From 1de4b5f1205c033f315042b8826a2f45c3d8475b Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 17:51:30 +0800
Subject: [PATCH 1/8] fix #7186

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 monai/losses/dice.py              |  6 +++---
 monai/losses/focal_loss.py        |  2 --
 monai/metrics/confusion_matrix.py |  7 ++-----
 monai/metrics/f_beta_score.py     |  7 ++-----
 monai/metrics/meaniou.py          |  3 ---
 monai/metrics/regression.py       | 12 ------------
 monai/metrics/surface_dice.py     |  3 ---
 monai/metrics/utils.py            |  4 ++--
 8 files changed, 9 insertions(+), 35 deletions(-)

diff --git a/monai/losses/dice.py b/monai/losses/dice.py
index b3c0f57c6e..f29865fa8e 100644
--- a/monai/losses/dice.py
+++ b/monai/losses/dice.py
@@ -377,7 +377,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         denominator = ground_o + pred_o
 
-        w = self.w_func(ground_o.float())
+        w = self.w_func(ground_o.int())
         infs = torch.isinf(w)
         if self.batch:
             w[infs] = 0.0
@@ -623,11 +623,11 @@ def _compute_alpha_generalized_true_positives(self, flat_target: torch.Tensor) -
         Args:
             flat_target: the target tensor.
         """
-        alpha: torch.Tensor = torch.ones((flat_target.size(0), self.num_classes)).float().to(flat_target.device)
+        alpha: torch.Tensor = torch.ones((flat_target.size(0), self.num_classes)).int().to(flat_target.device)
         if self.alpha_mode == "GDL":  # GDL style
             # Define alpha like in the generalized dice loss
             # i.e. the inverse of the volume of each class.
-            one_hot_f = F.one_hot(flat_target, num_classes=self.num_classes).permute(0, 2, 1).float()
+            one_hot_f = F.one_hot(flat_target, num_classes=self.num_classes).permute(0, 2, 1).int()
             volumes = torch.sum(one_hot_f, dim=2)
             alpha = 1.0 / (volumes + 1.0)
         else:  # default, i.e. like in the original paper
diff --git a/monai/losses/focal_loss.py b/monai/losses/focal_loss.py
index 98c1a071b6..778c2fb27b 100644
--- a/monai/losses/focal_loss.py
+++ b/monai/losses/focal_loss.py
@@ -154,8 +154,6 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
             raise ValueError(f"ground truth has different shape ({target.shape}) from input ({input.shape})")
 
         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
diff --git a/monai/metrics/confusion_matrix.py b/monai/metrics/confusion_matrix.py
index 9083b7733f..2a883348e1 100644
--- a/monai/metrics/confusion_matrix.py
+++ b/monai/metrics/confusion_matrix.py
@@ -153,9 +153,6 @@ def get_confusion_matrix(y_pred: torch.Tensor, y: torch.Tensor, include_backgrou
     if not include_background:
         y_pred, y = ignore_background(y_pred=y_pred, y=y)
 
-    y = y.float()
-    y_pred = y_pred.float()
-
     if y.shape != y_pred.shape:
         raise ValueError(f"y_pred and y should have same shapes, got {y_pred.shape} and {y.shape}.")
 
@@ -165,8 +162,8 @@ def get_confusion_matrix(y_pred: torch.Tensor, y: torch.Tensor, include_backgrou
     # As for classification tasks, S equals to 1.
     y_pred = y_pred.reshape(batch_size, n_class, -1)
     y = y.reshape(batch_size, n_class, -1)
-    tp = ((y_pred + y) == 2).float()
-    tn = ((y_pred + y) == 0).float()
+    tp = ((y_pred + y) == 2).int()
+    tn = ((y_pred + y) == 0).int()
 
     tp = tp.sum(dim=[2])
     tn = tn.sum(dim=[2])
diff --git a/monai/metrics/f_beta_score.py b/monai/metrics/f_beta_score.py
index a5703105a2..bc35ca9534 100644
--- a/monai/metrics/f_beta_score.py
+++ b/monai/metrics/f_beta_score.py
@@ -63,9 +63,6 @@ def get_f_beta_score(y_pred: torch.Tensor, y: torch.Tensor, include_background:
     if not include_background:
         y_pred, y = ignore_background(y_pred=y_pred, y=y)
 
-    y = y.float()
-    y_pred = y_pred.float()
-
     if y.shape != y_pred.shape:
         raise ValueError(f"y_pred and y should have same shapes, got {y_pred.shape} and {y.shape}.")
 
