解析caffe生成的caffemodel文件

要想了解caffe生成的caffemodel文件里的内容,我们就需要解析.caffemodel文件(caffemodel里不仅存储了权重和偏置等信息,还存储了整个训练网络的结构信息,即.prototxt信息,当然solver.prototxt信息是看不见的)。

1.单独查看

要是只是想看看权重信息正不正确(有时候学习率设置的太大,可能会导致梯度爆炸,但是又不确定的情况下,可以查看下caffemodel文件中的权重是否是nan,是的话说明梯度爆炸了),可以通过caffe中的python接口来快速查看,基本代码如下:

import caffe
import numpy as np

deploy='/home/b622/caffe-action_recog/evaluation/prototxt/pspnet101_VOC2012_deploy.prototxt'   
model='/media/b622/My Passport/pspnet101resultbn/train_iter_100.caffemodel'

net = caffe.Net(deploy,model,caffe.TEST)    
w=net.params['conv1_1_3x3_s2/bn'][0].data   
print w

其中deploy后面的是你自己的网络的deploy.prototxt文件路径;model是训练好的caffemodel文件的路径;net.params中的'conv1_1_3x3_s2/bn'是deploy.prototxt文件中某一层的名字,即该层的'name:'后面的内容,caffe是根据这个名字来索引该层的权重信息的(我这里用的是PSPNet网络中的某一层)。

2.整体输出

如果想输出caffemodel中所有的信息,可以通过以下的小程序来解析:

(1)定义主函数文件

打开终端输入以下命令,创建一个.cpp文件:

gedit test.cpp

在弹出的文件中写入以下代码,并保存:

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/io.hpp"
 
using namespace caffe;
using namespace std;
using google::protobuf::io::FileInputStream;
using google::protobuf::io::FileOutputStream;
using google::protobuf::io::ZeroCopyInputStream;
using google::protobuf::io::CodedInputStream;
using google::protobuf::io::ZeroCopyOutputStream;
using google::protobuf::io::CodedOutputStream;
using google::protobuf::Message;
 
int main()
{
	NetParameter proto;
	ReadProtoFromBinaryFile("/home/b622/caffe-action_recog/evaluation/pspnet101_VOC2012.caffemodel", &proto);
	WriteProtoToTextFile(proto, "test.txt");
	return 0;
}

其中ReadProtoFromBinaryFile()为读取函数,WriteProtoToTextFile()为写入函数。

(2)定义CMakeLists.txt文件(注意不要改文件名)

cmake_minimum_required (VERSION 2.8)
 
project (test)
 
add_executable(test test.cpp)
 
include_directories ( /home/b622/caffe-action_recog/include
    /usr/local/include
    /usr/local/cuda/include
    /usr/include )
 
target_link_libraries(test
    /home/b622/caffe-action_recog/build/lib/libcaffe.so
    /usr/lib/x86_64-linux-gnu/libglog.so
    /usr/lib/x86_64-linux-gnu/libboost_system.so
    )

只需要将上面的/home/b622/PSPNet-master/include和/home/b622/PSPNet-master/build/lib/libcaffe.so替换成你自己的caffe中的include和libcaffe.so的路径即可。

将上述两个文件放在同一文件夹下,然后依次输入以下命令进行编译:

cmake .
make

如下图所示:

解析caffe生成的caffemodel文件_第1张图片

如果在make过程中出现以下错误:

/home/b622/caffe-action_recog/include/caffe/blob.hpp:9:34: fatal error: caffe/proto/caffe.pb.h: No such file or directory
compilation terminated.
CMakeFiles/test.dir/build.make:62: recipe for target 'CMakeFiles/test.dir/test.cpp.o' failed
make[2]: *** [CMakeFiles/test.dir/test.cpp.o] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/test.dir/all' failed
make[1]: *** [CMakeFiles/test.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

则需要手动编译出caffe.pb.h文件,打开终端输入以下命令:

protoc --cpp_out=/home/b622/caffe-action_recog/include/caffe/proto caffe.proto

如下图所示:

