呼市网站设计,东莞关键词优化排名,wordpress 语言文件夹,网站新闻打不开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#xff0c;内容包括#xff1a; 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 … 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_numberclassName x1min y1min x1max y1maxclassname 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(fillBOTH, expand1)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 WE)self.ldBtn Button(self.frame, text Load, command self.loadDir)self.ldBtn.grid(row 0, column 2, sticky WE)# main panel for labelingself.mainPanel Canvas(self.frame, cursortcross)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 WN)# showing bbox info delete bboxself.lb1 Label(self.frame, text Bounding boxes:)self.lb1.grid(row 1, column 2, sticky WN)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 WEN)self.btnClear Button(self.frame, text ClearAll, command self.clearBBox)self.btnClear.grid(row 4, column 2, sticky WEN)#select class typeself.classPanel Frame(self.frame)self.classPanel.grid(row 5, column 1, columnspan 10, sticky WE)label Label(self.classPanel, text class:)label.grid(row 5, column 1, sticky WN)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 WE)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, textNext , 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 rD:\workspace\python\labelGUI
## if not os.path.isdir(s):
## tkMessageBox.showerror(Error!, message The specified dir doesnt 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, anchorNW)self.progLabel.config(text %04d/%04d %(self.cur, self.total))# load labelsself.clearBBox()self.imagename os.path.split(imagepath)[-1].split(.)[0]labelname self.imagename .txtself.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 tmpself.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 matself.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 bedself.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 rtest2.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, anchorNW)if __name__ __main__:root Tk()tool LabelTool(root)root.mainloop() main.py 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 version1.0 ?
annotationfolderVOC2007/folderfilename1.jpg/filenamesourcedatabaseMy Database/databaseannotationVOC2007/annotationimageflickr/imageflickridNULL/flickrid/sourceownerflickridNULL/flickridnameidaneel/name/ownersizewidth320/widthheight240/heightdepth3/depth/sizesegmented0/segmentedobjectnamedoor/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin109/xminymin3/yminxmax199/xmaxymax204/ymax/bndbox/object
/annotation VOC XML内容信息 (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 Documentlabelslabel
imgpathJPEGImages/
xmlpath_newAnnotations/
foldernameVOC2007def 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]:fidinopen(walk[0] / each,r)objIndex 0for data in islice(fidin, 1, None): objIndex 1datadata.strip(\n)datas data.split( )if 5 ! len(datas):print bounding box information errorcontinuepictureName 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() createXml.py 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-start1
except:print Please input picture rangeprint ./createTest.py 1 1500 500os._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(i1)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() createTest.py 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_dirImageSets/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.txtcp $dataset_file $img_filesed -i s/^/$name\/JPEGImages\//g $img_filesed -i s/$/.jpg/g $img_filelabel_file$bash_dir/$dataset_label.txtcp $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.txtfi# Shuffle trainval file.if [ $dataset trainval ]thenrand_file$dst_file.randomcat $dst_file | perl -MList::Utilshuffle -e print shuffle(STDIN); $rand_filemv $rand_file $dst_filefi
done create_list_indoor.sh cur_dir$(cd $( dirname ${BASH_SOURCE[0]} ) pwd )
root_dir$cur_dir/../..cd $root_dirredo1
data_root_dir$HOME/data/VOCdevkit
dataset_nameindoor
mapfile$root_dir/data/$dataset_name/labelmap_indoor.prototxt
anno_typedetection
dblmdb
min_dim0
max_dim0
width0
height0extra_cmd--encode-typejpg --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/$dataset_name
done create_data_indoor.sh (5)修改labelmap_indoor.prototxt将该文件中的类别修改成和自己的数据集相匹配注意需要保留一个label 0 , background类别 item {name: none_of_the_abovelabel: 0display_name: background
}
item {name: doorlabel: 1display_name: door
} labelmap_indoor.prototxt 完成上面步骤的修改后可以开始LMDB数据数据的制作在$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 1train_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_lmdb2 num_test_image该变量修改成自己数据集中测试数据的数量3num_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_batchnormTrue):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_1ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1)from_layer out_layerout_layer conv6_2ConvBNLayer(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], poolP.Pooling.AVE, global_poolingTrue)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_id0
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 parametersbase_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 parameterstest_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 dont 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_sizebatch_size_per_device,trainTrue, output_labelTrue, label_map_filelabel_map_file,transform_paramtrain_transform_param, batch_samplerbatch_sampler)VGGNetBody(net, from_layerdata, fully_convTrue, reducedTrue, dilatedTrue,dropoutFalse, freeze_layersfreeze_layers)AddExtraLayers(net, use_batchnorm)mbox_layers CreateMultiBoxHead(net, data_layerdata, from_layersmbox_source_layers,use_batchnormuse_batchnorm, min_sizesmin_sizes, max_sizesmax_sizes,aspect_ratiosaspect_ratios, normalizationsnormalizations,num_classesnum_classes, share_locationshare_location, flipflip, clipclip,prior_varianceprior_variance, kernel_size3, pad1)# Create the MultiBoxLossLayer.
