def _focal_loss(pred, gt):
'''Modified focal loss. Exactly the same as CornerNet.
Modified for more stability by using log_sigmoid function
Arguments:
pred (batch x c x h x w): logit (must be values before sigmoid activation)
gt_regr (batch x c x h x w)
'''
alpha = 2
beta = 4
pos_inds = F.greater_equal_scalar(gt, 1)
neg_inds = 1 - pos_inds
neg_weights = F.pow_scalar(1.0 - gt, beta)
prob_pred = F.sigmoid(pred)
pos_loss = F.log_sigmoid(pred) * F.pow_scalar(1.0 - prob_pred,
alpha) * pos_inds
pos_loss = F.sum(pos_loss)
neg_loss = F.log_sigmoid(-pred) * F.pow_scalar(
prob_pred, alpha) * neg_weights * neg_inds
neg_loss = F.sum(neg_loss)
num_pos = F.maximum_scalar(F.sum(pos_inds), 1)
loss = -(1 / num_pos) * (pos_loss + neg_loss)
return loss
def _loss_minus(self, dout):
return -F.log_sigmoid(-dout)
def _loss_plus(self, dout):
return -F.log_sigmoid(dout)