如何加快caffe classifer在python [英] How to speed up caffe classifer in python

问题描述

我使用python使用caffe分类器。我从我的相机获得图像，并从训练集预测图像。它工作很好，但问题是速度非常缓慢。我认为只有4帧/秒。你能建议我一些方法来提高我的代码的计算时间吗？
问题可以解释如下。我必须重新加载网络模型 age_net.caffemodel ，其大小约80MB，通过以下代码

I am using python to use caffe classifier. I got image from my camera and peform predict image from training set. It work well but the problem is speed very slow. I thinks just 4 frames/second. Could you suggest to me some way to improve computational time in my code? The problem can be explained as following. I have to reload an network model age_net.caffemodel that its size about 80MB by following code

age_net_pretrained='./age_net.caffemodel'
age_net_model_file='./deploy_age.prototxt'
age_net = caffe.Classifier(age_net_model_file, age_net_pretrained,
           mean=mean,
           channel_swap=(2,1,0),
           raw_scale=255,
           image_dims=(256, 256))

对于每个输入图像（ caffe_input ），我调用predict函数

And for each input image (caffe_input), I call the predict function

prediction = age_net.predict([caffe_input])

我认为由于网络的规模非常大。然后预测函数需要很长时间来预测图像。我认为缓慢的时间是从它。

这是我的完整的参考代码。它改变了我。

I think that due to size of network is very large. Then predict function takes long time to predict image. I think the slow time is from it.
This is my full reference code. It changed by me.

from conv_net import *

import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import os
caffe_root = './caffe' 
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
DATA_PATH = './face/'
cnn_params = './params/gender_5x5_5_5x5_10.param'
face_params = './params/haarcascade_frontalface_alt.xml'
def format_frame(frame):
    img = frame.astype(np.float32)/255.
    img = img[...,::-1]
    return img   

if __name__ == '__main__':    
    files = glob.glob(os.path.join(DATA_PATH, '*.*'))

    # This is the configuration of the full convolutional part of the CNN
    # `d` is a list of dicts, where each dict represents a convolution-maxpooling
    # layer. 
    # Eg c1 - first layer, convolution window size
    # p1 - first layer pooling window size
    # f_in1 - first layer no. of input feature arrays
    # f_out1 - first layer no. of output feature arrays
    d = [{'c1':(5,5),
          'p1':(2,2),
          'f_in1':1, 'f_out1':5},
         {'c2':(5,5),
          'p2':(2,2),
          'f_in2':5, 'f_out2':10}]

    # This is the configuration of the mlp part of the CNN
    # first tuple has the fan_in and fan_out of the input layer
    # of the mlp and so on.
    nnet =  [(800,256),(256,2)]    
    c = ConvNet(d,nnet, (45,45))
    c.load_params(cnn_params)        
    face_cascade = cv2.CascadeClassifier(face_params)
    cap = cv2.VideoCapture(0)
    cv2.namedWindow("Image", cv2.WINDOW_NORMAL)

    plt.rcParams['figure.figsize'] = (10, 10)
    plt.rcParams['image.interpolation'] = 'nearest'
    plt.rcParams['image.cmap'] = 'gray'
    mean_filename='./mean.binaryproto'
    proto_data = open(mean_filename, "rb").read()
    a = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
    mean  = caffe.io.blobproto_to_array(a)[0]
    age_net_pretrained='./age_net.caffemodel'
    age_net_model_file='./deploy_age.prototxt'
    age_net = caffe.Classifier(age_net_model_file, age_net_pretrained,
               mean=mean,
               channel_swap=(2,1,0),
               raw_scale=255,
               image_dims=(256, 256))
    age_list=['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']
    while(True):

        val, image = cap.read()        
        if image is None:
            break
        image = cv2.resize(image, (320,240))
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, 1.3, 5, minSize=(30,30))

        for f in faces:
            x,y,w,h = f
            cv2.rectangle(image, (x,y), (x+w,y+h), (0,255,255))            
            face_image_rgb = image[y:y+h, x:x+w]
            caffe_input = cv2.resize(face_image_rgb, (256, 256)).astype(np.float32)
            prediction = age_net.predict([caffe_input]) 
            print 'predicted age:', age_list[prediction[0].argmax()]       
        cv2.imshow('Image', image)
        ch = 0xFF & cv2.waitKey(1)
        if ch == 27:
            break
        #break

推荐答案

尝试使用 age_net.predict（[caffe_input]） .com / BVLC / caffe / blob / master / python / caffe / classifier.py＃L47rel =nofollow> oversmaple = False ： p>

Try calling age_net.predict([caffe_input]) with oversmaple=False:

prediction = age_net.predict([caffe_input], oversample=False)

predict 的默认行为是创建10个略有不同的输入图像作物，网络分类，通过禁用此选项你应该得到一个x10加速。

The default behavior of predict is to create 10, slightly different, crops of the input image and feed them to the network to classify, by disabling this option you should get a x10 speedup.

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