Add 3D support and confusion matrix output to PanopticQualityMetric

monai/metrics/panoptic_quality.py

@@ -21,7 +21,7 @@
 
 linear_sum_assignment, _ = optional_import("scipy.optimize", name="linear_sum_assignment")
 
-__all__ = ["PanopticQualityMetric", "compute_panoptic_quality"]
+__all__ = ["PanopticQualityMetric", "compute_panoptic_quality", "compute_mean_iou"]
 
 
 class PanopticQualityMetric(CumulativeIterationMetric):
@@ -55,6 +55,8 @@ class PanopticQualityMetric(CumulativeIterationMetric):
             If set `match_iou_threshold` < 0.5, this function uses Munkres assignment to find the
             maximal amount of unique pairing.
         smooth_numerator: a small constant added to the numerator to avoid zero.
+        return_confusion_matrix: if True, returns raw confusion matrix values (tp, fp, fn, iou_sum)
+            instead of computed metrics. Default is False.
 
     """
 
@@ -65,19 +67,22 @@ def __init__(
         reduction: MetricReduction | str = MetricReduction.MEAN_BATCH,
         match_iou_threshold: float = 0.5,
         smooth_numerator: float = 1e-6,
+        return_confusion_matrix: bool = False,
     ) -> None:
         super().__init__()
         self.num_classes = num_classes
         self.reduction = reduction
         self.match_iou_threshold = match_iou_threshold
         self.smooth_numerator = smooth_numerator
         self.metric_name = ensure_tuple(metric_name)
+        self.return_confusion_matrix = return_confusion_matrix
 
     def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
         """
         Args:
-            y_pred: Predictions. It must be in the form of B2HW and have integer type. The first channel and the
-                second channel represent the instance predictions and classification predictions respectively.
+            y_pred: Predictions. It must be in the form of B2HW (2D) or B2HWD (3D) and have integer type.
+                The first channel and the second channel represent the instance predictions and classification
+                predictions respectively.
             y: ground truth. It must have the same shape as `y_pred` and have integer type. The first channel and the
                 second channel represent the instance labels and classification labels respectively.
                 Values in the second channel of `y_pred` and `y` should be in the range of 0 to `self.num_classes`,
@@ -86,7 +91,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor
         Raises:
             ValueError: when `y_pred` and `y` have different shapes.
             ValueError: when `y_pred` and `y` have != 2 channels.
-            ValueError: when `y_pred` and `y` have != 4 dimensions.
+            ValueError: when `y_pred` and `y` have != 4 or 5 dimensions.
 
         """
         if y_pred.shape != y.shape:
@@ -98,8 +103,10 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor
             )
 
         dims = y_pred.ndimension()
-        if dims != 4:
-            raise ValueError(f"y_pred should have 4 dimensions (batch, 2, h, w), got {dims}.")
+        if dims not in (4, 5):
+            raise ValueError(
+                f"y_pred should have 4 dimensions (batch, 2, h, w) or 5 dimensions (batch, 2, h, w, d), got {dims}."
+            )
 
         batch_size = y_pred.shape[0]
 
@@ -131,13 +138,22 @@ def aggregate(self, reduction: MetricReduction | str | None = None) -> torch.Ten
                 available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
                 ``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
 
+        Returns:
+            If `return_confusion_matrix` is True, returns the raw confusion matrix [tp, fp, fn, iou_sum].
+            Otherwise, returns the computed metric(s) based on `metric_name`.
+
         """
         data = self.get_buffer()
         if not isinstance(data, torch.Tensor):
             raise ValueError("the data to aggregate must be PyTorch Tensor.")
 
         # do metric reduction
         f, _ = do_metric_reduction(data, reduction or self.reduction)
+
+        if self.return_confusion_matrix:
+            # Return raw confusion matrix values
+            return f
+
         tp, fp, fn, iou_sum = f[..., 0], f[..., 1], f[..., 2], f[..., 3]
         results = []
         for metric_name in self.metric_name:
@@ -169,7 +185,7 @@ def compute_panoptic_quality(
     calculate PQ, and returning them directly enables further calculation over all images.
 
