SSD框架训练自己的数据集
SSD demo中详细介绍了如何在VOC数据集上使用SSD进行物体检测的训练和验证。
本文介绍如何使用SSD实现对自己数据集的训练和验证过程，内容包括： 1 数据集的标注
2 数据集的转换
3 使用SSD如何训练
4 使用SSD如何测试
1 数据集的标注
数据的标注使用BBox-Label-Tool工具，该工具使用python实现，使用简单方便。修改后的工具支持多label的标签标注。
该工具生成的标签格式是:
object_number
className x1min y1min x1max y1max
classname x2min y2min x2max y2max
...
1.1 labelTool工具的使用说明
BBox-Label-Tool工具实现较简单，原始的git版本使用起来有一些小问题，进行了简单的修改，修改后的版本
#------------------------------------------------------------------------------- # Name: Object bounding box label tool # Purpose: Label object bboxes for ImageNet Detection data # Author: Qiushi # Created: 06/06/2014# #------------------------------------------------------------------------------- from __future__ import division from Tkinter import * import tkMessageBox from PIL import Image, ImageTk import os import glob import random# colors for the bboxes COLORS = ['red', 'blue', 'yellow', 'pink', 'cyan', 'green', 'black'] # image sizes for the examples SIZE = 256, 256classLabels=['mat', 'door', 'sofa', 'chair', 'table', 'bed', 'ashcan', 'shoe']class LabelTool():def __init__(self, master):# set up the main frameself.parent = masterself.parent.title("LabelTool")self.frame = Frame(self.parent)self.frame.pack(fill=BOTH, expand=1)self.parent.resizable(width = False, height = False)# initialize global stateself.imageDir = ''self.imageList= []self.egDir = ''self.egList = []self.outDir = ''self.cur = 0self.total = 0self.category = 0self.imagename = ''self.labelfilename = ''self.tkimg = None# initialize mouse stateself.STATE = {}self.STATE['click'] = 0self.STATE['x'], self.STATE['y'] = 0, 0# reference to bboxself.bboxIdList = []self.bboxId = Noneself.bboxList = []self.hl = Noneself.vl = Noneself.currentClass = ''# ----------------- GUI stuff ---------------------# dir entry & loadself.label = Label(self.frame, text = "Image Dir:")self.label.grid(row = 0, column = 0, sticky = E)self.entry = Entry(self.frame)self.entry.grid(row = 0, column = 1, sticky = W+E)self.ldBtn = Button(self.frame, text = "Load", command = self.loadDir)self.ldBtn.grid(row = 0, column = 2, sticky = W+E)# main panel for labelingself.mainPanel = Canvas(self.frame, cursor='tcross')self.mainPanel.bind("<Button-1>", self.mouseClick)self.mainPanel.bind("<Motion>", self.mouseMove)self.parent.bind("<Escape>", self.cancelBBox) # press <Espace> to cancel current bboxself.parent.bind("s", self.cancelBBox)self.parent.bind("a", self.prevImage) # press 'a' to go backforwardself.parent.bind("d", self.nextImage) # press 'd' to go forwardself.mainPanel.grid(row = 1, column = 1, rowspan = 4, sticky = W+N)# showing bbox info & delete bboxself.lb1 = Label(self.frame, text = 'Bounding boxes:')self.lb1.grid(row = 1, column = 2, sticky = W+N)self.listbox = Listbox(self.frame, width = 22, height = 12)self.listbox.grid(row = 2, column = 2, sticky = N)self.btnDel = Button(self.frame, text = 'Delete', command = self.delBBox)self.btnDel.grid(row = 3, column = 2, sticky = W+E+N)self.btnClear = Button(self.frame, text = 'ClearAll', command = self.clearBBox)self.btnClear.grid(row = 4, column = 2, sticky = W+E+N)#select class typeself.classPanel = Frame(self.frame)self.classPanel.grid(row = 5, column = 1, columnspan = 10, sticky = W+E)label = Label(self.classPanel, text = 'class:')label.grid(row = 5, column = 1, sticky = W+N)self.classbox = Listbox(self.classPanel, width = 4, height = 2)self.classbox.grid(row = 5,column = 2)for each in range(len(classLabels)):function = 'select' + classLabels[each]print classLabels[each]btnMat = Button(self.classPanel, text = classLabels[each], command = getattr(self, function))btnMat.grid(row = 5, column = each + 3)# control panel for image navigationself.ctrPanel = Frame(self.frame)self.ctrPanel.grid(row = 6, column = 1, columnspan = 2, sticky = W+E)self.prevBtn = Button(self.ctrPanel, text='<< Prev', width = 10, command = self.prevImage)self.prevBtn.pack(side = LEFT, padx = 5, pady = 3)self.nextBtn = Button(self.ctrPanel, text='Next >>', width = 10, command = self.nextImage)self.nextBtn.pack(side = LEFT, padx = 5, pady = 3)self.progLabel = Label(self.ctrPanel, text = "Progress: / ")self.progLabel.pack(side = LEFT, padx = 5)self.tmpLabel = Label(self.ctrPanel, text = "Go to Image No.")self.tmpLabel.pack(side = LEFT, padx = 5)self.idxEntry = Entry(self.ctrPanel, width = 5)self.idxEntry.pack(side = LEFT)self.goBtn = Button(self.ctrPanel, text = 'Go', command = self.gotoImage)self.goBtn.pack(side = LEFT)# example pannel for illustrationself.egPanel = Frame(self.frame, border = 10)self.egPanel.grid(row = 1, column = 0, rowspan = 5, sticky = N)self.tmpLabel2 = Label(self.egPanel, text = "Examples:")self.tmpLabel2.pack(side = TOP, pady = 5)self.egLabels = []for i in range(3):self.egLabels.append(Label(self.egPanel))self.egLabels[-1].pack(side = TOP)# display mouse positionself.disp = Label(self.ctrPanel, text='')self.disp.pack(side = RIGHT)self.frame.columnconfigure(1, weight = 1)self.frame.rowconfigure(10, weight = 1)# for debugging ## self.setImage() ## self.loadDir()def loadDir(self, dbg = False):if not dbg:s = self.entry.get()self.parent.focus()self.category = int(s)else:s = r'D:\workspace\python\labelGUI' ## if not os.path.isdir(s): ## tkMessageBox.showerror("Error!", message = "The specified dir doesn't exist!") ## return# get image listself.imageDir = os.path.join(r'./Images', '%d' %(self.category))self.imageList = glob.glob(os.path.join(self.imageDir, '*.jpg'))if len(self.imageList) == 0:print 'No .JPEG images found in the specified dir!'return # set up output dirself.outDir = os.path.join(r'./Labels', '%d' %(self.category))if not os.path.exists(self.outDir):os.mkdir(self.outDir)labeledPicList = glob.glob(os.path.join(self.outDir, '*.txt'))for label in labeledPicList:data = open(label, 'r')if '0\n' == data.read():data.close()continuedata.close()picture = label.replace('Labels', 'Images').replace('.txt', '.jpg')if picture in self.imageList:self.imageList.remove(picture)# default to the 1st image in the collectionself.cur = 1self.total = len(self.imageList)self.loadImage()print '%d images loaded from %s' %(self.total, s)def loadImage(self):# load imageimagepath = self.imageList[self.cur - 1]self.img = Image.open(imagepath)self.imgSize = self.img.sizeself.tkimg = ImageTk.PhotoImage(self.img)self.mainPanel.config(width = max(self.tkimg.width(), 400), height = max(self.tkimg.height(), 400))self.mainPanel.create_image(0, 0, image = self.tkimg, anchor=NW)self.progLabel.config(text = "%04d/%04d" %(self.cur, self.total))# load labels self.clearBBox()self.imagename = os.path.split(imagepath)[-1].split('.')[0]labelname = self.imagename + '.txt'self.labelfilename = os.path.join(self.outDir, labelname)bbox_cnt = 0if os.path.exists(self.labelfilename):with open(self.labelfilename) as f:for (i, line) in enumerate(f):if i == 0:bbox_cnt = int(line.strip())continuetmp = [int(t.strip()) for t in line.split()] ## print tmp self.bboxList.append(tuple(tmp))tmpId = self.mainPanel.create_rectangle(tmp[0], tmp[1], \tmp[2], tmp[3], \width = 2, \outline = COLORS[(len(self.bboxList)-1) % len(COLORS)])self.bboxIdList.append(tmpId)self.listbox.insert(END, '(%d, %d) -> (%d, %d)' %(tmp[0], tmp[1], tmp[2], tmp[3]))self.listbox.itemconfig(len(self.bboxIdList) - 1, fg = COLORS[(len(self.bboxIdList) - 1) % len(COLORS)])def saveImage(self):with open(self.labelfilename, 'w') as f:f.write('%d\n' %len(self.bboxList))for bbox in self.bboxList:f.write(' '.join(map(str, bbox)) + '\n')print 'Image No. %d saved' %(self.cur)def mouseClick(self, event):if self.STATE['click'] == 0:self.STATE['x'], self.STATE['y'] = event.x, event.y#self.STATE['x'], self.STATE['y'] = self.imgSize[0], self.imgSize[1]else:x1, x2 = min(self.STATE['x'], event.x), max(self.STATE['x'], event.x)y1, y2 = min(self.STATE['y'], event.y), max(self.STATE['y'], event.y)if x2 > self.imgSize[0]:x2 = self.imgSize[0]if y2 > self.imgSize[1]:y2 = self.imgSize[1] self.bboxList.append((self.currentClass, x1, y1, x2, y2))self.bboxIdList.append(self.bboxId)self.bboxId = Noneself.listbox.insert(END, '(%d, %d) -> (%d, %d)' %(x1, y1, x2, y2))self.listbox.itemconfig(len(self.bboxIdList) - 1, fg = COLORS[(len(self.bboxIdList) - 1) % len(COLORS)])self.STATE['click'] = 1 - self.STATE['click']def