openvino.runtime.opset4.fake_quantize¶
- openvino.runtime.opset4.fake_quantize(data: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], input_low: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], input_high: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], output_low: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], output_high: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], levels: int, auto_broadcast: str = 'NUMPY', name: Optional[str] = None) openvino._pyopenvino.Node ¶
Perform an element-wise linear quantization on input data.
- Parameters
data – The node with data tensor.
input_low – The node with the minimum for input values.
input_high – The node with the maximum for input values.
output_low – The node with the minimum quantized value.
output_high – The node with the maximum quantized value.
levels – The number of quantization levels. Integer value.
auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors.
- Returns
New node with quantized value.
Input floating point values are quantized into a discrete set of floating point values.
if x <= input_low: output = output_low if x > input_high: output = output_high else: output = fake_quantize(output)
Fake quantize uses the following logic:
- f[ output =
dfrac{round( dfrac{data - input_low}{(input_high - input_low)cdot (levels-1)})} {(levels-1)cdot (output_high - output_low)} + output_low f]