log_processor = LOG_PROCESSORS.build(log_processor_cfg) else: log_processor = LogProcessor(**log_processor_cfg) # type: ignore return log_processor # type: ignore
def get_hooks_info(self) -> str: # Get hooks info in each stage stage_hook_map: Dict[str, list] = {stage: [] for stage in Hook.stages} for hook in self.hooks: try: priority = Priority(hook.priority).name # type: ignore except ValueError: priority = hook.priority # type: ignore classname = hook.__class__.__name__ hook_info = f'({priority:<12}) {classname:<35}' for trigger_stage in hook.get_triggered_stages(): stage_hook_map[trigger_stage].append(hook_info) stage_hook_infos = [] for stage in Hook.stages: hook_infos = stage_hook_map[stage] if len(hook_infos) > 0: info = f'{stage}:\n' info += '\n'.join(hook_infos) info += '\n -------------------- ' stage_hook_infos.append(info) return '\n'.join(stage_hook_infos)
[docs] def load_or_resume(self) -> None: """load or resume checkpoint.""" if self._has_loaded: return None # decide to load from checkpoint or resume from checkpoint resume_from = None if self._resume and self._load_from is None: # auto resume from the latest checkpoint resume_from = find_latest_checkpoint(self.work_dir) self.logger.info( f'Auto resumed from the latest checkpoint {resume_from}.') elif self._resume and self._load_from is not None: # resume from the specified checkpoint resume_from = self._load_from if resume_from is not None: self.resume(resume_from) self._has_loaded = True elif self._load_from is not None: self.load_checkpoint(self._load_from) self._has_loaded = True
[docs] def train(self) -> nn.Module: """Launch training. Returns: nn.Module: The model after training. """ if is_model_wrapper(self.model): ori_model = self.model.module else: ori_model = self.model assert hasattr(ori_model, 'train_step'), ( 'If you want to train your model, please make sure your model ' 'has implemented `train_step`.') if self._val_loop is not None: assert hasattr(ori_model, 'val_step'), ( 'If you want to validate your model, please make sure your ' 'model has implemented `val_step`.') if self._train_loop is None: raise RuntimeError( '`self._train_loop` should not be None when calling train ' 'method. Please provide `train_dataloader`, `train_cfg`, ' '`optimizer` and `param_scheduler` arguments when ' 'initializing runner.') self._train_loop = self.build_train_loop( self._train_loop) # type: ignore # `build_optimizer` should be called before `build_param_scheduler` # because the latter depends on the former self.optim_wrapper = self.build_optim_wrapper(self.optim_wrapper) # Automatically scaling lr by linear scaling rule self.scale_lr(self.optim_wrapper, self.auto_scale_lr) if self.param_schedulers is not None: self.param_schedulers = self.build_param_scheduler( # type: ignore self.param_schedulers) # type: ignore if self._val_loop is not None: self._val_loop = self.build_val_loop( self._val_loop) # type: ignore # TODO: add a contextmanager to avoid calling `before_run` many times self.call_hook('before_run') # initialize the model weights self._init_model_weights() # make sure checkpoint-related hooks are triggered after `before_run` self.load_or_resume() # Initiate inner count of `optim_wrapper`. self.optim_wrapper.initialize_count_status( self.model, self._train_loop.iter, # type: ignore self._train_loop.max_iters) # type: ignore # Maybe compile the model according to options in self.cfg.compile # This must be called **AFTER** model has been wrapped. self._maybe_compile('train_step') model = self.train_loop.run() # type: ignore self.call_hook('after_run') return model
[docs] def val(self) -> dict: """Launch validation. Returns: dict: A dict of metrics on validation set. """ if self._val_loop is None: raise RuntimeError( '`self._val_loop` should not be None when calling val method.' 'Please provide `val_dataloader`, `val_cfg` and ' '`val_evaluator` arguments when initializing runner.') self._val_loop = self.build_val_loop(self._val_loop) # type: ignore self.call_hook('before_run') # make sure checkpoint-related hooks are triggered after `before_run` self.load_or_resume() metrics = self.val_loop.run() # type: ignore self.call_hook('after_run') return metrics