mouseMove(self, event):self.disp.config(text = 'x: %d, y: %d' %(event.x, event.y))if self.tkimg:if self.hl:self.mainPanel.delete(self.hl)self.hl = self.mainPanel.create_line(0, event.y, self.tkimg.width(), event.y, width = 2)if self.vl:self.mainPanel.delete(self.vl)self.vl = self.mainPanel.create_line(event.x, 0, event.x, self.tkimg.height(), width = 2)if 1 == self.STATE['click']:if self.bboxId:self.mainPanel.delete(self.bboxId)self.bboxId = self.mainPanel.create_rectangle(self.STATE['x'], self.STATE['y'], \event.x, event.y, \width = 2, \outline = COLORS[len(self.bboxList) % len(COLORS)])def cancelBBox(self, event):if 1 == self.STATE['click']:if self.bboxId:self.mainPanel.delete(self.bboxId)self.bboxId = Noneself.STATE['click'] = 0def delBBox(self):sel = self.listbox.curselection()if len(sel) != 1 :returnidx = int(sel[0])self.mainPanel.delete(self.bboxIdList[idx])self.bboxIdList.pop(idx)self.bboxList.pop(idx)self.listbox.delete(idx)def clearBBox(self):for idx in range(len(self.bboxIdList)):self.mainPanel.delete(self.bboxIdList[idx])self.listbox.delete(0, len(self.bboxList))self.bboxIdList = []self.bboxList = []def selectmat(self):self.currentClass = 'mat'self.classbox.delete(0,END)self.classbox.insert(0, 'mat')self.classbox.itemconfig(0,fg = COLORS[0])def selectdoor(self):self.currentClass = 'door' self.classbox.delete(0,END) self.classbox.insert(0, 'door')self.classbox.itemconfig(0,fg = COLORS[0])def selectsofa(self):self.currentClass = 'sofa' self.classbox.delete(0,END) self.classbox.insert(0, 'sofa')self.classbox.itemconfig(0,fg = COLORS[0])def selectchair(self):self.currentClass = 'chair' self.classbox.delete(0,END) self.classbox.insert(0, 'chair')self.classbox.itemconfig(0,fg = COLORS[0])def selecttable(self):self.currentClass = 'table' self.classbox.delete(0,END) self.classbox.insert(0, 'table')self.classbox.itemconfig(0,fg = COLORS[0])def selectbed(self):self.currentClass = 'bed'self.classbox.delete(0,END) self.classbox.insert(0, 'bed')self.classbox.itemconfig(0,fg = COLORS[0])def selectashcan(self):self.currentClass = 'ashcan' self.classbox.delete(0,END) self.classbox.insert(0, 'ashcan')self.classbox.itemconfig(0,fg = COLORS[0])def selectshoe(self):self.currentClass = 'shoe' self.classbox.delete(0,END) self.classbox.insert(0, 'shoe')self.classbox.itemconfig(0,fg = COLORS[0]) def prevImage(self, event = None):self.saveImage()if self.cur > 1:self.cur -= 1self.loadImage()def nextImage(self, event = None):self.saveImage()if self.cur < self.total:self.cur += 1self.loadImage()def gotoImage(self):idx = int(self.idxEntry.get())if 1 <= idx and idx <= self.total:self.saveImage()self.cur = idxself.loadImage()## def setImage(self, imagepath = r'test2.png'): ## self.img = Image.open(imagepath) ## self.tkimg = ImageTk.PhotoImage(self.img) ## self.mainPanel.config(width = self.tkimg.width()) ## self.mainPanel.config(height = self.tkimg.height()) ## self.mainPanel.create_image(0, 0, image = self.tkimg, anchor=NW)if __name__ == '__main__':root = Tk()tool = LabelTool(root)root.mainloop()
使用方法：
(1) 在BBox-Label-Tool/Images目录下创建保存图片的目录， 目录以数字命名(BBox-Label-Tool/Images/1), 然后将待标注的图片copy到1这个目录下;
(2) 在BBox-Label-Tool目录下执行命令 python main.py
(3) 在工具界面上, Image Dir 框中输入需要标记的目录名(比如 1), 然后点击load按钮, 工具自动将Images/1目录下的图片加载进来;
需要说明一下, 如果目录中的图片已经标注过,点击load时不会被重新加载进来.
(4) 该工具支持多类别标注, 画bounding boxs框标定之前,需要先选定类别,然后再画框.
(5) 一张图片标注完后, 点击Next>>按钮, 标注下一张图片, 图片label成功后,会在BBox-Label-Tool/Labels对应的目录下生成与图片文件名对应的label文件.
2 数据集的转换
caffe训练使用LMDB格式的数据，ssd框架中提供了voc数据格式转换成LMDB格式的脚本。
所以实践中先将BBox-Label-Tool标注的数据转换成voc数据格式，然后再转换成LMDB格式。
2.1 voc数据格式
(1)Annotations中保存的是xml格式的label信息
<?xml version="1.0" ?> <annotation><folder>VOC2007</folder><filename>1.jpg</filename><source><database>My Database</database><annotation>VOC2007</annotation><image>flickr</image><flickrid>NULL</flickrid></source><owner><flickrid>NULL</flickrid><name>idaneel</name></owner><size><width>320</width><height>240</height><depth>3</depth></size><segmented>0</segmented><object><name>door</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>109</xmin><ymin>3</ymin><xmax>199</xmax><ymax>204</ymax></bndbox></object> </annotation>
