def test_validate_data_files():
"""
Test validate_data_files function looks for inconsistencies
in the fixed/moving image and label lists.
If there is any issue it will raise an error, otherwise it returns None.
"""
for key_file_loader, file_loader in FileLoaderDict.items():
for train_split in ["train", "test"]:
for labeled in [True, False]:
data_dir_paths = [join(DataPaths[key_file_loader], train_split)]
common_args = dict(
file_loader=file_loader,
labeled=labeled,
sample_label="all",
intra_group_prob=1,
intra_group_option="forward",
sample_image_in_group=False,
seed=None if train_split == "train" else 0,
)
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths,
image_shape=image_shape,
**common_args,
)
assert data_loader.validate_data_files() is None
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_inter_sample_indices():
"""
Test all possible intergroup sampling indices are correctly calculated
"""
for key_file_loader, file_loader in FileLoaderDict.items():
data_dir_paths = [join(DataPaths[key_file_loader], "train")]
common_args = dict(
file_loader=file_loader,
labeled=True,
sample_label="all",
intra_group_prob=0,
intra_group_option="forward",
sample_image_in_group=False,
seed=None,
)
data_loader = GroupedDataLoader(
data_dir_paths=data_dir_paths, image_shape=image_shape, **common_args
)
ni = np.array(data_loader.num_images_per_group)
num_samples = np.sum(ni) * (np.sum(ni) - 1) - sum(ni * (ni - 1))
sample_indices = data_loader.sample_indices
sample_indices.sort()
unique_indices = list(set(sample_indices))
unique_indices.sort()
assert data_loader._num_samples == num_samples
assert sample_indices == unique_indices
def get_single_data_loader(data_type, data_config, common_args, data_dir_path):
if data_type == "paired":
moving_image_shape = data_config["moving_image_shape"]
fixed_image_shape = data_config["fixed_image_shape"]
return PairedDataLoader(
data_dir_path=data_dir_path,
moving_image_shape=moving_image_shape,
fixed_image_shape=fixed_image_shape,
**common_args,
)
elif data_type == "grouped":
image_shape = data_config["image_shape"]
intra_group_prob = data_config["intra_group_prob"]
intra_group_option = data_config["intra_group_option"]
sample_image_in_group = data_config["sample_image_in_group"]
return GroupedDataLoader(
data_dir_path=data_dir_path,
intra_group_prob=intra_group_prob,
intra_group_option=intra_group_option,
sample_image_in_group=sample_image_in_group,
image_shape=image_shape,
**common_args,
)
elif data_type == "unpaired":
image_shape = data_config["image_shape"]
return UnpairedDataLoader(
data_dir_path=data_dir_path, image_shape=image_shape, **common_args
)
else:
raise ValueError(
"Unknown data format. "
"Supported types are paired, unpaired, and grouped, got {}\n".format(
data_type
)
)
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 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:
# sample with 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:
# 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)
def get_single_data_loader(
data_type: str, data_config: dict, common_args: dict, data_dir_path: str
) -> DataLoader:
"""
Return one single data loader.
:param data_type: type of the data, paired / unpaired / grouped
:param data_config: dictionary containing the configuration of the data
:param common_args: some shared arguments for all data loaders
:param data_dir_path: path of the directory containing data
:return: a basic data loader
"""
try:
if data_type == "paired":
moving_image_shape = data_config["moving_image_shape"]
fixed_image_shape = data_config["fixed_image_shape"]
return PairedDataLoader(
data_dir_path=data_dir_path,
moving_image_shape=moving_image_shape,
fixed_image_shape=fixed_image_shape,
**common_args,
)
elif data_type == "grouped":
image_shape = data_config["image_shape"]
intra_group_prob = data_config["intra_group_prob"]
intra_group_option = data_config["intra_group_option"]
sample_image_in_group = data_config["sample_image_in_group"]
return GroupedDataLoader(
data_dir_path=data_dir_path,
intra_group_prob=intra_group_prob,
intra_group_option=intra_group_option,
sample_image_in_group=sample_image_in_group,
image_shape=image_shape,
**common_args,
)
elif data_type == "unpaired":
image_shape = data_config["image_shape"]
return UnpairedDataLoader(
data_dir_path=data_dir_path, image_shape=image_shape, **common_args
)
except KeyError as e:
msg = f"{e.args[0]} is not provided in the dataset config for paired data.\n"
if data_type == "paired":
msg += (
"Paired Loader requires 'moving_image_shape' and 'fixed_image_shape'.\n"
)
elif data_type == "grouped":
msg += (
"Grouped Loader requires 'image_shape', "
"as the data are not paired and will be resized to the same shape.\n"
"It also requires 'intra_group_prob', 'intra_group_option', and 'sample_image_in_group'.\n"
)
elif data_type == "unpaired":
msg += (
"Unpaired Loader requires 'image_shape', "
"as the data are not paired and will be resized to the same shape.\n"
)
raise ValueError(f"{msg}" f"The given dataset config is {data_config}\n")
raise ValueError(
f"Unknown data format {data_type}. "
f"Supported types are paired, unpaired, and grouped.\n"
)