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Revert "Merge branch 'tensorrt8-rds' into depth"

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This reverts commit 00f06f7bcc, reversing
changes made to 6a133d8dec.
This commit is contained in:
perseusdg
2022-01-25 18:30:39 +05:30
parent ef564f134d
commit b3bc93693f
1 changed files with 0 additions and 241 deletions
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include/tkDNN/SegmentationNN.h
-241
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@@ -18,7 +18,6 @@
#include "tkdnn.h"
#include "NetworkViz.h"
#include "kernelsThrust.h"
#define SLAM_MODE
namespace tk { namespace dnn {
@@ -100,62 +99,6 @@ class SegmentationNN {
*
* @param bi batch index
*/
#ifdef SLAM_MODE
cv::Mat postprocess(const int bi=0,bool apply_colormap=true){
cv::Mat maskMatrix;
dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
dataDim_t odim = netRT->output_dim;
matrixTranspose(cublasHandle, rt_out, tmpInputData_d, odim.c, odim.w*odim.h);
maxElem(tmpInputData_d, tmpOutData_d, odim.c, odim.h, odim.w);
checkCuda(cudaMemcpy(tmpOutData_h, tmpOutData_d, odim.w*odim.h * sizeof(float), cudaMemcpyDeviceToHost));
dataDim_t vdim = odim;
vdim.c = 1;
dnnType *dataTemp = nullptr;
if(isCudaPointer(tmpOutData_h))
{
dataTemp = new dnnType[vdim.tot()];
checkCuda(cudaMemcpy(dataTemp,tmpOutData_h,vdim.tot()*sizeof(dnnType),cudaMemcpyDeviceToHost));
}
else
{
dataTemp = tmpOutData_h;
}
for(int i =0;i<vdim.c;i++){
maskMatrix = cv::Mat(originalSize[bi],CV_32FC1,dataTemp + vdim.h*vdim.c*i);
}
cv::Mat colored;
if(apply_colormap)
colored = vizData2Mat(dataTemp, vdim, netRT->input_dim.h, netRT->input_dim.w, 0, classes, classes);
else{
cv::Mat colored_fp32 (cv::Size(odim.w, odim.h),CV_32FC1, dataTemp);
colored_fp32.convertTo(colored, CV_8UC1);
}
int max_dim = (originalSize[bi].width > originalSize[bi].height) ? originalSize[bi].width : originalSize[bi].height;
resize(colored, colored, cv::Size(max_dim, max_dim));
int top, bottom, left, right;
computeBorders(originalSize[bi].width, originalSize[bi].height, top, bottom, left, right);
cv::Rect roi(left,top,originalSize[bi].width, originalSize[bi].height);
cv::Mat or_size (colored, roi);
segmented[bi] = or_size;
if(isCudaPointer(tmpOutData_h))
{
delete [] dataTemp;
}
return maskMatrix;
}
#elif
void postprocess(const int bi=0, bool appy_colormap = true) {
dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
@@ -185,7 +128,6 @@ class SegmentationNN {
cv::Mat or_size (colored, roi);
segmented[bi] = or_size;
};
#endif
public:
int classes = 0;
@@ -295,184 +237,6 @@ class SegmentationNN {
}
}
#ifdef SLAM_MODE
cv::Mat updateOriginal(cv::Mat frame,bool apply_colormap=true){
std::vector<cv::Mat> splitted_frames;
cv::Mat maskMatrix;
int H, W, net_H, net_W;
int top = 0, bottom = 0, left = 0, right = 0;
std::vector<std::pair<int,int>> pos;
{
TKDNN_TSTART
cv::Size original_size = frame.size();
frame.convertTo(frame, CV_32FC3, 1 / 255.0, 0);
H = frame.rows;
W = frame.cols;
net_H = netRT->input_dim.h;
net_W = netRT->input_dim.w;
cv::Mat frame_cropped;
if( H <= net_H && W <= net_W ){ // smaller size wrt network
top = (net_H - H)/2;
bottom = net_H - H - top ;
left = (net_W - W)/2;
right = net_W - W - left ;
cv::copyMakeBorder(frame, frame_cropped, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0,0,0) );
splitted_frames.push_back(frame_cropped);
}
else{ //bigger size wrt network
if(H < net_H || W < net_W){
if(H < net_H){
top = (net_H - H)/2;
bottom = net_H - H - top ;
}
else{
left = (net_W - W)/2;
right = net_W - W - left ;
}
