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)