     Args:
-        pred: input data to compute, it must be in the form of HW and have integer type.
+        pred: input data to compute, it must be in the form of HW (2D) or HWD (3D) and have integer type.
         gt: ground truth. It must have the same shape as `pred` and have integer type.
         metric_name: output metric. The value can be "pq", "sq" or "rq".
         remap: whether to remap `pred` and `gt` to ensure contiguous ordering of instance id.
@@ -294,3 +310,24 @@ def _check_panoptic_metric_name(metric_name: str) -> str:
     if metric_name in ["recognition_quality", "rq"]:
         return "rq"
     raise ValueError(f"metric name: {metric_name} is wrong, please use 'pq', 'sq' or 'rq'.")
+
+
+def compute_mean_iou(confusion_matrix: torch.Tensor, smooth_numerator: float = 1e-6) -> torch.Tensor:
+    """Compute mean IoU from confusion matrix values.
+
+    Args:
+        confusion_matrix: tensor with shape (..., 4) where the last dimension contains
+            [tp, fp, fn, iou_sum] as returned by `compute_panoptic_quality` with `output_confusion_matrix=True`.
+        smooth_numerator: a small constant added to the numerator to avoid zero.
+
+    Returns:
+        Mean IoU computed as iou_sum / (tp + smooth_numerator).
+
+    """
+    if confusion_matrix.shape[-1] != 4:
+        raise ValueError(
+            f"confusion_matrix should have shape (..., 4) with [tp, fp, fn, iou_sum], "
+            f"got shape {confusion_matrix.shape}."
+        )
+    tp, iou_sum = confusion_matrix[..., 0], confusion_matrix[..., 3]
+    return iou_sum / (tp + smooth_numerator)

tests/metrics/test_compute_panoptic_quality.py

@@ -88,6 +88,37 @@
     [torch.as_tensor([[0.0, 1.0, 0.0], [0.6667, 0.0, 0.4]]), torch.as_tensor([[0.0, 0.5, 0.0], [0.3333, 0.0, 0.4]])],
 ]
 
+# 3D test cases
+sample_3d_pred = torch.as_tensor(
+    [
+        [
+            [[[2, 0], [1, 1]], [[0, 1], [2, 1]]],  # instance channel
+            [[[0, 1], [3, 0]], [[1, 0], [1, 1]]],  # class channel
+        ]
+    ],
+    device=_device,
+)
+
+sample_3d_gt = torch.as_tensor(
+    [
+        [
+            [[[2, 0], [0, 0]], [[2, 2], [2, 3]]],  # instance channel
+            [[[3, 3], [3, 2]], [[2, 2], [3, 3]]],  # class channel
+        ]
+    ],
+    device=_device,
+)
+
+# test 3D sample, num_classes = 3, match_iou_threshold = 0.5
+TEST_3D_CASE_1 = [{"num_classes": 3, "match_iou_threshold": 0.5}, sample_3d_pred, sample_3d_gt]
+
+# test confusion matrix return
+TEST_CM_CASE_1 = [
+    {"num_classes": 3, "match_iou_threshold": 0.5, "return_confusion_matrix": True},
+    sample_3_pred,
+    sample_3_gt,
+]
+
 
 @SkipIfNoModule("scipy.optimize")
 class TestPanopticQualityMetric(unittest.TestCase):
@@ -108,6 +139,98 @@ def test_value_class(self, input_params, y_pred, y_gt, expected_value):
         else:
             np.testing.assert_allclose(outputs.cpu().numpy(), np.asarray(expected_value), atol=1e-4)
 
