diff --git a/monai/losses/focal_loss.py b/monai/losses/focal_loss.py
index 4d1c723402..d6071edd71 100644
--- a/monai/losses/focal_loss.py
+++ b/monai/losses/focal_loss.py
@@ -28,10 +28,10 @@ class FocalLoss(_Loss):
     FocalLoss is an extension of BCEWithLogitsLoss that down-weights loss from
     high confidence correct predictions.
 
-    Reimplementation of the Focal Loss (with a build-in sigmoid activation) described in:
+    Reimplementation of the Focal Loss described in:
 
-        - "Focal Loss for Dense Object Detection", T. Lin et al., ICCV 2017
-        - "AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy",
+        - ["Focal Loss for Dense Object Detection"](https://arxiv.org/abs/1708.02002), T. Lin et al., ICCV 2017
+        - "AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy",
           Zhu et al., Medical Physics 2018
 
     Example:
@@ -70,19 +70,23 @@ def __init__(
         include_background: bool = True,
         to_onehot_y: bool = False,
         gamma: float = 2.0,
+        alpha: float | None = None,
         weight: Sequence[float] | float | int | torch.Tensor | None = None,
         reduction: LossReduction | str = LossReduction.MEAN,
+        use_softmax: bool = False,
     ) -> None:
         """
         Args:
-            include_background: if False, channel index 0 (background category) is excluded from the calculation.
-            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
-            gamma: value of the exponent gamma in the definition of the Focal loss.
+            include_background: if False, channel index 0 (background category) is excluded from the loss calculation.
+                If False, `alpha` is invalid when using softmax.
+            to_onehot_y: whether to convert the label `y` into the one-hot format. Defaults to False.
+            gamma: value of the exponent gamma in the definition of the Focal loss. Defaults to 2.
+            alpha: value of the alpha in the definition of the alpha-balanced Focal loss.
+                The value should be in [0, 1]. Defaults to None.
             weight: weights to apply to the voxels of each class. If None no weights are applied.
-                This corresponds to the weights `\alpha` in [1].
                 The input can be a single value (same weight for all classes), a sequence of values (the length
-                of the sequence should be the same as the number of classes, if not ``include_background``, the
-                number should not include class 0).
+                of the sequence should be the same as the number of classes. If not ``include_background``,
+                the number of classes should not include the background category class 0).
                 The value/values should be no less than 0. Defaults to None.
             reduction: {``"none"``, ``"mean"``, ``"sum"``}
                 Specifies the reduction to apply to the output. Defaults to ``"mean"``.
@@ -91,6 +95,9 @@ def __init__(
                 - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
                 - ``"sum"``: the output will be summed.
 
+            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 FocalLoss
@@ -103,14 +110,16 @@ def __init__(
         self.include_background = include_background
         self.to_onehot_y = to_onehot_y
         self.gamma = gamma
-        self.weight: Sequence[float] | float | int | torch.Tensor | None = weight
+        self.alpha = alpha
+        self.weight = weight
+        self.use_softmax = use_softmax
 
     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         """
         Args:
             input: the shape should be BNH[WD], where N is the number of classes.
                 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.
             target: the shape should be BNH[WD] or B1H[WD], where N is the number of classes.
 
         Raises:
@@ -141,63 +150,106 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         if target.shape != input.shape:
             raise ValueError(f"ground truth has different shape ({target.shape}) from input ({input.shape})")
 
-        i = input
-        t = target
-
-        # Change the shape of input and target to B x N x num_voxels.
-        b, n = t.shape[:2]
-        i = i.reshape(b, n, -1)
-        t = t.reshape(b, n, -1)
-
-        # computing binary cross entropy with logits
-        # see also https://github.com/pytorch/pytorch/blob/v1.9.0/aten/src/ATen/native/Loss.cpp#L231
-        max_val = (-i).clamp(min=0)
-        ce = i - i * t + max_val + ((-max_val).exp() + (-i - max_val).exp()).log()
+        loss: Optional[torch.Tensor] = None
+        input = input.float()
+        target = target.float()
+        if self.use_softmax:
+            if not self.include_background and self.alpha is not None:
+                self.alpha = None
+                warnings.warn("`include_background=False`, `alpha` ignored when using softmax.")
+            loss = softmax_focal_loss(input, target, self.gamma, self.alpha)
+        else:
+            loss = sigmoid_focal_loss(input, target, self.gamma, self.alpha)
 
