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)