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Merge branch 'cnet' of https://github.com/ceccocats/tkDNN into cnet

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xavier
2020-02-21 10:49:35 +01:00
parent 38a1b9dcb2 2c1df5619f
commit 209a3e8492
4 changed files with 152 additions and 170 deletions
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src/CenternetDetection.cpp
+101 -144
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@@ -37,10 +37,11 @@ bool CenternetDetection::init(std::string tensor_path) {
coco_class_name = std::vector<std::string>(coco_class_name_, std::end( coco_class_name_ ));
src = cv::Mat(cv::Size(2,3), CV_32F);
dst = cv::Mat(cv::Size(2,3), CV_32F);
dst2 = cv::Mat(cv::Size(2,3), CV_32F);
trans = cv::Mat(cv::Size(3,2), CV_32F);
trans2 = cv::Mat(cv::Size(3,2), CV_32F);
// dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
checkCuda(cudaMallocHost(&input_h, sizeof(dnnType)*netRT->input_dim.tot()));
checkCuda(cudaMallocHost(&input, sizeof(dnnType)*netRT->input_dim.tot()));
checkCuda(cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot()));
// dim_hm = tk::dnn::dataDim_t(1, 80, 56, 56, 1);
@@ -94,61 +95,31 @@ bool CenternetDetection::init(std::string tensor_path) {
checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
mean << 0.408, 0.447, 0.47;
stddev << 0.289, 0.274, 0.278;
checkCuda( cudaMalloc(&mean_d, 3 * sizeof(float)) );
checkCuda( cudaMalloc(&stddev_d, 3 * sizeof(float)) );
float mean[3] = {0.408, 0.447, 0.47};
float stddev[3] = {0.289, 0.274, 0.278};
checkCuda(cudaMemcpy(mean_d, mean, 3*sizeof(float), cudaMemcpyHostToDevice));
checkCuda(cudaMemcpy(stddev_d, stddev, 3*sizeof(float), cudaMemcpyHostToDevice));
checkCuda( cudaMalloc(&d_ptrs, dim.c * dim.h*dim.w * sizeof(float)) );
// mean << 0.408, 0.447, 0.47;
// stddev << 0.289, 0.274, 0.278;
// Alloc array used in the kernel
checkCuda( cudaMalloc(&src_out, K *sizeof(float)) );
checkCuda( cudaMalloc(&ids_out, K *sizeof(int)) );
// checkCuda( cudaFree(src_out) );
// checkCuda( cudaFree(ids_out) );
}
void CenternetDetection::testdog() {
readBinaryFile(input_bin, dim.tot(), &input_h, &input_d);
// -------- transofrm compose
cv::Mat imageORIG = cv::imread("../../dog.jpg");
imageORIG.convertTo(imageF, CV_32FC3, 1/255.0);
sz = imageF.size();
std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
resize(imageF, imageF, cv::Size(512, 512));
const int cropSize = 512;
const int offsetW = (imageF.cols - cropSize) / 2;
const int offsetH = (imageF.rows - cropSize) / 2;
const cv::Rect roi(offsetW, offsetH, cropSize, cropSize);
imageF = imageF(roi).clone();
std::cout << "Cropped image dimension: " << imageF.cols << " X " << imageF.rows << std::endl;
mean << 0.485, 0.456, 0.406;
stddev << 0.229, 0.224, 0.225;
sz = imageF.size();
// std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
// std::cout<<"mean: "<<mean<<", std: "<<stddev<<std::endl;
cv::add(imageF, -mean, imageF);
cv::divide(imageF, stddev, imageF);
//split channels
cv::split(imageF,bgr);//split source
dim2 = dim;
//write channels
for(int i=0; i<dim2.c; i++) {
int idx = i*imageF.rows*imageF.cols;
int ch = dim2.c-1 -i;
memcpy((void*)&input[idx], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
TIMER_START
netRT->infer(dim2, input_d);
TIMER_STOP
dim2.print();
}
// checkResult(dim2.tot(), input_h, input);
dst2.at<float>(0,0)=width * 0.5;
dst2.at<float>(0,1)=width * 0.5;
dst2.at<float>(1,0)=width * 0.5;
dst2.at<float>(1,1)=width * 0.5 + width * -0.5;
dst2.at<float>(2,0)=dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
dst2.at<float>(2,1)=dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );
}
cv::Mat CenternetDetection::draw(cv::Mat &imageORIG) {
@@ -196,7 +167,7 @@ cv::Mat CenternetDetection::draw(cv::Mat &imageORIG) {
void CenternetDetection::update(cv::Mat &imageORIG) {
if(!imageORIG.data) {
std::cout<<"YOLO: NO IMAGE DATA\n";
