//获取输入,输出tensor索引 int inputIndex = engine->getBindingIndex(INPUT_LAYER_NAME), int outputIndex = engine->getBindingIndex(OUTPUT_LAYER_NAME); //申请GPU显存 // Allocate GPU memory for Input / Output data void* buffers =
// 假设你已经创建好了TensorRT引擎 nvinfer1::ICudaEngine* engine = ...; nvinfer1::IExecutionContext* context = engine->createExecutionContext(); // 获取输入和输出张量的索引 int inputIndex = engine->getBindingIndex("input_tensor"); int outputIndex = engine->getBindingIndex("output_tensor")...
int32_t inputIndex = engine->getBindingIndex(INPUT_NAME); int32_t outputIndex = engine->getBindingIndex(OUTPUT_NAME); 使用这些索引,设置一个缓冲区数组,指向 GPU 上的输入和输出缓冲区: void* buffers[2]; buffers[inputIndex] = inputBuffer; buffers[outputIndex] = outputBuffer; 然后,您可以调用 ...
// input and output tensors. //获取输入,输出tensor索引 int inputIndex = engine->getBindingIndex(INPUT_LAYER_NAME), int outputIndex = engine->getBindingIndex(OUTPUT_LAYER_NAME); //申请GPU显存 // Allocate GPU memory for Input / Output data void* buffers = malloc(engine->getNbBindings() *...
getInput的参数0代表输入的张量索引,因为我们的输入输出都只有一个张量,因此填索引0即可。最后创建TensorRT最关键的用于推理的Engine(引擎) auto engine = builder->buildEngineWithConfig(*network, *config); 至此,构建阶段就算完成了。至于engine的序列化为模型文件以及通过读取模型文件来加载engine这里线掠过。
outputSize = engine_context->getBindingDimensions(output_index); 步骤3:前处理,多batch输入图片 for (size_t j = 0; j < BATCH_SIZE; j++) { ///cv2读图片 cv::Mat image = images[i*BATCH_SIZE+j]; std::cout << fn[i] << std::endl; ...
//获取输入,输出tensor索引 int inputIndex = engine->getBindingIndex(INPUT_LAYER_NAME), int outputIndex = engine->getBindingIndex(OUTPUT_LAYER_NAME); //申请GPU显存 // Allocate GPU memory for Input / Output data void* buffers = malloc(engine->getNbBindings() * sizeof(void*)); cudaMalloc(...
输出tensor索引int inputIndex=engine->getBindingIndex(INPUT_LAYER_NAME),int outputIndex=engine->getBindingIndex(OUTPUT_LAYER_NAME);//申请GPU显存// Allocate GPU memory for Input / Output datavoid*buffers=malloc(engine->getNbBindings()*sizeof(void*));cudaMalloc(&buffers[inputIndex],batchSize*size...
tensorrt.ICudaEngine.get_binding_index(tensor name:str) The above is the usage before version 8.6. I want to know which API can be used in 10.1 to achieve the same effect. The warning in 8.6 suggests get_tensor_name, but after checking the API documentation, I found that it cannot be...
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME); const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME); DebugP(inputIndex); DebugP(outputIndex); // create GPU buffers and a stream CHECK(cudaMalloc(&buffers[inputIndex], batchSize * INPUT_C * INPUT_H * INPUT_W...