玩转Jetson Nano（五）：TensorRT加速YOLOv5目标检测

玩转Jetson Nano（五）：TensorRT加速YOLOv5目标检测

• 前言
• 实验环境
• YOLOv5目标检测

• 克隆下载yolov5项目
• 安装所需的环境
• 下载yolov5s.pt权重
• 测试图片

• TensorRT加速

• 克隆下载tensorrtx项目
• 转换生成yolov5s.wts文件
• 生成引擎文件

• 编译
• 生成yolov5s.engine文件

• 测试图片

• 常见问题

• AttributeError: Can‘t get attribute ‘SPPF‘ on ＜module 'models.common' from '/home/yolov5/models/common.py'>

• 解决方法

• RuntimeError: Couldn't load custom C++ ops.

• 解决方法

• RuntimeError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend.

• 解决方法

• ModuleNotFoundError: No module named 'pycuda'

• 解决方法

• 参考文献

前言

• 本文是个人使用Jetson Nano的电子笔记，由于水平有限，难免出现错漏，敬请批评改正。
• 更多精彩内容，可点击进入
  玩转Jetson Nano专栏或我的个人主页查看

实验环境

• matplotlib>=3.2.2
• numpy>=1.18.5
• opencv-python>=4.1.1
• scipy>=1.4.1
• torch>=1.7.0
• torchvision>=0.8.1
• tqdm>=4.41.0
• tensorboard>=2.4.1
• seaborn>=0.11.0

YOLOv5目标检测

克隆下载yolov5项目

由于yolov5最新版本可能会出现了不兼容问题，所以这里克隆下载yolov5 5.0版本。

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

安装所需的环境

在yolov5项目下，打开终端输入：

sudo pip3 install -r requirements.txt

下载yolov5s.pt权重

https://github.com/ultralytics/yolov5/releases/tag/v5.0

如果没有下载yolov5s.pt权重，后续在测试图片，运行python3 detect.py时，会自动下载。

测试图片

python3 detect.py --source data/images/person.jpg

TensorRT加速

克隆下载tensorrtx项目

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

转换生成yolov5s.wts文件

cd tensorrtx
cp yolov5/gen_wts.py ~/yolov5
cd ~/yolov5
python3 gen_wts.py -w yolov5s.pt -o yolov5s.wts

生成引擎文件

编译

cd ~/tensorrtx/yolov5
mkdir build && cd build
mv ~/yolov5/yolov5s.wts ./
cmake ..
make -j4

生成yolov5s.engine文件

# ./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw]
./yolov5 -s yolov5s.wts yolov5s.engine s

测试图片

./yolov5 -d yolov5s.engine ../samples # 推理samples文件夹里的图片

从结果上看，预测一张图片所需时间，从5.262s提升到了0.316s。

新建并编辑yolov5_trt_test.py

vi yolov5_trt_img_test.py

yolov5_trt_img_test.py内容如下：

"""
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)

    try:
        thread1 = inferThread(yolov5_wrapper)
        thread1.start()
        thread1.join()
        frame = cv2.imread('samples/person.jpg')
        img,t=thread1.infer(frame)
        cv2.imshow("result", img)
        cv2.waitKey(0)

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

运行yolov5_trt_img_test.py文件

python3 yolov5_trt_img_test.py

常见问题

AttributeError: Can‘t get attribute ‘SPPF‘ on ＜module ‘models.common’ from ‘/home/yolov5/models/common.py’>

解决方法

vi utils/google_utils.py

将

response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api

改为

response = requests.get(f'https://api.github.com/repos/{repo}/releases/tags/v5.0').json() # github api

修改保存并退出，即可！

RuntimeError: Couldn’t load custom C++ ops.

RuntimeError: Couldn't load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.__version__ and your torchvision version with torchvision.__version__ and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.

解决方法

这可能是由于torch和torchvision版本不一致所引起的报错。torch所对应的torchvision版本，如下图所示。

RuntimeError: Could not run ‘torchvision::nms’ with arguments from the ‘CUDA’ backend.

RuntimeError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. 'torchvision::nms' is only available for these backends: [CPU, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, Tracer, Autocast, Batched, VmapMode].

解决方法

sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev

# 下载torchvision-0.8.1压缩包
wget https://github.com/pytorch/vision/archive/v0.8.1.zip
unzip vision-0.8.1.zip
mv vision-0.8.1 ~/torchvision

# 编译
cd torchvision
export BUILD_VERSION=0.8.1  # torch-1.7.0 对应 torchvision-0.8.1  
python3 setup.py install --user

ModuleNotFoundError: No module named ‘pycuda’

解决方法

网上有教程，直接用sudo pip3 install pycuda下载，你们可以试试，我这里无法安装成功，只能使用编译安装的方法。

pycuda下载链接 提取码: cuda

下载完成后，解压文件，运行以下命令：

cd pycuda-2019.1.2
python3 configure.py --cuda-root=/usr/local/cuda-10.2
sudo python3 setup.py install

参考文献

[1] Jetson Nano Developer Kit User Guide [2] https://docs.nvidia.com/deeplearning/frameworks/install-pytorch-jetson-platform-release-notes/index.html
[3] https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-11-now-available/72048
[4] https://github.com/ultralytics/yolov5.git
[5] https://zhuanlan.zhihu.com/p/425891581