Francesco Gatti
69 lines
2.2 KiB
C++
69 lines
2.2 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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const char *input_bin = "../tests/simple/input.bin";
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const char *c0_bin = "../tests/simple/layers/c0.bin";
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const char *c1_bin = "../tests/simple/layers/c1.bin";
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const char *d2_bin = "../tests/simple/layers/d2.bin";
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const char *output_bin = "../tests/simple/output.bin";
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int main() {
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// Network layout
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tk::dnn::dataDim_t dim(1, 1, 10, 10, 1);
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tk::dnn::Network net(dim);
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tk::dnn::Conv2d l0(&net, 2, 4, 4, 2, 2, 0, 0, c0_bin);
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tk::dnn::Activation l1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d l2(&net, 4, 2, 2, 1, 1, 0, 0, c1_bin);
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tk::dnn::Activation l3(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Dense l5(&net, 4, d2_bin);
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tk::dnn::Activation l6(&net, CUDNN_ACTIVATION_RELU);
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// Load input
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dnnType *data;
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dnnType *input_h;
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readBinaryFile(input_bin, dim.tot(), &input_h, &data);
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// Print input
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std::cout<<"\n======= INPUT =======\n";
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printDeviceVector(dim.tot(), data);
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std::cout<<"\n";
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//convert network to tensorRT
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tk::dnn::NetworkRT netRT(&net, "simple.rt");
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dnnType *out_data, *out_data2; // cudnn output, tensorRT output
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tk::dnn::dataDim_t dim1 = dim; //input dim
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printCenteredTitle(" CUDNN inference ", '=', 30); {
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dim1.print();
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TIMER_START
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out_data = net.infer(dim1, data);
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TIMER_STOP
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dim1.print();
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}
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tk::dnn::dataDim_t dim2 = dim;
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printCenteredTitle(" TENSORRT inference ", '=', 30); {
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dim2.print();
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TIMER_START
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out_data2 = netRT.infer(dim2, data);
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TIMER_STOP
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dim2.print();
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}
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std::cout<<"\n======= CUDNN =======\n";
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printDeviceVector(dim.tot(), out_data);
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std::cout<<"\n======= TENSORRT =======\n";
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printDeviceVector(dim.tot(), out_data2);
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printCenteredTitle(" CHECK RESULTS ", '=', 30);
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dnnType *out, *out_h;
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int out_dim = net.getOutputDim().tot();
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//readBinaryFile(output_bin, out_dim, &out_h, &out);
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//std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
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//std::cout<<"TRT vs correct"; checkResult(out_dim, out_data2, out);
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std::cout<<"CUDNN vs TRT "; checkResult(out_dim, out_data, out_data2);
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return 0;
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}
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