def test_num_classes_not_equal(self):
metric = segmentation_metrics.DiceScore(num_classes=4)
y_pred = tf.constant(0.5,
shape=[2, 128, 128, 128, 2],
dtype=tf.float32)
y_true = tf.ones(shape=[2, 128, 128, 128, 2], dtype=tf.float32)
with self.assertRaisesRegex(
ValueError,
'The number of classes from groundtruth labels and `num_classes` '
'should equal'):
metric.update_state(y_true=y_true, y_pred=y_pred)
def test_forward_dice_score(self, num_classes, metric_type, output,
expected_score):
metric = segmentation_metrics.DiceScore(num_classes=num_classes,
metric_type=metric_type,
per_class_metric=True)
y_pred = tf.constant(output,
shape=[2, 128, 128, 128, num_classes],
dtype=tf.float32)
y_true = tf.ones(shape=[2, 128, 128, 128, num_classes],
dtype=tf.float32)
metric.update_state(y_true=y_true, y_pred=y_pred)
actual_score = metric.result().numpy()
self.assertAllClose(
actual_score,
expected_score,
atol=1e-2,
msg='Output metric {} does not match expected metric {}.'.format(
actual_score, expected_score))
def build_metrics(self,
training: bool = True) -> Sequence[tf.keras.metrics.Metric]:
"""Gets streaming metrics for training/validation."""
metrics = []
num_classes = self.task_config.model.num_classes
if training:
metrics.extend([
tf.keras.metrics.CategoricalAccuracy(
name='train_categorical_accuracy', dtype=tf.float32)
])
else:
self.metrics = [
segmentation_metrics.DiceScore(
num_classes=num_classes,
metric_type='generalized',
per_class_metric=self.task_config.evaluation
.report_per_class_metric,
name='val_generalized_dice',
dtype=tf.float32)
]
return metrics