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Adapt detection classes to use batches, adapt demos, update README

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-05-14 16:41:51 +02:00
parent 2e3cb52cff
commit 40456592fc
10 changed files with 149 additions and 103 deletions
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src/MobilenetDetection.cpp
+12 -10
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@@ -126,11 +126,12 @@ float MobilenetDetection::iou(const tk::dnn::box &a, const tk::dnn::box &b){
return iou;
}
bool MobilenetDetection::init(const std::string& tensor_path, const int n_classes){
bool MobilenetDetection::init(const std::string& tensor_path, const int n_classes, const int n_batches){
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str());
imageSize = netRT->input_dim.h;
classes = n_classes;
nBatches = n_batches;
SSDSpec specs[N_SSDSPEC];
@@ -157,9 +158,9 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
generate_ssd_priors(specs, N_SSDSPEC);
#ifndef OPENCV_CUDACONTRIB
checkCuda(cudaMallocHost(&input, sizeof(dnnType) * netRT->input_dim.tot()));
checkCuda(cudaMallocHost(&input, sizeof(dnnType) * netRT->input_dim.tot() * nBatches));
#endif
checkCuda(cudaMalloc(&input_d, sizeof(dnnType) * netRT->input_dim.tot()));
checkCuda(cudaMalloc(&input_d, sizeof(dnnType) * netRT->input_dim.tot() * nBatches));
locations_h = (float *)malloc(N_COORDS * nPriors * sizeof(float));
confidences_h = (float *)malloc(nPriors * classes * sizeof(float));
@@ -208,7 +209,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
return 1;
}
void MobilenetDetection::preprocess(cv::Mat &frame){
void MobilenetDetection::preprocess(cv::Mat &frame, const int bi){
#ifdef OPENCV_CUDACONTRIB
//move original image on GPU
cv::cuda::GpuMat orig_img, frame_nomean;
@@ -224,7 +225,7 @@ void MobilenetDetection::preprocess(cv::Mat &frame){
for(int i=0; i < netRT->input_dim.c; i++){
int idx = i * imagePreproc.rows * imagePreproc.cols;
checkCuda( cudaMemcpy((void *)&input_d[idx], (void *)bgr[i].data, imagePreproc.rows * imagePreproc.cols* sizeof(float), cudaMemcpyDeviceToDevice) );
checkCuda( cudaMemcpy((void *)&input_d[idx + netRT->input_dim.tot()*bi], (void *)bgr[i].data, imagePreproc.rows * imagePreproc.cols* sizeof(float), cudaMemcpyDeviceToDevice) );
}
#else
//resize image, remove mean, divide by std
@@ -237,17 +238,17 @@ void MobilenetDetection::preprocess(cv::Mat &frame){
cv::split(imagePreproc, bgr);
for (int i = 0; i < netRT->input_dim.c; i++){
int idx = i * imagePreproc.rows * imagePreproc.cols;
memcpy((void *)&input[idx], (void *)bgr[i].data, imagePreproc.rows * imagePreproc.cols * sizeof(dnnType));
memcpy((void *)&input[idx + netRT->input_dim.tot()*bi], (void *)bgr[i].data, imagePreproc.rows * imagePreproc.cols * sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_d, input, netRT->input_dim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
checkCuda(cudaMemcpyAsync(input_d+ netRT->input_dim.tot()*bi, input + netRT->input_dim.tot()*bi, netRT->input_dim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
#endif
}
void MobilenetDetection::postprocess(const bool mAP){
void MobilenetDetection::postprocess(const int bi, const bool mAP){
//get confidences and locations_h
dnnType *rt_out[2];
rt_out[0] = (dnnType *)netRT->buffersRT[3];
rt_out[1] = (dnnType *)netRT->buffersRT[4];
rt_out[0] = (dnnType *)netRT->buffersRT[3]+ netRT->buffersDIM[3].tot()*bi;
rt_out[1] = (dnnType *)netRT->buffersRT[4]+ netRT->buffersDIM[4].tot()*bi;
detected.clear();
@@ -302,6 +303,7 @@ void MobilenetDetection::postprocess(const bool mAP){
boxes = remaining;
}
}
batchDetected.push_back(detected);
}