d1f4a9f474c547b099aa67619f7ba035a9f8ffbc,scripts/detection/ssd/train_ssd.py,,train,#Any#Any#Any#Any#Any#,95

Before Change

                        num_positive.append(nd.sum(box_masks >= 0).asscalar())
                        valid_cls = nd.sum(cls_targets >= 0, axis=0, exclude=True)
                        valid_cls = nd.maximum(valid_cls, nd.ones_like(valid_cls))
                        valid_box = nd.sum(box_masks > 0, axis=0, exclude=True)

                    l1 = cls_loss(cls_preds, cls_targets, (cls_targets >= 0).expand_dims(axis=-1))
                    // losses3.append(l1 * cls_targets.size / cls_targets.shape[0])
                    l1 = l1 / valid_cls * cls_targets.shape[-1]

After Change

                    box_labels.append(box_targets)
                    // losses1.append(l1)
                    // losses2.append(l2)
                n_pos = max(1, sum(num_positive))
                for l3, l4 in zip(losses3, losses4):
                    L = l3 / n_pos + l4 / n_pos
                    Ls.append(L)
                    losses1.append(l3 / n_pos)
                    losses2.append(l4 / n_pos)
                autograd.backward(Ls)

Instances