Micaela Verucchi
539 lines
13 KiB
C++
539 lines
13 KiB
C++
#ifndef LAYER_H
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#define LAYER_H
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#include<iostream>
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#include<vector>
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#include "utils.h"
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#include "Network.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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enum layerType_t {
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LAYER_INPUT,
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LAYER_DENSE,
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LAYER_CONV2D,
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LAYER_LSTM,
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LAYER_ACTIVATION,
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LAYER_FLATTEN,
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LAYER_MULADD,
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LAYER_POOLING,
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LAYER_SOFTMAX,
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LAYER_ROUTE,
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LAYER_REORG,
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LAYER_SHORTCUT,
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LAYER_UPSAMPLE,
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LAYER_REGION,
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LAYER_YOLO
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};
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#define TKDNN_BN_MIN_EPSILON 1e-5
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/**
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Simple layer Father class
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*/
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class Layer
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{
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public:
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Layer(Network *net);
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virtual ~Layer();
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virtual layerType_t getLayerType() = 0;
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData)
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{
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std::cout << "No infer action for this layer\n";
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return NULL;
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}
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dataDim_t input_dim, output_dim;
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dnnType *dstData; //where results will be putted
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std::string getLayerName()
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{
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layerType_t type = getLayerType();
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switch(type) {
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case LAYER_INPUT: return "Input";
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case LAYER_DENSE: return "Dense";
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case LAYER_CONV2D: return "Conv2d";
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case LAYER_LSTM: return "LSTM";
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case LAYER_ACTIVATION: return "Activation";
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case LAYER_FLATTEN: return "Flatten";
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case LAYER_MULADD: return "MulAdd";
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case LAYER_POOLING: return "Pooling";
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case LAYER_SOFTMAX: return "Softmax";
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case LAYER_ROUTE: return "Route";
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case LAYER_REORG: return "Reorg";
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case LAYER_SHORTCUT: return "Shortcut";
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case LAYER_UPSAMPLE: return "Upsample";
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case LAYER_REGION: return "Region";
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case LAYER_YOLO: return "Yolo";
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default: return "unknown";
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}
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}
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protected:
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Network *net;
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cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
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};
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/**
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Father class of all layer that need to load trained weights
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*/
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class LayerWgs : public Layer
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{
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public:
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LayerWgs(Network *net, int inputs, int outputs, int kh, int kw, int kt,
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std::string fname_weights, bool batchnorm = false);
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virtual ~LayerWgs();
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int inputs, outputs;
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std::string weights_path;
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dnnType *data_h, *data_d;
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dnnType *bias_h, *bias_d;
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//batchnorm
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bool batchnorm;
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dnnType *power_h;
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dnnType *scales_h, *scales_d;
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dnnType *mean_h, *mean_d;
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dnnType *variance_h, *variance_d;
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//fp16
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__half *data16_h, *bias16_h;
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__half *data16_d, *bias16_d;
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__half *power16_h, *power16_d;
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__half *scales16_h, *scales16_d;
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__half *mean16_h, *mean16_d;
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__half *variance16_h, *variance16_d;
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};
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/**
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Input layer (it doesnt need weigths)
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*/
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class Input : public Layer {
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public:
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Input(Network *net, dataDim_t &dim, dnnType* srcData) : Layer(net) {
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input_dim = dim;
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output_dim = dim;
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dstData = srcData;
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}
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virtual ~Input() {}
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virtual layerType_t getLayerType() { return LAYER_INPUT; };
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virtual dnnType* infer(dataDim_t &dim, dnnType* srcData) {
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return dstData;
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}
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};
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/**
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Dense (full interconnection) layer
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*/
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class Dense : public LayerWgs
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{
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public:
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Dense(Network *net, int out_ch, std::string fname_weights);
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virtual ~Dense();
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virtual layerType_t getLayerType() { return LAYER_DENSE; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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};
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/**
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Avaible activation functions
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*/
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typedef enum
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{
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ACTIVATION_ELU = 100,
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ACTIVATION_LEAKY = 101
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} tkdnnActivationMode_t;
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/**
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Activation layer (it doesnt need weigths)
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*/
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class Activation : public Layer
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{
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public:
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int act_mode;
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Activation(Network *net, int act_mode);
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virtual ~Activation();
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virtual layerType_t getLayerType() { return LAYER_ACTIVATION; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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protected:
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cudnnActivationDescriptor_t activDesc;
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};
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/**
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Convolutional 2D layer
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WEIGHTS shape: OUTCH, INCH, KH, KW ...
