caffe模型调用预测示例

灰太狼 2022-04-25 08:46 274阅读 0赞

如下给出调用已经训练好的caffe模型进行测试的示例代码：

包括：模型调用，时间统计，结果写入txt文件。

import os
    import caffe
    import numpy as np
    import time
    
    caffe.set_mode_cpu()
    
    root = '/home/xuqiong/makeall/caffe/'
    deploy = root + 'examples/xq0523pm/shufflenet_deploy.prototxt'
    caffe_model = root + 'examples/xq0523pm/shufflenet_train_iter_50000.caffemodel'
    #mean_file = '/mnt/data2/xuqiong/data/split/mean.binaryprototxt
    
    test = '/mnt/data2/xuqiong/data/split/test/'
    filelist = []
    
    dir = test + 'pristine_images/'
    filenames = os.listdir(dir)
    for fn in filenames:
        fullfilename = os.path.join(dir, fn)
        filelist.append(fullfilename)
    
    count = 0
    timei = 0
    timef = 0
    timem = 0
    timea = 0
    timei_all = 0
    timef_all = 0
    timem_all = 0
    timea_all = 0
    for i in range(0, len(filelist)):
        img = filelist[i]
        #Test(img)
    
        caffe.set_mode_cpu()
        net = caffe.Net(deploy, caffe_model, caffe.TEST)
    
        transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  # shape(1,3,28,28)
        transformer.set_transpose('data', (2, 0, 1))  # (28,28,3) to (3,28,28)
        #transformer.set_mean('data', np.load(mean_file).mean(1).mean(1))  # minus mean
        transformer.set_raw_scale('data', 255)  # [0，255]
        transformer.set_channel_swap('data', (2, 1, 0))  # RGB2BGR
    
        time0 = time.time()
        im = caffe.io.load_image(img)
        net.blobs['data'].data[...] = transformer.preprocess('data', im)
        time1 = time.time()
    
        out = net.forward()
        time2 = time.time()
    
        prob = net.blobs['fcout6'].data[0].flatten()  # last layer（prob）
        order = prob.argsort()[5]  # argsort() small-big
        time3 = time.time()
    
        timei = time1 - time0
        timef = time2 - time1
        timem = time3 - time2
        timea = timei + timef + timem
        timei_all = timei + timei_all
        timef_all = timef + timef_all
        timem_all = timem + timem_all
        timea_all = timea + timea_all
        print("timei: ", timei*1000, "ms")
        print("timef: ", timef*1000, "ms")
        print("timem: ", timem*1000, "ms")
        print("timea: ", timea*1000, "ms")
        print("the class is: ", order)
    
        f = open("/home/xuqiong/makeall/caffe/examples/xq0523pm/label.txt", "a+")
        f.writelines(img + ' ' + str(order) + '\n')
    
        #calcute accuracy
        path = img.split('/')[-2]
        if(path == 'pristine_images'):
            if order == 5:
                count = count + 1
        elif(path[-1] == 1):
            if order == 4:
                count = count + 1
        elif (path[-1] == 2):
            if order == 3:
                count = count + 1
        elif (path[-1] == 3):
            if order == 2:
                count = count + 1
        elif (path[-1] == 4):
            if order == 1:
                count = count + 1
        elif (path[-1] == 5):
            if order == 0:
                count = count + 1
    
    print("shufflenetv1, cpu")
    print("ok count: ", count)
    print("all count: ", len(filelist))
    print("timei average: %.2f", timei_all*1000/len(filelist), "ms")
    print("timef average: %.2f", timef_all*1000/len(filelist), "ms")
    print("timem average: %.2f", timem_all*1000/len(filelist), "ms")
    print("timea average: %.2f", timea_all*1000/len(filelist), "ms")
    
    f.close()