解析caffe生成的caffemodel文件_第2张图片

成功后会在caffe-action_recog/include/caffe/proto文件夹下生成caffe.pb.h文件,如下图

解析caffe生成的caffemodel文件_第3张图片

完成后再次make,即可通过,如果再出现其他错误,请自行百度。

生成的文件如下:

解析caffe生成的caffemodel文件_第4张图片

在终端输入以下命令,生成解析出的test.txt文档:

./test

注:先cd到test执行文件的根目录下,在输入上述语句。

成功后,可以查看tets.txt文件中的内容,我解析的是官方给的PSPNet网络模型,如下(部分)所示:

name: ""
layer {
  name: "data"
  type: "ImageSegData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  phase: TRAIN
  transform_param {
    mirror: true
    crop_size: 473
    mean_value: 103.939
    mean_value: 116.779
    mean_value: 123.68
    8: 0x3f000000
    8: 0x40000000
    9: 0xc1200000
    9: 0x41200000
    10: 1
  }
  image_data_param {
    source: "/mnt/lustre/zhaohengshuang/dataset/VOC2012/list/trainval.txt"
    batch_size: 16
    shuffle: true
    root_folder: "/mnt/lustre/zhaohengshuang/dataset/VOC2012"
    16: 2
  }
}
layer {
  name: "label_gather"
  type: "Gather"
  bottom: "label"
  top: "label_gather"
  phase: TRAIN
}
layer {
  name: "conv1_1_3x3_s2"
  type: "ConvolutionData"
  bottom: "data"
  top: "conv1_1_3x3_s2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  blobs {
    data: 0.0022265632
    data: 0.0063848812
    data: -0.021314347
    data: 0.0087224049
    data: -0.0017450363
    data: -0.040702991
    data: 0.040957987
    data: 0.030024877
    data: -0.00040234922
    data: 0.035385851
    data: 0.03061888
    data: 0.00975236
    data: 0.029203681
    data: 0.027859332
    data: -0.02282569
    data: 0.051132251
    data: 0.050982021
    data: 0.028273048
    data: 0.00045456921
    data: 0.0046192827
    data: -0.014982899
    data: 0.0010559497
    data: -0.01121246
    data: -0.043569062
    data: 0.018522989
    data: 0.013444615
    data: -0.012451748
    data: -0.0035903456
    data: -0.0036976787
    data: -0.0086726872
    data: 0.013544118
    data: 0.039940353
    data: 0.0332526
    data: 0.0174512
    data: 0.030373706
    data: 0.015946979
    data: -0.03056127
    data: -0.077426851
    data: -0.064679071
    data: -0.049140476
    data: -0.12691125
    data: -0.15443584
    data: -0.067412965
    data: -0.13174506
    data: -0.13033487
    data: -0.04345879
    data: -0.022715034
    data: 0.033402845
    data: -0.0118775
    data: 0.032909606
    data: 0.031529181
    data: 0.0028268516
    data: 0.030321535
    data: 0.055880059
    data: -0.012543317
    data: 0.29960331
    data: -0.26908255
    data: -0.060308661
    data: 0.053303137
    data: -0.035357445
    data: -0.027739143
    data: 0.013819358
    data: 0.028950721
    data: 0.0096227145
    data: 0.600619
    data: -0.47760749
    data: -0.085985318
    data: 0.12866144
    data: -0.085491486
    data: -0.069523379
    data: 0.0065062055
    data: 0.02735726
    data: -0.011153187
    data: 0.43719363
    data: -0.38543427
    data: -0.082773171
    data: 0.07562118
    data: -0.070219189
    data: -0.041084472
    data: 0.01311215
    data: 0.036830757
    data: 0.024847485
    data: -0.0090860045
    data: 0.051152926
    data: 0.03113804
    data: 0.022111731
    data: 0.056659479
    data: 0.087109923
    data: 0.069432892
    data: -0.004762331
    data: 0.0077367546
    data: -0.11521299
    data: -0.12150885

 

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