def compute_loss(self, logits: torch.Tensor, labels: torch.Tensor, task: EdgeProbingTask):
""" Compute loss & eval metrics.
Expect logits and labels to be already "selected" for good targets,
i.e. this function does not do any masking internally.
Args:
logits: [total_num_targets, n_classes] Tensor of float scores
labels: [total_num_targets, n_classes] Tensor of sparse binary targets
Returns:
loss: scalar Tensor
"""
binary_preds = logits.ge(0).long() # {0,1}
# Matthews coefficient and accuracy computed on {0,1} labels.
task.mcc_scorer(binary_preds, labels.long())
task.acc_scorer(binary_preds, labels.long())
# F1Measure() expects [total_num_targets, n_classes, 2]
# to compute binarized F1.
binary_scores = torch.stack([-1 * logits, logits], dim=2)
task.f1_scorer(binary_scores, labels)
if self.loss_type == "sigmoid":
return F.binary_cross_entropy(torch.sigmoid(logits), labels.float())
else:
raise ValueError("Unsupported loss type '%s' " "for edge probing." % self.loss_type)
def compute_loss(self, logits: torch.Tensor, labels: torch.Tensor,
task: EdgeProbingTask):
""" Compute loss & eval metrics.
Expect logits and labels to be already "selected" for good targets,
i.e. this function does not do any masking internally.
Args:
logits: [total_num_targets, n_classes] Tensor of float scores
labels: [total_num_targets, n_classes] Tensor of sparse binary targets
Returns:
loss: scalar Tensor
"""
if self.loss_type == "sigmoid":
binary_preds = logits.ge(0).long() # {0,1}
# Matthews coefficient and accuracy computed on {0,1} labels.
task.mcc_scorer(binary_preds, labels.long())
task.acc_scorer(binary_preds, labels.long())
#print("\n\n\n", torch.sum(labels, dim=1), "\n\n\n")
# F1Measure() expects [total_num_targets, n_classes, 2]
# to compute binarized F1.
binary_scores = torch.stack([-1 * logits, logits], dim=2)
task.f1_scorer(binary_scores, labels)
loss = F.binary_cross_entropy(torch.sigmoid(logits),
labels.float())
task.xent_scorer(loss.mean().item())
return loss
elif self.loss_type == "softmax":
preds = one_hot(logits.argmax(dim=-1), depth=logits.shape[-1])
# Matthews coefficient and accuracy computed on {0,1} labels.
task.mcc_scorer(preds.long(), labels.long())
task.acc_scorer(preds.long(), labels.long())
# print("\n\n\n", torch.sum(labels, dim=1), "\n\n\n")
# F1Measure() expects [total_num_targets, n_classes, 2]
# to compute binarized F1.
binary_scores = torch.stack([-1 * logits, logits], dim=2)
task.f1_scorer(binary_scores, labels)
loss = F.cross_entropy(logits, labels.argmax(dim=-1))
task.xent_scorer(loss.mean().item())
return loss
else:
raise ValueError("Unsupported loss type '%s' "
"for edge probing." % self.loss_type)