How to fix, "error: (-215) pbBlob.raw_data_type() == caffe::FLOAT16 in function blobFromProto" when running neural network in OpenCV

Question

我目前正在尝试使用 Nvidia DIGITS 在用于对象检测的自定义数据集上训练 CNN，最终我想运行在 Nvidia Jetson TX2 上使用该网络。我按照推荐的说明从 Docker 下载了 DIGITS 图像，我能够以合理的精度成功训练网络。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：函数中的 (-215) pbBlob.raw_data_type() == caffe::FLOAT16 blobFromProto

我在其他一些帖子中了解到，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，我尝试在 DIGITS 中选择选项，该选项应该使网络与其他软件兼容，但是这似乎根本没有改变网络，当我遇到同样的错误时运行宁我的 python 脚本。这是我运行创建错误的脚本（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)

这应该输出脚本调用中指定的图像，神经网络的输出绘制在图像之上。但是相反，脚本因前面提到的错误而崩溃。我已经看到其他人的线程也有同样的错误，但到目前为止，none 他们已经找到了适用于当前版本 DIGITS 的解决方案。

我的完整设置如下：

OS: Ubuntu 16.04

Nvidia DIGITS Docker 图片版本：19.01-caffe

DIGITS 版本：6.1.1

咖啡版本：0.17.2

咖啡风味：Nvidia

OpenCV 版本：4.0.0

Python版本：3.5

非常感谢任何帮助。

Answer 1

哈里森·麦金太尔，谢谢！此 PR 修复了它：https://github.com/opencv/opencv/pull/13800. Please note that there is a layer with type "ClusterDetections". It's not supported by OpenCV but you can implement it using custom layers mechanic (see a tutorial)

How to fix, "error: (-215) pbBlob.raw_data_type() == caffe::FLOAT16 in function blobFromProto" when running neural network in OpenCV

How to fix, "error: (-215) pbBlob.raw_data_type() == caffe::FLOAT16 in function blobFromProto" when running neural network in OpenCV

python

opencv

deep-learning

caffe

nvidia-digits