Francesco Gatti
73 lines
2.1 KiB
C++
73 lines
2.1 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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const char *input_bin = "../tests/mnist/input.bin";
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const char *c0_bin = "../tests/mnist/layers/c0.bin";
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const char *c1_bin = "../tests/mnist/layers/c1.bin";
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const char *d2_bin = "../tests/mnist/layers/d2.bin";
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const char *d3_bin = "../tests/mnist/layers/d3.bin";
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const char *output_bin = "../tests/mnist/output.bin";
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int main() {
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// Network layout
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tkDNN::dataDim_t dim(1, 1, 28, 28, 1);
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tkDNN::Network net(dim);
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tkDNN::Conv2d l0(&net, 20, 5, 5, 1, 1, 0, 0, c0_bin);
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tkDNN::Pooling l1(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
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tkDNN::Conv2d l2(&net, 50, 5, 5, 1, 1, 0, 0, c1_bin);
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tkDNN::Pooling l3(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
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tkDNN::Dense l4(&net, 500, d2_bin);
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tkDNN::Activation l5(&net, tkDNN::ACTIVATION_LEAKY);
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tkDNN::Dense l6(&net, 10, d3_bin);
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tkDNN::Softmax l7(&net);
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tkDNN::NetworkRT netRT(&net, "mnist.rt");
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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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dnnType *out_data, *out_data2;
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std::cout<<"CUDNN inference:\n"; {
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dim.print(); //print initial dimension
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TIMER_START
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out_data = net.infer(dim, data);
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TIMER_STOP
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dim.print();
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}
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// Print result
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//std::cout<<"\n======= CUDNN RESULT =======\n";
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//printDeviceVector(10, out_data);
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tkDNN::dataDim_t dim2(1, 1, 28, 28, 1);
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std::cout<<"TENSORRT inference:\n"; {
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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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// Print result
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//std::cout<<"\n======= TENRT RESULT =======\n";
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//printDeviceVector(10, out_data);
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std::cout<<"\n======= CHECK RESULT =======\n";
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checkResult(dim.tot(), out_data, out_data2);
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/*
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// Print real test
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std::cout<<"\n==== CHECK RESULT ====\n";
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dnnType *out;
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dnnType *out_h;
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readBinaryFile(output_bin, dim.tot(), &out_h, &out);
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printDeviceVector(dim.tot(), out);
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*/
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return 0;
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}
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