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V2 #1

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d1140c8
add proto_old2new.cpp; modify align_test to a friendly version
seed93 Mar 17, 2015
eccf529
remove caffe.pb.cc .h
seed93 Mar 17, 2015
bed5006
update align_test and makefile
seed93 Mar 17, 2015
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3 changes: 3 additions & 0 deletions Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,9 @@ feat_net_raw: feat_net_raw.cpp $(STATIC_NAME)
align_test: align_test.cpp $(STATIC_NAME)
$(CXX) $< $(CXXFLAGS) -o $@ -L. -lcaffe $(LDFLAGS) $(shell pkg-config --libs opencv)

proto_old2new: proto_old2new.cpp $(STATIC_NAME)
$(CXX) $< $(CXXFLAGS) -o $@ -L. -lcaffe $(LDFLAGS)

clean:
@- $(RM) $(NAME) $(STATIC_NAME)
@- $(RM) $(PROTO_GEN_HEADER) $(PROTO_GEN_CC) $(PROTO_GEN_PY)
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234 changes: 85 additions & 149 deletions align_test.cpp
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@@ -1,175 +1,151 @@
// Copyright 2013 Yangqing Jia
//
// This is a simple script that allows one to quickly test a network whose
// structure is specified by text format protocol buffers, and whose parameter
// are loaded from a pre-trained network.
// Usage:
// test_net net_proto pretrained_net_proto iterations [CPU/GPU]

#include <cstring>
#include <cstdlib>
#include <cstdio>
#include <vector>
#include <string>

#include "caffe/caffe.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>

#define CROP_WINSIZE 39
#define CROP_PADDING 2.5

using namespace caffe; // NOLINT(build/namespaces)
using namespace cv;
using namespace caffe;

#define square(x) ((x)*(x))

float getMean( float * p )
float getMean( float * p, int width, int height )
{
float ans = 0.0f;
for(int i = 0 ; i < CROP_WINSIZE * CROP_WINSIZE ; i++, p++)
float ans = 0;
for(int i = 0 ; i < width * height ; i++, p++)
ans += *p;
ans = ans/ float( CROP_WINSIZE * CROP_WINSIZE );
ans = ans/ float( width * height );
return ans;
}

float getStd( float * p , float mean)
float getStd( float * p , float mean, int width, int height)
{
float ans = 0.0f;
for(int i = 0 ; i < CROP_WINSIZE * CROP_WINSIZE ; i++, p++)
float ans = 0;
for(int i = 0 ; i < width * height ; i++, p++)
ans += ( (*p-mean) * (*p-mean) );
ans = ans/ float( CROP_WINSIZE * CROP_WINSIZE - 1);
ans = sqrt( ans ) + 1e-5f;
ans = ans/ float( width * height - 1);
ans = sqrt( ans );
return ans;
}
void getZscore( Mat & img, int left, int right, int top, int bottom, float * & score )
void getZscore( cv::Mat & img, int left, int right, int top, int bottom, float * & score, int width, int height )
{
if( img.type()==CV_8UC3 )
{
std::cerr << "warning! a color image input" << std::endl;
cv::cvtColor( img , img , CV_RGB2GRAY );
}
double scale = (right - left) / double( CROP_WINSIZE );

double scale = sqrt((square(right - left) + square(bottom - top))/2) / double( (width + height)/2 );

left -= int( scale * CROP_PADDING );
right += int( scale * CROP_PADDING );
right += int( scale * CROP_PADDING )+1;
top -= int( scale * CROP_PADDING );
bottom+= int( scale * CROP_PADDING );
bottom+= int( scale * CROP_PADDING )+1;

if( top<0 || left < 0 || right >= img.cols || bottom >= img.rows )
if( top<0 || left < 0 || right > img.cols || bottom > img.rows )
{
std::cerr << "warning! invalid bounding box " << std::endl;
return;
}

cv::Mat patch = img( cv::Range( top, bottom ), cv::Range( left, right ) );
cv::resize( patch , patch, cv::Size( height, width ) );
patch.convertTo( patch, CV_32F );

