add sigmoid/softmax support and multi-class extension for AsymmetricU…

monai/losses/unified_focal_loss.py

@@ -14,6 +14,7 @@
 import warnings
 
 import torch
+import torch.nn.functional as F
 from torch.nn.modules.loss import _Loss
 
 from monai.networks import one_hot
@@ -24,7 +25,7 @@ class AsymmetricFocalTverskyLoss(_Loss):
     """
     AsymmetricFocalTverskyLoss is a variant of FocalTverskyLoss, which attentions to the foreground class.
 
-    Actually, it's only supported for binary image segmentation now.
+    It supports both binary and multi-class segmentation.
 
     Reimplementation of the Asymmetric Focal Tversky Loss described in:
 
@@ -35,6 +36,7 @@ class AsymmetricFocalTverskyLoss(_Loss):
     def __init__(
         self,
         to_onehot_y: bool = False,
+        use_softmax: bool = False,
         delta: float = 0.7,
         gamma: float = 0.75,
         epsilon: float = 1e-7,
@@ -43,17 +45,29 @@ def __init__(
         """
         Args:
             to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
+            use_softmax: whether to use softmax to transform the original logits into probabilities.
+                If True, softmax is used. If False, sigmoid is used. Defaults to False.
             delta : weight of the background. Defaults to 0.7.
             gamma : value of the exponent gamma in the definition of the Focal loss  . Defaults to 0.75.
-            epsilon : it defines a very small number each time. simmily smooth value. Defaults to 1e-7.
+            epsilon : it defines a very small number each time. similarly smooth value. Defaults to 1e-7.
         """
         super().__init__(reduction=LossReduction(reduction).value)
         self.to_onehot_y = to_onehot_y
+        self.use_softmax = use_softmax
         self.delta = delta
         self.gamma = gamma
         self.epsilon = epsilon
 
     def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+        if self.use_softmax:
+            y_pred = torch.softmax(y_pred, dim=1)
+        else:
+            y_pred = torch.sigmoid(y_pred)
+
+        if y_pred.shape[1] == 1:
+            y_pred = torch.cat([1 - y_pred, y_pred], dim=1)
+            y_true = torch.cat([1 - y_true, y_true], dim=1)
+
         n_pred_ch = y_pred.shape[1]
 
         if self.to_onehot_y:
@@ -75,9 +89,16 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
         fp = torch.sum((1 - y_true) * y_pred, dim=axis)
         dice_class = (tp + self.epsilon) / (tp + self.delta * fn + (1 - self.delta) * fp + self.epsilon)
 
-        # Calculate losses separately for each class, enhancing both classes
+        # Class 0 is Background
         back_dice = 1 - dice_class[:, 0]
-        fore_dice = (1 - dice_class[:, 1]) * torch.pow(1 - dice_class[:, 1], -self.gamma)
+
+        # Class 1+ is Foreground
+        fore_dice = torch.pow(1 - dice_class[:, 1:], 1 - self.gamma)
+
+        if fore_dice.shape[1] > 1:
+            fore_dice = torch.mean(fore_dice, dim=1)
+        else:
+            fore_dice = fore_dice.squeeze(1)
 
         # Average class scores
         loss = torch.mean(torch.stack([back_dice, fore_dice], dim=-1))
@@ -88,7 +109,7 @@ class AsymmetricFocalLoss(_Loss):
     """
     AsymmetricFocalLoss is a variant of FocalTverskyLoss, which attentions to the foreground class.
 
-    Actually, it's only supported for binary image segmentation now.
+    It supports both binary and multi-class segmentation.
 
     Reimplementation of the Asymmetric Focal Loss described in:
 
@@ -99,6 +120,7 @@ class AsymmetricFocalLoss(_Loss):
     def __init__(
         self,
         to_onehot_y: bool = False,
+        use_softmax: bool = False,
         delta: float = 0.7,
         gamma: float = 2,
         epsilon: float = 1e-7,
@@ -107,17 +129,32 @@ def __init__(
         """
         Args:
             to_onehot_y : whether to convert `y` into the one-hot format. Defaults to False.
+            use_softmax: whether to use softmax to transform the original logits into probabilities.
+                If True, softmax is used. If False, sigmoid is used. Defaults to False.
             delta : weight of the background. Defaults to 0.7.
-            gamma : value of the exponent gamma in the definition of the Focal loss  . Defaults to 0.75.
-            epsilon : it defines a very small number each time. simmily smooth value. Defaults to 1e-7.
+            gamma : value of the exponent gamma in the definition of the Focal loss  . Defaults to 2.
+            epsilon : it defines a very small number each time. similarly smooth value. Defaults to 1e-7.
         """
         super().__init__(reduction=LossReduction(reduction).value)
         self.to_onehot_y = to_onehot_y
+        self.use_softmax = use_softmax
         self.delta = delta
         self.gamma = gamma
         self.epsilon = epsilon
 
