def convert_libsvm(model, name=None, initial_types=None, doc_string='', target_opset=None, targeted_onnx=onnx.__version__, custom_conversion_functions=None, custom_shape_calculators=None): if not utils.libsvm_installed(): raise RuntimeError( 'libsvm is not installed. Please install libsvm to use this feature.' ) from .libsvm.convert import convert return convert(model, name, initial_types, doc_string, target_opset, targeted_onnx, custom_conversion_functions, custom_shape_calculators)
def convert_tensorflow(frozen_graph_def, name=None, input_names=None, output_names=None, doc_string='', target_opset=None, channel_first_inputs=None, debug_mode=False, custom_op_conversions=None): if not utils.keras2onnx_installed(): raise RuntimeError( 'keras2onnx is not installed. Please install it to use this feature.' ) from keras2onnx import convert_tensorflow as convert return convert(frozen_graph_def, name, input_names, output_names, doc_string, target_opset, channel_first_inputs, debug_mode, custom_op_conversions)
def convert_sparkml(model, name=None, initial_types=None, doc_string='', target_opset=None, targeted_onnx=onnx.__version__, custom_conversion_functions=None, custom_shape_calculators=None, spark_session=None): if not utils.sparkml_installed(): raise RuntimeError( 'Spark is not installed. Please install Spark to use this feature.' ) from .sparkml.convert import convert return convert(model, name, initial_types, doc_string, target_opset, targeted_onnx, custom_conversion_functions, custom_shape_calculators, spark_session)
def convert_keras(model, name=None, initial_types=None, doc_string='', target_opset=None, targeted_onnx=onnx.__version__, channel_first_inputs=None, custom_conversion_functions=None, custom_shape_calculators=None, default_batch_size=1): if not utils.keras2onnx_installed(): raise RuntimeError( 'keras2onnx is not installed. Please install it to use this feature.' ) if custom_conversion_functions: warnings.warn( 'custom_conversion_functions is not supported any more. Please set it to None.' ) from keras2onnx import convert_keras as convert return convert(model, name, doc_string, target_opset, channel_first_inputs)
def convert_xgboost(*args, **kwargs): if not utils.xgboost_installed(): raise RuntimeError('xgboost is not installed. Please install xgboost to use this feature.') from .xgboost.convert import convert return convert(*args, **kwargs)