[docs] def test(self) -> dict: """Launch test. Returns: dict: A dict of metrics on testing set. """ if self._test_loop is None: raise RuntimeError( '`self._test_loop` should not be None when calling test ' 'method. Please provide `test_dataloader`, `test_cfg` and ' '`test_evaluator` arguments when initializing runner.') self._test_loop = self.build_test_loop(self._test_loop) # type: ignore self.call_hook('before_run') # make sure checkpoint-related hooks are triggered after `before_run` self.load_or_resume() metrics = self.test_loop.run() # type: ignore self.call_hook('after_run') return metrics
[docs] def call_hook(self, fn_name: str, **kwargs) -> None: """Call all hooks. Args: fn_name (str): The function name in each hook to be called, such as "before_train_epoch". **kwargs: Keyword arguments passed to hook. """ for hook in self._hooks: # support adding additional custom hook methods if hasattr(hook, fn_name): try: getattr(hook, fn_name)(self, **kwargs) except TypeError as e: raise TypeError(f'{e} in {hook}') from None
[docs] def register_hook( self, hook: Union[Hook, Dict], priority: Optional[Union[str, int, Priority]] = None) -> None: """Register a hook into the hook list. The hook will be inserted into a priority queue, with the specified priority (See :class:`Priority` for details of priorities). For hooks with the same priority, they will be triggered in the same order as they are registered. Priority of hook will be decided with the following priority: - ``priority`` argument. If ``priority`` is given, it will be priority of hook. - If ``hook`` argument is a dict and ``priority`` in it, the priority will be the value of ``hook['priority']``. - If ``hook`` argument is a dict but ``priority`` not in it or ``hook`` is an instance of ``hook``, the priority will be ``hook.priority``. Args: hook (:obj:`Hook` or dict): The hook to be registered. priority (int or str or :obj:`Priority`, optional): Hook priority. Lower value means higher priority. """ if not isinstance(hook, (Hook, dict)): raise TypeError( f'hook should be an instance of Hook or dict, but got {hook}') _priority = None if isinstance(hook, dict): if 'priority' in hook: _priority = hook.pop('priority') hook_obj = HOOKS.build(hook) else: hook_obj = hook if priority is not None: hook_obj.priority = priority elif _priority is not None: hook_obj.priority = _priority inserted = False for i in range(len(self._hooks) - 1, -1, -1): if get_priority(hook_obj.priority) >= get_priority( self._hooks[i].priority): self._hooks.insert(i + 1, hook_obj) inserted = True break if not inserted: self._hooks.insert(0, hook_obj)
[docs] def register_default_hooks( self, hooks: Optional[Dict[str, Union[Hook, Dict]]] = None) -> None: """Register default hooks into hook list. ``hooks`` will be registered into runner to execute some default actions like updating model parameters or saving checkpoints. Default hooks and their priorities: +----------------------+-------------------------+ | Hooks | Priority | +======================+=========================+ | RuntimeInfoHook | VERY_HIGH (10) | +----------------------+-------------------------+ | IterTimerHook | NORMAL (50) | +----------------------+-------------------------+ | DistSamplerSeedHook | NORMAL (50) | +----------------------+-------------------------+ | LoggerHook | BELOW_NORMAL (60) | +----------------------+-------------------------+ | ParamSchedulerHook | LOW (70) | +----------------------+-------------------------+ | CheckpointHook | VERY_LOW (90) | +----------------------+-------------------------+ If ``hooks`` is None, above hooks will be registered by default:: default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), logger=dict(type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), ) If not None, ``hooks`` will be merged into ``default_hooks``. If there are None value in default_hooks, the corresponding