DequantizeLinear#

DequantizeLinear - 13 #

Version

name: DequantizeLinear (GitHub)
domain: main
since_version: 13
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 13.

Summary

The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is y = (x - x_zero_point) * x_scale. ‘x_scale’ and ‘x_zero_point’ must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. ‘x_zero_point’ and ‘x’ must have same type. ‘x’ and ‘y’ must have same shape. In the case of dequantizing int32, there’s no zero point (zero point is supposed to be 0).

Attributes

axis: (Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).

Inputs

Between 2 and 3 inputs.

x (heterogeneous) - T: N-D quantized input tensor to be de-quantized.
x_scale (heterogeneous) - tensor(float): Scale for input ‘x’. It can be a scalar, which means a per- tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
x_zero_point (optional, heterogeneous) - T: Zero point for input ‘x’. Shape must match x_scale. It’s optional. Zero point is 0 when it’s not specified.

Outputs

y (heterogeneous) - tensor(float): N-D full precision output tensor. It has same shape as input ‘x’.

Type Constraints

T in ( tensor(int32), tensor(int8), tensor(uint8) ): Constrain ‘x_zero_point’ and ‘x’ to 8-bit/32-bit integer tensor.

Examples

default

import numpy as np
import onnx

node = onnx.helper.make_node(
    "DequantizeLinear",
    inputs=["x", "x_scale", "x_zero_point"],
    outputs=["y"],
)

# scalar zero point and scale
x = np.array([0, 3, 128, 255]).astype(np.uint8)
x_scale = np.float32(2)
x_zero_point = np.uint8(128)
y = np.array([-256, -250, 0, 254], dtype=np.float32)

expect(
    node,
    inputs=[x, x_scale, x_zero_point],
    outputs=[y],
    name="test_dequantizelinear",
)

_axis

import numpy as np
import onnx

node = onnx.helper.make_node(
    "DequantizeLinear",
    inputs=["x", "x_scale", "x_zero_point"],
    outputs=["y"],
)

# 1-D tensor zero point and scale of size equal to axis 1 of the input tensor
x = np.array(
    [
        [
            [[3, 89], [34, 200], [74, 59]],
            [[5, 24], [24, 87], [32, 13]],
            [[245, 99], [4, 142], [121, 102]],
        ],
    ],
    dtype=np.uint8,
)
x_scale = np.array([2, 4, 5], dtype=np.float32)
x_zero_point = np.array([84, 24, 196], dtype=np.uint8)
y = (
    x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32)
) * x_scale.reshape(1, 3, 1, 1)

expect(
    node,
    inputs=[x, x_scale, x_zero_point],
    outputs=[y],
    name="test_dequantizelinear_axis",
)

DequantizeLinear - 10 vs 13