def test_warn_random_but_has_no_invertible(self):
transforms = Compose(
[AddChanneld("image"), RandFlipd("image", prob=1.0), RandScaleIntensityd("image", 0.1, prob=1.0)]
)
with self.assertWarns(UserWarning):
tta = TestTimeAugmentation(transforms, 5, 0, orig_key="image")
tta(self.get_data(1, (20, 20), data_type=np.float32))
def test_single_transform(self):
transforms = RandFlipd(["image", "label"])
tta = TestTimeAugmentation(transforms,
batch_size=5,
num_workers=0,
inferrer_fn=lambda x: x)
tta(self.get_data(1, (20, 20)))
def test_single_transform(self):
for p in TEST_NDARRAYS:
transforms = RandFlipd(["image", "label"], prob=1.0)
tta = TestTimeAugmentation(transforms,
batch_size=5,
num_workers=0,
inferrer_fn=lambda x: x)
tta(self.get_data(1, (20, 20), data_type=p))
def test_image_no_label(self):
transforms = RandFlipd(["image"], prob=1.0)
tta = TestTimeAugmentation(transforms,
batch_size=5,
num_workers=0,
inferrer_fn=lambda x: x,
orig_key="image")
tta(self.get_data(1, (20, 20), include_label=False))
def test_requires_meta_dict(self):
transforms = Compose([RandFlipd("image"), Spacingd("image", (1, 1))])
tta = TestTimeAugmentation(transforms,
batch_size=5,
num_workers=0,
inferrer_fn=lambda x: x,
label_key="image")
tta(self.get_data(1, (20, 20), include_label=False))