@@ -56,6 +56,8 @@ def weighted_sigmoid_focal_loss(pred,
 def mask_cross_entropy(pred, target, label):
     num_rois = pred.size()[0]
     inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
+    if pred.shape[1] == 1:
+        label.zero_()
     pred_slice = pred[inds, label].squeeze(1)
     return F.binary_cross_entropy_with_logits(
         pred_slice, target, reduction='elementwise_mean')[None]
# TODO merge naive and weighted loss.
import torch
import torch.nn.functional as F


def weighted_nll_loss(pred, label, weight, avg_factor=None):
    if avg_factor is None:
        avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
    raw = F.nll_loss(pred, label, reduction='none')
    return torch.sum(raw * weight)[None] / avg_factor


def weighted_cross_entropy(pred, label, weight, avg_factor=None):
    if avg_factor is None:
        avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
    raw = F.cross_entropy(pred, label, reduction='none')
    return torch.sum(raw * weight)[None] / avg_factor


def weighted_binary_cross_entropy(pred, label, weight, avg_factor=None):
    if avg_factor is None:
        avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
    return F.binary_cross_entropy_with_logits(
        pred, label.float(), weight.float(),
        reduction='sum')[None] / avg_factor


def sigmoid_focal_loss(pred,
                       target,
                       weight,
                       gamma=2.0,
                       alpha=0.25,
                       reduction='elementwise_mean'):
    pred_sigmoid = pred.sigmoid()
    pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
    weight = (alpha * target + (1 - alpha) * (1 - target)) * weight
    weight = weight * pt.pow(gamma)
    return F.binary_cross_entropy_with_logits(
        pred, target, weight, reduction=reduction)


def weighted_sigmoid_focal_loss(pred,
                                target,
                                weight,
                                gamma=2.0,
                                alpha=0.25,
                                avg_factor=None,
                                num_classes=80):
    if avg_factor is None:
        avg_factor = torch.sum(weight > 0).float().item() / num_classes + 1e-6
    return sigmoid_focal_loss(
        pred, target, weight, gamma=gamma, alpha=alpha,
        reduction='sum')[None] / avg_factor


def mask_cross_entropy(pred, target, label):
    num_rois = pred.size()[0]
    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
    pred_slice = pred[inds, label].squeeze(1)
    return F.binary_cross_entropy_with_logits(
        pred_slice, target, reduction='elementwise_mean')[None]


def smooth_l1_loss(pred, target, beta=1.0, reduction='elementwise_mean'):
    assert beta > 0
    assert pred.size() == target.size() and target.numel() > 0
    diff = torch.abs(pred - target)
    loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
                       diff - 0.5 * beta)
    reduction = F._Reduction.get_enum(reduction)
    # none: 0, elementwise_mean:1, sum: 2
    if reduction == 0:
        return loss
    elif reduction == 1:
        return loss.sum() / pred.numel()
    elif reduction == 2:
        return loss.sum()


def weighted_smoothl1(pred, target, weight, beta=1.0, avg_factor=None):
    if avg_factor is None:
        avg_factor = torch.sum(weight > 0).float().item() / 4 + 1e-6
    loss = smooth_l1_loss(pred, target, beta, reduction='none')
    return torch.sum(loss * weight)[None] / avg_factor


def accuracy(pred, target, topk=1):
    if isinstance(topk, int):
        topk = (topk, )
        return_single = True

    maxk = max(topk)
    _, pred_label = pred.topk(maxk, 1, True, True)
    pred_label = pred_label.t()
    correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
        res.append(correct_k.mul_(100.0 / pred.size(0)))
    return res[0] if return_single else res

We could avoid the inplace modification of `label` in case that it will be used afterwards.