(l-onnx-doc-InstanceNormalization)=

# InstanceNormalization


(l-onnx-op-instancenormalization-22)=

## InstanceNormalization - 22

### Version

- **name**: [InstanceNormalization (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#InstanceNormalization)
- **domain**: `main`
- **since_version**: `22`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 22**.

### Summary

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

* **epsilon - FLOAT** (default is `'1e-05'`):

  The epsilon value to use to avoid division by zero.

### Inputs

- **input** (heterogeneous) - **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** (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 tensor of the same shape as input.

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.

```{toctree}
text_diff_InstanceNormalization_6_22
```

(l-onnx-op-instancenormalization-6)=

## InstanceNormalization - 6

### Version

- **name**: [InstanceNormalization (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#InstanceNormalization)
- **domain**: `main`
- **since_version**: `6`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 6**.

### Summary

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

* **epsilon - FLOAT** (default is `'1e-05'`):

  The epsilon value to use to avoid division by zero.

### Inputs

- **input** (heterogeneous) - **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** (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 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.

```{toctree}
text_diff_InstanceNormalization_1_22
text_diff_InstanceNormalization_1_6
```

(l-onnx-op-instancenormalization-1)=

## InstanceNormalization - 1

### Version

- **name**: [InstanceNormalization (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#InstanceNormalization)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

This version of the operator has been available
**since version 1**.

### Summary

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.

### Inputs

- **input** (heterogeneous) - **T**:

  The input 4-dimensional tensor of shape NCHW.
- **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.

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.