16337
b0ddb6ee1a
- Moved all project files and directories (config, core, models, etc.) from edge_inference_service/ to the repository root ai_edge/ - Updated model path in config/settings.py to reflect new structure - Revised usage paths in __init__.py documentation
449 lines
14 KiB
Python
449 lines
14 KiB
Python
"""
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TensorRT推理引擎模块
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实现引擎加载、显存优化、异步推理、性能监控
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"""
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import logging
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import threading
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import time
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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try:
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import tensorrt as trt
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TRT_AVAILABLE = True
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except ImportError:
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TRT_AVAILABLE = False
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trt = None
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from config.settings import get_settings, InferenceConfig
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from utils.logger import get_logger
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logger = logging.getLogger(__name__)
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class TensorRTEngine:
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"""TensorRT引擎管理类
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实现engine文件加载、显存管理、异步推理
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"""
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def __init__(self, config: Optional[InferenceConfig] = None):
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"""
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初始化TensorRT引擎
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Args:
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config: 推理配置
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"""
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if not TRT_AVAILABLE:
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raise RuntimeError("TensorRT未安装，请先安装tensorrt库")
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if config is None:
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settings = get_settings()
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config = settings.inference
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self.config = config
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self._engine = None
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self._context = None
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self._input_binding = None
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self._output_bindings = []
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self._stream = None
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self._logger = get_logger("tensorrt")
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self._lock = threading.Lock()
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self._memory_pool: Dict[str, np.ndarray] = {}
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self._performance_stats = {
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"inference_count": 0,
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"total_inference_time_ms": 0.0,
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"avg_inference_time_ms": 0.0,
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"throughput_fps": 0.0,
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"last_inference_time_ms": 0.0,
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}
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self._logger.info(
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f"TensorRT引擎初始化配置: "
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f"模型={config.model_path}, "
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f"输入尺寸={config.input_width}x{config.input_height}, "
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f"Batch={config.batch_size}, "
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f"FP16={config.fp16_mode}"
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)
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def load_engine(self, engine_path: Optional[str] = None) -> bool:
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"""
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加载TensorRT engine文件
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Args:
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engine_path: engine文件路径
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Returns:
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是否加载成功
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"""
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if engine_path is None:
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engine_path = self.config.model_path
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with self._lock:
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try:
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if self._context is not None:
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self._release_resources()
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TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
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with open(engine_path, "rb") as f:
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runtime = trt.Runtime(TRT_LOGGER)
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self._engine = runtime.deserialize_cuda_engine(f.read())
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self._context = self._engine.create_execution_context()
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self._setup_bindings()
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self._allocate_memory_pool()
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self._logger.log_connection_event(
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"load", "TensorRT", engine_path, True
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)
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self._logger.info(f"TensorRT引擎加载成功: {engine_path}")
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return True
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except Exception as e:
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self._logger.error(f"TensorRT引擎加载失败: {e}")
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return False
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def _setup_bindings(self):
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"""设置输入输出绑定"""
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self._input_binding = None
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self._output_bindings = []
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for i in range(self._engine.num_bindings):
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binding_name = self._engine.get_binding_name(i)
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binding_shape = self._engine.get_binding_shape(i)
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binding_dtype = self._engine.get_binding_dtype(i)
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if self._engine.binding_is_input(i):
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self._input_binding = {
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"name": binding_name,
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"shape": binding_shape,
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"dtype": binding_dtype,
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"index": i,
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}
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else:
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self._output_bindings.append({
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"name": binding_name,
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"shape": binding_shape,
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"dtype": binding_dtype,
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"index": i,
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})
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self._logger.debug(
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f"输入绑定: {self._input_binding}, "
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f"输出绑定: {len(self._output_bindings)}"
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)
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def _allocate_memory_pool(self):
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"""分配显存池"""
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self._memory_pool.clear()
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if self._input_binding:
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shape = self._input_binding["shape"]
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dtype = self._get_numpy_dtype(self._input_binding["dtype"])
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self._memory_pool["input"] = np.zeros(shape, dtype=dtype)
