Function: load_from_file_with_mem

Function Usage

Loads offline model data (offline model adapted to the AI processor) from a file. The model workspace is managed by the user.

Returns the model ID after the model is loaded. The model ID is used for model identification in subsequent operations.

Prototype

C Prototype

        
             aclError aclmdlLoadFromFileWithMem(const char *modelPath, uint32_t *modelId, void *workPtr, size_t workSize, void *weightPtr, size_t weightSize)

Python Function

        
             model_id, ret  = acl.mdl.load_from_file_with_mem(model_path, work_ptr, work_size, weight_ptr, weight_size)

Parameters

Parameter	Description
model_path	Str, directory of an offline model file, including the file name. The user who runs the app must have the permission to access the storage path. The .om file is an offline model adapted to the AI processor. NOTE: For details about how to obtain the .om file, see Model Building.
work_ptr	Int, pointer address of the workspace (for storing model input and output data) required by the model on the device. The memory is managed by the user and cannot be freed during model execution. If 0 is passed for this parameter, the system manages the memory. NOTE: In the event where the memory is managed by the user, if multiple models are executed in serial, the models can share a workspace. However, users need to guarantee the serial execution sequence of the models and the workspace size (the same as the total size of the workspaces needed by all the models). Refer to the following description to ensure serial execution: For synchronous model execution, add a lock to ensure that tasks are executed in serial. For asynchronous model execution, use a single stream to ensure that tasks are executed in serial.
work_size	Int, workspace size required for model execution, in bytes. This parameter is invalid when work_ptr is set to 0.
weight_ptr	Int, pointer address of the model weight memory (for storing weight data) on the device. The memory is managed by the user and cannot be freed during model execution. If 0 is passed for this parameter, the system manages the memory. NOTE: When the user-managed weight memory is used, in multi-thread scenarios, if a model is loaded once in each thread, the weight_ptr sharing mode can be selected because the weight_ptr memory is read-only during inference. Note that the weight_ptr cannot be released during weight_ptr sharing.
weight_size	Int, weight memory size, in bytes. This parameter is invalid when weight_ptr is set to 0.

Returns

Return Value	Description
model_id	Int, model ID generated after the model is loaded.
ret	Int, error code. 0 indicates success, and other values indicate failure.

Restrictions

Model loading, execution, and unloading must be performed in the same context. For details about how to create a context, see acl.rt.set_device and acl.rt.create_context.

Reference

For the API call sequence, see Model Loading.
The acl.mdl.set_config_opt and acl.mdl.load_with_config APIs are also provided for model loading. The caller needs to set the attributes in the configuration object passed to the API call to decide how the model will be loaded and who will manage the memory.

Parent topic: Model Loading and Unloading