【Cffe特征图可视化】【特征图】【非权重图】

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现在Caffe的Matlab接口 (matcaffe3) 和python接口都非常强大, 可以直接提取任意层的feature map以及parameters, 所以本文仅仅作为参考, 更多最新的信息请参考:

http://caffe.berkeleyvision.org/tutorial/interfaces.html

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.txtsed "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 descriptorfrom google.protobuf import messagefrom google.protobuf import reflectionfrom 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'] = _DATUMclass Datum(message.Message):  __metaclass__ = reflection.GeneratedProtocolMessageType  DESCRIPTOR = _DATUM    # @@protoc_insertion_point(class_scope:feat_extract.Datum)# @@protoc_insertion_point(module_scope)

leveldb2mat.py

import leveldbimport feat_helper_pb2import numpy as npimport scipy.io as sioimport timedef 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_FEATURETOOL=../../build/toolsMODEL=../../examples/imagenet/caffe_reference_imagenet_model #下载得到的caffe modelPROTOTXT=../../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 MATDIM=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 $BATCHSIZEpython 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 allif ~exist('opt_normalize', 'var') || isempty(opt_normalize)    opt_normalize= true;endif ~exist('opt_graycolor', 'var') || isempty(opt_graycolor)    opt_graycolor= true;endif ~exist('opt_colmajor', 'var') || isempty(opt_colmajor)    opt_colmajor = false;end% rescaleA = 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;    endelse    n = cols;    m = ceil(M/n);endarray=-ones(buf+m*(sz+buf),buf+n*(sz+buf));if ~opt_graycolor    array = 0.1.* array;endif ~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    endelse    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    endendif opt_graycolor    h=imagesc(array,'EraseMode','none',[-1 1]);else    h=imagesc(array,'EraseMode','none',[-1 1]);endaxis image offdrawnow;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.matwidth = 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

文章摘自 http://www.cnblogs.com/platero/p/3967208.html

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