def testIgnorePositiveExampleLossViaAlphaMultiplier(self): prediction_tensor = tf.constant( [[[_logit(0.55)], [_logit(0.52)], [_logit(0.50)], [_logit(0.48)], [_logit(0.45)]]], tf.float32) target_tensor = tf.constant([[[1], [1], [1], [0], [0]]], tf.float32) weights = tf.constant([[1, 1, 1, 1, 1]], tf.float32) focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=0.0) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss() focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor, weights=weights), axis=2) sigmoid_loss = tf.reduce_sum(sigmoid_loss_op(prediction_tensor, target_tensor, weights=weights), axis=2) with self.test_session() as sess: sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss]) self.assertAllClose(focal_loss[0][:3], [0., 0., 0.]) order_of_ratio = np.power( 10, np.floor(np.log10(sigmoid_loss[0][3:] / focal_loss[0][3:]))) self.assertAllClose(order_of_ratio, [1., 1.])
def testEasyExamplesProduceSmallLossComparedToSigmoidXEntropy(self): prediction_tensor = tf.constant( [[[_logit(0.97)], [_logit(0.91)], [_logit(0.73)], [_logit(0.27)], [_logit(0.09)], [_logit(0.03)]]], tf.float32) target_tensor = tf.constant([[[1], [1], [1], [0], [0], [0]]], tf.float32) weights = tf.constant([[1, 1, 1, 1, 1, 1]], tf.float32) focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=None) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss() focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor, weights=weights), axis=2) sigmoid_loss = tf.reduce_sum(sigmoid_loss_op(prediction_tensor, target_tensor, weights=weights), axis=2) with self.test_session() as sess: sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss]) order_of_ratio = np.power( 10, np.floor(np.log10(sigmoid_loss / focal_loss))) self.assertAllClose(order_of_ratio, [[1000, 100, 10, 10, 100, 1000]])
def testReturnsCorrectLossWithClassIndices(self): prediction_tensor = tf.constant( [[[-100, 100, -100, 100], [100, -100, -100, -100], [100, 0, -100, 100], [-100, -100, 100, -100]], [[-100, 0, 100, 100], [-100, 100, -100, 100], [100, 100, 100, 100], [0, 0, -1, 100]]], tf.float32) target_tensor = tf.constant( [[[0, 1, 0, 0], [1, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]], [[0, 0, 1, 0], [0, 1, 0, 0], [1, 1, 1, 0], [1, 0, 0, 0]]], tf.float32) weights = tf.constant([[1, 1, 1, 1], [1, 1, 1, 0]], tf.float32) # Ignores the last class. class_indices = tf.constant([0, 1, 2], tf.int32) loss_op = losses.WeightedSigmoidClassificationLoss() loss = loss_op(prediction_tensor, target_tensor, weights=weights, class_indices=class_indices) loss = tf.reduce_sum(loss, axis=2) exp_loss = np.matrix([[0, 0, -math.log(.5), 0], [-math.log(.5), 0, 0, 0]]) with self.test_session() as sess: loss_output = sess.run(loss) self.assertAllClose(loss_output, exp_loss)
def testReturnsCorrectLoss(self): prediction_tensor = tf.constant( [[[-100, 100, -100], [100, -100, -100], [100, 0, -100], [-100, -100, 100]], [[-100, 0, 100], [-100, 100, -100], [100, 100, 100], [0, 0, -1]]], tf.float32) target_tensor = tf.constant( [[[0, 1, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1]], [[0, 0, 1], [0, 1, 0], [1, 1, 1], [1, 0, 0]]], tf.float32) weights = tf.constant([[1, 1, 1, 1], [1, 1, 1, 0]], tf.float32) loss_op = losses.WeightedSigmoidClassificationLoss() loss = loss_op(prediction_tensor, target_tensor, weights=weights) loss = tf.reduce_sum(loss) exp_loss = -2 * math.log(.5) with self.test_session() as sess: loss_output = sess.run(loss) self.assertAllClose(loss_output, exp_loss)
def testNonAnchorWiseOutputComparableToSigmoidXEntropy(self): prediction_tensor = tf.constant( [[[_logit(0.55)], [_logit(0.52)], [_logit(0.50)], [_logit(0.48)], [_logit(0.45)]]], tf.float32) target_tensor = tf.constant([[[1], [1], [1], [0], [0]]], tf.float32) weights = tf.constant([[1, 1, 1, 1, 1]], tf.float32) focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=None) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss() focal_loss = tf.reduce_sum( focal_loss_op(prediction_tensor, target_tensor, weights=weights)) sigmoid_loss = tf.reduce_sum( sigmoid_loss_op(prediction_tensor, target_tensor, weights=weights)) with self.test_session() as sess: sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss]) order_of_ratio = np.power( 10, np.floor(np.log10(sigmoid_loss / focal_loss))) self.assertAlmostEqual(order_of_ratio, 1.)
