Caffe提取任意层特征并进行可视化
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.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
