Fix the issue when both numerator and denominator are 0, the dice score is...

Fix the issue when both numerator and denominator are 0, the dice score is incorrectly computed as 1, which should be 0.

PiperOrigin-RevId: 384257731

official/vision/beta/projects/volumetric_models/evaluation/segmentation_metrics.py

@@ -63,6 +63,7 @@ class DiceScore:
       self._dice_scores_per_class = [
           tf.Variable(0.0) for _ in range(num_classes)
       ]
+      self._count_per_class = [tf.Variable(0.0) for _ in range(num_classes)]
 
     self.name = name
     self.dtype = dtype
@@ -87,15 +88,21 @@ class DiceScore:
               self._num_classes,
               y_true.get_shape()[-1]))
 
-    self._count.assign_add(1.)
-    self._dice_scores_overall.assign_add(1 -
-                                         self._dice_op_overall(y_pred, y_true))
-    if self._per_class_metric:
-      for class_id in range(self._num_classes):
-        self._dice_scores_per_class[class_id].assign_add(
-            1 -
-            self._dice_op_per_class(y_pred[..., class_id], y_true[...,
-                                                                  class_id]))
+    # If both y_pred and y_true are all 0s, we skip computing the metrics;
+    # otherwise the averaged metrics will be erroneously lower.
+    if tf.reduce_sum(y_true) != 0 or tf.reduce_sum(y_pred) != 0:
+      self._count.assign_add(1.)
+      self._dice_scores_overall.assign_add(
+          1 - self._dice_op_overall(y_pred, y_true))
+      if self._per_class_metric:
+        for class_id in range(self._num_classes):
+          if tf.reduce_sum(y_true[..., class_id]) != 0 or tf.reduce_sum(
+              y_pred[..., class_id]) != 0:
+            self._count_per_class[class_id].assign_add(1.)
+            self._dice_scores_per_class[class_id].assign_add(
+                1 - self._dice_op_per_class(y_pred[...,
+                                                   class_id], y_true[...,
+                                                                     class_id]))
 
   def result(self) -> tf.Tensor:
     """Computes and returns the metric.
@@ -114,7 +121,7 @@ class DiceScore:
       for class_id in range(self._num_classes):
         dice_scores.append(
             tf.math.divide_no_nan(self._dice_scores_per_class[class_id],
-                                  self._count))
+                                  self._count_per_class[class_id]))
       return tf.stack(dice_scores)
     else:
       return tf.math.divide_no_nan(self._dice_scores_overall, self._count)

official/vision/beta/projects/volumetric_models/evaluation/segmentation_metrics_test.py

@@ -22,10 +22,10 @@ from official.vision.beta.projects.volumetric_models.evaluation import segmentat
 
 class SegmentationMetricsTest(parameterized.TestCase, tf.test.TestCase):
 
-  @parameterized.parameters((1, 'generalized', 0.5, [0.74, 0.67]),
+  @parameterized.parameters((1, 'generalized', 0.5, [0.67, 0.67]),
                             (1, 'adaptive', 0.5, [0.93, 0.67]),
                             (2, None, 0.5, [0.67, 0.67, 0.67]),
-                            (3, 'generalized', 0.5, [0.7, 0.67, 0.67, 0.67]))
+                            (3, 'generalized', 0.5, [0.67, 0.67, 0.67, 0.67]))
   def test_forward_dice_score(self, num_classes, metric_type, output,
                               expected_score):
     metric = segmentation_metrics.DiceScore(

official/vision/beta/projects/volumetric_models/losses/segmentation_losses.py

@@ -66,26 +66,43 @@ class SegmentationLossDiceScore(object):
       raise ValueError('The labels and logits must be at least rank 2.')
 
     epsilon = tf.keras.backend.epsilon()
-    axis = list(range(len(logits.shape) - 1))
+    keep_label_axis = list(range(len(logits.shape) - 1))
+    keep_batch_axis = list(range(1, len(logits.shape)))
+
+    # Compute sample mask to filter out samples with both all-0's labels and
+    # predictions because such samples should not contribute to mean dice score
+    # in this batch.
+    sample_mask = tf.logical_or(
+        tf.cast(tf.reduce_sum(labels, axis=keep_batch_axis), dtype=tf.bool),
+        tf.cast(tf.reduce_sum(logits, axis=keep_batch_axis), dtype=tf.bool))
+    labels = tf.boolean_mask(labels, sample_mask)
+    logits = tf.boolean_mask(logits, sample_mask)
+
+    # If all samples are filtered out, return 0 as the loss so this batch does
+    # not contribute.
+    if labels.shape[0] == 0:
+      return tf.convert_to_tensor(0.0)
 
