Function: set_input_dynamic_dims
Applicability
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Function Usage
Sets the runtime dimensions for model inference before model execution, assuming that the input shape is dynamic and the input data format is ND (supporting any format).
Prototype
- C Prototype
1aclError aclmdlSetInputDynamicDims(uint32_t modelId, aclmdlDataset *dataset, size_t index, const aclmdlIODims *dims)
- Python Function
1ret = acl.mdl.set_input_dynamic_dims(model_id, dataset_in, index, dims)
Parameter Description
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Parameter |
Description |
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modelId |
Int, model ID. You can obtain the model ID after the model is successfully loaded by calling the following APIs: |
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dataset |
Int, model input data. 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. |
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index |
Int, index of the dynamic dimension size input. The input index is obtained by the acl.mdl.get_input_index_by_name API. The input name is fixed to ascend_mbatch_shape_data. |
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dims |
Dict, dimension size for model inference. For details, see aclmdlIODims. You can call the acl.mdl.get_input_dynamic_dims API to obtain the number of dimension size profiles as well as the size of each profile supported by the model. Example: Use the following parameters to convert a model with ATC: input_shape="data:1,1,40,-1;label:1,-1;mask:-1,-1", dynamic_dims="20,20,1,1; 40,40,2,2; 80,60,4,4" If the actual dimension of the input data is (1,1,40,20,1,20,1,1), input dims as follows (name is not required): dims = {'dimCount': 8, 'name': '', 'dims': [1,1,40,20,1,20,1,1]} |
Return Value Description
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Return Value |
Description |
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ret |
Int, error code: 0 on success; else, failure. |