def test_one_of_weight(self):
for weight in [[1.0, 0.0], torch.tensor([1.0, 0.0])]:
with self.subTest(weight=weight):
trafo0 = Mirror((0, ))
trafo1 = Mirror((1, ))
comp = OneOf(trafo0, trafo1, weights=weight)
out = comp(**self.batch)
expected_out = trafo0(**self.batch)
self.assertTrue(out["data"].allclose(expected_out["data"]))
def test_dropout_compose(self):
compose = DropoutCompose(self.transforms[0], dropout=0.0)
outp = compose(**self.batch)
expected = Mirror(dims=(0, ), prob=1.)(**self.batch)
self.assertTrue((expected["data"] == outp["data"]).all())
compose = DropoutCompose(self.transforms[0], dropout=1.0)
outp = compose(**self.batch)
self.assertTrue((self.batch["data"] == outp["data"]).all())
def test_dropout_compose(self):
compose = DropoutCompose(self.transforms[0], dropout=0.0)
self.assertEqual(len(compose.transform_order), 1)
outp = compose(**self.batch)
expected = Mirror(dims=(0, ))(**self.batch)
self.assertTrue((expected["data"] == outp["data"]).all())
compose = DropoutCompose(self.transforms, dropout=1.0)
outp = compose(**self.batch)
self.assertEqual(len(compose.transform_order), 2)
self.assertTrue((self.batch["data"] == outp["data"]).all())
def test_compose_shuffle(self):
compose = Compose([Mirror(dims=(0, ))] * 10, shuffle=True)
random.seed(0)
outp = compose(**self.batch)
order = list(range(len(compose.transforms)))
expected_order = copy.deepcopy(order)
random.seed(0)
random.shuffle(expected_order)
self.assertEqual(compose.transform_order, expected_order)
self.assertNotEqual(expected_order, order)
def test_compose_single(self):
single_compose = Compose(self.transforms[0])
outp = single_compose(**self.batch)
expected = Mirror(dims=(0, ))(**self.batch)
self.assertTrue((expected["data"] == outp["data"]).all())
def setUp(self) -> None:
self.batch = {"data": torch.rand(1, 1, 10, 10)}
self.transforms = [Mirror(dims=(0, )), Mirror(dims=(0, ))]
def test_one_of_weight_error(self):
with self.assertRaises(ValueError):
trafo = Mirror((0, ))
comp = OneOf(trafo, trafo, weights=[0.33, 0.33, 0.33])
def test_one_of_no_weight(self):
trafo = Mirror((0, ))
comp = OneOf(trafo, trafo)
out = comp(**self.batch)
expected_out = trafo(**self.batch)
self.assertTrue(out["data"].allclose(expected_out["data"]))