Merge with master
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
@@ -1,7 +1,8 @@
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#ifndef LAYER_H
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#define LAYER_H
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#include <iostream>
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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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@@ -10,10 +11,11 @@ namespace tk
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namespace dnn
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{
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enum layerType_t
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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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@@ -52,36 +54,23 @@ public:
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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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{
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case LAYER_DENSE:
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return "Dense";
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case LAYER_CONV2D:
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return "Conv2d";
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case LAYER_ACTIVATION:
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return "Activation";
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case LAYER_FLATTEN:
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return "Flatten";
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case LAYER_MULADD:
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return "MulAdd";
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case LAYER_POOLING:
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return "Pooling";
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case LAYER_SOFTMAX:
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return "Softmax";
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case LAYER_ROUTE:
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return "Route";
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case LAYER_REORG:
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return "Reorg";
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case LAYER_SHORTCUT:
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return "Shortcut";
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case LAYER_UPSAMPLE:
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return "Upsample";
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case LAYER_REGION:
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return "Region";
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case LAYER_YOLO:
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return "Yolo";
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default:
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return "unknown";
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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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@@ -97,8 +86,8 @@ 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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const char *fname_weights, bool batchnorm = false);
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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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@@ -124,6 +113,27 @@ public:
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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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@@ -131,7 +141,7 @@ class Dense : public LayerWgs
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{
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public:
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Dense(Network *net, int out_ch, const char *fname_weights);
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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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@@ -168,14 +178,22 @@ protected:
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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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const char *fname_weights, bool batchnorm = false);
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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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@@ -193,6 +211,72 @@ protected:
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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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@@ -384,13 +468,14 @@ public:
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int sort_class;
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};
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Yolo(Network *net, int classes, int num, const char *fname_weights);
