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模板网站视频,丰台网站建设报价,网站seo入门基础教程,社保局网站建设意义classification 如上图所示#xff0c;由于直接export出的onnx文件有两个输出节点#xff0c;不方便处理#xff0c;所以编写脚本删除不需要的输出节点193#xff1a; import onnxonnx_model onnx.load(cls.onnx) graph onnx_model.graphinputs graph.inpu…classification 如上图所示由于直接export出的onnx文件有两个输出节点不方便处理所以编写脚本删除不需要的输出节点193 import onnxonnx_model onnx.load(cls.onnx) graph onnx_model.graphinputs graph.input for input in inputs:print(input,input.name)outputs graph.output for output in outputs:print(output,output.name)graph.output.remove(outputs[1]) onnx.save(onnx_model, cls_modified.onnx)C推理代码 #include iostream #include fstream #include vector #include algorithm #include cuda_runtime.h #include NvInfer.h #include NvInferRuntime.h #include NvOnnxParser.hconst int point_num 1024;void pc_normalize(std::vectorfloat points) {float mean_x 0, mean_y 0, mean_z 0;for (size_t i 0; i point_num; i){mean_x points[3 * i];mean_y points[3 * i 1];mean_z points[3 * i 2];}mean_x / point_num;mean_y / point_num;mean_z / point_num;for (size_t i 0; i point_num; i){points[3 * i] - mean_x;points[3 * i 1] - mean_y;points[3 * i 2] - mean_z;}float m 0;for (size_t i 0; i point_num; i){if (sqrt(pow(points[3 * i], 2) pow(points[3 * i 1], 2) pow(points[3 * i 2], 2)) m)m sqrt(pow(points[3 * i], 2) pow(points[3 * i 1], 2) pow(points[3 * i 2], 2));}for (size_t i 0; i point_num; i){points[3 * i] / m;points[3 * i 1] / m;points[3 * i 2] / m;} }class TRTLogger : public nvinfer1::ILogger { public:virtual void log(Severity severity, nvinfer1::AsciiChar const* msg) noexcept override{if (severity Severity::kINFO) printf(msg);} } logger;std::vectorunsigned char load_file(const std::string file) {std::ifstream in(file, std::ios::in | std::ios::binary);if (!in.is_open())return {};in.seekg(0, std::ios::end);size_t length in.tellg();std::vectoruint8_t data;if (length 0) {in.seekg(0, std::ios::beg);data.resize(length);in.read((char) data[0], length);}in.close();return data; }void classfier(std::vectorfloat points) {TRTLogger logger;nvinfer1::ICudaEngine engine;//#define BUILD_ENGINE#ifdef BUILD_ENGINEnvinfer1::IBuilder* builder nvinfer1::createInferBuilder(logger);nvinfer1::IBuilderConfig* config builder-createBuilderConfig();nvinfer1::INetworkDefinition* network builder-createNetworkV2(1);nvonnxparser::IParser* parser nvonnxparser::createParser(*network, logger);if (!parser-parseFromFile(cls_modified.onnx, 1)){printf(Failed to parser onnx\n);return;}int maxBatchSize 1;config-setMaxWorkspaceSize(1 32);engine builder-buildEngineWithConfig(*network, config);if (engine nullptr) {printf(Build engine failed.\n);return;}nvinfer1::IHostMemory model_data engine-serialize();FILE* f fopen(cls.engine, wb);fwrite(model_data-data(), 1, model_data-size(), f);fclose(f);model_data-destroy();parser-destroy();engine-destroy();network-destroy();config-destroy();builder-destroy(); #endif auto engine_data load_file(cls.engine);nvinfer1::IRuntime* runtime nvinfer1::createInferRuntime(logger);engine runtime-deserializeCudaEngine(engine_data.data(), engine_data.size());if (engine nullptr){printf(Deserialize cuda engine failed.\n);runtime-destroy();return;}nvinfer1::IExecutionContext* execution_context engine-createExecutionContext();cudaStream_t stream nullptr;cudaStreamCreate(stream);float* input_data_host nullptr;const size_t input_numel 1 * 3 * point_num;cudaMallocHost(input_data_host, input_numel * sizeof(float));for (size_t i 0; i 3; i){for (size_t j 0; j point_num; j){input_data_host[point_num * i j] points[3 * j i];}}float* input_data_device nullptr;float output_data_host[10];float* output_data_device nullptr;cudaMalloc(input_data_device, input_numel * sizeof(float));cudaMalloc(output_data_device, sizeof(output_data_host));cudaMemcpyAsync(input_data_device, input_data_host, input_numel * sizeof(float), cudaMemcpyHostToDevice, stream);float* bindings[] { input_data_device, output_data_device };bool success