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"""
positional encoding
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class Embedder:
"adapted from https://github.com/yenchenlin/nerf-pytorch/blob/master/run_nerf_helpers.py#L48"
def __init__(self, **kwargs):
"""
default config:
:param kwargs:
"""
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs['input_dims']
out_dim = 0
if self.kwargs['include_input']:
embed_fns.append(lambda x: x)
out_dim += d
max_freq = self.kwargs['max_freq_log2']
N_freqs = self.kwargs['num_freqs']
if self.kwargs['log_sampling']:
freq_bands = 2. ** torch.linspace(0., max_freq, steps=N_freqs)
else:
freq_bands = torch.linspace(2. ** 0., 2. ** max_freq, steps=N_freqs)
for freq in freq_bands:
for p_fn in self.kwargs['periodic_fns']:
embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
out_dim += d
self.embed_fns = embed_fns
self.out_dim = out_dim
def embed(self, inputs):
"""
:param inputs: (N_rays, N_samples, 3)
:return: (N_rays, N_samples, D)
"""
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
def get_embedder(multires, i=0, input_dims=3):
if i == -1:
return nn.Identity(), 3
embed_kwargs = {
'include_input': True,
'input_dims': input_dims,
'max_freq_log2': multires - 1,
'num_freqs': multires,
'log_sampling': True,
'periodic_fns': [torch.sin, torch.cos],
}
embedder_obj = Embedder(**embed_kwargs)
embed = lambda x, eo=embedder_obj: eo.embed(x)
return embed, embedder_obj.out_dim
def test():
""
# x = torch.randn(10, 50, 3)
# embed, _ = get_embedder(10)
# enc = embed(x)
# print(enc.shape) # torch.Size([10, 50, 63])
# print(x[0, :2])
# print(enc[0, :2]) # this encoding already includes the input coordinates
embed, _ = get_embedder(15, input_dims=1)
enc = embed(torch.randn(1, 1, 1))
print(enc.shape) # (1, 1, 31) 2*multires + input_dims
if __name__ == '__main__':
test() |