Francesco Gatti
62 lines
1.9 KiB
C++
62 lines
1.9 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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#include <stdlib.h> /* srand, rand */
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int main(int argc, char *argv[]) {
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if(argc < 2 || !fileExist(argv[1]))
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FatalError("unable to read serialRT file");
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int BATCH_SIZE = 1;
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if(argc >2)
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BATCH_SIZE = atoi(argv[2]);
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//always same test
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srand (0);
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//convert network to tensorRT
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tk::dnn::NetworkRT netRT(NULL, argv[1]);
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tk::dnn::dataDim_t idim = netRT.input_dim;
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tk::dnn::dataDim_t odim = netRT.output_dim;
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idim.n = BATCH_SIZE;
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odim.n = BATCH_SIZE;
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dnnType *input = new float[idim.tot()];
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dnnType *output = new float[odim.tot()];
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dnnType *input_d;
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checkCuda( cudaMalloc(&input_d, idim.tot()*sizeof(dnnType)));
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int ret_tensorrt = 0;
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std::cout<<"Testing with batchsize: "<<BATCH_SIZE<<"\n";
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printCenteredTitle(" TENSORRT inference ", '=', 30);
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for(int i=0; i<10; i++) {
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// generate input
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for(int j=0; j<netRT.input_dim.tot(); j++) {
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dnnType val = ((float) rand() / (RAND_MAX));
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for(int b=0; b<BATCH_SIZE; b++)
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input[netRT.input_dim.tot()*b + j] = val;
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}
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checkCuda(cudaMemcpy(input_d, input, idim.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
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tk::dnn::dataDim_t dim = idim;
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TIMER_START
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netRT.infer(dim, input_d);
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TIMER_STOP
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// control output
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std::cout<<"Output Buffers: "<<netRT.getBuffersN()-1<<"\n";
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for(int o=1; o<netRT.getBuffersN(); o++) {
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for(int b=1; b<BATCH_SIZE; b++) {
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dnnType *out_d = (dnnType*) netRT.buffersRT[o];
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dnnType *out0_d = out_d;
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dnnType *outI_d = out_d + netRT.buffersDIM[o].tot()*b;
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ret_tensorrt |= checkResult(netRT.buffersDIM[o].tot(), outI_d, out0_d) == 0 ? 0 : ERROR_TENSORRT;
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}
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}
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}
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return ret_tensorrt;
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}
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