megenginelite.network 源代码

# -*- coding: utf-8 -*-

from ctypes import *

import numpy as np

from .base import _Cnetwork, _Ctensor, _lib, _LiteCObjBase
from .struct import *
from .tensor import *


[文档]class LiteOptions(Structure): """ the inference options which can optimize the network forwarding performance Attributes: weight_preprocess: is the option which optimize the inference performance with processing the weights of the network ahead fuse_preprocess: fuse preprocess patten, like astype + pad_channel + dimshuffle fake_next_exec: whether only to perform non-computing tasks (like memory allocation and queue initialization) for next exec. This will be reset to false when the graph is executed. var_sanity_check_first_run: Disable var sanity check on the first run. Var sanity check is enabled on the first-time execution by default, and can be used to find some potential memory access errors in the operator const_shape: used to reduce memory usage and improve performance since some static inference data structures can be omitted and some operators can be compute before forwarding force_dynamic_alloc: force dynamic allocate memory for all vars force_output_dynamic_alloc: force dynamic allocate memory for output tensor which are used as the input of CallbackCaller Operator no_profiling_on_shape_change: do not re-profile to select best implement algo when input shape changes (use previous algo) jit_level: Execute supported operators with JIT (support MLIR, NVRTC). Can only be used on Nvidia GPUs and X86 CPU, this value indicates JIT level: level 1: for JIT execute with basic elemwise operator level 2: for JIT execute elemwise and reduce operators record_level: flags to optimize the inference performance with record the kernel tasks in first run, hereafter the inference all need is to execute the recorded tasks. level = 0 means the normal inference level = 1 means use record inference level = 2 means record inference with free the extra memory graph_opt_level: network optimization level: 0: disable 1: level-1: inplace arith transformations during graph construction 2: level-2: level-1, plus global optimization before graph compiling 3: also enable JIT async_exec_level: level of dispatch on separate threads for different comp_node. 0: do not perform async dispatch 1: dispatch async if there are more than one comp node with limited queue mask 0b10: async if there are multiple comp nodes with mask 0b100: always async Examples: .. code-block:: from megenginelite import * options = LiteOptions() options.weight_preprocess = true options.record_level = 1 options.fuse_preprocess = true """ _fields_ = [ ("weight_preprocess", c_int), ("fuse_preprocess", c_int), ("fake_next_exec", c_int), ("var_sanity_check_first_run", c_int), ("const_shape", c_int), ("force_dynamic_alloc", c_int), ("force_output_dynamic_alloc", c_int), ("force_output_use_user_specified_memory", c_int), ("no_profiling_on_shape_change", c_int), ("jit_level", c_int), ("comp_node_seq_record_level", c_int), ("graph_opt_level", c_int), ("async_exec_level", c_int), # layout transform options ("enable_nchw44", c_int), ("enable_nchw44_dot", c_int), ("enable_nchw88", c_int), ("enable_nhwcd4", c_int), ("enable_nchw4", c_int), ("enable_nchw32", c_int), ("enable_nchw64", c_int), ] def __init__(self): self.weight_preprocess = False self.fuse_preprocess = False self.fake_next_exec = False self.var_sanity_check_first_run = True self.const_shape = False self.force_dynamic_alloc = False self.force_output_dynamic_alloc = False self.force_output_use_user_specified_memory = False self.no_profiling_on_shape_change = False self.jit_level = 0 self.comp_node_seq_record_level = 0 self.graph_opt_level = 2 self.async_exec_level = 1 def __repr__(self): data = { "weight_preprocess": bool(self.weight_preprocess), "fuse_preprocess": bool(self.fuse_preprocess), "fake_next_exec": bool(self.fake_next_exec), "var_sanity_check_first_run": bool(self.var_sanity_check_first_run), "const_shape": bool(self.const_shape), "force_dynamic_alloc": bool(self.force_dynamic_alloc), "force_output_dynamic_alloc": bool(self.force_output_dynamic_alloc), "force_output_use_user_specified_memory": bool( self.force_output_use_user_specified_memory ), "no_profiling_on_shape_change": bool(self.no_profiling_on_shape_change), "jit_level": self.jit_level, "comp_node_seq_record_level": self.comp_node_seq_record_level, "graph_opt_level": self.graph_opt_level, "async_exec_level": self.async_exec_level, } return data.__repr__()
