void LRNLayerTest<Dtype>::ReferenceLRNForward(
const Blob<Dtype>& blob_bottom, const LayerParameter& layer_param,
Blob<Dtype>* blob_top) {
blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
blob_bottom.height(), blob_bottom.width());
const Dtype* bottom_data = blob_bottom.cpu_data();
Dtype* top_data = blob_top->mutable_cpu_data();
Dtype alpha = layer_param.alpha();
Dtype beta = layer_param.beta();
int size = layer_param.local_size();
for (int n = 0; n < blob_bottom.num(); ++n) {
for (int c = 0; c < blob_bottom.channels(); ++c) {
for (int h = 0; h < blob_bottom.height(); ++h) {
for (int w = 0; w < blob_bottom.width(); ++w) {
int c_start = c - (size - 1) / 2;
int c_end = min(c_start + size, blob_bottom.channels());
c_start = max(c_start, 0);
Dtype scale = 1.;
for (int i = c_start; i < c_end; ++i) {
Dtype value = blob_bottom.data_at(n, i, h, w);
scale += value * value * alpha / size;
}
*(top_data + blob_top->offset(n, c, h, w)) =
blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
}
}
}
}
}
void LRNLayerTest<Dtype>::ReferenceLRNForward(
const Blob<Dtype>& blob_bottom, const LayerParameter& layer_param,
Blob<Dtype>* blob_top) {
blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
blob_bottom.height(), blob_bottom.width());
Dtype* top_data = blob_top->mutable_cpu_data();
LRNParameter lrn_param = layer_param.lrn_param();
Dtype alpha = lrn_param.alpha();
Dtype beta = lrn_param.beta();
int size = lrn_param.local_size();
switch (lrn_param.norm_region()) {
case LRNParameter_NormRegion_ACROSS_CHANNELS:
for (int n = 0; n < blob_bottom.num(); ++n) {
for (int c = 0; c < blob_bottom.channels(); ++c) {
for (int h = 0; h < blob_bottom.height(); ++h) {
for (int w = 0; w < blob_bottom.width(); ++w) {
int c_start = c - (size - 1) / 2;
int c_end = min(c_start + size, blob_bottom.channels());
c_start = max(c_start, 0);
Dtype scale = 1.;
for (int i = c_start; i < c_end; ++i) {
Dtype value = blob_bottom.data_at(n, i, h, w);
scale += value * value * alpha / size;
}
*(top_data + blob_top->offset(n, c, h, w)) =
blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
}
}
}
}
break;
case LRNParameter_NormRegion_WITHIN_CHANNEL:
for (int n = 0; n < blob_bottom.num(); ++n) {
for (int c = 0; c < blob_bottom.channels(); ++c) {
for (int h = 0; h < blob_bottom.height(); ++h) {
int h_start = h - (size - 1) / 2;
int h_end = min(h_start + size, blob_bottom.height());
h_start = max(h_start, 0);
for (int w = 0; w < blob_bottom.width(); ++w) {
Dtype scale = 1.;
int w_start = w - (size - 1) / 2;
int w_end = min(w_start + size, blob_bottom.width());
w_start = max(w_start, 0);
for (int nh = h_start; nh < h_end; ++nh) {
for (int nw = w_start; nw < w_end; ++nw) {
Dtype value = blob_bottom.data_at(n, c, nh, nw);
scale += value * value * alpha / (size * size);
}
}
*(top_data + blob_top->offset(n, c, h, w)) =
blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
}
}
}
}
break;
default:
LOG(FATAL) << "Unknown normalization region.";
}
}
void CaffeMobile::putImage(AndroidBitmapInfo* info, void* pixels, const vector<Blob<float>*>& resImage) {
Blob<float> * srcBlob = *resImage.data();
LOG(DEBUG) << "srcBlob received";
vector<int> shape = {1, 3, (int) info->width, (int) info->height };
LOG(DEBUG) << "shape configured";
Blob<float>* imgBlob = new Blob<float>();
LOG(DEBUG) << "Blob created";
imgBlob->Reshape(shape);
LOG(DEBUG) << "imgBlob reshaped";
imgBlob->CopyFrom(*srcBlob, false, true);
LOG(DEBUG) << "imgBlob copied";
int size = imgBlob->count();