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BIAS shape: OUTCH
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with BATCHNORM:
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scales: OUTCH
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means: OUTCH
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variance: OUTCH
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*/
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class Conv2d : public LayerWgs
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{
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public:
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Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
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int strideH, int strideW, int paddingH, int paddingW,
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std::string fname_weights, bool batchnorm = false);
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virtual ~Conv2d();
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virtual layerType_t getLayerType() { return LAYER_CONV2D; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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int kernelH, kernelW, strideH, strideW, paddingH, paddingW;
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protected:
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cudnnFilterDescriptor_t filterDesc;
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cudnnConvolutionDescriptor_t convDesc;
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cudnnConvolutionFwdAlgo_t algo;
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cudnnTensorDescriptor_t biasTensorDesc;
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void *workSpace;
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size_t ws_sizeInBytes;
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};
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/**
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Bidirectional LSTM layer
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ONLY BIDIRECTIONAL (TODO: more configurable)
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currently implemented as 2 inferences: forward and backward (TODO: only 1 cudnn inference)
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implementation info:
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https://github.com/jiangnanhugo/seq2seq_cuda/blob/e4dbdcfa0517c972bfd4beea9f11a5233954093c/src/rnn.cpp
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https://github.com/Jeffery-Song/mxnet-test/blob/aab666faad44011f7a67b527b5f6c960367d0422/src/operator/cudnn_rnn-inl.h
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https://stackoverflow.com/a/38737941
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https://colah.github.io/posts/2015-08-Understanding-LSTMs/
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PARAMS (numlayers*2):
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layer0:
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( INCH, ? ) ???
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( HIDDEN, ? ) ???
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( HIDDEN * 8 ) ???
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layer2:
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( INCH, ? ) ???
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( HIDDEN, ? ) ???
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( HIDDEN * 8 ) ???
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OUTPUT shape:
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(N, C, 1, W) ---> LSTM(HIDDEN, returnSeq=True) ---> (N, 2*HIDDEN, 1, W) # W is seqLength
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(N, C, 1, W) ---> LSTM(HIDDEN, returnSeq=False) ---> (N, 2*HIDDEN, 1, 1)
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*/
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class LSTM : public Layer {
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public:
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LSTM(Network *net, int hiddensize, bool returnSeq, std::string fname_weights);
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virtual ~LSTM();
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virtual layerType_t getLayerType() { return LAYER_LSTM; };
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virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
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const bool bidirectional = true; /**> is the net bidir */
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bool returnSeq = false; /**> if false return only the result of last timestep */
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int stateSize = 0; /**> number of hidden states */
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int seqLen = 0; /**> number of timesteps */
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int numLayers = 1; /**> number of internal layers */
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protected:
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cudnnRNNDescriptor_t rnnDesc;
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cudnnDropoutDescriptor_t dropoutDesc;
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dnnType *dropout_states_, *work_space_;
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size_t workspace_byte_, dropout_byte_;
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int workspace_size_, dropout_size_;
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std::vector<cudnnTensorDescriptor_t> x_desc_vec_, y_desc_vec_;
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cudnnTensorDescriptor_t hx_desc_, cx_desc_;
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cudnnTensorDescriptor_t hy_desc_, cy_desc_;
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dnnType *hx_ptr, *cx_ptr, *hy_ptr, *cy_ptr;
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int stateDataDim;
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cudnnFilterDescriptor_t w_desc_;
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dnnType *w_ptr;
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dnnType *w_h;
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dnnType *wf_ptr, *wb_ptr; // params pointer forward and backward layer
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// used during inference
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dataDim_t one_output_dim; // output dim of as single inference
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dnnType *srcF, *srcB; // input of single inference
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dnnType *dstF, *dstB_NR, *dstB; // output of single inference, dstB_NR = dstB not reversed
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};
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/**
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Flatten layer
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is actually a matrix transposition
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*/
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class Flatten : public Layer
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{
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public:
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Flatten(Network *net);
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virtual ~Flatten();
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virtual layerType_t getLayerType() { return LAYER_FLATTEN; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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};
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/**
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MulAdd layer
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apply a multiplication and then an addition for each data
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*/
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class MulAdd : public Layer
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{
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public:
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MulAdd(Network *net, dnnType mul, dnnType add);
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virtual ~MulAdd();
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virtual layerType_t getLayerType() { return LAYER_MULADD; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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protected:
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dnnType mul, add;
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dnnType *add_vector;
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};
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/**
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Avaible pooling functions (padding on tkDNN is not supported)
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*/
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typedef enum
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{
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POOLING_MAX = 0,
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POOLING_AVERAGE = 1, // count for average includes padded values
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POOLING_AVERAGE_EXCLUDE_PADDING = 2 // count for average does not include padded values
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} tkdnnPoolingMode_t;
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/**
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Pooling layer
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currenty supported only 2d pooing (also on 3d input)
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*/
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class Pooling : public Layer
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{
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public:
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int winH, winW;
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int strideH, strideW;
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int paddingH, paddingW;
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Pooling(Network *net, int winH, int winW,
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int strideH, int strideW, tkdnnPoolingMode_t pool_mode);
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virtual ~Pooling();
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virtual layerType_t getLayerType() { return LAYER_POOLING; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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protected:
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cudnnPoolingDescriptor_t poolingDesc;
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tkdnnPoolingMode_t pool_mode;
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dnnType *tmpInputData, *tmpOutputData;
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bool poolOn3d;
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};
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/**
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Softmax layer
