openvino/docs/ops/normalization/GroupNormalization_12.md

GroupNormalization

@sphinxdirective

.. meta:: :description: Learn about GroupNormalization-12 - a normalization operation, which can be performed on three required input tensors.

Versioned name: GroupNormalization-12

Category: Normalization

Short description: Performs normalization of the input tensor according to the method described in https://arxiv.org/abs/1803.08494

Detailed description

The GroupNormalization operation performs the following transformation of the input tensor:

.. math::

y = scale * (x - mean) / sqrt(variance + epsilon) + bias

The operation is applied per batch, per group of channels. This means that the example input with N x C x H x W layout is transformed to the N x G x C/G x H x W form. The scale and bias coefficients are the inputs to the model and need to be specified separately for each channel. The mean and variance are calculated for each group.

Attributes

• num_groups

  • Description: Specifies the number of groups G that the channel dimension will be divided into.
  • Range of values: between 1 and the number of channels C in the input tensor
  • Type: int
  • Required: yes

• epsilon

  • Description: A very small value added to the variance for numerical stability. Ensures that division by zero does not occur for any normalized element.
  • Range of values: a positive floating-point number
  • Type: float
  • Required: yes

Inputs

• 1: data - The input tensor to be normalized. The type of this tensor is T. The tensor's shape is arbitrary but the first two dimensions are interpreted as batch and channels respectively. Required.

• 2: scale - 1D tensor of type T containing the scale values for each channel. The expected shape of this tensor is [C] where C is the number of channels in the data tensor. Required.

• 3: bias - 1D tensor of type T containing the bias values for each channel. The expected shape of this tensor is [C] where C is the number of channels in the data tensor. Required.

Outputs

• 1: Output tensor of the same shape and type as the data input tensor.

Types

• T: any supported floating point type.

Example

.. code-block:: xml :force:

<layer ... type="GroupNormalization"> 3 12 100 100 12 12 3 12 100 100

@endsphinxdirective