python读取caffemodel文件

caffemodel是二进制的protobuf文件，利用protobuf的python接口可以读取它，解析出需要的内容

不少算法都是用预训练模型在自己数据上微调，即加载“caffemodel”作为网络初始参数取值，然后在此基础上更新。使用方式往往是：同时给定solver的prototxt文件，以及caffemodel权值文件，然后从solver创建网络，并从caffemodel读取网络权值的初值。能否不加载solver的prototxt，只加载caffemodel并看看它里面都有什么东西？

利用protobuf的python接口（C++接口也可以，不过编写代码和编译都略麻烦），能够读取caffemodel内容。教程当然是参考protobuf官网的例子了。

阶段1：完全模仿protobuf官网例子

我这里贴一个最noob的用法吧，用protobuf的python接口读取caffemodel文件。配合jupyter-notebook命令开启的jupyter笔记本，可以用tab键补全，比较方便：

# coding:utf-8# 首先请确保编译了caffe的python接口，以及编译后的输出目录<caffe_root>/python加载到了PYTHONPATH环境变量中. 或者，在代码中向os.path中添加import caffe.proto.caffe_pb2 as caffe_pb2      # 载入caffe.proto编译生成的caffe_pb2文件# 载入模型caffemodel_filename = '/home/chris/py-faster-rcnn/imagenet_models/ZF.v2.caffemodel'ZFmodel = caffe_pb2.NetParameter()        # 为啥是NetParameter()而不是其他类，呃，目前也还没有搞清楚，这个是试验的f = open(caffemodel_filename, 'rb')ZFmodel.ParseFromString(f.read())f.close()# noob阶段，只知道print输出print ZFmodel.name    print ZFmodel.input

阶段2：根据caffe.proto，读取caffemodel中的字段

这一阶段从caffemodel中读取出了大量信息。首先把caffemodel作为一个NetParameter类的对象看待，那么解析出它的名字(name)和各层(layer)。然后，解析每一层(layer）。如何确定layer表示所有层，能被遍历呢？需要参考caffe.proto文件，发现layer定义为：

repeated LayerParameter layer = 100;

看到repeated关键字，可以确定layer是一个“数组”了。不断地、迭代第查看caffe.proto中的各个字段，就可以解析了。

能否从caffemodel文件中解析出信息并输出为网络训练的train.prototxt文件呢？：显然是可以的。这里以mnist训练10000次产生的caffemodel文件进行解析，将得到的信息拼接出网络训练所使用的lenet_train.prototxt（输出到stdout)(代码实现比较naive，是逐个字段枚举的方式进行输出的，后续可以改进）:

# coding:utf-8# author:ChrisZZ# description: 从caffemodel文件解析出网络训练信息，以类似train.prototxt的形式输出到屏幕import _init_pathsimport caffe.proto.caffe_pb2 as caffe_pb2caffemodel_filename = '/home/chris/work/py-faster-rcnn/caffe-fast-rcnn/examples/mnist/lenet_iter_10000.caffemodel'model = caffe_pb2.NetParameter()f=open(caffemodel_filename, 'rb')model.ParseFromString(f.read())f.close()layers = model.layerprint 'name: "%s"'%model.namelayer_id=-1for layer in layers:    layer_id = layer_id + 1    print 'layer {'    print '  name: "%s"'%layer.name    print '  type: "%s"'%layer.type        tops = layer.top    for top in tops:        print '  top: "%s"'%top        bottoms = layer.bottom    for bottom in bottoms:        print '  bottom: "%s"'%bottom        if len(layer.include)>0:        print '  include {'        includes = layer.include        phase_mapper={            '0': 'TRAIN',            '1': 'TEST'        }                for include in includes:            if include.phase is not None:                print '    phase: ', phase_mapper[str(include.phase)]        print '  }'        if layer.transform_param is not None and layer.transform_param.scale is not None and layer.transform_param.scale!=1:        print '  transform_param {'        print '    scale: %s'%layer.transform_param.scale        print '  }'    if layer.data_param is not None and (layer.data_param.source!="" or layer.data_param.batch_size!=0 or layer.data_param.backend!=0):        print '  data_param: {'        if layer.data_param.source is not None:            print '    source: "%s"'%layer.data_param.source        if layer.data_param.batch_size is not None:            print '    batch_size: %d'%layer.data_param.batch_size        if layer.data_param.backend is not None:            print '    backend: %s'%layer.data_param.backend        print '  }'            if layer.param is not None:        params = layer.param        for param in params:            print '  param {'            if param.lr_mult is not None:                print '    lr_mult: %s'% param.lr_mult            print '  }'        if layer.convolution_param is not None:        print '  convolution_param {'        conv_param = layer.convolution_param        if conv_param.num_output is not None:            print '    num_output: %d'%conv_param.num_output        if len(conv_param.kernel_size) > 0:            for kernel_size in conv_param.kernel_size:                print '    kernel_size: ',kernel_size        if len(conv_param.stride) > 0:            for stride in conv_param.stride:                print '    stride: ', stride        if conv_param.weight_filler is not None:            print '    weight_filler {'            print '      type: "%s"'%conv_param.weight_filler.type            print '    }'        if conv_param.bias_filler is not None:            print '    bias_filler {'            print '      type: "%s"'%conv_param.bias_filler.type            print '    }'        print '  }'        print '}'