@@ -75,8 +72,8 @@ def get_f_beta_score(y_pred: torch.Tensor, y: torch.Tensor, include_background:
     # As for classification tasks, S equals to 1.
     y_pred = y_pred.view(batch_size, n_class, -1)
     y = y.view(batch_size, n_class, -1)
-    tp = ((y_pred + y) == 2).float()
-    tn = ((y_pred + y) == 0).float()
+    tp = ((y_pred + y) == 2).int()
+    tn = ((y_pred + y) == 0).int()
 
     tp = tp.sum(dim=[2])
     tn = tn.sum(dim=[2])
diff --git a/monai/metrics/meaniou.py b/monai/metrics/meaniou.py
index 7d1ae49f25..65c53f7aa5 100644
--- a/monai/metrics/meaniou.py
+++ b/monai/metrics/meaniou.py
@@ -130,9 +130,6 @@ def compute_iou(
     if not include_background:
         y_pred, y = ignore_background(y_pred=y_pred, y=y)
 
-    y = y.float()
-    y_pred = y_pred.float()
-
     if y.shape != y_pred.shape:
         raise ValueError(f"y_pred and y should have same shapes, got {y_pred.shape} and {y.shape}.")
 
diff --git a/monai/metrics/regression.py b/monai/metrics/regression.py
index f37230f09e..9d29654ee3 100644
--- a/monai/metrics/regression.py
+++ b/monai/metrics/regression.py
@@ -111,9 +111,6 @@ def __init__(self, reduction: MetricReduction | str = MetricReduction.MEAN, get_
         self.sq_func = partial(torch.pow, exponent=2.0)
 
     def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-        y_pred = y_pred.float()
-        y = y.float()
-
         return compute_mean_error_metrics(y_pred, y, func=self.sq_func)
 
 
@@ -143,9 +140,6 @@ def __init__(self, reduction: MetricReduction | str = MetricReduction.MEAN, get_
         self.abs_func = torch.abs
 
     def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-        y_pred = y_pred.float()
-        y = y.float()
-
         return compute_mean_error_metrics(y_pred, y, func=self.abs_func)
 
 
@@ -176,9 +170,6 @@ def __init__(self, reduction: MetricReduction | str = MetricReduction.MEAN, get_
         self.sq_func = partial(torch.pow, exponent=2.0)
 
     def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-        y_pred = y_pred.float()
-        y = y.float()
-
         mse_out = compute_mean_error_metrics(y_pred, y, func=self.sq_func)
         return torch.sqrt(mse_out)
 
@@ -218,9 +209,6 @@ def __init__(
         self.sq_func = partial(torch.pow, exponent=2.0)
 
     def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> Any:
-        y_pred = y_pred.float()
-        y = y.float()
-
         mse_out = compute_mean_error_metrics(y_pred, y, func=self.sq_func)
         return 20 * math.log10(self.max_val) - 10 * torch.log10(mse_out)
 
diff --git a/monai/metrics/surface_dice.py b/monai/metrics/surface_dice.py
index f8c402a756..635eb1bc24 100644
--- a/monai/metrics/surface_dice.py
+++ b/monai/metrics/surface_dice.py
@@ -228,9 +228,6 @@ def compute_surface_dice(
             f"y_pred and y should have same shape, but instead, shapes are {y_pred.shape} (y_pred) and {y.shape} (y)."
         )
 
-    y = y.float()
-    y_pred = y_pred.float()
-
     batch_size, n_class = y_pred.shape[:2]
 
     if n_class != len(class_thresholds):
diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py
index fe145b0f50..d4a0ec441d 100644
--- a/monai/metrics/utils.py
+++ b/monai/metrics/utils.py
@@ -95,7 +95,7 @@ def do_metric_reduction(
     # some elements might be Nan (if ground truth y was missing (zeros))
     # we need to account for it
     nans = torch.isnan(f)
-    not_nans = (~nans).float()
+    not_nans = (~nans).int()
 
     t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
     reduction = look_up_option(reduction, MetricReduction)
@@ -108,7 +108,7 @@ def do_metric_reduction(
         not_nans = not_nans.sum(dim=1)
         f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero)  # channel average
 
