RetinaNet losses (#9536)

* add sigmoid_focal_loss and l1_loss

* update ref implementation comment

examples/mlperf/losses.py

@@ -1,6 +1,29 @@
 from examples.mlperf.metrics import dice_score
+from tinygrad import Tensor
 
 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
+
+def sigmoid_focal_loss(pred:Tensor, tgt:Tensor, alpha:float=0.25, gamma:float=2.0, reduction:str="none") -> Tensor:
+   assert reduction in ["mean", "sum", "none"], f"unsupported reduction {reduction}"
+   p, ce_loss = pred.sigmoid(), pred.binary_crossentropy_logits(tgt, reduction="none")
+   p_t = p * tgt + (1 - p) * (1 - tgt)
+   loss = ce_loss * ((1 - p_t) ** gamma)
+
+   if alpha >= 0:
+     alpha_t = alpha * tgt + (1 - alpha) * (1 - tgt)
+     loss = loss * alpha_t
+
+   if reduction == "mean": loss = loss.mean()
+   elif reduction == "sum": loss = loss.sum()
+   return loss
+
+def l1_loss(pred:Tensor, tgt:Tensor, reduction:str="none") -> Tensor:
+  assert reduction in ["mean", "sum", "none"], f"unsupported reduction {reduction}"
+  loss = (pred - tgt).abs()
+
+  if reduction == "mean": loss = loss.mean()
+  elif reduction == "sum": loss = loss.sum()
+  return loss