【寒武纪硬件产品型号】必填*：
必填项，例如：MLU370

【MagicMind版本号】必填*：

0.14.1
【出错信息】必填*：

2023-09-16 02:50:17.790200: ERROR:  magicmind/runtime/core/runtime_tensor.cc:276] Not found: Failed to find IRTTensor with name: input

2023-09-16 02:50:17.790233: ERROR:  magicmind/runtime/core/runtime_tensor.cc:276] Not found: Failed to find IRTTensor with name: output

【操作步骤】选填：

【相关日志文档】选填
如有，可附件

【出错代码】选填：

#include <memory>

#include <dlfcn.h>

#include "cnrt.h"

#include "common/device.h"

#include "common/data.h"

#include "common/logger.h"

#include "mm_common.h"

#include "mm_builder.h"

#include "basic_samples/sample_runtime/sample_runtime.h"

using namespace magicmind; 

int main()

{

IParser<ModelKind::kOnnx, std::string>* parser = CreateIParser<ModelKind::kOnnx, std::string>();

INetwork* network = CreateINetwork();

parser->Parse(network, "/root/resnet50-v1-7.onnx");

IBuilder* builder = CreateIBuilder();

IBuilderConfig* config = CreateIBuilderConfig();

config->ParseFromFile("/root/MLU370/build_config.json");

IModel* model_out = builder->BuildModel("/root/resnet50-v1-7.onnx", network, config);

std::cout << model_out << std::endl;

model_out->SerializeToFile("resnet50.mm");

model_out->Destroy();

IModel* model = CreateIModel();

model->DeserializeFromFile("/root/resnet50.mm");

cnrtSetDevice(0);

// 创建 IEngine

IEngine *engine = model->CreateIEngine();

IContext *context = engine->CreateIContext();

//为IContext设置输入输出地址、输入形状等信息，示例如下：

std::vector<IRTTensor*> inputs, outputs;

// 从IModel中获取输入输出Tensor的句柄

context->CreateInputTensors(&inputs);

context->CreateOutputTensors(&outputs);

// 按照Tensor名称获取Tensor的句柄

IRTTensor* input_tensor = FindIRTTensorByName(inputs, "input");

IRTTensor* output_tensor = FindIRTTensorByName(outputs, "output");

if (input_tensor == nullptr)

std::cout << "input_tensor empty" << std::endl;

if (output_tensor == nullptr)

std::cout << "output_tensor empty" << std::endl;

// 给输入Tensor设置形状和地址

input_tensor->SetDimensions(Dims({1, 224, 224, 3}));

// 根据获取的TensorLocation类型来决定给输入Tensor设置的是设备侧地址还是主机侧地址

// mlu_input_addr是由用户预分配的MLU设备侧输入内存，host_input_addr是由用户预分配的主机侧输入内存

if ((input_tensor->GetMemoryLocation() == magicmind::TensorLocation::kMLU) ||

        (input_tensor->GetMemoryLocation() == magicmind::TensorLocation::kRemoteMLU)) {

  input_tensor->SetData(const_cast<char*>("/root/image.jpg"));

} else {

  input_tensor->SetData(const_cast<char*>("host_input_addr"));

}

// 使用IContext进行输出的形状推导，获取输出形状

context->InferOutputShape(inputs, outputs);

// 根据获取的TensorLocation类型来决定给输出Tensor设置的是设备侧地址还是主机侧地址

// mlu_output_addr是由用户预分配的MLU设备侧输出内存，host_output_addr是由用户预分配的主机侧输出内存

if ((output_tensor->GetMemoryLocation() == magicmind::TensorLocation::kMLU) ||

        (output_tensor->GetMemoryLocation() == magicmind::TensorLocation::kRemoteMLU)) {

  output_tensor->SetData(const_cast<char*>("./output"));

} else {

  output_tensor->SetData(const_cast<char*>(""));

}

//magicmind::IRpcDevice *dev_      = nullptr;

//magicmind::IRpcQueue *queue_rpc_ = dev_->CreateQueue();

cnrtQueue_t queue            = nullptr;

//queue_rpc_ = dev_->CreateQueue();

    //queue_ = queue_rpc_->get();

// context->Enqueue(inputs, outputs, queue);

//使用capacity的情况下不必调用形状推导

std::vector<IRTTensor*> inputs1, outputs1;

context->CreateInputTensors(&inputs1);

context->CreateOutputTensors(&outputs1);

FindIRTTensorByName(inputs1, "input")->SetDimensions(Dims({1, 224, 224, 3}));

FindIRTTensorByName(inputs1, "input")->SetData(const_cast<char*>("./output"));

// 用户传入的输出形状仅供参考，可以与实际形状不一致，需要小于容量大小

FindIRTTensorByName(outputs1, "output")->SetDimensions(Dims({1, 224, 224, 3}));

// 设置容量大小，并传入满足容量大小的地址空间

FindIRTTensorByName(outputs1, "output")->SetCapacity(100);

FindIRTTensorByName(outputs1, "output")->SetData(const_cast<char*>(""));

context->Enqueue(inputs1, outputs1, queue);

const auto output_shape = FindIRTTensorByName(outputs1, "output")->GetDimensions();

for (auto tensor : inputs) {

  tensor->Destroy();

}

for (auto tensor : outputs) {

  tensor->Destroy();

}

context->Destroy();

engine->Destroy();

model->Destroy();

return 0;

}