要解决“could not create tensor from given input list”这一错误,你可以按照以下步骤进行排查和修复: 确认输入列表的数据类型和结构: 确保输入列表中的所有元素都是相同的数据类型(如整数、浮点数等)。如果列表中包含不同类型的元素,尝试将它们转换为统一的数据类型。 例如,如果列表包含字符串和数字,你需要将字...
GetStringList SetTensorList GetTensorList SetNamedAttrsList GetNamedAttrsList SetGraphList GetGraphList SetBufferList GetBufferList SetTensorDescList GetTensorDescList CreateFrom GetValueType IsEmpty operator== InitDefault GetProtoOwner GetProtoMsg CopyValueFrom MoveValueFrom ...
Tensor*> inputTensors;/** output tensors map */std::map<std::string, Tensor*> outputTensor;/** all tensors */std::vector<std::shared_ptr<Tensor
safetensors 0.5.3 scikit-image 0.24.0 scikit-learn 1.2.2 scipy 1.15.2 seaborn 0.13.2 semantic-version 2.10.0 semver 3.0.2 Send2Trash 1.8.3 sentencepiece 0.2.0 setuptools 68.2.2 shellingham 1.5.4 shtab 1.7.1 six 1.17.0 sklearn 0.0.post12 sklearn-pandas 2.2.0 smart-open 7.0.4 sm...
public JobCreateParameters withTensorFlowSettings(TensorFlowSettings tensorFlowSettings) Set the tensorFlowSettings property: Settings for Tensor Flow job. Parameters: tensorFlowSettings - the tensorFlowSettings value to set. Returns: the JobCreateParameters object itself.Applies...
std::map<Tensor*, const Session*> tensorMap; Session::ModeGroup modes; AutoStorage<uint8_t> cacheBuffer; std::string cacheFile; std::mutex lock; size_t lastCacheSize = 0; std::string bizCode; std::string uuid; std::string externalFile; ...
priorityList; priorityList.push_back(MNN_FORWARD_USER_0); //HIAI priorityList.push_back(MNN_FORWARD_NN); //CoreML priorityList.push_back(MNN_FORWARD_USER_1); //TensoRT priorityList.push_back(MNN_FORWARD_CUDA); //CUDA priorityList.push_back(MNN_FORWARD_OPENCL); //OpenCL priorityList....
std::vector<MNNForwardType> priorityList; priorityList.push_back(MNN_FORWARD_USER_0); //HIAI priorityList.push_back(MNN_FORWARD_NN); //CoreML priorityList.push_back(MNN_FORWARD_USER_1); //TensoRT priorityList.push_back(MNN_FORWARD_CUDA); //CUDA ...
E = Tensor(shape=[1024, 1024], op.name=E) inputs (list of Tensors)– The inputs of the group. [A, B] = [Tensor(shape=[1024, 1024], op.name=A), Tensor(shape=[1024, 1024], op.name=B)] include_inputs (boolean, optional)– Whether include input operations in the group if ...
forepochinrange(10): optimizer.zero_grad() outputs=self.model(X_tensor) loss=criterion(outputs,y_tensor) loss.backward() optimizer.step() def_init_model(self,input_dim): self.model=nn.Sequential( nn.Linear(input_dim,256), nn.ReLU