ExtendedInstanceNormalization

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.

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