def focal_loss(
input: torch.Tensor,
target: torch.Tensor,
alpha: float,
gamma: float = 2.0,
reduction: str = 'mean', ) -> torch.Tensor:
r"""Function that computes Focal loss.
See :class:`fastreid.modeling.losses.FocalLoss` for details.
"""
if not torch.is_tensor(input):
raise TypeError("Input type is not a torch.Tensor. Got {}"
.format(type(input)))
if not len(input.shape) >= 2:
raise ValueError("Invalid input shape, we expect BxCx*. Got: {}"
.format(input.shape))
if input.size(0) != target.size(0):
raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
.format(input.size(0), target.size(0)))
n = input.size(0)
out_size = (n,) + input.size()[2:]
if target.size()[1:] != input.size()[2:]:
raise ValueError('Expected target size {}, got {}'.format(
out_size, target.size()))
if not input.device == target.device:
raise ValueError(
"input and target must be in the same device. Got: {}".format(
input.device, target.device))
# compute softmax over the classes axis
input_soft = F.softmax(input, dim=1)
# create the labels one hot tensor
target_one_hot = one_hot(
target, num_classes=input.shape[1],
dtype=input.dtype)
# compute the actual focal loss
weight = torch.pow(-input_soft + 1., gamma)
focal = -alpha * weight * torch.log(input_soft)
loss_tmp = torch.sum(target_one_hot * focal, dim=1)
if reduction == 'none':
loss = loss_tmp
elif reduction == 'mean':
loss = torch.mean(loss_tmp)
elif reduction == 'sum':
loss = torch.sum(loss_tmp)
else:
raise NotImplementedError("Invalid reduction mode: {}"
.format(reduction))
return loss
def forward(self, features, targets):
sim_mat = F.linear(F.normalize(features), F.normalize(self.weight))
alpha_p = F.relu(-sim_mat.detach() + 1 + self._m)
alpha_n = F.relu(sim_mat.detach() + self._m)
delta_p = 1 - self._m
delta_n = self._m
s_p = self._s * alpha_p * (sim_mat - delta_p)
s_n = self._s * alpha_n * (sim_mat - delta_n)
targets = one_hot(targets, self._num_classes)
pred_class_logits = targets * s_p + (1.0 - targets) * s_n
return pred_class_logits
def forward(self, features, targets):
# get cos(theta)
cosine = F.linear(F.normalize(features), F.normalize(self.weight))
# add margin
theta = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7))
phi = torch.cos(theta + self._m)
# --------------------------- convert label to one-hot ---------------------------
targets = one_hot(targets, self._num_classes)
pred_class_logits = targets * phi + (1.0 - targets) * cosine
# logits re-scale
pred_class_logits *= self._s
return pred_class_logits