(2)ImageSet目录下的Main目录里存放的是用于表示训练的图片集和测试的图片集
(3)JPEGImages目录下存放所有图片集
(4)label目录下保存的是BBox-Label-Tool工具标注好的bounding box坐标文件，
该目录下的文件就是待转换的label标签文件。
2.2 Label转换成VOC数据格式 BBox-Label-Tool工具标注好的bounding box坐标文件转换成VOC数据格式的形式.
具体的转换过程包括了两个步骤：
（1）将BBox-Label-Tool下的txt格式保存的bounding box信息转换成VOC数据格式下以xml方式表示；
（2）生成用于训练的数据集和用于测试的数据集。
用python实现了上述两个步骤的换转。
createXml.py 完成txt到xml的转换； 执行脚本./createXml.py
#!/usr/bin/env pythonimport os import sys import cv2 from itertools import islice from xml.dom.minidom import Documentlabels='label' imgpath='JPEGImages/' xmlpath_new='Annotations/' foldername='VOC2007'def insertObject(doc, datas):obj = doc.createElement('object')name = doc.createElement('name')name.appendChild(doc.createTextNode(datas[0]))obj.appendChild(name)pose = doc.createElement('pose')pose.appendChild(doc.createTextNode('Unspecified'))obj.appendChild(pose)truncated = doc.createElement('truncated')truncated.appendChild(doc.createTextNode(str(0)))obj.appendChild(truncated)difficult = doc.createElement('difficult')difficult.appendChild(doc.createTextNode(str(0)))obj.appendChild(difficult)bndbox = doc.createElement('bndbox')xmin = doc.createElement('xmin')xmin.appendChild(doc.createTextNode(str(datas[1])))bndbox.appendChild(xmin)ymin = doc.createElement('ymin') ymin.appendChild(doc.createTextNode(str(datas[2])))bndbox.appendChild(ymin) xmax = doc.createElement('xmax') xmax.appendChild(doc.createTextNode(str(datas[3])))bndbox.appendChild(xmax) ymax = doc.createElement('ymax') if '\r' == str(datas[4])[-1] or '\n' == str(datas[4])[-1]:data = str(datas[4])[0:-1]else:data = str(datas[4])ymax.appendChild(doc.createTextNode(data))bndbox.appendChild(ymax)obj.appendChild(bndbox) return objdef create():for walk in os.walk(labels):for each in walk[2]:fidin=open(walk[0] + '/'+ each,'r')objIndex = 0for data in islice(fidin, 1, None): objIndex += 1data=data.strip('\n')datas = data.split(' ')if 5 != len(datas):print 'bounding box information error'continuepictureName = each.replace('.txt', '.jpg')imageFile = imgpath + pictureNameimg = cv2.imread(imageFile)imgSize = img.shapeif 1 == objIndex:xmlName = each.replace('.txt', '.xml')f = open(xmlpath_new + xmlName, "w")doc = Document()annotation = doc.createElement('annotation')doc.appendChild(annotation)folder = doc.createElement('folder')folder.appendChild(doc.createTextNode(foldername))annotation.appendChild(folder)filename = doc.createElement('filename')filename.appendChild(doc.createTextNode(pictureName))annotation.appendChild(filename)source = doc.createElement('source') database = doc.createElement('database')database.appendChild(doc.createTextNode('My Database'))source.appendChild(database)source_annotation = doc.createElement('annotation')source_annotation.appendChild(doc.createTextNode(foldername))source.appendChild(source_annotation)image = doc.createElement('image')image.appendChild(doc.createTextNode('flickr'))source.appendChild(image)flickrid = doc.createElement('flickrid')flickrid.appendChild(doc.createTextNode('NULL'))source.appendChild(flickrid)annotation.appendChild(source)owner = doc.createElement('owner')flickrid = doc.createElement('flickrid')flickrid.appendChild(doc.createTextNode('NULL'))owner.appendChild(flickrid)name = doc.createElement('name')name.appendChild(doc.createTextNode('idaneel'))owner.appendChild(name)annotation.appendChild(owner)size = doc.createElement('size')width = doc.createElement('width')width.appendChild(doc.createTextNode(str(imgSize[1])))size.appendChild(width)height = doc.createElement('height')height.appendChild(doc.createTextNode(str(imgSize[0])))size.appendChild(height)depth = doc.createElement('depth')depth.appendChild(doc.createTextNode(str(imgSize[2])))size.appendChild(depth)annotation.appendChild(size)segmented = doc.createElement('segmented')segmented.appendChild(doc.createTextNode(str(0)))annotation.appendChild(segmented) annotation.appendChild(insertObject(doc, datas))else:annotation.appendChild(insertObject(doc, datas))try:f.write(doc.toprettyxml(indent = ' '))f.close()fidin.close()except:passif __name__ == '__main__':create()
createTest.py 生成训练集和测试集标识文件； 执行脚本
./createTest.py %startID% %endID% %testNumber%
#!/usr/bin/env pythonimport os import sys import randomtry:start = int(sys.argv[1])end = int(sys.argv[2])test = int(sys.argv[3])allNum = end-start+1 except:print 'Please input picture range'print './createTest.py 1 1500 500'os._exit(0)b_list = range(start,end) blist_webId = random.sample(b_list, test) blist_webId = sorted(blist_webId) allFile = []testFile = open('ImageSets/Main/test.txt', 'w') trainFile = open('ImageSets/Main/trainval.txt', 'w')for i in range(allNum):allFile.append(i+1)for test in blist_webId:allFile.remove(test)testFile.write(str(test) + '\n')for train in allFile:trainFile.write(str(train) + '\n') testFile.close() trainFile.close()
