Spaces:
Running
Running
| 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 | |