产生的输出如下：

name: "LeNet"layer {  name: "mnist"  type: "Data"  top: "data"  top: "label"  include {    phase:  TRAIN  }  transform_param {    scale: 0.00390625  }  data_param: {    source: "examples/mnist/mnist_train_lmdb"    batch_size: 64    backend: 1  }  convolution_param {    num_output: 0    weight_filler {      type: "constant"    }    bias_filler {      type: "constant"    }  }}layer {  name: "conv1"  type: "Convolution"  top: "conv1"  bottom: "data"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  convolution_param {    num_output: 20    kernel_size:  5    stride:  1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  top: "pool1"  bottom: "conv1"  convolution_param {    num_output: 0    weight_filler {      type: "constant"    }    bias_filler {      type: "constant"    }  }}layer {  name: "conv2"  type: "Convolution"  top: "conv2"  bottom: "pool1"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  convolution_param {    num_output: 50    kernel_size:  5    stride:  1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool2"  type: "Pooling"  top: "pool2"  bottom: "conv2"  convolution_param {    num_output: 0    weight_filler {      type: "constant"    }    bias_filler {      type: "constant"    }  }}layer {  name: "ip1"  type: "InnerProduct"  top: "ip1"  bottom: "pool2"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  convolution_param {    num_output: 0    weight_filler {      type: "constant"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  top: "ip1"  bottom: "ip1"  convolution_param {    num_output: 0    weight_filler {      type: "constant"    }    bias_filler {      type: "constant"    }  }}layer {  name: "ip2"  type: "InnerProduct"  top: "ip2"  bottom: "ip1"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  convolution_param {    num_output: 0    weight_filler {      type: "constant"    }    bias_filler {      type: "constant"    }  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  top: "loss"  bottom: "ip2"  bottom: "label"  convolution_param {    num_output: 0    weight_filler {      type: "constant"    }    bias_filler {      type: "constant"    }  }}

阶段3：读出caffemodel的所有字段

阶段2是手工指定要打印输出的字段，需要参照caffe.proto,一个个字段去找，遇到嵌套的情况需要递归查找，比较繁琐。能否一口气读出caffemodel的所有字段呢？可以的，使用__str__就可以了，比如：

# coding:utf-8import _init_pathsimport caffe.proto.caffe_pb2 as caffe_pb2caffemodel_filename = '/home/chris/work/py-faster-rcnn/caffe-fast-rcnn/examples/mnist/lenet_iter_10000.caffemodel'model = caffe_pb2.NetParameter()f = open(caffemodel_filename, 'rb')model.ParseFromString(f.read())f.close()print model.__str__

得到的输出几乎就是网络训练用的train.prototxt了，只不过里面还把blobs字段给打印出来了。这个字段里面有太多的内容，是经过多次迭代学习出来的卷积核以及bias的数值。这些字段应当忽略。以及，__str__输出的首尾有不必要的字符串也要去掉，不妨将__str__输出到文件，然后用sed删除不必要的内容。除了过滤掉blobs字段包含的内容，还去掉了"phase: TRAIN"这个不必要显示的内容，处理完后再写回同一文件。代码如下（依然以lenet训练10000次的caffemodel为例）：

