def classification_loss_using_mask_iou_func_unbatched(
embeddings, instance_ids, sampled_embeddings, sampled_instance_ids,
sampled_class_labels, sampled_logits, similarity_strategy,
is_balanced):
"""Classification loss using mask iou.
Args:
embeddings: A tf.float32 tensor of size [n, f].
instance_ids: A tf.int32 tensor of size [n].
sampled_embeddings: A tf.float32 tensor of size [num_samples, f].
sampled_instance_ids: A tf.int32 tensor of size [num_samples].
sampled_class_labels: A tf.int32 tensor of size [num_samples, 1].
sampled_logits: A tf.float32 tensor of size [num_samples, num_classes].
similarity_strategy: Defines the method for computing similarity between
embedding vectors. Possible values are 'dotproduct' and
'distance'.
is_balanced: If True, the per-voxel losses are re-weighted to have equal
total weight for foreground vs. background voxels.
Returns:
A tf.float32 loss scalar tensor.
"""
predicted_soft_masks = metric_learning_utils.embedding_centers_to_soft_masks(
embedding=embeddings,
centers=sampled_embeddings,
similarity_strategy=similarity_strategy)
predicted_masks = tf.cast(tf.greater(predicted_soft_masks, 0.5),
dtype=tf.float32)
gt_masks = tf.cast(tf.equal(tf.expand_dims(sampled_instance_ids, axis=1),
tf.expand_dims(instance_ids, axis=0)),
dtype=tf.float32)
pairwise_iou = instance_segmentation_utils.points_mask_pairwise_iou(
masks1=predicted_masks, masks2=gt_masks)
num_classes = sampled_logits.get_shape().as_list()[1]
sampled_class_labels_one_hot = tf.one_hot(indices=tf.reshape(
sampled_class_labels, [-1]),
depth=num_classes)
sampled_class_labels_one_hot_fg = sampled_class_labels_one_hot[:, 1:]
iou_coefs = tf.tile(tf.reshape(pairwise_iou, [-1, 1]),
[1, num_classes - 1])
sampled_class_labels_one_hot_fg *= iou_coefs
sampled_class_labels_one_hot_bg = tf.maximum(
1.0 - tf.math.reduce_sum(
sampled_class_labels_one_hot_fg, axis=1, keepdims=True), 0.0)
sampled_class_labels_one_hot = tf.concat(
[sampled_class_labels_one_hot_bg, sampled_class_labels_one_hot_fg],
axis=1)
params = {}
if is_balanced:
weights = loss_utils.get_balanced_loss_weights_multiclass(
labels=tf.expand_dims(sampled_instance_ids, axis=1))
params['weights'] = weights
return classification_loss_fn(logits=sampled_logits,
labels=sampled_class_labels_one_hot,
**params)
def test_points_mask_pairwise_iou(self):
masks1 = tf.constant([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1],
[1, 0, 1, 0, 1], [0, 1, 0, 1, 0]],
dtype=tf.int32)
masks2 = tf.constant([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1],
[1, 1, 1, 1, 0], [1, 0, 1, 1, 1]],
dtype=tf.int32)
pairwise_iou = isu.points_mask_pairwise_iou(masks1=masks1,
masks2=masks2)
expected_iou = tf.constant([0, 1, 0.4, 0.2], dtype=tf.float32)
self.assertAllClose(pairwise_iou.numpy(), expected_iou.numpy())