(self, x, y)
| 382 | self.size_average = size_average |
| 383 | |
| 384 | def abs(self, x, y): |
| 385 | num_examples = x.size()[0] |
| 386 | |
| 387 | # Assume uniform mesh |
| 388 | h = 1.0 / (x.size()[1] - 1.0) |
| 389 | |
| 390 | all_norms = (h**(self.d/self.p))*torch.norm(x.view(num_examples,-1) - y.view(num_examples,-1), self.p, 1) |
| 391 | |
| 392 | if self.reduction: |
| 393 | if self.size_average: |
| 394 | return torch.mean(all_norms) |
| 395 | else: |
| 396 | return torch.sum(all_norms) |
| 397 | |
| 398 | return all_norms |
| 399 | |
| 400 | def rel(self, x, y): |
| 401 | num_examples = x.size()[0] |
no outgoing calls
no test coverage detected