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>
120 lines
3.1 KiB
C++
120 lines
3.1 KiB
C++
#include <iostream>
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#include <string.h>
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#include "tkdnn.h"
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#include "Network.h"
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#include "Layer.h"
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namespace tk { namespace dnn {
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Network::Network(dataDim_t input_dim) {
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this->input_dim = input_dim;
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float tk_ver = float(TKDNN_VERSION)/1000;
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float cu_ver = float(cudnnGetVersion())/1000;
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std::cout<<"New NETWORK (tkDNN v"<<tk_ver
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<<", CUDNN v"<<cu_ver<<")\n";
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dataType = CUDNN_DATA_FLOAT;
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tensorFormat = CUDNN_TENSOR_NCHW;
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dontLoadWeights = false;
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num_layers = 0;
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fp16 = false;
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dla = false;
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int8 = false;
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if(const char* env_p = std::getenv("TKDNN_MODE")) {
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if(strcmp(env_p, "FP16") == 0)
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fp16 = true;
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else if(strcmp(env_p, "DLA") == 0) {
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dla = true;
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fp16 = true;
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}
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else if(strcmp(env_p, "INT8") == 0) {
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int8 = true;
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}
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}
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if(fp16)
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std::cout<<COL_REDB<<"!! FP16 INFERENCE ENABLED !!"<<COL_END<<"\n";
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if(dla)
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std::cout<<COL_GREENB<<"!! DLA INFERENCE ENABLED !!"<<COL_END<<"\n";
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if(int8)
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std::cout<<COL_ORANGEB<<"!! INT8 INFERENCE ENABLED !!"<<COL_END<<"\n";
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checkCUDNN( cudnnCreate(&cudnnHandle) );
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checkERROR( cublasCreate(&cublasHandle) );
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}
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Network::~Network() {
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checkCUDNN( cudnnDestroy(cudnnHandle) );
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checkERROR( cublasDestroy(cublasHandle) );
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}
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dnnType* Network::infer(dataDim_t &dim, dnnType* data) {
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//do infer for every layer
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for(int i=0; i<num_layers; i++) {
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data = layers[i]->infer(dim, data);
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}
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checkCuda(cudaDeviceSynchronize());
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return data;
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}
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bool Network::addLayer(Layer *l) {
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if(num_layers == MAX_LAYERS)
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return false;
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l->id = num_layers;
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layers[num_layers++] = l;
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return true;
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}
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dataDim_t Network::getOutputDim() {
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if(num_layers == 0)
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return input_dim;
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else
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return layers[num_layers-1]->output_dim;
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}
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void Network::print() {
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printCenteredTitle(" NETWORK MODEL ", '=', 60);
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std::cout.width(3); std::cout<<std::left<<"N.";
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std::cout<<" ";
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std::cout.width(17); std::cout<<std::left<<"Layer type";
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std::cout.width(22); std::cout<<std::left<<"input (H*W,CH)";
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std::cout.width(16); std::cout<<std::left<<"output (H*W,CH)";
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std::cout<<"\n";
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for(int i=0; i<num_layers; i++) {
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dataDim_t in = layers[i]->input_dim;
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dataDim_t out = layers[i]->output_dim;
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std::cout.width(3); std::cout<<std::right<<i;
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std::cout<<" ";
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std::cout.width(16); std::cout<<std::left<<layers[i]->getLayerName();
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std::cout.width(4); std::cout<<std::right<<in.h;
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std::cout<<" x ";
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std::cout.width(4); std::cout<<std::right<<in.w;
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std::cout<<", ";
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std::cout.width(4); std::cout<<std::right<<in.c;
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std::cout<<" -> ";
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std::cout.width(4); std::cout<<std::right<<out.h;
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std::cout<<" x ";
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std::cout.width(4); std::cout<<std::right<<out.w;
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std::cout<<", ";
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std::cout.width(4); std::cout<<std::right<<out.c;
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std::cout<<"\n";
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}
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printCenteredTitle("", '=', 60);
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std::cout<<"\n";
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}
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}}
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