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# ExtendedInstanceNormalization

## ExtendedInstanceNormalization - 1

### Version

- name: ExtendedInstanceNormalization

- domain: com.amd.quark

### Summary

This is a customized version of the official operator InstanceNormalization, it carries out instance normalization with bfloat16.

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(float) ):

Constrain input and output types to float tensors.