fix kldiv when stop grad is trur
@@ -95,9 +95,15 @@ class DMLLoss(nn.Layer):
self.act = None
self.use_log = use_log
-
self.jskl_loss = KLJSLoss(mode="js")
+ def _kldiv(self, x, target):
+ eps = 1.0e-10
+ loss = target * (paddle.log(target + eps) - x)
+ # batch mean loss
+ loss = paddle.sum(loss) / loss.shape[0]
+ return loss
+
def forward(self, out1, out2):
if self.act is not None:
out1 = self.act(out1)
@@ -106,9 +112,8 @@ class DMLLoss(nn.Layer):
# for recognition distillation, log is needed for feature map
log_out1 = paddle.log(out1)
log_out2 = paddle.log(out2)
- loss = (F.kl_div(
- log_out1, out2, reduction='batchmean') + F.kl_div(
- log_out2, out1, reduction='batchmean')) / 2.0
+ loss = (
+ self._kldiv(log_out1, out2) + self._kldiv(log_out2, out1)) / 2.0
else:
# for detection distillation log is not needed
loss = self.jskl_loss(out1, out2)
