Francesco Gatti
35 lines
1.1 KiB
C++
35 lines
1.1 KiB
C++
#include<iostream>
|
|
#include "tkdnn.h"
|
|
#include <stdlib.h> /* srand, rand */
|
|
|
|
int main(int argc, char *argv[]) {
|
|
|
|
if(argc < 2 || !fileExist(argv[1]))
|
|
FatalError("unable to read serialRT file");
|
|
|
|
//always same test
|
|
srand (0);
|
|
|
|
//convert network to tensorRT
|
|
tkDNN::NetworkRT netRT(NULL, argv[1]);
|
|
|
|
dnnType *input = new float[netRT.input_dim.tot()];
|
|
dnnType *output = new float[netRT.input_dim.tot()];
|
|
|
|
printCenteredTitle(" TENSORRT inference ", '=', 30);
|
|
for(int i=0; i<100; i++) {
|
|
for(int j=0; j<netRT.input_dim.tot(); j++)
|
|
input[j] = ((float) rand() / (RAND_MAX));
|
|
TIMER_START
|
|
checkCuda( cudaMemcpyAsync(netRT.buffersRT[netRT.buf_input_idx], input,
|
|
netRT.input_dim.tot()*sizeof(float), cudaMemcpyHostToDevice, netRT.stream));
|
|
netRT.enqueue();
|
|
checkCuda( cudaMemcpyAsync(output, netRT.buffersRT[netRT.buf_output_idx],
|
|
netRT.output_dim.tot()*sizeof(float), cudaMemcpyDeviceToHost, netRT.stream));
|
|
cudaStreamSynchronize(netRT.stream);
|
|
TIMER_STOP
|
|
}
|
|
|
|
return 0;
|
|
}
|