def test_close():
"""
Test the close function
Since fixed and moving loaders are the same only need to test the moving
"""
for key_file_loader, file_loader in FileLoaderDict.items():
for split in ["train", "test"]:
data_dir_paths = [join(DataPaths[key_file_loader], split)]
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths,
image_shape=image_shape,
file_loader=file_loader,
labeled=True,
sample_label="all",
intra_group_prob=1,
intra_group_option="forward",
sample_image_in_group=True,
seed=0,
)
if key_file_loader == "h5":
data_loader.close()
for f in data_loader.loader_moving_image.h5_files.values():
assert not f.__bool__()
def test_get_intra_sample_indices():
"""
Test all possible intragroup sampling indices are correctly calculated
Ensure exception is thrown for unsupported group_option
"""
for key_file_loader, file_loader in FileLoaderDict.items():
for split in ["train", "test"]:
data_dir_paths = [join(DataPaths[key_file_loader], split)]
common_args = dict(
file_loader=file_loader,
labeled=True,
sample_label="all",
intra_group_prob=1,
sample_image_in_group=False,
seed=None,
)
# test feasible intra_group_option
for intra_group_option in ["forward", "backward", "unconstrained"]:
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths,
image_shape=image_shape,
intra_group_option=intra_group_option,
**common_args,
)
ni = data_loader.num_images_per_group
num_samples = sample_count(ni, intra_group_option)
sample_indices = data_loader.sample_indices
sample_indices.sort()
unique_indices = list(set(sample_indices))
unique_indices.sort()
# test all possible indices are generated
assert data_loader._num_samples == num_samples
assert sample_indices == unique_indices
# test exception thrown for unsupported group option
with pytest.raises(ValueError) as err_info:
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths,
image_shape=image_shape,
intra_group_option="wrong",
**common_args,
)
data_loader.close()
assert "Unknown intra_group_option," in str(err_info.value)
def test_init():
"""
Test __init__ catches exceptions with appropriate messages and counts samples correctly
"""
for key_file_loader, file_loader in FileLoaderDict.items():
for train_split in ["test", "train"]:
for prob in [0, 0.5, 1]:
for sample_in_group in [True, False]:
data_dir_paths = [
join(DataPaths[key_file_loader], train_split)
]
common_args = dict(
file_loader=file_loader,
labeled=True,
sample_label="all",
intra_group_prob=prob,
intra_group_option="forward",
sample_image_in_group=sample_in_group,
seed=None,
)
if train_split == "test" and prob < 1:
# catch exception when trying to sample between fewer than 2 groups. In "test" we only have one
# group
with pytest.raises(ValueError) as err_info:
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths,
image_shape=image_shape,
**common_args,
)
data_loader.close()
assert "we need at least two groups" in str(
err_info.value)
elif train_split == "train" and sample_in_group is True:
# ensure sample count is accurate (only for train dir, test dir uses same logic)
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths,
image_shape=image_shape,
**common_args,
)
assert data_loader.sample_indices is None
assert data_loader._num_samples == 2
data_loader.close()
elif sample_in_group is False and 0 < prob < 1:
# catch exception when specifying conflicting intra/inter group parameters
with pytest.raises(ValueError) as err_info:
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths,
image_shape=image_shape,
**common_args,
)
data_loader.close()
assert "Mixing intra and inter groups is not supported" in str(
err_info.value)
def test_sample_index_generator():
"""
Test to check the randomness and deterministic index generator for train
Test dir not checked because it contains only a single group of 2 images
"""
for key_file_loader, file_loader in FileLoaderDict.items():
common_args = dict(
image_shape=image_shape,
data_dir_paths=[join(DataPaths[key_file_loader], "train")],
file_loader=file_loader,
labeled=True,
sample_label="all",
)
# test feasible intra_group_option
for sample_in_group in [False, True]:
probs = [0, 0.5, 1] if sample_in_group else [0, 1]
for prob in probs:
for direction in ["forward", "backward", "unconstrained"]:
indices_to_compare = []
for seed in [0, 1, 0]:
data_loader = GroupedDataLoader(
intra_group_prob=prob,
intra_group_option=direction,
sample_image_in_group=sample_in_group,
seed=seed,
**common_args,
)
data_indices = []
for (
moving_index,
fixed_index,
indices,
) in data_loader.sample_index_generator():
assert isinstance(moving_index, tuple)
assert isinstance(fixed_index, tuple)
assert isinstance(indices, list)
data_indices += indices
data_loader.close()
indices_to_compare.append(data_indices)
# test different seeds give different indices
assert not np.allclose(indices_to_compare[0], indices_to_compare[1])
# test same seeds give the same indices
assert np.allclose(indices_to_compare[0], indices_to_compare[2])
# test exception thrown for unsupported intra_group_option option
data_loader = GroupedDataLoader(
intra_group_prob=1,
intra_group_option="wrong",
sample_image_in_group=True,
seed=0,
**common_args,
)
with pytest.raises(ValueError) as err_info:
next(data_loader.sample_index_generator())
data_loader.close()
assert "Unknown intra_group_option" in str(err_info.value)