(probas, labels, only_present=False)
return mean(losses)
Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
Multi - class Lovasz - Softmax loss probas : [ P C ] Variable class probabilities at each prediction ( between 0 and 1 ) labels : [ P ] Tensor ground truth labels ( between 0 and C - 1 ) only_present : average only on classes present in ground truth
def lovasz_softmax_flat(probas, labels, only_present=False):
    """
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
    """
    C = probas.size(1)
    losses = []
    for c in range(C):
        fg = (labels == c).float() # foreground for class c
        if only_present and fg.sum() == 0:
            continue

        errors = (fg - probas[:, c]).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        losses.append(torch.dot(errors_sorted, lovasz_grad(fg_sorted)))
    return mean(losses)
def lovasz_softmax_flat ( probas , labels , only_present = False ) : C = probas . size ( 1 ) losses = [ ] for c in range ( C ) : fg = ( labels == c ) . float ( ) # foreground for class c if only_present and fg . sum ( ) == 0 : continue errors = ( fg - probas [ : , c ] ) . abs ( ) errors_sorted , perm = torch . sort ( errors , 0 , descending = True ) perm = perm . data fg_sorted = fg [ perm ] losses . append ( torch . dot ( errors_sorted , lovasz_grad ( fg_sorted ) ) ) return mean ( losses )