name mbox_loss
mbox_layers.append(net.label)
net[name] L.MultiBoxLoss(*mbox_layers, multibox_loss_parammultibox_loss_param,loss_paramloss_param, includedict(phasecaffe_pb2.Phase.Value(TRAIN)),propagate_down[True, True, False, False])with open(train_net_file, w) as f:print(name: {}_train.format(model_name), filef)print(net.to_proto(), filef)
shutil.copy(train_net_file, job_dir)# Create test net.
net caffe.NetSpec()
net.data, net.label CreateAnnotatedDataLayer(test_data, batch_sizetest_batch_size,trainFalse, output_labelTrue, label_map_filelabel_map_file,transform_paramtest_transform_param)VGGNetBody(net, from_layerdata, fully_convTrue, reducedTrue, dilatedTrue,dropoutFalse, freeze_layersfreeze_layers)AddExtraLayers(net, use_batchnorm)mbox_layers CreateMultiBoxHead(net, data_layerdata, from_layersmbox_source_layers,use_batchnormuse_batchnorm, min_sizesmin_sizes, max_sizesmax_sizes,aspect_ratiosaspect_ratios, normalizationsnormalizations,num_classesnum_classes, share_locationshare_location, flipflip, clipclip,prior_varianceprior_variance, kernel_size3, pad1)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], shapedict(dim[0, -1, num_classes]))softmax_name {}_softmax.format(conf_name)net[softmax_name] L.Softmax(net[reshape_name], axis2)flatten_name {}_flatten.format(conf_name)net[flatten_name] L.Flatten(net[softmax_name], axis1)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_paramdet_out_param,includedict(phasecaffe_pb2.Phase.Value(TEST)))
net.detection_eval L.DetectionEvaluate(net.detection_out, net.label,detection_evaluate_paramdet_eval_param,includedict(phasecaffe_pb2.Phase.Value(TEST)))with open(test_net_file, w) as f:print(name: {}_test.format(model_name), filef)print(net.to_proto(), filef)
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, filef)
shutil.copy(deploy_net_file, job_dir)# Create solver.
solver caffe_pb2.SolverParameter(train_nettrain_net_file,test_net[test_net_file],snapshot_prefixsnapshot_prefix,**solver_param)with open(solver_file, w) as f:print(solver, filef)
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 {} 21 | tee {}/{}.log\n.format(gpus, job_dir, model_name))else:f.write(21 | 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, shellTrue) ssd_pascal_indoor.py 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.bin models/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 好文要顶 关注我 收藏该文 machineLearning 关注 - 14 粉丝 - 5 加关注 2 0 « 上一篇object detect links » 下一篇神经网络向量化求反向传播梯度 posted 2016-08-17 16:36 machineLearning 阅读(4639) 评论(27) 编辑 收藏
