def test_cosine_arithmetic_loss_zeros_mask(self):
all_zeros_mask = np.zeros((BATCH_SIZE))
loss = losses.CosineArithmeticLoss(fake_data['pregrasp_embedding'],
fake_data['goal_embedding'],
fake_data['postgrasp_embedding'],
all_zeros_mask)
with tf.Session() as sess:
output = sess.run(loss)
self.assertEqual(output, 0)
def test_cosine_arithmetic_loss_mixed_mask(self):
mixed_mask = np.zeros((BATCH_SIZE))
mixed_mask[0] = 1
loss = losses.CosineArithmeticLoss(fake_data['pregrasp_embedding'],
fake_data['goal_embedding'],
fake_data['postgrasp_embedding'],
mixed_mask)
with tf.Session() as sess:
output = sess.run(loss)
true_answer = cosine_distance((fake_data['pregrasp_embedding'][:1] -
fake_data['postgrasp_embedding'][:1]),
fake_data['goal_embedding'][:1])
self.assertAlmostEqual(output, true_answer[0], places=3)
def test_cosine_arithmetic_loss_ones_mask(self):
all_ones_mask = np.ones((BATCH_SIZE))
loss = losses.CosineArithmeticLoss(fake_data['pregrasp_embedding'],
fake_data['goal_embedding'],
fake_data['postgrasp_embedding'],
all_ones_mask)
with tf.Session() as sess:
output = sess.run(loss)
true_answer = cosine_distance(
fake_data['pregrasp_embedding'] - fake_data['postgrasp_embedding'],
fake_data['goal_embedding'])
self.assertAlmostEqual(output, np.mean(true_answer), places=3)
