InstanceNormalization - version 1#

This page documents version 1 of operator InstanceNormalization. See InstanceNormalization for the latest version (since version 22).

  • Domain: ai.onnx

  • 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.

Inputs

  • input (T): The input 4-dimensional tensor of shape NCHW.

  • scale (T): The input 1-dimensional scale tensor of size C.

  • B (T): The input 1-dimensional bias tensor of size C.

Outputs

  • output (T): The output 4-dimensional tensor of the same shape as input.

Attributes

  • consumed_inputs (int[]): legacy optimization attribute.

  • epsilon (float): The epsilon value to use to avoid division by zero.

Type Constraints

  • T: Constrain input and output types to float tensors. Allowed types: tensor(double), tensor(float), tensor(float16).