     def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+        if self.use_softmax:
+            y_log_pred = F.log_softmax(y_pred, dim=1)
+            y_pred = torch.exp(y_log_pred)
+        else:
+            y_log_pred = F.logsigmoid(y_pred)
+            y_pred = torch.sigmoid(y_pred)
+
+        if y_pred.shape[1] == 1:
+            y_pred = torch.cat([1 - y_pred, y_pred], dim=1)
+            y_log_pred = torch.log(torch.clamp(y_pred, 1e-7, 1.0))
+            y_true = torch.cat([1 - y_true, y_true], dim=1)
+
         n_pred_ch = y_pred.shape[1]
 
         if self.to_onehot_y:
@@ -130,23 +167,30 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
             raise ValueError(f"ground truth has different shape ({y_true.shape}) from input ({y_pred.shape})")
 
         y_pred = torch.clamp(y_pred, self.epsilon, 1.0 - self.epsilon)
-        cross_entropy = -y_true * torch.log(y_pred)
+        cross_entropy = -y_true * y_log_pred
 
+        # Class 0: Background
         back_ce = torch.pow(1 - y_pred[:, 0], self.gamma) * cross_entropy[:, 0]
         back_ce = (1 - self.delta) * back_ce
 
-        fore_ce = cross_entropy[:, 1]
+        # Class 1+: Foreground
+        fore_ce = cross_entropy[:, 1:]
         fore_ce = self.delta * fore_ce
 
-        loss = torch.mean(torch.sum(torch.stack([back_ce, fore_ce], dim=1), dim=1))
+        if fore_ce.shape[1] > 1:
+            fore_ce = torch.sum(fore_ce, dim=1)
+        else:
+            fore_ce = fore_ce.squeeze(1)
+
+        loss = torch.mean(torch.stack([back_ce, fore_ce], dim=-1))
         return loss
 
 
 class AsymmetricUnifiedFocalLoss(_Loss):
     """
     AsymmetricUnifiedFocalLoss is a variant of Focal Loss.
 
-    Actually, it's only supported for binary image segmentation now
+    It supports both binary and multi-class segmentation.
 
     Reimplementation of the Asymmetric Unified Focal Tversky Loss described in:
 
@@ -157,74 +201,50 @@ class AsymmetricUnifiedFocalLoss(_Loss):
     def __init__(
         self,
         to_onehot_y: bool = False,
-        num_classes: int = 2,
         weight: float = 0.5,
         gamma: float = 0.5,
         delta: float = 0.7,
+        use_softmax: bool = False,
         reduction: LossReduction | str = LossReduction.MEAN,
     ):
         """
         Args:
             to_onehot_y : whether to convert `y` into the one-hot format. Defaults to False.
-            num_classes : number of classes, it only supports 2 now. Defaults to 2.
+            weight : weight for each loss function. Defaults to 0.5.
+            gamma : value of the exponent gamma in the definition of the Focal loss. Defaults to 0.5.
             delta : weight of the background. Defaults to 0.7.
-            gamma : value of the exponent gamma in the definition of the Focal loss. Defaults to 0.75.
-            epsilon : it defines a very small number each time. simmily smooth value. Defaults to 1e-7.
-            weight : weight for each loss function, if it's none it's 0.5. Defaults to None.
+            use_softmax: whether to use softmax to transform the original logits into probabilities.
+                If True, softmax is used. If False, sigmoid is used. Defaults to False.
 
         Example:
             >>> import torch
             >>> from monai.losses import AsymmetricUnifiedFocalLoss
-            >>> pred = torch.ones((1,1,32,32), dtype=torch.float32)
-            >>> grnd = torch.ones((1,1,32,32), dtype=torch.int64)
-            >>> fl = AsymmetricUnifiedFocalLoss(to_onehot_y=True)
+            >>> pred = torch.randn((1, 3, 32, 32))
+            >>> grnd = torch.randint(0, 3, (1, 1, 32, 32))
+            >>> fl = AsymmetricUnifiedFocalLoss(use_softmax=True, to_onehot_y=True)
             >>> fl(pred, grnd)
         """
         super().__init__(reduction=LossReduction(reduction).value)
         self.to_onehot_y = to_onehot_y
-        self.num_classes = num_classes
+        self.weight: float = weight
         self.gamma = gamma
         self.delta = delta
-        self.weight: float = weight
-        self.asy_focal_loss = AsymmetricFocalLoss(gamma=self.gamma, delta=self.delta)
-        self.asy_focal_tversky_loss = AsymmetricFocalTverskyLoss(gamma=self.gamma, delta=self.delta)
+        self.use_softmax = use_softmax
+        self.asy_focal_loss = AsymmetricFocalLoss(
+            to_onehot_y=to_onehot_y, gamma=self.gamma, delta=self.delta, use_softmax=use_softmax
+        )
+        self.asy_focal_tversky_loss = AsymmetricFocalTverskyLoss(
+            to_onehot_y=to_onehot_y, gamma=self.gamma, delta=self.delta, use_softmax=use_softmax
+        )
 
