OPENVINO模型优化

OPENVINO开发环境部署

参考1：IT虾米网
参考2：IT虾米网
这一部分是使用virtualbox + ubuntu来实现的。

模型来源

IT虾米网
这个repo的模型本来是为测试NCSDK准备的，但是NCS2不支持，拿来主义，OPENVINO也可以使用。

测试一：Gender net

修改deploy.prototxt:
input_dim: 1

mv deploy.prototxt gender_net.prototxt 
python3 model_optimizer/mo.py --input_model models/GenderNet/gender_net.caffemodel --output_dir . 

生成三个文件，但是我们只需要.xml、.bin文件。

import cv2 
import imutils 
from imutils.video.pivideostream import PiVideoStream 
from imutils.video import FPS 
import time 
 
stat = r'../gender/stat.txt' 
xml = r'../gender/gender_net.xml' 
file = r'../gender/gender_net.bin' 
with open(stat,'r') as f: 
	lines = f.readlines() 
	mean = lines[0].strip().split(" ") 
	std = lines[1].strip().split(" ") 
mean = [eval(x) for x in mean] 
std = [eval(x) for x in std] 
print(mean) 
print(std) 
net = cv2.dnn.readNet(xml, file) 
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD) 
winName = 'gender test' 
cv2.namedWindow(winName, cv2.WINDOW_AUTOSIZE) 
print("[INFO] sampling THREADED frames from `picamera` module...") 
vs = PiVideoStream().start() 
time.sleep(2.0) 
fps = FPS().start() 
# loop over some frames...this time using the threaded stream 
while True: 
	# grab the frame from the threaded video stream and resize it 
	# to have a maximum width of 400 pixels 
	frame = vs.read() 
	img = frame.copy() 
	img[:,:,0] = (img[:,:,0] - mean[0]) * std[0] 
	img[:,:,1] = (img[:,:,1] - mean[1]) * std[1] 
	img[:,:,2] = (img[:,:,2] - mean[2]) * std[2] 
	image = cv2.UMat(imutils.resize(img, width=227)) 
	blob = cv2.dnn.blobFromImage(image, size=(227,227), ddepth=cv2.CV_8U) 
	net.setInput(blob) 
	out = net.forward() 
	if out[0,0] > out[0,1]: 
		cv2.putText(frame,"MAN detected",(10,10),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,0,255),0) 
		#cv2.putText(frame,"MAN detected,[INFO] elasped time: {:.2f}  FPS: {:.2f}".format(fps.elapsed(),fps.fps()),(10,10),cv2.FONT_HERSHEY_COMPLEX,6,(0,0,255),25) 
	else: 
		cv2.putText(frame,"Female detected",(10,10),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,0,255),0) 
	cv2.imshow(winName,frame) 
	 
	key = cv2.waitKey(1) & 0xFF 
	# if the `q` key was pressed, break from the loop 
	if key == ord("q"): 
		break 
  
	# update the FPS counter 
	fps.update() 
  
# stop the timer and display FPS information 
fps.stop() 
print("[INFO] elasped time: {:.2f}".format(fps.elapsed())) 
print("[INFO] approx. FPS: {:.2f}".format(fps.fps())) 
  
# do a bit of cleanup 
cv2.destroyAllWindows() 
vs.stop() 
 

• 测试结果可能与上位机训练的结果不一致，可能的原因：
  ** 模型转换过程丢失精度？
  ** 没有正确运行，比如图片预处理。这个与DEMO一致。
  ** 训练集合可能不是针对黄种人，造成数据缺口。这个是最可能的原因。
• 可能的改进措施：
  ** fine tuning

测试二：AgeNet

同上，结果相似，非常不准确。
Any idea?