def load_classifier(path, extractor_class, feature_computer_class, session):
extractor = extractor_class.load(os.path.join(path, 'extractor'))
feature_computer = feature_computer_class.load(
os.path.join(path, "feature_computer"))
saver = load_tf_model_with_saver(path, "model", session)
graph = _convert_load_graph(session, load_with_pickle(path, "graph.pkl"))
return extractor, feature_computer, graph, saver
def load(cls, path, session):
extractor, feature_computer, graph, saver = load_classifier(
path, RelExtFeatureExtractor, CompositeFeatureComputer, session)
return cls(graph, extractor, feature_computer, session, saver, load_with_pickle(path, "collapser"))
def load(path):
return load_with_pickle(path, "feature_extractor.pkl")
def load(cls, path, session):
extractor, feature_computer, graph, saver = load_classifier(
path, NERFeatureExtractor, SyntacticFeatureComputer, session)
return cls(graph, extractor, feature_computer, session, saver,
load_with_pickle(path, "post_processor"))
def load(cls, path, session):
extractor, feature_computer, graph, saver = load_classifier(
path, NETFeatureExtractor, SyntacticFeatureComputer, session)
return cls(graph, extractor, feature_computer, session, saver,
load_with_pickle(path, "grouper_collapser"))
def load(path):
return load_with_pickle(path, "feature_computer.pkl")
def load_file(path: str, name: str) -> List[Document]:
return load_with_pickle(path, name)
def load(path: str) -> List[Document]:
return load_with_pickle(path, "docs.pkl")
def load(cls, path, session):
extractor, feature_computer, graph, saver = load_classifier(
path, CorefFeatureExtractor, FeatureComputer, session)
classifiers = load_with_pickle(path, "classifiers.pkl")
return cls(graph, extractor, feature_computer, session, saver, classifiers)