         if self.weight is not None:
+            # make sure the lengths of weights are equal to the number of classes
             class_weight: Optional[torch.Tensor] = None
+            num_of_classes = target.shape[1]
             if isinstance(self.weight, (float, int)):
-                class_weight = torch.as_tensor([self.weight] * i.size(1))
+                class_weight = torch.as_tensor([self.weight] * num_of_classes)
             else:
                 class_weight = torch.as_tensor(self.weight)
-                if class_weight.size(0) != i.size(1):
+                if class_weight.shape[0] != num_of_classes:
                     raise ValueError(
-                        "the length of the weight sequence should be the same as the number of classes. "
-                        + "If `include_background=False`, the number should not include class 0."
+                        """the length of the `weight` sequence should be the same as the number of classes.
+                        If `include_background=False`, the weight should not include
+                        the background category class 0."""
                     )
             if class_weight.min() < 0:
-                raise ValueError("the value/values of weights should be no less than 0.")
-            class_weight = class_weight.to(i)
-            # Convert the weight to a map in which each voxel
-            # has the weight associated with the ground-truth label
-            # associated with this voxel in target.
-            at = class_weight[None, :, None]  # N => 1,N,1
-            at = at.expand((t.size(0), -1, t.size(2)))  # 1,N,1 => B,N,H*W
-            # Multiply the log proba by their weights.
-            ce = ce * at
-
-        # Compute the loss mini-batch.
-        # (1-p_t)^gamma * log(p_t) with reduced chance of overflow
-        p = F.logsigmoid(-i * (t * 2.0 - 1.0))
-        flat_loss: torch.Tensor = (p * self.gamma).exp() * ce
-
-        # Previously there was a mean over the last dimension, which did not
-        # return a compatible BCE loss. To maintain backwards compatible
-        # behavior we have a flag that performs this extra step, disable or
-        # parameterize if necessary. (Or justify why the mean should be there)
-        average_spatial_dims = True
+                raise ValueError("the value/values of the `weight` should be no less than 0.")
+            # apply class_weight to loss
+            class_weight = class_weight.to(loss)
+            broadcast_dims = [-1] + [1] * len(target.shape[2:])
+            class_weight = class_weight.view(broadcast_dims)
+            loss = class_weight * loss
 
         if self.reduction == LossReduction.SUM.value:
+            # Previously there was a mean over the last dimension, which did not
+            # return a compatible BCE loss. To maintain backwards compatible
+            # behavior we have a flag that performs this extra step, disable or
+            # parameterize if necessary. (Or justify why the mean should be there)
+            average_spatial_dims = True
             if average_spatial_dims:
-                flat_loss = flat_loss.mean(dim=-1)
-            loss = flat_loss.sum()
+                loss = loss.mean(dim=list(range(2, len(target.shape))))
+            loss = loss.sum()
         elif self.reduction == LossReduction.MEAN.value:
-            if average_spatial_dims:
-                flat_loss = flat_loss.mean(dim=-1)
-            loss = flat_loss.mean()
+            loss = loss.mean()
         elif self.reduction == LossReduction.NONE.value:
-            spacetime_dims = input.shape[2:]
-            loss = flat_loss.reshape([b, n] + list(spacetime_dims))
+            pass
         else:
             raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')
         return loss
+
+
+def softmax_focal_loss(
+    input: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, alpha: Optional[float] = None
+) -> torch.Tensor:
+    """
+    FL(pt) = -alpha * (1 - pt)**gamma * log(pt)
+
+    where p_i = exp(s_i) / sum_j exp(s_j), t is the target (ground truth) class, and
+    s_j is the unnormalized score for class j.
+    """
+    input_ls = input.log_softmax(1)
+    loss: torch.Tensor = -(1 - input_ls.exp()).pow(gamma) * input_ls * target
+
+    if alpha is not None:
+        # (1-alpha) for the background class and alpha for the other classes
+        alpha_fac = torch.tensor([1 - alpha] + [alpha] * (target.shape[1] - 1)).to(loss)
+        broadcast_dims = [-1] + [1] * len(target.shape[2:])
+        alpha_fac = alpha_fac.view(broadcast_dims)
+        loss = alpha_fac * loss
+
+    return loss
+
+
+def sigmoid_focal_loss(
+    input: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, alpha: Optional[float] = None
+) -> torch.Tensor:
+    """
+    FL(pt) = -alpha * (1 - pt)**gamma * log(pt)
+
+    where p = sigmoid(x), pt = p if label is 1 or 1 - p if label is 0
+    """
+    # computing binary cross entropy with logits
+    # equivalent to F.binary_cross_entropy_with_logits(input, target, reduction='none')
+    # see also https://github.com/pytorch/pytorch/blob/v1.9.0/aten/src/ATen/native/Loss.cpp#L231
+    max_val = (-input).clamp(min=0)
+    loss: torch.Tensor = input - input * target + max_val + ((-max_val).exp() + (-input - max_val).exp()).log()
+
+    # sigmoid(-i) if t==1; sigmoid(i) if t==0 <=>
+    # 1-sigmoid(i) if t==1; sigmoid(i) if t==0 <=>
+    # 1-p if t==1; p if t==0 <=>
+    # pfac, that is, the term (1 - pt)
+    invprobs = F.logsigmoid(-input * (target * 2 - 1))  # reduced chance of overflow
+    # (pfac.log() * gamma).exp() <=>
+    # pfac.log().exp() ^ gamma <=>
+    # pfac ^ gamma
+    loss = (invprobs * gamma).exp() * loss
+
+    if alpha is not None:
+        # alpha if t==1; (1-alpha) if t==0
+        alpha_factor = target * alpha + (1 - target) * (1 - alpha)
+        loss = alpha_factor * loss
+
+    return loss
diff --git a/tests/test_focal_loss.py b/tests/test_focal_loss.py
index 5f30b7b07d..46a947ea7c 100644
--- a/tests/test_focal_loss.py
+++ b/tests/test_focal_loss.py
@@ -13,16 +13,78 @@
 