# coding:utf-8import _init_pathsimport caffe.proto.caffe_pb2 as caffe_pb2caffemodel_filename = '/home/chris/work/py-faster-rcnn/caffe-fast-rcnn/examples/mnist/lenet_iter_10000.caffemodel'model = caffe_pb2.NetParameter()f = open(caffemodel_filename, 'rb')model.ParseFromString(f.read())f.close()import sysold=sys.stdoutsave_filename = 'lenet_from_caffemodel.prototxt' sys.stdout=open( save_filename, 'w')print model.__str__sys.stdout=oldf.close()import oscmd_1 = 'sed -i "1s/^.\{38\}//" ' + save_filename     # 删除第一行前面38个字符cmd_2 = "sed -i '$d' " + save_filename      # 删除最后一行os.system(cmd_1)os.system(cmd_2)# 打开刚刚存储的文件，输出里面的内容，输出时过滤掉“blobs”块和"phase: TRAIN"行。f=open(save_filename, 'r')lines = f.readlines()f.close()wr = open(save_filename, 'w')now_have_blobs = Falsenu = 1for line in lines:    #print nu    nu = nu + 1    content = line.strip('\n')    if (content == '  blobs {'):        now_have_blobs = True    elif (content == '  }' and now_have_blobs==True):        now_have_blobs = False        continue    if (content == '  phase: TRAIN'):        continue            if (now_have_blobs):        continue    else:        wr.write(content+'\n')wr.close()

现在，查看下得到的lenet_from_caffemodel.prototxt文件内容，也就是从caffemodel文件解析出来的字段并过滤后的结果：

name: "LeNet"layer {  name: "mnist"  type: "Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  transform_param {    scale: 0.00390625  }  data_param {    source: "examples/mnist/mnist_train_lmdb"    batch_size: 64    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  convolution_param {    num_output: 20    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  convolution_param {    num_output: 50    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool2"  top: "ip1"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1.0  }  param {    lr_mult: 2.0  }  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "ip2"  bottom: "label"  top: "loss"  loss_weight: 1.0}

可以说，得到的这个lenet_from_caffemodel.prototxt就是用于网络训练的配置文件了。
这里其实还存在一个问题：caffemodel->__str__->文件，这个文件会比caffemodel大很多，因为各种blobs数据占据了太多空间。当把要解析的caffemodel从lenet_iter_10000.caffemodel换成imagenet数据集上训练的ZFnet的权值文件ZF.v2.caffemodel，这个文件本身就有200多M(lenet那个只有不到2M），再运行本阶段的python代码尝试得到网络结构，会报错提示说内存不足。看来，这个解析方法还需要改进。

阶段4：不完美的解析，但是肯定够用

既然阶段3的尝试失败，那就回到阶段2的方法，手动指定需要解析的字段，获取其内容，然后打印输出。对照着caffe.proto，把一些参数的默认值过滤掉，以及blobs过滤掉。
此处以比lenet5更复杂的ZFnet（论文：Visualizing and Understanding Convolutional Networks）来解析，因为在py-faster-rcnn中使用到了这个网络，而其配置文件中又增加了RPN和ROIPooling等层，想要知道到底增加了那些层以及换掉了哪些参数，不妨看看ZFnet的原版使用了哪些层：

