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Add OPENCV_CUDA define, to allow having preprocess both in CPU and GPU (Mobilenet and Centernet)

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-03-20 10:21:11 +01:00
parent 58fe723b6a
commit c7d9c38ea0
5 changed files with 111 additions and 33 deletions
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include/tkDNN/CenternetDetection.h
+14 -6
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@@ -35,9 +35,7 @@ class CenternetDetection {
int ndets = 0;
// tk::dnn::Yolo::detection *dets = nullptr;
cv::Mat imageF;
cv::cuda::GpuMat imageF1_d, imageF2_d;
cv::cuda::GpuMat bgr[3];
cv::Mat imageOrig;
// std::vector< cv::cuda::GpuMat > bgr;
// variable to test cnet on dog pictures
@@ -71,8 +69,16 @@ class CenternetDetection {
float *target_coords;
float *mean_d;
float *stddev_d;
#ifdef OPENCV_CUDA
float *mean_d;
float *stddev_d;
#else
cv::Vec<float, 3> mean;
cv::Vec<float, 3> stddev;
dnnType *input;
#endif
float *d_ptrs;
@@ -88,6 +94,8 @@ class CenternetDetection {
// pointer used in the kernels
float *src_out;
int *ids_out;
void preprocess();
public:
dnnType *rt_out[4];
@@ -97,7 +105,7 @@ class CenternetDetection {
int classes = 80;
int num = 0;
int n_masks = 0;
float thresh = 0.0;
float thresh = 0.3;
cv::Scalar colors[256];
// this is filled with results
include/tkDNN/MobilenetDetection.h
+1 -1
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@@ -83,7 +83,7 @@ private:
void generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp = true);
void convert_locatios_to_boxes_and_center();
float iou(const tk::dnn::box &a, const tk::dnn::box &b);
void preprocess(const bool gpu = true);
void preprocess();
std::vector<tk::dnn::box> postprocess(const int width, const int height);
float get_color2(int c, int x, int max);
include/tkDNN/utils.h
+2
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@@ -14,6 +14,8 @@
#define dnnType float
#define OPENCV_CUDA
// Colored output
#define COL_END "\033[0m"
src/CenternetDetection.cpp
+90 -21
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@@ -103,6 +103,8 @@ bool CenternetDetection::init(std::string tensor_path) {
checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
#ifdef OPENCV_CUDA
checkCuda( cudaMalloc(&mean_d, 3 * sizeof(float)) );
checkCuda( cudaMalloc(&stddev_d, 3 * sizeof(float)) );
float mean[3] = {0.408, 0.447, 0.47};
@@ -110,6 +112,11 @@ bool CenternetDetection::init(std::string tensor_path) {
checkCuda(cudaMemcpy(mean_d, mean, 3*sizeof(float), cudaMemcpyHostToDevice));
checkCuda(cudaMemcpy(stddev_d, stddev, 3*sizeof(float), cudaMemcpyHostToDevice));
#else
checkCuda(cudaMallocHost(&input, sizeof(dnnType)*netRT->input_dim.tot()));
mean << 0.408, 0.447, 0.47;
stddev << 0.289, 0.274, 0.278;
#endif
checkCuda( cudaMalloc(&d_ptrs, dim.c * dim.h*dim.w * sizeof(float)) );
// mean << 0.408, 0.447, 0.47;
@@ -130,7 +137,7 @@ bool CenternetDetection::init(std::string tensor_path) {
}
cv::Mat CenternetDetection::draw(cv::Mat &imageORIG) {
cv::Mat CenternetDetection::draw(cv::Mat &imageOrig) {
tk::dnn::box b;
int x0, w, x1, y0, h, y1;
@@ -158,33 +165,30 @@ cv::Mat CenternetDetection::draw(cv::Mat &imageORIG) {
y1 = b.y + h;
objClass = b.cl;
det_class = coco_class_name[objClass];
cv::rectangle(imageORIG, cv::Point(x0, y0), cv::Point(x1, y1), colors[objClass], 2);
cv::rectangle(imageOrig, cv::Point(x0, y0), cv::Point(x1, y1), colors[objClass], 2);
// draw label
cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
cv::rectangle(imageORIG, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), colors[b.cl], -1);
cv::putText(imageORIG, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
cv::rectangle(imageOrig, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), colors[b.cl], -1);
cv::putText(imageOrig, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
}
return imageORIG;
return imageOrig;
// cv::namedWindow("cnet", cv::WINDOW_NORMAL);
// cv::imshow("cnet", imageOrig);
// cv::waitKey(10000);
}
void CenternetDetection::update(cv::Mat &imageORIG) {
if(!imageORIG.data) {
std::cout<<"CENTERNET: NO IMAGE DATA\n";
return;
}
TIMER_START
void CenternetDetection::preprocess()
{
auto start_t = std::chrono::steady_clock::now();
auto step_t = std::chrono::steady_clock::now();
auto end_t = std::chrono::steady_clock::now();
// -----------------------------------pre-process ------------------------------------------
