def build(cfg, registry, default_args=None):
"""Build a module.
Args:
cfg (dict, list[dict]): The config of modules, is is either a dict
or a list of configs.
registry (:obj:`Registry`): A registry the module belongs to.
default_args (dict, optional): Default arguments to build the module.
Defaults to None.
Returns:
nn.Module: A built nn module.
"""
if isinstance(cfg, list):
modules = [
build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
]
return nn.Sequential(*modules)
else:
return build_from_cfg(cfg, registry, default_args)
def build_sampler(cfg, default_args: Optional[Dict] = None):
world_size = dist_info.get_world_size()
if world_size > 1:
rank = dist_info.get_local_rank()
default_args.update({'world_size': world_size, 'rank': rank})
sampler_type = getattr(cfg, 'type', None)
if sampler_type != 'DistributedSampler':
logger = logging.getLogger(__name__)
logger.warning(
f'because world_size equal {world_size},expected DistributedSampler type,but got {sampler_type}'
)
cfg.type = 'DistributedSampler'
elif world_size == 1:
sampler_type = getattr(cfg, 'type', None)
if sampler_type == 'DistributedSampler':
logger = logging.getLogger(__name__)
logger.warning(
f'because world_size equal {world_size},and the preset sampling is {sampler_type}, '
f'here it will set sampler is None')
return None
SamplerAdaptor.adaptor(cfg=cfg, default_args=default_args)
return build_from_cfg(cfg, SAMPLER, default_args)
def build_dataset(dataset_cfg, default_args=None):
return build_from_cfg(dataset_cfg, DATASETS, default_args)
def build_processor(cfg, defualt_args=None):
return build_from_cfg(cfg, PROCESSOR, defualt_args)
def build_preprocessor(cfg):
"""Build preprocessor."""
return build_from_cfg(cfg, PREPROCESSOR)
def build_vocab(cfg):
"""Build vocab."""
return build_from_cfg(cfg, VOCAB)
def build_feature_reader(cfg, default_args=None):
return build_from_cfg(cfg=cfg,
registry=FEATURE_READERS,
default_args=default_args)
def build_hook(cfg, default_args: Optional[Dict] = None):
return build_from_cfg(cfg=cfg, registry=HOOKS, default_args=default_args)
def build_batch_sampler(cfg, default_args: Optional[Dict] = None):
SamplerAdaptor.adaptor(cfg=cfg, default_args=default_args)
return build_from_cfg(cfg, BATCH_SAMPLER, default_args)