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  • 3.0.31

    • hmct.api.build_model

    Interface Description

    The model conversion function provided by HMCT, inputs an onnx model and outputs the quantized model after model conversion and quantization.

    Interface Form

    def build_model(
    onnx_model: ModelProto,
    march: str,
    cali_data: Union[Sequence[np.ndarray], Dict[str, Sequence[np.ndarray]]],
    quant_config: Optional[Union[str, Dict[str, Any]]] = None,
    input_dict: Optional[Dict[str, Any]] = None,
    name_prefix: Optional[str] = None,
    verbose: Optional[bool] = True,
) -> ModelProto

    Return Value

    Output a quantized onnx model that can be used for quantized model accuracy evaluation, as well as compiled into a deployment model via hbdk.

    Parameter

    ParameterTypeDefault ValueDescription
    onnx_modelModelProtoRequired, no default valueThe input onnx model.
    marchstrRequired, no default valueComputing platform.
    cali_dataUnion[Sequence[np.ndarray], Dict[str, Sequence[np.ndarray]]]Required, no default value

    Calibration data:

    1. Single-input models can specify calibration data via Sequence[np.ndarray].
      2. cali_data = [sample0, sample1, ...]
    2. Multi-input models require calibration data to be specified via Dict[str, Sequence[np.ndarray]].
      3. cali_data = {'input_name0': [sample0, sample1, ...], 
'input_name1': [sample0, sample1, ...]}
    quant_configOptional[Union[str, Dict[str, Any]]]None

    Specify quantized configuration parameters, detailed quantized parameter configuration description reference The quant_config Introduction。

    1. Use the default quantization method.
      2. quant_config = None
    2. Quantization parameters can be configured via a json file.
      3. quant_config = quant_config_json_file
    3. Configure quantization parameters via a dict type.
      4. quant_config = {'node_config' : {'Conv0', {'qtype', 'int16'}}}
    input_dictOptional[Dict[str, Any]]None

    Modify the converted model inputs according to the specified parameters. Note: The caller needs to ensure that the modifications to the model inputs are legal.

    1. Modify the input_shape of the specified input:
      2. input_dict = {'input_name0': {'input_shape':[x,x,x,x]}}
    2. Modify the batch_size of the specified input:
      3. input_dict = {'input_name0': {'input_batch': 4}}
    name_prefixOptional[str]None

    Specify the save path for the generated object during the model conversion process.

    1. None, saved in the current directory.
    2. Specify a path relative to the current directory in which to save the generated objects during the conversion process.
      3. # Saved in the current directory, saved files are prefixed with temp_;
name_prefix = 'temp_'
# Save in the . /tmp01 directory, saved files are prefixed with temp_;
name_prefix = './tmp01/temp_'
    verboseOptional[bool]Trueverbose=True/False, specify whether detailed information is printed during the model conversion.

    Generator Description

    Generator NameDescription
    original_float_model.onnxThe output after the original model conversion, which is included in this phase of the conversion: opset, ir version conversion, input_shape modification operation.
    optimized_float_model.onnxThe output of the model optimization phase, which is included in this phase of the conversion: constant folding, operator fusion, useless operator removal, operator replacement, and operator splitting.
    calibrated_model.onnxThe output of the model calibration phase, which is included in this phase of the quantization: inserting calibration nodes, counting data distributions, and calculating quantization parameters.
    ptq_model.onnxThe output of the model quantization phase, which is included in this phase of the quantization: tuning and converting the quantization parameters based on the specified march.