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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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@@ -422,8 +507,8 @@ 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, const char *fname_weights);
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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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@@ -46,6 +46,9 @@ public:
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// this is filled with results
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std::vector<tk::dnn::box> detected;
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// keep track of inference times (ms)
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std::vector<double> stats;
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Yolo3Detection() {}
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virtual ~Yolo3Detection() {}
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@@ -58,6 +61,15 @@ public:
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bool init(std::string tensor_path);
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void addBorders(cv::Mat &imageORIG, cv::Mat &imageWBorders, int &top, int &left);
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void update(cv::Mat &frame);
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tk::dnn::Yolo* getYoloLayer(int n=0) {
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if(n<3)
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return yolo[n];
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else
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return nullptr;
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}
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};
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} // namespace dnn
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@@ -0,0 +1,289 @@
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int preYoloFilters = (classes+5)*3;
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std::string input_bin = bin_path + "/layers/input.bin";
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std::vector<std::string> output_bins = {
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bin_path + "/debug/layer82_out.bin",
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bin_path + "/debug/layer94_out.bin",
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bin_path + "/debug/layer106_out.bin"
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};
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std::string c0_bin = bin_path + "/layers/c0.bin";
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std::string c1_bin = bin_path + "/layers/c1.bin";
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std::string c2_bin = bin_path + "/layers/c2.bin";
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std::string c3_bin = bin_path + "/layers/c3.bin";
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std::string c5_bin = bin_path + "/layers/c5.bin";
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std::string c6_bin = bin_path + "/layers/c6.bin";
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std::string c7_bin = bin_path + "/layers/c7.bin";
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std::string c9_bin = bin_path + "/layers/c9.bin";
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std::string c10_bin = bin_path + "/layers/c10.bin";
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std::string c12_bin = bin_path + "/layers/c12.bin";
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std::string c13_bin = bin_path + "/layers/c13.bin";
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std::string c14_bin = bin_path + "/layers/c14.bin";
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std::string c16_bin = bin_path + "/layers/c16.bin";
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std::string c17_bin = bin_path + "/layers/c17.bin";
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std::string c19_bin = bin_path + "/layers/c19.bin";
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std::string c20_bin = bin_path + "/layers/c20.bin";
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std::string c22_bin = bin_path + "/layers/c22.bin";
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std::string c23_bin = bin_path + "/layers/c23.bin";
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std::string c25_bin = bin_path + "/layers/c25.bin";
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std::string c26_bin = bin_path + "/layers/c26.bin";
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std::string c28_bin = bin_path + "/layers/c28.bin";
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std::string c29_bin = bin_path + "/layers/c29.bin";
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std::string c31_bin = bin_path + "/layers/c31.bin";
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std::string c32_bin = bin_path + "/layers/c32.bin";
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std::string c34_bin = bin_path + "/layers/c34.bin";
