在 OpenCV 中运行神经网络时如何修复、"error: (-215) pbBlob.raw_data_type() == caffe::FLOAT16 in function blobFromPr

Question

我目前正试图使用Nvidia DIGITS在用于对象检测的自定义数据集上训练CNN，最终我想在Nvidia Jetson TX2上运行该网络。我按照推荐的说明从Docker下载了DIGITS图像，并且我能够以合理的精度成功地训练网络。但是当我尝试使用OpenCv在python中运行我的网络时，我得到了这个错误，

"错误：(-215)pbBlob.raw_data_type()==函数中的caffe:：FLOAT16blobFromProto">

我在其他一些线程中读到，这是由于DIGITS以与OpenCv的DNN功能不兼容的形式存储其网络。

在训练我的网络之前，我曾尝试在DIGITS中选择一个选项，该选项应该使网络与其他软件兼容，但这似乎根本不会改变网络，而且我在运行python脚本时也遇到了同样的错误。这是我运行的创建错误的脚本(它来自本教程https://www.pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/)

# 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 = ["dontcare", "HatchPanel"]
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, (300, 300)), 0.007843,
(300, 300), 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]
# 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)

这应该输出在对脚本的调用中指定的图像，并在图像的顶部绘制神经网络的输出。但相反，脚本会因前面提到的错误而崩溃。我看到其他线程中有人也有同样的错误，但到目前为止，他们都没有找到一个适用于当前版本DIGITS的解决方案。

我的完整设置如下：

操作系统：Ubuntu 16.04

Nvidia DIGITS Docker镜像版本：19.01-caffe

DIGITS版本：6.1.1

咖啡版本：0.17.2

咖啡口味：Nvidia

OpenCV版本：4.0.0

Python版本：3.5

非常感谢您的帮助。