说明： 由于BBox-Label-Tool实现相对简单，该工具每次只能对一个类别进行打标签，所以转换脚本
每一次也是对一个类别进行数据的转换，这个问题后续需要优化改进。
优化后的BBox-Label-Tool工具，支持多类别标定，生成的label文件中增加了类别名称信息。
使用时修改classLabels，改写成自己的类别, 修改后的工具代码参见1.1中的main.py
2.3 VOC数据转换成LMDB数据
SSD提供了VOC数据到LMDB数据的转换脚本 data/VOC0712/create_list.sh 和 ./data/VOC0712/create_data.sh，这两个脚本是完全针对VOC0712目录下的数据进行的转换。
实现中为了不破坏VOC0712目录下的数据内容，针对我们自己的数据集，修改了上面这两个脚本，
将脚本中涉及到VOC0712的信息替换成我们自己的目录信息。
在处理我们的数据集时，将VOC0712替换成indoor。
具体的步骤如下：
(1) 在 $HOME/data/VOCdevkit目录下创建indoor目录，该目录中存放自己转换完成的VOC数据集；
(2) $CAFFE_ROOT/examples目录下创建indoor目录；
(3) $CAFFE_ROOT/data目录下创建indoor目录，同时将data/VOC0712下的create_list.sh,create_data.sh,labelmap_voc.prototxt
这三个文件copy到indoor目录下，分别重命名为create_list_indoor.sh,create_data_indoor.sh, labelmap_indoor.prototxt
(4)对上面新生成的两个create文件进行修改，主要修改是将VOC0712相关的信息替换成indoor
修改后的这两个文件分别为： #!/bin/bashroot_dir=$HOME/data/VOCdevkit/ sub_dir=ImageSets/Main bash_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"for dataset in trainval test dodst_file=$bash_dir/$dataset.txtif [ -f $dst_file ]thenrm -f $dst_filefifor name in indoordoif [[ $dataset == "test" && $name == "VOC2012" ]]thencontinuefiecho "Create list for $name $dataset..."dataset_file=$root_dir/$name/$sub_dir/$dataset.txtimg_file=$bash_dir/$dataset"_img.txt"cp $dataset_file $img_filesed -i "s/^/$name\/JPEGImages\//g" $img_filesed -i "s/$/.jpg/g" $img_filelabel_file=$bash_dir/$dataset"_label.txt"cp $dataset_file $label_filesed -i "s/^/$name\/Annotations\//g" $label_filesed -i "s/$/.xml/g" $label_filepaste -d' ' $img_file $label_file >> $dst_filerm -f $label_filerm -f $img_filedone# Generate image name and size infomation.if [ $dataset == "test" ]then$bash_dir/../../build/tools/get_image_size $root_dir $dst_file $bash_dir/$dataset"_name_size.txt"fi# Shuffle trainval file.if [ $dataset == "trainval" ]thenrand_file=$dst_file.randomcat $dst_file | perl -MList::Util=shuffle -e 'print shuffle(<STDIN>);' > $rand_filemv $rand_file $dst_filefi done
cur_dir=$(cd $( dirname ${BASH_SOURCE[0]} ) && pwd ) root_dir=$cur_dir/../..cd $root_dirredo=1 data_root_dir="$HOME/data/VOCdevkit" dataset_name="indoor" mapfile="$root_dir/data/$dataset_name/labelmap_indoor.prototxt" anno_type="detection" db="lmdb" min_dim=0 max_dim=0 width=0 height=0extra_cmd="--encode-type=jpg --encoded" if [ $redo ] thenextra_cmd="$extra_cmd --redo" fi for subset in test trainval dopython $root_dir/scripts/create_annoset.py --anno-type=$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 $root_dir/data/$dataset_name/$subset.txt $data_root_dir/$dataset_name/$db/$dataset_name"_"$subset"_"$db examples/$dataset_name done
(5)修改labelmap_indoor.prototxt，将该文件中的类别修改成和自己的数据集相匹配，注意需要保留一个label 0 , background类别
item {name: "none_of_the_above"label: 0display_name: "background"
}
item {name: "door"label: 1display_name: "door"
} 完成上面步骤的修改后，可以开始LMDB数据数据的制作，在$CAFFE_ROOT目录下分别运行：
./data/indoor/create_list_indoor.sh
./data/indoor/create_data_indoor.sh
命令执行完毕后，可以在$CAFFE_ROOT/indoor目录下查看转换完成的LMDB数据数据。
3 使用SSD进行自己数据集的训练
训练时使用ssd demo中提供的预训练好的VGGnet model : VGG_ILSVRC_16_layers_fc_reduced.caffemodel
将该模型保存到$CAFFE_ROOT/models/VGGNet下。
将ssd_pascal.py copy一份 ssd_pascal_indoor.py文件， 根据自己的数据集修改ssd_pascal_indoor.py
主要修改点：
（1）train_data和test_data修改成指向自己的数据集LMDB
train_data = "examples/indoor/indoor_trainval_lmdb"
test_data = "examples/indoor/indoor_test_lmdb"
（2） num_test_image该变量修改成自己数据集中测试数据的数量
（3）num_classes 该变量修改成自己数据集中 标签类别数量数 + 1
针对我的数据集，ssd_pascal_indoor.py的内容为：
from __future__ import print_function import caffe from caffe.model_libs import * from google.protobuf import text_formatimport math import os import shutil import stat import subprocess import sys# Add extra layers on top of a "base" network (e.g. VGGNet or Inception). def AddExtraLayers(net, use_batchnorm=True):use_relu = True# Add additional convolutional layers.from_layer = net.keys()[-1]# TODO(weiliu89): Construct the name using the last layer to avoid duplication.out_layer = "conv6_1"ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1)from_layer = out_layerout_layer = "conv6_2"ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 512, 3, 1, 2)for i in xrange(7, 9):from_layer = out_layerout_layer = "conv{}_1".format(i)ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1)from_layer = out_layerout_layer = "conv{}_2".format(i)ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 2)# Add global pooling layer.name = net.keys()[-1]net.pool6 = L.Pooling(net[name], pool=P.Pooling.AVE, global_pooling=True)return net### Modify the following parameters accordingly ### # The directory which contains the caffe code. # We assume you are running the script at the CAFFE_ROOT. caffe_root = os.getcwd()# Set true if you want to start training right after generating all files. run_soon = True # Set true if you want to load from most recently saved snapshot. # Otherwise, we will load from the pretrain_model defined below. resume_training = True # If true, Remove old model files. remove_old_models = False# The database file for training data. Created by data/VOC0712/create_data.sh train_data = "examples/indoor/indoor_trainval_lmdb" # The database file for testing data. Created by data/VOC0712/create_data.sh test_data = "examples/indoor/indoor_test_lmdb" # Specify the batch sampler. resize_width = 300 resize_height = 300 resize = "{}x{}".format(resize_width, resize_height) batch_sampler = [{'sampler': {},'max_trials': 1,'max_sample': 1,},{'sampler': {'min_scale': 0.3,'max_scale': 1.0,'min_aspect_ratio': 0.5,'max_aspect_ratio': 2.0,},'sample_constraint': {'min_jaccard_overlap': 0.1,},'max_trials': 50,'max_sample': 1,},{'sampler': {'min_scale': 0.3,'max_scale': 1.0,'min_aspect_ratio': 0.5,'max_aspect_ratio': 2.0,},'sample_constraint': {'min_jaccard_overlap': 0.3,},'max_trials': 50,'max_sample': 1,},{'sampler': {'min_scale': 0.3,'max_scale': 1.0,'min_aspect_ratio': 0.5,'max_aspect_ratio': 2.0,},'sample_constraint': {'min_jaccard_overlap': 0.5,},'max_trials': 50,'max_sample': 1,},{'sampler': {'min_scale': 0.3,'max_scale': 1.0,'min_aspect_ratio': 0.5,'max_aspect_ratio': 2.0,},'sample_constraint': {'min_jaccard_overlap': 0.7,},'max_trials': 50,'max_sample': 1,},{'sampler': {'min_scale': 0.3,'max_scale': 1.0,'min_aspect_ratio': 0.5,'max_aspect_ratio': 2.0,},'sample_constraint': {'min_jaccard_overlap': 0.9,},'max_trials': 50,'max_sample': 1,},{'sampler': {'min_scale': 0.3,'max_scale': 1.0,'min_aspect_ratio': 0.5,'max_aspect_ratio': 2.0,},'sample_constraint': {'max_jaccard_overlap': 1.0,},'max_trials': 50,'max_sample': 1,},] train_transform_param = {'mirror': True,'mean_value': [104, 117, 123],'resize_param': {'prob': 1,'resize_mode': P.Resize.WARP,'height': resize_height,'width': resize_width,'interp_mode': [P.Resize.LINEAR,P.Resize.AREA,P.Resize.NEAREST,P.Resize.CUBIC,P.Resize.LANCZOS4,],},'emit_constraint': {'emit_type': caffe_pb2.EmitConstraint.CENTER,}} test_transform_param = {'mean_value': [104, 117, 123],'resize_param': {'prob': 1,'resize_mode': P.Resize.WARP,'height': resize_height,'width': resize_width,'interp_mode': [P.Resize.LINEAR],},}# If true, use batch norm for all newly added layers. # Currently only the non batch norm version has been tested. use_batchnorm = False # Use different initial learning rate. if use_batchnorm:base_lr = 0.0004 else:# A learning rate for batch_size = 1, num_gpus = 1.base_lr = 0.00004# Modify the job name if you want. job_name = "SSD_{}".format(resize) # The name of the model. Modify it if you want. model_name = "VGG_VOC0712_{}".format(job_name)# Directory which stores the model .prototxt file. save_dir = "models/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the snapshot of models. snapshot_dir = "models/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the job script and log file. job_dir = "jobs/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the detection results. output_result_dir = "{}/data/VOCdevkit/results/VOC2007/{}/Main".format(os.environ['HOME'], job_name)# model definition files. train_net_file = "{}/train.prototxt".format(save_dir) test_net_file = "{}/test.prototxt".format(save_dir) deploy_net_file = "{}/deploy.prototxt".format(save_dir) solver_file = "{}/solver.prototxt".format(save_dir) # snapshot prefix. snapshot_prefix = "{}/{}".format(snapshot_dir, model_name) # job script path. job_file = "{}/{}.sh".format(job_dir, model_name)# Stores the test image names and sizes. Created by data/VOC0712/create_list.sh name_size_file = "data/indoor/test_name_size.txt" # The pretrained model. We use the Fully convolutional reduced (atrous) VGGNet. pretrain_model = "models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel" # Stores LabelMapItem. label_map_file = "data/indoor/labelmap_indoor.prototxt"# MultiBoxLoss parameters. num_classes = 2 share_location = True background_label_id=0 train_on_diff_gt = True normalization_mode = P.Loss.VALID code_type = P.PriorBox.CENTER_SIZE neg_pos_ratio = 3. loc_weight = (neg_pos_ratio + 1.) / 4. multibox_loss_param = {'loc_loss_type': P.MultiBoxLoss.SMOOTH_L1,'conf_loss_type': P.MultiBoxLoss.SOFTMAX,'loc_weight': loc_weight,'num_classes': num_classes,'share_location': share_location,'match_type': P.MultiBoxLoss.PER_PREDICTION,'overlap_threshold': 0.5,'use_prior_for_matching': True,'background_label_id': background_label_id,'use_difficult_gt': train_on_diff_gt,'do_neg_mining': True,'neg_pos_ratio': neg_pos_ratio,'neg_overlap': 0.5,'code_type': code_type,} loss_param = {'normalization': normalization_mode,}# parameters for generating priors. # minimum dimension of input image min_dim = 300 # conv4_3 ==> 38 x 38 # fc7 ==> 19 x 19 # conv6_2 ==> 10 x 10 # conv7_2 ==> 5 x 5 # conv8_2 ==> 3 x 3 # pool6 ==> 1 x 1 mbox_source_layers = ['conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'pool6'] # in percent % min_ratio = 20 max_ratio = 95 step = int(math.