-        not_nans = (not_nans > 0).float().sum(dim=0)
+        not_nans = (not_nans > 0).int().sum(dim=0)
         f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero)  # batch average
 
     elif reduction == MetricReduction.SUM:

From 21157f60c5c3817d46285644924645761a9156ca Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 18:04:26 +0800
Subject: [PATCH 2/8] add unit test

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 tests/test_compute_confusion_matrix.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/tests/test_compute_confusion_matrix.py b/tests/test_compute_confusion_matrix.py
index a886d8b7e4..2a8ae37745 100644
--- a/tests/test_compute_confusion_matrix.py
+++ b/tests/test_compute_confusion_matrix.py
@@ -210,6 +210,10 @@
 
 TEST_CASES_CLF = [data_clf.copy(), result_clf]
 
+TEST_CASE_PRECISION = [
+    {"y_pred": torch.zeros([1, 1, 1024, 1024, 44], device=_device), "y": torch.zeros([1, 1, 1024, 1024, 44], device=_device)},
+    torch.tensor([[[0.0, 0.0, 46137344.0, 0.0]]])
+]
 
 class TestConfusionMatrix(unittest.TestCase):
     @parameterized.expand([TEST_CASE_CONFUSION_MATRIX])
@@ -274,6 +278,13 @@ def test_clf_with_nan(self, input_data, expected_value):
         expected_value = compute_confusion_matrix_metric("tpr", expected_value)
         assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)
 
+    @parameterized.expand([TEST_CASE_PRECISION])
+    def test_precision(self, input_data, expected_value):
+        # include or ignore background
+        result = get_confusion_matrix(**input_data)
+        assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)
+        np.testing.assert_equal(result.device, input_data["y_pred"].device)
+
 
 if __name__ == "__main__":
     unittest.main()

From 3c650d0841d360e5f471c255bdf53644908385da Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 18:05:27 +0800
Subject: [PATCH 3/8] fix flake8

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 tests/test_compute_confusion_matrix.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/tests/test_compute_confusion_matrix.py b/tests/test_compute_confusion_matrix.py
index 2a8ae37745..e0a92aec67 100644
--- a/tests/test_compute_confusion_matrix.py
+++ b/tests/test_compute_confusion_matrix.py
@@ -211,10 +211,14 @@
 TEST_CASES_CLF = [data_clf.copy(), result_clf]
 
 TEST_CASE_PRECISION = [
-    {"y_pred": torch.zeros([1, 1, 1024, 1024, 44], device=_device), "y": torch.zeros([1, 1, 1024, 1024, 44], device=_device)},
-    torch.tensor([[[0.0, 0.0, 46137344.0, 0.0]]])
+    {
+        "y_pred": torch.zeros([1, 1, 1024, 1024, 44], device=_device),
+        "y": torch.zeros([1, 1, 1024, 1024, 44], device=_device),
+    },
+    torch.tensor([[[0.0, 0.0, 46137344.0, 0.0]]]),
 ]
 
+
 class TestConfusionMatrix(unittest.TestCase):
     @parameterized.expand([TEST_CASE_CONFUSION_MATRIX])
     def test_value(self, input_data, expected_value):

From 166a62864aa210d8e7819e152d01a00e93a20862 Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 18:32:35 +0800
Subject: [PATCH 4/8] revert loss

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 monai/losses/dice.py       | 6 +++---
 monai/losses/focal_loss.py | 2 ++
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/monai/losses/dice.py b/monai/losses/dice.py
index f29865fa8e..b3c0f57c6e 100644
--- a/monai/losses/dice.py
+++ b/monai/losses/dice.py
@@ -377,7 +377,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         denominator = ground_o + pred_o
 