LOG(DEBUG) << "imgBlob size is: " << size;
/*Partially from https://github.com/ruckus/android-image-filter-ndk*/
uint32_t* pixelRow;
int ix, iy, red, green, blue;
for(iy = 0; iy < (int) info->height; iy++){
pixelRow = (uint32_t*) pixels;
for(ix =0; ix < (int) info->width; ix++){
red = (int) clip(imgBlob->data_at(0,0,iy,ix), 0, 255);
green = (int) clip(imgBlob->data_at(0,1,iy,ix), 0, 255);
blue = (int) clip(imgBlob->data_at(0,2,iy,ix), 0, 255);
pixelRow[ix] =
((red << 16) & 0x00FF0000) |
((green << 8) & 0x0000FF00) |
(blue & 0x000000FF);
}
pixels = (char*)pixels + info->stride;
}
LOG(DEBUG) << "before return putImage " << size;
return;
}
void HDF5OutputLayerTest<TypeParam>::CheckBlobEqual(const Blob<Dtype>& b1,
const Blob<Dtype>& b2) {
EXPECT_EQ(b1.num(), b2.num());
EXPECT_EQ(b1.channels(), b2.channels());
EXPECT_EQ(b1.height(), b2.height());
EXPECT_EQ(b1.width(), b2.width());
for (int n = 0; n < b1.num(); ++n) {
for (int c = 0; c < b1.channels(); ++c) {
for (int h = 0; h < b1.height(); ++h) {
for (int w = 0; w < b1.width(); ++w) {
EXPECT_EQ(b1.data_at(n, c, h, w), b2.data_at(n, c, h, w));
}
}
}
}
}
int main(int argc, char** argv)
{
cudaSetDevice(0);
Caffe::set_phase(Caffe::TEST);
Caffe::SetDevice(3);
//Caffe::set_mode(Caffe::CPU);
if (argc == 8 && strcmp(argv[7], "CPU") == 0) {
LOG(ERROR) << "Using CPU";
Caffe::set_mode(Caffe::CPU);
} else {
LOG(ERROR) << "Using GPU";
Caffe::set_mode(Caffe::GPU);
}
// Caffe::set_mode(Caffe::CPU);
NetParameter test_net_param;
ReadProtoFromTextFile(argv[1], &test_net_param);
Net<float> caffe_test_net(test_net_param);
NetParameter trained_net_param;
ReadProtoFromBinaryFile(argv[2], &trained_net_param);
caffe_test_net.CopyTrainedLayersFrom(trained_net_param);
vector<shared_ptr<Layer<float> > > layers = caffe_test_net.layers();
const DataLayer<float> *datalayer = dynamic_cast<const DataLayer<float>* >(layers[0].get());
CHECK(datalayer);
string labelFile(argv[3]);
int data_counts = 0;
FILE * file = fopen(labelFile.c_str(), "r");
while(fgets(buf,100000,file) > 0)
{
data_counts++;
}
fclose(file);
vector<Blob<float>*> dummy_blob_input_vec;
string rootfolder(argv[4]);
rootfolder.append("/");
CreateDir(rootfolder.c_str(), rootfolder.size() - 1);
string folder;
string fName;
float output;
int counts = 0;
file = fopen(labelFile.c_str(), "r");
Blob<float>* c1 = (*(caffe_test_net.bottom_vecs().rbegin()))[0];
int c2 = c1->num();
int batchCount = std::ceil(data_counts / (floor)(c2));//(test_net_param.layers(0).layer().batchsize()));// (test_net_param.layers(0).layer().batchsize() ));
string resulttxt = rootfolder + "3dNormalResult.txt";
FILE * resultfile = fopen(resulttxt.c_str(), "w");
for (int batch_id = 0; batch_id < batchCount; ++batch_id)
{
LOG(INFO)<< "processing batch :" << batch_id+1 << "/" << batchCount <<"...";
const vector<Blob<float>*>& result = caffe_test_net.Forward(dummy_blob_input_vec);
Blob<float>* bboxs = (*(caffe_test_net.bottom_vecs().rbegin()))[0];
int bsize = bboxs->num();
const Blob<float>* labels = (*(caffe_test_net.bottom_vecs().rbegin()))[1];
for (int i = 0; i < bsize && counts < data_counts; i++, counts++)
{
char fname[1010];
fscanf(file, "%s", fname);
/*for(int w = 0; w < LABEL_SIZE; w ++)
for(int h = 0; h < LABEL_SIZE; h ++)
{
int lbl;
fscanf(file,"%d",&lbl);
}*/
fprintf(resultfile, "%s ", fname);
int len = LABEL_SIZE * LABEL_SIZE * LABEL_LEN;
for(int j = 0; j < len; j ++)
{
fprintf(resultfile, "%f ", (float)(bboxs->data_at(i, j, 0, 0)) );
}
fprintf(resultfile, "\n");
}
}
fclose(resultfile);
