neural_compressor.tensorflow.quantization.quantize
Intel Neural Compressor Tensorflow quantization base API.
Functions
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Whether to apply the algorithm. |
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The main entry to quantize model. |
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Quantize model using single config. |
Module Contents
- neural_compressor.tensorflow.quantization.quantize.need_apply(configs_mapping: Dict[Tuple[str, callable], neural_compressor.common.base_config.BaseConfig], algo_name)[source]
Whether to apply the algorithm.
- neural_compressor.tensorflow.quantization.quantize.quantize_model(model: str | tensorflow.keras.Model | neural_compressor.tensorflow.utils.BaseModel, quant_config: neural_compressor.common.base_config.BaseConfig | list, calib_dataloader: Callable = None, calib_iteration: int = 100, calib_func: Callable = None)[source]
The main entry to quantize model.
- Parameters:
model – a fp32 model to be quantized.
quant_config – single or lists of quantization configuration.
calib_dataloader – a data loader for calibration.
calib_iteration – the iteration of calibration.
calib_func – the function used for calibration, should be a substitution for calib_dataloader
inference. (when the built-in calibration function of INC does not work for model)
- Returns:
the quantized model.
- Return type:
q_model
- neural_compressor.tensorflow.quantization.quantize.quantize_model_with_single_config(q_model: neural_compressor.tensorflow.utils.BaseModel, quant_config: neural_compressor.common.base_config.BaseConfig, calib_dataloader: Callable = None, calib_iteration: int = 100, calib_func: Callable = None)[source]
Quantize model using single config.
- Parameters:
model – a model wrapped by INC TF model class.
quant_config – a quantization configuration.
calib_dataloader – a data loader for calibration.
calib_iteration – the iteration of calibration.
calib_func – the function used for calibration, should be a substitution for calib_dataloader
inference. (when the built-in calibration function of INC does not work for model)
- Returns:
the quantized model.
- Return type:
q_model