xavier
808a84131c
The new test works both with TensorRT and cuDNN. Preprocessing and Postprocessing are missing. Add ClippedReLU (for ReLU6), groups for Conv2d, additional bias for convolution. Other minors: -move the timer in the detector to measure all the processing time for a given frame (both centernet and yolo); -add int8 flag. Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com> Davide Sapienza <sapienza.dav@gmail.com>
68 lines
1.2 KiB
C++
68 lines
1.2 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 { namespace dnn {
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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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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) :
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n(_n), c(_c), h(_h), w(_w), l(_l) {};
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void print() {
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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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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 = 512;
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class Network {
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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, int8;
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bool dontLoadWeights;
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};
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}}
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#endif //NETWORK_H
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