diff --git a/monai/losses/dice.py b/monai/losses/dice.py
index d74d40fe37..b3c0f57c6e 100644
--- a/monai/losses/dice.py
+++ b/monai/losses/dice.py
@@ -111,8 +111,9 @@ def __init__(
         self.smooth_nr = float(smooth_nr)
         self.smooth_dr = float(smooth_dr)
         self.batch = batch
-        self.weight = weight
-        self.register_buffer("class_weight", torch.ones(1))
+        weight = torch.as_tensor(weight) if weight is not None else None
+        self.register_buffer("class_weight", weight)
+        self.class_weight: None | torch.Tensor
 
     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         """
@@ -189,13 +190,12 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         f: torch.Tensor = 1.0 - (2.0 * intersection + self.smooth_nr) / (denominator + self.smooth_dr)
 
-        if self.weight is not None and target.shape[1] != 1:
+        num_of_classes = target.shape[1]
+        if self.class_weight is not None and num_of_classes != 1:
             # make sure the lengths of weights are equal to the number of classes
-            num_of_classes = target.shape[1]
-            if isinstance(self.weight, (float, int)):
-                self.class_weight = torch.as_tensor([self.weight] * num_of_classes)
+            if self.class_weight.ndim == 0:
+                self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
             else:
-                self.class_weight = torch.as_tensor(self.weight)
                 if self.class_weight.shape[0] != num_of_classes:
                     raise ValueError(
                         """the length of the `weight` sequence should be the same as the number of classes.
diff --git a/monai/losses/focal_loss.py b/monai/losses/focal_loss.py
index fbd0e6efb8..98c1a071b6 100644
--- a/monai/losses/focal_loss.py
+++ b/monai/losses/focal_loss.py
@@ -113,7 +113,9 @@ def __init__(
         self.alpha = alpha
         self.weight = weight
         self.use_softmax = use_softmax
-        self.register_buffer("class_weight", torch.ones(1))
+        weight = torch.as_tensor(weight) if weight is not None else None
+        self.register_buffer("class_weight", weight)
+        self.class_weight: None | torch.Tensor
 
     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         """
@@ -162,13 +164,12 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         else:
             loss = sigmoid_focal_loss(input, target, self.gamma, self.alpha)
 
-        if self.weight is not None:
+        num_of_classes = target.shape[1]
+        if self.class_weight is not None and num_of_classes != 1:
             # make sure the lengths of weights are equal to the number of classes
-            num_of_classes = target.shape[1]
-            if isinstance(self.weight, (float, int)):
-                self.class_weight = torch.as_tensor([self.weight] * num_of_classes)
+            if self.class_weight.ndim == 0:
+                self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
             else:
-                self.class_weight = torch.as_tensor(self.weight)
                 if self.class_weight.shape[0] != num_of_classes:
                     raise ValueError(
                         """the length of the `weight` sequence should be the same as the number of classes.