std::cout<<"CENTERNET: NO IMAGE DATA\n";
return;
}
TIMER_START
@@ -211,82 +182,91 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
float scale = 1.0;
float new_height = sz.height * scale;
float new_width = sz.width * scale;
float c[] = {new_width / 2.0, new_height /2.0};
float s[2];
if(sz.height != sz_old.height && sz.width != sz_old.width){
float c[] = {new_width / 2.0, new_height /2.0};
float s[2];
if(sz.width > sz.height){
s[0] = sz.width * 1.0;
s[1] = sz.width * 1.0;
if(sz.width > sz.height){
s[0] = sz.width * 1.0;
s[1] = sz.width * 1.0;
}
else{
s[0] = sz.height * 1.0;
s[1] = sz.height * 1.0;
}
// ----------- get_affine_transform
// rot_rad = pi * 0 / 100 --> 0
src.at<float>(0,0)=c[0];
src.at<float>(0,1)=c[1];
src.at<float>(1,0)=c[0];
src.at<float>(1,1)=c[1] + s[0] * -0.5;
dst.at<float>(0,0)=inp_width * 0.5;
dst.at<float>(0,1)=inp_height * 0.5;
dst.at<float>(1,0)=inp_width * 0.5;
dst.at<float>(1,1)=inp_height * 0.5 + inp_width * -0.5;
src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
trans = cv::getAffineTransform( src, dst );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME gett affine trans: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
trans2 = cv::getAffineTransform( dst2, src );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME getAffineTrans 2: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
}
else{
s[0] = sz.height * 1.0;
s[1] = sz.height * 1.0;
}
// ----------- get_affine_transform
// rot_rad = pi * 0 / 100 --> 0
src.at<float>(0,0)=c[0];
src.at<float>(0,1)=c[1];
src.at<float>(1,0)=c[0];
src.at<float>(1,1)=c[1] + s[0] * -0.5;
dst.at<float>(0,0)=inp_width * 0.5;
dst.at<float>(0,1)=inp_height * 0.5;
dst.at<float>(1,0)=inp_width * 0.5;
dst.at<float>(1,1)=inp_height * 0.5 + inp_width * -0.5;
sz_old = sz;
cv::cuda::GpuMat im_Orig;
im_Orig = cv::cuda::GpuMat(imageORIG);
cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
checkCuda( cudaDeviceSynchronize() );
src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
cv::Mat trans = cv::getAffineTransform( src, dst );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME gett affine trans: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
step_t = end_t;
resize(imageORIG, imageF, cv::Size(new_width, new_height));
sz = imageF.size();
sz = imageF1_d.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::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
cv::warpAffine(imageF, imageF, 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::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
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);
imageF2_d.convertTo(imageF1_d, CV_32FC3, 1/255.0);
checkCuda( cudaDeviceSynchronize() );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME convert: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME convert: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
dim2 = dim;
//split channels
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];
}
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;
//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));
}
for(int i=0; i<dim.c; i++)
checkCuda( cudaMemcpy(d_ptrs + i*dim.h * dim.w, (float*)bgr[i].data, dim.h * dim.w * sizeof(float), cudaMemcpyDeviceToDevice) );
normalize(d_ptrs, dim.c, dim.h, dim.w, mean_d, stddev_d);
end_t = std::chrono::steady_clock::now();
std::cout << " TIME normalize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
checkCuda(cudaMemcpyAsync(input_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
checkCuda(cudaMemcpy(input_d, d_ptrs, dim2.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
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;
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
@@ -295,7 +275,6 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
TIMER_STOP
dim2.print();
}
// checkResult(dim2.tot(), input_h, input);
