neuralop.layers.embeddings.SinusoidalEmbedding

class neuralop.layers.embeddings.SinusoidalEmbedding(in_channels: int, num_frequencies: int | None = None, embedding_type: str = 'transformer', max_positions: int = 10000)[source]

SinusoidalEmbedding provides a unified sinusoidal positional embedding in the styles of Transformers [1] and Neural Radiance Fields (NERFs) [2].

Parameters:

in_channelsint

Number of input channels to embed

num_freqsint, optional

Number of frequencies in positional embedding. By default, set to the number of input channels

embedding{‘transformer’, ‘nerf’}

Type of embedding to apply. For a function with N input channels, each channel value p is embedded via a function g with 2L channels such that g(p) is a 2L-dim vector. For 0 <= k < L:

• ‘transformer’ for transformer-style encoding.

  g(p)_k = sin((p / max_positions) ^ {k / N})

  g(p)_{k+1} = cos((p / max_positions) ^ {k / N})

• ‘nerf’ : NERF-style encoding.

  g(p)_k = sin(2^(k) * Pi * p)

  g(p)_{k+1} = cos(2^(k) * Pi * p)

max_positionsint, optional

Maximum number of positions for the encoding, default 10000 Only used if embedding == transformer.

Attributes:

out_channels

out_channels: required property for linking/composing model layers

Methods

forward(x)

References

[1]

:

Vaswani, A. et al (2017)

“Attention Is All You Need”. NeurIPS 2017, https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.

[2]

:

Mildenhall, B. et al (2020)

“NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis”. ArXiv, https://arxiv.org/pdf/2003.08934.

property out_channels

out_channels: required property for linking/composing model layers

forward(x)[source]

Parameters:

x: torch.Tensor, shape (n_in, self.in_channels) or (batch, n_in, self.in_channels)