本篇博客主要讲述如何使用ssd在Caffe下针对自己的数据集进行finetune训练

• NEUDataset介绍
• LMDB数据集制作
• 将数据集分为trainval和test
• 获得trainval.txt和test.txt
• 修改labelmap文件
• 生成LMDB数据集
• 使用caffe-ssd进行网络训练
• 代码修改
• 网络说明及修改
• 实验结果

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同时，本篇博客不会再涉及到路径和名称修改问题，关于caffe-ssd的安装编译、网络训练和测试以及路径和名称修改等问题请参考我这篇博客： 目标检测SSD网络在Caffe下的实现

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数据集来源： NEU surface defect database（
模型：ssd
系统平台：linux-ubuntu

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NEUDataset介绍

该数据集是东北大学宋克臣团队制作而成，是钢材表面缺陷数据集，共有1800张图片，包含六种类型：

• crazing
• inclusion
• patches
• pitted_surface
• rolled-in_scale
• scratches

LMDB数据集制作

将数据集分为trainval和test

我写了个python脚本，将1800张images和labels按照8:2的比例随机分为trainval和test两个数据集

• trainval:1440
• test:360
  代码如下：

"""this code is to split randomly images and xml files to train and test file"""

    import os
    import cv2
    #import string
    import random
    import numpy as np
    import shutil
    
    os.makedirs('/home1/xxx/caffe_ssd/data/NEU/neu/trainval/ANNOTATIONS')
    os.makedirs('/home1/xxx/caffe_ssd/data/NEU/neu/trainval/IMAGES')
    os.makedirs('/home1/xxx/caffe_ssd/data/NEU/neu/test/ANNOTATIONS')
    os.makedirs('/home1/xxx/caffe_ssd/data/NEU/neu/test/IMAGES')
    
    open_dir = "/home1/xxx/caffe_ssd/data/NEU/NEU-DET"                           #the file you want to split
    save_dir = '/home1/xxx/caffe_ssd/data/NEU/neu'            #the file you want to save
    sum_samples = 300                                                 #the sums of each class
    img_resize = 300
    sample_class=['crazing', 'inclusion', 'patches', 'pitted_surface', 'rolled-in_scale', 'scratches']   #samples class
    
    def get_specific_suffix(dirname, suffix='.jpg'):     #get specific suffix images and xml files
        images_path = 'IMAGES'                           #the file name of images
        annotations_path = 'ANNOTATIONS'                 #the file name of annotations
    img_dir = os.path.join(dirname, images_path)
    img_list = os.listdir(img_dir)

    xml_dir = os.path.join(dirname, annotations_path)
    xml_list = os.listdir(xml_dir)

    img_list_suffix = []
    for img_array in img_list:
        if os.path.splitext(img_array)[1] == suffix:
            img_list_suffix.append(img_array)
        else:
            continue

    return img_list_suffix, xml_list  #['crazing_1.jpg', 'crazing_10.jpg']   #return img list and xml list of content

    def get_random_list(sum_samples, scale=0.8):     #get random list to split train and test with scale
    list_random = random.sample(range(1, sum_samples), int(sum_samples * scale))   #get random figures without repetition
    list_sort = sorted(list_random)
    return list_sort

    #get random images and annotations, split them to train and test file

    def get_random_img_anno(img_list_suffix, xml_list, sum_samples, img_anno_path='./data/', save_path='./'):
        images_path = 'IMAGES/'              #the file name of images
        annotations_path = 'ANNOTATIONS/'    #the file name of annotations

    random_list = get_random_list(sum_samples)  #get random list
    #split images to train and test according sample class
    for sam_class in sample_class:
        
        for img_name in img_list_suffix:
            count = 0
            
            for i in random_list:           
                if img_name.find(sam_class) != -1:
                    
                    if img_name.split('.')[0] == sam_class + '_' + str(i):
                        shutil.copy(os.path.join(img_anno_path, images_path, img_name),os.path.join(save_path, 'trainval/', images_path, img_name))
                    if img_name.split('.')[0] != sam_class + '_' + str(i):
                        count = count + 1
                        if count == len(random_list):
                            count = 0
                            shutil.copy(os.path.join(img_anno_path, images_path, img_name),os.path.join(save_path, 'test/', images_path, img_name))

