InstanceNormalization¶

InstanceNormalization - 22¶

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

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.

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

The epsilon value to use to avoid division by zero.

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.

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

Constrain input and output types to float tensors.

This version of the operator has been available 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.

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

The epsilon value to use to avoid division by zero.

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.

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

Constrain input and output types to float tensors.

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

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.

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.

output (heterogeneous) - T:

The output 4-dimensional tensor of the same shape as input.

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

Constrain input and output types to float tensors.