Spaces:
Running
on
L40S
Running
on
L40S
File size: 2,053 Bytes
616f571 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import math
import torch
import torch.nn as nn
class PhaseModulatedFourierEmbedder(torch.nn.Module):
def __init__(
self,
num_freqs: int,
input_dim: int = 3,
):
"""
Initializes the PhaseModulatedFourierEmbedder class.
Args:
num_freqs (int): The number of frequencies to be used.
input_dim (int, optional): The dimension of the input. Defaults to 3.
Attributes:
weight (torch.nn.Parameter): The weight parameter initialized with random values.
carrier (torch.Tensor): The carrier frequencies calculated based on the Nyquist-Shannon sampling theorem.
out_dim (int): The output dimension calculated based on the input dimension and number of frequencies.
"""
super().__init__()
self.weight = nn.Parameter(
torch.randn(input_dim, num_freqs) * math.sqrt(0.5 * num_freqs)
)
# NOTE this is the highest frequency we can get (2 for peaks, 2 for zeros, and 4 for interpolation points), see also https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem
carrier = (num_freqs / 8) ** torch.linspace(1, 0, num_freqs)
carrier = (carrier + torch.linspace(0, 1, num_freqs)) * 2 * torch.pi
self.register_buffer("carrier", carrier, persistent=False)
self.out_dim = input_dim * (num_freqs * 2 + 1)
def forward(self, x):
"""
Perform the forward pass of the embedder model.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, ..., input_dim).
Returns:
torch.Tensor: Output tensor of shape (batch_size, ..., output_dim) where
output_dim = input_dim + 2 * input_dim.
"""
m = x.float().unsqueeze(-1)
fm = (m * self.weight).view(*x.shape[:-1], -1)
pm = (m * 0.5 * torch.pi + self.carrier).view(*x.shape[:-1], -1)
embedding = torch.cat([x, fm.cos() + pm.cos(), fm.sin() + pm.sin()], dim=-1)
return embedding
|