    #split annotations to train and test according sample class
    for sam_class in sample_class:
        #count_val = 0
        for xml_name in xml_list:
            count = 0
            
            for i in random_list:
                if xml_name.find(sam_class) != -1:
                    
                    if xml_name.split('.')[0] == sam_class + '_' + str(i):
                        shutil.copy(os.path.join(img_anno_path, annotations_path, xml_name),
                                    os.path.join(save_path, 'trainval/', annotations_path, xml_name))
    
                    if xml_name.split('.')[0] != sam_class + '_' + str(i):
                        count = count + 1
                        if count == len(random_list):
                            count = 0
                            shutil.copy(os.path.join(img_anno_path, annotations_path, xml_name),
                                        os.path.join(save_path, 'test/', annotations_path, xml_name))
    if __name__=='__main__':
        img_list_suffix, xml_list = get_specific_suffix(open_dir)
        get_random_img_anno(img_list_suffix, xml_list, sum_samples=sum_samples, img_anno_path=open_dir, save_path=save_dir)

执行该程序后，会生成两个文件夹train，test

trainval和test均有ANNOTATIONS和IMAGES两个文件夹。

• ANNOTATIONS：标签
• IMAGES：图片

自己写了个python脚本获取train.txt和test.txt，并将其保存在trainval/MAIN和test/MAIN中。

import os

    trainval_dirname = '/home1/xxx/caffe_ssd/data/NEU/neu/trainval/IMAGES'
    test_dirname = '/home1/xxx/caffe_ssd/data/NEU/neu/test/IMAGES'
    
    os.makedirs('/home1/xxx/caffe_ssd/data/NEU/neu/trainval/MAIN')
    os.makedirs('/home1/xxx/caffe_ssd/data/NEU/neu/test/MAIN')
    
    save_trainval_txt_dirname = '/home1/xxx/caffe_ssd/data/NEU/neu/trainval/MAIN/'
    save_test_txt_dirname = '/home1/xxx/caffe_ssd/data/NEU/neu/test/MAIN/'
    
    trainval_list = os.listdir(trainval_dirname)
    test_list = os.listdir(test_dirname)
    
    trainval_txt = open(os.path.join(save_trainval_txt_dirname, 'trainval.txt'), 'w')
    test_txt = open(os.path.join(save_test_txt_dirname, 'test.txt'), 'w')
    
    for trainval in trainval_list:
        if trainval != '':
            trainval = trainval.split('.')[0]
            trainval_txt.write(trainval)
            trainval_txt.write('\n')
    trainval_txt.close()
    for test in test_list:
        if test != '':
            test = test.split('.')[0]
            test_txt.write(test)
            test_txt.write('\n')
    test_txt.close()

执行该脚本文件，执行完毕后，此时trainval和test下均具有三个文件夹：

以trainval为例

• ANNOTATIONS：标签
  
• IMAGES：图片
  
• MAIN：txt文本文件所在路径，打开后如下图所示
  

获得trainval.txt和test.txt

利用create_list.sh生成具有images和labels信息的trainval.txt和test.txt。
脚本命令：

 #!/bin/bash
    root_dir=/home1/xxx/caffe_ssd/data/NEU/neu/
    sub_dir=MAIN/
    bash_dir=/home1/xxx/caffe_ssd/data/NEU/neu && pwd
    
    for dataset in trainval test 
    do
      dst_file=$bash_dir/$dataset.txt
      echo "dst_file  $dst_file" 
      if [ -f $dst_file ]
      then
        rm -f $dst_file
      fi
        echo "Create list for  $dataset..."   #VOC2012 test
        
        dataset_file=$root_dir$dataset/$sub_dir/$dataset.txt    
      echo "dataset_file  $dataset_file"
      
        img_file=$bash_dir/$dataset/$dataset"_img.txt"
      echo "img_file  $img_file"   
        
        cp $dataset_file $img_file
      echo "dataset_file_change  $dataset_file"
      echo "img_file_change  $img_file" 

        sed -i "s/^/$dataset\/IMAGES\//g" $img_file
      echo "img_file $img_file"
        sed -i "s/$/.jpg/g" $img_file
      
        label_file=$bash_dir/$name$dataset/$dataset"_label.txt"
        cp $dataset_file $label_file
    
        sed -i "s/^/$name$dataset\/ANNOTATIONS\//g" $label_file
        sed -i "s/$/.xml/g" $label_file
        paste -d' ' $img_file $label_file >> $dst_file
    
