Micaela Verucchi
65 lines
1.4 KiB
C++
65 lines
1.4 KiB
C++
#include <iostream>
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#include "Layer.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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Dense::Dense(Network *net, int out_ch, const char *fname_weights) : LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights)
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{
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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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{
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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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{
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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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} // namespace dnn
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} // namespace tk
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