Jetson AGX Xavier实现TensorRT加速YOLOv5进行实时检测_xavier tensorrt

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一、前言

        由于YOLOv5在Xavier上对实时画面的检测速度较慢，需要采用TensorRT对其进行推理加速。接下来记录一下我的实现过程。

二、环境准备

 如果还没有搭建YOLOv5的python环境，按照下文步骤执行。反之，直接跳过第一步执行第二步。

1、参考文章《Jetson AGX Xavier配置yolov5虚拟环境》建立YOLOv5的Python环境，并参照《Jetson AGX Xavier安装Archiconda虚拟环境管理器与在虚拟环境中调用opencv》，将opencv导入环境，本文Opencv采用的是3.4.3版本。

2、在环境中导入TensorRT的库。与opencv的导入相同。将路径 /usr/lib/python3.6/dist-packages/  下关于TensorRT的文件夹，复制到自己所创建环境的site-packages文件夹下。例如：复制到/home/jetson/archiconda3/envs/yolov5env/lib/python3.6/site-packages/之下。

3、在环境中安装pycuda，如果pip安装不成功，网上有许多解决办法。 

conda activate yolov5env
pip install pycuda

三、加速步骤

         以加速YOLOv5s模型为例，以下有v4.0与v5.0两个版本，大家任选其一即可。

1、克隆工程

①v4.0

git clone -b v4.0 https://github.com/ultralytics/yolov5.git 
git clone -b yolov5-v4.0 https://github.com/wang-xinyu/tensorrtx.git

②v5.0

git clone -b v5.0 https://github.com/ultralytics/yolov5.git 
git clone -b yolov5-v5.0 https://github.com/wang-xinyu/tensorrtx.git

2、生成引擎文件

①下载yolov5s.pt到yolov5工程的weights文件夹下。

②复制tensorrtx/yolov5文件夹下的gen_wts.py文件到yolov5工程下。

③生成yolov5s.wts文件。

conda activate yolov5env
cd /xxx/yolov5
 
以下按照自己所下版本选择
#v4.0
python gen_wts.py
 
#v5.0
python gen_wts.py -w yolov5s.pt -o yolov5s.wts

④生成引擎文件

        进入tensorrtx/yolov5文件夹下。

mkdir build

        复制yolov5工程中生成的yolov5s.wts文件到tensorrtx/yolov5/build文件夹中。并在build文件夹中打开终端：

cmake ..
make
#v4.0   sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x/]
#v5.0   sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw]
sudo ./yolov5 -s yolov5s.wts yolov5s.engine s

生成yolov5s.engine文件。

四、加速实现

1、图片检测加速

sudo ./yolov5 -d yolov5s.engine ../samples
 
或者
 
conda activate yolov5env
python yolov5_trt.py
 

2、摄像头实时检测加速

        由于本人没有学习过C++语言，所以只能硬着头皮修改了下yolov5_trt.py脚本，脚本的代码格式较差，但是能够实现加速，有需要的可以作为一个参考。

        在tensorrt工程下新建一个yolo_trt_test.py文件。复制下面 v4.0或者v5.0的代码到yolo_trt_test.py。注意yolov5s.engine的路径，自行更改。

  ①v4.0代码

"""
An example that uses TensorRT's Python api to make inferences.
"""
import ctypes
import os
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
 
INPUT_W = 608
INPUT_H = 608
CONF_THRESH = 0.15
IOU_THRESHOLD = 0.45
int_box=[0,0,0,0]
int_box1=[0,0,0,0]
fps1=0.0
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]))
    C2 = c2
    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] + 8
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(
            img,
            label,
            (c1[0], c1[1]+t_size[1] + 5),
            0,
            tl / 3,
            [255,255,255],
            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.cfx = 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:
            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):
                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
 
