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import torch
import torch.nn as nn
# Positional encoding embedding. Code was taken from https://github.com/bmild/nerf.
class Embedder:
def __init__(self, **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, N_freqs)
else:
freq_bands = torch.linspace(2.**0., 2.**max_freq, 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):
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
def get_embedder(multires, input_dims=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)
def embed(x, eo=embedder_obj): return eo.embed(x)
return embed, embedder_obj.out_dim
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