adds weight parameter to dice and lovasz_softmax losses (#2879)

* adds weight parameter to dice and lovasz_softmax losses, similarly how we have focal loss

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* lint

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

kornia/losses/dice.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from typing import Optional
+
 import torch
 from torch import nn
 
@@ -12,7 +14,9 @@
 # https://github.com/Lightning-AI/metrics/blob/v0.11.3/src/torchmetrics/functional/classification/dice.py#L66-L207
 
 
-def dice_loss(pred: Tensor, target: Tensor, average: str = "micro", eps: float = 1e-8) -> Tensor:
+def dice_loss(
+    pred: Tensor, target: Tensor, average: str = "micro", eps: float = 1e-8, weight: Optional[Tensor] = None
+) -> Tensor:
     r"""Criterion that computes Sørensen-Dice Coefficient loss.
 
     According to [1], we compute the Sørensen-Dice Coefficient as follows:
@@ -43,6 +47,7 @@ def dice_loss(pred: Tensor, target: Tensor, average: str = "micro", eps: float =
             - ``'micro'`` [default]: Calculate the loss across all classes.
             - ``'macro'``: Calculate the loss for each class separately and average the metrics across classes.
         eps: Scalar to enforce numerical stabiliy.
+        weight: weights for classes with shape :math:`(num\_of\_classes,)`.
 
     Return:
         One-element tensor of the computed loss.
@@ -64,7 +69,7 @@ def dice_loss(pred: Tensor, target: Tensor, average: str = "micro", eps: float =
 
     if not pred.device == target.device:
         raise ValueError(f"pred and target must be in the same device. Got: {pred.device} and {target.device}")
-
+    num_of_classes = pred.shape[1]
     possible_average = {"micro", "macro"}
     KORNIA_CHECK(average in possible_average, f"The `average` has to be one of {possible_average}. Got: {average}")
 
@@ -80,6 +85,18 @@ def dice_loss(pred: Tensor, target: Tensor, average: str = "micro", eps: float =
         dims = (1, *dims)
 
     # compute the actual dice score
+    if weight is not None:
+        KORNIA_CHECK_IS_TENSOR(weight, "weight must be Tensor or None.")
+        KORNIA_CHECK(
+            (weight.shape[0] == num_of_classes and weight.numel() == num_of_classes),
+            f"weight shape must be (num_of_classes,): ({num_of_classes},), got {weight.shape}",
+        )
+        KORNIA_CHECK(
+            weight.device == pred.device,
+            f"weight and pred must be in the same device. Got: {weight.device} and {pred.device}",
+        )
+        pred_soft = pred_soft * weight
+        target_one_hot = target_one_hot * weight
     intersection = torch.sum(pred_soft * target_one_hot, dims)
     cardinality = torch.sum(pred_soft + target_one_hot, dims)
 
@@ -120,6 +137,7 @@ class DiceLoss(nn.Module):
             - ``'micro'`` [default]: Calculate the loss across all classes.
             - ``'macro'``: Calculate the loss for each class separately and average the metrics across classes.
         eps: Scalar to enforce numerical stabiliy.
+        weight: weights for classes with shape :math:`(num\_of\_classes,)`.
 
     Shape:
         - Pred: :math:`(N, C, H, W)` where C = number of classes.
@@ -135,10 +153,11 @@ class DiceLoss(nn.Module):
         >>> output.backward()
     """
 
-    def __init__(self, average: str = "micro", eps: float = 1e-8) -> None:
+    def __init__(self, average: str = "micro", eps: float = 1e-8, weight: Optional[Tensor] = None) -> None:
         super().__init__()
         self.average = average
         self.eps = eps
+        self.weight = weight
 
     def forward(self, pred: Tensor, target: Tensor) -> Tensor:
-        return dice_loss(pred, target, self.average, self.eps)
+        return dice_loss(pred, target, self.average, self.eps, self.weight)

kornia/losses/lovasz_softmax.py

@@ -1,15 +1,17 @@
 from __future__ import annotations
 
+from typing import Optional
+
 import torch
 from torch import Tensor, nn
 
-from kornia.core.check import KORNIA_CHECK_SHAPE
+from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_IS_TENSOR, KORNIA_CHECK_SHAPE
 
 # based on:
 # https://github.com/bermanmaxim/LovaszSoftmax
 
 
-def lovasz_softmax_loss(pred: Tensor, target: Tensor) -> Tensor:
+def lovasz_softmax_loss(pred: Tensor, target: Tensor, weight: Optional[Tensor] = None) -> Tensor:
     r"""Criterion that computes a surrogate multi-class intersection-over-union (IoU) loss.
 
     According to [1], we compute the IoU as follows:
@@ -22,7 +24,7 @@ def lovasz_softmax_loss(pred: Tensor, target: Tensor) -> Tensor:
 
     Where:
        - :math:`X` expects to be the scores of each class.
-       - :math:`Y` expects to be the binary tensor with the class labels.
+       - :math:`Y` expects to be the long tensor with the class labels.
 
     the loss, is finally computed as:
 
@@ -41,6 +43,7 @@ def lovasz_softmax_loss(pred: Tensor, target: Tensor) -> Tensor:
         pred: logits tensor with shape :math:`(N, C, H, W)` where C = number of classes > 1.
         labels: labels tensor with shape :math:`(N, H, W)` where each value
           is :math:`0 ≤ targets[i] ≤ C-1`.
+        weight: weights for classes with shape :math:`(num\_of\_classes,)`.
 