-    # TODO: Implement this  function to support multiple classes segmentation
     def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
         """
         Args:
             y_pred : the shape should be BNH[WD], where N is the number of classes.
-                It only supports binary segmentation.
                 The input should be the original logits since it will be transformed by
-                    a sigmoid in the forward function.
+                    a sigmoid/softmax in the forward function.
             y_true : the shape should be BNH[WD], where N is the number of classes.
-                It only supports binary segmentation.
-
-        Raises:
-            ValueError: When input and target are different shape
-            ValueError: When len(y_pred.shape) != 4 and len(y_pred.shape) != 5
-            ValueError: When num_classes
-            ValueError: When the number of classes entered does not match the expected number
         """
-        if y_pred.shape != y_true.shape:
-            raise ValueError(f"ground truth has different shape ({y_true.shape}) from input ({y_pred.shape})")
-
-        if len(y_pred.shape) != 4 and len(y_pred.shape) != 5:
-            raise ValueError(f"input shape must be 4 or 5, but got {y_pred.shape}")
-
-        if y_pred.shape[1] == 1:
-            y_pred = one_hot(y_pred, num_classes=self.num_classes)
-            y_true = one_hot(y_true, num_classes=self.num_classes)
-
-        if torch.max(y_true) != self.num_classes - 1:
-            raise ValueError(f"Please make sure the number of classes is {self.num_classes-1}")
-
-        n_pred_ch = y_pred.shape[1]
-        if self.to_onehot_y:
-            if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
-            else:
-                y_true = one_hot(y_true, num_classes=n_pred_ch)
 
         asy_focal_loss = self.asy_focal_loss(y_pred, y_true)
         asy_focal_tversky_loss = self.asy_focal_tversky_loss(y_pred, y_true)

tests/losses/test_unified_focal_loss.py

@@ -20,28 +20,41 @@
 from monai.losses import AsymmetricUnifiedFocalLoss
 
 TEST_CASES = [
-    [  # shape: (2, 1, 2, 2), (2, 1, 2, 2)
+    # Case 0: Binary segmentation
+    [
+        {},
         {
-            "y_pred": torch.tensor([[[[1.0, 0], [0, 1.0]]], [[[1.0, 0], [0, 1.0]]]]),
+            "y_pred": torch.tensor([[[[20.0, -20.0], [-20.0, 20.0]]], [[[20.0, -20.0], [-20.0, 20.0]]]]),
             "y_true": torch.tensor([[[[1.0, 0], [0, 1.0]]], [[[1.0, 0], [0, 1.0]]]]),
         },
         0.0,
     ],
-    [  # shape: (2, 1, 2, 2), (2, 1, 2, 2)
+    # Case 1: Same as above but explicit arguments
+    [
+        {"use_softmax": False, "to_onehot_y": False},
         {
-            "y_pred": torch.tensor([[[[1.0, 0], [0, 1.0]]], [[[1.0, 0], [0, 1.0]]]]),
+            "y_pred": torch.tensor([[[[20.0, -20.0], [-20.0, 20.0]]], [[[20.0, -20.0], [-20.0, 20.0]]]]),
             "y_true": torch.tensor([[[[1.0, 0], [0, 1.0]]], [[[1.0, 0], [0, 1.0]]]]),
         },
         0.0,
     ],
+    # Case 2: Multi-class segmentation
+    [
+        {"use_softmax": True, "to_onehot_y": True},
+        {
+            "y_pred": torch.tensor([[[[-20.0]], [[-20.0]], [[20.0]]]]).repeat(2, 1, 1, 1),
+            "y_true": torch.tensor([[[[2]]]]).repeat(2, 1, 1, 1),
+        },
+        0.0,
+    ],
 ]
 
 
 class TestAsymmetricUnifiedFocalLoss(unittest.TestCase):
 
     @parameterized.expand(TEST_CASES)
-    def test_result(self, input_data, expected_val):
-        loss = AsymmetricUnifiedFocalLoss()
+    def test_result(self, input_param, input_data, expected_val):
+        loss = AsymmetricUnifiedFocalLoss(**input_param)
         result = loss(**input_data)
         np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, atol=1e-4, rtol=1e-4)
 
@@ -52,7 +65,7 @@ def test_ill_shape(self):
 
     def test_with_cuda(self):
         loss = AsymmetricUnifiedFocalLoss()
-        i = torch.tensor([[[[1.0, 0], [0, 1.0]]], [[[1.0, 0], [0, 1.0]]]])
+        i = torch.tensor([[[[20.0, -20.0], [-20.0, 20.0]]], [[[20.0, -20.0], [-20.0, 20.0]]]])
         j = torch.tensor([[[[1.0, 0], [0, 1.0]]], [[[1.0, 0], [0, 1.0]]]])
         if torch.cuda.is_available():
             i = i.cuda()