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