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std::string c35_bin = bin_path + "/layers/c35.bin";
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std::string c37_bin = bin_path + "/layers/c37.bin";
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std::string c38_bin = bin_path + "/layers/c38.bin";
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std::string c39_bin = bin_path + "/layers/c39.bin";
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std::string c41_bin = bin_path + "/layers/c41.bin";
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std::string c42_bin = bin_path + "/layers/c42.bin";
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std::string c44_bin = bin_path + "/layers/c44.bin";
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std::string c45_bin = bin_path + "/layers/c45.bin";
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std::string c47_bin = bin_path + "/layers/c47.bin";
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std::string c48_bin = bin_path + "/layers/c48.bin";
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std::string c50_bin = bin_path + "/layers/c50.bin";
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std::string c51_bin = bin_path + "/layers/c51.bin";
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std::string c53_bin = bin_path + "/layers/c53.bin";
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std::string c54_bin = bin_path + "/layers/c54.bin";
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std::string c56_bin = bin_path + "/layers/c56.bin";
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std::string c57_bin = bin_path + "/layers/c57.bin";
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std::string c59_bin = bin_path + "/layers/c59.bin";
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std::string c60_bin = bin_path + "/layers/c60.bin";
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std::string c62_bin = bin_path + "/layers/c62.bin";
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std::string c63_bin = bin_path + "/layers/c63.bin";
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std::string c64_bin = bin_path + "/layers/c64.bin";
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std::string c66_bin = bin_path + "/layers/c66.bin";
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std::string c67_bin = bin_path + "/layers/c67.bin";
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std::string c69_bin = bin_path + "/layers/c69.bin";
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std::string c70_bin = bin_path + "/layers/c70.bin";
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std::string c72_bin = bin_path + "/layers/c72.bin";
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std::string c73_bin = bin_path + "/layers/c73.bin";
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std::string c75_bin = bin_path + "/layers/c75.bin";
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std::string c76_bin = bin_path + "/layers/c76.bin";
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std::string c77_bin = bin_path + "/layers/c77.bin";
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std::string c78_bin = bin_path + "/layers/c78.bin";
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std::string c79_bin = bin_path + "/layers/c79.bin";
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std::string c80_bin = bin_path + "/layers/c80.bin";
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std::string c81_bin = bin_path + "/layers/c81.bin";
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std::string g82_bin = bin_path + "/layers/g82.bin";
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std::string c84_bin = bin_path + "/layers/c84.bin";
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std::string c87_bin = bin_path + "/layers/c87.bin";
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std::string c88_bin = bin_path + "/layers/c88.bin";
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std::string c89_bin = bin_path + "/layers/c89.bin";
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std::string c90_bin = bin_path + "/layers/c90.bin";
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std::string c91_bin = bin_path + "/layers/c91.bin";
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std::string c92_bin = bin_path + "/layers/c92.bin";
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std::string c93_bin = bin_path + "/layers/c93.bin";
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std::string g94_bin = bin_path + "/layers/g94.bin";
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std::string c96_bin = bin_path + "/layers/c96.bin";
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std::string c99_bin = bin_path + "/layers/c99.bin";
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std::string c100_bin = bin_path + "/layers/c100.bin";
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std::string c101_bin = bin_path + "/layers/c101.bin";