 
    def infer(self, input_image_path):
        global int_box,int_box1,fps1
        # threading.Thread.__init__(self)
        # Make self the active context, pushing it on top of the context stack.
        self.cfx.push()
        # 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
        input_image, image_raw, origin_h, origin_w = self.preprocess_image(
            input_image_path
        )
        # Copy input image to host buffer
        np.copyto(host_inputs[0], input_image.ravel())
        # Transfer input data  to the GPU.
        cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
        # Run inference.
        context.execute_async(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()
        # Remove any context from the top of the context stack, deactivating it.
        self.cfx.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 i in range(len(result_boxes)):
            box1 = result_boxes[i]
            plot_one_box(
                box1,
                image_raw,
                label="{}:{:.2f}".format(
                    categories[int(result_classid[i])], result_scores[i]
                ),
            )
        return image_raw
 
        # parent, filename = os.path.split(input_image_path)
        # save_name = os.path.join(parent, "output_" + filename)
        # # 　Save image
        # cv2.imwrite(save_name, image_raw)
 
    def destroy(self):
        # Remove any context from the top of the context stack, deactivating it.
        self.cfx.pop()
 
    def preprocess_image(self, input_image_path):
        """
        description: Read an image from image path, convert it 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 = INPUT_W / w
        r_h = INPUT_H / h
        if r_h > r_w:
            tw = INPUT_W
            th = int(r_w * h)
            tx1 = tx2 = 0
            ty1 = int((INPUT_H - th) / 2)
            ty2 = INPUT_H - th - ty1
        else:
            tw = int(r_h * w)
            th = INPUT_H
            tx1 = int((INPUT_W - tw) / 2)
            tx2 = 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 = INPUT_W / origin_w
        r_h = 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 - (INPUT_H - r_w * origin_h) / 2
            y[:, 3] = x[:, 1] + x[:, 3] / 2 - (INPUT_H - r_w * origin_h) / 2
            y /= r_w
        else:
            y[:, 0] = x[:, 0] - x[:, 2] / 2 - (INPUT_W - r_h * origin_w) / 2
            y[:, 2] = x[:, 0] + x[:, 2] / 2 - (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 myThread(threading.Thread):
    def __init__(self, func, args):
        threading.Thread.__init__(self)
        self.func = func
        self.args = args
 
    def run(self):
        self.func(*self.args)
 
 
if __name__ == "__main__":
    # load custom plugins
    PLUGIN_LIBRARY = "build/libmyplugins.so"
    ctypes.CDLL(PLUGIN_LIBRARY)
    engine_file_path = "yolov5s.engine"
 
    # 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(0)
    while 1:
        _,image =cap.read()
        img=yolov5_wrapper.infer(image)
        cv2.imshow("result", img)
        if cv2.waitKey(1) & 0XFF == ord('q'):  # 1 millisecond
            break
    cap.release()
    cv2.destroyAllWindows()
    yolov5_wrapper.destroy()

  ②v5.0代码

"""
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(0)
    try:
        thread1 = inferThread(yolov5_wrapper)
        thread1.start()
        thread1.join()
        while 1:
            _,frame = cap.read()
            img,t=thread1.infer(frame)
            cv2.imshow("result", img)
            if cv2.waitKey(1) & 0XFF == ord('q'):  # 1 millisecond
                break
 
 
    finally:
        # destroy the instance
        cap.release()
        cv2.destroyAllWindows()
        yolov5_wrapper.destroy()

          最后在yolov5env环境中执行yolo_trt_test.py脚本。

conda activate yolov5env
python yolo_trt_test.py

 3、实现效果

    

                          （a）未加速                                                         (b)加速                                                                        

五、总结

       TensorRT加速对于深度学习模型在移动嵌入式部署十分重要，解决了一些算力较低的嵌入式设备无法部署深度学习算法或者部署效果差的情况。个人感觉当然使用v5.0的最好，它支持YOLOv5新出的几个模型加速。到此，我使用TensorRT加速yolov5的过程就到此结束，如果有问题可以随时问我，希望得到点赞和关注。翻过一座山又是一座山，下座山峰见。

六、参考文章

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