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Perf(test): shrink synthetic volumes in image loading and dataset tests #9034

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Perf(test): shrink synthetic volumes in image loading and dataset tes…
aymuos15 Jul 31, 2026
1f7bde9
Merge branch 'dev' into perf/test-shrink-volumes
aymuos15 Jul 31, 2026
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16 changes: 8 additions & 8 deletions tests/data/test_arraydataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,14 +28,14 @@
Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandGaussianNoise(prob=1.0)]),
Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandGaussianNoise(prob=1.0)]),
(0, 1),
(1, 128, 128, 128),
(1, 32, 32, 32),
]

TEST_CASE_2 = [
Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandAdjustContrast(prob=1.0)]),
Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandAdjustContrast(prob=1.0)]),
(0, 1),
(1, 128, 128, 128),
(1, 32, 32, 32),
]


Expand Down Expand Up @@ -69,20 +69,20 @@ def __call__(self, input_, lazy=False):
]
),
(0, 2),
(1, 64, 64, 33),
(1, 16, 16, 9),
]

TEST_CASE_4 = [
Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandGaussianNoise(prob=1.0)]),
(1, 128, 128, 128),
(1, 32, 32, 32),
]


class TestArrayDataset(unittest.TestCase):

@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
def test_shape(self, img_transform, label_transform, indices, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(32, 32, 32)).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_seg1 = os.path.join(tempdir, "test_seg1.nii.gz")
Expand Down Expand Up @@ -116,7 +116,7 @@ def test_shape(self, img_transform, label_transform, indices, expected_shape):

@parameterized.expand([TEST_CASE_4])
def test_default_none(self, img_transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(32, 32, 32)).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_image2 = os.path.join(tempdir, "test_image2.nii.gz")
Expand All @@ -139,7 +139,7 @@ def test_default_none(self, img_transform, expected_shape):

@parameterized.expand([TEST_CASE_4])
def test_dataloading_img(self, img_transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(32, 32, 32)).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_image2 = os.path.join(tempdir, "test_image2.nii.gz")
Expand All @@ -160,7 +160,7 @@ def test_dataloading_img(self, img_transform, expected_shape):

@parameterized.expand([TEST_CASE_4])
def test_dataloading_img_label(self, img_transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(32, 32, 32)).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_image2 = os.path.join(tempdir, "test_image2.nii.gz")
Expand Down
8 changes: 4 additions & 4 deletions tests/data/test_cachedataset.py
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Expand Up @@ -23,9 +23,9 @@
from monai.data import CacheDataset, DataLoader, PersistentDataset, SmartCacheDataset
from monai.transforms import Compose, Lambda, LoadImaged, RandLambda, ThreadUnsafe, Transform

TEST_CASE_1 = [Compose([LoadImaged(keys=["image", "label", "extra"])]), (128, 128, 128)]
TEST_CASE_1 = [Compose([LoadImaged(keys=["image", "label", "extra"])]), (32, 32, 32)]

TEST_CASE_2 = [None, (128, 128, 128)]
TEST_CASE_2 = [None, (32, 32, 32)]

TEST_DS = []
for c in (0, 1, 2):
Expand All @@ -41,7 +41,7 @@ class TestCacheDataset(unittest.TestCase):

@parameterized.expand([TEST_CASE_1, TEST_CASE_2])
def test_shape(self, transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[32, 32, 32]).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_data = []
for i in ["1", "2"]:
Expand Down Expand Up @@ -192,7 +192,7 @@ def test_thread_safe(self, persistent_workers, cache_workers, loader_workers):

@parameterized.expand([TEST_CASE_1, TEST_CASE_2])
def test_hash_as_key(self, transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[32, 32, 32]).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_data = []
for i in ["1", "2", "2", "3", "3"]:
Expand Down
6 changes: 3 additions & 3 deletions tests/data/test_dataset.py
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Expand Up @@ -26,13 +26,13 @@
from monai.transforms import Compose, Lambda, LoadImage, LoadImaged, SimulateDelay, SimulateDelayd
from tests.transforms.compose.test_compose import TEST_COMPOSE_LAZY_ON_CALL_LOGGING_TEST_CASES, data_from_keys

