tf.lite.experimental.QuantizationDebugOptions

Debug options to set up a given QuantizationDebugger.

tf.lite.experimental.QuantizationDebugOptions(
    layer_debug_metrics: Optional[Mapping[str, Callable[[np.ndarray], float]]] = None,
    model_debug_metrics: Optional[Mapping[str, Callable[[Sequence[np.ndarray], Sequence[np.ndarray]],
        float]]] = None,
    layer_direct_compare_metrics: Optional[Mapping[str, Callable[[Sequence[np.ndarray], Sequence[np.ndarray],
        float, int], float]]] = None,
    denylisted_ops: Optional[List[str]] = None,
    denylisted_nodes: Optional[List[str]] = None,
    fully_quantize: bool = False
) -> None

layer_debug_metrics a dict to specify layer debug functions {function_name_str: function} where the function accepts result of NumericVerify Op, which is value difference between float and dequantized op results. The function returns single scalar value.
model_debug_metrics a dict to specify model debug functions {function_name_str: function} where the function accepts outputs from two models, and returns single scalar value for a metric. (e.g. accuracy, IoU)
layer_direct_compare_metrics a dict to specify layer debug functions {function_name_str: function}. The signature is different from that of layer_debug_metrics, and this one gets passed (original float value, original quantized value, scale, zero point). The function's implementation is responsible for correctly dequantize the quantized value to compare. Use this one when comparing diff is not enough. (Note) quantized value is passed as int8, so cast to int32 is needed.
denylisted_ops a list of op names which is expected to be removed from quantization.
denylisted_nodes a list of op's output tensor names to be removed from quantization.
fully_quantize Bool indicating whether to fully quantize the model. Besides model body, the input/output will be quantized as well. Corresponding to mlir_quantize's fully_quantize parameter.

ValueError when there are duplicate keys