-        w = self.w_func(ground_o.int())
+        w = self.w_func(ground_o.float())
         infs = torch.isinf(w)
         if self.batch:
             w[infs] = 0.0
@@ -623,11 +623,11 @@ def _compute_alpha_generalized_true_positives(self, flat_target: torch.Tensor) -
         Args:
             flat_target: the target tensor.
         """
-        alpha: torch.Tensor = torch.ones((flat_target.size(0), self.num_classes)).int().to(flat_target.device)
+        alpha: torch.Tensor = torch.ones((flat_target.size(0), self.num_classes)).float().to(flat_target.device)
         if self.alpha_mode == "GDL":  # GDL style
             # Define alpha like in the generalized dice loss
             # i.e. the inverse of the volume of each class.
-            one_hot_f = F.one_hot(flat_target, num_classes=self.num_classes).permute(0, 2, 1).int()
+            one_hot_f = F.one_hot(flat_target, num_classes=self.num_classes).permute(0, 2, 1).float()
             volumes = torch.sum(one_hot_f, dim=2)
             alpha = 1.0 / (volumes + 1.0)
         else:  # default, i.e. like in the original paper
diff --git a/monai/losses/focal_loss.py b/monai/losses/focal_loss.py
index 778c2fb27b..98c1a071b6 100644
--- a/monai/losses/focal_loss.py
+++ b/monai/losses/focal_loss.py
@@ -154,6 +154,8 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
             raise ValueError(f"ground truth has different shape ({target.shape}) from input ({input.shape})")
 
         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

From 4f51f20ffdf13d80b0f3e5a3e17aad0b99dabcde Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 18:40:44 +0800
Subject: [PATCH 5/8] remove int

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 monai/metrics/confusion_matrix.py | 4 ++--
 monai/metrics/f_beta_score.py     | 4 ++--
 monai/metrics/utils.py            | 6 +++---
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/monai/metrics/confusion_matrix.py b/monai/metrics/confusion_matrix.py
index 2a883348e1..0189b33955 100644
--- a/monai/metrics/confusion_matrix.py
+++ b/monai/metrics/confusion_matrix.py
@@ -162,8 +162,8 @@ def get_confusion_matrix(y_pred: torch.Tensor, y: torch.Tensor, include_backgrou
     # As for classification tasks, S equals to 1.
     y_pred = y_pred.reshape(batch_size, n_class, -1)
     y = y.reshape(batch_size, n_class, -1)
-    tp = ((y_pred + y) == 2).int()
-    tn = ((y_pred + y) == 0).int()
+    tp = ((y_pred + y) == 2)
+    tn = ((y_pred + y) == 0)
 
     tp = tp.sum(dim=[2])
     tn = tn.sum(dim=[2])
diff --git a/monai/metrics/f_beta_score.py b/monai/metrics/f_beta_score.py
index bc35ca9534..a92fa09a10 100644
--- a/monai/metrics/f_beta_score.py
+++ b/monai/metrics/f_beta_score.py
@@ -72,8 +72,8 @@ def get_f_beta_score(y_pred: torch.Tensor, y: torch.Tensor, include_background:
     # As for classification tasks, S equals to 1.
     y_pred = y_pred.view(batch_size, n_class, -1)
     y = y.view(batch_size, n_class, -1)
-    tp = ((y_pred + y) == 2).int()
-    tn = ((y_pred + y) == 0).int()
+    tp = ((y_pred + y) == 2)
+    tn = ((y_pred + y) == 0)
 
     tp = tp.sum(dim=[2])
     tn = tn.sum(dim=[2])
diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py
index d4a0ec441d..eed230b3bf 100644
--- a/monai/metrics/utils.py
+++ b/monai/metrics/utils.py
@@ -95,12 +95,12 @@ def do_metric_reduction(
     # some elements might be Nan (if ground truth y was missing (zeros))
     # we need to account for it
     nans = torch.isnan(f)
-    not_nans = (~nans).int()
+    not_nans = (~nans)
 
     t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
     reduction = look_up_option(reduction, MetricReduction)
     if reduction == MetricReduction.NONE:
-        return f, not_nans
+        return f, not_nans.float()
 
     f[nans] = 0
     if reduction == MetricReduction.MEAN:
@@ -108,7 +108,7 @@ def do_metric_reduction(
         not_nans = not_nans.sum(dim=1)
         f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero)  # channel average
 
