def test_losses(self):
tf.random.set_seed(1111)
box_loss = train_lib.BoxLoss()
box_iou_loss = train_lib.BoxIouLoss(iou_loss_type='ciou',
min_level=3,
max_level=3,
num_scales=1,
aspect_ratios=[1.0],
anchor_scale=1.0,
image_size=32)
alpha = 0.25
gamma = 1.5
focal_loss_v2 = train_lib.FocalLoss(
alpha, gamma, reduction=tf.keras.losses.Reduction.NONE)
box_outputs = tf.random.normal([64, 4])
box_targets = tf.random.normal([64, 4])
num_positives = tf.constant(4.0)
self.assertEqual(
legacy_fn._box_loss(box_outputs, box_targets, num_positives),
box_loss([num_positives, box_targets], box_outputs))
self.assertAllEqual(
legacy_fn.focal_loss(box_outputs, box_targets, alpha, gamma,
num_positives),
focal_loss_v2([num_positives, box_targets], box_outputs))
# TODO(tanmingxing): Re-enable this test after fixing this failing test.
# self.assertEqual(
# legacy_fn._box_iou_loss(box_outputs, box_targets, num_positives,
# 'ciou'),
# box_iou_loss([num_positives, box_targets], box_outputs))
iou_loss = box_iou_loss([num_positives, box_targets], box_outputs)
self.assertAlmostEqual(iou_loss.numpy(), 4.924635, places=5)
def test_focal_loss(self):
tf.random.set_seed(1111)
y_pred = tf.random.uniform([4, 32, 32, 90])
y_true = tf.ones([4, 32, 32, 90])
alpha, gamma, n = 0.25, 1.5, 100
legacy_output = legacy_focal_loss(y_pred, y_true, alpha, gamma, n)
new_output = det_model_fn.focal_loss(y_pred, y_true, alpha, gamma, n)
self.assertAllClose(legacy_output, new_output)
def test_focal_loss_with_label_smoothing(self):
tf.random.set_seed(1111)
shape = [2, 2, 2, 2]
y_pred = tf.random.uniform(shape)
# A binary classification target [0.0, 1.0] becomes [.1, .9]
# with smoothing .2
y_true = tf.ones(shape) * [0.0, 1.0]
y_true_presmoothed = tf.ones(shape) * [0.1, 0.9]
alpha, gamma, n = 1, 0, 100
presmoothed = det_model_fn.focal_loss(y_pred, y_true_presmoothed,
alpha, gamma, n, 0)
alpha, gamma, n = 0.9, 0, 100
unsmoothed = det_model_fn.focal_loss(y_pred, y_true, alpha, gamma, n,
0.2)
self.assertAllClose(presmoothed, unsmoothed)
def test_losses(self):
box_loss = train_lib.BoxLoss()
box_iou_loss = train_lib.BoxIouLoss('ciou')
alpha = 0.25
gamma = 1.5
focal_loss_v2 = train_lib.FocalLoss(
alpha, gamma, reduction=tf.keras.losses.Reduction.NONE)
box_outputs = tf.ones([8])
box_targets = tf.zeros([8])
num_positives = 4.0
self.assertEqual(
legacy_fn._box_loss(box_outputs, box_targets, num_positives),
box_loss([num_positives, box_targets], box_outputs))
self.assertEqual(
legacy_fn._box_iou_loss(box_outputs, box_targets, num_positives,
'ciou'),
box_iou_loss([num_positives, box_targets], box_outputs))
self.assertAllEqual(
legacy_fn.focal_loss(box_outputs, box_targets, alpha, gamma,
num_positives),
focal_loss_v2([num_positives, box_targets], box_outputs))
