research/object_detection/builders/losses_builder_test.py
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for losses_builder."""
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
from object_detection.builders import losses_builder
from object_detection.core import losses
from object_detection.protos import losses_pb2
from object_detection.utils import ops
class LocalizationLossBuilderTest(tf.test.TestCase):
def test_build_weighted_l2_localization_loss(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)
_, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
self.assertIsInstance(localization_loss,
losses.WeightedL2LocalizationLoss)
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.assertIsInstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss)
self.assertAlmostEqual(localization_loss._delta, 1.0)
def test_build_weighted_smooth_l1_localization_loss_non_default_delta(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
delta: 0.1
}
}
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.assertIsInstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss)
self.assertAlmostEqual(localization_loss._delta, 0.1)
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.assertIsInstance(localization_loss,
losses.WeightedIOULocalizationLoss)
def test_build_weighted_giou_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_giou {
}
}
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.assertIsInstance(localization_loss,
losses.WeightedGIOULocalizationLoss)
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.assertIsInstance(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])
def test_raise_error_on_empty_localization_config(self):
losses_text_proto = """
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
with self.assertRaises(ValueError):
losses_builder._build_localization_loss(losses_proto)
class ClassificationLossBuilderTest(tf.test.TestCase):
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.assertIsInstance(classification_loss,
losses.WeightedSigmoidClassificationLoss)
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.assertIsInstance(classification_loss,
losses.SigmoidFocalClassificationLoss)
self.assertAlmostEqual(classification_loss._alpha, None)
self.assertAlmostEqual(classification_loss._gamma, 2.0)
def test_build_weighted_sigmoid_focal_loss_non_default(self):
losses_text_proto = """
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 3.0
}
}
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.assertIsInstance(classification_loss,
losses.SigmoidFocalClassificationLoss)
self.assertAlmostEqual(classification_loss._alpha, 0.25)
self.assertAlmostEqual(classification_loss._gamma, 3.0)
def test_build_weighted_softmax_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_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.assertIsInstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)
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.assertIsInstance(
classification_loss,
losses.WeightedSoftmaxClassificationAgainstLogitsLoss)
def test_build_weighted_softmax_classification_loss_with_logit_scale(self):
losses_text_proto = """
classification_loss {
weighted_softmax {
logit_scale: 2.0
}
}
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.assertIsInstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)
def test_build_bootstrapped_sigmoid_classification_loss(self):
losses_text_proto = """
classification_loss {
bootstrapped_sigmoid {
alpha: 0.5
}
}
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.assertIsInstance(classification_loss,
losses.BootstrappedSigmoidClassificationLoss)
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.assertIsInstance(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, 1.0], [1.0, 1.0, 1.0]]])
loss = classification_loss(predictions, targets, weights=weights)
self.assertEqual(loss.shape, [1, 2, 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_build_penalty_reduced_logistic_focal_loss(self):
losses_text_proto = """
classification_loss {
penalty_reduced_logistic_focal_loss {
alpha: 2.0
beta: 4.0
}
}
localization_loss {
l1_localization_loss {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
self.assertIsInstance(classification_loss,
losses.PenaltyReducedLogisticFocalLoss)
self.assertAlmostEqual(classification_loss._alpha, 2.0)
self.assertAlmostEqual(classification_loss._beta, 4.0)
def test_build_dice_loss(self):
losses_text_proto = """
classification_loss {
weighted_dice_classification_loss {
squared_normalization: true
}
}
localization_loss {
l1_localization_loss {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
self.assertIsInstance(classification_loss,
losses.WeightedDiceClassificationLoss)
assert classification_loss._squared_normalization
class HardExampleMinerBuilderTest(tf.test.TestCase):
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_hard_example_miner_for_classification_loss(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
loss_type: CLASSIFICATION
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
self.assertIsInstance(hard_example_miner, losses.HardExampleMiner)
self.assertEqual(hard_example_miner._loss_type, 'cls')
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.assertIsInstance(hard_example_miner, losses.HardExampleMiner)
self.assertEqual(hard_example_miner._loss_type, 'loc')
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.assertIsInstance(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)
class LossBuilderTest(tf.test.TestCase):
def test_build_all_loss_parameters(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.assertIsInstance(hard_example_miner, losses.HardExampleMiner)
self.assertIsInstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)
self.assertIsInstance(localization_loss,
losses.WeightedL2LocalizationLoss)
self.assertAlmostEqual(classification_weight, 0.8)
self.assertAlmostEqual(localization_weight, 0.2)
def test_build_expected_sampling(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.assertIsInstance(hard_example_miner, losses.HardExampleMiner)
self.assertIsInstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)
self.assertIsInstance(localization_loss, losses.WeightedL2LocalizationLoss)
self.assertAlmostEqual(classification_weight, 0.8)
self.assertAlmostEqual(localization_weight, 0.2)
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.assertIsInstance(hard_example_miner, losses.HardExampleMiner)
self.assertIsInstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)
self.assertIsInstance(localization_loss, losses.WeightedL2LocalizationLoss)
self.assertAlmostEqual(classification_weight, 0.8)
self.assertAlmostEqual(localization_weight, 0.2)
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)
class FasterRcnnClassificationLossBuilderTest(tf.test.TestCase):
def test_build_sigmoid_loss(self):
losses_text_proto = """
weighted_sigmoid {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertIsInstance(classification_loss,
losses.WeightedSigmoidClassificationLoss)
def test_build_softmax_loss(self):
losses_text_proto = """
weighted_softmax {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertIsInstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)
def test_build_logits_softmax_loss(self):
losses_text_proto = """
weighted_logits_softmax {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSoftmaxClassificationAgainstLogitsLoss))
def test_build_sigmoid_focal_loss(self):
losses_text_proto = """
weighted_sigmoid_focal {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertIsInstance(classification_loss,
losses.SigmoidFocalClassificationLoss)
def test_build_softmax_loss_by_default(self):
losses_text_proto = """
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertIsInstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)
if __name__ == '__main__':
tf.test.main()