TEST_CASE_1 = [(128, 128, 128)]
TEST_CASE_1 = [(32, 32, 32)]


class TestDataset(unittest.TestCase):
@parameterized.expand([TEST_CASE_1])
def test_shape(self, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[32, 32, 32]).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
nib.save(test_image, os.path.join(tempdir, "test_image1.nii.gz"))
nib.save(test_image, os.path.join(tempdir, "test_label1.nii.gz"))
Expand Down Expand Up @@ -101,7 +101,7 @@ def test_dataset_lazy_on_call(self):
class TestTupleDataset(unittest.TestCase):
@parameterized.expand([TEST_CASE_1])
def test_shape(self, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[32, 32, 32]).astype(float), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
nib.save(test_image, os.path.join(tempdir, "test_image1.nii.gz"))
nib.save(test_image, os.path.join(tempdir, "test_label1.nii.gz"))
Expand Down
2 changes: 1 addition & 1 deletion tests/data/test_mapping_file.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@


def create_input_file(temp_dir, name):
test_image = np.random.rand(128, 128, 128)
test_image = np.random.rand(16, 16, 16)
output_ext = ".nii.gz"
input_file = os.path.join(temp_dir, name + output_ext)
nib.save(nib.Nifti1Image(test_image, np.eye(4)), input_file)
Expand Down
62 changes: 31 additions & 31 deletions tests/transforms/test_load_image.py
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Expand Up @@ -52,64 +52,64 @@ def get_data(self, _obj):
return np.zeros((1, 1, 1)), {"name": "my test"}


TEST_CASE_1 = [{}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_1 = [{}, ["test_image.nii.gz"], (32, 32, 32)]

TEST_CASE_2 = [{}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_2 = [{}, ["test_image.nii.gz"], (32, 32, 32)]

TEST_CASE_3 = [{}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], (3, 128, 128, 128)]
TEST_CASE_3 = [{}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], (3, 32, 32, 32)]

TEST_CASE_3_1 = [ # .mgz format
{"reader": "nibabelreader"},
["test_image.mgz", "test_image2.mgz", "test_image3.mgz"],
(3, 128, 128, 128),
(3, 32, 32, 32),
]

TEST_CASE_4 = [{}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], (3, 128, 128, 128)]
TEST_CASE_4 = [{}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], (3, 32, 32, 32)]

TEST_CASE_4_1 = [ # additional parameter
{"mmap": False},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
(3, 32, 32, 32),
]

TEST_CASE_5 = [{"reader": NibabelReader(mmap=False)}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_5 = [{"reader": NibabelReader(mmap=False)}, ["test_image.nii.gz"], (32, 32, 32)]

TEST_CASE_GPU_1 = [{"reader": "nibabelreader", "to_gpu": True}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_GPU_1 = [{"reader": "nibabelreader", "to_gpu": True}, ["test_image.nii.gz"], (32, 32, 32)]

TEST_CASE_GPU_2 = [{"reader": "nibabelreader", "to_gpu": True}, ["test_image.nii"], (128, 128, 128)]
TEST_CASE_GPU_2 = [{"reader": "nibabelreader", "to_gpu": True}, ["test_image.nii"], (32, 32, 32)]

TEST_CASE_GPU_3 = [
{"reader": "nibabelreader", "to_gpu": True},
["test_image.nii", "test_image2.nii", "test_image3.nii"],
(3, 128, 128, 128),
(3, 32, 32, 32),
]

TEST_CASE_GPU_4 = [
{"reader": "nibabelreader", "to_gpu": True},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
(3, 32, 32, 32),
]

TEST_CASE_6 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_6 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], (32, 32, 32)]

TEST_CASE_7 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_7 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], (32, 32, 32)]