 import unittest
 
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from parameterized import parameterized
 
 from monai.losses import FocalLoss
 from monai.networks import one_hot
 from tests.utils import test_script_save
 
+TEST_CASES = []
+for device in ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]:
+    input_data = {
+        "input": torch.tensor(
+            [[[[1.0, 1.0], [0.5, 0.0]], [[1.0, 1.0], [0.5, 0.0]], [[1.0, 1.0], [0.5, 0.0]]]], device=device
+        ),  # (1, 3, 2, 2)
+        "target": torch.tensor([[[[0, 1], [2, 0]]]], device=device),  # (1, 1, 2, 2)
+    }
+    TEST_CASES.append([{"to_onehot_y": True}, input_data, 0.34959])
+    TEST_CASES.append(
+        [
+            {"to_onehot_y": False},
+            {
+                "input": input_data["input"],  # (1, 3, 2, 2)
+                "target": F.one_hot(input_data["target"].squeeze(1)).permute(0, 3, 1, 2),  # (1, 3, 2, 2)
+            },
+            0.34959,
+        ]
+    )
+    TEST_CASES.append([{"to_onehot_y": True, "include_background": False}, input_data, 0.36498])
+    TEST_CASES.append([{"to_onehot_y": True, "alpha": 0.8}, input_data, 0.08423])
+    TEST_CASES.append(
+        [
+            {"to_onehot_y": True, "reduction": "none"},
+            input_data,
+            np.array(
+                [
+                    [
+                        [[0.02266, 0.70187], [0.37741, 0.17329]],
+                        [[0.70187, 0.02266], [0.37741, 0.17329]],
+                        [[0.70187, 0.70187], [0.06757, 0.17329]],
+                    ]
+                ]
+            ),
+        ]
+    )
+    TEST_CASES.append(
+        [
+            {"to_onehot_y": True, "weight": torch.tensor([0.5, 0.1, 0.2]), "reduction": "none"},
+            input_data,
+            np.array(
+                [
+                    [
+                        [[0.01133, 0.35093], [0.18871, 0.08664]],
+                        [[0.07019, 0.00227], [0.03774, 0.01733]],
+                        [[0.14037, 0.14037], [0.01352, 0.03466]],
+                    ]
+                ]
+            ),
+        ]
+    )
+    TEST_CASES.append([{"to_onehot_y": True, "use_softmax": True}, input_data, 0.16276])
+    TEST_CASES.append([{"to_onehot_y": True, "alpha": 0.8, "use_softmax": True}, input_data, 0.08138])
+
 
 class TestFocalLoss(unittest.TestCase):
+    @parameterized.expand(TEST_CASES)
+    def test_result(self, input_param, input_data, expected_val):
+        focal_loss = FocalLoss(**input_param)
+        result = focal_loss(**input_data)
+        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, atol=1e-4, rtol=1e-4)
+
     def test_consistency_with_cross_entropy_2d(self):
         """For gamma=0 the focal loss reduces to the cross entropy loss"""
         focal_loss = FocalLoss(to_onehot_y=False, gamma=0.0, reduction="mean", weight=1.0)
@@ -144,86 +206,116 @@ def test_consistency_with_cross_entropy_classification_01(self):
         self.assertNotAlmostEqual(max_error, 0.0, places=3)
 
