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csresnext50-panet-spp works with TensorRT

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Signed-off-by: Francesco Gatti <gattifrancesco@hotmail.it>
This commit is contained in:
Francesco Gatti
2020-03-12 18:24:41 +01:00
parent 5f4508a5a6
commit 4845edbc88
8 changed files with 1225 additions and 102 deletions
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tests/csresnext50-panet-spp/csresnext50-panet-spp.cfg
+1018
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File diff suppressed because it is too large Load Diff
tests/csresnext50-panet-spp/csresnext50-panet-spp.cpp
+66 -75
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@@ -339,7 +339,7 @@ int main()
tk::dnn::Conv2d c83(&net, 512, 1, 1, 1, 1, 0, 0, c83_bin, true);
tk::dnn::Activation a83(&net, tk::dnn::ACTIVATION_LEAKY);
// //SPP
//SPP
tk::dnn::Pooling p84(&net, 5, 5, 1, 1,0,0, tk::dnn::POOLING_MAX, false, true);
tk::dnn::Layer *r85_layers[1] = {&a83};
tk::dnn::Route r85(&net, r85_layers, 1);
@@ -351,7 +351,7 @@ int main()
tk::dnn::Pooling p88(&net, 13, 13, 1, 1, 12, 12, tk::dnn::POOLING_MAX, false, true);
tk::dnn::Layer *r89_layers[4] = {&p88, &p86, &p84, &a83};
tk::dnn::Route r89(&net, r89_layers, 4);
// //END SPP
//END SPP
tk::dnn::Conv2d c90(&net, 512, 1, 1, 1, 1, 0, 0, c90_bin, true);
tk::dnn::Activation a90(&net, tk::dnn::ACTIVATION_LEAKY);
@@ -454,17 +454,15 @@ int main()
tk::dnn::Conv2d c136(&net, 255, 1, 1, 1, 1, 0, 0, c136_bin, false);
tk::dnn::Yolo yolo137(&net, classes, 3, g137_bin);
yolo[0] = &yolo115;
yolo[1] = &yolo126;
yolo[2] = &yolo137;
// yolo[0] = &yolo115;
// yolo[1] = &yolo126;
// yolo[2] = &yolo137;
// // fill classes names
// for (int i = 0; i < 3; i++)
// {
// yolo[i]->classesNames = {"person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"};
// }
// fill classes names
for (int i = 0; i < 3; i++)
{
yolo[i]->classesNames = {"person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"};
}
// Load input
dnnType *data;
@@ -475,13 +473,13 @@ int main()
net.print();
// //convert network to tensorRT
// tk::dnn::NetworkRT netRT(&net, "csresnext50-panet-spp.rt");
tk::dnn::NetworkRT netRT(&net, "csresnext50-panet-spp.rt");
// the network have 3 outputs
// tk::dnn::dataDim_t out_dim[3];
// for (int i = 0; i < 3; i++)
// out_dim[i] = yolo[i]->output_dim;
// dnnType *cudnn_out[3], *rt_out[3];
tk::dnn::dataDim_t out_dim[3];
for (int i = 0; i < 3; i++)
out_dim[i] = yolo[i]->output_dim;
dnnType *cudnn_out[3], *rt_out[3];
tk::dnn::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30);
@@ -492,69 +490,62 @@ int main()
TIMER_STOP
dim1.print();
}
dnnType *cudnn_out = net.layers[net.num_layers-1]->dstData;
tk::dnn::dataDim_t out_dim = net.layers[net.num_layers-1]->output_dim;
dnnType *out1, *out1_h;
int odim1 = out_dim.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
std::cout << "CUDNN vs correct" << std::endl;
// printDeviceVector(odim1, cudnn_out);
checkResult(odim1, cudnn_out, out1);
for (int i = 0; i < 3; i++)
cudnn_out[i] = yolo[i]->dstData;
// for (int i = 0; i < 3; i++)
// cudnn_out[i] = yolo[i]->dstData;
printCenteredTitle(" compute detections ", '=', 30);
TIMER_START
int ndets = 0;
tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
for (int i = 0; i < 3; i++)
yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
// printCenteredTitle(" compute detections ", '=', 30);
// TIMER_START
// int ndets = 0;
// tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
// for (int i = 0; i < 3; i++)
// yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
// tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
for (int j = 0; j < ndets; j++)
{
tk::dnn::Yolo::box b = dets[j].bbox;
int x0 = (b.x - b.w / 2.);
int x1 = (b.x + b.w / 2.);
int y0 = (b.y - b.h / 2.);
int y1 = (b.y + b.h / 2.);
// for (int j = 0; j < ndets; j++)
// {
// tk::dnn::Yolo::box b = dets[j].bbox;
// int x0 = (b.x - b.w / 2.);
// int x1 = (b.x + b.w / 2.);
// int y0 = (b.y - b.h / 2.);
// int y1 = (b.y + b.h / 2.);
int cl = 0;
for (int c = 0; c < classes; ++c)
{
float prob = dets[j].prob[c];
if (prob > 0)
cl = c;
}
std::cout << cl << ": " << x0 << " " << y0 << " " << x1 << " " << y1 << "\n";
}
TIMER_STOP
// int cl = 0;
// for (int c = 0; c < classes; ++c)
// {
// float prob = dets[j].prob[c];
// if (prob > 0)
// cl = c;
// }
// std::cout << cl << ": " << x0 << " " << y0 << " " << x1 << " " << y1 << "\n";
// }
// TIMER_STOP
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TIMER_START
netRT.infer(dim2, data);
TIMER_STOP
dim2.print();
}
// tk::dnn::dataDim_t dim2 = dim;
// printCenteredTitle(" TENSORRT inference ", '=', 30);
// {
// dim2.print();
// TIMER_START
// netRT.infer(dim2, data);
// TIMER_STOP
// dim2.print();
// }
// for (int i = 0; i < 3; i++)
// rt_out[i] = (dnnType *)netRT.buffersRT[i + 1];
for (int i = 0; i < 3; i++)
rt_out[i] = (dnnType *)netRT.buffersRT[i + 1];
// for (int i = 0; i < 3; i++)
// {
// printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
// dnnType *out, *out_h;
// int odim = out_dim[i].tot();
// readBinaryFile(output_bins[i], odim, &out_h, &out);
// std::cout << "CUDNN vs correct";
// checkResult(odim, cudnn_out[i], out);
// std::cout << "TRT vs correct";
// checkResult(odim, rt_out[i], out);
// std::cout << "CUDNN vs TRT ";
// checkResult(odim, cudnn_out[i], rt_out[i]);
// }
for (int i = 0; i < 3; i++)
{
printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
dnnType *out, *out_h;
int odim = out_dim[i].tot();
readBinaryFile(output_bins[i], odim, &out_h, &out);
std::cout << "CUDNN vs correct";
checkResult(odim, cudnn_out[i], out);
std::cout << "TRT vs correct";
checkResult(odim, rt_out[i], out);
std::cout << "CUDNN vs TRT ";
checkResult(odim, cudnn_out[i], rt_out[i]);
}
return 0;
}