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tests/yolo_berkeley/yolo_berkeley.cpp

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This commit is contained in:
Francesco Gatti
2018-12-18 18:07:37 +01:00
parent 6eb63160c8
commit a9970f43fb
2 changed files with 40 additions and 56 deletions
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demo/live/live.cpp
+12 -28
View File
@@ -7,15 +7,7 @@
#include <opencv2/highgui/highgui.hpp> #include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp> #include <opencv2/imgproc/imgproc.hpp>
#define VOC const char *reg_bias = "../tests/yolo_berkeley/layers/g31.bin";
#ifdef VOC
const char *reg_bias = "../tests/yolo_voc/layers/g31.bin";
#define CLASS 20
#else
const char *reg_bias = "../tests/yolo/layers/g31.bin";
#define CLASS 80
#endif
int prob_sort(const void *pa, const void *pb) { int prob_sort(const void *pa, const void *pb) {
tk::dnn::box a = *(tk::dnn::box *)pa; tk::dnn::box a = *(tk::dnn::box *)pa;
@@ -26,30 +18,22 @@ int prob_sort(const void *pa, const void *pb) {
return 0; return 0;
} }
cv::Mat GetSquareImage(const cv::Mat& img, int target_width) { cv::Mat GetSquareImage(const cv::Mat& img, int target_height, int target_width) {
int width = img.cols, height = img.rows; int width = img.cols, height = img.rows;
cv::Mat square = cv::Mat::zeros( target_width, target_width, img.type() ); cv::Mat square = cv::Mat::zeros( target_height, target_width, img.type() );
int max_dim = ( width >= height ) ? width : height; int max_dim = ( width >= height ) ? width : height;
float scale = ( ( float ) target_width ) / max_dim; float scale = ( ( float ) target_width ) / max_dim;
cv::Rect roi; cv::Rect roi;
if ( width >= height )
{
roi.width = target_width; roi.width = target_width;
roi.x = 0; roi.x = 0;
roi.height = height * scale; roi.height = target_height;
roi.y = ( target_width - roi.height ) / 2;
}
else
{
roi.y = 0; roi.y = 0;
roi.height = target_width;
roi.width = width * scale;
roi.x = ( target_width - roi.width ) / 2;
}
cv::resize( img, square( roi ), roi.size() );
cv::resize( img, square(roi), roi.size() );
return square; return square;
} }
@@ -61,7 +45,7 @@ double compute_image( cv::Mat imageORIG,
TIMER_START TIMER_START
//Resize with padding and convert to float //Resize with padding and convert to float
cv::Mat image = GetSquareImage(imageORIG, netRT->input_dim.w); cv::Mat image = GetSquareImage(imageORIG, netRT->input_dim.h, netRT->input_dim.w);
cv::Mat imageF; cv::Mat imageF;
image.convertTo(imageF, CV_32FC3, 1/255.0); image.convertTo(imageF, CV_32FC3, 1/255.0);
@@ -109,7 +93,7 @@ int main(int argc, char *argv[]) {
//params //params
char *tensor_path = NULL; char *tensor_path = NULL;
int device = 0; char *device = 0;
float thresh = 0.3f; float thresh = 0.3f;
bool show = false; bool show = false;
@@ -127,7 +111,7 @@ int main(int argc, char *argv[]) {
if(argc - optind == 2) { if(argc - optind == 2) {
tensor_path = argv[optind]; tensor_path = argv[optind];
device = atoi(argv[optind+1]); device = argv[optind+1];
} else { } else {
std::cout<<"not enough arguments.\n"; std::cout<<"not enough arguments.\n";
return print_usage(); return print_usage();
@@ -149,7 +133,7 @@ int main(int argc, char *argv[]) {
//convert network to tensorRT //convert network to tensorRT
tk::dnn::NetworkRT netRT(NULL, tensor_path); tk::dnn::NetworkRT netRT(NULL, tensor_path);
tk::dnn::RegionInterpret rI(netRT.input_dim, netRT.output_dim, CLASS, 4, 5, thresh, reg_bias); tk::dnn::RegionInterpret rI(netRT.input_dim, netRT.output_dim, 10, 4, 5, thresh, reg_bias);
dnnType *input = new float[netRT.input_dim.tot()]; dnnType *input = new float[netRT.input_dim.tot()];
dnnType *output = new float[netRT.output_dim.tot()]; dnnType *output = new float[netRT.output_dim.tot()];
@@ -163,7 +147,7 @@ int main(int argc, char *argv[]) {
//LOAD IMAGE //LOAD IMAGE
cv::Mat img; //= cv::imread("../demo/live/test.jpeg", CV_LOAD_IMAGE_COLOR); cv::Mat img; //= cv::imread("../demo/live/test.jpeg", CV_LOAD_IMAGE_COLOR);
cap >> img; cap >> img;
//show results
if(!img.data) if(!img.data)
FatalError("Could not open image"); FatalError("Could not open image");
std::cout<<"Image size: ("<<img.cols<<"x"<<img.rows<<")\n"; std::cout<<"Image size: ("<<img.cols<<"x"<<img.rows<<")\n";
tests/yolo_berkeley/yolo_berkeley.cpp
+28 -28
View File
@@ -1,32 +1,32 @@
