MeanVarianceNormalization#
MeanVarianceNormalization - 13#
Version
domain: main
since_version: 13
function: True
support_level: SupportType.COMMON
shape inference: False
This version of the operator has been available since version 13.
Summary
A MeanVarianceNormalization Function: Perform mean variance normalization
on the input tensor X using formula: <br/> ` (X-EX)/sqrt(E(X-EX)^2) `
Attributes
axes: A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.
Inputs
X (heterogeneous) - T: Input tensor
Outputs
Y (heterogeneous) - T: Output tensor
Type Constraints
T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to all numeric tensors.
Examples
default
import numpy as np
import onnx
node = onnx.helper.make_node(
"MeanVarianceNormalization", inputs=["X"], outputs=["Y"]
)
input_data = np.array(
[
[
[[0.8439683], [0.5665144], [0.05836735]],
[[0.02916367], [0.12964272], [0.5060197]],
[[0.79538304], [0.9411346], [0.9546573]],
],
[
[[0.17730942], [0.46192095], [0.26480448]],
[[0.6746842], [0.01665257], [0.62473077]],
[[0.9240844], [0.9722341], [0.11965699]],
],
[
[[0.41356155], [0.9129373], [0.59330076]],
[[0.81929934], [0.7862604], [0.11799799]],
[[0.69248444], [0.54119414], [0.07513223]],
],
],
dtype=np.float32,
)
# Calculate expected output data
data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
data_mean_squared = np.power(data_mean, 2)
data_squared = np.power(input_data, 2)
data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
std = np.sqrt(data_squared_mean - data_mean_squared)
expected_output = (input_data - data_mean) / (std + 1e-9)
expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn")
MeanVarianceNormalization - 9#
Version
domain: main
since_version: 9
function: True
support_level: SupportType.COMMON
shape inference: False
This version of the operator has been available since version 9.
Summary
A MeanVarianceNormalization Function: Perform mean variance normalization
on the input tensor X using formula: <br/> ` (X-EX)/sqrt(E(X-EX)^2) `
Attributes
axes: A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.
Inputs
X (heterogeneous) - T: Input tensor
Outputs
Y (heterogeneous) - T: Output tensor
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to all numeric tensors.