| import torch |
| from torch.nn.functional import _Reduction |
| from .Criterion import Criterion |
| |
| |
| class MultiMarginCriterion(Criterion): |
| |
| def __init__(self, p=1, weights=None, margin=1, sizeAverage=True): |
| super(MultiMarginCriterion, self).__init__() |
| if p != 1 and p != 2: |
| raise ValueError("only p == 1 and p == 2 supported") |
| self.p = p |
| self.margin = margin |
| self.sizeAverage = sizeAverage |
| if weights is not None: |
| assert weights.dim() == 1 |
| self.weights = weights |
| self.output_tensor = None |
| |
| def updateOutput(self, input, target): |
| if self.output_tensor is None: |
| self.output_tensor = input.new(1) |
| target = target.long() |
| self._backend.MultiMarginCriterion_updateOutput( |
| self._backend.library_state, |
| input, |
| target, |
| self.output_tensor, |
| _Reduction.legacy_get_enum(self.sizeAverage, True, emit_warning=False), |
| self.p, |
| self.weights, |
| self.margin, |
| ) |
| self.output = self.output_tensor[0].item() |
| return self.output |
| |
| def updateGradInput(self, input, target): |
| target = target.long() |
| implicit_gradOutput = torch.ones(1).type_as(input) |
| self._backend.MultiMarginCriterion_updateGradInput( |
| self._backend.library_state, |
| input, |
| target, |
| implicit_gradOutput, |
| self.gradInput, |
| _Reduction.legacy_get_enum(self.sizeAverage, True, emit_warning=False), |
| self.p, |
| self.weights, |
| self.margin, |
| ) |
| return self.gradInput |
