Caffe 总是 returns 一个标签

Question

我已经在 bin 下使用 caffe 工具训练了一个模型，现在我正在尝试使用 python 脚本进行测试，我读入图像并自己对其进行预处理（就像我对训练数据集所做的那样）和我将预训练的权重加载到网络中，但我几乎总是（99.99% 的时间）收到相同的结果 -0- 对于每个测试图像。我确实考虑过我的模型可能过度拟合，但在训练了几个模型之后，我开始意识到我从预测中获得的标签很可能是原因。我还增加了 dropout 并随机裁剪以克服过度拟合，我有大约 60K 用于训练。数据集也大致平衡。我在训练的评估步骤中获得了 77 到 87 的准确率（取决于我处理数据的方式、我使用的架构等）

请原谅我的超级 hacky 代码，我已经远离 caffe 测试一段时间了，所以我怀疑问题是我如何将输入数据传递到网络，但我无法确定：

import h5py, os
import sys
sys.path.append("/home/X/Desktop/caffe-caffe-0.16/python")
from caffe.io import oversample
from caffe.io import resize_image
import caffe
from random import randint
import numpy as np
import cv2
import matplotlib.pyplot as plt
from collections import Counter as Cnt

meanImg = cv2.imread('/home/caffe/data/Ch/Final_meanImg.png')

model_def = '/home/X/Desktop/caffe-caffe-0.16/models/bvlc_googlenet/deploy.prototxt'
model_weights = '/media/X/DATA/SDet/Google__iter_140000.caffemodel'

# load the model
#caffe.set_mode_gpu()
#caffe.set_device(0)

net = caffe.Net(model_def,      # defines the structure of the model
                model_weights,  # contains the trained weights
                caffe.TEST)     # use test mode (e.g., don't perform dropout)


with open( '/home/caffe/examples/sdet/SDet/test_random.txt', 'r' ) as T, open('/media/X/DATA/SDet/results/testResults.txt','w') as testResultsFile:


    readImgCounter = 0
    runningCorrect = 0
    runningAcc = 0.0

    #testResultsFile.write('filename'+' '+'prediction'+' '+'GT')
    lines = T.readlines()

    for i,l in enumerate(lines):


        sp = l.split(' ')

        video = sp[0].split('_')[0]

        impath =  '/home/caffe/data/Ch/images/'+video+'/'+sp[0] +'.jpg' 

        img = cv2.imread(impath)

        resized_img = resize_image(img, (255,255))

        oversampledImages = oversample([resized_img], (224,224)) #5 crops x 2 mirror flips = return 10 images

        transposed_img = np.zeros( (10, 3, 224, 224), dtype='f4' ) 
        tp =  np.zeros( (1, 3, 224, 224), dtype='f4' ) 

        predictedLabels = []

        for j in range(0,oversampledImages.shape[0]-1):
            transposed_img[j] = oversampledImages[j].transpose((2,0,1))


            tp[0] = transposed_img[j]
            net.blobs['data'].data[0]  = tp

            pred =  net.forward(data=tp)

            predictedLabels.append(pred['prob'].argmax())


        print(predictedLabels)

        prediction,num_most_common = Cnt(predictedLabels).most_common(1)[0]


        print(prediction)

        readImgCounter = readImgCounter + 1

        if (prediction == int(sp[1])):
            runningCorrect = runningCorrect + 1

        runningAcc = runningCorrect / readImgCounter
        print('runningAcc:')
        print(runningAcc)
        print('-----------')
        print('runningCorrect:')
        print(runningCorrect)
        print('-----------')
        print('totalImgRead:')
        print(readImgCounter)
        print('-----------')

        testResultsFile.write(sp[0]+' '+str(prediction)+' '+sp[1])
        testResultsFile.write('\n')

Answer 1

我最终解决了这个问题。我不是 100% 确定什么有效，但很可能是在学习时将偏差更改为 0。

Caffe 总是 returns 一个标签

Caffe always returns one label

testing

input

caffe

conv-neural-network

pycaffe