def test_always_single_batch_shape(self):
a = ltd.TermDynamic(torch.randn(1, 100, 2))
b = ltd.TermDynamic(torch.randn(1, 100, 2))
con = ltd.Always(ltd.EQ(a, b), 100)
self.assertEqual(con.loss(0).shape, torch.Size([1]))
self.assertEqual(con.satisfy(0).shape, torch.Size([1]))
def test_gt_batch_shape(self):
a = ltd.TermDynamic(torch.randn(32, 100, 2))
b = ltd.TermDynamic(torch.randn(32, 100, 2))
con = ltd.GT(a, b)
self.assertEqual(con.loss(0).shape, torch.Size([32]))
self.assertEqual(con.satisfy(0).shape, torch.Size([32]))
def test_eventually_false_stressfull_large(self):
a = ltd.TermDynamic(torch.ones(32, 100, 7))
b = ltd.TermDynamic(torch.ones(32, 100, 7) + 100)
con = ltd.Eventually(ltd.EQ(a, b), 100)
self.assertAlmostEqual(con.loss(0)[0].item(), np.sqrt(70000), places=3)
self.assertEqual(con.satisfy(0)[0], False)
def test_eventually_false_is_nonzero(self):
a = ltd.TermDynamic(torch.ones(32, 10, 2))
b = ltd.TermDynamic(torch.ones(32, 10, 2) + 1)
con = ltd.Eventually(ltd.EQ(a, b), 10)
self.assertAlmostEqual(con.loss(0)[0].item(), np.sqrt(2.0), places=3)
self.assertEqual(con.satisfy(0)[0], False)
def test_eventually_shape(self):
a = ltd.TermDynamic(torch.ones(64, 10000, 7))
b = ltd.TermDynamic(torch.ones(64, 10000, 7))
con = ltd.Eventually(ltd.EQ(a, b), 10000)
self.assertEqual(con.loss(0).shape, torch.Size([64]))
self.assertEqual(con.satisfy(0).shape, torch.Size([64]))
def test_always_equal(self):
a = ltd.TermDynamic(torch.ones(64, 10000, 7))
b = ltd.TermDynamic(torch.ones(64, 10000, 7))
con = ltd.Always(ltd.EQ(a, b), 10000)
actual_loss = con.loss(0)
self.assertEqual(actual_loss[0], 0.0)
self.assertEqual(con.satisfy(0).all(), True)
def test_eventually_false_stressful_small(self):
a = ltd.TermDynamic(torch.ones(32, 100, 1))
b = ltd.TermDynamic(torch.ones(32, 100, 1) + 0.1)
con = ltd.Eventually(ltd.EQ(a, b), 100)
actual_loss = con.loss(0)
self.assertNotEqual(con.loss(0)[0], 0.0)
self.assertAlmostEqual(con.loss(0)[0].item(), 0.1)
self.assertEqual(con.satisfy(0)[0], False)
def test_eventually_true_is_zero(self):
a = ltd.TermDynamic(torch.ones(32, 10, 2))
b = torch.ones(32, 10, 2) + 1
b[0][5] -= 1
b = ltd.TermDynamic(b)
con = ltd.Eventually(ltd.EQ(a, b), 10)
self.assertEqual(con.loss(0)[0], 0.0)
self.assertEqual(con.satisfy(0)[0], True)
def test_always_unequal_huge(self):
a = ltd.TermDynamic(torch.ones(64, 10000, 7))
b = ltd.TermDynamic(torch.ones(64, 10000, 7) + 1000)
con = ltd.Always(ltd.EQ(a, b), 10000)
expected_loss = torch.ones(64) * np.sqrt(7000000)
actual_loss = con.loss(0)
self.assertAlmostEqual(actual_loss[0].item(),
expected_loss[0].item(),
places=1)
self.assertEqual(con.satisfy(0).all(), False)
def test_always_unequal_stress(self):
a = ltd.TermDynamic(torch.ones(64, 10000, 7))
b = ltd.TermDynamic(torch.ones(64, 10000, 7) + 1)
con = ltd.Always(ltd.EQ(a, b), 10000)
expected_loss = torch.ones(64) * np.sqrt(7)
actual_loss = con.loss(0)
# Note that the numerical errors start to mount up
# In the end we can only get it to about 1 decimal place of accuracy
self.assertAlmostEqual(actual_loss[0].item(),
expected_loss[0].item(),
places=4)
self.assertEqual(con.satisfy(0).all(), False)
def test_eq_dynamic_batch_shape(self):
a = ltd.TermDynamic(torch.randn(32, 100, 2))
b = ltd.TermDynamic(torch.randn(32, 100, 2))
con = ltd.EQ(a, b)
self.assertEqual(con.loss(0).shape, torch.Size([32]))
