Shortcuts

mmengine.runner._flexible_runner 源代码

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

import torch.nn as nn
from torch.utils.data import DataLoader

import mmengine
from mmengine._strategy import BaseStrategy
from mmengine.config import Config, ConfigDict
from mmengine.dataset import worker_init_fn
from mmengine.dist import get_rank, infer_launcher, master_only
from mmengine.evaluator import Evaluator
from mmengine.fileio import FileClient, join_path
from mmengine.hooks import Hook
from mmengine.logging import MessageHub, print_log
from mmengine.optim import OptimWrapper, OptimWrapperDict, _ParamScheduler
from mmengine.registry import (DATA_SAMPLERS, DATASETS, EVALUATOR, FUNCTIONS,
                               HOOKS, LOG_PROCESSORS, LOOPS, RUNNERS,
                               STRATEGIES, VISUALIZERS, DefaultScope)
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
from mmengine.visualization import Visualizer
from .base_loop import BaseLoop
from .checkpoint import find_latest_checkpoint
from .log_processor import LogProcessor
from .loops import EpochBasedTrainLoop, IterBasedTrainLoop, TestLoop, ValLoop
from .priority import Priority, get_priority

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


[文档]@RUNNERS.register_module() class FlexibleRunner: """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. Warning: This is an experimental feature, and its interface is subject to change. Args: model (:obj:`torch.nn.Module` or dict): The model to be run. It can be a dict used for build a model. Kwargs: work_dir (str, optional): The working directory to save checkpoints. The logs will be saved in the subdirectory of `work_dir` named :attr:`timestamp`. Defaults to 'work_dir'. experiment_name (str, optional): Name of current experiment. If not specified, timestamp will be used as ``experiment_name``. Defaults to None. 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. 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. 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_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. 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. 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. 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. Defaults to None. See :meth:`build_val_loop` for more details. 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. 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. strategy (BaseStrategy or dict, optional): A strategy object or a dict to build a strategy. Defaults to None. If not specified, the strategy will be inferred automatically. 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. 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, optional): Way to launcher multi-process. Supported launchers are 'pytorch', 'mpi', 'slurm' and 'none'. If 'none' is provided, non-distributed environment will be launched. If launcher is None, the launcher will be inferred according some specified environments. Defaults to None. 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. compile (bool or dict, optional): Whether to enable ``torch.compile``. Defaults to False. 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 `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 = 'work_dirs', experiment_name: Optional[str] = None, train_dataloader: Optional[Union[DataLoader, Dict]] = None, optim_wrapper: Optional[Union[OptimWrapper, Dict]] = None, param_scheduler: Optional[Union[_ParamScheduler, Dict, List]] = None, train_cfg: Optional[Dict] = None, val_dataloader: Optional[Union[DataLoader, Dict]] = None, val_evaluator: Optional[Union[Evaluator, Dict, List]] = None, val_cfg: Optional[Dict] = None, test_dataloader: Optional[Union[DataLoader, Dict]] = None, test_evaluator: Optional[Union[Evaluator, Dict, List]] = None, test_cfg: Optional[Dict] = None, strategy: Optional[Union[BaseStrategy, Dict]] = None, auto_scale_lr: Optional[Dict] = 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: Union[str, bool] = False, launcher: Optional[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: Optional[str] = 'mmengine', randomness: Dict = dict(seed=None), compile: Union[bool, Dict] = False, cfg: Optional[ConfigType] = None, ): 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 = model 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}') 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 if not isinstance(compile, bool) and not isinstance(compile, dict): raise TypeError( f'compile should be a bool or dict, but got {type(compile)}') self._compile = compile if isinstance(resume, str) and load_from is not None: raise ValueError('If resume is a str, load_from should be None.') self._load_from = load_from self._resume = resume # flag to mark whether checkpoint has been loaded or resumed self._has_loaded = False if launcher is None: launcher = infer_launcher() if experiment_name is None and self.cfg.filename is not None: experiment_name = osp.splitext(osp.basename(self.cfg.filename))[0] self._randomness_cfg = randomness self.strategy = self.build_strategy( strategy, launcher=launcher, randomness=randomness, env_cfg=env_cfg, experiment_name=experiment_name, log_level=log_level, ) # 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) # Collect and log environment information. self._log_env() # 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._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()
