def add_positive_pairwise_loss():
"""Adds positive pairwise loss."""
(positive_pairwise_anchor_embeddings,
positive_pairwise_positive_embeddings) = tf.unstack(
pipeline_utils.stack_embeddings(
outputs,
configs['positive_pairwise_embedding_keys'],
common_module=common_module),
axis=1)
if FLAGS.use_normalized_embeddings_for_positive_pairwise_loss:
positive_pairwise_anchor_embeddings = tf.math.l2_normalize(
positive_pairwise_anchor_embeddings, axis=-1)
positive_pairwise_positive_embeddings = tf.math.l2_normalize(
positive_pairwise_positive_embeddings, axis=-1)
positive_pairwise_loss, positive_pairwise_loss_summaries = (
loss_utils.compute_positive_pairwise_loss(
positive_pairwise_anchor_embeddings,
positive_pairwise_positive_embeddings,
loss_weight=FLAGS.positive_pairwise_loss_weight,
distance_fn=configs[
'positive_pairwise_embedding_sample_distance_fn']))
tf.losses.add_loss(positive_pairwise_loss,
loss_collection=tf.GraphKeys.LOSSES)
summaries.update(positive_pairwise_loss_summaries)
summaries[
'train/positive_pairwise_loss'] = positive_pairwise_loss
def test_compute_positive_pairwise_loss(self):
anchor_embeddings = tf.constant([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],
[[7.0, 8.0], [9.0, 10.0], [11.0, 12.0]]])
positive_embeddings = tf.constant([[[12.0, 11.0], [10.0, 9.0], [8.0, 7.0]],
[[6.0, 5.0], [4.0, 3.0], [2.0, 1.0]]])
weighted_loss, summaries = loss_utils.compute_positive_pairwise_loss(
anchor_embeddings, positive_embeddings, loss_weight=6.0)
self.assertAlmostEqual(weighted_loss, 572.0)
expected_summaries = {
'pairwise_loss/PositivePair/Loss/Original': 95.333333333,
'pairwise_loss/PositivePair/Loss/Weighted': 572.0,
'pairwise_loss/PositivePair/Loss/Weight': 6.0,
}
self._assert_dict_equal_or_almost_equal(summaries, expected_summaries)