def from_archive(
cls,
archive: Archive,
predictor_name: str = None,
dataset_reader_to_load: str = "validation",
frozen: bool = True,
language: str = "en_core_web_sm",
restrict_frames: bool = False,
restrict_roles: bool = False,
) -> "Predictor":
# Duplicate the config so that the config inside the archive doesn't get consumed
config = archive.config.duplicate()
if not predictor_name:
model_type = config.get("model").get("type")
model_class, _ = Model.resolve_class_name(model_type)
predictor_name = model_class.default_predictor
predictor_class: Type[Predictor] = (
Predictor.by_name(predictor_name) if predictor_name is not None else cls # type: ignore
)
if dataset_reader_to_load == "validation" and "validation_dataset_reader" in config:
dataset_reader_params = config["validation_dataset_reader"]
else:
dataset_reader_params = config["dataset_reader"]
dataset_reader = DatasetReader.from_params(dataset_reader_params)
model = archive.model
if frozen:
model.restrict_frames = restrict_frames
model.restrict_roles = restrict_roles
model.eval()
return predictor_class(model, dataset_reader, language)
def predictor_from_archive(archive: Archive,
predictor_name: str = None,
paper_features_path: str = None) -> 'Predictor':
"""
Extends allennlp.predictors.predictor.from_archive to allow processing multiprocess reader
paper_features_path is passed to replace the correct one if the dataset_reader is multiprocess
"""
# Duplicate the config so that the config inside the archive doesn't get consumed
config = archive.config.duplicate()
if not predictor_name:
model_type = config.get("model").get("type")
if not model_type in DEFAULT_PREDICTORS:
raise ConfigurationError(f"No default predictor for model type {model_type}.\n"\
f"Please specify a predictor explicitly.")
predictor_name = DEFAULT_PREDICTORS[model_type]
dataset_config = config["dataset_reader"].as_dict()
if dataset_config['type'] == 'multiprocess':
dataset_config = dataset_config['base_reader']
if paper_features_path:
dataset_config['paper_features_path'] = paper_features_path
dataset_reader_params = Params(dataset_config)
else:
dataset_reader_params = config["dataset_reader"]
dataset_reader = DatasetReader.from_params(dataset_reader_params)
model = archive.model
model.eval()
return Predictor.by_name(predictor_name)(model, dataset_reader)
def __init__(self, model: MultiTaskModel,
dataset_reader: MultiTaskDatasetReader) -> None:
if not isinstance(dataset_reader, MultiTaskDatasetReader):
raise ConfigurationError(self._WRONG_READER_ERROR)
if not isinstance(model, MultiTaskModel):
raise ConfigurationError(
"MultiTaskPredictor is designed to work only with MultiTaskModel."
)
super().__init__(model, dataset_reader)
self.predictors = {}
for name, head in model._heads.items():
predictor_name = head.default_predictor
predictor_class: Type[Predictor] = (
Predictor.by_name(predictor_name)
if predictor_name is not None else Predictor # type: ignore
)
self.predictors[name] = predictor_class(
model, dataset_reader.readers[name].inner)
