Francesco Gatti
84 lines
3.1 KiB
C++
84 lines
3.1 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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const char *i0_bin = "../tests/imuodom/layers/input0.bin";
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const char *i1_bin = "../tests/imuodom/layers/input1.bin";
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const char *i2_bin = "../tests/imuodom/layers/input2.bin";
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const char *o0_bin = "../tests/imuodom/layers/output0.bin";
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const char *o1_bin = "../tests/imuodom/layers/output1.bin";
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const char *c0_bin = "../tests/imuodom/layers/conv1d_7.bin";
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const char *c1_bin = "../tests/imuodom/layers/conv1d_8.bin";
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const char *c2_bin = "../tests/imuodom/layers/conv1d_9.bin";
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const char *c3_bin = "../tests/imuodom/layers/conv1d_10.bin";
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const char *c4_bin = "../tests/imuodom/layers/conv1d_11.bin";
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const char *c5_bin = "../tests/imuodom/layers/conv1d_12.bin";
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const char *l0_bin = "../tests/imuodom/layers/bidirectional_3.bin";
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const char *l1_bin = "../tests/imuodom/layers/bidirectional_4.bin";
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const char *d0_bin = "../tests/imuodom/layers/dense_3.bin";
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const char *d1_bin = "../tests/imuodom/layers/dense_4.bin";
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int main() {
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// Network layout
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tk::dnn::dataDim_t dim0(1, 4, 1, 100);
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tk::dnn::dataDim_t dim1(1, 3, 1, 100);
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tk::dnn::dataDim_t dim2(1, 3, 1, 100);
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// Load input
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dnnType *i0_d, *i1_d, *i2_d;
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dnnType *i0_h, *i1_h, *i2_h;
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readBinaryFile(i0_bin, dim0.tot(), &i0_h, &i0_d);
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readBinaryFile(i1_bin, dim1.tot(), &i1_h, &i1_d);
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readBinaryFile(i2_bin, dim2.tot(), &i2_h, &i2_d);
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tk::dnn::Network net(dim0);
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tk::dnn::Input x0 (&net, dim0, i0_d);
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tk::dnn::Conv2d x0_0(&net, 128, 1, 11, 1, 1, 0, 0, c0_bin);
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tk::dnn::Conv2d x0_1(&net, 128, 1, 11, 1, 1, 0, 0, c1_bin);
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tk::dnn::Pooling x0_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
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tk::dnn::Input x1 (&net, dim1, i1_d);
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tk::dnn::Conv2d x1_0(&net, 128, 1, 11, 1, 1, 0, 0, c2_bin);
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tk::dnn::Conv2d x1_1(&net, 128, 1, 11, 1, 1, 0, 0, c3_bin);
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tk::dnn::Pooling x1_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
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tk::dnn::Input x2 (&net, dim2, i2_d);
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tk::dnn::Conv2d x2_0(&net, 128, 1, 11, 1, 1, 0, 0, c4_bin);
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tk::dnn::Conv2d x2_1(&net, 128, 1, 11, 1, 1, 0, 0, c5_bin);
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tk::dnn::Pooling x2_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
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tk::dnn::Layer *concat_l[3] = { &x0_2, &x1_2, &x2_2 };
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tk::dnn::Route concat (&net, concat_l, 3);
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tk::dnn::LSTM lstm0(&net, 128, true, l0_bin);
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tk::dnn::LSTM lstm1(&net, 128, false, l1_bin);
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tk::dnn::Dense d0 (&net, 3, d0_bin);
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tk::dnn::Layer *lstm1_l[1] = { &lstm1 };
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tk::dnn::Route lstm1_link (&net, lstm1_l, 1);
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tk::dnn::Dense d1 (&net, 4, d1_bin);
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net.print();
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dnnType *data;
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tk::dnn::dataDim_t dim;
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TIMER_START
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// Inference
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data = net.infer(dim, data);
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TIMER_STOP
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// Print real test
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std::cout<<"\n==== CHECK RESULT ====\n";
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dnnType *out0, *out1;
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dnnType *out0_h, *out1_h;
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readBinaryFile(o0_bin, d0.output_dim.tot(), &out0_h, &out0);
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readBinaryFile(o1_bin, d1.output_dim.tot(), &out1_h, &out1);
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d0.output_dim.print();
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checkResult(d0.output_dim.tot(), d0.dstData, out0);
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d1.output_dim.print();
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checkResult(d1.output_dim.tot(), d1.dstData, out1);
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return 0;
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}
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