OH_NN_QuantParam

Overview

Quantization information.

In quantization scenarios, the 32-bit floating-point data type is quantized into the fixed-point data type according to the following formula:

s and z are quantization parameters, which are stored by scale and zeroPoint in OH_NN_QuantParam. r is a floating point number, q is the quantization result, q_min is the lower bound of the quantization result, and q_max is an upper bound of a quantization result. The calculation method is as follows:

The clamp function is defined as follows:

Since: 9

Related Modules:

NeuralNeworkRuntime

Summary

Member Variables

Member Variable Description

numBits

const uint32_t* OH_NN_QuantParam::numBits

Description
Number of quantization bits

quantCount

uint32_t OH_NN_QuantParam::quantCount

Description
Specifies the length of the numBits, scale, and zeroPoint arrays. In the per-layer quantization scenario, quantCount is usually set to 1. That is, all channels of a tensor share a set of quantization parameters. In the per-channel quantization scenario, quantCount is usually the same as the number of tensor channels, and each channel uses its own quantization parameters.

scale

const double* OH_NN_QuantParam::scale

Description
Pointer to the scale data in the quantization formula

zeroPoint

const int32_t* OH_NN_QuantParam::zeroPoint

Description
Pointer to the zero point data in the quantization formula