【TensorRT】在Jetson设备Xavier（TX2等通用）上使用TensorRT加速yolov5，详细记录过程

硬件：Xavier
jetpack版本：sudo apt-cache show nvidia-jetpack

一、克隆代码：

1、第一个代码是yolov5的官方代码：

https://github.com/ultralytics/yolov5.git

2、第二个代码是TensorRT编译yolov5的代码：

git clone https://github.com/wang-xinyu/tensorrtx.git

其中tensorrtx文件夹我只保留了yolov5文件夹，如图：

下载yolov5s.pt(官方提供的，注意版本不能错)这里我将我使用的上传到百度网盘，需要自取：链接: https://pan.baidu.com/s/1LwOJ56f6-faUlIl0fsDCng?pwd=b3h9 提取码: b3h9 复制这段内容后打开百度网盘手机App，操作更方便哦

二、模型转换（将yolotest.pt也就是yolov5s.pt转换成yolov5s.wts）

环境说明：
（1）Xavier上需创建完成yolov5所需要的库环境（其中比较难装的torch和tarchision见我另外的博客）
（2）如果不想在xavier上转换，可以在pc机转换

转换模型文件：
注意：yolov5官方仓库已经提供了转换代码gen_wts.py
步骤一：将下载的yolotest.pt拷贝到yolov5目录下

步骤二：打开终端，进入yolov5目录下，启动虚拟环境

cd yolov5
conda activate yolov5 #启动虚拟环境

步骤三：执行gen_wts.py程序，生成转换文件

python gen_wts.py -w yolotest.pt -o yolov5s.wts

三、编译引擎文件

步骤一：在tensortrtx/yolov5/目录下新建build文件夹

cd work/tensorrtx/yolov5 #你自己的目录
mkdir bulid

将上一步生成的yolov5.wts拷贝到build文件夹下
然后cmake 、make

cmake ..
make

以上两部顺利编译的基础是opencv库等都已经配置好的，我使用的是jetpack4.5，这些库已经是系统配置好的，不需要再装。我在加速别的模型时，需要使用一些cuda编译的cv库，这时就需要将默认的库卸载，重新编译opencv了，在本文中，不需要重新编译opencv，我说这些就是描述一下我遇到的一些问题，给大家一个参考。

使用yolov5s.wts生成yolov5s.engine

sudo ./yolov5 -s yolov5s.wts yolov5s.engine s

四、测试

一些bug：
jetson xavier在使用tensorRT对yolov5加速时，报错ModuleNotFoundError: No module named ‘pycuda‘
jetson xavier在使用tensorRT对yolov5加速时，报错ModuleNotFoundError: No module named ‘tensorrt‘
其中pycuda可以直接pip install 安装
tensorrt解决参见链接

./yolov5 -d yolov5s.engine ../test/

可见推理一张图片的时间30ms左右。

五、提供一个python测试代码，调用摄像头的。
感谢：https://blog.csdn.net/qq_40691868/article/details/117331162?spm=1001.2014.3001.5506
我在这个代码基础上增加了FPS显示，我的是外接摄像头，注意修改摄像头编号

"""
An example that uses TensorRT's Python api to make inferences.
"""
import ctypes
import os
import shutil
import random
import sys
import threading
import time
import cv2
import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
import torch
import torchvision
import argparse

CONF_THRESH = 0.5
IOU_THRESHOLD = 0.4


def get_img_path_batches(batch_size, img_dir):
    ret = []
    batch = []
    for root, dirs, files in os.walk(img_dir):
        for name in files:
            if len(batch) == batch_size:
                ret.append(batch)
                batch = []
            batch.append(os.path.join(root, name))
    if len(batch) > 0:
        ret.append(batch)
    return ret


def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    """
    description: Plots one bounding box on image img,
                 this function comes from YoLov5 project.
    param:
        x:      a box likes [x1,y1,x2,y2]
        img:    a opencv image object
        color:  color to draw rectangle, such as (0,255,0)
        label:  str
        line_thickness: int
    return:
        no return
    """
    tl = (
            line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
    )  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(
            img,
            label,
            (c1[0], c1[1] - 2),
            0,
            tl / 3,
            [225, 255, 255],
            thickness=tf,
            lineType=cv2.LINE_AA,
        )


class YoLov5TRT(object):
    """
    description: A YOLOv5 class that warps TensorRT ops, preprocess and postprocess ops.
    """

    def __init__(self, engine_file_path):
        # Create a Context on this device,
        self.ctx = cuda.Device(0).make_context()
        stream = cuda.Stream()
        TRT_LOGGER = trt.Logger(trt.Logger.INFO)
        runtime = trt.Runtime(TRT_LOGGER)

