| """ |
| Positional encoding embedding. |
| Based on NeRF / NeuS implementation (same as original). |
| """ |
| import torch |
| import torch.nn as nn |
|
|
| 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 |
|
|