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*/
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class Softmax : public Layer
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{
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public:
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Softmax(Network *net);
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virtual ~Softmax();
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virtual layerType_t getLayerType() { return LAYER_SOFTMAX; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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};
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/**
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Route layer
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Merge a list of layers
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*/
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class Route : public Layer
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{
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public:
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Route(Network *net, Layer **layers, int layers_n);
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virtual ~Route();
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virtual layerType_t getLayerType() { return LAYER_ROUTE; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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public:
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Layer **layers; //ids of layers to be merged
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int layers_n; //number of layers
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};
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/**
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Reorg layer
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Mantain same dimension but change C*H*W distribution
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*/
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class Reorg : public Layer
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{
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public:
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Reorg(Network *net, int stride);
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virtual ~Reorg();
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virtual layerType_t getLayerType() { return LAYER_REORG; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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int stride;
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};
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/**
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Shortcut layer
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sum with stride another layer
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*/
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class Shortcut : public Layer
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{
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public:
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Shortcut(Network *net, Layer *backLayer);
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virtual ~Shortcut();
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virtual layerType_t getLayerType() { return LAYER_SHORTCUT; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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public:
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Layer *backLayer;
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};
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/**
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Upsample layer
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Mantain same dimension but change C*H*W distribution
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*/
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class Upsample : public Layer
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{
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public:
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Upsample(Network *net, int stride);
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virtual ~Upsample();
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virtual layerType_t getLayerType() { return LAYER_UPSAMPLE; };
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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int stride;
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bool reverse;
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};
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struct box
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{
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int cl;
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float x, y, w, h;
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float prob;
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};
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struct sortable_bbox
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{
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int index;
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int cl;
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float **probs;
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};
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/**
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Yolo3 layer
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*/
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class Yolo : public Layer
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{
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public:
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struct box
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{
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float x, y, w, h;
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};
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struct detection
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{
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Yolo::box bbox;
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int classes;
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float *prob;
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float *mask;
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float objectness;
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int sort_class;
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};
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Yolo(Network *net, int classes, int num, std::string fname_weights);
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virtual ~Yolo();
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virtual layerType_t getLayerType() { return LAYER_YOLO; };
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int classes, num;
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dnnType *mask_h, *mask_d; //anchors
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dnnType *bias_h, *bias_d; //anchors
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std::vector<std::string> classesNames;
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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int computeDetections(Yolo::detection *dets, int &ndets, int netw, int neth, float thresh);
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dnnType *predictions;
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static const int MAX_DETECTIONS = 256;
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static Yolo::detection *allocateDetections(int nboxes, int classes);
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static void mergeDetections(Yolo::detection *dets, int ndets, int classes);
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};
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/**
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Region layer
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*/
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class Region : public Layer
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{
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public:
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Region(Network *net, int classes, int coords, int num);
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virtual ~Region();
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virtual layerType_t getLayerType() { return LAYER_REGION; };
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int classes, coords, num;
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virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
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};
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class RegionInterpret
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{
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public:
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RegionInterpret(dataDim_t input_dim, dataDim_t output_dim,
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int classes, int coords, int num, float thresh, std::string fname_weights);
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~RegionInterpret();
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dataDim_t input_dim, output_dim;
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dnnType *bias_h, *bias_d; //anchors
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int classes, coords, num;
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float thresh;
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box *boxes;
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float **probs;
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sortable_bbox *s;
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box res_boxes[256];
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int res_boxes_n;
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box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride);
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void get_region_boxes(float *input, int w, int h, int netw, int neth, float thresh,
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float **probs, box *boxes, int only_objectness,
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int *map, float tree_thresh, int relative);
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void correct_region_boxes(box *boxes, int n, int w, int h, int netw, int neth, int relative);
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void interpretData(dnnType *data_h, int imageW = 0, int imageH = 0);
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void showImageResult(dnnType *input_h);
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static float box_iou(box a, box b);
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};
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} // namespace dnn
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} // namespace tk
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#endif //LAYER_H
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