tensorrt加速yolov5 具体流程

TensorRT可以用来加速Yolo系列的目标检测算法，下面是大致的流程：

1. 下载并安装TensorRT

2. 下载Yolov5的模型文件（.pt或.onnx格式）

3. 将模型文件转换为TensorRT可用的格式(.trt)

- 使用torch2trt工具将.pt文件转换为.trt文件
- 使用onnx2trt工具将.onnx文件转换为.trt文件

4. 在C++代码中加载.trt文件并运行推理

- 创建TensorRT引擎
- 创建输入和输出Tensor
- 将输入数据传递给引擎
- 运行推理
- 获取输出结果

5. 可以使用OpenCV等库将输出结果可视化

具体实现过程可以参考以下链接：

- https://github.com/wang-xinyu/tensorrtx/tree/master/yolov5

- https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/TensorRTx

相关问题

TensorRT加速yolov4-tiny的具体流程

感谢您的提问。TensorRT加速yolov4-tiny的具体流程包括以下几个步骤：首先，将yolov4-tiny模型转换为TensorRT可读取的格式；然后，使用TensorRT API创建一个TensorRT引擎；接着，将输入数据传递给TensorRT引擎进行推理；最后，将输出结果从TensorRT引擎中读取出来。希望这个回答能够帮助您。

tensorrt推理yolov5流程Python

TensorRT是NVIDIA开发的高性能推理引擎，可以用于加速深度学习模型的推理。而YoloV5是一种目标检测模型，可以用于检测图像中的物体。

下面是使用TensorRT进行YoloV5推理的Python流程：

1. 导入必要的库和模块：

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
import cv2
import os
import time

2. 加载YoloV5模型并构建TensorRT引擎：

def build_engine(onnx_file_path, engine_file_path):
    TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(TRT_LOGGER)

    explicit_batch = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
    network = builder.create_network(explicit_batch)
    parser = trt.OnnxParser(network, TRT_LOGGER)

    with open(onnx_file_path, 'rb') as model:
        if not parser.parse(model.read()):
            for error in range(parser.num_errors):
                print(parser.get_error(error))
            return None

    builder.max_batch_size = 1
    builder.max_workspace_size = 1 << 30

    engine = builder.build_cuda_engine(network)
    with open(engine_file_path, "wb") as f:
        f.write(engine.serialize())
    
    return engine

3. 加载TensorRT引擎：

def load_engine(engine_file_path):
    with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
        engine = runtime.deserialize_cuda_engine(f.read())
    return engine

4. 加载测试图片并预处理：

def preprocess(image, input_shape):
    image = cv2.resize(image, (input_shape[1], input_shape[0]))
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = np.transpose(image, (2, 0, 1)).astype(np.float32)
    image /= 255.0
    image = np.expand_dims(image, axis=0)
    return image

5. 执行推理：

def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
    context.execute_async(bindings=bindings, batch_size=batch_size, stream_handle=stream.handle)
    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
    stream.synchronize()
    return [out.host for out in outputs]

6. 解析推理结果：

def postprocess(outputs, anchors, masks, input_shape, image_shape, conf_thres=0.5, iou_thres=0.5):
    num_classes = 80
    num_anchors = 3
    num_layers = 3
    anchor_masks = masks

    anchors = np.array(anchors).reshape(num_layers, -1, 2)
    input_h, input_w = input_shape
    image_h, image_w, _ = image_shape

    scale_h, scale_w = image_h / input_h, image_w / input_w

    box_mins = np.zeros((0, 2))
    box_maxes = np.zeros((0, 2))
    box_classes = np.zeros((0))
    box_scores = np.zeros((0))

    for i in range(num_layers):
        grid_h, grid_w = input_h // 32 // (2 ** i), input_w // 32 // (2 ** i)
        outputs_i = outputs[i]
        outputs_i = np.reshape(outputs_i, (batch_size, num_anchors * (5 + num_classes), grid_h * grid_w)).transpose(0, 2, 1)

        outputs_i[..., :2] = 1 / (1 + np.exp(-outputs_i[..., :2]))
        outputs_i[..., 2:4] = np.exp(outputs_i[..., 2:4])
        outputs_i[..., 4:] = 1 / (1 + np.exp(-outputs_i[..., 4:]))

        anchors_scale = anchors[i]
        anchors_scale = anchors_scale[np.newaxis, :, :]

        box_xy = outputs_i[..., :2]
        box_wh = outputs_i[..., 2:4]
        box_confidence = outputs_i[..., 4:5]
        box_class_probs = outputs_i[..., 5:]

        box_xy += (np.arange(grid_w, dtype=np.float32) + 0.5)[np.newaxis, :, np.newaxis]
        box_xy += (np.arange(grid_h, dtype=np.float32) + 0.5)[:, np.newaxis, np.newaxis]
        box_xy *= 32 * (2 ** i)

        box_wh *= anchors_scale
        box_wh *= np.array([image_w / input_w, image_h / input_h])[np.newaxis, np.newaxis, :]

        box_mins = np.concatenate([box_mins, box_xy - box_wh / 2], axis=0)
        box_maxes = np.concatenate([box_maxes, box_xy + box_wh / 2], axis=0)
        box_scores = np.concatenate([box_scores, box_confidence], axis=0)
        box_classes = np.concatenate([box_classes, np.argmax(box_class_probs, axis=-1).flatten()], axis=0)

    boxes = np.concatenate([box_mins, box_maxes], axis=-1)
    boxes /= np.array([scale_w, scale_h, scale_w, scale_h])[np.newaxis, :]

    nms_indices = cv2.dnn.NMSBoxes(boxes.tolist(), box_scores.flatten().tolist(), conf_thres, iou_thres)

    results = []
    for i in nms_indices:
        i = i[0]
        box = boxes[i]
        score = box_scores.flatten()[i]
        label = box_classes.flatten()[i]
        results.append((box[0], box[1], box[2], box[3], score, label))

    return results

完整代码请参考：https://github.com/wang-xinyu/tensorrtx/tree/master/yolov5

需要注意的是，使用TensorRT进行YoloV5推理需要先将YoloV5模型转换为ONNX格式，然后再使用TensorRT构建引擎。