# 模块级常量和函数 from rknnlite.api import RKNNLite import numpy as np import os import warnings import logging from typing import List, Dict, Union, Optional try: import onnxruntime as ort HAS_ORT = True except ImportError: HAS_ORT = False warnings.warn("onnxruntime未安装,只能使用RKNN后端", ImportWarning) # 配置日志 logger = logging.getLogger("somemodelruntime_rknnlite2") logger.setLevel(logging.ERROR) # 默认只输出错误信息 if not logger.handlers: handler = logging.StreamHandler() handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')) logger.addHandler(handler) # ONNX Runtime日志级别到Python logging级别的映射 _LOGGING_LEVEL_MAP = { 0: logging.DEBUG, # Verbose 1: logging.INFO, # Info 2: logging.WARNING, # Warning 3: logging.ERROR, # Error 4: logging.CRITICAL # Fatal } # 检查环境变量中的日志级别设置 try: env_log_level = os.getenv('ZTU_MODELRT_RKNNL2_LOG_LEVEL') if env_log_level is not None: log_level = int(env_log_level) if log_level in _LOGGING_LEVEL_MAP: logger.setLevel(_LOGGING_LEVEL_MAP[log_level]) logger.info(f"从环境变量设置日志级别: {log_level}") else: logger.warning(f"环境变量ZTU_MODELRT_RKNNL2_LOG_LEVEL的值无效: {log_level}, 应该是0-4之间的整数") except ValueError: logger.warning(f"环境变量ZTU_MODELRT_RKNNL2_LOG_LEVEL的值无效: {env_log_level}, 应该是0-4之间的整数") def set_default_logger_severity(level: int) -> None: """ Sets the default logging severity. 0:Verbose, 1:Info, 2:Warning, 3:Error, 4:Fatal Args: level: 日志级别(0-4) """ if level not in _LOGGING_LEVEL_MAP: raise ValueError(f"无效的日志级别: {level}, 应该是0-4之间的整数") logger.setLevel(_LOGGING_LEVEL_MAP[level]) def set_default_logger_verbosity(level: int) -> None: """ Sets the default logging verbosity level. To activate the verbose log, you need to set the default logging severity to 0:Verbose level. Args: level: 日志级别(0-4) """ set_default_logger_severity(level) # RKNN tensor type到numpy dtype的映射 RKNN_DTYPE_MAP = { 0: np.float32, # RKNN_TENSOR_FLOAT32 1: np.float16, # RKNN_TENSOR_FLOAT16 2: np.int8, # RKNN_TENSOR_INT8 3: np.uint8, # RKNN_TENSOR_UINT8 4: np.int16, # RKNN_TENSOR_INT16 5: np.uint16, # RKNN_TENSOR_UINT16 6: np.int32, # RKNN_TENSOR_INT32 7: np.uint32, # RKNN_TENSOR_UINT32 8: np.int64, # RKNN_TENSOR_INT64 9: bool, # RKNN_TENSOR_BOOL 10: np.int8, # RKNN_TENSOR_INT4 (用int8表示) } def get_available_providers() -> List[str]: """ 获取可用的设备提供者列表(为保持接口兼容性的占位函数) Returns: list: 可用的设备提供者列表,总是返回["CPUExecutionProvider", "somemodelruntime_rknnlite2_ExecutionProvider"] """ return ["CPUExecutionProvider", "somemodelruntime_rknnlite2_ExecutionProvider"] def get_device() -> str: """ 获取当前设备 Returns: str: 当前设备 """ return "RKNN2" def get_version_info() -> Dict[str, str]: """ 获取版本信息 Returns: dict: 包含API和驱动版本信息的字典 """ runtime = RKNNLite() version = runtime.get_sdk_version() return { "api_version": version.split('\n')[2].split(': ')[1].split(' ')[0], "driver_version": version.split('\n')[3].split(': ')[1] } class IOTensor: """输入/输出张量的信息封装类""" def __init__(self, name, shape, type=None): self.name = name.decode() if isinstance(name, bytes) else name self.shape = shape self.type = type def __str__(self): return f"IOTensor(name='{self.name}', shape={self.shape}, type={self.type})" class SessionOptions: """会话选项类""" def __init__(self): self.enable_profiling = False # 是否使用性能分析 self.intra_op_num_threads = 1 # 设置RKNN的线程数, 对应rknn的core_mask self.log_severity_level = -1 # 另一个设置日志级别的参数 