.. _l-onnx-op-instancenormalization-d1-6:

InstanceNormalization - 1 vs 6
==============================

Next section compares an older to a newer version of the same operator 
after both definition are converted into markdown text.
Green means an addition to the newer version, red means a deletion.
Anything else is unchanged.

.. raw:: html


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    --- a/InstanceNormalization1
    +++ b/InstanceNormalization6
    @@ -1 +1 @@
      Carries out instance normalization as described in the paper
      https://arxiv.org/abs/1607.08022.
  
      y = scale * (x - mean) / sqrt(variance + epsilon) + B,
      where mean and variance are computed per instance per channel.
  
      ### Attributes
  
    - * **consumed_inputs - INTS** :
    - 
    -   legacy optimization attribute.
    - 
      * **epsilon - FLOAT** (default is '1e-05'):
  
    -   The epsilon value to use to avoid division by zero, default is 1e-5f.
    ?                                                     ------------------
    +   The epsilon value to use to avoid division by zero.
  
      ### Inputs
  
      - **input** (heterogeneous) - **T**:
  
    -   The input 4-dimensional tensor of shape NCHW.
    +   Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
      - **scale** (heterogeneous) - **T**:
  
        The input 1-dimensional scale tensor of size C.
      - **B** (heterogeneous) - **T**:
  
        The input 1-dimensional bias tensor of size C.
  
      ### Outputs
  
      - **output** (heterogeneous) - **T**:
  
    -   The output 4-dimensional tensor of the same shape as input.
    ?             --------------
    +   The output tensor of the same shape as input.
  
      ### Type Constraints
  
      * **T** in ( tensor(double), tensor(float), tensor(float16) ):
  
        Constrain input and output types to float tensors.
    `;

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