InstanceNormalization

InstanceNormalization - 22

Version

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.

InstanceNormalization - 6

Version

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.

InstanceNormalization - 1

Version

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.