| 123456 |
- from examples.mlperf.metrics import dice_score
- def dice_ce_loss(pred, tgt):
- ce = pred.permute(0, 2, 3, 4, 1).sparse_categorical_crossentropy(tgt.squeeze(1))
- dice = (1.0 - dice_score(pred, tgt, argmax=False, to_one_hot_x=False)).mean()
- return (dice + ce) / 2
|
