Function: set_dynamic_batch_size

In the dynamic batch scenario, sets the batch size (number of images processed at a time) for model inference before model execution.

C Prototype

        
             aclError aclmdlSetDynamicBatchSize(uint32_t modelId, aclmdlDataset *dataset, size_t index, uint64_t batchSize)

Python Function

        
             ret = acl.mdl.set_dynamic_batch_size(model_id, dataset, index, batch_size)

Parameter	Description
model_id	Int, model ID. You can obtain the model ID after the model is successfully loaded by calling the following APIs: acl.mdl.load_from_file acl.mdl.load_from_mem acl.mdl.load_from_file_with_mem acl.mdl.load_from_mem_with_mem
dataset	Int, pointer address of the input data of a model. Data of type aclmdlDataset describes the input data for model inference, while data of type aclDataBuffer describes the input buffer size and address. For details, see aclmdlDataset. index: int, index of the input dynamic batch, obtained by calling acl.mdl.get_input_index_by_name. For dynamic batch and image size, the input name is fixed to ascend_mbatch_shape_data. For dynamic AIPP, the input name is fixed to ascend_dynamic_aipp_data.
index	Int, index of the input dynamic batch, obtained by calling acl.mdl.get_input_index_by_name. For dynamic batch and image size, the input name is fixed to ascend_mbatch_shape_data. For dynamic AIPP, the input name is fixed to ascend_dynamic_aipp_data.
batch_size	Int, batch size for model inference. The configured batch_size must be among the batch size profiles set during model building. You can also call acl.mdl.get_dynamic_batch to obtain the number of batch size profiles supported by a specified model and the number of batches in each profile.

Return Value	Description
ret	Int, error code. 0 indicates success, and other values indicate failure.

Parent topic: Model Execution