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_paths
import caffe.proto.caffe_pb2 as caffe_pb2

caffemodel_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.layer
print 'name: "%s"'%model.name
layer_id=-1
for 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-8

import _init_paths
import caffe.proto.caffe_pb2 as caffe_pb2

caffemodel_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-8

import _init_paths
import caffe.proto.caffe_pb2 as caffe_pb2

caffemodel_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 sys
old=sys.stdout
save_filename = 'lenet_from_caffemodel.prototxt' 
sys.stdout=open( save_filename, 'w')
print model.__str__
sys.stdout=old
f.close()

import os
cmd_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 = False
nu = 1
for 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_paths
import 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.layer
print 'name: ' + model.name
layer_id=-1
for 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）