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561 lines
18 KiB
561 lines
18 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for losses_builder."""
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import tensorflow as tf
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from google.protobuf import text_format
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from object_detection.builders import losses_builder
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from object_detection.core import losses
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from object_detection.protos import losses_pb2
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from object_detection.utils import ops
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class LocalizationLossBuilderTest(tf.test.TestCase):
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def test_build_weighted_l2_localization_loss(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(localization_loss,
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losses.WeightedL2LocalizationLoss))
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def test_build_weighted_smooth_l1_localization_loss_default_delta(self):
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losses_text_proto = """
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localization_loss {
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weighted_smooth_l1 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(localization_loss,
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losses.WeightedSmoothL1LocalizationLoss))
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self.assertAlmostEqual(localization_loss._delta, 1.0)
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def test_build_weighted_smooth_l1_localization_loss_non_default_delta(self):
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losses_text_proto = """
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localization_loss {
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weighted_smooth_l1 {
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delta: 0.1
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(localization_loss,
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losses.WeightedSmoothL1LocalizationLoss))
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self.assertAlmostEqual(localization_loss._delta, 0.1)
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def test_build_weighted_iou_localization_loss(self):
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losses_text_proto = """
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localization_loss {
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weighted_iou {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(localization_loss,
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losses.WeightedIOULocalizationLoss))
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def test_anchorwise_output(self):
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losses_text_proto = """
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localization_loss {
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weighted_smooth_l1 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(localization_loss,
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losses.WeightedSmoothL1LocalizationLoss))
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predictions = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
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targets = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
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weights = tf.constant([[1.0, 1.0]])
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loss = localization_loss(predictions, targets, weights=weights)
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self.assertEqual(loss.shape, [1, 2])
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def test_raise_error_on_empty_localization_config(self):
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losses_text_proto = """
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classification_loss {
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weighted_softmax {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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with self.assertRaises(ValueError):
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losses_builder._build_localization_loss(losses_proto)
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class ClassificationLossBuilderTest(tf.test.TestCase):
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def test_build_weighted_sigmoid_classification_loss(self):
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losses_text_proto = """
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classification_loss {
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weighted_sigmoid {
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(classification_loss,
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losses.WeightedSigmoidClassificationLoss))
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def test_build_weighted_sigmoid_focal_classification_loss(self):
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losses_text_proto = """
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classification_loss {
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weighted_sigmoid_focal {
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(classification_loss,
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losses.SigmoidFocalClassificationLoss))
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self.assertAlmostEqual(classification_loss._alpha, None)
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self.assertAlmostEqual(classification_loss._gamma, 2.0)
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def test_build_weighted_sigmoid_focal_loss_non_default(self):
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losses_text_proto = """
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classification_loss {
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weighted_sigmoid_focal {
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alpha: 0.25
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gamma: 3.0
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(classification_loss,
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losses.SigmoidFocalClassificationLoss))
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self.assertAlmostEqual(classification_loss._alpha, 0.25)
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self.assertAlmostEqual(classification_loss._gamma, 3.0)
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def test_build_weighted_softmax_classification_loss(self):
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losses_text_proto = """
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classification_loss {
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weighted_softmax {
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(classification_loss,
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losses.WeightedSoftmaxClassificationLoss))
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def test_build_weighted_logits_softmax_classification_loss(self):
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losses_text_proto = """
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classification_loss {
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weighted_logits_softmax {
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(
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isinstance(classification_loss,
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losses.WeightedSoftmaxClassificationAgainstLogitsLoss))
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def test_build_weighted_softmax_classification_loss_with_logit_scale(self):
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losses_text_proto = """
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classification_loss {
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weighted_softmax {
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logit_scale: 2.0
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(classification_loss,
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losses.WeightedSoftmaxClassificationLoss))
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def test_build_bootstrapped_sigmoid_classification_loss(self):
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losses_text_proto = """
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classification_loss {
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bootstrapped_sigmoid {
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alpha: 0.5
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(classification_loss,
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losses.BootstrappedSigmoidClassificationLoss))
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def test_anchorwise_output(self):
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losses_text_proto = """
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classification_loss {
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weighted_sigmoid {
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anchorwise_output: true
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}
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}
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(classification_loss,
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losses.WeightedSigmoidClassificationLoss))
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predictions = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.5, 0.5]]])
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targets = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])
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weights = tf.constant([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]])
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loss = classification_loss(predictions, targets, weights=weights)
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self.assertEqual(loss.shape, [1, 2, 3])