        #rm -f $label_file
        #rm -f $img_file

      # Generate image name and size infomation.
      if [ $dataset == "test" ]
      then
       
        /home1/xxx/caffe_ssd/build/tools/get_image_size $root_dir $dst_file $bash_dir/$dataset"_name_size.txt"
    echo "$root_dir $dst_file $bash_dir/$dataset _name_size.txt"
      fi
    
      # Shuffle trainval file.
      if [ $dataset == "trainval" ]
      then
        rand_file=$dst_file.random
        cat $dst_file | perl -MList::Util=shuffle -e 'print shuffle(<STDIN>);' > $rand_file
        mv $rand_file $dst_file
      fi    
    done

执行该脚本命令，生成三个文件：

• trainval.txt
• test.txt
  
• test_name_size.txt
  

修改labelmap文件

除此之外我们需要修改labelmap_voc.prototxt为labelmao_neu.prototxt，内容如下：

item {
  name: "none_of_the_above"
  label: 0
  display_name: "background"
}
item {
  name: "crazing"
  label: 1
  display_name: "crazing"
}
item {
  name: "inclusion"
  label: 2
  display_name: "inclusion"
}
item {
  name: "patches"
  label: 3
  display_name: "patches"
}
item {
  name: "pitted_surface"
  label: 4
  display_name: "pitted_surface"
}
item {
  name: "rolled-in_scale"
  label: 5
  display_name: "rolled-in_scale"
}
item {
  name: "scratches"
  label: 6
  display_name: "scratches"
}

生成LMDB数据集

 #cur_dir=$(cd $( dirname ${BASH_SOURCE[0]} ) && pwd )
    #root_dir=$cur_dir/../..
    root_dir="/home1/xxx/caffe_ssd/data/NEU/neu"
    
    cd $root_dir
    echo $root_dir
    
    redo=1
    data_root_dir="/home1/xxx/caffe_ssd/data/"
    dataset_name="NEU"
    mapfile="$root_dir/labelmap_neu.prototxt"
    anno_type="detection"
    db="lmdb"
    min_dim=0
    max_dim=0
    width=0
    height=0
    
    extra_cmd="--encode-type=jpg --encoded"
    if [ $redo ]
    then
      extra_cmd="$extra_cmd --redo"
    fi
    for subset in test trainval
    do
      python2 /home1/jsk/caffe_ssd/scripts/create_annoset.py --anno-type=$anno_type --label-map-file=$mapfile --min-dim=$min_dim --max-dim=$max_dim --resize-width=$width --resize-height=$height --check-label $extra_cmd $data_root_dir$dataset_name/'neu' $root_dir/$subset.txt $root_dir/$db/$subset"_"$db examples/
    done

执行脚本命令：

  sudo sh create_data.sh

会生成两个文件夹如下：

均值的求解我还是使用compute_image_mean工具，可参考我的这篇博客： Caffe制作LMDB数据并进行分类网络训练和测试

求解出均值为：【128.329，128.329，128.329】

使用caffe-ssd进行网络训练

代码修改

主要是对ssd_pascal.py进行修改：

路径和名称修改不再赘述，请参考我的这篇博客： 目标检测SSD网络在Caffe下的实现
其他修改如下：
266行、359行那里：

    num_classes = 7               	 # 21改为7
    num_test_image = 360      		 # 4952改为360

网络说明及修改

执行python2 ssd_pascal_neu.py，出现如下问题：

因为我在finetune的时候，用到的模型是在VOC下训练迭代120000次后的caffemodel，所以这里出现了一个参数不匹配的问题，source参数，也就是VGG_VOC0712_SSD_300x300_iter_120000.caffemodel中的类别是21，conv4_3_norm_mbox_conf层的维度为21x4=84；