step_t = std::chrono::steady_clock::now();
// ------------------------------------ process --------------------------------------------
@@ -307,10 +286,10 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
activationSIGMOIDForward(rt_out[0], rt_out[0], dim_hm.tot());
checkCuda( cudaDeviceSynchronize() );
subtractWithThreshold(rt_out[0], rt_out[0] + dim_hm.tot(), rt_out[1], rt_out[0]);
subtractWithThreshold(rt_out[0], rt_out[0] + dim_hm.tot(), rt_out[1], rt_out[0], op);
end_t = std::chrono::steady_clock::now();
std::cout << " TIME threshold: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME threshold: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
// ----------- nms end
// ----------- topk
@@ -327,7 +306,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
ids_d);
checkCuda( cudaDeviceSynchronize() );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME sort: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME sort: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
topk(rt_out[0], ids_d, K, scores_d,
@@ -335,14 +314,14 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
checkCuda( cudaDeviceSynchronize() );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME topk: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME topk: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
checkCuda( cudaMemcpy(scores, scores_d, K *sizeof(float), cudaMemcpyDeviceToHost) );
topKxyclasses(topk_inds_d, topk_inds_d+K, K, width, dim_hm.w*dim_hm.h, clses_d, inttopk_xs_d, inttopk_ys_d);
end_t = std::chrono::steady_clock::now();
std::cout << " TIME topk x y clses 2: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME topk x y clses 2: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
checkCuda( cudaMemcpy(topk_xs_d, (float *)inttopk_xs_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
@@ -356,7 +335,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
// checkCuda( cudaDeviceSynchronize() );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME add offset: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME add offset: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
bboxes(topk_inds_d, K, dim_wh.h*dim_wh.w, topk_xs_d, topk_ys_d, rt_out[2], bbx0_d, bbx1_d, bby0_d, bby1_d, src_out, ids_out);
@@ -368,35 +347,13 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
checkCuda( cudaMemcpy(bby1, bby1_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME bboxes: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME bboxes: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
// ---------------------------------- post-process -----------------------------------------
// --------- ctdet_post_process
// --------- transform_preds
src.at<float>(0,0)=c[0];
src.at<float>(0,1)=c[1];
src.at<float>(1,0)=c[0];
src.at<float>(1,1)=c[1] + s[0] * -0.5;
dst.at<float>(0,0)=width * 0.5;
dst.at<float>(0,1)=width * 0.5;
dst.at<float>(1,0)=width * 0.5;
dst.at<float>(1,1)=width * 0.5 + width * -0.5;
src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
cv::Mat trans2(cv::Size(3,2), CV_32F);
trans2 = cv::getAffineTransform( dst, src );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME getAffineTrans 2: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
step_t = end_t;
cv::Mat new_pt1(cv::Size(1,2), CV_32F);
cv::Mat new_pt2(cv::Size(1,2), CV_32F);
@@ -420,7 +377,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
target_coords[i*4+2] = new_pt2.at<float>(0,0);
target_coords[i*4+3] = new_pt2.at<float>(0,1);
}
detected.clear();
for(int i = 0; i<classes; i++){
for(int j=0; j<K; j++)
@@ -449,7 +406,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
}
end_t = std::chrono::steady_clock::now();
std::cout << " TIME detections: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
std::cout << " TIME detections: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
std::cout<<"TOTAL: \n";