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def test_raise_error_on_empty_config(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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with self.assertRaises(ValueError):
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losses_builder.build(losses_proto)
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class HardExampleMinerBuilderTest(tf.test.TestCase):
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def test_do_not_build_hard_example_miner_by_default(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
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self.assertEqual(hard_example_miner, None)
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def test_build_hard_example_miner_for_classification_loss(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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hard_example_miner {
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loss_type: CLASSIFICATION
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
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self.assertEqual(hard_example_miner._loss_type, 'cls')
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def test_build_hard_example_miner_for_localization_loss(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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hard_example_miner {
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loss_type: LOCALIZATION
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
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self.assertEqual(hard_example_miner._loss_type, 'loc')
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def test_build_hard_example_miner_with_non_default_values(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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hard_example_miner {
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num_hard_examples: 32
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iou_threshold: 0.5
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loss_type: LOCALIZATION
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max_negatives_per_positive: 10
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min_negatives_per_image: 3
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}
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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_, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
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self.assertEqual(hard_example_miner._num_hard_examples, 32)
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self.assertAlmostEqual(hard_example_miner._iou_threshold, 0.5)
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self.assertEqual(hard_example_miner._max_negatives_per_positive, 10)
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self.assertEqual(hard_example_miner._min_negatives_per_image, 3)
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class LossBuilderTest(tf.test.TestCase):
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def test_build_all_loss_parameters(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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hard_example_miner {
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}
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classification_weight: 0.8
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localization_weight: 0.2
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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(classification_loss, localization_loss, classification_weight,
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localization_weight, hard_example_miner, _,
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_) = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
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self.assertTrue(isinstance(classification_loss,
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losses.WeightedSoftmaxClassificationLoss))
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self.assertTrue(isinstance(localization_loss,
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losses.WeightedL2LocalizationLoss))
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self.assertAlmostEqual(classification_weight, 0.8)
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self.assertAlmostEqual(localization_weight, 0.2)
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def test_build_expected_sampling(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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hard_example_miner {
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}
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classification_weight: 0.8
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localization_weight: 0.2
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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(classification_loss, localization_loss, classification_weight,
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localization_weight, hard_example_miner, _,
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_) = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
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self.assertTrue(
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isinstance(classification_loss,
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losses.WeightedSoftmaxClassificationLoss))
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self.assertTrue(
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isinstance(localization_loss, losses.WeightedL2LocalizationLoss))
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self.assertAlmostEqual(classification_weight, 0.8)
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self.assertAlmostEqual(localization_weight, 0.2)
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def test_build_reweighting_unmatched_anchors(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_softmax {
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}
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}
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hard_example_miner {
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}
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classification_weight: 0.8
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localization_weight: 0.2
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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(classification_loss, localization_loss, classification_weight,
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localization_weight, hard_example_miner, _,
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_) = losses_builder.build(losses_proto)
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self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
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self.assertTrue(
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isinstance(classification_loss,
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losses.WeightedSoftmaxClassificationLoss))
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self.assertTrue(
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isinstance(localization_loss, losses.WeightedL2LocalizationLoss))
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self.assertAlmostEqual(classification_weight, 0.8)
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self.assertAlmostEqual(localization_weight, 0.2)
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def test_raise_error_when_both_focal_loss_and_hard_example_miner(self):
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losses_text_proto = """
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localization_loss {
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weighted_l2 {
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}
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}
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classification_loss {
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weighted_sigmoid_focal {
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}
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}
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hard_example_miner {
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}
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classification_weight: 0.8
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localization_weight: 0.2
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"""
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losses_proto = losses_pb2.Loss()
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text_format.Merge(losses_text_proto, losses_proto)
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with self.assertRaises(ValueError):
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losses_builder.build(losses_proto)
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class FasterRcnnClassificationLossBuilderTest(tf.test.TestCase):
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def test_build_sigmoid_loss(self):
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losses_text_proto = """
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weighted_sigmoid {
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}
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"""
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losses_proto = losses_pb2.ClassificationLoss()
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text_format.Merge(losses_text_proto, losses_proto)
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classification_loss = losses_builder.build_faster_rcnn_classification_loss(
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losses_proto)
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self.assertTrue(isinstance(classification_loss,
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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.assertTrue(isinstance(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.assertTrue(
|
|
isinstance(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.assertTrue(isinstance(classification_loss,
|
|
losses.WeightedSoftmaxClassificationLoss))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|