(self, model_input)
| 494 | print(self) |
| 495 | |
| 496 | def forward(self, model_input): |
| 497 | |
| 498 | coords = model_input['coords'] |
| 499 | |
| 500 | if self.nl != 'sine' and not self.no_pe: |
| 501 | coords_pe = self.pe(coords) |
| 502 | output = self.net(coords_pe) |
| 503 | if self.use_sigmoid: |
| 504 | output = torch.sigmoid(output) |
| 505 | else: |
| 506 | output = self.net(coords) |
| 507 | |
| 508 | if self.is_sdf: |
| 509 | return {'model_in': model_input, 'model_out': output} |
| 510 | |
| 511 | else: |
| 512 | return {'model_in': model_input, 'model_out': {'output': output}} |
| 513 | |
| 514 | |
| 515 | def IntegratedPositionalEncoding(coords, radius, L=8): |
nothing calls this directly
no outgoing calls
no test coverage detected