def __init__(self,
model_path,
tag_lookup_file=None,
vocabulary_lookup_file=None):
self.tag_lookup_table = Lookuper.load_from_file(tag_lookup_file)
self.vocabulary_lookup_table = Lookuper.load_from_file(
vocabulary_lookup_file)
super(KerasInferenceBase, self).__init__(model_path)
def __init__(self, model_path, tag_lookup_file=None, vocabulary_lookup_file=None):
# load model
self.model_dir = model_path
# TODO: temp bugfix
self.model = tf.keras.models.load_model(model_path, custom_objects={"crf_accuracy": crf_accuracy, "sequence_span_accuracy": sequence_span_accuracy})
self.predict_fn = self.model.predict
self.tag_lookup_table = Lookuper.load_from_file(tag_lookup_file)
self.vocabulary_lookup_table = Lookuper.load_from_file(vocabulary_lookup_file)
def __init__(self,
model_path,
tag_lookup_file=None,
vocabulary_lookup_file=None):
# load model
self.model_dir = model_path
# TODO: temp bugfix
self.model = tf.keras.models.load_model(model_path)
self.predict_fn = self.model.predict
self.tag_lookup_table = Lookuper.load_from_file(tag_lookup_file)
self.vocabulary_lookup_table = Lookuper.load_from_file(
vocabulary_lookup_file)
def load(cls, parameter: dict, asset_dir) -> "ProcessorBase":
from seq2annotation.input import Lookuper
lookup_table_registry = {}
for instance_name in parameter["lookup_table"]:
instance_asset = asset_dir / instance_name
lookup_table_instance = Lookuper.load_from_file(instance_asset)
lookup_table_registry[instance_name] = lookup_table_instance
init_parameter = copy.deepcopy(parameter)
init_parameter.pop("lookup_table")
init_parameter["lookup_table_registry"] = lookup_table_registry
self = cls(**init_parameter)
return self