     Return:
         a scalar with the computed loss.
@@ -65,6 +68,19 @@ def lovasz_softmax_loss(pred: Tensor, target: Tensor) -> Tensor:
     if not pred.device == target.device:
         raise ValueError(f"pred and target must be in the same device. Got: {pred.device} and {target.device}")
 
+    num_of_classes = pred.shape[1]
+    # compute the actual dice score
+    if weight is not None:
+        KORNIA_CHECK_IS_TENSOR(weight, "weight must be Tensor or None.")
+        KORNIA_CHECK(
+            (weight.shape[0] == num_of_classes and weight.numel() == num_of_classes),
+            f"weight shape must be (num_of_classes,): ({num_of_classes},), got {weight.shape}",
+        )
+        KORNIA_CHECK(
+            weight.device == pred.device,
+            f"weight and pred must be in the same device. Got: {weight.device} and {pred.device}",
+        )
+
     # flatten pred [B, C, -1] and target [B, -1] and to float
     pred_flatten: Tensor = pred.reshape(pred.shape[0], pred.shape[1], -1)
     target_flatten: Tensor = target.reshape(target.shape[0], -1).float()
@@ -91,7 +107,7 @@ def lovasz_softmax_loss(pred: Tensor, target: Tensor) -> Tensor:
         gradient: Tensor = 1.0 - intersection / union
         if N > 1:
             gradient[..., 1:] = gradient[..., 1:] - gradient[..., :-1]
-        loss: Tensor = (errors_sorted.relu() * gradient).sum(1).mean()
+        loss: Tensor = (errors_sorted.relu() * gradient).sum(1).mean() * (1.0 if weight is None else weight[c])
         losses.append(loss)
     final_loss: Tensor = torch.stack(losses, dim=0).mean()
     return final_loss
@@ -129,6 +145,7 @@ class LovaszSoftmaxLoss(nn.Module):
         pred: logits tensor with shape :math:`(N, C, H, W)` where C = number of classes > 1.
         labels: labels tensor with shape :math:`(N, H, W)` where each value
           is :math:`0 ≤ targets[i] ≤ C-1`.
+        weight: weights for classes with shape :math:`(num\_of\_classes,)`.
 
     Return:
         a scalar with the computed loss.
@@ -142,8 +159,9 @@ class LovaszSoftmaxLoss(nn.Module):
         >>> output.backward()
     """
 
-    def __init__(self) -> None:
+    def __init__(self, weight: Optional[Tensor] = None) -> None:
         super().__init__()
+        self.weight = weight
 
     def forward(self, pred: Tensor, target: Tensor) -> Tensor:
-        return lovasz_softmax_loss(pred=pred, target=target)
+        return lovasz_softmax_loss(pred=pred, target=target, weight=self.weight)

tests/losses/test_dice.py

@@ -65,6 +65,17 @@ def test_averaging_micro(self, device, dtype):
         loss = criterion(logits, labels)
         self.assert_close(loss, expected_loss, rtol=1e-3, atol=1e-3)
 
+    def test_weight(self, device, dtype):
+        num_classes = 3
+        eps = 1e-8
+        logits = torch.zeros(2, num_classes, 4, 1, device=device, dtype=dtype)
+        labels = torch.zeros(2, 4, 1, device=device, dtype=torch.int64)
+        expected_loss = torch.tensor([2.0 / 3.0], device=device, dtype=dtype).squeeze()
+        weight = torch.tensor([0.0, 1.0, 1.0], device=device, dtype=dtype)
+        criterion = kornia.losses.DiceLoss(average="micro", eps=eps, weight=weight)
+        loss = criterion(logits, labels)
+        self.assert_close(loss, expected_loss, rtol=1e-3, atol=1e-3)
+
     def test_averaging_macro(self, device, dtype):
         num_classes = 2
         eps = 1e-8

tests/losses/test_lovaz_softmax.py

@@ -62,6 +62,19 @@ def test_all_ones(self, device, dtype):
 
         self.assert_close(loss, torch.zeros_like(loss), rtol=1e-3, atol=1e-3)
 
+    def test_weight(self, device, dtype):
+        num_classes = 2
+        # make perfect prediction
+        # note that softmax(prediction[:, 1]) == 1. softmax(prediction[:, 0]) == 0.
+        prediction = torch.zeros(2, num_classes, 1, 2, device=device, dtype=dtype)
+        prediction[:, 0] = 100.0
+        labels = torch.ones(2, 1, 2, device=device, dtype=torch.int64)
+
+        criterion = kornia.losses.LovaszSoftmaxLoss(weight=torch.tensor([1.0, 0.0], device=device, dtype=dtype))
+        loss = criterion(prediction, labels)
+
+        self.assert_close(loss, 0.5 * torch.ones_like(loss), rtol=1e-3, atol=1e-3)
+
     def test_gradcheck(self, device):
         num_classes = 4
         logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=torch.float64)