TEST_CASE_8 = [
{"reader": ITKReader() if has_itk else "itkreader"},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
(3, 32, 32, 32),
]

TEST_CASE_8_1 = [
{"reader": ITKReader(channel_dim=0) if has_itk else "itkreader"},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(384, 128, 128),
(96, 32, 32),
]

TEST_CASE_9 = [
{"reader": ITKReader() if has_itk else "itkreader"},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
(3, 32, 32, 32),
]

TEST_CASE_10 = [
Expand All @@ -128,24 +128,24 @@ def get_data(self, _obj):
(4, 16, 16),
]

TEST_CASE_13 = [{"reader": "nibabelreader", "channel_dim": 0}, "test_image.nii.gz", (3, 128, 128, 128)]
TEST_CASE_13 = [{"reader": "nibabelreader", "channel_dim": 0}, "test_image.nii.gz", (3, 32, 32, 32)]

TEST_CASE_14 = [
{"reader": "nibabelreader", "channel_dim": -1, "ensure_channel_first": True},
"test_image.nii.gz",
(128, 128, 128, 3),
(32, 32, 32, 3),
]

TEST_CASE_15 = [{"reader": "nibabelreader", "channel_dim": 2}, "test_image.nii.gz", (128, 128, 3, 128)]
TEST_CASE_15 = [{"reader": "nibabelreader", "channel_dim": 2}, "test_image.nii.gz", (32, 32, 3, 32)]

TEST_CASE_16 = [{"reader": "itkreader", "channel_dim": 0}, "test_image.nii.gz", (3, 128, 128, 128)]
TEST_CASE_16 = [{"reader": "itkreader", "channel_dim": 0}, "test_image.nii.gz", (3, 32, 32, 32)]

TEST_CASE_17 = [{"reader": "monai.data.ITKReader", "channel_dim": -1}, "test_image.nii.gz", (128, 128, 128, 3)]
TEST_CASE_17 = [{"reader": "monai.data.ITKReader", "channel_dim": -1}, "test_image.nii.gz", (32, 32, 32, 3)]

TEST_CASE_18 = [
{"reader": "ITKReader", "channel_dim": 2, "ensure_channel_first": True},
"test_image.nii.gz",
(128, 128, 3, 128),
(32, 32, 3, 32),
]

# test same dicom data with PydicomReader
Expand Down Expand Up @@ -180,8 +180,8 @@ def get_data(self, _obj):

TESTS_META = []
for track_meta in (False, True):
TESTS_META.append([{}, (128, 128, 128), track_meta])
TESTS_META.append([{"reader": "ITKReader", "fallback_only": False}, (128, 128, 128), track_meta])
TESTS_META.append([{}, (32, 32, 32), track_meta])
TESTS_META.append([{"reader": "ITKReader", "fallback_only": False}, (32, 32, 32), track_meta])


@unittest.skipUnless(has_itk, "itk not installed")
Expand Down Expand Up @@ -209,7 +209,7 @@ def tearDownClass(cls):
[TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_3_1, TEST_CASE_4, TEST_CASE_4_1, TEST_CASE_5]
)
def test_nibabel_reader(self, input_param, filenames, expected_shape):
test_image = np.random.rand(128, 128, 128)
test_image = np.random.rand(32, 32, 32)
with tempfile.TemporaryDirectory() as tempdir:
for i, name in enumerate(filenames):
filenames[i] = os.path.join(tempdir, name)
Expand All @@ -229,9 +229,9 @@ def test_nibabel_reader_gpu(self, input_param, filenames, expected_shape):
if torch.__version__.endswith("nv24.8"):
# related issue: https://github.com/Project-MONAI/MONAI/issues/8274
# for this version, use randint test case to avoid the issue
test_image = torch.randint(0, 256, (128, 128, 128), dtype=torch.uint8).numpy()
test_image = torch.randint(0, 256, (32, 32, 32), dtype=torch.uint8).numpy()
else:
test_image = np.random.rand(128, 128, 128)
test_image = np.random.rand(32, 32, 32)
with tempfile.TemporaryDirectory() as tempdir:
for i, name in enumerate(filenames):
filenames[i] = os.path.join(tempdir, name)
Expand All @@ -251,7 +251,7 @@ def test_nibabel_reader_gpu(self, input_param, filenames, expected_shape):