Mat patch = img( Range( top, bottom ), Range( left, right ) );
cv::resize( patch , patch, Size( CROP_WINSIZE, CROP_WINSIZE ) );

patch.convertTo( patch, CV_32F );
//cv::imshow("AA", patch / 255.0);

float mu = getMean( patch.ptr<float>() );
float sigma = getStd( patch.ptr<float>() , mu);
fprintf(stderr, "mu %f, sigma %f\n", mu, sigma);
float mu = getMean( patch.ptr<float>(), width, height);
float sigma = getStd( patch.ptr<float>() , mu, width, height);

score = new( float[ CROP_WINSIZE * CROP_WINSIZE ] );
score = new float [ width*height ];

float * p_patch = patch.ptr<float>();

for(int i = 0 ; i < CROP_WINSIZE * CROP_WINSIZE ; i++)
for(int i = 0 ; i < width*height ; i++)
score[i] = ( p_patch[i] - mu ) / sigma;
}


template <typename Dtype>
static void save_blob(const string& fn, Blob<Dtype> *b){
LOG(INFO) << "Saving " << fn;
FILE *f = fopen(fn.c_str(), "wb");
CHECK(f != NULL);
fwrite(b->cpu_data(), sizeof(Dtype), b->count(), f);
fclose(f);
}

static void draw(const float *buf, const float *pt){
const int ph = 39, pw = 39;
void draw(Blob<float>* data_blob, const Blob<float>* feature_blob)
{
CHECK_EQ(data_blob->num(), feature_blob->num());
int batch_size = data_blob->num();
int img_length = data_blob->height()*data_blob->width();
int pt_length = feature_blob->channels()/2;
const float scale = 4.0f;
cv::Mat m = cv::Mat::zeros(ph, pw, CV_32FC1);
memcpy(m.data, buf, sizeof(float)*pw*ph);
cv::Mat dsp;
cv::normalize(m, dsp, 0, 255, cv::NORM_MINMAX, CV_8UC1);
cv::resize(dsp, dsp, cv::Size(), scale, scale);
cv::cvtColor(dsp, dsp, CV_GRAY2BGR);

#if 1
for(int i=0;i<5;i++){
const float * pt = feature_blob->cpu_data();
for (int i=0; i<batch_size; i++)
{
cv::Mat img(data_blob->height(), data_blob->width(), CV_32FC1);
img.data = (uchar *)(data_blob->mutable_cpu_data() + i*img_length);
cv::Mat dsp;
cv::normalize(img, dsp, 0, 255, cv::NORM_MINMAX, CV_8UC1);
cv::resize(dsp, dsp, cv::Size(), scale, scale);
cv::cvtColor(dsp, dsp, CV_GRAY2BGR);
for(int i=0;i<pt_length;i++){
const float *t = pt + 2*i;
cv::circle(dsp, cv::Point(t[0]*scale, t[1]*scale), 2, cv::Scalar(255,0,0), 2);
}
#endif
cv::imshow("A", dsp);
}
pt += pt_length*2;
cv::imshow("image", dsp);
cv::waitKey(0);
}
}


int main(int argc, char** argv) {
LogMessage::Enable(true);
if (argc < 3) {
LOG(ERROR) << "test_net net_proto pretrained_net_proto iterations inputbin output_dir"
<< " [CPU/GPU]";
//check input
if (argc != 4) {
LOG(ERROR) << "test_align test_net_proto pretrained_net_proto imagelist";
return 0;
}

//enable screen output
LogMessage::Enable(true);
//set CPU mode
Caffe::set_mode(Caffe::CPU);

//initial test net
NetParameter test_net_param;
ReadNetParamsFromTextFileOrDie(argv[1], &test_net_param);

Net<float> caffe_test_net(test_net_param);
//read in pretrained net
NetParameter trained_net_param;
ReadNetParamsFromBinaryFileOrDie(argv[2], &trained_net_param);
caffe_test_net.CopyTrainedLayersFrom(trained_net_param);

#if 0
SolverState state;
std::string state_file = std::string(argv[2]) + ".solverstate";
ReadProtoFromBinaryFile(state_file, &state);
#endif

vector<Blob<float>*> dummy_blob_input_vec;

//save layer
//find layer ID
int feature_layer_idx = -1;
int data_layer_idx = -1;
for(int i=0;i<caffe_test_net.layer_names().size();i++)
if(caffe_test_net.layer_names()[i] == "ip2"){
for(int i=0;i<caffe_test_net.layer_names().size();i++){
if(caffe_test_net.layer_names()[i] == "ip2")
feature_layer_idx = i;
break;
}
for(int i=0;i<caffe_test_net.layer_names().size();i++)
if(caffe_test_net.layer_names()[i] == "image_input"){
else if (caffe_test_net.layer_names()[i] == "image_input")
data_layer_idx = i;
break;
}

}
//check layer ID and log info
CHECK_NE(feature_layer_idx, -1);
CHECK_NE(data_layer_idx, -1);
LOG(INFO) << "Data layer: " << data_layer_idx;
LOG(INFO) << "Feature layer: " << feature_layer_idx;

