xavier
38a1b9dcb2
The new test works both with TensorRT and cuDNN. Preprocessing and Postprocessing are missing. Add ClippedReLU (for ReLU6), groups for Conv2d, additional bias for convolution. Other minors: -move the timer in the detector to measure all the processing time for a given frame (both centernet and yolo); -add int8 flag. Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com> Davide Sapienza <sapienza.dav@gmail.com>
67 lines
2.3 KiB
C++
67 lines
2.3 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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Activation::Activation(Network *net, int act_mode, const float ceiling) :
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Layer(net) {
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this->act_mode = act_mode;
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this->ceiling = ceiling;
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checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(dnnType)) );
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if(int(act_mode) < 100) {
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checkCUDNN( cudnnSetTensor4dDescriptor(srcTensorDesc,
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net->tensorFormat,
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net->dataType,
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input_dim.n*input_dim.l,
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input_dim.c,
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input_dim.h, input_dim.w) );
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checkCUDNN( cudnnSetTensor4dDescriptor(dstTensorDesc,
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net->tensorFormat,
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net->dataType,
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input_dim.n*input_dim.l,
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input_dim.c,
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input_dim.h, input_dim.w) );
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checkCUDNN( cudnnCreateActivationDescriptor(&activDesc) );
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checkCUDNN( cudnnSetActivationDescriptor(activDesc,
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(cudnnActivationMode_t) act_mode,
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CUDNN_PROPAGATE_NAN,
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ceiling) );
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}
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}
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Activation::~Activation() {
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checkCuda( cudaFree(dstData) );
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if(int(act_mode) < 100)
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checkCUDNN( cudnnDestroyActivationDescriptor(activDesc) );
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}
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dnnType* Activation::infer(dataDim_t &dim, dnnType* srcData) {
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if(act_mode == ACTIVATION_LEAKY) {
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activationLEAKYForward(srcData, dstData, dim.tot());
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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( cudnnActivationForward(net->cudnnHandle,
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activDesc,
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&alpha,
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srcTensorDesc,
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srcData,
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&beta,
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dstTensorDesc,
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dstData) );
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}
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return dstData;
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}
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}}
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