GroupNorm(channels::Integer, G::Integer, λ=identity;
          initβ=zeros32, initγ=ones32,
          affine=true, track_stats=false,
          ϵ=1f-5, momentum=0.1f0)

Group Normalization layer.

chs is the number of channels, the channel dimension of your input. For an array of N dimensions, the N-1th index is the channel dimension.

G is the number of groups along which the statistics are computed. The number of channels must be an integer multiple of the number of groups.

channels should be the size of the channel dimension in your data (see below).

Given an array with N > 2 dimensions, call the N-1th the channel dimension. For WHCN images it's the usual channel dimension.

If affine=true, it also applies a shift and a rescale to the input through to learnable per-channel bias β and scale γ parameters.

If track_stats=true, accumulates mean and var statistics in training phase that will be used to renormalize the input in test phase.

Examples

			julia> using Statistics

julia> xs = rand(3, 3, 4, 2);  # a batch of 2 images, each having 4 channels

julia> m = GroupNorm(4, 2);

julia> y = m(xs);

julia> isapprox(std(y[:, :, 1:2, 1]), 1, atol=0.1) && std(xs[:, :, 1:2, 1]) != std(y[:, :, 1:2, 1])
true

julia> isapprox(std(y[:, :, 3:4, 2]), 1, atol=0.1) && std(xs[:, :, 3:4, 2]) != std(y[:, :, 3:4, 2])
true