mbosi
59 lines
1.4 KiB
C++
59 lines
1.4 KiB
C++
#include <iostream>
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#include "Layer.h"
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namespace tk { namespace dnn {
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Dense::Dense(Network *net, int out_ch, std::string fname_weights) :
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LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights) {
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output_dim.n = 1;
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output_dim.c = out_ch;
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output_dim.h = 1;
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output_dim.w = 1;
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output_dim.l = 1;
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//allocate data for infer result
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checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
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}
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Dense::~Dense() {
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checkCuda( cudaFree(dstData) );
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}
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dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
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if (dim.n != 1)
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FatalError("Not Implemented");
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int dim_x = dim.tot();
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int dim_y = output_dim.tot();
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if (dim_x != input_dim.tot())
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FatalError("Input mismatch");
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dnnType alpha = dnnType(1), beta = dnnType(1);
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// place bias into dstData
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checkCuda( cudaMemcpy(dstData, bias_d, dim_y*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
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//do matrix moltiplication
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checkERROR( cublasSgemv(net->cublasHandle, CUBLAS_OP_T,
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dim_x, dim_y,
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&alpha,
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data_d, dim_x,
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srcData, 1,
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&beta,
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dstData, 1) );
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//update data dimensions
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dim.h = 1;
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dim.w = 1;
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dim.l = 1;
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dim.c = dim_y;
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return dstData;
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}
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}}
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