def testExpectedLossWithAlpha75AndZeroGamma(self): # All zeros correspond to 0.5 probability. prediction_tensor = tf.constant([[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], 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, 0, 0], [1, 0, 0]]], tf.float32) weights = tf.constant([[1, 1, 1, 1], [1, 1, 1, 1]], tf.float32) focal_loss_op = losses.SigmoidFocalClassificationLoss( anchorwise_output=False, alpha=0.75, gamma=0.0) focal_loss = focal_loss_op(prediction_tensor, target_tensor, weights=weights) with self.test_session() as sess: focal_loss = sess.run(focal_loss) self.assertAllClose( (-math.log(.5) * # x-entropy per class per anchor. ((0.75 * # alpha for positives. 8) + # positives from 8 anchors. (0.25 * # alpha for negatives. 8 * 2))), # negatives from 8 anchors for two classes. focal_loss)
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( anchorwise_output=True, alpha=None, gamma=0.0) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss( anchorwise_output=True) 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 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( anchorwise_output=True, gamma=2.0, alpha=0.0) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss( anchorwise_output=True) 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(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 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( anchorwise_output=False, gamma=2.0, alpha=None) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss( anchorwise_output=False) 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]) order_of_ratio = np.power(10, np.floor(np.log10(sigmoid_loss / focal_loss))) self.assertAlmostEqual(order_of_ratio, 1.)
def testEasyExamplesProduceSmallLossComparedToSigmoidXEntropy(self): prediction_tensor = tf.constant([[[_logit(0.97)], [_logit(0.90)], [_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( anchorwise_output=True, gamma=2.0, alpha=None) sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss( anchorwise_output=True) 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]) 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 _build_classification_loss(loss_config): """Builds a classification loss based on the loss config. Args: loss_config: A losses_pb2.ClassificationLoss object. Returns: Loss based on the config. Raises: ValueError: On invalid loss_config. """ if not isinstance(loss_config, losses_pb2.ClassificationLoss): raise ValueError( 'loss_config not of type losses_pb2.ClassificationLoss.') loss_type = loss_config.WhichOneof('classification_loss') if loss_type == 'weighted_sigmoid': config = loss_config.weighted_sigmoid return losses.WeightedSigmoidClassificationLoss( anchorwise_output=config.anchorwise_output) if loss_type == 'weighted_sigmoid_focal': config = loss_config.weighted_sigmoid_focal alpha = None if config.HasField('alpha'): alpha = config.alpha return losses.SigmoidFocalClassificationLoss( anchorwise_output=config.anchorwise_output, gamma=config.gamma, alpha=alpha) if loss_type == 'weighted_softmax': config = loss_config.weighted_softmax return losses.WeightedSoftmaxClassificationLoss( anchorwise_output=config.anchorwise_output, logit_scale=config.logit_scale) if loss_type == 'bootstrapped_sigmoid': config = loss_config.bootstrapped_sigmoid return losses.BootstrappedSigmoidClassificationLoss( alpha=config.alpha, bootstrap_type=('hard' if config.hard_bootstrap else 'soft'), anchorwise_output=config.anchorwise_output) raise ValueError('Empty loss config.')