execution_context-enqueueV2((void)bindings, stream, nullptr);cudaMemcpyAsync(output_data_host, output_data_device, sizeof(output_data_host), cudaMemcpyDeviceToHost, stream);cudaStreamSynchronize(stream);int predict_label std::max_element(output_data_host, output_data_host 10) - output_data_host;std::cout \npredict_label: predict_label std::endl;cudaStreamDestroy(stream);execution_context-destroy();engine-destroy();runtime-destroy(); }int main() {std::vectorfloat points;std::ifstream infile;float x, y, z, nx, ny, nz;char ch;infile.open(bed_0610.txt);for (size_t i 0; i point_num; i){infile x ch y ch z ch nx ch ny ch nz;points.push_back(x);points.push_back(y);points.push_back(z);}infile.close();pc_normalize(points);classfier(points);return 0; }其中推理引擎的构建也可以直接使用tensorrt的bin目录下的trtexec.exe。 LZ也实现了cuda版本的前处理代码但似乎效率比cpu前处理还低。可能是数据量不够大吧才10^3数量级而且目前LZ的cuda水平也只是入门阶段… #include iostream #include fstream #include vector #include algorithm #include cuda_runtime.h #include cuda_runtime_api.h #include NvInfer.h #include NvInferRuntime.h #include NvOnnxParser.hconst int point_num 1024; const int thread_num 1024; const int block_num 1;global void array_sum(float* data, float* val, int N) {shared double share_dTemp[thread_num];const int nStep gridDim.x * blockDim.x;const int tid blockIdx.x * blockDim.x threadIdx.x;double dTempSum 0.0;for (int i tid; i N; i nStep){dTempSum data[i];}share_dTemp[threadIdx.x] dTempSum;syncthreads();for (int i blockDim.x / 2; i ! 0; i / 2){if (threadIdx.x i){share_dTemp[threadIdx.x] share_dTemp[threadIdx.x i];}syncthreads();}if (0 threadIdx.x){atomicAdd(val, share_dTemp[0]);} }global void array_sub(float* data, float val, int N) {const int tid blockIdx.x * blockDim.x threadIdx.x;const int nStep blockDim.x * gridDim.x;for (int i tid; i N; i nStep){data[i] data[i] - val;} }global void array_L2(float* in, float* out, int N) {const int tid blockIdx.x * blockDim.x threadIdx.x;const int nStep blockDim.x * gridDim.x;for (int i tid; i N; i nStep){out[i] sqrt(pow(in[i], 2) pow(in[i N], 2) pow(in[i 2 * N], 2));} }global void array_max(float* mem, int numbers) {int tid threadIdx.x;int idof blockIdx.x * blockDim.x;int idx tid idof;extern shared float tep[];if (idx numbers) return;tep[tid] mem[idx];unsigned int bi 0;for (int s 1; s blockDim.x; s (s 1)){unsigned int kid tid (bi 1);if ((kid s) blockDim.x || (idof kid s) numbers) break;tep[kid] tep[kid] tep[kid s] ? tep[kid] : tep[kid s];bi;syncthreads();}if (tid 0) {mem[blockIdx.x] tep[0];} }global__ void array_div(float* data, float val, int N) {const int tid blockIdx.x * blockDim.x threadIdx.x;const int nStep blockDim.x * gridDim.x;for (int i tid; i N; i nStep){data[i] data[i] / val;} }void pc_normalize_gpu(float* points) {float *mean_x NULL, *mean_y NULL, *mean_z NULL;cudaMalloc((void) mean_x, sizeof(float));cudaMalloc((void) mean_y, sizeof(float));cudaMalloc((void) mean_z, sizeof(float));array_sum thread_num, block_num (points 0 * point_num, mean_x, point_num);array_sum thread_num, block_num (points 1 * point_num, mean_y, point_num);array_sum thread_num, block_num (points 2 * point_num, mean_z, point_num);float mx, my, mz;cudaMemcpy(mx, mean_x, sizeof(float), cudaMemcpyDeviceToHost);cudaMemcpy(my, mean_y, sizeof(float), cudaMemcpyDeviceToHost);cudaMemcpy(mz, mean_z, sizeof(float), cudaMemcpyDeviceToHost);array_sub thread_num, block_num (points 0 * point_num, mx / point_num, point_num);array_sub thread_num, block_num (points 1 * point_num, my / point_num, point_num);array_sub thread_num, block_num (points 2 * point_num, mz / point_num, point_num);//float* pts (float*)malloc(sizeof(float) * point_num);//cudaMemcpy(pts, points, sizeof(float) * point_num, cudaMemcpyDeviceToHost);//for (size_t i 0; i point_num; i)//{// std::cout pts[i] std::endl;//}float* L2 NULL;cudaMalloc((void) L2, sizeof(float) * point_num);array_L2 thread_num, block_num (points, L2, point_num);//float* l2 (float*)malloc(sizeof(float) * point_num);//cudaMemcpy(l2, L2, sizeof(float) * point_num, cudaMemcpyDeviceToHost);//for (size_t i 0; i point_num; i)//{// std::cout l2[i] std::endl;//}int tmp_num point_num;int share_size sizeof(float) * thread_num;int block_num (tmp_num thread_num - 1) / thread_num;do {array_max block_num, thread_num, share_size (L2, thread_num);tmp_num block_num;block_num (tmp_num thread_num - 1) / thread_num;} while (tmp_num 1);float max;cudaMemcpy(max, L2, sizeof(float), cudaMemcpyDeviceToHost);//std::cout max std::endl;array_div thread_num, block_num (points 0 * point_num, max, point_num);array_div thread_num, block_num (points 1 * point_num, max, point_num);array_div thread_num, block_num (points 2 * point_num, max, point_num);}class TRTLogger : public nvinfer1::ILogger { public:virtual void log(Severity severity, nvinfer1::AsciiChar const* msg) noexcept override{if (severity Severity::kINFO) printf(msg);} } logger;std::vectorunsigned char load_file(const std::string file) {std::ifstream in(file, std::ios::in | std::ios::binary);if (!in.is_open())return {};in.seekg(0, std::ios::end);size_t length in.tellg();std::vectoruint8_t data;if (length 0) {in.seekg(0, std::ios::beg);data.resize(length);in.read((char) data[0], length);}in.close();return data; }void classfier(std::vectorfloat points) {TRTLogger logger;nvinfer1::ICudaEngine engine;//#define BUILD_ENGINE#ifdef BUILD_ENGINEnvinfer1::IBuilder* builder nvinfer1::createInferBuilder(logger);nvinfer1::IBuilderConfig* config builder-createBuilderConfig();nvinfer1::INetworkDefinition* network builder-createNetworkV2(1);nvonnxparser::IParser* parser nvonnxparser::createParser(*network, logger);if (!parser-parseFromFile(cls_modified.onnx, 1)){printf(Failed to parser onnx\n);return;}int maxBatchSize 1;config-setMaxWorkspaceSize(1 32);engine builder-buildEngineWithConfig(*network, config);if (engine nullptr) {printf(Build engine failed.\n);return;}nvinfer1::IHostMemory model_data engine-serialize();FILE* f fopen(cls.engine, wb);fwrite(model_data-data(), 1, model_data-size(), f);fclose(f);model_data-destroy();parser-destroy();engine-destroy();network-destroy();config-destroy();builder-destroy(); #endif auto engine_data load_file(cls.engine);nvinfer1::IRuntime* runtime nvinfer1::createInferRuntime(logger);engine runtime-deserializeCudaEngine(engine_data.data(), engine_data.size());if (engine nullptr){printf(Deserialize cuda engine failed.\n);runtime-destroy();return;}nvinfer1::IExecutionContext* execution_context engine-createExecutionContext();cudaStream_t stream nullptr;cudaStreamCreate(stream);float* input_data_host nullptr;const size_t input_numel 1 * 3 * point_num;cudaMallocHost(input_data_host, input_numel * sizeof(float));for (size_t i 0; i 3; i){for (size_t j 0; j point_num; j){input_data_host[point_num * i j] points[3 * j i];}}float* input_data_device nullptr;float output_data_host[10];float* output_data_device nullptr;cudaMalloc(input_data_device, input_numel * sizeof(float));cudaMalloc(output_data_device, sizeof(output_data_host));cudaMemcpyAsync(input_data_device, input_data_host, input_numel * sizeof(float), cudaMemcpyHostToDevice, stream);pc_normalize_gpu(input_data_device);float* bindings[] { input_data_device, output_data_device };bool success execution_context-enqueueV2((void)bindings, stream, nullptr);cudaMemcpyAsync(output_data_host, output_data_device, sizeof(output_data_host), cudaMemcpyDeviceToHost, stream);cudaStreamSynchronize(stream);int predict_label std::max_element(output_data_host, output_data_host 10) - output_data_host;std::cout \npredict_label: predict_label std::endl;cudaStreamDestroy(stream);execution_context-destroy();engine-destroy();runtime-destroy(); }int main() {std::vectorfloat points;std::ifstream infile;float x, y, z, nx, ny, nz;char ch;infile.open(sofa_0020.txt);for (size_t i 0; i point_num; i){infile x ch y ch z ch nx ch ny ch nz;points.push_back(x);points.push_back(y);points.push_back(z);}infile.close();classfier(points);return 0; }