[文档]class LiteConfig(Structure): """ Configuration when load and compile a network Attributes: has_compression: flag whether the model is compressed, the compress method is stored in the model device_id: configure the device id of a network device_type: configure the device type of a network backend: configure the inference backend of a network, now only support megengine bare_model_cryption_name: is the bare model encryption method name, bare model is not packed with json information, this encryption method name is useful to decrypt the encrypted bare model options: configuration of Options auto_optimize_inference: lite will detect the device information add set the options heuristically Examples: .. code-block:: from megenginelite import * config = LiteConfig() config.has_compression = False config.device_type = LiteDeviceType.LITE_CPU config.backend = LiteBackend.LITE_DEFAULT config.bare_model_cryption_name = "AES_default".encode("utf-8") config.auto_optimize_inference = False """ _fields_ = [ ("has_compression", c_int), ("device_id", c_int), ("device_type", c_int), ("backend", c_int), ("_bare_model_cryption_name", c_char_p), ("options", LiteOptions), ("auto_optimize_inference", c_int), ] def __init__(self, device_type=LiteDeviceType.LITE_CPU, option=None): self.device_type = device_type if option: self.options = option else: self.options = LiteOptions() self._bare_model_cryption_name = c_char_p(b"") self.use_loader_dynamic_param = 0 self.has_compression = 0 self.backend = LiteBackend.LITE_DEFAULT self.auto_optimize_inference = 0 @property def bare_model_cryption_name(self): return self._bare_model_cryption_name.decode("utf-8") @bare_model_cryption_name.setter def bare_model_cryption_name(self, name): if isinstance(name, str): self._bare_model_cryption_name = name.encode("utf-8") else: assert isinstance(name, bytes), "name should be str or bytes type." self._bare_model_cryption_name = name def __repr__(self): data = { "has_compression": bool(self.has_compression), "device_id": LiteDeviceType(self.device_id), "device_type": LiteDeviceType(self.device_type), "backend": LiteBackend(self.backend), "bare_model_cryption_name": self.bare_model_cryption_name, "options": self.options, "auto_optimize_inference": self.auto_optimize_inference, } return data.__repr__()
class LiteExtraConfig(Structure): """ Extra configuration when load and compile the graph disable_configure_by_model_info: disable the configuration dumped with model, if set true, all configuration in the model will not apply, users should configure the network. """ _fields_ = [ ("disable_configure_by_model_info", c_int), ] def __init__(self, disable_model_config=False): self.disable_configure_by_model_info = disable_model_config def __repr__(self): data = { "disable_configure_by_model_info": bool( self.disable_configure_by_model_info ), } return data.__repr__()
[文档]class LiteIO(Structure): """ config the network input and output item, the input and output tensor information will describe there Attributes: name: the tensor name in the graph corresponding to the IO is_host: Used to mark where the input tensor comes from and where the output tensor will copy to, if is_host is true, the input is from host and output copy to host, otherwise in device. Sometimes the input is from device and output no need copy to host, default is true. io_type: The IO type, it can be SHAPE or VALUE, when SHAPE is set, the input or output tensor value is invaid, only shape will be set, default is VALUE config_layout: The layout of the config from user, if other layout is set before forward or get after forward, this layout will by pass. if no other layout is set before forward, this layout will work. if this layout is no set, the model will forward with its origin layout. if in output, it will used to check. Note: if other layout is set to input tensor before forwarding, this layout will not work if no layout is set before forwarding, the model will forward with its origin layout if layout is set in output tensor, it will used to check whether the layout computed from the network is correct Examples: .. code-block:: from megenginelite import * io = LiteIO( "data2", is_host=True, io_type=LiteIOType.LITE_IO_SHAPE, layout=LiteLayout([2, 4, 4]), ) """ _fields_ = [ ("_name", c_char_p), ("is_host", c_int), ("io_type", c_int), ("config_layout", LiteLayout), ] def __init__( self, name, is_host=True, io_type=LiteIOType.LITE_IO_VALUE, layout=None ): if type(name) == str: self._name = c_char_p(name.encode("utf-8")) else: self._name = c_char_p(name) if layout: self.config_layout = layout else: self.config_layout = LiteLayout() self.is_host = is_host self.io_type = io_type @property def name(self): """ get the name of IO item """ return self._name.decode("utf-8") @name.setter def name(self, name): """ set the name of IO item """ if isinstance(name, str): self._name = name.encode("utf-8") else: assert isinstance(name, bytes), "name should be str or bytes type." self._name = name def __repr__(self): data = { "name": self.name, "is_host": bool(self.is_host), "io_type": LiteIOType(self.io_type), "config_layout": self.config_layout, } return data.__repr__() def __hash__(self): return hash(self.name)
class _LiteNetworkIO(Structure): _fields_ = [ ("inputs", POINTER(LiteIO)), ("outputs", POINTER(LiteIO)), ("input_size", c_size_t), ("output_size", c_size_t), ] def __init__(self): self.inputs = POINTER(LiteIO)() self.outputs = POINTER(LiteIO)() self.input_size = 0 self.output_size = 0
[文档]class LiteNetworkIO(object): """ the input and output information when load the network for user the NetworkIO will remain in the network until the network is destroyed. Attributes: inputs: The all input tensors information that will configure to the network outputs: The all output tensors information that will configure to the network Examples: .. code-block:: from megenginelite import * input_io = LiteIO("data", is_host=False, io_type=LiteIOType.LITE_IO_VALUE) io = LiteNetworkIO() io.add_input(input_io) output_io = LiteIO("out", is_host=True, layout=LiteLayout([1, 1000])) io.add_output(output_io) """ def __init__(self, inputs=None, outputs=None): self.inputs = [] self.outputs = [] if inputs: for i in inputs: if isinstance(i, list): self.inputs.append(LiteIO(*i)) else: assert isinstance( i, LiteIO ), "the param to construct LiteNetworkIO must be list of the LiteIO member or the LiteIO." self.inputs.append(i) if outputs: for i in outputs: if isinstance(i, list): self.outputs.append(LiteIO(*i)) else: assert isinstance( i, LiteIO ), "the param to construct LiteNetworkIO must be list of the LiteIO member or the LiteIO." self.outputs.append(i)
[文档] def add_input( self, obj, is_host=True, io_type=LiteIOType.LITE_IO_VALUE, layout=None ): """ add input information into LiteNetworkIO """ if isinstance(obj, LiteIO): self.inputs.append(obj) else: name = obj self.add_input(LiteIO(name, is_host, io_type, layout))
[文档] def add_output( self, obj, is_host=True, io_type=LiteIOType.LITE_IO_VALUE, layout=None ): """ add output information into LiteNetworkIO """ if isinstance(obj, LiteIO): self.outputs.append(obj) else: name = obj self.add_output(LiteIO(name, is_host, io_type, layout))
def _create_network_io(self): network_io = _LiteNetworkIO() length = 1 if len(self.inputs) == 0 else len(self.inputs) self.c_inputs = (LiteIO * length)(*self.inputs) length = 1 if len(self.outputs) == 0 else len(self.outputs) self.c_outputs = (LiteIO * length)(*self.outputs) network_io.inputs = pointer(self.c_inputs[0]) network_io.outputs = pointer(self.c_outputs[0]) network_io.input_size = len(self.inputs) network_io.output_size = len(self.outputs) return network_io def __repr__(self): data = {"inputs": list(self.inputs), "outputs": list(self.outputs)} return data.__repr__()
LiteAsyncCallback = CFUNCTYPE(c_int) LiteStartCallback = CFUNCTYPE(c_int, POINTER(LiteIO), POINTER(_Ctensor), c_size_t) LiteFinishCallback = CFUNCTYPE(c_int, POINTER(LiteIO), POINTER(_Ctensor), c_size_t) def wrap_async_callback(func): global wrapper @CFUNCTYPE(c_int) def wrapper(): return func() return wrapper def start_finish_callback(func): global wrapper @CFUNCTYPE(c_int, POINTER(LiteIO), POINTER(_Ctensor), c_size_t) def wrapper(c_ios, c_tensors, size): ios = {} for i in range(size): tensor = LiteTensor(physic_construct=False) tensor._tensor = c_void_p(c_tensors[i]) tensor.update() io = c_ios[i] ios[io] = tensor return func(ios) return wrapper class _NetworkAPI(_LiteCObjBase): """ get the network api from the lib """ _api_ = [ ("LITE_make_default_network", [POINTER(_Cnetwork)]), ("LITE_make_network", [POINTER(_Cnetwork), LiteConfig, _LiteNetworkIO]), ("LITE_load_model_from_mem", [_Cnetwork, c_void_p, c_size_t]), ("LITE_load_model_from_path", [_Cnetwork, c_char_p]), ("LITE_shared_weight_with_network", [_Cnetwork, _Ctensor]), ("LITE_destroy_network", [_Cnetwork]), ("LITE_forward", [_Cnetwork]), ("LITE_wait", [_Cnetwork]), ("LITE_get_io_tensor", [_Cnetwork, c_char_p, c_int, POINTER(_Ctensor)]), ("LITE_get_input_name", [_Cnetwork, c_size_t, POINTER(c_char_p)]), ("LITE_get_output_name", [_Cnetwork, c_size_t, POINTER(c_char_p)]), ("LITE_get_all_input_name", [_Cnetwork, POINTER(c_size_t), POINTER(c_char_p)]), ("LITE_get_all_output_name", [_Cnetwork, POINTER(c_size_t), POINTER(c_char_p)]), ("LITE_is_cpu_inplace_mode", [_Cnetwork, POINTER(c_int)]), ("LITE_get_cpu_threads_number", [_Cnetwork, POINTER(c_size_t)]), ("LITE_get_device_id", [_Cnetwork, POINTER(c_int)]), ("LITE_set_device_id", [_Cnetwork, c_int]), ("LITE_set_cpu_inplace_mode", [_Cnetwork]), ("LITE_use_tensorrt", [_Cnetwork]), ("LITE_set_cpu_threads_number", [_Cnetwork, c_size_t]), ("LITE_set_stream_id", [_Cnetwork, c_int]), ("LITE_get_stream_id", [_Cnetwork, POINTER(c_int)]), ("LITE_set_network_algo_policy", [_Cnetwork, c_int]), ("LITE_set_network_algo_fastrun_config", [_Cnetwork, c_int, c_int]), ("LITE_set_network_algo_workspace_limit", [_Cnetwork, c_size_t]), ("LITE_share_runtime_memroy", [_Cnetwork, _Cnetwork]), ("LITE_enable_profile_performance", [_Cnetwork, c_char_p]), ("LITE_enable_io_txt_dump", [_Cnetwork, c_char_p]), ("LITE_enable_io_bin_dump", [_Cnetwork, c_char_p]), ("LITE_set_async_callback", [_Cnetwork, LiteAsyncCallback]), ("LITE_set_start_callback", [_Cnetwork, LiteStartCallback]), ("LITE_set_finish_callback", [_Cnetwork, LiteFinishCallback]), ("LITE_get_static_memory_alloc_info", [_Cnetwork, c_char_p]), ("LITE_enable_global_layout_transform", [_Cnetwork]), ("LITE_dump_layout_transform_model", [_Cnetwork, c_char_p]), ( "LITE_get_model_io_info_by_path", [c_char_p, LiteConfig, POINTER(_LiteNetworkIO)], ), ( "LITE_get_model_io_info_by_memory", [c_char_p, c_size_t, LiteConfig, POINTER(_LiteNetworkIO)], ), ("LITE_extra_configure", [_Cnetwork, LiteExtraConfig]), ]
[文档]class LiteNetwork(object): """ the network to load a model and forward Examples: .. code-block:: from megenginelite import * config = LiteConfig() config.device_type = LiteDeviceType.LITE_CPU network = LiteNetwork(config) network.load("model_path") input_name = network.get_input_name(0) input_tensor = network.get_io_tensor(input_name) output_name = network.get_output_name(0) output_tensor = network.get_io_tensor(output_name) input_tensor.set_data_by_copy(input_data) network.forward() network.wait() """ _api = _NetworkAPI()._lib def __init__(self, config=None, io=None): """ create a network with config and networkio """ self._network = _Cnetwork() if config: self.config = config else: self.config = LiteConfig() if io: self.network_io = io else: self.network_io = LiteNetworkIO() c_network_io = self.network_io._create_network_io() self._api.LITE_make_network(byref(self._network), self.config, c_network_io) def __repr__(self): data = {"config": self.config, "IOs": self.network_io} return data.__repr__() def __del__(self): self._api.LITE_destroy_network(self._network)
[文档] def load(self, path): """ load network from given path """ c_path = c_char_p(path.encode("utf-8")) self._api.LITE_load_model_from_path(self._network, c_path)
[文档] def forward(self): """ forward the network with filled input data and fill the output data to the output tensor """ self._api.LITE_forward(self._network)
[文档] def wait(self): """ wait until forward finish in sync model """ self._api.LITE_wait(self._network)
[文档] def is_cpu_inplace_mode(self): """ whether the network run in cpu inpalce mode Returns: if use inpalce mode return True, else return False """ inplace = c_int() self._api.LITE_is_cpu_inplace_mode(self._network, byref(inplace)) return bool(inplace.value)
[文档] def enable_cpu_inplace_mode(self): """ set cpu forward in inplace mode with which cpu forward only create one thread Note: this must be set before the network loaded """ self._api.LITE_set_cpu_inplace_mode(self._network)
[文档] def use_tensorrt(self): """ use TensorRT Note: this must be set before the network loaded """ self._api.LITE_use_tensorrt(self._network)