self.log_verbosity_level = -1 # 另一个设置日志级别的参数 class InferenceSession: """ RKNNLite运行时封装类,API风格类似ONNX Runtime """ def __new__(cls, model_path: str, sess_options: Optional[SessionOptions] = None, **kwargs): processed_path = InferenceSession._process_model_path(model_path, sess_options) if isinstance(processed_path, str) and processed_path.lower().endswith('.onnx'): logger.info("使用ONNX Runtime加载模型") if not HAS_ORT: raise RuntimeError("未安装onnxruntime,无法加载ONNX模型") return ort.InferenceSession(processed_path, sess_options=sess_options, **kwargs) else: # 如果不是 ONNX 模型，则调用父类的 __new__ 创建 InferenceSession 实例 instance = super().__new__(cls) # 保存处理后的路径 instance._processed_path = processed_path return instance def __init__(self, model_path: str, sess_options: Optional[SessionOptions] = None, **kwargs): """ 初始化运行时并加载模型 Args: model_path: 模型文件路径(.rknn或.onnx) sess_options: 会话选项 **kwargs: 其他初始化参数 """ options = sess_options or SessionOptions() # 只在未设置环境变量时使用SessionOptions中的日志级别 if os.getenv('ZTU_MODELRT_RKNNL2_LOG_LEVEL') is None: if options.log_severity_level != -1: set_default_logger_severity(options.log_severity_level) if options.log_verbosity_level != -1: set_default_logger_verbosity(options.log_verbosity_level) # 使用__new__中处理好的路径 model_path = getattr(self, '_processed_path', model_path) if isinstance(model_path, str) and model_path.lower().endswith('.onnx'): # 避免重复加载 ONNX 模型 return # ... 现有的 RKNN 模型加载和初始化代码 ... self.model_path = model_path if not os.path.exists(self.model_path): logger.error(f"模型文件不存在: {self.model_path}") raise FileNotFoundError(f"模型文件不存在: {self.model_path}") self.runtime = RKNNLite(verbose=options.enable_profiling) logger.debug(f"正在加载模型: {self.model_path}") ret = self.runtime.load_rknn(self.model_path) if ret != 0: logger.error(f"加载RKNN模型失败: {self.model_path}") raise RuntimeError(f'加载RKNN模型失败: {self.model_path}') logger.debug("模型加载成功") if options.intra_op_num_threads == 1: core_mask = RKNNLite.NPU_CORE_AUTO elif options.intra_op_num_threads == 2: core_mask = RKNNLite.NPU_CORE_0_1 elif options.intra_op_num_threads == 3: core_mask = RKNNLite.NPU_CORE_0_1_2 else: raise ValueError(f"intra_op_num_threads的值无效: {options.intra_op_num_threads}, 只能是1,2或3") logger.debug("正在初始化运行时环境") ret = self.runtime.init_runtime(core_mask=core_mask) if ret != 0: logger.error("初始化运行时环境失败") raise RuntimeError('初始化运行时环境失败') logger.debug("运行时环境初始化成功") self._init_io_info() self.options = options def get_performance_info(self) -> Dict[str, float]: """ 获取性能信息 Returns: dict: 包含性能信息的字典 """ if not self.options.perf_debug: raise RuntimeError("性能分析未启用,请在SessionOptions中设置perf_debug=True") perf = self.runtime.rknn_runtime.get_run_perf() return { "run_duration": perf.run_duration / 1000.0 # 转换为毫秒 } def set_core_mask(self, core_mask: int) -> None: """ 设置NPU核心使用模式 Args: core_mask: NPU核心掩码,使用NPU_CORE_*常量 """ ret = self.runtime.rknn_runtime.set_core_mask(core_mask) if ret != 0: raise RuntimeError("设置NPU核心模式失败") @staticmethod def _process_model_path(model_path, sess_options): """ 处理模型路径,支持.onnx和.rknn文件 Args: model_path: 模型文件路径 """ # 如果是ONNX文件,检查是否需要自动加载RKNN if model_path.lower().endswith('.onnx'): logger.info("检测到ONNX模型文件") # 获取需要跳过自动加载的模型列表 skip_models = os.getenv('ZTU_MODELRT_RKNNL2_SKIP', '').strip() if skip_models: skip_list = [m.strip() for m in skip_models.split(',')] # 