Blob<float>* output = caffe_test_net.top_vecs()[feature_layer_idx][0],
//get pointer of the outputs of these layers
Blob<float>* feature_blob = caffe_test_net.top_vecs()[feature_layer_idx][0],
*data_blob = caffe_test_net.top_vecs()[data_layer_idx][0];
//log info
LOG(INFO) << "DATA BLOB dim: " << data_blob->num() << ' '
<< data_blob->channels() << ' '
<< data_blob->width() << ' '
<< data_blob->height();
LOG(INFO) << "OUTPUT BLOB dim: " << feature_blob->num() << ' '
<< feature_blob->channels() << ' '
<< feature_blob->width() << ' '
<< feature_blob->height();
//read image list
FILE *finput = fopen(argv[3], "r");
CHECK(finput != NULL)<<"read file error "<<argv[3];
//setup data layer
MemoryDataLayer<float> *data_layer = dynamic_cast<MemoryDataLayer<float>* >(caffe_test_net.layers()[data_layer_idx].get());
CHECK(data_layer != 0);

LOG(INFO) << "OUTPUT BLOB dim: " << output->num() << ' '
<< output->channels() << ' '
<< output->width() << ' '
<< output->height();
const int ih = data_blob->height(), iw = data_blob->width(), ic = data_blob->channels();
//double buf[ih*iw*ic];
FILE *finput = fopen(argv[3], "r");
CHECK(finput != NULL);
for (;;) {
//creat a dummy vector
vector<Blob<float>*> dummy_blob_input_vec;
CHECK_EQ(data_blob->num(), 1)<<"batch size should be 1";
int input_size = data_blob->channels()*data_blob->width()*data_blob->height();
while(1){
char fn[1024];
int l,r,t,b;
int nread = fscanf(finput, "%s%d%d%d%d", fn, &l, &r, &t, &b);
Expand All @@ -181,56 +157,16 @@ int main(int argc, char** argv) {
continue;
}
cv::cvtColor(mat, mat, CV_BGR2GRAY);
float * p = 0;
getZscore( mat, l, r, t, b, p);
if(!p)
continue;

//float *d = data_blob->mutable_cpu_data();
size_t len = ih * iw * ic;
//XXX
float * p = NULL;
getZscore( mat, l, r, t, b, p, data_blob->width(), data_blob->height());
CHECK(p != NULL);
float dummy_label[1] = {0};
float tmp[32] = {0};
//draw(p, tmp);
#if 1
data_layer->Reset(p, dummy_label, 1);
for(int j = 0; j < data_blob->num(); j++){
//memcpy(d, p, sizeof(float)*CROP_WINSIZE*CROP_WINSIZE);
/*
size_t nread = fread(buf, sizeof(double), len, finput);
CHECK_EQ(nread, len);
for(int k=0;k<len;k++){
d[k] = buf[k];
}
d += len;
*/
}
const vector<Blob<float>*>& result =
caffe_test_net.Forward(dummy_blob_input_vec);

printf("%s %d %d %d %d ", fn, l, r, t, b);
const float *pt = output->cpu_data();
for(int i=0;i<output->num();i++){
for(int j=0;j<output->channels();j++)
printf("%f\t", pt[j]);
printf("\n");
}
fflush(stdout);

draw(p, pt);
#endif
delete [] p;

//sprintf(output_dir, "%s/feat_%05d", argv[4], i);
//save_blob(output_dir, output);

//test_accuracy += result[0]->cpu_data()[0];
//LOG(ERROR) << "Batch " << i << ", accuracy: " << result[0]->cpu_data()[0];
caffe_test_net.Forward(dummy_blob_input_vec);
draw(data_blob, feature_blob);
delete p;
}
fclose(finput);
//test_accuracy /= total_iter;
//LOG(ERROR) << "Test accuracy:" << test_accuracy;

return 0;
}

27 changes: 27 additions & 0 deletions proto_old2new.cpp
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Original file line number Diff line number Diff line change
@@ -0,0 +1,27 @@
#include "caffe/caffe.hpp"

using namespace caffe;

int main(int argc, char** argv) {
//enable screen output
LogMessage::Enable(true);
//check input
if (argc != 4) {
LOG(ERROR) << "proto_old2new old_proto new_proto mode(0:bin2txt 1:txt2txt)";
return 0;
}
//set CPU mode
Caffe::set_mode(Caffe::CPU);
//initial test net
NetParameter net_param;
if(argv[3][0] == '0'){
ReadNetParamsFromBinaryFileOrDie(argv[1], &net_param);
LOG(INFO)<<"bin2txt: "<<argv[1]<<" to "<<argv[2];
}else {
ReadNetParamsFromTextFileOrDie(argv[1], &net_param);
LOG(INFO)<<"txt2txt: "<<argv[1]<<" to "<<argv[2];
}
WriteProtoToTextFile(net_param, argv[2]);
return 0;
}

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