mmr/tkDNN
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compute detections

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This commit is contained in:
Francesco Gatti
2019-02-04 20:34:15 +00:00
parent 88097a3774
commit c8dea4668d
3 changed files with 140 additions and 6 deletions
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tests/yolo3_berkeley/yolo3_berkeley.cpp
+13 -3
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@@ -61,6 +61,7 @@ const char *c78_bin = "../tests/yolo3_berkeley/layers/c78.bin";
const char *c79_bin = "../tests/yolo3_berkeley/layers/c79.bin";
const char *c80_bin = "../tests/yolo3_berkeley/layers/c80.bin";
const char *c81_bin = "../tests/yolo3_berkeley/layers/c81.bin";
const char *c82_bin = "../tests/yolo3_berkeley/layers/g82.bin";
const char *c84_bin = "../tests/yolo3_berkeley/layers/c84.bin";
const char *c87_bin = "../tests/yolo3_berkeley/layers/c87.bin";
const char *c88_bin = "../tests/yolo3_berkeley/layers/c88.bin";
@@ -69,6 +70,7 @@ const char *c90_bin = "../tests/yolo3_berkeley/layers/c90.bin";
const char *c91_bin = "../tests/yolo3_berkeley/layers/c91.bin";
const char *c92_bin = "../tests/yolo3_berkeley/layers/c92.bin";
const char *c93_bin = "../tests/yolo3_berkeley/layers/c93.bin";
const char *c94_bin = "../tests/yolo3_berkeley/layers/g94.bin";
const char *c96_bin = "../tests/yolo3_berkeley/layers/c96.bin";
const char *c99_bin = "../tests/yolo3_berkeley/layers/c99.bin";
const char *c100_bin = "../tests/yolo3_berkeley/layers/c100.bin";
@@ -77,6 +79,7 @@ const char *c102_bin = "../tests/yolo3_berkeley/layers/c102.bin";
const char *c103_bin = "../tests/yolo3_berkeley/layers/c103.bin";
const char *c104_bin = "../tests/yolo3_berkeley/layers/c104.bin";
const char *c105_bin = "../tests/yolo3_berkeley/layers/c105.bin";
const char *c106_bin = "../tests/yolo3_berkeley/layers/g106.bin";
const char *output_bins[3] = {
"../tests/yolo3_berkeley/debug/layer82_out.bin",
"../tests/yolo3_berkeley/debug/layer94_out.bin",
@@ -238,7 +241,7 @@ int main() {
tk::dnn::Conv2d c80 (&net,1024, 3, 3, 1, 1, 1, 1, c80_bin, true);
tk::dnn::Activation a80 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c81 (&net, 45, 1, 1, 1, 1, 0, 0, c81_bin, false);
tk::dnn::Yolo yolo0 (&net, 10, 3);
tk::dnn::Yolo yolo0 (&net, 10, 3, c84_bin);
tk::dnn::Layer *m83_layers[1] = { &a79 };
tk::dnn::Route m83 (&net, m83_layers, 1);
@@ -262,7 +265,7 @@ int main() {
tk::dnn::Conv2d c92 (&net, 512, 3, 3, 1, 1, 1, 1, c92_bin, true);
tk::dnn::Activation a92 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c93 (&net, 45, 1, 1, 1, 1, 0, 0, c93_bin, false);
tk::dnn::Yolo yolo1 (&net, 10, 3);
tk::dnn::Yolo yolo1 (&net, 10, 3, c94_bin);
tk::dnn::Layer *m95_layers[1] = { &a91 };
tk::dnn::Route m95 (&net, m95_layers, 1);
@@ -286,7 +289,7 @@ int main() {
tk::dnn::Conv2d c104 (&net, 256, 3, 3, 1, 1, 1, 1, c104_bin, true);
tk::dnn::Activation a104 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c105 (&net, 45, 1, 1, 1, 1, 0, 0, c105_bin, false);
tk::dnn::Yolo yolo2 (&net, 10, 3);
tk::dnn::Yolo yolo2 (&net, 10, 3, c106_bin);
// Load input
dnnType *data;
@@ -317,6 +320,13 @@ int main() {
cudnn_out[0] = yolo0.dstData;
cudnn_out[1] = yolo1.dstData;
cudnn_out[2] = yolo2.dstData;
printCenteredTitle(" compute detections ", '=', 30);
TIMER_START
yolo0.computeDetections(640, 480, 0.5);
yolo1.computeDetections(640, 480, 0.5);
yolo2.computeDetections(640, 480, 0.5);
TIMER_STOP
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30); {