(self, inputs)
| 92 | self.out_dim = out_dim |
| 93 | |
| 94 | def embed(self, inputs): |
| 95 | # inputs [65536, 3] |
| 96 | if self.kwargs['max_freq_log2'] != 0: |
| 97 | ret = torch.cat([fn(inputs) for fn in self.embed_fns], -1) # cos, sin embedding # ret.shape [65536, 63] |
| 98 | else: |
| 99 | ret = inputs |
| 100 | return ret |
| 101 | |
| 102 | def get_embed_weight(self, epoch, num_freqs, N): |
| 103 | ''' Nerfie Paper Eq.(8) ''' |