item will be popped from ``default_hooks``:: hooks = dict(timer=None) The final registered default hooks will be :obj:`RuntimeInfoHook`, :obj:`DistSamplerSeedHook`, :obj:`LoggerHook`, :obj:`ParamSchedulerHook` and :obj:`CheckpointHook`. Args: hooks (dict[str, Hook or dict], optional): Default hooks or configs to be registered. """ default_hooks: dict = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), logger=dict(type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), ) if hooks is not None: for name, hook in hooks.items(): if name in default_hooks and hook is None: # remove hook from _default_hooks default_hooks.pop(name) else: assert hook is not None default_hooks[name] = hook for hook in default_hooks.values(): self.register_hook(hook)
[docs] def register_custom_hooks(self, hooks: List[Union[Hook, Dict]]) -> None: """Register custom hooks into hook list. Args: hooks (list[Hook | dict]): List of hooks or configs to be registered. """ for hook in hooks: self.register_hook(hook)
[docs] def register_hooks( self, default_hooks: Optional[Dict[str, Union[Hook, Dict]]] = None, custom_hooks: Optional[List[Union[Hook, Dict]]] = None) -> None: """Register default hooks and custom hooks into hook list. Args: default_hooks (dict[str, dict] or dict[str, Hook], optional): Hooks to execute default actions like updating model parameters and saving checkpoints. Defaults to None. custom_hooks (list[dict] or list[Hook], optional): Hooks to execute custom actions like visualizing images processed by pipeline. Defaults to None. """ self.register_default_hooks(default_hooks) if custom_hooks is not None: self.register_custom_hooks(custom_hooks)
[docs] def resume(self, filename: str, resume_optimizer: bool = True, resume_param_scheduler: bool = True, map_location: Union[str, Callable] = 'default') -> None: """Resume model from checkpoint. Args: filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. resume_optimizer (bool): Whether to resume optimizer state. Defaults to True. resume_param_scheduler (bool): Whether to resume param scheduler state. Defaults to True. map_location (str or callable):A string or a callable function to specifying how to remap storage locations. Defaults to 'default'. """ if map_location == 'default': device = get_device() checkpoint = self.load_checkpoint(filename, map_location=device) else: checkpoint = self.load_checkpoint( filename, map_location=map_location) self.train_loop._epoch = checkpoint['meta']['epoch'] self.train_loop._iter = checkpoint['meta']['iter'] # check whether the number of GPU used for current experiment # is consistent with resuming from checkpoint if 'config' in checkpoint['meta']: config = mmengine.Config.fromstring( checkpoint['meta']['config'], file_format='.py') previous_gpu_ids = config.get('gpu_ids', None) if (previous_gpu_ids is not None and len(previous_gpu_ids) > 0 and len(previous_gpu_ids) != self._world_size): # TODO, should we modify the iteration? if (self.auto_scale_lr is None or not self.auto_scale_lr.get('enable', False)): raise RuntimeError( 'Number of GPUs used for current experiment is not ' 'consistent with the checkpoint being resumed from. ' 'This will result in poor performance due to the ' 'learning rate. You must set the ' '`auto_scale_lr` parameter for Runner and make ' '`auto_scale_lr["enable"]=True`.') else: self.logger.info( 'Number of GPU used for current experiment is not ' 'consistent with resuming from checkpoint but the ' 'leaning rate will be adjusted according to the ' f'setting in auto_scale_lr={self.auto_scale_lr}') # resume random seed resumed_seed = checkpoint['meta'].get('seed', None) current_seed = self._randomness_cfg.get('seed') if resumed_seed is not None and resumed_seed != current_seed: if current_seed is not None: self.logger.warning(f'The value of random seed in the ' f'checkpoint "{resumed_seed}" is ' f'different from the value in ' f'`randomness` config "{current_seed}"') self._randomness_cfg.update(seed=resumed_seed) self.set_randomness(**self._randomness_cfg) resumed_dataset_meta = checkpoint['meta'].get('dataset_meta', None) dataset_meta = getattr(self.train_dataloader.dataset, 'metainfo', None) # `resumed_dataset_meta` and `dataset_meta` could be object like # np.ndarray, which cannot be directly judged as equal or not, # therefore we just compared their dumped results. if pickle.dumps(resumed_dataset_meta) != pickle.dumps(dataset_meta): self.logger.warning( 'The dataset metainfo from the resumed checkpoint is ' 'different from the current training dataset, please ' 'check the correctness of the checkpoint or the training ' 'dataset.') self.message_hub.load_state_dict(checkpoint['message_hub']) # resume optimizer if 'optimizer' in checkpoint and resume_optimizer: self.optim_wrapper = self.build_optim_wrapper(self.optim_wrapper) self.optim_wrapper.load_state_dict( # type: ignore checkpoint['optimizer']) # resume param scheduler if resume_param_scheduler and self.param_schedulers is None: self.logger.warning( '`resume_param_scheduler` is True but `self.param_schedulers` ' 'is None, so skip resuming parameter schedulers') resume_param_scheduler = False if 'param_schedulers' in checkpoint and resume_param_scheduler: self.param_schedulers = self.build_param_scheduler( # type: ignore self.param_schedulers) # type: ignore if isinstance(self.param_schedulers, dict): for name, schedulers in self.param_schedulers.items(): for scheduler, ckpt_scheduler in zip( schedulers, checkpoint['param_schedulers'][name]): scheduler.load_state_dict(ckpt_scheduler) else: for scheduler, ckpt_scheduler in zip( self.param_schedulers, # type: ignore checkpoint['param_schedulers']): scheduler.load_state_dict(ckpt_scheduler) self._has_loaded = True self.logger.info(f'resumed epoch: {self.epoch}, iter: {self.iter}')
[docs] def load_checkpoint(self, filename: str, map_location: Union[str, Callable] = 'cpu', strict: bool = False, revise_keys: list = [(r'^module.', '')]): """Load checkpoint from given ``filename``. Args: filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. map_location (str or callable): A string or a callable function to specifying how to remap storage locations. Defaults to 'cpu'. strict (bool): strict (bool): Whether to allow different params for the model and checkpoint. revise_keys (list): A list of customized keywords to modify the state_dict in checkpoint. Each item is a (pattern, replacement) pair of the regular expression operations. Defaults to strip the prefix 'module.' by [(r'^module\\.', '')]. """ checkpoint = _load_checkpoint(filename, map_location=map_location) # Add comments to describe the usage of `after_load_ckpt` self.call_hook('after_load_checkpoint', checkpoint=checkpoint) if is_model_wrapper(self.model): model = self.model.module else: model = self.model checkpoint = _load_checkpoint_to_model( model, checkpoint, strict, revise_keys=revise_keys) self._has_loaded = True self.logger.info(f'Load checkpoint from {filename}') return checkpoint
[docs] @master_only def save_checkpoint( self, out_dir: str, filename: str, file_client_args: Optional[dict] = None, save_optimizer: bool = True, save_param_scheduler: bool = True, meta: Optional[dict] = None, by_epoch: bool = True, backend_args: Optional[dict] = None, ): """Save checkpoints. ``CheckpointHook`` invokes this method to save checkpoints periodically. Args: out_dir (str): The directory that checkpoints are saved. filename (str): The checkpoint filename. file_client_args (dict, optional): Arguments to instantiate a FileClient. See :class:`mmengine.fileio.FileClient` for details. Defaults to None. It will be deprecated in future. Please use `backend_args` instead. save_optimizer (bool): Whether to save the optimizer to the checkpoint. Defaults to True. save_param_scheduler (bool): Whether to save the param_scheduler to the checkpoint. Defaults to True. meta (dict, optional): The meta information to be saved in the checkpoint. Defaults to None. by_epoch (bool): Decide the number of epoch or iteration saved in checkpoint. Defaults to True. backend_args (dict, optional): Arguments to instantiate