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std::string c102_bin = bin_path + "/layers/c102.bin";
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std::string c103_bin = bin_path + "/layers/c103.bin";
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std::string c104_bin = bin_path + "/layers/c104.bin";
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std::string c105_bin = bin_path + "/layers/c105.bin";
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std::string g106_bin = bin_path + "/layers/g106.bin";
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tk::dnn::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
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tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c1 (&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
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tk::dnn::Activation a1 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c2 (&net, 32, 1, 1, 1, 1, 0, 0, c2_bin, true);
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tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c3 (&net, 64, 3, 3, 1, 1, 1, 1, c3_bin, true);
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tk::dnn::Activation a3 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s4 (&net, &a1);
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tk::dnn::Conv2d c5 (&net, 128, 3, 3, 2, 2, 1, 1, c5_bin, true);
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tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c6 (&net, 64, 1, 1, 1, 1, 0, 0, c6_bin, true);
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tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c7 (&net, 128, 3, 3, 1, 1, 1, 1, c7_bin, true);
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tk::dnn::Activation a7 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s8 (&net, &a5);
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tk::dnn::Conv2d c9 (&net, 64, 1, 1, 1, 1, 0, 0, c9_bin, true);
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tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c10 (&net, 128, 3, 3, 1, 1, 1, 1, c10_bin, true);
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tk::dnn::Activation a10 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s11 (&net, &s8);
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tk::dnn::Conv2d c12 (&net, 256, 3, 3, 2, 2, 1, 1, c12_bin, true);
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tk::dnn::Activation a12 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c13 (&net, 128, 1, 1, 1, 1, 0, 0, c13_bin, true);
|
||||
tk::dnn::Activation a13 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c14 (&net, 256, 3, 3, 1, 1, 1, 1, c14_bin, true);
|
||||
tk::dnn::Activation a14 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s15 (&net, &a12);
|
||||
|
||||
tk::dnn::Conv2d c16 (&net, 128, 1, 1, 1, 1, 0, 0, c16_bin, true);
|
||||
tk::dnn::Activation a16 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c17 (&net, 256, 3, 3, 1, 1, 1, 1, c17_bin, true);
|
||||
tk::dnn::Activation a17 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s18 (&net, &s15);
|
||||
tk::dnn::Conv2d c19 (&net, 128, 1, 1, 1, 1, 0, 0, c19_bin, true);
|
||||
tk::dnn::Activation a19 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c20 (&net, 256, 3, 3, 1, 1, 1, 1, c20_bin, true);
|
||||
tk::dnn::Activation a20 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s21 (&net, &s18);
|
||||
tk::dnn::Conv2d c22 (&net, 128, 1, 1, 1, 1, 0, 0, c22_bin, true);
|
||||
tk::dnn::Activation a22 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c23 (&net, 256, 3, 3, 1, 1, 1, 1, c23_bin, true);
|
||||
tk::dnn::Activation a23 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s24 (&net, &s21);
|
||||
tk::dnn::Conv2d c25 (&net, 128, 1, 1, 1, 1, 0, 0, c25_bin, true);
|
||||
tk::dnn::Activation a25 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c26 (&net, 256, 3, 3, 1, 1, 1, 1, c26_bin, true);
|
||||
tk::dnn::Activation a26 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s27 (&net, &s24);
|
||||
tk::dnn::Conv2d c28 (&net, 128, 1, 1, 1, 1, 0, 0, c28_bin, true);
|
||||
tk::dnn::Activation a28 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c29 (&net, 256, 3, 3, 1, 1, 1, 1, c29_bin, true);
|
||||
tk::dnn::Activation a29 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s30 (&net, &s27);
|
||||
tk::dnn::Conv2d c31 (&net, 128, 1, 1, 1, 1, 0, 0, c31_bin, true);
|
||||
tk::dnn::Activation a31 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c32 (&net, 256, 3, 3, 1, 1, 1, 1, c32_bin, true);
|
||||
tk::dnn::Activation a32 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s33 (&net, &s30);
|
||||
tk::dnn::Conv2d c34 (&net, 128, 1, 1, 1, 1, 0, 0, c34_bin, true);
|
||||
tk::dnn::Activation a34 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c35 (&net, 256, 3, 3, 1, 1, 1, 1, c35_bin, true);
|
||||
tk::dnn::Activation a35 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s36 (&net, &s33);
|
||||
|
||||
tk::dnn::Conv2d c37 (&net, 512, 3, 3, 2, 2, 1, 1, c37_bin, true);
