def test_select_keypoints_by_name(self):
input_keypoints = tf.constant([
[0.0, 0.0, 0.0],
[1.0, 1.0, 1.0],
[2.0, 2.0, 2.0],
[3.0, 3.0, 3.0],
[4.0, 4.0, 4.0],
[5.0, 5.0, 5.0],
[6.0, 6.0, 6.0],
[7.0, 7.0, 7.0],
[8.0, 8.0, 8.0],
[9.0, 9.0, 9.0],
[10.0, 10.0, 10.0],
[11.0, 11.0, 11.0],
[12.0, 12.0, 12.0],
[13.0, 13.0, 13.0],
[14.0, 14.0, 14.0],
[15.0, 15.0, 15.0],
[16.0, 16.0, 16.0],
])
keypoint_profile_3d = (
keypoint_profiles.create_keypoint_profile_or_die('LEGACY_3DH36M17')
)
keypoint_profile_2d = (
keypoint_profiles.create_keypoint_profile_or_die('LEGACY_2DCOCO13')
)
output_keypoints, _ = keypoint_utils.select_keypoints_by_name(
input_keypoints,
input_keypoint_names=keypoint_profile_3d.keypoint_names,
output_keypoint_names=(
keypoint_profile_2d.
compatible_keypoint_name_dict['LEGACY_3DH36M17']))
self.assertAllClose(output_keypoints, [
[1.0, 1.0, 1.0],
[4.0, 4.0, 4.0],
[5.0, 5.0, 5.0],
[6.0, 6.0, 6.0],
[7.0, 7.0, 7.0],
[8.0, 8.0, 8.0],
[9.0, 9.0, 9.0],
[11.0, 11.0, 11.0],
[12.0, 12.0, 12.0],
[13.0, 13.0, 13.0],
[14.0, 14.0, 14.0],
[15.0, 15.0, 15.0],
[16.0, 16.0, 16.0],
])
def add_triplet_loss():
"""Adds triplet loss."""
anchor_keypoints_3d = tf.unstack(
inputs[common_module.KEY_KEYPOINTS_3D], num=2, axis=1)[0]
if (configs['keypoint_profile_3d'].keypoint_names !=
configs['target_keypoint_profile_3d'].keypoint_names):
# Select target keypoints to use if they are different than input.
anchor_keypoints_3d, _ = keypoint_utils.select_keypoints_by_name(
anchor_keypoints_3d,
input_keypoint_names=(
configs['keypoint_profile_3d'].keypoint_names),
output_keypoint_names=(
configs['target_keypoint_profile_3d'].
keypoint_names))
triplet_anchor_embeddings, triplet_positive_embeddings = tf.unstack(
pipeline_utils.stack_embeddings(
outputs, configs['triplet_embedding_keys']),
axis=1)
if FLAGS.use_normalized_embeddings_for_triplet_loss:
triplet_anchor_embeddings = tf.math.l2_normalize(
triplet_anchor_embeddings, axis=-1)
triplet_positive_embeddings = tf.math.l2_normalize(
triplet_positive_embeddings, axis=-1)
triplet_anchor_mining_embeddings, triplet_positive_mining_embeddings = (
tf.unstack(pipeline_utils.stack_embeddings(
outputs, configs['triplet_mining_embedding_keys']),
axis=1))
if FLAGS.use_normalized_embeddings_for_triplet_mining:
triplet_anchor_mining_embeddings = tf.math.l2_normalize(
triplet_anchor_mining_embeddings, axis=-1)
triplet_positive_mining_embeddings = tf.math.l2_normalize(
triplet_positive_mining_embeddings, axis=-1)
triplet_loss, triplet_loss_summaries = (
loss_utils.compute_keypoint_triplet_losses(
anchor_embeddings=triplet_anchor_embeddings,
positive_embeddings=triplet_positive_embeddings,
match_embeddings=triplet_anchor_embeddings,
anchor_keypoints=anchor_keypoints_3d,
match_keypoints=anchor_keypoints_3d,
margin=FLAGS.triplet_loss_margin,
min_negative_keypoint_distance=(
configs['min_negative_keypoint_distance']),
use_semi_hard=FLAGS.use_semi_hard_triplet_negatives,
exclude_inactive_triplet_loss=(
FLAGS.exclude_inactive_triplet_loss),
embedding_sample_distance_fn=(
configs['triplet_embedding_sample_distance_fn']),
keypoint_distance_fn=configs['keypoint_distance_fn'],
anchor_mining_embeddings=
triplet_anchor_mining_embeddings,
positive_mining_embeddings=
triplet_positive_mining_embeddings,
match_mining_embeddings=
triplet_anchor_mining_embeddings,
summarize_percentiles=FLAGS.summarize_percentiles))
tf.losses.add_loss(triplet_loss,
loss_collection=tf.GraphKeys.LOSSES)
summaries.update(triplet_loss_summaries)
summaries['train/triplet_loss'] = triplet_loss