def optimize(model, passes=[]): # type: (ModelProto, Sequence[Text]) -> ModelProto
if len(passes) == 0:
passes = ['eliminate_nop_transpose',
'fuse_consecutive_transposes',
'fuse_transpose_into_gemm']
if not isinstance(model, ModelProto):
raise ValueError('Optimizer only accepts ModelProto, incorrect type: {}'.format(type(model)))
model_str = model.SerializeToString()
optimized_model_str = C.optimize(model_str, passes)
return onnx.load_from_string(optimized_model_str)
def optimize(model, passes=[]): # type: (ModelProto, Sequence[Text]) -> ModelProto
if len(passes) == 0:
passes = ['eliminate_nop_transpose',
'eliminate_nop_pad',
'fuse_consecutive_transposes',
'fuse_transpose_into_gemm']
if not isinstance(model, ModelProto):
raise ValueError('Optimizer only accepts ModelProto, incorrect type: {}'.format(type(model)))
model_str = model.SerializeToString()
optimized_model_str = C.optimize(model_str, passes)
return onnx.load_from_string(optimized_model_str)
def optimize(
model,
passes=None,
fixed_point=False
): # type: (ModelProto, Optional[Sequence[Text]], bool) -> ModelProto
if passes is None:
passes = [
'eliminate_nop_transpose', 'eliminate_nop_pad',
'fuse_consecutive_transposes', 'fuse_transpose_into_gemm'
]
if not isinstance(model, ModelProto):
raise ValueError(
'Optimizer only accepts ModelProto, incorrect type: {}'.format(
type(model)))
model_str = model.SerializeToString()
if fixed_point:
optimized_model_str = C.optimize_fixedpoint(model_str, passes)
else:
optimized_model_str = C.optimize(model_str, passes)
return onnx.load_from_string(optimized_model_str)