Micaela Verucchi
141 lines
4.1 KiB
C++
141 lines
4.1 KiB
C++
#include <iostream>
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#include "Layer.h"
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#include "kernels.h"
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namespace tk { namespace dnn {
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Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
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int paddingH, int paddingW,
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tkdnnPoolingMode_t pool_mode, float p) :
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Layer(net) {
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this->winH = winH;
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this->winW = winW;
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this->strideH = strideH;
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this->strideW = strideW;
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this->pool_mode = pool_mode;
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this->paddingH = paddingH;
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this->paddingW = paddingW;
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this->padding = winH -1;
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this->pow_param = p;
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checkCUDNN( cudnnCreatePoolingDescriptor(&poolingDesc) );
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int n = input_dim.n;
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int c = input_dim.c;
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int h = input_dim.h;
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int w = input_dim.w;
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int l = input_dim.l;
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poolOn3d = false;
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if(l > 1) {
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poolOn3d = true;
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if(n != 1)
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FatalError("N value on 3d pool must be 1");
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//use batch as l
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n = l;
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}
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cudnnPoolingMode_t cudnn_pool_mode = cudnnPoolingMode_t(pool_mode);
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if(pool_mode == POOLING_MAX_FIXEDSIZE) cudnn_pool_mode = cudnnPoolingMode_t(tkdnnPoolingMode_t::POOLING_MAX);
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if(pool_mode == POOLING_GENERALIZED_MEAN_P) cudnn_pool_mode = cudnnPoolingMode_t(tkdnnPoolingMode_t::POOLING_AVERAGE);
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checkCUDNN( cudnnSetPooling2dDescriptor(poolingDesc, cudnn_pool_mode,
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CUDNN_NOT_PROPAGATE_NAN, winH, winW, paddingH, paddingW, strideH, strideW) );
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checkCUDNN( cudnnSetTensor4dDescriptor(srcTensorDesc,
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net->tensorFormat, net->dataType, n, c, h, w) );
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//get out dim
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// checkCUDNN( cudnnGetPooling2dForwardOutputDim(poolingDesc, srcTensorDesc, &n, &c, &h, &w));
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//compute w and h as in darknet
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if(pool_mode == tkdnnPoolingMode_t::POOLING_MAX_FIXEDSIZE){
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int padH = paddingH == 0? winH -1 : paddingH;
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int padW = paddingW == 0? winW -1 : paddingW;
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h = (h + padH - winH)/strideH +1;
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w = (w + padW - winW)/strideW +1;
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}
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else{
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h = (h + 2*paddingH - winH)/strideH +1 ;
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w = (w + 2*paddingW - winW)/strideW +1;
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}
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// h = (h + winH*this->paddingH)/strideH;
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// w = (w + winW*this->paddingW)/strideW;
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checkCUDNN( cudnnSetTensor4dDescriptor(dstTensorDesc,
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net->tensorFormat, net->dataType, n, c, h, w) );
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output_dim.n = n;
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output_dim.c = c;
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output_dim.h = h;
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output_dim.w = w;
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output_dim.l = l;
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checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
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//pool on 3d data need transposition at the enter and on the exit
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//allocate for initial and final transposition
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if(poolOn3d) {
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output_dim.n = 1;
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checkCuda( cudaMalloc(&tmpInputData, input_dim.tot()*sizeof(dnnType)) );
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checkCuda( cudaMalloc(&tmpOutputData, output_dim.tot()*sizeof(dnnType)) );
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}
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}
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Pooling::~Pooling() {
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if(poolOn3d) {
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checkCuda( cudaFree(tmpInputData) );
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checkCuda( cudaFree(tmpOutputData) );
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}
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checkCUDNN( cudnnDestroyPoolingDescriptor(poolingDesc) );
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checkCuda( cudaFree(dstData) );
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}
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dnnType* Pooling::infer(dataDim_t &dim, dnnType* srcData) {
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dnnType *poolSrc = srcData;
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dnnType *poolDst = dstData;
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if(poolOn3d) {
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matrixTranspose(net->cublasHandle, srcData, tmpInputData, dim.h*dim.w*dim.c, dim.l);
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poolSrc = tmpInputData;
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poolDst = tmpOutputData;
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}
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if(pool_mode == tkdnnPoolingMode_t::POOLING_MAX_FIXEDSIZE){
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MaxPoolingForward(poolSrc, poolDst, dim.n, dim.c, dim.h, dim.w, this->strideH, this->strideW, this->winH, this->winH-1);
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}
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else if(pool_mode == tkdnnPoolingMode_t::POOLING_GENERALIZED_MEAN_P){
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GeneralizedMeanPoolingP(poolSrc, poolDst, dim.n, dim.c, dim.h, dim.w, pow_param);
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}
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else{
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dnnType alpha = dnnType(1);
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dnnType beta = dnnType(0);
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checkCUDNN( cudnnPoolingForward(net->cudnnHandle, poolingDesc,
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&alpha, srcTensorDesc, poolSrc,
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&beta, dstTensorDesc, poolDst) );
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}
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//update dim
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dim = output_dim;
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if(poolOn3d)
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matrixTranspose(net->cublasHandle, tmpOutputData, dstData, dim.l, dim.h*dim.w*dim.c);
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return dstData;
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}
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}}
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