Unofficial pytorch implementation of the paper "Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding", NeurIPS 2021. https://arxiv.org/pdf/2106.02795.pdf
Basic usage is as below:
from positional_encoding import LearnableFourierFeatures as LFF
lff = LFF(pos_dim=2, f_dim=128, h_dim=256, d_dim=64) # learnable fourier features module
pos = torch.randn([4, 1024, 1, 2]) # random positional coordinates
pe = lff(pos) # forwardMore detailed explanation of usage can be found in the file positional_encoding.py, and other popular positional encoding methods such as the sinusoidal positional encoding from the paper "Attention is All You Need", and fourier features from the paper "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains" are also implemented in the file.