diff --git a/monai/losses/dice.py b/monai/losses/dice.py
index 610327ef63..67802abc0a 100644
--- a/monai/losses/dice.py
+++ b/monai/losses/dice.py
@@ -796,8 +796,6 @@ def __init__(
         """
         super().__init__()
         self.dice = DiceLoss(
-            include_background=include_background,
-            to_onehot_y=to_onehot_y,
             sigmoid=sigmoid,
             softmax=softmax,
             other_act=other_act,
@@ -808,19 +806,15 @@ def __init__(
             smooth_dr=smooth_dr,
             batch=batch,
         )
-        self.focal = FocalLoss(
-            include_background=include_background,
-            to_onehot_y=to_onehot_y,
-            gamma=gamma,
-            weight=focal_weight,
-            reduction=reduction,
-        )
+        self.focal = FocalLoss(gamma=gamma, weight=focal_weight, reduction=reduction)
         if lambda_dice < 0.0:
             raise ValueError("lambda_dice should be no less than 0.0.")
         if lambda_focal < 0.0:
             raise ValueError("lambda_focal should be no less than 0.0.")
         self.lambda_dice = lambda_dice
         self.lambda_focal = lambda_focal
+        self.to_onehot_y = to_onehot_y
+        self.include_background = include_background
 
     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         """
@@ -837,6 +831,22 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         if len(input.shape) != len(target.shape):
             raise ValueError("the number of dimensions for input and target should be the same.")
 
+        n_pred_ch = input.shape[1]
+
+        if self.to_onehot_y:
+            if n_pred_ch == 1:
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+            else:
+                target = one_hot(target, num_classes=n_pred_ch)
+
+        if not self.include_background:
+            if n_pred_ch == 1:
+                warnings.warn("single channel prediction, `include_background=False` ignored.")
+            else:
+                # if skipping background, removing first channel
+                target = target[:, 1:]
+                input = input[:, 1:]
+
         dice_loss = self.dice(input, target)
         focal_loss = self.focal(input, target)
         total_loss: torch.Tensor = self.lambda_dice * dice_loss + self.lambda_focal * focal_loss