Caffe提取任意层特征并进行可视化

原图

 

 

conv1层可视化结果 （96个filter得到的结果）

 

 

数据模型与准备

安装好Caffe后，在examples/images文件夹下有两张示例图像，本文即在这两张图像上，用Caffe提供的预训练模型，进行特征提取，并进行可视化。

1. 进入caffe根目录，创建临时文件夹，用于存放所需要的临时文件

 

mkdir examples/_temp

 

2. 根据examples/images文件夹中的图片，创建包含图像列表的txt文件，并添加标签（0）

 

find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt
sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt

 

3. 执行下列脚本，下载imagenet12图像均值文件，在后面的网络结构定义prototxt文件中，需要用到该文件 （data/ilsvrc212/imagenet_mean.binaryproto）

data/ilsvrc12/get_ilsvrc_aux.sh

4. 将网络定义prototxt文件复制到_temp文件夹下

cp examples/feature_extraction/imagenet_val.prototxt examples/_temp

提取特征

 1. 创建 src/youname/ 文件夹， 存放我们自己的脚本

mkdir src/yourname

2. caffe的  extract_features 将提取出的图像特征存为leveldb格式， 为了方便观察特征，我们将利用下列两个python脚本将图像转化为matlab的.mat格式 （请先安装caffe的python依赖库）

feat_helper_pb2.py

# Generated by the protocol buffer compiler.  DO NOT EDIT!

from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)



DESCRIPTOR = descriptor.FileDescriptor(
  name='datum.proto',
  package='feat_extract',
  serialized_pb='\n\x0b\x64\x61tum.proto\x12\x0c\x66\x65\x61t_extract\"i\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 \x03(\x02')




_DATUM = descriptor.Descriptor(
  name='Datum',
  full_name='feat_extract.Datum',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  fields=[
    descriptor.FieldDescriptor(
      name='channels', full_name='feat_extract.Datum.channels', index=0,
      number=1, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='height', full_name='feat_extract.Datum.height', index=1,
      number=2, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='width', full_name='feat_extract.Datum.width', index=2,
      number=3, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='data', full_name='feat_extract.Datum.data', index=3,
      number=4, type=12, cpp_type=9, label=1,
      has_default_value=False, default_value="",
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='label', full_name='feat_extract.Datum.label', index=4,
      number=5, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='float_data', full_name='feat_extract.Datum.float_data', index=5,
      number=6, type=2, cpp_type=6, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
  ],
  extensions=[
  ],
  nested_types=[],
  enum_types=[
  ],
  options=None,
  is_extendable=False,
  extension_ranges=[],
  serialized_start=29,
  serialized_end=134,
)

DESCRIPTOR.message_types_by_name['Datum'] = _DATUM

class Datum(message.Message):
  __metaclass__ = reflection.GeneratedProtocolMessageType
  DESCRIPTOR = _DATUM
  
  # @@protoc_insertion_point(class_scope:feat_extract.Datum)

# @@protoc_insertion_point(module_scope)

 

leveldb2mat.py

import leveldb
import feat_helper_pb2
import numpy as np
import scipy.io as sio
import time

def main(argv):
    leveldb_name = sys.argv[1]
    print "%s" % sys.argv[1]
    batch_num = int(sys.argv[2]);
    batch_size = int(sys.argv[3]);
    window_num = batch_num*batch_size;

    start = time.time()
    if 'db' not in locals().keys():
        db = leveldb.LevelDB(leveldb_name)
        datum = feat_helper_pb2.Datum()

    ft = np.zeros((window_num, int(sys.argv[4])))
    for im_idx in range(window_num):
        datum.ParseFromString(db.Get('%d' %(im_idx)))
        ft[im_idx, :] = datum.float_data

    print 'time 1: %f' %(time.time() - start)
    sio.savemat(sys.argv[5], {'feats':ft})
    print 'time 2: %f' %(time.time() - start)
    print 'done!'

