def test_unweighted_soft():
num_data = 20
feat_dim = 6
margin = 1.0
num_classes = 4
embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
labels = np.random.randint(0, num_classes, size=(num_data))
loss_np = triplet_hard_loss_np(labels, embedding, margin, soft=True)
# Compute the loss in TF.
y_true = tf.constant(labels)
y_pred = tf.constant(embedding)
cce_obj = triplet.TripletHardLoss(soft=True)
loss = cce_obj(y_true, y_pred)
np.testing.assert_allclose(loss, loss_np, rtol=1e-6, atol=1e-6)
def test_unweighted_soft(self):
num_data = 20
feat_dim = 6
margin = 1.0
num_classes = 4
embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
labels = np.random.randint(0, num_classes, size=(num_data))
loss_np = triplet_hard_loss_np(labels, embedding, margin, soft=True)
# Compute the loss in TF.
y_true = tf.constant(labels)
y_pred = tf.constant(embedding)
cce_obj = triplet.TripletHardLoss(soft=True)
loss = cce_obj(y_true, y_pred)
self.assertAlmostEqual(self.evaluate(loss), loss_np, 3)
def test_hard_tripled_loss_angular(dtype, soft, dist_func, dist_metric):
num_data = 20
feat_dim = 6
margin = 1.0
num_classes = 4
embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
labels = np.random.randint(0, num_classes, size=(num_data))
# Compute the loss in NP.
loss_np = triplet_hard_loss_np(labels, embedding, margin, dist_func, soft)
# Compute the loss in TF.
y_true = tf.constant(labels)
y_pred = tf.constant(embedding, dtype=dtype)
cce_obj = triplet.TripletHardLoss(soft=soft, distance_metric=dist_metric)
loss = cce_obj(y_true, y_pred)
test_utils.assert_allclose_according_to_type(loss.numpy(), loss_np)
def test_serialization_hard():
loss = triplet.TripletHardLoss()
tf.keras.losses.deserialize(tf.keras.losses.serialize(loss))
