mmr/tkDNN
Archived
1
0
Fork 0
Code Issues 46 Pull Requests 12 Releases 4 Activity

namespace change

Browse Source
This commit is contained in:
Francesco Gatti
2018-12-14 21:55:16 +01:00
parent 443179359d
commit 6249956469
32 changed files with 374 additions and 374 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/yolo_tiny/yolo_tiny.cpp
+28 -28
View File
@@ -18,38 +18,38 @@ const char *output_bin = "../tests/yolo_tiny/layers/output.bin";
int main() {
// Network layout
tkDNN::dataDim_t dim(1, 3, 416, 416, 1);
tkDNN::Network net(dim);
tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
tk::dnn::Network net(dim);
tkDNN::Conv2d c0 (&net, 16, 3, 3, 1, 1, 1, 1, c0_bin, true);
tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tk::dnn::Conv2d c0 (&net, 16, 3, 3, 1, 1, 1, 1, c0_bin, true);
tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Pooling p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
tkDNN::Conv2d c2 (&net, 32, 3, 3, 1, 1, 1, 1, c2_bin, true);
tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tk::dnn::Conv2d c2 (&net, 32, 3, 3, 1, 1, 1, 1, c2_bin, true);
tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Pooling p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
tkDNN::Conv2d c4 (&net, 64, 3, 3, 1, 1, 1, 1, c4_bin, true);
tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p5 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tk::dnn::Conv2d c4 (&net, 64, 3, 3, 1, 1, 1, 1, c4_bin, true);
tk::dnn::Activation a4 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Pooling p5 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
tkDNN::Conv2d c6 (&net, 128, 3, 3, 1, 1, 1, 1, c6_bin, true);
tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p7(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tk::dnn::Conv2d c6 (&net, 128, 3, 3, 1, 1, 1, 1, c6_bin, true);
tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Pooling p7(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
tkDNN::Conv2d c8(&net, 256, 3, 3, 1, 1, 1, 1, c8_bin, true);
tkDNN::Activation a8(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p9(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tk::dnn::Conv2d c8(&net, 256, 3, 3, 1, 1, 1, 1, c8_bin, true);
tk::dnn::Activation a8(&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Pooling p9(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
tkDNN::Conv2d c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
tk::dnn::Conv2d c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_LEAKY);
tkDNN::Conv2d c11(&net, 1024, 3, 3, 1, 1, 1, 1, c11_bin, true);
tkDNN::Activation a11(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c12(&net, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c13(&net, 425, 1, 1, 1, 1, 0, 0, c13_bin, false);
tkDNN::Region g14(&net, 80, 4, 5);
tk::dnn::Conv2d c11(&net, 1024, 3, 3, 1, 1, 1, 1, c11_bin, true);
tk::dnn::Activation a11(&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c12(&net, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
tk::dnn::Activation a12(&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c13(&net, 425, 1, 1, 1, 1, 0, 0, c13_bin, false);
tk::dnn::Region g14(&net, 80, 4, 5);
// Load input
dnnType *data;
@@ -60,11 +60,11 @@ int main() {
net.print();
//convert network to tensorRT
tkDNN::NetworkRT netRT(&net, "yolo_tiny.rt");
tk::dnn::NetworkRT netRT(&net, "yolo_tiny.rt");
dnnType *out_data, *out_data2; // cudnn output, tensorRT output
tkDNN::dataDim_t dim1 = dim; //input dim
tk::dnn::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30); {
dim1.print();
TIMER_START
@@ -73,7 +73,7 @@ int main() {
dim1.print();
}
tkDNN::dataDim_t dim2 = dim;
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
TIMER_START