@property def device_id(self): """ get the device id Returns: the device id of current network used """ device_id = c_int() self._api.LITE_get_device_id(self._network, byref(device_id)) return device_id.value @device_id.setter def device_id(self, device_id): """ set the device id Note: this must be set before the network loaded """ self._api.LITE_set_device_id(self._network, device_id) @property def stream_id(self): """ get the stream id Returns: the value of stream id set for detwork """ stream_id = c_int() self._api.LITE_get_stream_id(self._network, byref(stream_id)) return stream_id.value @stream_id.setter def stream_id(self, stream_id): """ set the stream id Note: this must be set before the network loaded """ self._api.LITE_set_stream_id(self._network, stream_id) @property def threads_number(self): """ get the thread number of the netwrok Returns: the number of thread set in the network """ nr_thread = c_size_t() self._api.LITE_get_cpu_threads_number(self._network, byref(nr_thread)) return nr_thread.value @threads_number.setter def threads_number(self, nr_threads): """ set the network forward in multithread mode, and the thread number Note: this must be set before the network loaded """ self._api.LITE_set_cpu_threads_number(self._network, nr_threads)
[文档] def get_io_tensor(self, name, phase=LiteTensorPhase.LITE_IO): """ get input or output tensor by its name Args: name: the name of io tensor phase: the type of LiteTensor, this is useful to separate input or output tensor with the same name Returns: the tensor with given name and type """ if type(name) == str: c_name = c_char_p(name.encode("utf-8")) else: c_name = c_char_p(name) tensor = LiteTensor(physic_construct=False) self._api.LITE_get_io_tensor( self._network, c_name, phase, byref(tensor._tensor) ) tensor.update() return tensor
[文档] def get_input_name(self, index): """ get the input name by the index in the network Args: index: the index of the input name Returns: the name of input tesor with given index """ c_name = c_char_p() self._api.LITE_get_input_name(self._network, index, byref(c_name)) return c_name.value.decode("utf-8")
[文档] def get_output_name(self, index): """ get the output name by the index in the network Args: index: the index of the output name Returns: the name of output tesor with given index """ c_name = c_char_p() self._api.LITE_get_output_name(self._network, index, byref(c_name)) return c_name.value.decode("utf-8")
[文档] def get_all_input_name(self): """ get all the input tensor name in the network Returns: the names of all input tesor in the network """ nr_input = c_size_t() self._api.LITE_get_all_input_name(self._network, byref(nr_input), None) if nr_input.value > 0: names = (c_char_p * nr_input.value)() self._api.LITE_get_all_input_name(self._network, None, names) ret_name = [names[i].decode("utf-8") for i in range(nr_input.value)] return ret_name
[文档] def get_all_output_name(self): """ get all the output tensor name in the network Returns: the names of all output tesor in the network """ nr_output = c_size_t() self._api.LITE_get_all_output_name(self._network, byref(nr_output), None) if nr_output.value > 0: names = (c_char_p * nr_output.value)() self._api.LITE_get_all_output_name(self._network, None, names) ret_name = [names[i].decode("utf-8") for i in range(nr_output.value)] return ret_name
[文档] def extra_configure(self, extra_config): """ Extra Configuration to the network. """ self._api.LITE_extra_configure(self._network, extra_config)
[文档] def share_weights_with(self, src_network): """ share weights with the loaded network Args: src_network: the network to share weights """ assert isinstance(src_network, LiteNetwork) self._api.LITE_shared_weight_with_network(self._network, src_network._network)
[文档] def share_runtime_memroy(self, src_network): """ share runtime memory with the srouce network Args: src_network: the network to share runtime memory """ assert isinstance(src_network, LiteNetwork) self._api.LITE_share_runtime_memroy(self._network, src_network._network)
[文档] def async_with_callback(self, async_callback): """ set the network forwarding in async mode and set the AsyncCallback callback function Args: async_callback: the callback to set for network """ callback = wrap_async_callback(async_callback) self._api.LITE_set_async_callback(self._network, callback)