|
||||
tk::dnn::Activation a37 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c38 (&net, 256, 1, 1, 1, 1, 0, 0, c38_bin, true);
|
||||
tk::dnn::Activation a38 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c39 (&net, 512, 3, 3, 1, 1, 1, 1, c39_bin, true);
|
||||
tk::dnn::Activation a39 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s40 (&net, &a37);
|
||||
|
||||
tk::dnn::Conv2d c41 (&net, 256, 1, 1, 1, 1, 0, 0, c41_bin, true);
|
||||
tk::dnn::Activation a41 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c42 (&net, 512, 3, 3, 1, 1, 1, 1, c42_bin, true);
|
||||
tk::dnn::Activation a42 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s43 (&net, &s40);
|
||||
tk::dnn::Conv2d c44 (&net, 256, 1, 1, 1, 1, 0, 0, c44_bin, true);
|
||||
tk::dnn::Activation a44 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c45 (&net, 512, 3, 3, 1, 1, 1, 1, c45_bin, true);
|
||||
tk::dnn::Activation a45 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s46 (&net, &s43);
|
||||
tk::dnn::Conv2d c47 (&net, 256, 1, 1, 1, 1, 0, 0, c47_bin, true);
|
||||
tk::dnn::Activation a47 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c48 (&net, 512, 3, 3, 1, 1, 1, 1, c48_bin, true);
|
||||
tk::dnn::Activation a48 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s49 (&net, &s46);
|
||||
tk::dnn::Conv2d c50 (&net, 256, 1, 1, 1, 1, 0, 0, c50_bin, true);
|
||||
tk::dnn::Activation a50 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c51 (&net, 512, 3, 3, 1, 1, 1, 1, c51_bin, true);
|
||||
tk::dnn::Activation a51 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s52 (&net, &s49);
|
||||
tk::dnn::Conv2d c53 (&net, 256, 1, 1, 1, 1, 0, 0, c53_bin, true);
|
||||
tk::dnn::Activation a53 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c54 (&net, 512, 3, 3, 1, 1, 1, 1, c54_bin, true);
|
||||
tk::dnn::Activation a54 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s55 (&net, &s52);
|
||||
tk::dnn::Conv2d c56 (&net, 256, 1, 1, 1, 1, 0, 0, c56_bin, true);
|
||||
tk::dnn::Activation a56 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c57 (&net, 512, 3, 3, 1, 1, 1, 1, c57_bin, true);
|
||||
tk::dnn::Activation a57 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s58 (&net, &s55);
|
||||
tk::dnn::Conv2d c59 (&net, 256, 1, 1, 1, 1, 0, 0, c59_bin, true);
|
||||
tk::dnn::Activation a59 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c60 (&net, 512, 3, 3, 1, 1, 1, 1, c60_bin, true);
|
||||
tk::dnn::Activation a60 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s61 (&net, &s58);
|
||||
|
||||
tk::dnn::Conv2d c62 (&net,1024, 3, 3, 2, 2, 1, 1, c62_bin, true);
|
||||
tk::dnn::Activation a62 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c63 (&net, 512, 1, 1, 1, 1, 0, 0, c63_bin, true);
|
||||
tk::dnn::Activation a63 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c64 (&net,1024, 3, 3, 1, 1, 1, 1, c64_bin, true);
|
||||
tk::dnn::Activation a64 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s65 (&net, &a62);
|
||||
|
||||
tk::dnn::Conv2d c66 (&net, 512, 1, 1, 1, 1, 0, 0, c66_bin, true);
|
||||
tk::dnn::Activation a66 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c67 (&net,1024, 3, 3, 1, 1, 1, 1, c67_bin, true);
|
||||
tk::dnn::Activation a67 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s68 (&net, &s65);
|
||||
|
||||
tk::dnn::Conv2d c69 (&net, 512, 1, 1, 1, 1, 0, 0, c69_bin, true);
|
||||
tk::dnn::Activation a69 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c70 (&net,1024, 3, 3, 1, 1, 1, 1, c70_bin, true);
|
||||
tk::dnn::Activation a70 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s71 (&net, &s68);
|
||||
|
||||
tk::dnn::Conv2d c72 (&net, 512, 1, 1, 1, 1, 0, 0, c72_bin, true);
|
||||
tk::dnn::Activation a72 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c73 (&net,1024, 3, 3, 1, 1, 1, 1, c73_bin, true);
|
||||
tk::dnn::Activation a73 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s74 (&net, &s71);
|
||||
|
||||
tk::dnn::Conv2d c75 (&net, 512, 1, 1, 1, 1, 0, 0, c75_bin, true);
|
||||
tk::dnn::Activation a75 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c76 (&net,1024, 3, 3, 1, 1, 1, 1, c76_bin, true);
|
||||
tk::dnn::Activation a76 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c77 (&net, 512, 1, 1, 1, 1, 0, 0, c77_bin, true);
|
||||
tk::dnn::Activation a77 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c78 (&net,1024, 3, 3, 1, 1, 1, 1, c78_bin, true);
|
||||
tk::dnn::Activation a78 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c79 (&net, 512, 1, 1, 1, 1, 0, 0, c79_bin, true);
|
||||
tk::dnn::Activation a79 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c80 (&net,1024, 3, 3, 1, 1, 1, 1, c80_bin, true);
|
||||
tk::dnn::Activation a80 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c81 (&net, preYoloFilters, 1, 1, 1, 1, 0, 0, c81_bin, false);
|
||||
tk::dnn::Yolo yolo0 (&net, classes, 3, g82_bin);
|
||||
|
||||
tk::dnn::Layer *m83_layers[1] = { &a79 };
|
||||
tk::dnn::Route m83 (&net, m83_layers, 1);
|
||||
tk::dnn::Conv2d c84 (&net, 256, 1, 1, 1, 1, 0, 0, c84_bin, true);
|
||||
