def test_balanced_sample(self):
features = self._get_features_example()
instance_ids = self._get_instance_id_example()
sampled_features, sampled_instance_ids, sampled_indices = (
sampling_utils.balanced_sample(features=features,
instance_ids=instance_ids,
num_samples=20,
max_instance_id=4))
self.assertAllEqual(sampled_features.shape, np.array([2, 20, 3]))
self.assertAllEqual(sampled_instance_ids.shape, np.array([2, 20]))
self.assertAllEqual(sampled_indices.shape, np.array([2, 20]))
def classification_loss_using_mask_iou_func(embeddings,
logits,
instance_ids,
class_labels,
num_samples,
valid_mask=None,
max_instance_id=None,
similarity_strategy='dotproduct',
is_balanced=True):
"""Classification loss using mask iou.
Args:
embeddings: A tf.float32 tensor of size [batch_size, n, f].
logits: A tf.float32 tensor of size [batch_size, n, num_classes]. It is
assumed that background is class 0.
instance_ids: A tf.int32 tensor of size [batch_size, n].
class_labels: A tf.int32 tensor of size [batch_size, n]. It is assumed
that the background voxels are assigned to class 0.
num_samples: An int determining the number of samples.
valid_mask: A tf.bool tensor of size [batch_size, n] that is True when an
element is valid and False if it needs to be ignored. By default the value
is None which means it is not applied.
max_instance_id: If set, instance ids larger than that value will be
ignored. If not set, it will be computed from instance_ids tensor.
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 scalar loss tensor.
"""
batch_size = embeddings.get_shape().as_list()[0]
if batch_size is None:
raise ValueError('Unknown batch size at graph construction time.')
if max_instance_id is None:
max_instance_id = tf.reduce_max(instance_ids)
class_labels = tf.reshape(class_labels, [batch_size, -1, 1])
sampled_embeddings, sampled_instance_ids, sampled_indices = (
sampling_utils.balanced_sample(features=embeddings,
instance_ids=instance_ids,
num_samples=num_samples,
valid_mask=valid_mask,
max_instance_id=max_instance_id))
losses = []
for i in range(batch_size):
embeddings_i = embeddings[i, :, :]
instance_ids_i = instance_ids[i, :]
class_labels_i = class_labels[i, :, :]
logits_i = logits[i, :]
sampled_embeddings_i = sampled_embeddings[i, :, :]
sampled_instance_ids_i = sampled_instance_ids[i, :]
sampled_indices_i = sampled_indices[i, :]
sampled_class_labels_i = tf.gather(class_labels_i, sampled_indices_i)
sampled_logits_i = tf.gather(logits_i, sampled_indices_i)
if valid_mask is not None:
valid_mask_i = valid_mask[i]
embeddings_i = tf.boolean_mask(embeddings_i, valid_mask_i)
instance_ids_i = tf.boolean_mask(instance_ids_i, valid_mask_i)
loss_i = classification_loss_using_mask_iou_func_unbatched(
embeddings=embeddings_i,
instance_ids=instance_ids_i,
sampled_embeddings=sampled_embeddings_i,
sampled_instance_ids=sampled_instance_ids_i,
sampled_class_labels=sampled_class_labels_i,
sampled_logits=sampled_logits_i,
similarity_strategy=similarity_strategy,
is_balanced=is_balanced)
losses.append(loss_i)
return tf.math.reduce_mean(tf.stack(losses))
def npair_loss_func(embeddings,
instance_ids,
num_samples,
valid_mask=None,
max_instance_id=None,
similarity_strategy='dotproduct',
loss_strategy='softmax'):
"""N-pair metric learning loss for learning feature embeddings.
Args:
embeddings: A tf.float32 tensor of size [batch_size, n, f].
instance_ids: A tf.int32 tensor of size [batch_size, n].
num_samples: An int determinig the number of samples.
valid_mask: A tf.bool tensor of size [batch_size, n] that is True when an
element is valid and False if it needs to be ignored. By default the value
is None which means it is not applied.
max_instance_id: If set, instance ids larger than that value will be
ignored. If not set, it will be computed from instance_ids tensor.
similarity_strategy: Defines the method for computing similarity between
embedding vectors. Possible values are 'dotproduct' and
'distance'.
loss_strategy: Defines the type of loss including 'softmax' or 'sigmoid'.
Returns:
A tf.float32 scalar loss tensor.
"""
batch_size = embeddings.get_shape().as_list()[0]
if batch_size is None:
raise ValueError('Unknown batch size at graph construction time.')
if max_instance_id is None:
max_instance_id = tf.reduce_max(instance_ids)
sampled_embeddings, sampled_instance_ids, _ = sampling_utils.balanced_sample(
features=embeddings,
instance_ids=instance_ids,
num_samples=num_samples,
valid_mask=valid_mask,
max_instance_id=max_instance_id)
losses = []
for i in range(batch_size):
sampled_instance_ids_i = sampled_instance_ids[i, :]
sampled_embeddings_i = sampled_embeddings[i, :, :]
min_ids_i = tf.math.reduce_min(sampled_instance_ids_i)
max_ids_i = tf.math.reduce_max(sampled_instance_ids_i)
target_i = tf.one_hot(
sampled_instance_ids_i,
depth=(max_instance_id + 1),
dtype=tf.float32)
# pylint: disable=cell-var-from-loop
def npair_loss_i():
return metric_learning_losses.npair_loss(
embedding=sampled_embeddings_i,
target=target_i,
similarity_strategy=similarity_strategy,
loss_strategy=loss_strategy)
# pylint: enable=cell-var-from-loop
loss_i = tf.cond(
max_ids_i > min_ids_i, npair_loss_i,
lambda: tf.constant(0.0, dtype=tf.float32))
losses.append(loss_i)
return tf.math.reduce_mean(losses)