the prefix of uri corresponding backend. Defaults to None. New in v0.2.0. """ if meta is None: meta = {} elif not isinstance(meta, dict): raise TypeError( f'meta should be a dict or None, but got {type(meta)}') if by_epoch: # self.epoch increments 1 after # `self.call_hook('after_train_epoch)` but `save_checkpoint` is # called by `after_train_epoch`` method of `CheckpointHook` so # `epoch` should be `self.epoch + 1` meta.setdefault('epoch', self.epoch + 1) meta.setdefault('iter', self.iter) else: meta.setdefault('epoch', self.epoch) meta.setdefault('iter', self.iter + 1) if file_client_args is not None: warnings.warn( '"file_client_args" will be deprecated in future. ' 'Please use "backend_args" instead', DeprecationWarning) if backend_args is not None: raise ValueError( '"file_client_args" and "backend_args" cannot be set at ' 'the same time.') file_client = FileClient.infer_client(file_client_args, out_dir) filepath = file_client.join_path(out_dir, filename) else: filepath = join_path( # type: ignore out_dir, filename, backend_args=backend_args) meta.update( cfg=self.cfg.pretty_text, seed=self.seed, experiment_name=self.experiment_name, time=time.strftime('%Y%m%d_%H%M%S', time.localtime()), mmengine_version=mmengine.__version__ + get_git_hash()) if hasattr(self.train_dataloader.dataset, 'metainfo'): meta.update(dataset_meta=self.train_dataloader.dataset.metainfo) if is_model_wrapper(self.model): model = self.model.module else: model = self.model checkpoint = { 'meta': meta, 'state_dict': weights_to_cpu(model.state_dict()), 'message_hub': apply_to(self.message_hub.state_dict(), lambda x: hasattr(x, 'cpu'), lambda x: x.cpu()), } # save optimizer state dict to checkpoint if save_optimizer: if isinstance(self.optim_wrapper, OptimWrapper): checkpoint['optimizer'] = apply_to( self.optim_wrapper.state_dict(), lambda x: hasattr(x, 'cpu'), lambda x: x.cpu()) else: raise TypeError( 'self.optim_wrapper should be an `OptimWrapper` ' 'or `OptimWrapperDict` instance, but got ' f'{self.optim_wrapper}') # save param scheduler state dict if save_param_scheduler and self.param_schedulers is None: self.logger.warning( '`save_param_scheduler` is True but `self.param_schedulers` ' 'is None, so skip saving parameter schedulers') save_param_scheduler = False if save_param_scheduler: if isinstance(self.param_schedulers, dict): checkpoint['param_schedulers'] = dict() for name, schedulers in self.param_schedulers.items(): checkpoint['param_schedulers'][name] = [] for scheduler in schedulers: state_dict = scheduler.state_dict() checkpoint['param_schedulers'][name].append(state_dict) else: checkpoint['param_schedulers'] = [] for scheduler in self.param_schedulers: # type: ignore state_dict = scheduler.state_dict() # type: ignore checkpoint['param_schedulers'].append(state_dict) self.call_hook('before_save_checkpoint', checkpoint=checkpoint) save_checkpoint( checkpoint, filepath, file_client_args=file_client_args, backend_args=backend_args)
[docs] @master_only def dump_config(self) -> None: """Dump config to `work_dir`.""" if self.cfg.filename is not None: filename = osp.basename(self.cfg.filename) else: filename = f'{self.timestamp}.py' self.cfg.dump(osp.join(self.work_dir, filename))
def _check_scheduler_cfg( self, param_scheduler: Optional[Union[dict, list, _ParamScheduler]]) -> None: """Parse `param_scheduler` to a list of parameter schedulers, or a `dict` of which each value is a list of parameter schedulers. If only one optimizer is used, the parsed config should be a list of parameter scheduler configs or instances. If multiple optimizers are used, the parsed config should be `dict`. Its key should be consistent with the optimizer `dict` and its value should be a list of parameter scheduler configs or instances. See :meth:`build_param_scheduler` for more details. Examples: >>> # valid scheduler: >>> # empty scheduler >>> scheduler = None >>> # Single scheduler >>> scheduler = dict(type='MultiStepLR', milestones=[1, 2]) >>> # Single list schedulers >>> scheduler = [dict(type='MultiStepLR', milestones=[1, 