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for output in self._output_bindings:
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shape = output["shape"]
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dtype = self._get_numpy_dtype(output["dtype"])
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self._memory_pool[output["name"]] = np.zeros(shape, dtype=dtype)
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def _get_numpy_dtype(self, trt_dtype) -> np.dtype:
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"""转换TensorRT数据类型到numpy"""
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if trt_dtype == trt.float16:
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return np.float16
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elif trt_dtype == trt.float32:
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return np.float32
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elif trt_dtype == trt.int32:
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return np.int32
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elif trt_dtype == trt.int8:
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return np.int8
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else:
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return np.float32
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def _allocate_device_memory(self, batch_size: int) -> Tuple[np.ndarray, List[np.ndarray]]:
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"""
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分配设备显存
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Returns:
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tuple: (输入数据, 输出数据列表)
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"""
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input_shape = list(self._input_binding["shape"])
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input_shape[0] = batch_size
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input_data = np.zeros(input_shape, dtype=np.float16 if self.config.fp16_mode else np.float32)
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output_data_list = []
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for output in self._output_bindings:
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output_shape = list(output["shape"])
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output_shape[0] = batch_size
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output_data = np.zeros(output_shape, dtype=self._get_numpy_dtype(output["dtype"]))
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output_data_list.append(output_data)
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return input_data, output_data_list
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def set_input_shape(self, batch_size: int, height: int, width: int):
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"""
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动态设置输入形状
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Args:
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batch_size: 批次大小
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height: 输入高度
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width: 输入宽度
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"""
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if self._context is None:
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raise RuntimeError("引擎未加载")
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self._context.set_input_shape(
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self._input_binding["name"],
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[batch_size, 3, height, width]
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)
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self._logger.debug(f"输入形状已设置为: [{batch_size}, 3, {height}, {width}]")
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def infer(
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self,
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input_data: np.ndarray,
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async_mode: bool = False
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) -> Tuple[List[np.ndarray], float]:
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"""
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执行推理
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Args:
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input_data: 输入数据 (NCHW格式)
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async_mode: 是否使用异步模式
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Returns:
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tuple: (输出列表, 推理耗时ms)
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"""
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if self._engine is None or self._context is None:
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raise RuntimeError("引擎未加载")
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start_time = time.perf_counter()
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batch_size = input_data.shape[0]
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input_data = input_data.astype(np.float16 if self.config.fp16_mode else np.float32)
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self._context.set_input_shape(
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self._input_binding["name"],
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input_data.shape
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)
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input_tensor = input_data
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output_tensors = []
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for output in self._output_bindings:
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output_shape = list(output["shape"])
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output_shape[0] = batch_size
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output_tensor = np.zeros(output_shape, dtype=self._get_numpy_dtype(output["dtype"]))
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output_tensors.append(output_tensor)
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bindings = [input_tensor] + output_tensors
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self._context.execute_v2(bindings=bindings)
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inference_time_ms = (time.perf_counter() - start_time) * 1000
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self._update_performance_stats(inference_time_ms, batch_size)
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return output_tensors, inference_time_ms
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def infer_async(self, input_data: np.ndarray) -> Tuple[List[np.ndarray], float]:
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"""
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执行异步推理
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Args:
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input_data: 输入数据
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Returns:
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tuple: (输出列表, 推理耗时ms)
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"""
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return self.infer(input_data, async_mode=True)
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def infer_batch(
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self,
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batch_data: np.ndarray,
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batch_size: int
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) -> Tuple[List[np.ndarray], float]:
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"""
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推理批次数据
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Args:
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batch_data: 批次数据
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batch_size: 实际批次大小
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Returns:
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tuple: (输出列表, 推理耗时ms)
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"""
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if batch_data.shape[0] != batch_size:
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batch_data = batch_data[:batch_size]
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return self.infer(batch_data)
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def _update_performance_stats(self, inference_time_ms: float, batch_size: int):
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"""更新性能统计"""
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stats = self._performance_stats
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stats["inference_count"] += 1
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stats["total_inference_time_ms"] += inference_time_ms
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stats["last_inference_time_ms"] = inference_time_ms
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stats["avg_inference_time_ms"] = (
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stats["total_inference_time_ms"] / stats["inference_count"]
