:nosearch:

.. _op_ai_onnx_InstanceNormalization-6:

InstanceNormalization - version 6
=================================

This page documents version **6** of operator **InstanceNormalization**. See :doc:`InstanceNormalization` for the latest version (since version 22).

- **Domain**: ``ai.onnx``
- **Since version**: 6

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.

**Inputs**

- **input** (*T*): 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** (*T*): The input 1-dimensional scale tensor of size C.
- **B** (*T*): The input 1-dimensional bias tensor of size C.

**Outputs**

- **output** (*T*): The output tensor of the same shape as input.

**Attributes**

- **epsilon** (*float*): The epsilon value to use to avoid division by zero.

**Type Constraints**

- **T**: Constrain input and output types to float tensors.
  Allowed types: tensor(double), tensor(float), tensor(float16).

Differences with previous version (1)
-------------------------------------

**SchemaDiff**: ``InstanceNormalization`` (domain ``'ai.onnx'``)

* old version: 1
* new version: 6
* breaking: **yes**

**Breaking reasons:**

* attribute 'consumed_inputs' (removed): type=INTS; required=False

**Attributes:**

* [BREAKING] removed 'consumed_inputs': type=INTS; required=False