[文档] @classmethod def from_cfg(cls, cfg: ConfigType) -> 'FlexibleRunner': """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.get('work_dir', 'work_dirs'), experiment_name=cfg.get('experiment_name'), train_dataloader=cfg.get('train_dataloader'), optim_wrapper=cfg.get('optim_wrapper'), param_scheduler=cfg.get('param_scheduler'), train_cfg=cfg.get('train_cfg'), val_dataloader=cfg.get('val_dataloader'), val_evaluator=cfg.get('val_evaluator'), val_cfg=cfg.get('val_cfg'), test_dataloader=cfg.get('test_dataloader'), test_evaluator=cfg.get('test_evaluator'), test_cfg=cfg.get('test_cfg'), strategy=cfg.get('strategy'), auto_scale_lr=cfg.get('auto_scale_lr'), 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'), 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)), cfg=cfg, ) return runner
@property def experiment_name(self): """str: Name of experiment.""" return self.strategy.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.strategy.log_dir @property def logger(self): return self.strategy.logger @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 distributed(self): """bool: Whether current environment is distributed.""" return self.strategy.distributed @property def rank(self): """int: Rank of current process.""" return self.strategy.rank @property def world_size(self): """int: Number of processes participating in the job.""" return self.strategy.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.strategy.seed @property def timestamp(self): """str: Timestamp when creating experiment.""" return self.strategy.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
[文档] def build_strategy( self, strategy: Optional[Union[BaseStrategy, Dict]] = None, launcher: str = 'none', randomness: Optional[dict] = None, env_cfg: dict = dict(dist_cfg=dict(backend='nccl')), experiment_name: Optional[str] = None, log_level: Optional[str] = None, ) -> BaseStrategy: """Build a strategy. Args: strategy (BaseStrategy, optional): A strategy object or dict to build the strategy. Defaults to None. Returns: BaseStrategy: A strategy object. """ if isinstance(strategy, BaseStrategy): strategy_obj = strategy else: if launcher == 'none': if strategy is None: strategy = dict(type='SingleDeviceStrategy') else: if strategy is None: strategy = dict(type='DDPStrategy') assert isinstance(strategy, dict) # train_micro_batch_size_per_gpu is required by DeepSpeed if isinstance(strategy['type'], str): strategy_name = strategy['type'] else: strategy_name = strategy['type'].__name__ if strategy_name == 'DeepSpeedStrategy': if self._train_dataloader is None: strategy['train_micro_batch_size_per_gpu'] = 1 else: strategy['train_micro_batch_size_per_gpu'] = \ _get_batch_size(self._train_dataloader) strategy.setdefault('work_dir', self._work_dir) strategy.setdefault('experiment_name', experiment_name) strategy.setdefault('auto_scale_lr', self._auto_scale_lr) env_kwargs = dict( launcher=launcher, randomness=randomness, **env_cfg, ) strategy.setdefault('env_kwargs', env_kwargs) log_kwargs = dict(log_level=log_level) strategy.setdefault('log_kwargs', log_kwargs) strategy_obj = STRATEGIES.build(strategy) return strategy_obj
[文档] 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)
[文档] 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}')
[文档] 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}')
[文档] @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. Defaults to False. 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 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( 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')) collate_fn_type = collate_fn_cfg.pop('type') collate_fn = FUNCTIONS.get(collate_fn_type) collate_fn = partial(collate_fn, **collate_fn_cfg) # type: ignore 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
[文档] 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'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
[文档] 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'train_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
[文档] 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'train_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
[文档] 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)
[文档] def load_or_resume(self): """load or resume checkpoint.""" if self._has_loaded: return None if not self._resume and self._load_from is None: return None # decide to load from checkpoint or resume from checkpoint resume_from = None if isinstance(self._resume, str): resume_from = self._resume elif 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
