如果没有“ClusterDetections”层，如何运行Detectnet Caffemodel？

Question

我使用Nvidia Digitsdocker图像训练了一个自定义对象检测模型。现在我只想使用OpenCV python运行这个模型（即不是Caffe、TensorRT等），我可以使用OpenCV的DNN模块成功地加载和运行我的模型，但是我必须删除部署.prototxt“称为”ClusterDetections“，因为OpenCV不支持这种类型的层，抛出错误：

Can't create layer "cluster" of type "Python" in function 'getLayerInstance'

如前所述，如果我将不支持的层从部署.prototxt，但如果没有“ClusterDetections”层，我只剩下net.forward()返回的原始数据，它们的格式与网络的预期输出有很大不同。你知道吗

运行print(print(detections[0,0,i,2])（通常是每个类的置信度分数）返回：

-11.059842 -14.562948 -14.037464 -13.557558 -13.167087 -12.759864 -12.131538 -11.58218 -11.353993 -11.398977 -11.459799 -11.529523 -11.670803 -11.776192 -12.514015 -14.56443 -16.339668 -16.761234 -18.237602 -20.796967 -13.148532 -5.987872

这可能仅仅意味着在我传递给模型的图像中没有检测到任何对象，但是由于缺少“ClusterDetection”层，我倾向于认为在我的图像中可能检测到对象，我只是错误地解释了数据。你知道吗

我尝试过用python实现this post建议的自定义检测集群函数，但没有效果。Another solution我发现使用Caffe来实现不受支持的层。但正如我前面提到的，对于我的应用程序，我只想使用opencvpython来运行模型。在我之前问过的related question中，我被告知要使用OpenCV的自定义层机制。但即使读完了the documentation,，我仍然不能很好地理解如何利用上述层机制从头开始写东西。你知道吗

基本上，我的问题是，有没有一种简单的方法来补偿不受支持的“ClusterDetections”层，并且仍然使用OpenCV运行我的对象检测模型？因为我找到的每个解决方案要么都不起作用，要么说你必须使用另一个库，比如Caffe或TensorRT。你知道吗

以下是我的代码，摘自this tutorial：

# import the necessary packages
import numpy as np
import argparse
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
    help="path to input image")
ap.add_argument("-p", "--prototxt", required=True,
    help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
    help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
    help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# implementation)
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (1248, 352)), 0.007843,
    (1248, 352), 127.5)

# pass the blob through the network and obtain the detections and
# predictions
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()

# loop over the detections
for i in np.arange(0, detections.shape[2]):
    # extract the confidence (i.e., probability) associated with the
    # prediction
    confidence = detections[0, 0, i, 2]
    print(detections[0,0,i,2])
    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence > args["confidence"]:
        # extract the index of the class label from the `detections`,
        # then compute the (x, y)-coordinates of the bounding box for
        # the object
        idx = int(detections[0, 0, i, 1])
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")

        # display the prediction
        label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
        print("[INFO] {}".format(label))
        cv2.rectangle(image, (startX, startY), (endX, endY),
            COLORS[idx], 2)
        y = startY - 15 if startY - 15 > 15 else startY + 15
        cv2.putText(image, label, (startX, y),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)

Here是我运行模型的图像。你知道吗

我运行的是python3.6和opencv4.1.1。复制当前功能所需的文件可以在google驱动器here.上找到