| import torch |
| from .Criterion import Criterion |
| |
| |
| class L1HingeEmbeddingCriterion(Criterion): |
| |
| def __init__(self, margin=1): |
| super(L1HingeEmbeddingCriterion, self).__init__() |
| self.margin = float(margin) |
| self.gradInput = [torch.Tensor(), torch.Tensor()] |
| |
| def updateOutput(self, input, y): |
| self.output = float(input[0].dist(input[1], 1)) |
| if y == -1: |
| self.output = max(0, self.margin - self.output) |
| |
| return self.output |
| |
| def _mathsign(t): |
| return 1 if x > 0 else -1 |
| |
| def updateGradInput(self, input, y): |
| self.gradInput[0].resize_as_(input[0]) |
| self.gradInput[1].resize_as_(input[1]) |
| self.gradInput[0].copy_(input[0]) |
| self.gradInput[0].add_(-1, input[1]) |
| dist = self.gradInput[0].norm(1) |
| self.gradInput[0].sign_() |
| if y == -1: # just to avoid a mul by 1 |
| if dist > self.margin: |
| self.gradInput[0].zero_() |
| else: |
| self.gradInput[0].mul_(-1) |
| |
| self.gradInput[1].zero_().add_(-1, self.gradInput[0]) |
| return self.gradInput |
