def instantiate_board(config):
if config._type_ == "charuco":
schema = OmegaConf.structured(CharucoConfig)
return CharucoBoard(aruco_params=aruco_params, **merge_schema(config, schema))
elif config._type_ == "aprilgrid":
schema = OmegaConf.structured(AprilConfig)
return AprilGrid(**merge_schema(config, schema))
else:
assert False, f"unknown board type: {config._type_}, options are (charuco | aprilgrid | checkerboard)"
def legacy_model_config_to_new_model_config(model_cfg: DictConfig) -> DictConfig:
"""
Transform old style config into
:class:`~nemo.collections.nlp.models.token_classification.punctuation_capitalization_config.PunctuationCapitalizationModelConfig`.
Old style configs are configs which were used before ``common_dataset_parameters`` item was added. Old style
datasets use ``dataset`` instead of ``common_dataset_parameters``, ``batch_size`` instead of ``tokens_in_batch``.
Old style configs do not support tarred datasets.
Args:
model_cfg: old style config
Returns:
model config which follows dataclass
:class:`~nemo.collections.nlp.models.token_classification.punctuation_capitalization_config.PunctuationCapitalizationModelConfig`
"""
train_ds = model_cfg.get('train_ds')
validation_ds = model_cfg.get('validation_ds')
test_ds = model_cfg.get('test_ds')
dataset = model_cfg.dataset
punct_head_config = model_cfg.get('punct_head', {})
capit_head_config = model_cfg.get('capit_head', {})
return OmegaConf.structured(
PunctuationCapitalizationModelConfig(
class_labels=model_cfg.class_labels,
common_dataset_parameters=CommonDatasetParametersConfig(
pad_label=dataset.pad_label,
ignore_extra_tokens=dataset.get(
'ignore_extra_tokens', CommonDatasetParametersConfig.ignore_extra_tokens
),
ignore_start_end=dataset.get('ignore_start_end', CommonDatasetParametersConfig.ignore_start_end),
punct_label_ids=model_cfg.punct_label_ids,
capit_label_ids=model_cfg.capit_label_ids,
),
train_ds=None
if train_ds is None
else legacy_data_config_to_new_data_config(train_ds, dataset, train=True),
validation_ds=None
if validation_ds is None
else legacy_data_config_to_new_data_config(validation_ds, dataset, train=False),
test_ds=None if test_ds is None else legacy_data_config_to_new_data_config(test_ds, dataset, train=False),
punct_head=HeadConfig(
num_fc_layers=punct_head_config.get('punct_num_fc_layers', HeadConfig.num_fc_layers),
fc_dropout=punct_head_config.get('fc_dropout', HeadConfig.fc_dropout),
activation=punct_head_config.get('activation', HeadConfig.activation),
use_transformer_init=punct_head_config.get('use_transformer_init', HeadConfig.use_transformer_init),
),
capit_head=HeadConfig(
num_fc_layers=capit_head_config.get('capit_num_fc_layers', HeadConfig.num_fc_layers),
fc_dropout=capit_head_config.get('fc_dropout', HeadConfig.fc_dropout),
activation=capit_head_config.get('activation', HeadConfig.activation),
use_transformer_init=capit_head_config.get('use_transformer_init', HeadConfig.use_transformer_init),
),
tokenizer=model_cfg.tokenizer,
language_model=model_cfg.language_model,
optim=model_cfg.optim,
)
)
def _ensure_container(target: Any, flags: Optional[Dict[str, bool]] = None) -> Any:
from omegaconf import OmegaConf
if is_primitive_container(target):
assert isinstance(target, (list, dict))
target = OmegaConf.create(target, flags=flags)
elif is_structured_config(target):
target = OmegaConf.structured(target, flags=flags)
assert OmegaConf.is_config(target)
return target
def _ensure_container(target: Any, flags: Optional[Dict[str, bool]] = None) -> Any:
from omegaconf import OmegaConf
if is_primitive_container(target):
assert isinstance(target, (list, dict))
target = OmegaConf.create(target, flags=flags)
elif is_structured_config(target):
target = OmegaConf.structured(target, flags=flags)
elif not OmegaConf.is_config(target):
raise ValueError(
"Invalid input. Supports one of "
+ "[dict,list,DictConfig,ListConfig,dataclass,dataclass instance,attr class,attr class instance]"
)
return target