-        not_nans = (not_nans > 0).int().sum(dim=0)
+        not_nans = (not_nans > 0).sum(dim=0)
         f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero)  # batch average
 
     elif reduction == MetricReduction.SUM:

From bbc0f08b6ff89663cb759d6f55d9f50f724e7815 Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 18:58:21 +0800
Subject: [PATCH 6/8] bc fix

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 monai/metrics/confusion_matrix.py |  6 +++---
 monai/metrics/f_beta_score.py     |  6 +++---
 monai/metrics/utils.py            | 26 +++++++++++++-------------
 3 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/monai/metrics/confusion_matrix.py b/monai/metrics/confusion_matrix.py
index 0189b33955..f5da1b6b14 100644
--- a/monai/metrics/confusion_matrix.py
+++ b/monai/metrics/confusion_matrix.py
@@ -165,9 +165,9 @@ def get_confusion_matrix(y_pred: torch.Tensor, y: torch.Tensor, include_backgrou
     tp = ((y_pred + y) == 2)
     tn = ((y_pred + y) == 0)
 
-    tp = tp.sum(dim=[2])
-    tn = tn.sum(dim=[2])
-    p = y.sum(dim=[2])
+    tp = tp.sum(dim=[2]).float()
+    tn = tn.sum(dim=[2]).float()
+    p = y.sum(dim=[2]).float()
     n = y.shape[-1] - p
 
     fn = p - tp
diff --git a/monai/metrics/f_beta_score.py b/monai/metrics/f_beta_score.py
index a92fa09a10..79af7a5acd 100644
--- a/monai/metrics/f_beta_score.py
+++ b/monai/metrics/f_beta_score.py
@@ -75,9 +75,9 @@ def get_f_beta_score(y_pred: torch.Tensor, y: torch.Tensor, include_background:
     tp = ((y_pred + y) == 2)
     tn = ((y_pred + y) == 0)
 
-    tp = tp.sum(dim=[2])
-    tn = tn.sum(dim=[2])
-    p = y.sum(dim=[2])
+    tp = tp.sum(dim=[2]).float()
+    tn = tn.sum(dim=[2]).float()
+    p = y.sum(dim=[2]).float()
     n = y.shape[-1] - p
 
     fn = p - tp
diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py
index eed230b3bf..77e6510ab6 100644
--- a/monai/metrics/utils.py
+++ b/monai/metrics/utils.py
@@ -105,27 +105,27 @@ def do_metric_reduction(
     f[nans] = 0
     if reduction == MetricReduction.MEAN:
         # 2 steps, first, mean by channel (accounting for nans), then by batch
-        not_nans = not_nans.sum(dim=1)
-        f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero)  # channel average
+        not_nans = not_nans.sum(dim=1).float()
+        f = torch.where(not_nans > 0, f.sum(dim=1).float() / not_nans, t_zero)  # channel average
 
-        not_nans = (not_nans > 0).sum(dim=0)
-        f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero)  # batch average
+        not_nans = (not_nans > 0).sum(dim=0).float()
+        f = torch.where(not_nans > 0, f.sum(dim=0).float() / not_nans, t_zero)  # batch average
 
     elif reduction == MetricReduction.SUM:
-        not_nans = not_nans.sum(dim=[0, 1])
+        not_nans = not_nans.sum(dim=[0, 1]).float()
         f = torch.sum(f, dim=[0, 1])  # sum over the batch and channel dims
     elif reduction == MetricReduction.MEAN_BATCH:
-        not_nans = not_nans.sum(dim=0)
-        f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero)  # batch average
+        not_nans = not_nans.sum(dim=0).float()
+        f = torch.where(not_nans > 0, f.sum(dim=0).float() / not_nans, t_zero)  # batch average
     elif reduction == MetricReduction.SUM_BATCH:
-        not_nans = not_nans.sum(dim=0)
-        f = f.sum(dim=0)  # the batch sum
+        not_nans = not_nans.sum(dim=0).float()
+        f = f.sum(dim=0).float()  # the batch sum
     elif reduction == MetricReduction.MEAN_CHANNEL:
-        not_nans = not_nans.sum(dim=1)
-        f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero)  # channel average
+        not_nans = not_nans.sum(dim=1).float()
+        f = torch.where(not_nans > 0, f.sum(dim=1).float() / not_nans, t_zero)  # channel average
     elif reduction == MetricReduction.SUM_CHANNEL:
-        not_nans = not_nans.sum(dim=1)
-        f = f.sum(dim=1)  # the channel sum
+        not_nans = not_nans.sum(dim=1).float()
+        f = f.sum(dim=1).float()  # the channel sum
     elif reduction != MetricReduction.NONE:
         raise ValueError(
             f"Unsupported reduction: {reduction}, available options are "

