com.microsoft - QuantizeLinear#
QuantizeLinear - 1 (com.microsoft)#
Version
name: QuantizeLinear (GitHub)
domain: com.microsoft
since_version: 1
function:
support_level:
shape inference:
This version of the operator has been available since version 1 of domain com.microsoft.
Summary
The linear quantization operator. It consumes a full precision data, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point).For saturation, it saturates to [0, 255] if it’s uint8, or [-128, 127] if it’s int8. For (x / y_scale), it’s rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per ‘axis’).
Attributes
axis: The axis along which same quantization parameters are applied. It’s optional.If it’s not specified, it means per-tensor quantization and input ‘x_scale’ and ‘x_zero_point’ must be scalars.If it’s specified, it means per ‘axis’ quantization and input ‘x_scale’ and ‘x_zero_point’ must be 1-D tensors. Default value is
?
.
Inputs
x (heterogeneous) - T1: N-D full precision Input tensor to be quantized.
y_scale (heterogeneous) - T1: Scale for doing quantization to get ‘y’. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it’s a 1-D tensor, its number of elements should be equal to the dimension value of ‘axis’ dimension of input ‘x’.
y_zero_point (heterogeneous) - T2: Zero point for doing quantization to get ‘y’. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it’s a 1-D tensor, its number of elements should be equal to the dimension value of ‘axis’ dimension of input ‘x’.
Outputs
y (heterogeneous) - T2: N-D quantized output tensor. It has same shape as input ‘x’.
Examples
default
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
y_scale = np.float32(2)
y_zero_point = np.uint8(128)
y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear",
)
_axis
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array(
[
[
[[-162, 10], [-100, 232], [-20, -50]],
[[-76, 0], [0, 252], [32, -44]],
[[245, -485], [-960, -270], [-375, -470]],
],
],
dtype=np.float32,
)
y_scale = np.array([2, 4, 5], dtype=np.float32)
y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(
np.uint8
)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear_axis",
)