2]), >>> dict(type='MultiStepLR', milestones=[2, 3])] >>> # `dict` of schedulers >>> scheduler = dict(linear1=dict(type='MultiStepLR', milestones=[1, 2]), >>> linear2=dict(type='MultiStepLR', milestones=[1, 2])) >>> # `dict` of `list` of schedulers >>> scheduler = dict(linear1=[dict(type='MultiStepLR', milestones=[1, 2])], >>> linear2=[dict(type='MultiStepLR', milestones=[1, 2])]) >>> # Single built scheduler >>> from mmengine.optim import MultiStepLR >>> scheduler = MultiStepLR(milestones=[1, 2], optimizer=optimizer) >>> # Single built list schedulers >>> scheduler = [MultiStepLR(milestones=[1, 2], optimizer=optimizer)] >>> # dict of built scheduler >>> scheduler = dict(linear1=MultiStepLR(milestones=[1, 2], optimizer=optimizer), >>> linear2=MultiStepLR(milestones=[1, 2], optimizer=optimizer)) >>> # dict of built list schedulers >>> scheduler = dict(linear1=[MultiStepLR(milestones=[1, 2], optimizer=optimizer)], >>> linear2=[MultiStepLR(milestones=[1, 2], optimizer=optimizer)]) Args: param_scheduler (dict or list): The original parameter scheduler. """ # noqa: E501 if param_scheduler is None: return if isinstance(param_scheduler, _ParamScheduler): return if is_seq_of(param_scheduler, _ParamScheduler): return if is_seq_of(param_scheduler, dict): for _param_scheduler in param_scheduler: assert 'type' in _param_scheduler, ( 'Each parameter scheduler should contain the key type, ' f'but got {_param_scheduler}') elif isinstance(param_scheduler, dict): if 'type' not in param_scheduler: for key, _param_scheduler in param_scheduler.items(): assert isinstance( _param_scheduler, (dict, tuple, list, _ParamScheduler)), ( 'Each value of `param_scheduler` should be a ' f'dict or a list, but got {_param_scheduler} with ' f'type {type(_ParamScheduler)}') else: raise TypeError( '`param_scheduler` should be a `_ParamScheduler`, `dict`, ' f'list or a tuple, but got {type(param_scheduler)}. If ' '`param_scheduler` is a list of dict, it means a list of ' 'scheduler configs for single optimizer. If it is a dict and ' 'contains key `type`, it means a scheduler config for a ' 'single optimizer. If it does not contain key `type`, it ' 'means multiple lists of schedulers for multiple optimizers.') def _log_env(self, env_cfg: dict) -> None: """Logging environment information of the current task. Args: env_cfg (dict): The environment config of the runner. """ # Collect and log environment information. env = collect_env() runtime_env = OrderedDict() runtime_env.update(env_cfg) runtime_env.update(self._randomness_cfg) runtime_env['seed'] = self._seed runtime_env['Distributed launcher'] = self._launcher runtime_env['Distributed training'] = self._distributed runtime_env['GPU number'] = self._world_size env_info = '\n ' + '\n '.join(f'{k}: {v}' for k, v in env.items()) runtime_env_info = '\n ' + '\n '.join( f'{k}: {v}' for k, v in runtime_env.items()) dash_line = '-' * 60 self.logger.info('\n' + dash_line + '\nSystem environment:' + env_info + '\n' '\nRuntime environment:' + runtime_env_info + '\n' + dash_line + '\n') if self.cfg._cfg_dict: self.logger.info(f'Config:\n{self.cfg.pretty_text}') def _maybe_compile(self, target: str) -> None: """Use `torch.compile` to optimize model/wrapped_model.""" compile_cfg = self.cfg.get('compile', None) if compile_cfg is None: # no compile options given, won't compile return if isinstance(compile_cfg, bool): if not compile_cfg: # compile=False, compilation is disabled return # compile=True, use default configurations compile_cfg = dict() assert digit_version(TORCH_VERSION) >= digit_version('2.0.0'), ( 'PyTorch >= 2.0.0 is required to enable torch.compile') assert isinstance(compile_cfg, dict), ( f'`compile` should be a dict or bool, got {type(compile_cfg)}') func = getattr(self.model, target) compiled_func = torch.compile(func, **compile_cfg) setattr(self.model, target, compiled_func) self.logger.info('Model has been "compiled". The first few iterations' ' will be slow, please be patient.')

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