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2))) min_sizes = [] max_sizes = [] for ratio in xrange(min_ratio, max_ratio + 1, step):min_sizes.append(min_dim * ratio / 100.)max_sizes.append(min_dim * (ratio + step) / 100.) min_sizes = [min_dim * 10 / 100.] + min_sizes max_sizes = [[]] + max_sizes aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]] # L2 normalize conv4_3. normalizations = [20, -1, -1, -1, -1, -1] # variance used to encode/decode prior bboxes. if code_type == P.PriorBox.CENTER_SIZE:prior_variance = [0.1, 0.1, 0.2, 0.2] else:prior_variance = [0.1] flip = True clip = True# Solver parameters. # Defining which GPUs to use. gpus = "0" gpulist = gpus.split(",") num_gpus = len(gpulist)# Divide the mini-batch to different GPUs. batch_size = 4 accum_batch_size = 32 iter_size = accum_batch_size / batch_size solver_mode = P.Solver.CPU device_id = 0 batch_size_per_device = batch_size if num_gpus > 0:batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus))iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))solver_mode = P.Solver.GPUdevice_id = int(gpulist[0])if normalization_mode == P.Loss.NONE:base_lr /= batch_size_per_device elif normalization_mode == P.Loss.VALID:base_lr *= 25. / loc_weight elif normalization_mode == P.Loss.FULL:# Roughly there are 2000 prior bboxes per image.# TODO(weiliu89): Estimate the exact # of priors.base_lr *= 2000.# Which layers to freeze (no backward) during training. freeze_layers = ['conv1_1', 'conv1_2', 'conv2_1', 'conv2_2']# Evaluate on whole test set. num_test_image = 800 test_batch_size = 1 test_iter = num_test_image / test_batch_sizesolver_param = {# Train parameters'base_lr': base_lr,'weight_decay': 0.0005,'lr_policy': "step",'stepsize': 40000,'gamma': 0.1,'momentum': 0.9,'iter_size': iter_size,'max_iter': 60000,'snapshot': 40000,'display': 10,'average_loss': 10,'type': "SGD",'solver_mode': solver_mode,'device_id': device_id,'debug_info': False,'snapshot_after_train': True,# Test parameters'test_iter': [test_iter],'test_interval': 10000,'eval_type': "detection",'ap_version': "11point",'test_initialization': False,}# parameters for generating detection output. det_out_param = {'num_classes': num_classes,'share_location': share_location,'background_label_id': background_label_id,'nms_param': {'nms_threshold': 0.45, 'top_k': 400},'save_output_param': {'output_directory': output_result_dir,'output_name_prefix': "comp4_det_test_",'output_format': "VOC",'label_map_file': label_map_file,'name_size_file': name_size_file,'num_test_image': num_test_image,},'keep_top_k': 200,'confidence_threshold': 0.01,'code_type': code_type,}# parameters for evaluating detection results. det_eval_param = {'num_classes': num_classes,'background_label_id': background_label_id,'overlap_threshold': 0.5,'evaluate_difficult_gt': False,'name_size_file': name_size_file,}### Hopefully you don't need to change the following ### # Check file. check_if_exist(train_data) check_if_exist(test_data) check_if_exist(label_map_file) check_if_exist(pretrain_model) make_if_not_exist(save_dir) make_if_not_exist(job_dir) make_if_not_exist(snapshot_dir)# Create train net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer(train_data, batch_size=batch_size_per_device,train=True, output_label=True, label_map_file=label_map_file,transform_param=train_transform_param, batch_sampler=batch_sampler)VGGNetBody(net, from_layer='data', fully_conv=True, reduced=True, dilated=True,dropout=False, freeze_layers=freeze_layers)AddExtraLayers(net, use_batchnorm)mbox_layers = CreateMultiBoxHead(net, data_layer='data', from_layers=mbox_source_layers,use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes,aspect_ratios=aspect_ratios, normalizations=normalizations,num_classes=num_classes, share_location=share_location, flip=flip, clip=clip,prior_variance=prior_variance, kernel_size=3, pad=1)# Create the MultiBoxLossLayer. name = "mbox_loss" mbox_layers.append(net.label) net[name] = L.MultiBoxLoss(*mbox_layers, multibox_loss_param=multibox_loss_param,loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')),propagate_down=[True, True, False, False])with open(train_net_file, 'w') as f:print('name: "{}_train"'.format(model_name), file=f)print(net.to_proto(), file=f) shutil.copy(train_net_file, job_dir)# Create test