def loss_labels(self, outputs, targets, indices, num_boxes, log=True): assert 'pred_logits' in outputs src_logits = outputs['pred_logits'] idx = self._get_src_permutation_idx(indices) target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) target_classes = torch.full(src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device) target_classes[idx] = target_classes_o loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) losses = {'loss_ce': loss_ce} if log: # TODO this should probably be a separate loss, not hacked in this one here losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0] return losses怎么替换为focal损失函数
时间: 2024-01-31 21:03:57 浏览: 35
要将上述代码替换为Focal Loss,可以修改loss_labels函数的第8行,将F.cross_entropy替换为我们刚刚实现的FocalLoss。修改后的代码如下:
```python
def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
idx = self._get_src_permutation_idx(indices)
target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(src_logits.shape[:2], self.num_classes,
dtype=torch.int64, device=src_logits.device)
target_classes[idx] = target_classes_o
loss_fn = FocalLoss(gamma=2)
loss_ce = loss_fn(src_logits.transpose(1, 2), target_classes)
losses = {'loss_ce': loss_ce}
if log:
losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]
return losses
```
在代码中,我们首先创建一个Focal Loss函数loss_fn,然后将src_logits和target_classes作为输入,计算Focal Loss。最后返回Focal Loss作为损失值。这样,就将DETR模型中的分类损失替换为Focal Loss,可以更好地处理类别不平衡的情况。