     def test_bin_seg_2d(self):
-        # define 2d examples
-        target = torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])
-        # add another dimension corresponding to the batch (batch size = 1 here)
-        target = target.unsqueeze(0)  # shape (1, H, W)
-        pred_very_good = 100 * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float() - 50.0
-
-        # initialize the mean dice loss
-        loss = FocalLoss(to_onehot_y=True)
-
-        # focal loss for pred_very_good should be close to 0
-        target = target.unsqueeze(1)  # shape (1, 1, H, W)
-        focal_loss_good = float(loss(pred_very_good, target).cpu())
-        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+        for use_softmax in [True, False]:
+            # define 2d examples
+            target = torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])
+            # add another dimension corresponding to the batch (batch size = 1 here)
+            target = target.unsqueeze(0)  # shape (1, H, W)
+            pred_very_good = 100 * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float() - 50.0
+
+            # initialize the mean dice loss
+            loss = FocalLoss(to_onehot_y=True, use_softmax=use_softmax)
+
+            # focal loss for pred_very_good should be close to 0
+            target = target.unsqueeze(1)  # shape (1, 1, H, W)
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+
+            # with alpha
+            loss = FocalLoss(to_onehot_y=True, alpha=0.5, use_softmax=use_softmax)
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
 
     def test_empty_class_2d(self):
-        num_classes = 2
-        # define 2d examples
-        target = torch.tensor([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
-        # add another dimension corresponding to the batch (batch size = 1 here)
-        target = target.unsqueeze(0)  # shape (1, H, W)
-        pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2).float() - 500.0
-
-        # initialize the mean dice loss
-        loss = FocalLoss(to_onehot_y=True)
-
-        # focal loss for pred_very_good should be close to 0
-        target = target.unsqueeze(1)  # shape (1, 1, H, W)
-        focal_loss_good = float(loss(pred_very_good, target).cpu())
-        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+        for use_softmax in [True, False]:
+            num_classes = 2
+            # define 2d examples
+            target = torch.tensor([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
+            # add another dimension corresponding to the batch (batch size = 1 here)
+            target = target.unsqueeze(0)  # shape (1, H, W)
+            pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2).float() - 500.0
+
+            # initialize the mean dice loss
+            loss = FocalLoss(to_onehot_y=True, use_softmax=use_softmax)
+
+            # focal loss for pred_very_good should be close to 0
+            target = target.unsqueeze(1)  # shape (1, 1, H, W)
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+
+            # with alpha
+            loss = FocalLoss(to_onehot_y=True, alpha=0.5, use_softmax=use_softmax)
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
 
     def test_multi_class_seg_2d(self):
-        num_classes = 6  # labels 0 to 5
-        # define 2d examples
-        target = torch.tensor([[0, 0, 0, 0], [0, 1, 2, 0], [0, 3, 4, 0], [0, 0, 0, 0]])
-        # add another dimension corresponding to the batch (batch size = 1 here)
-        target = target.unsqueeze(0)  # shape (1, H, W)
-        pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2).float() - 500.0
-        # initialize the mean dice loss
-        loss = FocalLoss(to_onehot_y=True)
-        loss_onehot = FocalLoss(to_onehot_y=False)
-
-        # focal loss for pred_very_good should be close to 0
-        target_one_hot = F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2)  # test one hot
-        target = target.unsqueeze(1)  # shape (1, 1, H, W)
-
-        focal_loss_good = float(loss(pred_very_good, target).cpu())
-        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
-
-        focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())
-        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+        for use_softmax in [True, False]:
+            num_classes = 6  # labels 0 to 5
+            # define 2d examples
+            target = torch.tensor([[0, 0, 0, 0], [0, 1, 2, 0], [0, 3, 4, 0], [0, 0, 0, 0]])
+            # add another dimension corresponding to the batch (batch size = 1 here)
+            target = target.unsqueeze(0)  # shape (1, H, W)
+            pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2).float() - 500.0
+            # initialize the mean dice loss
+            loss = FocalLoss(to_onehot_y=True, use_softmax=use_softmax)
+            loss_onehot = FocalLoss(to_onehot_y=False, use_softmax=use_softmax)
+
+            # focal loss for pred_very_good should be close to 0
+            target_one_hot = F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2)  # test one hot
+            target = target.unsqueeze(1)  # shape (1, 1, H, W)
+
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+
+            focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+
+            # with alpha
+            loss = FocalLoss(to_onehot_y=True, alpha=0.5, use_softmax=use_softmax)
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+            loss_onehot = FocalLoss(to_onehot_y=False, alpha=0.5, use_softmax=use_softmax)
+            focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
 