tk::dnn::Activation a84 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Upsample u85 (&net, 2);
|
||||
|
||||
tk::dnn::Layer *m86_layers[2] = { &u85, &s61 };
|
||||
tk::dnn::Route m86 (&net, m86_layers, 2);
|
||||
tk::dnn::Conv2d c87 (&net, 256, 1, 1, 1, 1, 0, 0, c87_bin, true);
|
||||
tk::dnn::Activation a87 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c88 (&net, 512, 3, 3, 1, 1, 1, 1, c88_bin, true);
|
||||
tk::dnn::Activation a88 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c89 (&net, 256, 1, 1, 1, 1, 0, 0, c89_bin, true);
|
||||
tk::dnn::Activation a89 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c90 (&net, 512, 3, 3, 1, 1, 1, 1, c90_bin, true);
|
||||
tk::dnn::Activation a90 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c91 (&net, 256, 1, 1, 1, 1, 0, 0, c91_bin, true);
|
||||
tk::dnn::Activation a91 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
|
||||
tk::dnn::Conv2d c92 (&net, 512, 3, 3, 1, 1, 1, 1, c92_bin, true);
|
||||
tk::dnn::Activation a92 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c93 (&net, preYoloFilters, 1, 1, 1, 1, 0, 0, c93_bin, false);
|
||||
tk::dnn::Yolo yolo1 (&net, classes, 3, g94_bin);
|
||||
|
||||
tk::dnn::Layer *m95_layers[1] = { &a91 };
|
||||
tk::dnn::Route m95 (&net, m95_layers, 1);
|
||||
tk::dnn::Conv2d c96 (&net, 128, 1, 1, 1, 1, 0, 0, c96_bin, true);
|
||||
tk::dnn::Activation a96 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Upsample u97 (&net, 2);
|
||||
|
||||
tk::dnn::Layer *m98_layers[2] = { &u97, &s36 };
|
||||
tk::dnn::Route m98 (&net, m98_layers, 2);
|
||||
tk::dnn::Conv2d c99 (&net, 128, 1, 1, 1, 1, 0, 0, c99_bin, true);
|
||||
tk::dnn::Activation a99 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c100 (&net, 256, 3, 3, 1, 1, 1, 1, c100_bin, true);
|
||||
tk::dnn::Activation a100 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c101 (&net, 128, 1, 1, 1, 1, 0, 0, c101_bin, true);
|
||||
tk::dnn::Activation a101 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c102 (&net, 256, 3, 3, 1, 1, 1, 1, c102_bin, true);
|
||||
tk::dnn::Activation a102 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c103 (&net, 128, 1, 1, 1, 1, 0, 0, c103_bin, true);
|
||||
tk::dnn::Activation a103 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
|
||||
tk::dnn::Conv2d c104 (&net, 256, 3, 3, 1, 1, 1, 1, c104_bin, true);
|
||||
tk::dnn::Activation a104 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c105 (&net, preYoloFilters, 1, 1, 1, 1, 0, 0, c105_bin, false);
|
||||
tk::dnn::Yolo yolo2 (&net, classes, 3, g106_bin);
|
||||
|
||||
yolo[0] = &yolo0;
|
||||
yolo[1] = &yolo1;
|
||||
yolo[2] = &yolo2;
|
||||
@@ -1,6 +1,8 @@
|
||||
#include<cassert>
|
||||
#include "../kernels.h"
|
||||
|
||||
#define YOLORT_CLASSNAME_W 256
|
||||
|
||||
class YoloRT : public IPlugin {
|
||||
|
||||
|
||||
@@ -16,6 +18,7 @@ public:
|
||||
if(yolo != nullptr) {
|
||||
memcpy(mask, yolo->mask_h, sizeof(dnnType)*num);
|
||||
memcpy(bias, yolo->bias_h, sizeof(dnnType)*num*3*2);
|
||||
classesNames = yolo->classesNames;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -72,7 +75,7 @@ public:
|
||||
|
||||
|
||||
virtual size_t getSerializationSize() override {
|
||||
return 5*sizeof(int) + num*sizeof(dnnType) + num*3*2*sizeof(dnnType);
|
||||
return 5*sizeof(int) + num*sizeof(dnnType) + num*3*2*sizeof(dnnType) + YOLORT_CLASSNAME_W*classes*sizeof(char);
|
||||
}
|
||||
|
||||
virtual void serialize(void* buffer) override {
|
||||
@@ -86,10 +89,20 @@ public:
|
||||
tk::dnn::writeBUF(buf, mask[i]);
|
||||
for(int i=0; i<3*2*num; i++)
|
||||
tk::dnn::writeBUF(buf, bias[i]);
|
||||
|
||||
// save classes names
|
||||
for(int i=0; i<classes; i++) {
|
||||
char tmp[YOLORT_CLASSNAME_W];
|
||||
strcpy(tmp, classesNames[i].c_str());
|
||||
for(int j=0; j<YOLORT_CLASSNAME_W; j++) {
|
||||
tk::dnn::writeBUF(buf, tmp[j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
int c, h, w;
|
||||
int classes, num;
|
||||
std::vector<std::string> classesNames;
|
||||
|
||||
dnnType *mask;
|
||||
dnnType *bias;
|
||||
@@ -5,4 +5,4 @@
|
||||
#include "Layer.h"
|
||||
#include "NetworkRT.h"
|
||||
|
||||
#define TKDNN_VERSION 300
|
||||
#define TKDNN_VERSION 400
|
||||
@@ -104,9 +104,9 @@
|
||||
|
||||
void printCenteredTitle(const char *title, char fill, int dim);
|
||||
bool fileExist(const char *fname);
|
||||
void readBinaryFile(const char *fname, int size, dnnType **data_h, dnnType **data_d, int seek = 0);
|
||||
int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device = true);
|
||||
void printDeviceVector(int size, dnnType *vec_d, bool device = true);
|
||||
void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** data_d, int seek = 0);
|
||||
int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device = true, int limit = 10);
|
||||
void printDeviceVector(int size, dnnType* vec_d, bool device = true);
|
||||
void resize(int size, dnnType **data);
|
||||
|
||||
void matrixTranspose(cublasHandle_t handle, dnnType *srcData, dnnType *dstData, int rows, int cols);
|
||||
Reference in New Issue
Block a user