[文档] def set_start_callback(self, start_callback): """ when the network start forward, the callback will be called, the start_callback with param mapping from LiteIO to the corresponding LiteTensor Args: start_callback: the callback to set for network """ callback = start_finish_callback(start_callback) self._api.LITE_set_start_callback(self._network, callback)
[文档] def set_finish_callback(self, finish_callback): """ when the network finish forward, the callback will be called, the finish_callback with param mapping from LiteIO to the corresponding LiteTensor Args: finish_callback: the callback to set for network """ callback = start_finish_callback(finish_callback) self._api.LITE_set_finish_callback(self._network, callback)
[文档] def enable_profile_performance(self, profile_file): """ enable get the network performance profiled information and save into given file Args: profile_file: the file to save profile information """ c_file = profile_file.encode("utf-8") self._api.LITE_enable_profile_performance(self._network, c_file)
[文档] def set_network_algo_workspace_limit(self, size_limit): """ set the opr workspace limitation in the target network, some opr maybe use large of workspace to get good performance, set workspace limitation can save memory but may influence the performance Args: size_limit: the byte size of workspace limitation """ self._api.LITE_set_network_algo_workspace_limit(self._network, size_limit)
[文档] def set_network_algo_policy( self, policy, shared_batch_size=0, binary_equal_between_batch=False ): """ set the network algorithm search policy for fast-run Args: shared_batch_size: the batch size used by fastrun, Non-zero value means that fastrun use this batch size regardless of the batch size of the model. Zero means fastrun use batch size of the model binary_equal_between_batch: if the content of each input batch is binary equal,whether the content of each output batch is promised to be equal """ self._api.LITE_set_network_algo_policy(self._network, policy) self._api.LITE_set_network_algo_fastrun_config( self._network, shared_batch_size, binary_equal_between_batch )
[文档] def io_txt_dump(self, txt_file): """ dump all input/output tensor of all operators to the output file, in txt format, user can use this function to debug compute error Args: txt_file: the txt file """ c_file = txt_file.encode("utf-8") self._api.LITE_enable_io_txt_dump(self._network, c_file)
[文档] def io_bin_dump(self, bin_dir): """ dump all input/output tensor of all operators to the output file, in binary format, user can use this function to debug compute error Args: bin_dir: the binary file directory """ c_dir = bin_dir.encode("utf-8") self._api.LITE_enable_io_bin_dump(self._network, c_dir)
[文档] def get_static_memory_alloc_info(self, log_dir="logs/test"): """ get static peak memory info showed by Graph visualization Args: log_dir: the directory to save information log """ c_log_dir = log_dir.encode("utf-8") self._api.LITE_get_static_memory_alloc_info(self._network, c_log_dir)
[文档] def enable_global_layout_transform(self): """ set global layout transform optimization for network, global layout optimization can auto determine the layout of every operator in the network by profile, thus it can improve the performance of the network forwarding """ self._api.LITE_enable_global_layout_transform(self._network)
[文档] def dump_layout_transform_model(self, model_file): """ dump network after global layout transform optimization to the specific path Args: model_file: the file path to dump model """ c_file = model_file.encode("utf-8") self._api.LITE_dump_layout_transform_model(self._network, c_file)
def get_model_io_info(model_path, config=None): """ get the model io information before model loaded by model path. Args: model_path: the model path to get the model IO information config the model configuration Returns: the input and output information in the network configuration """ api = _NetworkAPI()._lib c_path = c_char_p(model_path.encode("utf-8")) ios = _LiteNetworkIO() if config is not None: api.LITE_get_model_io_info_by_path(c_path, config, byref(ios)) else: config = LiteConfig() api.LITE_get_model_io_info_by_path(c_path, config, byref(ios)) ret_ios = LiteNetworkIO() for i in range(ios.input_size): ret_ios.add_input(ios.inputs[i]) for i in range(ios.output_size): ret_ios.add_output(ios.outputs[i]) return ret_ios