cv::copyMakeBorder(frame, frame_cropped, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0,0,0));
}
for(int x=0; x+net_W<=W ;){
for(int y=0; y+net_H <=H ; ){
cv::Rect roi(x, y, net_W, net_H);
cv::Mat image_roi = frame(roi);
splitted_frames.push_back(image_roi);
pos.push_back(std::make_pair(x,y));
y += net_H;
if(y == H)
break;
if(y + net_H > H) y = H - net_H;
}
x += net_W;
if(x == W)
break;
if(x + net_W > W) x = W - net_W;
}
}
tk::dnn::dataDim_t idim = netRT->input_dim;
if(splitted_frames.size()> nBatches)
FatalError(std::to_string(splitted_frames.size()) + " min batches required");
for(int bi=0; bi<splitted_frames.size();++bi){
cv::split(splitted_frames[bi], bgr);
for (int i = 0; i < idim.c; i++){
int idx = i * splitted_frames[bi].rows * splitted_frames[bi].cols;
int ch = idim.c-1 -i;
memcpy((void *)&input[idx + idim.tot()*bi], (void *)bgr[ch].data, splitted_frames[bi].rows * splitted_frames[bi].cols * sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_d+ idim.tot()*bi, input + idim.tot()*bi, idim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
normalize(input_d + idim.tot()*bi, idim.c, idim.h, idim.w, mean_d, stddev_d);
}
TKDNN_TSTOP
stats_pre.push_back(t_ns);
}
tk::dnn::dataDim_t dim = netRT->input_dim;
dim.n = splitted_frames.size();
{
if(TKDNN_VERBOSE) dim.print();
TKDNN_TSTART
netRT->infer(dim, input_d);
TKDNN_TSTOP
if(TKDNN_VERBOSE) dim.print();
stats.push_back(t_ns);
}
dataDim_t odim = netRT->output_dim;
std::vector<cv::Mat> out_img;
std::vector<cv::Mat> out_mask;
{
TKDNN_TSTART
for(int bi=0; bi<splitted_frames.size();++bi){
dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
matrixTranspose(cublasHandle, rt_out, tmpInputData_d, odim.c, odim.w*odim.h);
maxElem(tmpInputData_d, tmpOutData_d, odim.c, odim.h, odim.w);
checkCuda(cudaMemcpy(tmpOutData_h, tmpOutData_d, odim.w*odim.h * sizeof(float), cudaMemcpyDeviceToHost));
dataDim_t vdim = odim;
vdim.c = 1;
dnnType *dataTemp = nullptr;
if(isCudaPointer(tmpOutData_h))
{
dataTemp = new dnnType[vdim.tot()];
checkCuda(cudaMemcpy(dataTemp,tmpOutData_h,vdim.tot()*sizeof(dnnType),cudaMemcpyDeviceToHost));
}
else
{
dataTemp = tmpOutData_h;
}
cv::Mat colored;
for(int i=0;i<vdim.c;i++){
out_mask.push_back(cv::Mat(cv::Size(odim.w,odim.h),CV_32FC1,dataTemp + dim.h*dim.c*i));
}
if(apply_colormap)
colored = vizData2Mat(tmpOutData_h, vdim, netRT->input_dim.h, netRT->input_dim.w, 0, classes, classes);
else{
cv::Mat colored_fp32 (cv::Size(odim.w, odim.h),CV_32FC1, tmpOutData_h);
colored_fp32.convertTo(colored, CV_8UC1);
}
out_img.push_back(colored);
if(isCudaPointer(tmpOutData_h))
{
delete [] dataTemp;
}
}
cv::Mat tempMask(frame.size(), out_mask[0].type());
cv::Mat seg(frame.size(), out_img[0].type());
if(out_img.size() == 1)
{
cv::Rect roi(left, top, W, H);
seg = out_img[0](roi);
tempMask = out_mask[0](roi);
}
else{
int bi=0;
if(top == 0 && left == 0){
for(int i=0; i<out_img.size(); ++i){
cv::Mat roi_collage = seg(cv::Rect( pos[i].first ,pos[i].second,out_img[i].cols,out_img[i].rows));
cv::Mat roi_collage2 = tempMask(cv::Rect( pos[i].first ,pos[i].second,out_img[i].cols,out_img[i].rows));
out_img[i].copyTo(roi_collage);
out_mask[i].copyTo(roi_collage2);
}
}
else{
FatalError("Not handled case")
}
}
maskMatrix = tempMask;
segmented[0] = seg;
TKDNN_TSTOP
stats_post.push_back(t_ns);
}
return maskMatrix;
}
#elif
void updateOriginal(cv::Mat frame, bool apply_colormap=true){
std::vector<cv::Mat> splitted_frames;
@@ -621,11 +385,6 @@ class SegmentationNN {
stats_post.push_back(t_ns);
}
}
#endif
/**
* Method to draw boundixg boxes and labels on a frame.