[文档] def train(self) -> nn.Module: """Launch training. Returns: nn.Module: The model after training. """ 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 if self._val_loop is not None: self._val_loop = self.build_val_loop( self._val_loop) # type: ignore compile: Union[dict, bool] = False if isinstance(self._compile, bool): if self._compile: compile = dict(target='train_step') else: compile = copy.copy(self._compile) compile.setdefault('target', 'train_step') dispatch_kwargs = dict( epoch_length=len(self.train_dataloader), max_epochs=self.max_epochs, max_iters=self.max_iters, ) self.strategy.prepare( self.model, optim_wrapper=self.optim_wrapper, param_scheduler=self.param_schedulers, compile=compile, dispatch_kwargs=dispatch_kwargs, ) self.model = self.strategy.model self.optim_wrapper = self.strategy.optim_wrapper # type: ignore if self.param_schedulers is not None: self.param_schedulers = self.strategy.param_schedulers self.load_or_resume() # TODO: add a contextmanager to avoid calling `before_run` many times self.call_hook('before_run') model = self.train_loop.run() # type: ignore self.call_hook('after_run') return model
[文档] 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 dispatch_kwargs = dict( init_weights_for_test_or_val=self.cfg.get( 'init_weights_for_test_or_val', True)) self.strategy.prepare(self.model, dispatch_kwargs=dispatch_kwargs) self.model = self.strategy.model self.load_or_resume() self.call_hook('before_run') metrics = self.val_loop.run() # type: ignore self.call_hook('after_run') return metrics
[文档] 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 dispatch_kwargs = dict( init_weights_for_test_or_val=self.cfg.get( 'init_weights_for_test_or_val', True)) self.strategy.prepare(self.model, dispatch_kwargs=dispatch_kwargs) self.model = self.strategy.model self.load_or_resume() self.call_hook('before_run') metrics = self.test_loop.run() # type: ignore self.call_hook('after_run') return metrics
[文档] 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
[文档] 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)
[文档] 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)
[文档] 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)
[文档] 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)
[文档] 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'. """ def callback(checkpoint): self.call_hook('after_load_checkpoint', checkpoint=checkpoint) checkpoint = self.strategy.resume( filename, resume_optimizer=resume_optimizer, resume_param_scheduler=resume_param_scheduler, map_location=map_location, callback=callback, ) 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? self.logger.info( 'Number of GPU used for current experiment is not ' 'consistent with resuming from checkpoint') if (self._auto_scale_lr is None or not self._auto_scale_lr.get('enable', False)): raise RuntimeError( 'Cannot automatically rescale lr in resuming. Please ' 'make sure the number of GPU is consistent with the ' 'previous training state resuming from the checkpoint ' 'or set `enable` in `auto_scale_lr to False.') 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']) self.logger.info(f'resumed epoch: {self.epoch}, iter: {self.iter}')
[文档] 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\\.', '')]. """ def callback(checkpoint): self.call_hook('after_load_checkpoint', checkpoint=checkpoint) self.strategy.load_checkpoint( filename, map_location=map_location, strict=strict, revise_keys=revise_keys, callback=callback)
[文档] 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: 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): Whether the scheduled momentum is updated by epochs. Defaults to True. backend_args (dict, optional): Arguments to instantiate the prefix of uri corresponding backend. Defaults to None. """ 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.update(epoch=self.epoch + 1, iter=self.iter) else: meta.update(epoch=self.epoch, 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, experiment_name=self.experiment_name) if hasattr(self.train_dataloader.dataset, 'metainfo'): meta.update(dataset_meta=self.train_dataloader.dataset.metainfo) checkpoint = { 'meta': meta, 'message_hub': self.message_hub.state_dict() } def callback(checkpoint): self.call_hook('before_save_checkpoint', checkpoint=checkpoint) self.strategy.save_checkpoint( filename=filepath, save_optimizer=save_optimizer, save_param_scheduler=save_param_scheduler, extra_ckpt=checkpoint, callback=callback, )
[文档] @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 _log_env(self) -> None: """Logging environment information of the current task. Args: env_cfg (dict): The environment config of the runner. """ # Collect and log environment information. system_env, runtime_env = self.strategy.collect_env() env_info = '\n ' + '\n '.join(f'{k}: {v}' for k, v in system_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 _get_batch_size(dataloader): if isinstance(dataloader, dict): if 'batch_size' in dataloader: return dataloader['batch_size'] elif ('batch_sampler' in dataloader and 'batch_size' in dataloader['batch_sampler']): return dataloader['batch_sampler']['batch_size'] else: raise ValueError('Please set batch_size in `Dataloader` or ' '`batch_sampler`') elif isinstance(dataloader, DataLoader): return dataloader.batch_sampler.batch_size else: raise ValueError('dataloader should be a dict or a Dataloader ' f'instance, but got {type(dataloader)}')

© Copyright 2022, mmengine contributors. Revision b2295a25.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: v0.8.1
Versions
latest
stable
v0.8.1
v0.8.0
v0.7.4
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.0
v0.5.0
v0.4.0
v0.3.0
v0.2.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.