.. _l-onnx-doc-MeanVarianceNormalization: ========================= MeanVarianceNormalization ========================= .. contents:: :local: .. _l-onnx-op-meanvariancenormalization-13: MeanVarianceNormalization - 13 ============================== **Version** * **name**: `MeanVarianceNormalization (GitHub) `_ * **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:
``` (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. Default value is ``[0 2 3]``. **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** :: 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") **Differences** .. raw:: html
00A MeanVarianceNormalization Function: Perform mean variance normalizationA MeanVarianceNormalization Function: Perform mean variance normalization
11on the input tensor X using formula:
(X-EX)/sqrt(E(X-EX)^2) 		on the input tensor X using formula:
(X-EX)/sqrt(E(X-EX)^2)
22
33**Attributes****Attributes**
44
55* **axes**:* **axes**:
66 A list of integers, along which to reduce. The default is to A list of integers, along which to reduce. The default is to
77 caculate along axes [0,2,3] for calculating mean and variance along caculate along axes [0,2,3] for calculating mean and variance along
88 each channel. Two variables with the same C-coordinate are each channel. Two variables with the same C-coordinate are
99 associated with the same mean and variance. Default value is [0 2 3]. associated with the same mean and variance. Default value is [0 2 3].
1010
1111**Inputs****Inputs**
1212
1313* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
1414 Input tensor Input tensor
1515
1616**Outputs****Outputs**
1717
1818* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1919 Output tensor Output tensor
2020
2121**Type Constraints****Type Constraints**
2222
2323* **T** in (* **T** in (
24 tensor(bfloat16),
2425 tensor(double), tensor(double),
2526 tensor(float), tensor(float),
2627 tensor(float16) tensor(float16)
2728 ): ):
2829 Constrain input and output types to all numeric tensors. Constrain input and output types to all numeric tensors.
.. _l-onnx-op-meanvariancenormalization-9: MeanVarianceNormalization - 9 ============================= **Version** * **name**: `MeanVarianceNormalization (GitHub) `_ * **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:
``` (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. Default value is ``[0 2 3]``. **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.