From a45ce671df65f7ab60a8c90ab2b9cd08df59a7c7 Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 19:07:03 +0800
Subject: [PATCH 7/8] fix flake8

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 monai/metrics/confusion_matrix.py | 4 ++--
 monai/metrics/f_beta_score.py     | 4 ++--
 monai/metrics/utils.py            | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/monai/metrics/confusion_matrix.py b/monai/metrics/confusion_matrix.py
index f5da1b6b14..26ec823081 100644
--- a/monai/metrics/confusion_matrix.py
+++ b/monai/metrics/confusion_matrix.py
@@ -162,8 +162,8 @@ def get_confusion_matrix(y_pred: torch.Tensor, y: torch.Tensor, include_backgrou
     # As for classification tasks, S equals to 1.
     y_pred = y_pred.reshape(batch_size, n_class, -1)
     y = y.reshape(batch_size, n_class, -1)
-    tp = ((y_pred + y) == 2)
-    tn = ((y_pred + y) == 0)
+    tp = (y_pred + y) == 2
+    tn = (y_pred + y) == 0
 
     tp = tp.sum(dim=[2]).float()
     tn = tn.sum(dim=[2]).float()
diff --git a/monai/metrics/f_beta_score.py b/monai/metrics/f_beta_score.py
index 79af7a5acd..61e4525662 100644
--- a/monai/metrics/f_beta_score.py
+++ b/monai/metrics/f_beta_score.py
@@ -72,8 +72,8 @@ def get_f_beta_score(y_pred: torch.Tensor, y: torch.Tensor, include_background:
     # As for classification tasks, S equals to 1.
     y_pred = y_pred.view(batch_size, n_class, -1)
     y = y.view(batch_size, n_class, -1)
-    tp = ((y_pred + y) == 2)
-    tn = ((y_pred + y) == 0)
+    tp = (y_pred + y) == 2
+    tn = (y_pred + y) == 0
 
     tp = tp.sum(dim=[2]).float()
     tn = tn.sum(dim=[2]).float()
diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py
index 77e6510ab6..774ebad47f 100644
--- a/monai/metrics/utils.py
+++ b/monai/metrics/utils.py
@@ -95,7 +95,7 @@ def do_metric_reduction(
     # some elements might be Nan (if ground truth y was missing (zeros))
     # we need to account for it
     nans = torch.isnan(f)
-    not_nans = (~nans)
+    not_nans = ~nans
 
     t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
     reduction = look_up_option(reduction, MetricReduction)

From 8bd7518487668de9b073e94d72323bb3893ae92f Mon Sep 17 00:00:00 2001
From: KumoLiu <yunl@nvidia.com>
Date: Thu, 2 Nov 2023 21:13:09 +0800
Subject: [PATCH 8/8] fix ci

Signed-off-by: KumoLiu <yunl@nvidia.com>
---
 monai/metrics/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py
index 774ebad47f..c139fc35ed 100644
--- a/monai/metrics/utils.py
+++ b/monai/metrics/utils.py
@@ -97,7 +97,7 @@ def do_metric_reduction(
     nans = torch.isnan(f)
     not_nans = ~nans
 
-    t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
+    t_zero = torch.zeros(1, device=f.device, dtype=torch.float)
     reduction = look_up_option(reduction, MetricReduction)
     if reduction == MetricReduction.NONE:
         return f, not_nans.float()