Francesco Gatti
81 lines
3.6 KiB
C++
81 lines
3.6 KiB
C++
#include<iostream>
|
|
#include "tkdnn.h"
|
|
|
|
const char *input_bin = "../tests/yolo3_berkeley/layers/input.bin";
|
|
const char *c0_bin = "../tests/yolo3_berkeley/layers/c0.bin";
|
|
const char *c1_bin = "../tests/yolo3_berkeley/layers/c1.bin";
|
|
const char *c2_bin = "../tests/yolo3_berkeley/layers/c2.bin";
|
|
const char *c3_bin = "../tests/yolo3_berkeley/layers/c3.bin";
|
|
const char *c5_bin = "../tests/yolo3_berkeley/layers/c5.bin";
|
|
const char *c6_bin = "../tests/yolo3_berkeley/layers/c6.bin";
|
|
const char *c7_bin = "../tests/yolo3_berkeley/layers/c7.bin";
|
|
const char *c9_bin = "../tests/yolo3_berkeley/layers/c9.bin";
|
|
const char *c10_bin = "../tests/yolo3_berkeley/layers/c10.bin";
|
|
const char *c12_bin = "../tests/yolo3_berkeley/layers/c12.bin";
|
|
const char *c13_bin = "../tests/yolo3_berkeley/layers/c13.bin";
|
|
const char *c14_bin = "../tests/yolo3_berkeley/layers/c14.bin";
|
|
const char *output_bin = "../tests/yolo3_berkeley/debug/layer15_out.bin";
|
|
|
|
int main() {
|
|
|
|
// Network layout
|
|
tk::dnn::dataDim_t dim(1, 3, 320, 544, 1);
|
|
tk::dnn::Network net(dim);
|
|
|
|
tk::dnn::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
|
|
tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c1 (&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
|
|
tk::dnn::Activation a1 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c2 (&net, 32, 1, 1, 1, 1, 0, 0, c2_bin, true);
|
|
tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c3 (&net, 64, 3, 3, 1, 1, 1, 1, c3_bin, true);
|
|
tk::dnn::Activation a3 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Shortcut s4 (&net, &a1);
|
|
tk::dnn::Conv2d c5 (&net, 128, 3, 3, 2, 2, 1, 1, c5_bin, true);
|
|
tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c6 (&net, 64, 1, 1, 1, 1, 0, 0, c6_bin, true);
|
|
tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c7 (&net, 128, 3, 3, 1, 1, 1, 1, c7_bin, true);
|
|
tk::dnn::Activation a7 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Shortcut s8 (&net, &a5);
|
|
tk::dnn::Conv2d c9 (&net, 64, 1, 1, 1, 1, 0, 0, c9_bin, true);
|
|
tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c10 (&net, 128, 3, 3, 1, 1, 1, 1, c10_bin, true);
|
|
tk::dnn::Activation a10 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Shortcut s11 (&net, &s8);
|
|
tk::dnn::Conv2d c12 (&net, 256, 3, 3, 2, 2, 1, 1, c12_bin, true);
|
|
tk::dnn::Activation a12 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c13 (&net, 128, 1, 1, 1, 1, 0, 0, c13_bin, true);
|
|
tk::dnn::Activation a13 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Conv2d c14 (&net, 256, 3, 3, 1, 1, 1, 1, c14_bin, true);
|
|
tk::dnn::Activation a14 (&net, tk::dnn::ACTIVATION_LEAKY);
|
|
tk::dnn::Shortcut s15 (&net, &a12);
|
|
|
|
// Load input
|
|
dnnType *data;
|
|
dnnType *input_h;
|
|
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
|
|
|
|
//print network model
|
|
net.print();
|
|
|
|
|
|
dnnType *out_data; // cudnn output
|
|
|
|
tk::dnn::dataDim_t dim1 = dim; //input dim
|
|
printCenteredTitle(" CUDNN inference ", '=', 30); {
|
|
dim1.print();
|
|
TIMER_START
|
|
out_data = net.infer(dim1, data);
|
|
TIMER_STOP
|
|
dim1.print();
|
|
}
|
|
|
|
printCenteredTitle(" CHECK RESULTS ", '=', 30);
|
|
dnnType *out, *out_h;
|
|
int out_dim = net.getOutputDim().tot();
|
|
readBinaryFile(output_bin, out_dim, &out_h, &out);
|
|
std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
|
|
return 0;
|
|
}
|