    #leveldb.DestroyDB(leveldb_name)

if __name__ == '__main__':
    import sys
    main(sys.argv)

3. 创建脚本文件extract_feature.sh， 并执行，将在examples/_temp文件夹下得到leveldb文件（features_conv1）和.mat文件（features.mat）

#!/usr/bin/env sh
# args for EXTRACT_FEATURE
TOOL=../../build/tools
MODEL=../../examples/imagenet/caffe_reference_imagenet_model #下载得到的caffe model
PROTOTXT=../../examples/_temp/imagenet_val.prototxt # 网络定义
LAYER=conv1 # 提取层的名字，如提取fc7等
LEVELDB=../../examples/_temp/features_conv1 # 保存的leveldb路径
BATCHSIZE=10

# args for LEVELDB to MAT
DIM=290400 # 需要手工计算feature长度
OUT=../../examples/_temp/features.mat #.mat文件保存路径
BATCHNUM=1 # 有多少哥batch， 本例只有两张图， 所以只有一个batch

$TOOL/extract_features.bin  $MODEL $PROTOTXT $LAYER $LEVELDB $BATCHSIZE
python leveldb2mat.py $LEVELDB $BATCHNUM  $BATCHSIZE $DIM $OUT 

4. 得到.mat文件后，需要对其进行可视化，这里用了UFLDL里的display_network函数，由于可视化出来结果进行了翻转，因此对原代码的67, 69, 83, 85行进行了修改

display_network.m 存放在 src/yourname文件夹下

function [h, array] = display_network(A, opt_normalize, opt_graycolor, cols, opt_colmajor)
% This function visualizes filters in matrix A. Each column of A is a
% filter. We will reshape each column into a square image and visualizes
% on each cell of the visualization panel. 
% All other parameters are optional, usually you do not need to worry
% about it.
% opt_normalize: whether we need to normalize the filter so that all of
% them can have similar contrast. Default value is true.
% opt_graycolor: whether we use gray as the heat map. Default is true.
% cols: how many columns are there in the display. Default value is the
% squareroot of the number of columns in A.
% opt_colmajor: you can switch convention to row major for A. In that
% case, each row of A is a filter. Default value is false.
warning off all

if ~exist('opt_normalize', 'var') || isempty(opt_normalize)
    opt_normalize= true;
end

if ~exist('opt_graycolor', 'var') || isempty(opt_graycolor)
    opt_graycolor= true;
end

if ~exist('opt_colmajor', 'var') || isempty(opt_colmajor)
    opt_colmajor = false;
end

% rescale
A = A - mean(A(:));

if opt_graycolor, colormap(gray); end

% compute rows, cols
[L M]=size(A);
sz=sqrt(L);
buf=1;
if ~exist('cols', 'var')
    if floor(sqrt(M))^2 ~= M
        n=ceil(sqrt(M));
        while mod(M, n)~=0 && n<1.2*sqrt(M), n=n+1; end
        m=ceil(M/n);
    else
        n=sqrt(M);
        m=n;
    end
else
    n = cols;
    m = ceil(M/n);
end

array=-ones(buf+m*(sz+buf),buf+n*(sz+buf));

if ~opt_graycolor
    array = 0.1.* array;
end


if ~opt_colmajor
    k=1;
    for i=1:m
        for j=1:n
            if k>M, 
                continue; 
            end
            clim=max(abs(A(:,k)));
            if opt_normalize
                array(buf+(i-1)*(sz+buf)+(1:sz),buf+(j-1)*(sz+buf)+(1:sz))=reshape(A(:,k),sz,sz)'/clim;
            else
                array(buf+(i-1)*(sz+buf)+(1:sz),buf+(j-1)*(sz+buf)+(1:sz))=reshape(A(:,k),sz,sz)'/max(abs(A(:)));
            end
            k=k+1;
        end
    end
else
    k=1;
    for j=1:n
        for i=1:m
            if k>M, 
                continue; 
            end
            clim=max(abs(A(:,k)));
            if opt_normalize
                array(buf+(i-1)*(sz+buf)+(1:sz),buf+(j-1)*(sz+buf)+(1:sz))=reshape(A(:,k),sz,sz)'/clim;
            else
                array(buf+(i-1)*(sz+buf)+(1:sz),buf+(j-1)*(sz+buf)+(1:sz))=reshape(A(:,k),sz,sz)';
            end
            k=k+1;
        end
    end
end

if opt_graycolor
    h=imagesc(array,'EraseMode','none',[-1 1]);
else
    h=imagesc(array,'EraseMode','none',[-1 1]);
end
axis image off

drawnow;

warning on all

 

5. 调用display_network 以及提取到的feature进行可视化：

在 examples/_temp/ 下创建如下matlab脚本， 并执行

addpath(genpath('../../src/wyang'));

nsample     = 3;
num_output  = 96;

load features.mat
width = size(feats, 2);
nmap  = width / num_output;

for i = 1:nsample
    feat = feats(i, :);
    feat = reshape(feat, [nmap num_output]);
    figure('name', sprintf('image #%d', i));
    display_network(feat);
end

 下图是在MNIST上用lenet进行conv1层卷积后得到的结果