def test_build_reweighting_unmatched_anchors(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
}
classification_weight: 0.8
localization_weight: 0.2
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
(classification_loss, localization_loss, classification_weight,
localization_weight, hard_example_miner, _,
_) = losses_builder.build(losses_proto)
self.assertTrue(isinstance(hard_example_miner,
losses.HardExampleMiner))
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss))
self.assertTrue(
isinstance(localization_loss, losses.WeightedL2LocalizationLoss))
self.assertAlmostEqual(classification_weight, 0.8)
self.assertAlmostEqual(localization_weight, 0.2)
def test_build_hard_example_miner_with_non_default_values(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
num_hard_examples: 32
iou_threshold: 0.5
loss_type: LOCALIZATION
max_negatives_per_positive: 10
min_negatives_per_image: 3
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, _, _, _, hard_example_miner, _, _ = losses_builder.build(
losses_proto)
self.assertTrue(isinstance(hard_example_miner,
losses.HardExampleMiner))
self.assertEqual(hard_example_miner._num_hard_examples, 32)
self.assertAlmostEqual(hard_example_miner._iou_threshold, 0.5)
self.assertEqual(hard_example_miner._max_negatives_per_positive, 10)
self.assertEqual(hard_example_miner._min_negatives_per_image, 3)
def test_raise_error_on_empty_config(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
with self.assertRaises(ValueError):
losses_builder.build(losses_proto)
def test_do_not_build_hard_example_miner_by_default(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, _, _, _, hard_example_miner = losses_builder.build(losses_proto)
self.assertEqual(hard_example_miner, None)
def test_build_weighted_iou_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_iou {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(
isinstance(localization_loss, losses.WeightedIOULocalizationLoss))
def test_build_weighted_sigmoid_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_sigmoid {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSigmoidClassificationLoss))
def test_build_weighted_logits_softmax_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_logits_softmax {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _, _, _ = losses_builder.build(
losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSoftmaxClassificationAgainstLogitsLoss))
def test_build_weighted_smooth_l1_localization_loss_default_delta(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(
isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss))
self.assertAlmostEqual(localization_loss._delta, 1.0)
def test_raise_error_when_both_focal_loss_and_hard_example_miner(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_sigmoid_focal {
}
}
hard_example_miner {
}
classification_weight: 0.8
localization_weight: 0.2
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
with self.assertRaises(ValueError):
losses_builder.build(losses_proto)
def test_build_weighted_sigmoid_focal_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_sigmoid_focal {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.SigmoidFocalClassificationLoss))
self.assertAlmostEqual(classification_loss._alpha, None)
self.assertAlmostEqual(classification_loss._gamma, 2.0)
def test_build_hard_example_miner_for_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
loss_type: LOCALIZATION
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, _, _, _, hard_example_miner = losses_builder.build(losses_proto)
self.assertTrue(isinstance(hard_example_miner,
losses.HardExampleMiner))
self.assertEqual(hard_example_miner._loss_type, 'loc')
def test_anchorwise_output(self):
losses_text_proto = """
classification_loss {
weighted_sigmoid {
anchorwise_output: true
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSigmoidClassificationLoss))
predictions = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.5, 0.5]]])
targets = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])
weights = tf.constant([[1.0, 1.0]])
loss = classification_loss(predictions, targets, weights=weights)
self.assertEqual(loss.shape, [1, 2])
def test_anchorwise_output(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(
isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss))
predictions = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0,
1.0]]])
targets = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
weights = tf.constant([[1.0, 1.0]])
loss = localization_loss(predictions, targets, weights=weights)
self.assertEqual(loss.shape, [1, 2])
