(probas, labels, only_present=False, per_image=False, ignore=None)
return loss
Multi-class Lovasz-Softmax loss
      probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
      per_image: compute the loss per image instead of per batch
      ignore: void class labels
Multi-class Lovasz-Softmax loss
      probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
      per_image: compute the loss per image instead of per batch
      ignore: void class labels
Multi - class Lovasz - Softmax loss probas : [ B C H W ] Variable class probabilities at each prediction ( between 0 and 1 ) labels : [ B H W ] Tensor ground truth labels ( between 0 and C - 1 ) only_present : average only on classes present in ground truth per_image : compute the loss per image instead of per batch ignore : void class labels
def lovasz_softmax(probas, labels, only_present=False, per_image=False, ignore=None):
    """
    Multi-class Lovasz-Softmax loss
      probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
      per_image: compute the loss per image instead of per batch
      ignore: void class labels
    """
    if per_image:
        loss = mean(lovasz_softmax_flat(*flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), only_present=only_present)
                          for prob, lab in zip(probas, labels))
    else:
        loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore), only_present=only_present)
    return loss
def lovasz_softmax ( probas , labels , only_present = False , per_image = False , ignore = None ) : if per_image : loss = mean ( lovasz_softmax_flat ( * flatten_probas ( prob . unsqueeze ( 0 ) , lab . unsqueeze ( 0 ) , ignore ) , only_present = only_present ) for prob , lab in zip ( probas , labels ) ) else : loss = lovasz_softmax_flat ( * flatten_probas ( probas , labels , ignore ) , only_present = only_present ) return loss