def testSameAsSigmoidXEntropyWithNoAlphaAndZeroGamma(self): prediction_tensor = tf.constant( [[[-100, 100, -100], [100, -100, -100], [100, 0, -100], [-100, -100, 100]], [[-100, 0, 100], [-100, 100, -100], [100, 100, 100], [0, 0, -1]]], tf.float32) target_tensor = tf.constant( [[[0, 1, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1]], [[0, 0, 1], [0, 1, 0], [1, 1, 1], [1, 0, 0]]], tf.float32) weights = tf.constant([[1, 1, 1, 1], [1, 1, 1, 0]], tf.float32) focal_loss_op = losses.SigmoidFocalClassificationLoss(alpha=None, gamma=0.0) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss() focal_loss = focal_loss_op(prediction_tensor, target_tensor, weights=weights) sigmoid_loss = sigmoid_loss_op(prediction_tensor, target_tensor, weights=weights) with self.test_session() as sess: sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss]) self.assertAllClose(sigmoid_loss, focal_loss)
def __init__(self): super(FakeDetectionModel, self).__init__(num_classes=NUMBER_OF_CLASSES) self._classification_loss = losses.WeightedSigmoidClassificationLoss() self._localization_loss = losses.WeightedSmoothL1LocalizationLoss()
def _create_model(self, apply_hard_mining=True, normalize_loc_loss_by_codesize=False, add_background_class=True, random_example_sampling=False, use_keras=False): is_training = False num_classes = 1 mock_anchor_generator = MockAnchorGenerator2x2() if use_keras: mock_box_predictor = test_utils.MockKerasBoxPredictor( is_training, num_classes) else: mock_box_predictor = test_utils.MockBoxPredictor( is_training, num_classes) mock_box_coder = test_utils.MockBoxCoder() if use_keras: fake_feature_extractor = FakeSSDKerasFeatureExtractor() else: fake_feature_extractor = FakeSSDFeatureExtractor() mock_matcher = test_utils.MockMatcher() region_similarity_calculator = sim_calc.IouSimilarity() encode_background_as_zeros = False def image_resizer_fn(image): return [tf.identity(image), tf.shape(image)] classification_loss = losses.WeightedSigmoidClassificationLoss() localization_loss = losses.WeightedSmoothL1LocalizationLoss() non_max_suppression_fn = functools.partial( post_processing.batch_multiclass_non_max_suppression, score_thresh=-20.0, iou_thresh=1.0, max_size_per_class=5, max_total_size=5) classification_loss_weight = 1.0 localization_loss_weight = 1.0 negative_class_weight = 1.0 normalize_loss_by_num_matches = False hard_example_miner = None if apply_hard_mining: # This hard example miner is expected to be a no-op. hard_example_miner = losses.HardExampleMiner( num_hard_examples=None, iou_threshold=1.0) random_example_sampler = None if random_example_sampling: random_example_sampler = sampler.BalancedPositiveNegativeSampler( positive_fraction=0.5) code_size = 4 model = ssd_meta_arch.SSDMetaArch( is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder, fake_feature_extractor, mock_matcher, region_similarity_calculator, encode_background_as_zeros, negative_class_weight, image_resizer_fn, non_max_suppression_fn, tf.identity, classification_loss, localization_loss, classification_loss_weight, localization_loss_weight, normalize_loss_by_num_matches, hard_example_miner, add_summaries=False, normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize, freeze_batchnorm=False, inplace_batchnorm_update=False, add_background_class=add_background_class, random_example_sampler=random_example_sampler) return model, num_classes, mock_anchor_generator.num_anchors(), code_size