     def test_bin_seg_3d(self):
-        num_classes = 2  # labels 0, 1
-        # define 3d examples
-        target = torch.tensor(
-            [
-                # raw 0
-                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],
-                # raw 1
-                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],
-                # raw 2
-                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],
-            ]
-        )
-        # add another dimension corresponding to the batch (batch size = 1 here)
-        target = target.unsqueeze(0)  # shape (1, H, W, D)
-        target_one_hot = F.one_hot(target, num_classes=num_classes).permute(0, 4, 1, 2, 3)  # test one hot
-        pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 4, 1, 2, 3).float() - 500.0
-
-        # initialize the mean dice loss
-        loss = FocalLoss(to_onehot_y=True)
-        loss_onehot = FocalLoss(to_onehot_y=False)
-
-        # focal loss for pred_very_good should be close to 0
-        target = target.unsqueeze(1)  # shape (1, 1, H, W)
-        focal_loss_good = float(loss(pred_very_good, target).cpu())
-        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
-
-        focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())
-        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+        for use_softmax in [True, False]:
+            num_classes = 2  # labels 0, 1
+            # define 3d examples
+            target = torch.tensor(
+                [
+                    # raw 0
+                    [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],
+                    # raw 1
+                    [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],
+                    # raw 2
+                    [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],
+                ]
+            )
+            # add another dimension corresponding to the batch (batch size = 1 here)
+            target = target.unsqueeze(0)  # shape (1, H, W, D)
+            target_one_hot = F.one_hot(target, num_classes=num_classes).permute(0, 4, 1, 2, 3)  # test one hot
+            pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 4, 1, 2, 3).float() - 500.0
+
+            # initialize the mean dice loss
+            loss = FocalLoss(to_onehot_y=True, use_softmax=use_softmax)
+            loss_onehot = FocalLoss(to_onehot_y=False, use_softmax=use_softmax)
+
+            # focal loss for pred_very_good should be close to 0
+            target = target.unsqueeze(1)  # shape (1, 1, H, W)
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+
+            focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+
+            # with alpha
+            loss = FocalLoss(to_onehot_y=True, alpha=0.5, use_softmax=use_softmax)
+            focal_loss_good = float(loss(pred_very_good, target).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
+            loss_onehot = FocalLoss(to_onehot_y=False, alpha=0.5, use_softmax=use_softmax)
+            focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())
+            self.assertAlmostEqual(focal_loss_good, 0.0, places=3)
 
     def test_foreground(self):
         background = torch.ones(1, 1, 5, 5)
@@ -260,10 +352,28 @@ def test_ill_class_weight(self):
         with self.assertRaisesRegex(ValueError, ""):
             FocalLoss(include_background=False, weight=(1.0, 1.0, -1.0))(chn_input, chn_target)
 
+    def test_warnings(self):
+        with self.assertWarns(Warning):
+            chn_input = torch.ones((1, 1, 3))
+            chn_target = torch.ones((1, 1, 3))
+            loss = FocalLoss(to_onehot_y=True)
+            loss(chn_input, chn_target)
+        with self.assertWarns(Warning):
+            chn_input = torch.ones((1, 1, 3))
+            chn_target = torch.ones((1, 1, 3))
+            loss = FocalLoss(include_background=False)
+            loss(chn_input, chn_target)
+        with self.assertWarns(Warning):
+            chn_input = torch.ones((1, 3, 3))
+            chn_target = torch.ones((1, 3, 3))
+            loss = FocalLoss(include_background=False, use_softmax=True, alpha=0.5)
+            loss(chn_input, chn_target)
+
     def test_script(self):
-        loss = FocalLoss()
-        test_input = torch.ones(2, 2, 8, 8)
-        test_script_save(loss, test_input, test_input)
+        for use_softmax in [True, False]:
+            loss = FocalLoss(use_softmax=use_softmax)
+            test_input = torch.ones(2, 2, 8, 8)
+            test_script_save(loss, test_input, test_input)
 
 
 if __name__ == "__main__":