def forward(
self,
pos_scores: FloatTensorType,
neg_scores: FloatTensorType,
) -> FloatTensorType:
# print(pos_scores.size())
num_pos = match_shape(pos_scores, -1)
# print(pos_scores.size())
num_neg = match_shape(neg_scores, num_pos, -1)
# print("%d --- %d",num_pos,num_neg)
# FIXME Workaround for https://github.com/pytorch/pytorch/issues/15870
# and https://github.com/pytorch/pytorch/issues/15223.
if num_pos == 0 or num_neg == 0:
return torch.zeros((), requires_grad=True)
# print("---", pos_scores.size())
# print("---", neg_scores.size())
scores = torch.cat([
pos_scores.unsqueeze(1),
neg_scores.logsumexp(dim=1, keepdim=True)
],
dim=1)
# print("scores ", type(scores))
# x = torch.zeros((), dtype=torch.long).expand(num_pos)
# print("x= ", x)
loss = F.cross_entropy(
scores,
torch.zeros((), dtype=torch.long).expand(num_pos),
reduction='sum',
)
# print("loss", loss)
return loss
def forward(
self,
pos_scores: FloatTensorType,
neg_scores: FloatTensorType,
) -> FloatTensorType:
num_pos = match_shape(pos_scores, -1)
num_neg = match_shape(neg_scores, num_pos, -1)
# FIXME Workaround for https://github.com/pytorch/pytorch/issues/15870
# and https://github.com/pytorch/pytorch/issues/15223.
if num_pos == 0 or num_neg == 0:
return torch.zeros((),
device=pos_scores.device,
requires_grad=True)
scores = torch.cat([
pos_scores.unsqueeze(1),
neg_scores.logsumexp(dim=1, keepdim=True)
],
dim=1)
loss = F.cross_entropy(
scores,
pos_scores.new_zeros((), dtype=torch.long).expand(num_pos),
reduction='sum',
)
return loss
def forward(
self,
pos_scores: FloatTensorType,
neg_scores: FloatTensorType,
weight: Optional[FloatTensorType],
) -> FloatTensorType:
num_pos = match_shape(pos_scores, -1)
num_neg = match_shape(neg_scores, num_pos, -1)
# FIXME Workaround for https://github.com/pytorch/pytorch/issues/15870
# and https://github.com/pytorch/pytorch/issues/15223.
if num_pos == 0 or num_neg == 0:
return torch.zeros((),
device=pos_scores.device,
requires_grad=True)
scores = torch.cat([
pos_scores.unsqueeze(1),
neg_scores.logsumexp(dim=1, keepdim=True)
],
dim=1)
if weight is not None:
loss_per_sample = F.cross_entropy(
scores,
pos_scores.new_zeros((), dtype=torch.long).expand(num_pos),
reduction="none",
)
match_shape(weight, num_pos)
loss_per_sample = loss_per_sample * weight
else:
loss_per_sample = F.cross_entropy(
scores,
pos_scores.new_zeros((), dtype=torch.long).expand(num_pos),
reduction="sum",
)
return loss_per_sample.sum()