net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer(test_data, batch_size=test_batch_size,train=False, output_label=True, label_map_file=label_map_file,transform_param=test_transform_param)VGGNetBody(net, from_layer='data', fully_conv=True, reduced=True, dilated=True,dropout=False, freeze_layers=freeze_layers)AddExtraLayers(net, use_batchnorm)mbox_layers = CreateMultiBoxHead(net, data_layer='data', from_layers=mbox_source_layers,use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes,aspect_ratios=aspect_ratios, normalizations=normalizations,num_classes=num_classes, share_location=share_location, flip=flip, clip=clip,prior_variance=prior_variance, kernel_size=3, pad=1)conf_name = "mbox_conf" if multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.SOFTMAX:reshape_name = "{}_reshape".format(conf_name)net[reshape_name] = L.Reshape(net[conf_name], shape=dict(dim=[0, -1, num_classes]))softmax_name = "{}_softmax".format(conf_name)net[softmax_name] = L.Softmax(net[reshape_name], axis=2)flatten_name = "{}_flatten".format(conf_name)net[flatten_name] = L.Flatten(net[softmax_name], axis=1)mbox_layers[1] = net[flatten_name] elif multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.LOGISTIC:sigmoid_name = "{}_sigmoid".format(conf_name)net[sigmoid_name] = L.Sigmoid(net[conf_name])mbox_layers[1] = net[sigmoid_name]net.detection_out = L.DetectionOutput(*mbox_layers,detection_output_param=det_out_param,include=dict(phase=caffe_pb2.Phase.Value('TEST'))) net.detection_eval = L.DetectionEvaluate(net.detection_out, net.label,detection_evaluate_param=det_eval_param,include=dict(phase=caffe_pb2.Phase.Value('TEST')))with open(test_net_file, 'w') as f:print('name: "{}_test"'.format(model_name), file=f)print(net.to_proto(), file=f) shutil.copy(test_net_file, job_dir)# Create deploy net. # Remove the first and last layer from test net. deploy_net = net with open(deploy_net_file, 'w') as f:net_param = deploy_net.to_proto()# Remove the first (AnnotatedData) and last (DetectionEvaluate) layer from test net.del net_param.layer[0]del net_param.layer[-1]net_param.name = '{}_deploy'.format(model_name)net_param.input.extend(['data'])net_param.input_shape.extend([caffe_pb2.BlobShape(dim=[1, 3, resize_height, resize_width])])print(net_param, file=f) shutil.copy(deploy_net_file, job_dir)# Create solver. solver = caffe_pb2.SolverParameter(train_net=train_net_file,test_net=[test_net_file],snapshot_prefix=snapshot_prefix,**solver_param)with open(solver_file, 'w') as f:print(solver, file=f) shutil.copy(solver_file, job_dir)max_iter = 0 # Find most recent snapshot. for file in os.listdir(snapshot_dir):if file.endswith(".solverstate"):basename = os.path.splitext(file)[0]iter = int(basename.split("{}_iter_".format(model_name))[1])if iter > max_iter:max_iter = itertrain_src_param = '--weights="{}" \\\n'.format(pretrain_model) if resume_training:if max_iter > 0:train_src_param = '--snapshot="{}_iter_{}.solverstate" \\\n'.format(snapshot_prefix, max_iter)if remove_old_models:# Remove any snapshots smaller than max_iter.for file in os.listdir(snapshot_dir):if file.endswith(".solverstate"):basename = os.path.splitext(file)[0]iter = int(basename.split("{}_iter_".format(model_name))[1])if max_iter > iter:os.remove("{}/{}".format(snapshot_dir, file))if file.endswith(".caffemodel"):basename = os.path.splitext(file)[0]iter = int(basename.split("{}_iter_".format(model_name))[1])if max_iter > iter:os.remove("{}/{}".format(snapshot_dir, file))# Create job file. with open(job_file, 'w') as f:f.write('cd {}\n'.format(caffe_root))f.write('./build/tools/caffe train \\\n')f.write('--solver="{}" \\\n'.format(solver_file))f.write(train_src_param)if solver_param['solver_mode'] == P.Solver.GPU:f.write('--gpu {} 2>&1 | tee {}/{}.log\n'.format(gpus, job_dir, model_name))else:f.write('2>&1 | tee {}/{}.log\n'.format(job_dir, model_name))# Copy the python script to job_dir. py_file = os.path.abspath(__file__) shutil.copy(py_file, job_dir)# Run the job. os.chmod(job_file, stat.S_IRWXU) if run_soon:subprocess.call(job_file, shell=True)
训练命令：
python examples/ssd/ssd_pascal_indoor.py4 测试
SSD框架中提供了测试代码，有C++版本和python版本
4.1 c++版本
编译完SSD后，C++版本的的可执行文件存放目录： .build_release/examples/ssd/ssd_detect.bin
测试命令 ./.build_release/examples/ssd/ssd_detect.binmodels/VGGNet/indoor/deploy.prototxt models/VGGNet/indoor/VGG_VOC0712_SSD_300x300_iter_60000.caffemodel pictures.txt
其中pictures.txt中保存的是待测试图片的list
4.2 python版本
python 版本的测试过程参见examples/detection.ipynb
参考：
1 将数据集做成VOC2007格式用于Faster-RCNN训练
2 SSD的配置安装与测试
分类: Computer Vision,object Detection
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posted @ 2016-08-17 16:36 machineLearning 阅读(4639) 评论(27) 编辑 收藏
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