def __init__(self,
num_classes: int,
metric_type: Optional[str] = None,
per_class_metric: bool = False,
name: Optional[str] = None,
dtype: Optional[str] = None):
"""Constructs segmentation evaluator class.
Args:
num_classes: The number of classes.
metric_type: An optional `str` of type of dice scores.
per_class_metric: Whether to report per-class metric.
name: A `str`, name of the metric instance..
dtype: The data type of the metric result.
"""
self._num_classes = num_classes
self._per_class_metric = per_class_metric
self._dice_op_overall = segmentation_losses.SegmentationLossDiceScore(
metric_type=metric_type)
self._dice_scores_overall = tf.Variable(0.0)
self._count = tf.Variable(0.0)
if self._per_class_metric:
# Always use raw dice score for per-class metrics, so metric_type is None
# by default.
self._dice_op_per_class = segmentation_losses.SegmentationLossDiceScore()
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
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)
actual_score = loss(logits=logits, labels=labels)
self.assertAlmostEqual(actual_score.numpy(), expected_score, places=1)
def build_losses(self,
labels: tf.Tensor,
model_outputs: tf.Tensor,
aux_losses=None) -> tf.Tensor:
"""Segmentation loss.
Args:
labels: labels.
model_outputs: Output logits of the classifier.
aux_losses: auxiliarly loss tensors, i.e. `losses` in keras.Model.
Returns:
The total loss tensor.
"""
segmentation_loss_fn = segmentation_losses.SegmentationLossDiceScore(
metric_type='adaptive')
total_loss = segmentation_loss_fn(model_outputs, labels)
if aux_losses:
total_loss += tf.add_n(aux_losses)
return total_loss
