def sigmoid_focal_loss(pred,
target,
weight=None,
gamma=2.0,
alpha=0.25,
reduction='mean',
avg_factor=None):
r"""A warpper of cuda version `Focal Loss
<https://arxiv.org/abs/1708.02002>`_.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the number
of classes.
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum".
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
# Function.apply does not accept keyword arguments, so the decorator
# "weighted_loss" is not applicable
loss = _sigmoid_focal_loss(pred.contiguous(), target, gamma, alpha, None,
'none')
if weight is not None:
if weight.shape != loss.shape:
if weight.size(0) == loss.size(0):
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = weight.view(-1, 1)
else:
# Sometimes, weight per anchor per class is also needed. e.g.
# in FSAF. But it may be flattened of shape
# (num_priors x num_class, ), while loss is still of shape
# (num_priors, num_class).
assert weight.numel() == loss.numel()
weight = weight.view(loss.size(0), -1)
assert weight.ndim == loss.ndim
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
def sigmoid_focal_loss(pred,
target,
one_hot_target,
weight=None,
gamma=2.0,
alpha=0.5,
class_weight=None,
valid_mask=None,
reduction='mean',
avg_factor=None):
r"""A warpper of cuda version `Focal Loss
<https://arxiv.org/abs/1708.02002>`_.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the number
of classes.
target (torch.Tensor): The learning label of the prediction. It's shape
should be (N, )
one_hot_target (torch.Tensor): The learning label with shape (N, C)
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float | list[float], optional): A balanced form for Focal Loss.
Defaults to 0.5.
class_weight (list[float], optional): Weight of each class.
Defaults to None.
valid_mask (torch.Tensor, optional): A mask uses 1 to mark the valid
samples and uses 0 to mark the ignored samples. Default: None.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum".
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
# Function.apply does not accept keyword arguments, so the decorator
# "weighted_loss" is not applicable
final_weight = torch.ones(1, pred.size(1)).type_as(pred)
if isinstance(alpha, list):
# _sigmoid_focal_loss doesn't accept alpha of list type. Therefore, if
# a list is given, we set the input alpha as 0.5. This means setting
# equal weight for foreground class and background class. By
# multiplying the loss by 2, the effect of setting alpha as 0.5 is
# undone. The alpha of type list is used to regulate the loss in the
# post-processing process.
loss = _sigmoid_focal_loss(pred.contiguous(), target.contiguous(),
gamma, 0.5, None, 'none') * 2
alpha = pred.new_tensor(alpha)
final_weight = final_weight * (alpha * one_hot_target + (1 - alpha) *
(1 - one_hot_target))
else:
loss = _sigmoid_focal_loss(pred.contiguous(), target.contiguous(),
gamma, alpha, None, 'none')
if weight is not None:
if weight.shape != loss.shape and weight.size(0) == loss.size(0):
# For most cases, weight is of shape (N, ),
# which means it does not have the second axis num_class
weight = weight.view(-1, 1)
assert weight.dim() == loss.dim()
final_weight = final_weight * weight
if class_weight is not None:
final_weight = final_weight * pred.new_tensor(class_weight)
if valid_mask is not None:
final_weight = final_weight * valid_mask
loss = weight_reduce_loss(loss, final_weight, reduction, avg_factor)
return loss