+    def test_3d_support(self):
+        """Test that 3D input is properly supported."""
+        input_params, y_pred, y_gt = TEST_3D_CASE_1
+        metric = PanopticQualityMetric(**input_params)
+        # Should not raise an error for 3D input
+        metric(y_pred, y_gt)
+        outputs = metric.aggregate()
+        # Check that output is a tensor
+        self.assertIsInstance(outputs, torch.Tensor)
+        # Check that output shape is correct (num_classes,)
+        self.assertEqual(outputs.shape, torch.Size([3]))
+
+    def test_confusion_matrix_return(self):
+        """Test that confusion matrix can be returned instead of computed metrics."""
+        input_params, y_pred, y_gt = TEST_CM_CASE_1
+        metric = PanopticQualityMetric(**input_params)
+        metric(y_pred, y_gt)
+        outputs = metric.aggregate()
+        # Check that output is a tensor with shape (batch_size, num_classes, 4)
+        self.assertIsInstance(outputs, torch.Tensor)
+        self.assertEqual(outputs.shape[-1], 4)
+        # Verify that values correspond to [tp, fp, fn, iou_sum]
+        tp, fp, fn, iou_sum = outputs[..., 0], outputs[..., 1], outputs[..., 2], outputs[..., 3]
+        # tp, fp, fn should be non-negative integers
+        self.assertTrue(torch.all(tp >= 0))
+        self.assertTrue(torch.all(fp >= 0))
+        self.assertTrue(torch.all(fn >= 0))
+        # iou_sum should be non-negative float
+        self.assertTrue(torch.all(iou_sum >= 0))
+
+    def test_compute_mean_iou(self):
+        """Test mean IoU computation from confusion matrix."""
+        from monai.metrics.panoptic_quality import compute_mean_iou
+
+        input_params, y_pred, y_gt = TEST_CM_CASE_1
+        metric = PanopticQualityMetric(**input_params)
+        metric(y_pred, y_gt)
+        confusion_matrix = metric.aggregate()
+        mean_iou = compute_mean_iou(confusion_matrix)
+        # Check shape is correct
+        self.assertEqual(mean_iou.shape, confusion_matrix.shape[:-1])
+        # Check values are non-negative
+        self.assertTrue(torch.all(mean_iou >= 0))
+
+    def test_metric_name_filtering(self):
+        """Test that metric_name parameter properly filters output."""
+        # Test single metric "sq"
+        metric_sq = PanopticQualityMetric(num_classes=3, metric_name="sq", match_iou_threshold=0.5)
+        metric_sq(sample_3_pred, sample_3_gt)
+        result_sq = metric_sq.aggregate()
+        self.assertIsInstance(result_sq, torch.Tensor)
+        self.assertEqual(result_sq.shape, torch.Size([3]))
+
+        # Test single metric "rq"
+        metric_rq = PanopticQualityMetric(num_classes=3, metric_name="rq", match_iou_threshold=0.5)
+        metric_rq(sample_3_pred, sample_3_gt)
+        result_rq = metric_rq.aggregate()
+        self.assertIsInstance(result_rq, torch.Tensor)
+        self.assertEqual(result_rq.shape, torch.Size([3]))
+
+        # Results should be different for different metrics
+        self.assertFalse(torch.allclose(result_sq, result_rq, atol=1e-4))
+
+    def test_invalid_3d_shape(self):
+        """Test that invalid 3D shapes are rejected."""
+        # Shape with 3 dimensions should fail
+        invalid_pred = torch.randint(0, 5, (2, 2, 10))
+        invalid_gt = torch.randint(0, 5, (2, 2, 10))
+        metric = PanopticQualityMetric(num_classes=3)
+        with self.assertRaises(ValueError):
+            metric(invalid_pred, invalid_gt)
+
+        # Shape with 6 dimensions should fail
+        invalid_pred = torch.randint(0, 5, (1, 2, 8, 8, 8, 8))
+        invalid_gt = torch.randint(0, 5, (1, 2, 8, 8, 8, 8))
+        with self.assertRaises(ValueError):
+            metric(invalid_pred, invalid_gt)
+
+    def test_compute_mean_iou_invalid_shape(self):
+        """Test that compute_mean_iou raises ValueError for invalid shapes."""
+        from monai.metrics.panoptic_quality import compute_mean_iou
+
+        # Shape (..., 3) instead of (..., 4) should fail
+        invalid_confusion_matrix = torch.zeros(3, 3)
+        with self.assertRaises(ValueError):
+            compute_mean_iou(invalid_confusion_matrix)
+
+        # Shape (..., 5) should also fail
+        invalid_confusion_matrix = torch.zeros(2, 5)
+        with self.assertRaises(ValueError):
+            compute_mean_iou(invalid_confusion_matrix)
+
 
 if __name__ == "__main__":
     unittest.main()