[英]How to fix, “error: (-215) pbBlob.raw_data_type() == caffe::FLOAT16 in function blobFromProto” when running neural network in OpenCV
我目前正在嘗試使用Nvidia DIGITS在自定義數據集上訓練CNN以進行對象檢測,最終我想在Nvidia Jetson TX2上運行該網絡。 我按照推薦的說明從Docker下載了DIGITS映像,並且能夠成功地以合理的精度訓練網絡。 但是,當我嘗試使用OpenCv在python中運行網絡時,出現此錯誤,
“錯誤:(-215)pbBlob.raw_data_type()==函數blobFromProto中的caffe :: FLOAT16”
我在其他一些線程中讀到,這是由於DIGITS以與OpenCv的DNN功能不兼容的形式存儲其網絡。
在訓練我的網絡之前,我嘗試過選擇DIGITS中的選項,該選項應該使網絡與其他軟件兼容,但是似乎根本不會改變網絡,並且在運行python腳本時出現相同的錯誤。 這是我運行的導致錯誤的腳本(它來自本教程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)
這應該輸出在腳本調用中指定的圖像,並在圖像上方繪制神經網絡的輸出。 但是,腳本會因上述錯誤而崩潰。 我見過其他人也有同樣的錯誤,但是到目前為止,還沒有一個人提出與當前版本的DIGITS兼容的解決方案。
我的完整設置如下:
操作系統:Ubuntu 16.04
Nvidia DIGITS Docker映像版本:19.01-caffe
數字版本:6.1.1
Caffe版本:0.17.2
咖啡口味:Nvidia
OpenCV版本:4.0.0
Python版本:3.5
任何幫助深表感謝。
哈里森·麥金太爾,謝謝! 此PR對其進行了修復: https : //github.com/opencv/opencv/pull/13800 。 請注意,存在一個類型為“ ClusterDetections”的圖層。 OpenCV不支持它,但是您可以使用自定義圖層機制來實現它(請參閱教程 )
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