InstanceNormalization¶
InstanceNormalization - 6¶
Version
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: 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.
Examples
InstanceNormalization - 1¶
Version
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: legacy optimization attribute.
epsilon: 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.