# coding:utf-8# author:ChrisZZ# description: 从caffemodel文件解析出网络训练信息，以类似train.prototxt的形式输出到屏幕import _init_pathsimport caffe.proto.caffe_pb2 as caffe_pb2#caffemodel_filename = '/home/chris/work/fuckubuntu/caffe-fast-rcnn/examples/mnist/lenet_iter_10000.caffemodel'caffemodel_filename = '/home/chris/work/py-faster-rcnn/data/imagenet_models/ZF.v2.caffemodel'    model = caffe_pb2.NetParameter()f=open(caffemodel_filename, 'rb')model.ParseFromString(f.read())f.close()layers = model.layerprint 'name: ' + model.namelayer_id=-1for layer in layers:    layer_id = layer_id + 1        res=list()        # name    res.append('layer {')    res.append('  name: "%s"' % layer.name)        # type    res.append('  type: "%s"' % layer.type)            # bottom    for bottom in layer.bottom:        res.append('  bottom: "%s"' % bottom)        # top    for top in layer.top:        res.append('  top: "%s"' % top)        # loss_weight    for loss_weight in layer.loss_weight:        res.append('  loss_weight: ' + loss_weight)        # param    for param in layer.param:        param_res = list()        if param.lr_mult is not None:            param_res.append('    lr_mult: %s' % param.lr_mult)        if param.decay_mult!=1:            param_res.append('    decay_mult: %s' % param.decay_mult)        if len(param_res)>0:            res.append('  param{')            res.extend(param_res)            res.append('  }')        # lrn_param    if layer.lrn_param is not None:        lrn_res = list()        if layer.lrn_param.local_size!=5:            lrn_res.append('    local_size: %d' % layer.lrn_param.local_size)        if layer.lrn_param.alpha!=1:            lrn_res.append('    alpha: %f' % layer.lrn_param.alpha)        if layer.lrn_param.beta!=0.75:            lrn_res.append('    beta: %f' % layer.lrn_param.beta)        NormRegionMapper={'0': 'ACROSS_CHANNELS', '1': 'WITHIN_CHANNEL'}        if layer.lrn_param.norm_region!=0:            lrn_res.append('    norm_region: %s' % NormRegionMapper[str(layer.lrn_param.norm_region)])        EngineMapper={'0': 'DEFAULT', '1':'CAFFE', '2':'CUDNN'}        if layer.lrn_param.engine!=0:            lrn_res.append('    engine: %s' % EngineMapper[str(layer.lrn_param.engine)])        if len(lrn_res)>0:            res.append('  lrn_param{')            res.extend(lrn_res)            res.append('  }')        # include    if len(layer.include)>0:        include_res = list()        includes = layer.include        phase_mapper={            '0': 'TRAIN',            '1': 'TEST'        }                for include in includes:            if include.phase is not None:                include_res.append('    phase: ', phase_mapper[str(include.phase)])                if len(include_res)>0:            res.append('  include {')            res.extend(include_res)            res.append('  }')        # transform_param    if layer.transform_param is not None:        transform_param_res = list()        if layer.transform_param.scale!=1:                       transform_param_res.append('    scale: %s'%layer.transform_param.scale)        if layer.transform_param.mirror!=False:            transform_param.res.append('    mirror: ' + layer.transform_param.mirror)        if len(transform_param_res)>0:            res.append('  transform_param {')            res.extend(transform_param_res)            res.res.append('  }')    # data_param    if layer.data_param is not None and (layer.data_param.source!="" or layer.data_param.batch_size!=0 or layer.data_param.backend!=0):        data_param_res = list()                if layer.data_param.source is not None:            data_param_res.append('    source: "%s"'%layer.data_param.source)        if layer.data_param.batch_size is not None:            data_param_res.append('    batch_size: %d'%layer.data_param.batch_size)        if layer.data_param.backend is not None:            data_param_res.append('    backend: %s'%layer.data_param.backend)                if len(data_param_res)>0:            res.append('  data_param: {')            res.extend(data_param_res)            res.append('  }')            # convolution_param    if layer.convolution_param is not None:        convolution_param_res = list()        conv_param = layer.convolution_param        if conv_param.num_output!=0:            convolution_param_res.append('    num_output: %d'%conv_param.num_output)        if len(conv_param.kernel_size) > 0:            for kernel_size in conv_param.kernel_size:                convolution_param_res.append('    kernel_size: %d' % kernel_size)        if len(conv_param.pad) > 0:            for pad in conv_param.pad:                convolution_param_res.append('    pad: %d' % pad)        if len(conv_param.stride) > 0:            for stride in conv_param.stride:                convolution_param_res.append('    stride: %d' % stride)        if conv_param.weight_filler is not None and conv_param.weight_filler.type!='constant':            convolution_param_res.append('    weight_filler {')            convolution_param_res.append('      type: "%s"'%conv_param.weight_filler.type)            convolution_param_res.append('    }')        if conv_param.bias_filler is not None and conv_param.bias_filler.type!='constant':            convolution_param_res.append('    bias_filler {')            convolution_param_res.append('      type: "%s"'%conv_param.bias_filler.type)            convolution_param_res.append('    }')                if len(convolution_param_res)>0:            res.append('  convolution_param {')            res.extend(convolution_param_res)            res.append('  }')        # pooling_param    if layer.pooling_param is not None:        pooling_param_res = list()        if layer.pooling_param.kernel_size>0:            pooling_param_res.append('    kernel_size: %d' % layer.pooling_param.kernel_size)            pooling_param_res.append('    stride: %d' % layer.pooling_param.stride)            pooling_param_res.append('    pad: %d' % layer.pooling_param.pad)            PoolMethodMapper={'0':'MAX', '1':'AVE', '2':'STOCHASTIC'}            pooling_param_res.append('    pool: %s' % PoolMethodMapper[str(layer.pooling_param.pool)])                if len(pooling_param_res)>0:            res.append('  pooling_param {')            res.extend(pooling_param_res)            res.append('  }')        # inner_product_param    if layer.inner_product_param is not None:        inner_product_param_res = list()        if layer.inner_product_param.num_output!=0:            inner_product_param_res.append('    num_output: %d' % layer.inner_product_param.num_output)                if len(inner_product_param_res)>0:            res.append('  inner_product_param {')            res.extend(inner_product_param_res)            res.append('  }')        # drop_param    if layer.dropout_param is not None:        dropout_param_res = list()        if layer.dropout_param.dropout_ratio!=0.5 or layer.dropout_param.scale_train!=True:            dropout_param_res.append('    dropout_ratio: %f' % layer.dropout_param.dropout_ratio)            dropout_param_res.append('    scale_train: ' + str(layer.dropout_param.scale_train))                if len(dropout_param_res)>0:            res.append('  dropout_param {')            res.extend(dropout_param_res)            res.append('  }')        res.append('}')        for line in res:        print line