        # Deserialize the engine from file
        with open(engine_file_path, "rb") as f:
            engine = runtime.deserialize_cuda_engine(f.read())
        context = engine.create_execution_context()

        host_inputs = []
        cuda_inputs = []
        host_outputs = []
        cuda_outputs = []
        bindings = []

        for binding in engine:
            print('bingding:', binding, engine.get_binding_shape(binding))
            size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
            dtype = trt.nptype(engine.get_binding_dtype(binding))
            # Allocate host and device buffers
            host_mem = cuda.pagelocked_empty(size, dtype)
            cuda_mem = cuda.mem_alloc(host_mem.nbytes)
            # Append the device buffer to device bindings.
            bindings.append(int(cuda_mem))
            # Append to the appropriate list.
            if engine.binding_is_input(binding):
                self.input_w = engine.get_binding_shape(binding)[-1]
                self.input_h = engine.get_binding_shape(binding)[-2]
                host_inputs.append(host_mem)
                cuda_inputs.append(cuda_mem)
            else:
                host_outputs.append(host_mem)
                cuda_outputs.append(cuda_mem)

        # Store
        self.stream = stream
        self.context = context
        self.engine = engine
        self.host_inputs = host_inputs
        self.cuda_inputs = cuda_inputs
        self.host_outputs = host_outputs
        self.cuda_outputs = cuda_outputs
        self.bindings = bindings
        self.batch_size = engine.max_batch_size

    def infer(self, input_image_path):
        threading.Thread.__init__(self)
        # Make self the active context, pushing it on top of the context stack.
        self.ctx.push()
        self.input_image_path = input_image_path
        # Restore
        stream = self.stream
        context = self.context
        engine = self.engine
        host_inputs = self.host_inputs
        cuda_inputs = self.cuda_inputs
        host_outputs = self.host_outputs
        cuda_outputs = self.cuda_outputs
        bindings = self.bindings
        # Do image preprocess
        batch_image_raw = []
        batch_origin_h = []
        batch_origin_w = []
        batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w])

        input_image, image_raw, origin_h, origin_w = self.preprocess_image(input_image_path
                                                                           )

        batch_origin_h.append(origin_h)
        batch_origin_w.append(origin_w)
        np.copyto(batch_input_image, input_image)
        batch_input_image = np.ascontiguousarray(batch_input_image)

        # Copy input image to host buffer
        np.copyto(host_inputs[0], batch_input_image.ravel())
        start = time.time()
        # Transfer input data  to the GPU.
        cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
        # Run inference.
        context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle)
        # Transfer predictions back from the GPU.
        cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
        # Synchronize the stream
        stream.synchronize()
        end = time.time()
        # Remove any context from the top of the context stack, deactivating it.
        self.ctx.pop()
        # Here we use the first row of output in that batch_size = 1
        output = host_outputs[0]
        # Do postprocess
        result_boxes, result_scores, result_classid = self.post_process(
            output, origin_h, origin_w)
        # Draw rectangles and labels on the original image
        for j in range(len(result_boxes)):
            box = result_boxes[j]
            plot_one_box(
                box,
                image_raw,
                label="{}:{:.2f}".format(
                    categories[int(result_classid[j])], result_scores[j]
                ),
            )
        return image_raw, end - start

    def destroy(self):
        # Remove any context from the top of the context stack, deactivating it.
        self.ctx.pop()

    def get_raw_image(self, image_path_batch):
        """
        description: Read an image from image path
        """
        for img_path in image_path_batch:
            yield cv2.imread(img_path)

    def get_raw_image_zeros(self, image_path_batch=None):
        """
        description: Ready data for warmup
        """
        for _ in range(self.batch_size):
            yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8)

    def preprocess_image(self, input_image_path):
        """
        description: Convert BGR image to RGB,
                     resize and pad it to target size, normalize to [0,1],
                     transform to NCHW format.
        param:
            input_image_path: str, image path
        return:
            image:  the processed image
            image_raw: the original image
            h: original height
            w: original width
        """
        image_raw = input_image_path
        h, w, c = image_raw.shape
        image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)
        # Calculate widht and height and paddings
        r_w = self.input_w / w
        r_h = self.input_h / h
        if r_h > r_w:
            tw = self.input_w
            th = int(r_w * h)
            tx1 = tx2 = 0
            ty1 = int((self.input_h - th) / 2)
            ty2 = self.input_h - th - ty1
        else:
            tw = int(r_h * w)
            th = self.input_h
            tx1 = int((self.input_w - tw) / 2)
            tx2 = self.input_w - tw - tx1
            ty1 = ty2 = 0
        # Resize the image with long side while maintaining ratio
        image = cv2.resize(image, (tw, th))
        # Pad the short side with (128,128,128)
        image = cv2.copyMakeBorder(
            image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, (128, 128, 128)
        )
        image = image.astype(np.float32)
        # Normalize to [0,1]
        image /= 255.0
        # HWC to CHW format:
        image = np.transpose(image, [2, 0, 1])
        # CHW to NCHW format
        image = np.expand_dims(image, axis=0)
        # Convert the image to row-major order, also known as "C order":
        image = np.ascontiguousarray(image)
        return image, image_raw, h, w

