ncdl.nn.LatticeGroupNorm

class ncdl.nn.LatticeGroupNorm(*args: Any, **kwargs: Any)

Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization .. math:

y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

The input channels are separated into num_groups groups, each containing num_channels / num_groups channels. num_channels must be divisible by num_groups. The mean and standard-deviation are calculated separately over the each group. \(\gamma\) and \(\beta\) are learnable per-channel affine transform parameter vectors of size num_channels if affine is True. The standard-deviation is calculated via the biased estimator, equivalent to torch.var(input, unbiased=False). This layer uses statistics computed from input data in both training and evaluation modes. :param num_groups: number of groups to separate the channels into :type num_groups: int :param num_channels: number of channels expected in input :type num_channels: int :param eps: a value added to the denominator for numerical stability. Default: 1e-5 :param affine: a boolean value that when set to True, this module

has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: True.

Shape:

• Input: \((N, C, *)\) where \(C=\text{num\_channels}\)

• Output: \((N, C, *)\) (same shape as input)

Examples::

>>> input = torch.randn(20, 6, 10, 10)
>>> # Separate 6 channels into 3 groups
>>> m = nn.GroupNorm(3, 6)
>>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm)
>>> m = nn.GroupNorm(6, 6)
>>> # Put all 6 channels into a single group (equivalent with LayerNorm)
>>> m = nn.GroupNorm(1, 6)
>>> # Activating the module
>>> output = m(input)

__init__(lattice: Lattice, num_groups: int, num_channels: int, eps: float = 1e-05, affine: bool = True, device=None, dtype=None) → None

Methods

__init__(lattice, num_groups, num_channels)
extra_repr()
forward(input)
reset_parameters()

Attributes

num_groups
num_channels
eps
affine