此处贴出ZFnet原版网络的prototxt描述文件：

name: "ImageNet_Zeiler_spm"layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  convolution_param {    num_output: 96    kernel_size: 7    pad: 1    stride: 2    weight_filler {      type: "gaussian"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "conv1"  top: "conv1"}layer {  name: "norm1"  type: "LRN"  bottom: "conv1"  top: "norm1"  lrn_param{    local_size: 3    alpha: 0.000050    norm_region: WITHIN_CHANNEL  }}layer {  name: "pool1"  type: "Pooling"  bottom: "norm1"  top: "pool1"  pooling_param {    kernel_size: 3    stride: 2    pad: 0    pool: MAX  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  convolution_param {    num_output: 256    kernel_size: 5    pad: 0    stride: 2    weight_filler {      type: "gaussian"    }  }}layer {  name: "relu2"  type: "ReLU"  bottom: "conv2"  top: "conv2"}layer {  name: "norm2"  type: "LRN"  bottom: "conv2"  top: "norm2"  lrn_param{    local_size: 3    alpha: 0.000050    norm_region: WITHIN_CHANNEL  }}layer {  name: "pool2"  type: "Pooling"  bottom: "norm2"  top: "pool2"  pooling_param {    kernel_size: 3    stride: 2    pad: 0    pool: MAX  }}layer {  name: "conv3"  type: "Convolution"  bottom: "pool2"  top: "conv3"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  convolution_param {    num_output: 384    kernel_size: 3    pad: 1    stride: 1    weight_filler {      type: "gaussian"    }  }}layer {  name: "relu3"  type: "ReLU"  bottom: "conv3"  top: "conv3"}layer {  name: "conv4"  type: "Convolution"  bottom: "conv3"  top: "conv4"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  convolution_param {    num_output: 384    kernel_size: 3    pad: 1    stride: 1    weight_filler {      type: "gaussian"    }  }}layer {  name: "relu4"  type: "ReLU"  bottom: "conv4"  top: "conv4"}layer {  name: "conv5"  type: "Convolution"  bottom: "conv4"  top: "conv5"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  convolution_param {    num_output: 256    kernel_size: 3    pad: 1    stride: 1    weight_filler {      type: "gaussian"    }  }}layer {  name: "relu5"  type: "ReLU"  bottom: "conv5"  top: "conv5"}layer {  name: "pool5_spm6"  type: "Pooling"  bottom: "conv5"  top: "pool5_spm6"  pooling_param {    kernel_size: 3    stride: 2    pad: 0    pool: MAX  }}layer {  name: "pool5_spm6_flatten"  type: "Flatten"  bottom: "pool5_spm6"  top: "pool5_spm6_flatten"}layer {  name: "fc6"  type: "InnerProduct"  bottom: "pool5_spm6_flatten"  top: "fc6"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  inner_product_param {    num_output: 4096  }}layer {  name: "relu6"  type: "ReLU"  bottom: "fc6"  top: "fc6"}layer {  name: "drop6"  type: "Dropout"  bottom: "fc6"  top: "fc6"}layer {  name: "fc7"  type: "InnerProduct"  bottom: "fc6"  top: "fc7"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  inner_product_param {    num_output: 4096  }}layer {  name: "relu7"  type: "ReLU"  bottom: "fc7"  top: "fc7"}layer {  name: "drop7"  type: "Dropout"  bottom: "fc7"  top: "fc7"}layer {  name: "fc8"  type: "InnerProduct"  bottom: "fc7"  top: "fc8"  param{    lr_mult: 1.0  }  param{    lr_mult: 2.0  }  inner_product_param {    num_output: 1000  }}layer {  name: "prob"  type: "Softmax"  bottom: "fc8"  top: "prob"}

根据得到的prototxt文件，容易绘制出原版ZFnet对应的网络结构图：（可参考这篇博客：http://www.cnblogs.com/zjutzz/p/5955218.html）