获取模型文件名(不含路径)用于匹配 model_name = os.path.basename(model_path) if model_name.lower() in [m.lower() for m in skip_list]: logger.info(f"模型{model_name}在跳过列表中,将使用ONNX Runtime") return model_path # 构造RKNN文件路径 rknn_path = os.path.splitext(model_path)[0] + '.rknn' if os.path.exists(rknn_path): logger.info(f"找到对应的RKNN模型,将使用RKNN: {rknn_path}") return rknn_path else: logger.info("未找到对应的RKNN模型,将使用ONNX Runtime") return model_path return model_path def _convert_nhwc_to_nchw(self, shape): """将NHWC格式的shape转换为NCHW格式""" if len(shape) == 4: # NHWC -> NCHW n, h, w, c = shape return [n, c, h, w] return shape def _init_io_info(self): """初始化模型的输入输出信息""" runtime = self.runtime.rknn_runtime # 获取输入输出数量 n_input, n_output = runtime.get_in_out_num() # 获取输入信息 self.input_tensors = [] for i in range(n_input): attr = runtime.get_tensor_attr(i) shape = [attr.dims[j] for j in range(attr.n_dims)] # 对四维输入进行NHWC到NCHW的转换 shape = self._convert_nhwc_to_nchw(shape) # 获取dtype dtype = RKNN_DTYPE_MAP.get(attr.type, None) tensor = IOTensor(attr.name, shape, dtype) self.input_tensors.append(tensor) # 获取输出信息 self.output_tensors = [] for i in range(n_output): attr = runtime.get_tensor_attr(i, is_output=True) shape = runtime.get_output_shape(i) # 获取dtype dtype = RKNN_DTYPE_MAP.get(attr.type, None) tensor = IOTensor(attr.name, shape, dtype) self.output_tensors.append(tensor) def get_inputs(self): """ 获取模型输入信息 Returns: list: 包含输入信息的列表 """ return self.input_tensors def get_outputs(self): """ 获取模型输出信息 Returns: list: 包含输出信息的列表 """ return self.output_tensors def run(self, output_names=None, input_feed=None, data_format="nchw", **kwargs): """ 执行模型推理 Args: output_names: 输出节点名称列表,指定需要返回哪些输出 input_feed: 输入数据字典或列表 data_format: 输入数据格式,"nchw"或"nhwc" **kwargs: 其他运行时参数 Returns: list: 模型输出结果列表,如果指定了output_names则只返回指定的输出 """ if input_feed is None: logger.error("input_feed不能为None") raise ValueError("input_feed不能为None") # 准备输入数据 if isinstance(input_feed, dict): # 如果是字典,按照模型输入顺序排列 inputs = [] input_map = {tensor.name: i for i, tensor in enumerate(self.input_tensors)} for tensor in self.input_tensors: if tensor.name not in input_feed: raise ValueError(f"缺少输入: {tensor.name}") inputs.append(input_feed[tensor.name]) elif isinstance(input_feed, (list, tuple)): # 如果是列表,确保长度匹配 if len(input_feed) != len(self.input_tensors): raise ValueError(f"输入数量不匹配: 期望{len(self.input_tensors)}, 实际{len(input_feed)}") inputs = list(input_feed) else: logger.error("input_feed必须是字典或列表类型") raise ValueError("input_feed必须是字典或列表类型") # 执行推理 try: logger.debug("开始执行推理") all_outputs = self.runtime.inference(inputs=inputs, data_format=data_format) # 如果没有指定output_names,返回所有输出 if output_names is None: return all_outputs # 获取指定的输出 output_map = {tensor.name: i for i, tensor in enumerate(self.output_tensors)} selected_outputs = [] for name in output_names: if name not in output_map: raise ValueError(f"未找到输出节点: {name}") selected_outputs.append(all_outputs[output_map[name]]) return selected_outputs except Exception as e: logger.error(f"推理执行失败: {str(e)}") raise RuntimeError(f"推理执行失败: {str(e)}") def close(self): """ 关闭会话,释放资源 """ if self.runtime is not None: logger.info("正在释放运行时资源") self.runtime.release() self.runtime = None def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.close() def end_profiling(self) -> Optional[str]: """ 结束性能分析的存根方法 Returns: Optional[str]: None """ warnings.warn("end_profiling()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return None def get_profiling_start_time_ns(self) -> int: """ 获取性能分析开始时间的存根方法 Returns: int: 0 """ warnings.warn("get_profiling_start_time_ns()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return 0 def get_modelmeta(self) -> Dict[str, str]: """ 获取模型元数据的存根方法 Returns: Dict[str, str]: 空字典 """ warnings.warn("get_modelmeta()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return {} def get_session_options(self) -> SessionOptions: """ 获取会话选项 Returns: SessionOptions: 当前会话选项 """ return self.options def get_providers(self) -> List[str]: """ 获取当前使用的providers的存根方法 Returns: List[str]: ["CPUExecutionProvider"] """ warnings.warn("get_providers()是存根方法,始终返回CPUExecutionProvider", RuntimeWarning, stacklevel=2) return ["CPUExecutionProvider"] def get_provider_options(self) -> Dict[str, Dict[str, str]]: """ 获取provider选项的存根方法 Returns: Dict[str, Dict[str, str]]: 空字典 """ warnings.warn("get_provider_options()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return {} def get_session_config(self) -> Dict[str, str]: """ 获取会话配置的存根方法 Returns: Dict[str, str]: 空字典 """ warnings.warn("get_session_config()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return {} def get_session_state(self) -> Dict[str, str]: """ 获取会话状态的存根方法 Returns: Dict[str, str]: 空字典 """ warnings.warn("get_session_state()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return {} def set_session_config(self, config: Dict[str, str]) -> None: """ 设置会话配置的存根方法 Args: config: 会话配置字典 """ warnings.warn("set_session_config()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) def get_memory_info(self) -> Dict[str, int]: """ 获取内存使用信息的存根方法 Returns: Dict[str, int]: 空字典 """ warnings.warn("get_memory_info()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return {} def set_memory_pattern(self, enable: bool) -> None: """ 设置内存模式的存根方法 Args: enable: 是否启用内存模式 """ warnings.warn("set_memory_pattern()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) def disable_memory_pattern(self) -> None: """ 禁用内存模式的存根方法 """ warnings.warn("disable_memory_pattern()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) def get_optimization_level(self) -> int: """ 获取优化级别的存根方法 Returns: int: 0 """ warnings.warn("get_optimization_level()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return 0 def set_optimization_level(self, level: int) -> None: """ 设置优化级别的存根方法 Args: level: 优化级别 """ warnings.warn("set_optimization_level()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) def get_model_metadata(self) -> Dict[str, str]: """ 获取模型元数据的存根方法(与get_modelmeta不同的接口) Returns: Dict[str, str]: 空字典 """ warnings.warn("get_model_metadata()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return {} def get_model_path(self) -> str: """ 获取模型路径 Returns: str: 模型文件路径 """ return self.model_path def get_input_type_info(self) -> List[Dict[str, str]]: """ 获取输入类型信息的存根方法 Returns: List[Dict[str, str]]: 空列表 """ warnings.warn("get_input_type_info()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return [] def get_output_type_info(self) -> List[Dict[str, str]]: """ 获取输出类型信息的存根方法 Returns: List[Dict[str, str]]: 空列表 """ warnings.warn("get_output_type_info()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2) return []