而NEUDataset中的类别是7，conv4_3_norm_mbox_conf层的维度为7x4=28；

所以需要对这些涉及到类别数量的层进行命名修改，表示不对这些层进行权重复制。

而SSD中涉及到类别数量的维度有六层：

• conv4_3_norm_mbox_conf
• fc7_mbox_conf
• conv6_2_mbox_conf
• conv7_2_mbox_conf
• conv8_2_mbox_conf
• conv9_2_mbox_conf

因此需要对这些层进行重新命名，同时以这些层作为输入的层的bottom也要进行相应的修改。
修改后的网络结构，有时间我会放到Github上，也会在这里同步更新。

修改之后，这个时候就没必要再通过 python2 ssd_pascal_neu.py进行网络训练了。

直接在SSD_300x300文件夹下创建finetune_ssd.sh文件，文件内容为：

#!/usr/bin/env sh  
    TOOLS=/home1/xxx/caffe_ssd/build/tools  
    GLOG_logtostderr=0 GLOG_log_dir=./log1/  $TOOLS/caffe train --solver=solver.prototxt --weights=/home1/xxx/caffe_ssd/models/VGGNet/VOC0712_1/SSD_300x300/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel -gpu 1  #加入 -gpu 选项 	

然后：

  sudo sh finetune_ssd.sh

即可
运行成功如下图所示：

实验结果

最后的mAP值是0.655686，loss是0.73928，比ssd在VOC数据集下的mAP值下降了14%。
不是特别清楚是什么原因，猜想是因为训练集太少的缘故，VGG模型参数又多造成了模型欠拟合。

对测试集360张图片进行实际测试，修改ssd_detect.py代码，以绘制类别和矩形框信息并且可以批量存储。

• ssd_detect.py:

	
    import os
    import sys
    import argparse
    import numpy as np
    from PIL import Image, ImageDraw
    # Make sure that caffe is on the python path:
    caffe_root = './'
    os.chdir(caffe_root)
    sys.path.insert(0, os.path.join(caffe_root, 'python'))
    import caffe
    
    from google.protobuf import text_format
    from caffe.proto import caffe_pb2
    
    
    def get_labelname(labelmap, labels):
        num_labels = len(labelmap.item)
        labelnames = []
        if type(labels) is not list:
            labels = [labels]
        for label in labels:
            found = False
            for i in xrange(0, num_labels):
                if label == labelmap.item[i].label:
                    found = True
                    labelnames.append(labelmap.item[i].display_name)
                    break
            assert found == True
        return labelnames
    
    class CaffeDetection:
        def __init__(self, gpu_id, model_def, model_weights, image_resize, labelmap_file):
            caffe.set_device(gpu_id)
            caffe.set_mode_gpu()

        self.image_resize = image_resize
        # Load the net in the test phase for inference, and configure input preprocessing.
        self.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)
         # input preprocessing: 'data' is the name of the input blob == net.inputs[0]
        self.transformer = caffe.io.Transformer({'data': self.net.blobs['data'].data.shape})
        self.transformer.set_transpose('data', (2, 0, 1))
        self.transformer.set_mean('data', np.array([104, 117, 123])) # mean pixel
        # the reference model operates on images in [0,255] range instead of [0,1]
        self.transformer.set_raw_scale('data', 255)
        # the reference model has channels in BGR order instead of RGB
        self.transformer.set_channel_swap('data', (2, 1, 0))

        # load PASCAL VOC labels
        file = open(labelmap_file, 'r')
        self.labelmap = caffe_pb2.LabelMap()
        text_format.Merge(str(file.read()), self.labelmap)

    def detect(self, image_file, conf_thresh=0.5, topn=5):
        '''
        SSD detection
        '''
        # set net to batch size of 1
        # image_resize = 300
        self.net.blobs['data'].reshape(1, 3, self.image_resize, self.image_resize)
       
        
        image = caffe.io.load_image(image_file)

        #Run the net and examine the top_k results
        transformed_image = self.transformer.preprocess('data', image)
        self.net.blobs['data'].data[...] = transformed_image

