Class Model

Defined in File model.h

Class Documentation

class Model

The Model class is used to define a MindSpore model, facilitating computational graph management.

Public Functions

Status Build(GraphCell graph, const std::shared_ptr<Context> &model_context = nullptr, const std::shared_ptr<TrainCfg> &train_cfg = nullptr)

Builds a model so that it can run on a device.

Parameters

graph – [in] GraphCell is a derivative of Cell. Cell is not available currently. GraphCell can be constructed from Graph, for example, model.Build(GraphCell(graph), context).
model_context – [in] A context used to store options during execution.
train_cfg – [in] A config used by training.

Returns

Status.

Status Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims)

Resizes the shapes of inputs.

Parameters

inputs – [in] A vector that includes all input tensors in order.
dims – [in] Defines the new shapes of inputs, should be consistent with inputs.

Returns

Status.

Status Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs, const MSKernelCallBack &before = nullptr, const MSKernelCallBack &after = nullptr)

Inference model.

Parameters

inputs – [in] A vector where model inputs are arranged in sequence.
outputs – [out] Which is a pointer to a vector. The model outputs are filled in the container in sequence.
before – [in] CallBack before predict.
after – [in] CallBack after predict.

Returns

Status.

Status PredictWithPreprocess(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs, const MSKernelCallBack &before = nullptr, const MSKernelCallBack &after = nullptr)

Inference model with preprocess in model.

Parameters

inputs – [in] A vector where model inputs are arranged in sequence.
outputs – [out] Which is a pointer to a vector. The model outputs are filled in the container in sequence.
whether – [in] to use data preprocess in model.
before – [in] CallBack before predict.
after – [in] CallBack after predict.

Returns

Status.

Status Preprocess(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs)

Apply data preprocess if it exits in model.

Parameters

inputs – [in] A vector where model inputs are arranged in sequence.
outputs – [out] Which is a pointer to a vector. The model outputs are filled in the container in sequence.

Returns

Status.

bool HasPreprocess()

Check if data preprocess exists in model.

Returns: true if data preprocess exists.

inline Status LoadConfig(const std::string &config_path)

Load config file.

Parameters: config_path – [in] config file path.
Returns: Status.

std::vector<MSTensor> GetInputs()

Obtains all input tensors of the model.

Returns: The vector that includes all input tensors.

inline MSTensor GetInputByTensorName(const std::string &tensor_name)

Obtains the input tensor of the model by name.

Returns: The input tensor with the given name, if the name is not found, an invalid tensor is returned.

std::vector<MSTensor> GetGradients() const

Obtains all gradient tensors of the model.

Returns: The vector that includes all gradient tensors.

Status ApplyGradients(const std::vector<MSTensor> &gradients)

update gradient tensors of the model.

Parameters: inputs – [in] A vector new gradients.
Returns: Status of operation

std::vector<MSTensor> GetOptimizerParams() const

Obtains optimizer params tensors of the model.

Returns: The vector that includes all params tensors.

Status SetOptimizerParams(const std::vector<MSTensor> &params)

update the optimizer parameters

Parameters: inputs – [in] A vector new optimizer params.
Returns: Status of operation

std::vector<MSTensor> GetOutputs()

Obtains all output tensors of the model.

Returns: The vector that includes all output tensors.

inline std::vector<std::string> GetOutputTensorNames()

Obtains names of all output tensors of the model.

Returns: A vector that includes names of all output tensors.

inline MSTensor GetOutputByTensorName(const std::string &tensor_name)

Obtains the output tensor of the model by name.

Returns: The output tensor with the given name, if the name is not found, an invalid tensor is returned.

inline std::vector<MSTensor> GetOutputsByNodeName(const std::string &node_name)

Get output MSTensors of model by node name.

Note

Deprecated, replace with GetOutputByTensorName

Parameters: node_name – [in] Define node name.
Returns: The vector of output MSTensor.

inline Status Build(const void *model_data, size_t data_size, ModelType model_type, const std::shared_ptr<Context> &model_context = nullptr, const Key &dec_key = {}, const std::string &dec_mode = kDecModeAesGcm)

Build a model from model buffer so that it can run on a device. Only valid for Lite.

Parameters

model_data – [in] Define the buffer read from a model file.
size – [in] Define bytes number of model buffer.
model_type – [in] Define The type of model file. Options: ModelType::kMindIR, ModelType::kOM. Only ModelType::kMindIR is valid for Lite.
model_context – [in] Define the context used to store options during execution.
dec_key – [in] Define the key used to decrypt the ciphertext model. The key length is 16, 24, or 32.
dec_mode – [in] Define the decryption mode. Options: AES-GCM, AES-CBC.

Returns

Status.

inline Status Build(const std::string &model_path, ModelType model_type, const std::shared_ptr<Context> &model_context = nullptr, const Key &dec_key = {}, const std::string &dec_mode = kDecModeAesGcm)

Load and build a model from model buffer so that it can run on a device. Only valid for Lite.

Parameters

model_path – [in] Define the model path.
model_type – [in] Define The type of model file. Options: ModelType::kMindIR, ModelType::kOM. Only ModelType::kMindIR is valid for Lite.
model_context – [in] Define the context used to store options during execution.
dec_key – [in] Define the key used to decrypt the ciphertext model. The key length is 16, 24, or 32.
dec_mode – [in] Define the decryption mode. Options: AES-GCM, AES-CBC.

Returns

Status.

Public Static Functions

static bool CheckModelSupport(enum DeviceType device_type, ModelType model_type)

Inference model.

Parameters

device_type – [in] Device type，options are kGPU, kAscend910, etc.
model_type – [in] The type of model file, options are ModelType::kMindIR, ModelType::kOM.

Returns

Is supported or not.