    def xywh2xyxy(self, origin_h, origin_w, x):
        """
        description:    Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
        param:
            origin_h:   height of original image
            origin_w:   width of original image
            x:          A boxes tensor, each row is a box [center_x, center_y, w, h]
        return:
            y:          A boxes tensor, each row is a box [x1, y1, x2, y2]
        """
        y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
        r_w = self.input_w / origin_w
        r_h = self.input_h / origin_h
        if r_h > r_w:
            y[:, 0] = x[:, 0] - x[:, 2] / 2
            y[:, 2] = x[:, 0] + x[:, 2] / 2
            y[:, 1] = x[:, 1] - x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
            y[:, 3] = x[:, 1] + x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
            y /= r_w
        else:
            y[:, 0] = x[:, 0] - x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
            y[:, 2] = x[:, 0] + x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
            y[:, 1] = x[:, 1] - x[:, 3] / 2
            y[:, 3] = x[:, 1] + x[:, 3] / 2
            y /= r_h

        return y

    def post_process(self, output, origin_h, origin_w):
        """
        description: postprocess the prediction
        param:
            output:     A tensor likes [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...]
            origin_h:   height of original image
            origin_w:   width of original image
        return:
            result_boxes: finally boxes, a boxes tensor, each row is a box [x1, y1, x2, y2]
            result_scores: finally scores, a tensor, each element is the score correspoing to box
            result_classid: finally classid, a tensor, each element is the classid correspoing to box
        """
        # Get the num of boxes detected
        num = int(output[0])
        # Reshape to a two dimentional ndarray
        pred = np.reshape(output[1:], (-1, 6))[:num, :]
        # to a torch Tensor
        pred = torch.Tensor(pred).cuda()
        # Get the boxes
        boxes = pred[:, :4]
        # Get the scores
        scores = pred[:, 4]
        # Get the classid
        classid = pred[:, 5]
        # Choose those boxes that score > CONF_THRESH
        si = scores > CONF_THRESH
        boxes = boxes[si, :]
        scores = scores[si]
        classid = classid[si]
        # Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2]
        boxes = self.xywh2xyxy(origin_h, origin_w, boxes)
        # Do nms
        indices = torchvision.ops.nms(boxes, scores, iou_threshold=IOU_THRESHOLD).cpu()
        result_boxes = boxes[indices, :].cpu()
        result_scores = scores[indices].cpu()
        result_classid = classid[indices].cpu()
        return result_boxes, result_scores, result_classid


class inferThread(threading.Thread):
    def __init__(self, yolov5_wrapper):
        threading.Thread.__init__(self)
        self.yolov5_wrapper = yolov5_wrapper

    def infer(self, frame):
        batch_image_raw, use_time = self.yolov5_wrapper.infer(frame)

        # for i, img_path in enumerate(self.image_path_batch):
        #     parent, filename = os.path.split(img_path)
        #     save_name = os.path.join('output', filename)
        #     # Save image
        #     cv2.imwrite(save_name, batch_image_raw[i])
        # print('input->{}, time->{:.2f}ms, saving into output/'.format(self.image_path_batch, use_time * 1000))
        return batch_image_raw, use_time


class warmUpThread(threading.Thread):
    def __init__(self, yolov5_wrapper):
        threading.Thread.__init__(self)
        self.yolov5_wrapper = yolov5_wrapper

    def run(self):
        batch_image_raw, use_time = self.yolov5_wrapper.infer(self.yolov5_wrapper.get_raw_image_zeros())
        print('warm_up->{}, time->{:.2f}ms'.format(batch_image_raw[0].shape, use_time * 1000))


if __name__ == "__main__":
    # load custom plugins
    parser = argparse.ArgumentParser()
    parser.add_argument('--engine', nargs='+', type=str, default="build/yolov5s.engine", help='.engine path(s)')
    parser.add_argument('--save', type=int, default=0, help='save?')
    opt = parser.parse_args()
    PLUGIN_LIBRARY = "build/libmyplugins.so"
    engine_file_path = opt.engine

    ctypes.CDLL(PLUGIN_LIBRARY)

    # load coco labels

    categories = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
                  "traffic light",
                  "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
                  "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase",
                  "frisbee",
                  "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard",
                  "surfboard",
                  "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
                  "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
                  "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
                  "cell phone",
                  "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
                  "teddy bear",
                  "hair drier", "toothbrush"]
    # a YoLov5TRT instance
    yolov5_wrapper = YoLov5TRT(engine_file_path)
    cap = cv2.VideoCapture(2)
    try:
        thread1 = inferThread(yolov5_wrapper)
        thread1.start()
        thread1.join()
        while 1:
            _, frame = cap.read()
            img, t = thread1.infer(frame)
            fps = 1/t
            imgout = cv2.putText(img, "FPS= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
            cv2.imshow("result", imgout)
            if cv2.waitKey(1) & 0XFF == ord('q'):  # 1 millisecond
                break


    finally:
        # destroy the instance
        cap.release()
        cv2.destroyAllWindows()
        yolov5_wrapper.destroy()