Micaela Verucchi
74 lines
1.3 KiB
C++
74 lines
1.3 KiB
C++
#ifndef NETWORK_H
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#define NETWORK_H
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#include "utils.h"
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namespace tk
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{
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namespace dnn
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{
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/**
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Data rapresentation beetween layers
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n = batch size
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c = channels
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h = heigth (lines)
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w = width (rows)
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l = lenght (3rd dimension)
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*/
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struct dataDim_t
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{
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int n, c, h, w, l;
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dataDim_t() : n(1), c(1), h(1), w(1), l(1){};
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dataDim_t(int _n, int _c, int _h, int _w, int _l = 1) : n(_n), c(_c), h(_h), w(_w), l(_l){};
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void print()
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{
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std::cout << "Data dim: " << n << " " << c << " " << h << " " << w << " " << l << "\n";
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}
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int tot()
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{
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return n * c * h * w * l;
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}
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};
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class Layer;
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const int MAX_LAYERS = 256;
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class Network
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{
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public:
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Network(dataDim_t input_dim);
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virtual ~Network();
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/**
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Do inferece for every added layer
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*/
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dnnType *infer(dataDim_t &dim, dnnType *data);
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bool addLayer(Layer *l);
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void print();
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cudnnDataType_t dataType;
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cudnnTensorFormat_t tensorFormat;
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cudnnHandle_t cudnnHandle;
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cublasHandle_t cublasHandle;
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Layer *layers[MAX_LAYERS]; //contains layers of the net
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int num_layers; //current number of layers
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dataDim_t input_dim;
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dataDim_t getOutputDim();
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bool fp16, dla;
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};
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} // namespace dnn
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} // namespace tk
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#endif //NETWORK_H
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