Precision issue in get_confusion_matrix (Project-MONAI#7187)

Fixes Project-MONAI#7186

### Description
remove unnecessary float()

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: KumoLiu <yunl@nvidia.com>
Co-authored-by: Wenqi Li <831580+wyli@users.noreply.github.com>
Signed-off-by: Yu0610 <612410030@alum.ccu.edu.tw>

Author: 2 people

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}.")
 
@@ -165,12 +162,12 @@ 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
+    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

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}.")
 
@@ -75,12 +72,12 @@ 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
+    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

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}.")
 

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)
 

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):

monai/metrics/utils.py

@@ -95,37 +95,37 @@ 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
 
-    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
+        return f, not_nans.float()
 
     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).float().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 "

tests/test_compute_confusion_matrix.py

@@ -210,6 +210,14 @@
 
 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]]]),
+]
+
 
 class TestConfusionMatrix(unittest.TestCase):
     @parameterized.expand([TEST_CASE_CONFUSION_MATRIX])
@@ -274,6 +282,13 @@ 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()