#include<iostream> #include<iostream>
#include "tkdnn.h" #include "tkdnn.h"
const char *input_bin = "../tests/yolo_berkely/layers/input.bin"; const char *input_bin = "../tests/yolo_berkeley/layers/input.bin";
const char *c0_bin = "../tests/yolo_berkely/layers/c0.bin"; const char *c0_bin = "../tests/yolo_berkeley/layers/c0.bin";
const char *c2_bin = "../tests/yolo_berkely/layers/c2.bin"; const char *c2_bin = "../tests/yolo_berkeley/layers/c2.bin";
const char *c4_bin = "../tests/yolo_berkely/layers/c4.bin"; const char *c4_bin = "../tests/yolo_berkeley/layers/c4.bin";
const char *c5_bin = "../tests/yolo_berkely/layers/c5.bin"; const char *c5_bin = "../tests/yolo_berkeley/layers/c5.bin";
const char *c6_bin = "../tests/yolo_berkely/layers/c6.bin"; const char *c6_bin = "../tests/yolo_berkeley/layers/c6.bin";
const char *c8_bin = "../tests/yolo_berkely/layers/c8.bin"; const char *c8_bin = "../tests/yolo_berkeley/layers/c8.bin";
const char *c9_bin = "../tests/yolo_berkely/layers/c9.bin"; const char *c9_bin = "../tests/yolo_berkeley/layers/c9.bin";
const char *c10_bin = "../tests/yolo_berkely/layers/c10.bin"; const char *c10_bin = "../tests/yolo_berkeley/layers/c10.bin";
const char *c12_bin = "../tests/yolo_berkely/layers/c12.bin"; const char *c12_bin = "../tests/yolo_berkeley/layers/c12.bin";
const char *c13_bin = "../tests/yolo_berkely/layers/c13.bin"; const char *c13_bin = "../tests/yolo_berkeley/layers/c13.bin";
const char *c14_bin = "../tests/yolo_berkely/layers/c14.bin"; const char *c14_bin = "../tests/yolo_berkeley/layers/c14.bin";
const char *c15_bin = "../tests/yolo_berkely/layers/c15.bin"; const char *c15_bin = "../tests/yolo_berkeley/layers/c15.bin";
const char *c16_bin = "../tests/yolo_berkely/layers/c16.bin"; const char *c16_bin = "../tests/yolo_berkeley/layers/c16.bin";
const char *c18_bin = "../tests/yolo_berkely/layers/c18.bin"; const char *c18_bin = "../tests/yolo_berkeley/layers/c18.bin";
const char *c19_bin = "../tests/yolo_berkely/layers/c19.bin"; const char *c19_bin = "../tests/yolo_berkeley/layers/c19.bin";
const char *c20_bin = "../tests/yolo_berkely/layers/c20.bin"; const char *c20_bin = "../tests/yolo_berkeley/layers/c20.bin";
const char *c21_bin = "../tests/yolo_berkely/layers/c21.bin"; const char *c21_bin = "../tests/yolo_berkeley/layers/c21.bin";
const char *c22_bin = "../tests/yolo_berkely/layers/c22.bin"; const char *c22_bin = "../tests/yolo_berkeley/layers/c22.bin";
const char *c23_bin = "../tests/yolo_berkely/layers/c23.bin"; const char *c23_bin = "../tests/yolo_berkeley/layers/c23.bin";
const char *c24_bin = "../tests/yolo_berkely/layers/c24.bin"; const char *c24_bin = "../tests/yolo_berkeley/layers/c24.bin";
const char *c26_bin = "../tests/yolo_berkely/layers/c26.bin"; const char *c26_bin = "../tests/yolo_berkeley/layers/c26.bin";
const char *c29_bin = "../tests/yolo_berkely/layers/c29.bin"; const char *c29_bin = "../tests/yolo_berkeley/layers/c29.bin";
const char *c30_bin = "../tests/yolo_berkely/layers/c30.bin"; const char *c30_bin = "../tests/yolo_berkeley/layers/c30.bin";
const char *g31_bin = "../tests/yolo_berkely/layers/g31.bin"; const char *g31_bin = "../tests/yolo_berkeley/layers/g31.bin";
const char *output_bin = "../tests/yolo_berkely/layers/output.bin"; const char *output_bin = "../tests/yolo_berkeley/layers/output.bin";
int main() { int main() {
@@ -97,7 +97,7 @@ int main() {
tk::dnn::Conv2d c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true); tk::dnn::Conv2d c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
tk::dnn::Activation a29(&net, tk::dnn::ACTIVATION_LEAKY); tk::dnn::Activation a29(&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c30(&net, 75, 1, 1, 1, 1, 0, 0, c30_bin, false); tk::dnn::Conv2d c30(&net, 75, 1, 1, 1, 1, 0, 0, c30_bin, false);
tk::dnn::Region g31(&net, 20, 4, 5); tk::dnn::Region g31(&net, 10, 4, 5);
tk::dnn::RegionInterpret rI(dim, g31.output_dim, 10, 4, 5, 0.3f, g31_bin); tk::dnn::RegionInterpret rI(dim, g31.output_dim, 10, 4, 5, 0.3f, g31_bin);
@@ -110,7 +110,7 @@ int main() {
net.print(); net.print();
//convert network to tensorRT //convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, "yolo_berkely.rt"); tk::dnn::NetworkRT netRT(&net, "yolo_berkeley.rt");
dnnType *out_data, *out_data2; // cudnn output, tensorRT output dnnType *out_data, *out_data2; // cudnn output, tensorRT output