harmonization and clarification of dice losses variants docs and associated tests

monai/losses/dice.py

@@ -778,12 +778,22 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         Raises:
             ValueError: When number of dimensions for input and target are different.
-            ValueError: When number of channels for target is neither 1 nor the same as input.
+            ValueError: When number of channels for target is neither 1 (without one-hot encoding) nor the same as input.
+
+        Returns:
+            torch.Tensor: value of the loss.
 
         """
-        if len(input.shape) != len(target.shape):
+        if input.dim() != target.dim():
             raise ValueError(
                 "the number of dimensions for input and target should be the same, "
+                f"got shape {input.shape} (nb dims: {len(input.shape)}) and {target.shape} (nb dims: {len(target.shape)}). "
+                "if target is not one-hot encoded, please provide a tensor with shape B1H[WD]."
+            )
+
+        if target.shape[1] != 1 and target.shape[1] != input.shape[1]:
+            raise ValueError(
+                "number of channels for target is neither 1 (without one-hot encoding) nor the same as input, "
                 f"got shape {input.shape} and {target.shape}."
             )
 
@@ -899,14 +909,24 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         Raises:
             ValueError: When number of dimensions for input and target are different.
-            ValueError: When number of channels for target is neither 1 nor the same as input.
+            ValueError: When number of channels for target is neither 1 (without one-hot encoding) nor the same as input.
 
+        Returns:
+            torch.Tensor: value of the loss.
         """
-        if len(input.shape) != len(target.shape):
+        if input.dim() != target.dim():
             raise ValueError(
                 "the number of dimensions for input and target should be the same, "
+                f"got shape {input.shape} (nb dims: {len(input.shape)}) and {target.shape} (nb dims: {len(target.shape)}). "
+                "if target is not one-hot encoded, please provide a tensor with shape B1H[WD]."
+            )
+
+        if target.shape[1] != 1 and target.shape[1] != input.shape[1]:
+            raise ValueError(
+                "number of channels for target is neither 1 (without one-hot encoding) nor the same as input, "
                 f"got shape {input.shape} and {target.shape}."
             )
+
         if self.to_onehot_y:
             n_pred_ch = input.shape[1]
             if n_pred_ch == 1:
@@ -1015,15 +1035,23 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
             target (torch.Tensor): the shape should be BNH[WD] or B1H[WD].
 
         Raises:
-            ValueError: When the input and target tensors have different numbers of dimensions, or the target
-                channel isn't either one-hot encoded or categorical with the same shape of the input.
+            ValueError: When number of dimensions for input and target are different.
+            ValueError: When number of channels for target is neither 1 (without one-hot encoding) nor the same as input.
 
         Returns:
             torch.Tensor: value of the loss.
         """
         if input.dim() != target.dim():
             raise ValueError(
-                f"Input - {input.shape} - and target - {target.shape} - must have the same number of dimensions."
+                "the number of dimensions for input and target should be the same, "
+                f"got shape {input.shape} (nb dims: {len(input.shape)}) and {target.shape} (nb dims: {len(target.shape)}). "
+                "if target is not one-hot encoded, please provide a tensor with shape B1H[WD]."
+            )
+
+        if target.shape[1] != 1 and target.shape[1] != input.shape[1]:
+            raise ValueError(
+                "number of channels for target is neither 1 (without one-hot encoding) nor the same as input, "
+                f"got shape {input.shape} and {target.shape}."
             )
 
         gdl_loss = self.generalized_dice(input, target)

tests/test_dice_ce_loss.py

@@ -93,10 +93,20 @@ def test_result(self, input_param, input_data, expected_val):
         result = diceceloss(**input_data)
         np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, atol=1e-4, rtol=1e-4)
 
-    # def test_ill_shape(self):
-    #     loss = DiceCELoss()
-    #     with self.assertRaisesRegex(ValueError, ""):
-    #         loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
+    def test_ill_shape(self):
+        loss = DiceCELoss()
+        with self.assertRaises(AssertionError):
+            loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 2, 5)))
+
+    def test_ill_shape2(self):
+        loss = DiceCELoss()
+        with self.assertRaises(ValueError):
+            loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
+
+    def test_ill_shape3(self):
+        loss = DiceCELoss()
+        with self.assertRaises(ValueError):
+            loss.forward(torch.ones((1, 3, 4, 4)), torch.ones((1, 2, 4, 4)))
 
     # def test_ill_reduction(self):
     #     with self.assertRaisesRegex(ValueError, ""):

tests/test_dice_focal_loss.py

@@ -69,8 +69,18 @@ def test_result_no_onehot_no_bg(self, size, onehot):
 
     def test_ill_shape(self):
         loss = DiceFocalLoss()
-        with self.assertRaisesRegex(ValueError, ""):
-            loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
+        with self.assertRaises(AssertionError):
+            loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 2, 5)))
+
+    def test_ill_shape2(self):
+        loss = DiceFocalLoss()
+        with self.assertRaises(ValueError):
+            loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
+
+    def test_ill_shape3(self):
+        loss = DiceFocalLoss()
+        with self.assertRaises(ValueError):
+            loss.forward(torch.ones((1, 3, 4, 4)), torch.ones((1, 2, 4, 4)))
 
     def test_ill_lambda(self):
         with self.assertRaisesRegex(ValueError, ""):

tests/test_generalized_dice_focal_loss.py

@@ -59,8 +59,18 @@ def test_result_no_onehot_no_bg(self):
 
     def test_ill_shape(self):
         loss = GeneralizedDiceFocalLoss()
-        with self.assertRaisesRegex(ValueError, ""):
-            loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
+        with self.assertRaises(AssertionError):
+            loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 2, 5)))
+
+    def test_ill_shape2(self):
+        loss = GeneralizedDiceFocalLoss()
+        with self.assertRaises(ValueError):
+            loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
+
+    def test_ill_shape3(self):
+        loss = GeneralizedDiceFocalLoss()
+        with self.assertRaises(ValueError):
+            loss.forward(torch.ones((1, 3, 4, 4)), torch.ones((1, 2, 4, 4)))
 
     def test_ill_lambda(self):
         with self.assertRaisesRegex(ValueError, ""):