Function: set_dynamic_batch_size
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C Prototype |
aclError aclmdlSetDynamicBatchSize(uint32_t modelId, aclmdlDataset *dataset, size_t index, uint64_t batchSize) |
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Python Function |
ret = acl.mdl.set_dynamic_batch_size(model_id, dataset, index, batch_size) |
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Function Usage |
Sets the batch size for dynamic-shape model inference before the model is executed. |
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Input Description |
model_id: int, model ID. You can obtain the model ID after the model is successfully loaded by calling the following APIs:
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. 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 profiles supported by a specified model and the number of batches in each profile. |
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Return Value |
ret: int, error code.
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Restrictions |
None |
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Reference |
For details about the API call sequence and example, see Dynamic Batch/Dynamic Image Size/Dynamic Dimension (Setting Multi-Dimension Profiles). |