fclose(file);
return 0;
}
int main(int argc, char** argv)
{
Caffe::set_phase(Caffe::TEST);
Caffe::SetDevice(0);
//Caffe::set_mode(Caffe::CPU);
if (argc == 8 && strcmp(argv[7], "CPU") == 0) {
LOG(ERROR) << "Using CPU";
Caffe::set_mode(Caffe::CPU);
} else {
LOG(ERROR) << "Using GPU";
Caffe::set_mode(Caffe::GPU);
}
//Caffe::set_mode(Caffe::CPU);
NetParameter test_net_param;
ReadProtoFromTextFile(argv[1], &test_net_param);
Net<float> caffe_test_net(test_net_param);
NetParameter trained_net_param;
ReadProtoFromBinaryFile(argv[2], &trained_net_param);
caffe_test_net.CopyTrainedLayersFrom(trained_net_param);
vector<shared_ptr<Layer<float> > > layers = caffe_test_net.layers();
const DataLayer<float> *datalayer = dynamic_cast<const DataLayer<float>* >(layers[0].get());
CHECK(datalayer);
string labelFile(argv[3]);
int data_counts = 0;
FILE * file = fopen(labelFile.c_str(), "r");
while(fgets(buf,100000,file) > 0)
{
data_counts++;
}
fclose(file);
vector<Blob<float>*> dummy_blob_input_vec;
string rootfolder(argv[4]);
rootfolder.append("/");
CreateDir(rootfolder.c_str(), rootfolder.size() - 1);
string folder;
string fName;
float output;
int counts = 0;
file = fopen(labelFile.c_str(), "r");
Blob<float>* c1 = (*(caffe_test_net.bottom_vecs().rbegin()))[0];
int c2 = c1->num();
int batchCount = std::ceil(data_counts / (floor)(c2));//(test_net_param.layers(0).layer().batchsize()));// (test_net_param.layers(0).layer().batchsize() ));
string resulttxt = rootfolder + "3dNormalResult.txt";
FILE * resultfile = fopen(resulttxt.c_str(), "w");
float * st = new float[LABEL_LEN * LABEL_HEIGHT * LABEL_WIDTH];
for (int batch_id = 0; batch_id < batchCount; ++batch_id)
{
LOG(INFO)<< "processing batch :" << batch_id+1 << "/" << batchCount <<"...";
const vector<Blob<float>*>& result = caffe_test_net.Forward(dummy_blob_input_vec);
Blob<float>* bboxs = (*(caffe_test_net.bottom_vecs().rbegin()))[0];
int bsize = bboxs->num();
const Blob<float>* labels = (*(caffe_test_net.bottom_vecs().rbegin()))[1];
for (int i = 0; i < bsize && counts < data_counts; i++, counts++)
{
char fname[1010];
fscanf(file, "%s", fname);
for(int c = 0; c < LABEL_LEN; c ++)
for(int w = 0; w < LABEL_WIDTH; w ++)
for(int h = 0; h < LABEL_HEIGHT; h ++)
{
float lbl;
fscanf(file,"%f", &lbl);
}
fprintf(resultfile, "%s ", fname);
//char ss2[1010];
//sprintf(ss2,"/home/dragon123/cnncode/3ddata/testReg/%d_fine_not.jpg",counts);
//Mat imgOut(Size(LABEL_SIZE,LABEL_SIZE),CV_8UC3);
//for(int c = 0; c < LABEL_LEN; c++)
{
for(int w = 0; w < LABEL_WIDTH; w ++)
for(int h = 0; h < LABEL_HEIGHT; h ++)
{
float tnum1 = (float)(bboxs->data_at(i, 0, h, w));
float tnum2 = (float)(bboxs->data_at(i, 1, h, w));
float tnum3 = (float)(bboxs->data_at(i, 2, h, w));
float z = tnum1 * tnum1 + tnum2 * tnum2 + tnum3 * tnum3;
z = sqrt(z);
st[w * LABEL_HEIGHT + h] = tnum1 / z;
st[LABEL_HEIGHT * LABEL_WIDTH + w * LABEL_HEIGHT + h] = tnum2 / z;
st[2 * LABEL_HEIGHT * LABEL_WIDTH + w * LABEL_HEIGHT + h] = tnum3 / z;
//fprintf(resultfile, "%f ", tnum);
//imgOut.at<cv::Vec3b>(h, w)[2 - c] = (uchar)(tnum) * 128 + 128);
}
}
for(int c = 0; c < LABEL_LEN; c++)
{
for(int w = 0; w < LABEL_WIDTH; w ++)
for(int h = 0; h < LABEL_HEIGHT; h ++)
{
float tnum = st[c * LABEL_HEIGHT * LABEL_WIDTH + w * LABEL_HEIGHT + h] ;
fprintf(resultfile, "%f ", tnum);
// imgOut.at<cv::Vec3b>(h, w)[2 - c] = (uchar)((tnum) * 128 + 128);
}
}
//imwrite(ss2,imgOut);
fprintf(resultfile, "\n");
}
}
delete st;
fclose(resultfile);
fclose(file);
return 0;
}