        # Forward pass.
        detections = self.net.forward()['detection_out']

        # Parse the outputs.
        det_label = detections[0,0,:,1]
        det_conf = detections[0,0,:,2]
        det_xmin = detections[0,0,:,3]
        det_ymin = detections[0,0,:,4]
        det_xmax = detections[0,0,:,5]
        det_ymax = detections[0,0,:,6]

        # Get detections with confidence higher than 0.6.
        top_indices = [i for i, conf in enumerate(det_conf) if conf >= conf_thresh]

        top_conf = det_conf[top_indices]
        top_label_indices = det_label[top_indices].tolist()
        top_labels = get_labelname(self.labelmap, top_label_indices)
        top_xmin = det_xmin[top_indices]
        top_ymin = det_ymin[top_indices]
        top_xmax = det_xmax[top_indices]
        top_ymax = det_ymax[top_indices]

        result = []
        for i in xrange(min(topn, top_conf.shape[0])):
            xmin = top_xmin[i] # xmin = int(round(top_xmin[i] * image.shape[1]))
            ymin = top_ymin[i] # ymin = int(round(top_ymin[i] * image.shape[0]))
            xmax = top_xmax[i] # xmax = int(round(top_xmax[i] * image.shape[1]))
            ymax = top_ymax[i] # ymax = int(round(top_ymax[i] * image.shape[0]))
            score = top_conf[i]
            label = int(top_label_indices[i])
            label_name = top_labels[i]
            result.append([xmin, ymin, xmax, ymax, label, score, label_name])
        return result

    def main(args):
        '''main '''
        detection = CaffeDetection(args.gpu_id,
                                   args.model_def, args.model_weights,
                                   args.image_resize, args.labelmap_file)
        test_image_list = os.listdir(args.image_file)
        print(test_image_list)
        count=0
        for test_image in test_image_list:
            print("test_image:", test_image)
            print('os',os.path.join(args.image_file, test_image))
            result = detection.detect(os.path.join(args.image_file, test_image))
            print("***result***",result)
        
        if len(result) == 0:
            count=count+1

        img = Image.open(os.path.join(args.image_file, test_image))
        draw = ImageDraw.Draw(img)
        width, height = img.size
        print width, height
        for item in result:
            xmin = int(round(item[0] * width))
            ymin = int(round(item[1] * height))
            xmax = int(round(item[2] * width))
            ymax = int(round(item[3] * height))
            draw.rectangle([xmin, ymin, xmax, ymax], outline=(255, 0, 0))
            draw.text([xmin, ymin], item[-1] + str(item[-2]), (0, 0, 255))
            print item
            print [xmin, ymin, xmax, ymax]
            print [xmin, ymin], item[-1]
        img.save(os.path.join('/home1/xxx/caffe_ssd/data/VOC0712/neulmdb/detect_image_3',test_image))
    print('count:',count)
    print("accuracy:", (len(test_image_list) - count)/len(test_image_list))


    def parse_args():
        '''parse args'''
        parser = argparse.ArgumentParser()
        parser.add_argument('--gpu_id', type=int, default=1, help='gpu id')
        parser.add_argument('--labelmap_file',
                            default='/home1/xxx/caffe_ssd/models/VGGNet/neu/labelmap_neu.prototxt')
        parser.add_argument('--model_def',
                            default='/home1/xxx/caffe_ssd/models/VGGNet/neu/SSD_300x300/deploy.prototxt')
        parser.add_argument('--image_resize', default=300, type=int)
        parser.add_argument('--model_weights',
                            default='/home1/xxx/caffe_ssd/models/VGGNet/neu/SSD_300x300/snapshot/snapshot_iter_119000.caffemodel')
        parser.add_argument('--image_file', default='/home1/xxx/caffe_ssd/data/VOC0712/neulmdb/test_image/')
        return parser.parse_args()
    
    if __name__ == '__main__':
        main(parse_args())

一些实际测试图片展示：

• crazing

• inclusion

• patches

• pitted_surface

• rolled-in_scale

• scatches

---

至此我们已经完成了ssd在NEUDatast上的finetune。

---

希望能帮到大家。谢谢。
2019.7.11

标签：name,img,finetune,caffe,file,path,test,ssd
来源： https://blog.csdn.net/jsk_learner/article/details/95476570