// -----------------------------------pre-process ------------------------------------------
// it will resize the images to `224 x 224` in GETTING_STARTED.md
cv::Size sz = imageORIG.size();
cv::Size sz = imageOrig.size();
std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
float scale = 1.0;
@@ -232,9 +236,12 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
step_t = end_t;
}
sz_old = sz;
#ifdef OPENCV_CUDA
cv::cuda::GpuMat im_Orig;
im_Orig = cv::cuda::GpuMat(imageORIG);
// cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
cv::cuda::GpuMat imageF1_d, imageF2_d;
im_Orig = cv::cuda::GpuMat(imageOrig);
cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
checkCuda( cudaDeviceSynchronize() );
sz = imageF1_d.size();
@@ -243,11 +250,9 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
// cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
checkCuda( cudaDeviceSynchronize() );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME warpAffine: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
imageF2_d.convertTo(imageF1_d, CV_32FC3, 1/255.0);
checkCuda( cudaDeviceSynchronize() );
@@ -256,7 +261,8 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
step_t = end_t;
dim2 = dim;
// cv::cuda::split(imageF1_d,bgr);//split source
cv::cuda::GpuMat bgr[3];
cv::cuda::split(imageF1_d,bgr);//split source
end_t = std::chrono::steady_clock::now();
std::cout << " TIME split: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
@@ -275,6 +281,69 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
end_t = std::chrono::steady_clock::now();
std::cout << " TIME Memcpy to input_d: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
#else
cv::Mat imageF;
resize(imageOrig, imageF, cv::Size(new_width, new_height));
sz = imageF.size();
std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
end_t = std::chrono::steady_clock::now();
std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
cv::Mat trans = cv::getAffineTransform( src, dst );
cv::warpAffine(imageF, imageF, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME warpAffine: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
sz = imageF.size();
std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
imageF.convertTo(imageF, CV_32FC3, 1/255.0);
end_t = std::chrono::steady_clock::now();
std::cout << " TIME convertto: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
dim2 = dim;
//split channels
cv::Mat bgr[3];
cv::split(imageF,bgr);//split source
for(int i=0; i<3; i++){
bgr[i] = bgr[i] - mean[i];
bgr[i] = bgr[i] / stddev[i];
}
//write channels
for(int i=0; i<dim2.c; i++) {
int idx = i*imageF.rows*imageF.cols;
int ch = dim2.c-3 +i;
// std::cout<<"i: "<<i<<", idx: "<<idx<<", ch: "<<ch<<std::endl;
memcpy((void*)&input[idx], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
#endif
}
void CenternetDetection::update(cv::Mat &image_orig) {
imageOrig = image_orig;
if(!imageOrig.data) {
std::cout<<"CENTERNET: NO IMAGE DATA\n";
return;
}
TIMER_START
auto start_t = std::chrono::steady_clock::now();
auto step_t = std::chrono::steady_clock::now();
auto end_t = std::chrono::steady_clock::now();
preprocess();
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
src/MobilenetDetection.cpp
+4 -5
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@@ -280,10 +280,10 @@ cv::Mat MobilenetDetection::draw()
return origImg;
}
void MobilenetDetection::preprocess(const bool gpu)
void MobilenetDetection::preprocess()
{
std::cout<<"preprocess"<<std::endl;
if(gpu){
#ifdef OPENCV_CUDA
//move original image on GPU
cv::cuda::GpuMat im_Orig, frame_resize, frame_nomean, frame_scaled;
im_Orig = cv::cuda::GpuMat(origImg);
@@ -301,8 +301,7 @@ void MobilenetDetection::preprocess(const bool gpu)
int idx = i * frame_scaled.rows * frame_scaled.cols;
checkCuda( cudaMemcpy((void *)&input_d[idx], (void *)bgr[i].data, frame_scaled.rows * frame_scaled.cols* sizeof(float), cudaMemcpyDeviceToDevice) );
}
}
else{
#else
//resize image, remove mean, divide by std
cv::Mat frame_resize, frame_nomean, frame_scaled;
resize(origImg, frame_resize, cv::Size(netRT->input_dim.w, netRT->input_dim.h));
@@ -317,7 +316,7 @@ void MobilenetDetection::preprocess(const bool gpu)
memcpy((void *)&input[idx], (void *)bgr[i].data, frame_scaled.rows * frame_scaled.cols * sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_d, input, netRT->input_dim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
}
#endif
}
void MobilenetDetection::update(cv::Mat &img)