def _generate_cache(self):
os.makedirs(self.cfg["cache_path"])
data_map = self._create_data_map()
for path, label_paths in tqdm(
data_map.items(),
total=len(data_map),
position=0,
leave=False,
desc="Caching pooled data",
):
subject_id = self._get_subject_id(path)
image = ScalarImage(path, check_nans=True)
label_map = LabelMap(label_paths, check_nans=True)
if not isinstance(label_map, list):
one_hot = OneHot(self.cfg["num_classes"] + 1)
label_map = one_hot(label_map)
label_map = LabelMap(tensor=label_map.tensor[1:]) # type: ignore
image.load()
label_map.load()
image, label_map = self._resize(image, label_map)
torch.save(
{"subject_id": subject_id, "image": image.data, "seg": label_map.data},
os.path.join(self.cfg["cache_path"], f"{subject_id}.pt"),
)
def test_with_a_list_of_images_with_different_affines(self):
path1 = self.get_image_path('path1', spacing=(1, 1, 1))
path2 = self.get_image_path('path2', spacing=(1, 2, 1))
image = ScalarImage(path=[path1, path2])
with self.assertWarns(RuntimeWarning):
image.load()
def test_with_a_list_of_images_with_different_shapes(self):
path1 = self.get_image_path('path1', shape=(5, 5, 5))
path2 = self.get_image_path('path2', shape=(7, 5, 5))
image = ScalarImage(path=[path1, path2])
with self.assertRaises(RuntimeError):
image.load()
def test_nans_file(self):
image = ScalarImage(self.get_image_path('repr_test', add_nans=True))
with self.assertWarns(UserWarning):
image.load()
