Francesco Gatti
45 lines
1.5 KiB
C++
45 lines
1.5 KiB
C++
#include<iostream>
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#include "Layer.h"
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const char *input_bin = "../tests/input.bin";
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const char *c0_bin = "../tests/conv0.bin";
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const char *c0_bias_bin = "../tests/conv0.bias.bin";
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const char *c1_bin = "../tests/conv1.bin";
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const char *c1_bias_bin = "../tests/conv1.bias.bin";
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const char *d2_bin = "../tests/dense2.bin";
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const char *d2_bias_bin = "../tests/dense2.bias.bin";
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int main() {
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// Network layout
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tkDNN::Network net;
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tkDNN::dataDim_t dim(1, 1, 10, 10, 4);
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tkDNN::Conv3d c0 (&net, dim, 2, 4, 4, 2, 2, 2, 1, c0_bin, c0_bias_bin);
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tkDNN::Activation a0 (&net, c0.output_dim, tkDNN::ACTIVATION_RELU);
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tkDNN::Conv3d c1 (&net, a0.output_dim, 4, 2, 2, 2, 1, 1, 1, c1_bin, c1_bias_bin);
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tkDNN::Activation a1 (&net, c1.output_dim, tkDNN::ACTIVATION_ELU);
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tkDNN::Flatten f1 (&net, a1.output_dim);
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tkDNN::MulAdd m1 (&net, f1.output_dim, 2, 1);
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// Load input
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value_type *data;
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value_type *input_h;
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readBinaryFile(input_bin, dim.tot(), &input_h, &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 = c0.infer(dim, data); dim.print();
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data = a0.infer(dim, data); dim.print();
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data = c1.infer(dim, data); dim.print();
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data = a1.infer(dim, data); dim.print();
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data = f1.infer(dim, data); dim.print();
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data = m1.infer(dim, data); dim.print();
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TIMER_STOP
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// Print result
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printDeviceVector(dim.tot(), data);
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return 0;
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} |