Francesco Gatti
48 lines
1.4 KiB
C++
48 lines
1.4 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::Flatten l4(&net);
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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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printDeviceVector(dim.tot(), data);
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dim.print(); //print initial dimension
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TIMER_START
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// Inference
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data = net.infer(dim, data); dim.print();
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TIMER_STOP
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// Print result
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std::cout<<"\n======= RESULT =======\n";
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printDeviceVector(dim.tot(), data);
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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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return 0;
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}
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