Francesco Gatti
55 lines
1.8 KiB
C++
55 lines
1.8 KiB
C++
#include<iostream>
|
|
#include "tkdnn.h"
|
|
|
|
const char *input_bin = "../tests/test/input.bin";
|
|
const char *c0_bin = "../tests/test/layers/conv0.bin";
|
|
const char *c0_bias_bin = "../tests/test/layers/conv0.bias.bin";
|
|
const char *c1_bin = "../tests/test/layers/conv1.bin";
|
|
const char *c1_bias_bin = "../tests/test/layers/conv1.bias.bin";
|
|
const char *d2_bin = "../tests/test/layers/dense2.bin";
|
|
const char *d2_bias_bin = "../tests/test/layers/dense2.bias.bin";
|
|
const char *output_bin = "../tests/test/output.bin";
|
|
|
|
int main() {
|
|
|
|
// Network layout
|
|
tkDNN::Network net;
|
|
tkDNN::dataDim_t dim(1, 1, 10, 10, 1);
|
|
tkDNN::Layer *l;
|
|
l = new tkDNN::Conv2d (&net, dim, 2, 4, 4, 2, 2, c0_bin, c0_bias_bin);
|
|
l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
|
|
l = new tkDNN::Conv2d (&net, l->output_dim, 4, 2, 2, 1, 1, c1_bin, c1_bias_bin);
|
|
l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
|
|
l = new tkDNN::Flatten (&net, l->output_dim);
|
|
l = new tkDNN::Dense (&net, l->output_dim, 4, d2_bin, d2_bias_bin);
|
|
l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
|
|
|
|
// Load input
|
|
value_type *data;
|
|
value_type *input_h;
|
|
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
|
|
|
|
printDeviceVector(dim.tot(), data);
|
|
dim.print(); //print initial dimension
|
|
|
|
TIMER_START
|
|
|
|
// Inference
|
|
data = net.infer(dim, data); dim.print();
|
|
|
|
|
|
TIMER_STOP
|
|
|
|
// Print result
|
|
std::cout<<"\n======= RESULT =======\n";
|
|
printDeviceVector(dim.tot(), data);
|
|
|
|
// Print real test
|
|
std::cout<<"\n==== CHECK RESULT ====\n";
|
|
value_type *out;
|
|
value_type *out_h;
|
|
readBinaryFile(output_bin, dim.tot(), &out_h, &out);
|
|
printDeviceVector(dim.tot(), out);
|
|
return 0;
|
|
}
|