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)
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total_time = stats["total_inference_time_ms"] / 1000.0
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if total_time > 0:
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stats["throughput_fps"] = stats["inference_count"] / total_time
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def get_performance_stats(self) -> Dict[str, Any]:
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"""获取性能统计"""
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stats = dict(self._performance_stats)
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stats["memory_usage"] = self.get_memory_usage()
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return stats
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def get_memory_usage(self) -> Dict[str, float]:
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"""获取显存使用情况"""
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try:
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import pynvml
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pynvml.nvmlInit()
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handle = pynvml.nvmlDeviceGetHandleByIndex(self.config.device_id)
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mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
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pynvml.nvmlShutdown()
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return {
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"total_mb": mem_info.total / (1024 ** 2),
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"used_mb": mem_info.used / (1024 ** 2),
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"free_mb": mem_info.free / (1024 ** 2),
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}
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except Exception as e:
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self._logger.warning(f"获取显存信息失败: {e}")
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return {"total_mb": 0, "used_mb": 0, "free_mb": 0}
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def _release_resources(self):
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"""释放资源"""
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if self._stream:
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try:
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self._stream.synchronize()
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except Exception:
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pass
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if self._context:
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self._context.destroy()
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self._context = None
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if self._engine:
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self._engine.destroy()
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self._engine = None
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self._memory_pool.clear()
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def release(self):
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"""释放引擎资源"""
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with self._lock:
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self._release_resources()
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self._logger.info("TensorRT引擎资源已释放")
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def __del__(self):
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"""析构函数"""
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self.release()
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class EngineManager:
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"""引擎管理器类
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管理多个TensorRT引擎实例
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"""
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def __init__(self):
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self._engines: Dict[str, TensorRTEngine] = {}
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self._logger = get_logger("tensorrt")
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self._lock = threading.Lock()
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def load_engine(
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self,
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engine_id: str,
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engine_path: str,
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config: Optional[InferenceConfig] = None
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) -> bool:
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"""
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加载引擎
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Args:
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engine_id: 引擎标识
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engine_path: engine文件路径
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config: 推理配置
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Returns:
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是否加载成功
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"""
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with self._lock:
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if engine_id in self._engines:
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self._engines[engine_id].release()
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engine = TensorRTEngine(config)
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if engine.load_engine(engine_path):
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self._engines[engine_id] = engine
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self._logger.info(f"引擎已加载: {engine_id}")
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return True
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else:
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return False
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def get_engine(self, engine_id: str) -> Optional[TensorRTEngine]:
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"""获取引擎"""
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return self._engines.get(engine_id)
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def release_engine(self, engine_id: str):
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"""释放引擎"""
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with self._lock:
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if engine_id in self._engines:
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self._engines[engine_id].release()
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del self._engines[engine_id]
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self._logger.info(f"引擎已释放: {engine_id}")
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def release_all(self):
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"""释放所有引擎"""
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with self._lock:
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for engine_id in list(self._engines.keys()):
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self._engines[engine_id].release()
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self._engines.clear()
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self._logger.info("所有引擎已释放")
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def get_all_stats(self) -> Dict[str, Any]:
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"""获取所有引擎统计"""
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with self._lock:
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return {
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engine_id: engine.get_performance_stats()
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for engine_id, engine in self._engines.items()
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}
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def create_tensorrt_engine(
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engine_path: str,
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config: Optional[InferenceConfig] = None
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) -> TensorRTEngine:
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"""
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创建TensorRT引擎的便捷函数
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Args:
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engine_path: engine文件路径
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config: 推理配置
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Returns:
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TensorRTEngine实例
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"""
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engine = TensorRTEngine(config)
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if engine.load_engine(engine_path):
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return engine
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else:
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raise RuntimeError(f"无法加载TensorRT引擎: {engine_path}")
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