     # Calculate intersections and unions per class.
-    intersection = tf.reduce_sum(labels * logits, axis=axis)
-    union = tf.reduce_sum(labels + logits, axis=axis)
+    intersection = tf.reduce_sum(labels * logits, axis=keep_label_axis)
+    union = tf.reduce_sum(labels + logits, axis=keep_label_axis)
 
     if self._metric_type == 'generalized':
       # Calculate the volume of groundtruth labels.
       w = tf.math.reciprocal(
-          tf.square(tf.reduce_sum(labels, axis=axis)) + epsilon)
+          tf.square(tf.reduce_sum(labels, axis=keep_label_axis)) + epsilon)
 
       # Calculate the weighted dice score and normalizer.
-      dice = 2 * tf.reduce_sum(w * intersection) + epsilon
-      normalizer = tf.reduce_sum(w * union) + epsilon
+      dice = 2 * tf.reduce_sum(w * intersection)
+      normalizer = tf.reduce_sum(w * union)
+      if normalizer == 0:
+        return tf.convert_to_tensor(1.0)
       dice = tf.cast(dice, dtype=tf.float32)
       normalizer = tf.cast(normalizer, dtype=tf.float32)
 
       return 1 - tf.reduce_mean(dice / normalizer)
     elif self._metric_type == 'adaptive':
-      dice = 2.0 * (intersection + epsilon) / (union + epsilon)
+      dice = 2.0 * intersection / (union + epsilon)
       # Calculate weights based on Dice scores.
       weights = tf.exp(-1.0 * dice)
 
@@ -94,12 +111,14 @@ class SegmentationLossDiceScore(object):
 
       # Calculate normalization factor.
       normalizer = tf.cast(tf.size(input=dice), dtype=tf.float32) * tf.exp(-1.0)
+      if normalizer == 0:
+        return tf.convert_to_tensor(1.0)
       weighted_dice = tf.cast(weighted_dice, dtype=tf.float32)
       return 1 - tf.reduce_mean(weighted_dice / normalizer)
     else:
       summation = tf.reduce_sum(
           labels, axis=self._axis) + tf.reduce_sum(
               logits, axis=self._axis)
-      dice = (2 * tf.reduce_sum(labels * logits, axis=self._axis) + epsilon) / (
+      dice = (2 * tf.reduce_sum(labels * logits, axis=self._axis)) / (
           summation + epsilon)
       return 1 - tf.reduce_mean(dice)

official/vision/beta/projects/volumetric_models/losses/segmentation_losses_test.py

@@ -27,8 +27,19 @@ class SegmentationLossDiceScoreTest(parameterized.TestCase, tf.test.TestCase):
   def test_supported_loss(self, metric_type, output, expected_score):
     loss = segmentation_losses.SegmentationLossDiceScore(
         metric_type=metric_type)
-    logits = tf.constant(output, shape=[1, 128, 128, 128, 1], dtype=tf.float32)
-    labels = tf.ones(shape=[1, 128, 128, 128, 1], dtype=tf.float32)
+    logits = tf.constant(output, shape=[2, 128, 128, 128, 1], dtype=tf.float32)
+    labels = tf.ones(shape=[2, 128, 128, 128, 1], dtype=tf.float32)
+    actual_score = loss(logits=logits, labels=labels)
+    self.assertAlmostEqual(actual_score.numpy(), expected_score, places=1)
+
+  @parameterized.parameters((None, 0, 0), ('generalized', 0, 0),
+                            ('adaptive', 0, 0))
+  def test_supported_loss_zero_labels_logits(self, metric_type, output,
+                                             expected_score):
+    loss = segmentation_losses.SegmentationLossDiceScore(
+        metric_type=metric_type)
+    logits = tf.constant(output, shape=[2, 128, 128, 128, 1], dtype=tf.float32)
+    labels = tf.zeros(shape=[2, 128, 128, 128, 1], dtype=tf.float32)
     actual_score = loss(logits=logits, labels=labels)
     self.assertAlmostEqual(actual_score.numpy(), expected_score, places=1)