Add a test for alpha usage in DiceFocalLoss

tests/test_dice_focal_loss.py

@@ -91,6 +91,28 @@ def test_script(self):
         test_input = torch.ones(2, 1, 8, 8)
         test_script_save(loss, test_input, test_input)
 
+    def test_result_with_alpha(self):
+        size = [3, 3, 5, 5]
+        label = torch.randint(low=0, high=2, size=size)
+        pred = torch.randn(size)
+        alpha_values = [0.25, 0.5, 0.75]
+        for reduction in ["sum", "mean", "none"]:
+            for weight in [None, torch.tensor([1.0, 1.0, 2.0]), (3, 2.0, 1)]:
+                common_params = {
+                    "include_background": True,
+                    "to_onehot_y": False,
+                    "reduction": reduction,
+                    "weight": weight,
+                }
+                for lambda_focal in [0.5, 1.0, 1.5]:
+                    for alpha in alpha_values:
+                        dice_focal = DiceFocalLoss(gamma=1.0, lambda_focal=lambda_focal, alpha=alpha, **common_params)
+                        dice = DiceLoss(**common_params)
+                        focal = FocalLoss(gamma=1.0, alpha=alpha, **common_params)
+                        result = dice_focal(pred, label)
+                        expected_val = dice(pred, label) + lambda_focal * focal(pred, label)
+                        np.testing.assert_allclose(result, expected_val)
+
 
 if __name__ == "__main__":
     unittest.main()