我目前正在尝试使用 Nvidia DIGITS 在自定义数据集上训练 CNN 以进行对象检测，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 映像，并且能够以合理的准确度成功训练网络。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() == caffe::FLOAT16 in function blobFromProto”
我在其他一些线程中读到，这是因为 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，我尝试在 DIGITS 中选择应该使网络与其他软件兼容的选项，但这似乎根本没有改变网络，并且在运行我的 python 脚本时出现同样的错误。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)
这应该输出在脚本调用中指定的图像，神经网络的输出绘制在图像的顶部。但相反，脚本因前面提到的错误而崩溃。我见过其他人也有同样的错误，但到目前为止，他们都没有找到适用于当前版本 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
数字版本：6.1.1
咖啡版本：0.17.2
咖啡口味：英伟达
OpenCV 版本：4.0.0
Python版本：3.5
任何帮助深表感谢。