hchandirasekar
78 lines
2.7 KiB
C++
78 lines
2.7 KiB
C++
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#include <tkdnn.h>
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int testInference(std::vector<std::string> input_bins, std::vector<std::string> output_bins,
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tk::dnn::Network *net, tk::dnn::NetworkRT *netRT = nullptr) {
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std::vector<tk::dnn::Layer*> outputs;
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for(int i=0; i<net->num_layers; i++) {
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if(net->layers[i]->final)
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outputs.push_back(net->layers[i]);
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}
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// no final layers, set last as output
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if(outputs.size() == 0) {
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outputs.push_back(net->layers[net->num_layers-1]);
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}
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// check input
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if(input_bins.size() != 1) {
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FatalError("currently support only 1 input");
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}
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if(output_bins.size() != outputs.size()) {
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std::cout<<output_bins.size()<<" "<<outputs.size()<<"\n";
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FatalError("outputs size mismatch");
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}
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// Load input
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dnnType *data;
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dnnType *input_h;
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readBinaryFile(input_bins[0], net->input_dim.tot(), &input_h, &data);
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// outputs
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//dnnType *cudnn_out[outputs.size()], *rt_out[outputs.size()];
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std::vector<dnnType *> cudnn_out,rt_out;
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tk::dnn::dataDim_t dim1 = net->input_dim; //input dim
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printCenteredTitle(" CUDNN inference ", '=', 30); {
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dim1.print();
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TKDNN_TSTART
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net->infer(dim1, data);
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TKDNN_TSTOP
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dim1.print();
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}
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for(int i=0; i<outputs.size(); i++) cudnn_out.push_back(outputs[i]->dstData);
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if(netRT != nullptr) {
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tk::dnn::dataDim_t dim2 = net->input_dim;
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printCenteredTitle(" TENSORRT inference ", '=', 30); {
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dim2.print();
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TKDNN_TSTART
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netRT->infer(dim2, data);
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TKDNN_TSTOP
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dim2.print();
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}
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for(int i=0; i<outputs.size(); i++) rt_out.push_back((dnnType*)netRT->buffersRT[i+1]);
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}
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int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
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for(int i=0; i<outputs.size(); i++) {
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printCenteredTitle((std::string(" OUTPUT ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
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dnnType *out, *out_h;
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int odim = outputs[i]->output_dim.tot();
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readBinaryFile(output_bins[i], odim, &out_h, &out);
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std::cout<<"CUDNN vs correct";
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ret_cudnn |= checkResult(odim, cudnn_out[i], out) == 0 ? 0: ERROR_CUDNN;
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if(netRT != nullptr) {
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std::cout<<"TRT vs correct";
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ret_tensorrt |= checkResult(odim, rt_out[i], out) == 0 ? 0 : ERROR_TENSORRT;
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std::cout<<"CUDNN vs TRT ";
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ret_cudnn_tensorrt |= checkResult(odim, cudnn_out[i], rt_out[i]) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
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}
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delete [] out_h;
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checkCuda( cudaFree(out) );
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}
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delete [] input_h;
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checkCuda( cudaFree(data) );
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return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
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} |