如何在 Darknet 架构上进行批量检测？

Question

我正在尝试使用 Darknet\YoloV4 进行批量检测。它适用于一批，然后第二批因 CUDA 错误而失败。我是否遗漏了以下代码段中的其他内容？ RTX GPU 卡 Batch 的正确参数是什么，如何确定正确的 Batch 大小？

我的系统配置如下-

System:    Host: ubox Kernel: 5.4.0-42-generic x86_64 bits: 64
           Desktop: Gnome 3.28.4 Distro: Ubuntu 18.04.4 LTS
Machine:   Device: desktop System: Alienware product: Alienware Aurora R9 v: 1.0.7 serial: N/A
           Mobo: Alienware model: 0T76PD v: A01 serial: N/A
           UEFI: Alienware v: 1.0.7 date: 12/23/2019
CPU:       8 core Intel Core i7-9700K (-MCP-) cache: 12288 KB
           clock speeds: max: 4900 MHz 1: 800 MHz 2: 800 MHz 3: 800 MHz
           4: 800 MHz 5: 801 MHz 6: 803 MHz 7: 808 MHz 8: 810 MHz
Graphics:  Card-1: Intel Device 3e98
           Card-2: NVIDIA Device 1e84
           Display Server: x11 (X.Org 1.20.8 )
           drivers: modesetting,nvidia (unloaded: fbdev,vesa,nouveau)
           Resolution: 2560x1440@59.95hz, 1920x1080@60.00hz
           OpenGL: renderer: GeForce RTX 2070 SUPER/PCIe/SSE2
           version: 4.6.0 NVIDIA 450.57

当我执行批量大小 3 的 performBatchDetectV2() 时，我得到 CUDA Error: out of memory。

如何在Yolov4架构上正确进行Batching？在我的用例中，我正在从相机获取帧，我想将 10 帧合并为一个并在下面调用。如果我只调用一次，下面的函数将完美运行，这意味着它会在 second batch of frames.

上抛出 Cuda 错误

def performBatchDetectV2(image_list, thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3):
    net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
    meta = load_meta(metaPath.encode('utf-8'))
    pred_height, pred_width, c = image_list[0].shape
    net_width, net_height = (network_width(net), network_height(net))
    img_list = []
    for custom_image_bgr in image_list:
        custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
        custom_image = cv2.resize(
            custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST)
        custom_image = custom_image.transpose(2, 0, 1)
        img_list.append(custom_image)

    arr = np.concatenate(img_list, axis=0)
    arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
    data = arr.ctypes.data_as(POINTER(c_float))
    im = IMAGE(net_width, net_height, c, data)

    batch_dets = network_predict_batch(net, im, batch_size, pred_width,
                                                pred_height, thresh, hier_thresh, None, 0, 0)
    batch_boxes = []
    batch_scores = []
    batch_classes = []
    for b in range(batch_size):
        num = batch_dets[b].num
        dets = batch_dets[b].dets
        if nms:
            do_nms_obj(dets, num, meta.classes, nms)
        boxes = []
        scores = []
        classes = []
        for i in range(num):
            det = dets[i]
            score = -1
            label = None
            for c in range(det.classes):
                p = det.prob[c]
                if p > score:
                    score = p
                    label = c
            if score > thresh:
                box = det.bbox
                left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2,
                                            box.x + box.w / 2, box.y + box.h / 2))
                boxes.append((top, left, bottom, right))
                scores.append(score)
                classes.append(label)
                # boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0)
                # cv2.rectangle(image_list[b], (left, top),
                #           (right, bottom), boxColor, 2)
        # cv2.imwrite(os.path.basename(img_samples[b]),image_list[b])

        batch_boxes.append(boxes)
        batch_scores.append(scores)
        batch_classes.append(classes)
    free_batch_detections(batch_dets, batch_size)
    return batch_boxes, batch_scores, batch_classes

Answer 1

问题是，每次调用 performBatchDetectV2 方法时加载网络。因此，您必须以恒定的批量大小一次性加载网络，并使用加载的网络进行预测。将您的 netwrok 变量设置为全局变量。因为你是

这是你的功能

def performBatchDetectV2(image_list, thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3):
    net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
    meta = load_meta(metaPath.encode('utf-8'))
    <your code>

改成这个

configPath = "./cfg/yolov4.cfg"
weightPath = "yolov4.weights"
metaPath= "./cfg/coco.data",
net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
meta = load_meta(metaPath.encode('utf-8'))
def performBatchDetectV2(image_list, thresh= 0.25, hier_thresh=.5, nms=.45, batch_size=3):
    global net, meta
    <your code>

如何在 Darknet 架构上进行批量检测？

How to do Batch Detection on Darknet architecture?

object-detection

computer-vision

darknet

yolo