MeanVarianceNormalization

• Domain: ai.onnx

• Since version: 13

A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula: (X-EX)/sqrt(E(X-EX)^2)

Inputs

• X (T): Input tensor

Outputs

• Y (T): Output tensor

Attributes

• axes (int[]): A list of integers, along which to reduce. The default is to calculate 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.

Type Constraints

• T: Constrain input and output types to all numeric tensors. Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16).

Examples

test_cc_mvn

Node:
  MeanVarianceNormalization(x) -> (y)

Inputs:
  x: shape=(3, 3, 3, 1, 2), dtype=float32
    [[[[[-1. ,  0. ]],

       [[ 0.5,  1. ]],

       [[ 2. ,  3. ]]],


      [[[ 4. ,  5. ]],

       [[ 6. ,  7. ]],

       [[ 8. ,  9. ]]],


      [[[10. , 11. ]],

       [[12. , 13. ]],

       [[14. , 15. ]]]],



     [[[[16. , 17. ]],

       [[18. , 19. ]],

       [[20. , 21. ]]],


      [[[22. , 23. ]],

       [[24. , 25. ]],

       [[26. , 27. ]]],


      [[[28. , 29. ]],

       [[30. , 31. ]],

       [[32. , 33. ]]]],



     [[[[34. , 35. ]],

       [[36. , 37. ]],

       [[38. , 39. ]]],


      [[[40. , 41. ]],

       [[42. , 43. ]],

       [[44. , 45. ]]],


      [[[46. , 47. ]],

       [[48. , 49. ]],

       [[50. , 51. ]]]]]

Outputs:
  y: shape=(3, 3, 3, 1, 2), dtype=float32
    [[[[[-1.3153384 , -1.3054299 ]],

       [[-1.2123989 , -1.2371227 ]],

       [[-1.1094594 , -1.1005079 ]]],


      [[[-1.3525045 , -1.3525045 ]],

       [[-1.217254  , -1.217254  ]],

       [[-1.0820036 , -1.0820036 ]]],


      [[[-1.3525045 , -1.3525045 ]],

       [[-1.217254  , -1.217254  ]],

       [[-1.0820036 , -1.0820036 ]]]],



     [[[[-0.14869043, -0.14420448]],

       [[-0.01143773, -0.00758971]],

       [[ 0.12581497,  0.12902506]]],


      [[[-0.13525045, -0.13525045]],

       [[ 0.        ,  0.        ]],

       [[ 0.13525045,  0.13525045]]],


      [[[-0.13525045, -0.13525045]],

       [[ 0.        ,  0.        ]],

       [[ 0.13525045,  0.13525045]]]],



     [[[[ 1.0865839 ,  1.0853285 ]],

       [[ 1.2238367 ,  1.2219431 ]],

       [[ 1.3610893 ,  1.3585579 ]]],


      [[[ 1.0820036 ,  1.0820036 ]],

       [[ 1.217254  ,  1.217254  ]],

       [[ 1.3525045 ,  1.3525045 ]]],


      [[[ 1.0820036 ,  1.0820036 ]],

       [[ 1.217254  ,  1.217254  ]],

       [[ 1.3525045 ,  1.3525045 ]]]]]

test_cc_mvn_explicit_axes

Node:
  MeanVarianceNormalization(x) -> (y)
  Attributes:
    axes = [0, 2, 3]

Inputs:
  x: shape=(3, 3, 3, 1, 2), dtype=float32
    [[[[[-2., -1.]],

       [[ 0.,  1.]],

       [[ 2.,  3.]]],


      [[[ 4.,  5.]],

       [[ 6.,  7.]],

       [[ 8.,  9.]]],


      [[[10., 11.]],

       [[12., 13.]],

       [[14., 15.]]]],



     [[[[16., 17.]],

       [[18., 19.]],

       [[20., 21.]]],


      [[[22., 23.]],

       [[24., 25.]],

       [[26., 27.]]],


      [[[28., 29.]],

       [[30., 31.]],

       [[32., 33.]]]],



     [[[[34., 35.]],

       [[36., 37.]],

       [[38., 39.]]],


      [[[40., 41.]],

       [[42., 43.]],

       [[44., 45.]]],


      [[[46., 47.]],

       [[48., 49.]],

       [[50., 51.]]]]]

Outputs:
  y: shape=(3, 3, 3, 1, 2), dtype=float32
    [[[[[-1.3525045 , -1.3525045 ]],

       [[-1.217254  , -1.217254  ]],

       [[-1.0820036 , -1.0820036 ]]],


      [[[-1.3525045 , -1.3525045 ]],

       [[-1.217254  , -1.217254  ]],

       [[-1.0820036 , -1.0820036 ]]],


      [[[-1.3525045 , -1.3525045 ]],

       [[-1.217254  , -1.217254  ]],

       [[-1.0820036 , -1.0820036 ]]]],



     [[[[-0.13525045, -0.13525045]],

       [[ 0.        ,  0.        ]],

       [[ 0.13525045,  0.13525045]]],


      [[[-0.13525045, -0.13525045]],

       [[ 0.        ,  0.        ]],

       [[ 0.13525045,  0.13525045]]],


      [[[-0.13525045, -0.13525045]],

       [[ 0.        ,  0.        ]],

       [[ 0.13525045,  0.13525045]]]],



     [[[[ 1.0820036 ,  1.0820036 ]],

       [[ 1.217254  ,  1.217254  ]],

       [[ 1.3525045 ,  1.3525045 ]]],


      [[[ 1.0820036 ,  1.0820036 ]],

       [[ 1.217254  ,  1.217254  ]],

       [[ 1.3525045 ,  1.3525045 ]]],


      [[[ 1.0820036 ,  1.0820036 ]],

       [[ 1.217254  ,  1.217254  ]],

       [[ 1.3525045 ,  1.3525045 ]]]]]

Differences with previous version (9)

SchemaDiff: MeanVarianceNormalization (domain 'ai.onnx')

• old version: 9

• new version: 13

• breaking: no

Type constraints:

• changed ‘T’: added types: [‘tensor(bfloat16)’]

Version History

• Version 9