@parameterized.expand([TEST_CASE_6, TEST_CASE_7, TEST_CASE_8, TEST_CASE_8_1, TEST_CASE_9])
def test_itk_reader(self, input_param, filenames, expected_shape):
test_image = torch.randint(0, 256, (128, 128, 128), dtype=torch.uint8).numpy()
test_image = torch.randint(0, 256, (32, 32, 32), dtype=torch.uint8).numpy()
print("Test image value range:", test_image.min(), test_image.max())
with tempfile.TemporaryDirectory() as tempdir:
for i, name in enumerate(filenames):
Expand Down Expand Up @@ -462,7 +462,7 @@ def test_channel_dim(self, input_param, filename, expected_shape):
result = LoadImage(image_only=True, **input_param)(filename) # with itk, meta has 'qto_xyz': itkMatrixF44

self.assertTupleEqual(
result.shape, (3, 128, 128, 128) if input_param.get("ensure_channel_first", False) else expected_shape
result.shape, (3, 32, 32, 32) if input_param.get("ensure_channel_first", False) else expected_shape
)
self.assertEqual(result.meta["original_channel_dim"], input_param["channel_dim"])

Expand All @@ -473,7 +473,7 @@ class TestLoadImageMeta(unittest.TestCase):
def setUpClass(cls):
super().setUpClass()
cls.tmpdir = tempfile.mkdtemp()
test_image = nib.Nifti1Image(np.random.rand(128, 128, 128), np.eye(4))
test_image = nib.Nifti1Image(np.random.rand(32, 32, 32), np.eye(4))
nib.save(test_image, os.path.join(cls.tmpdir, "im.nii.gz"))
cls.test_data = os.path.join(cls.tmpdir, "im.nii.gz")

Expand Down Expand Up @@ -502,7 +502,7 @@ def test_track_meta_false_ensure_channel_first(self):
try:
set_track_meta(False)
r = LoadImage(image_only=True, ensure_channel_first=True)(self.test_data)
self.assertTupleEqual(r.shape, (1, 128, 128, 128))
self.assertTupleEqual(r.shape, (1, 32, 32, 32))
self.assertIsInstance(r, torch.Tensor)
self.assertNotIsInstance(r, MetaTensor)
finally:
Expand Down
12 changes: 6 additions & 6 deletions tests/transforms/test_load_imaged.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,21 +34,21 @@

KEYS = ["image", "label", "extra"]

TEST_CASE_1 = [{"keys": KEYS}, (128, 128, 128)]
TEST_CASE_1 = [{"keys": KEYS}, (32, 32, 32)]

TEST_CASE_2 = [{"keys": KEYS, "reader": "ITKReader", "fallback_only": False}, (128, 128, 128)]
TEST_CASE_2 = [{"keys": KEYS, "reader": "ITKReader", "fallback_only": False}, (32, 32, 32)]

TESTS_META = []
for track_meta in (False, True):
TESTS_META.append([{"keys": KEYS}, (128, 128, 128), track_meta])
TESTS_META.append([{"keys": KEYS, "reader": "ITKReader", "fallback_only": False}, (128, 128, 128), track_meta])
TESTS_META.append([{"keys": KEYS}, (32, 32, 32), track_meta])
TESTS_META.append([{"keys": KEYS, "reader": "ITKReader", "fallback_only": False}, (32, 32, 32), track_meta])


@unittest.skipUnless(has_itk, "itk not installed")
class TestLoadImaged(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2])
def test_shape(self, input_param, expected_shape):
test_image = nib.Nifti1Image(np.random.rand(128, 128, 128), np.eye(4))
test_image = nib.Nifti1Image(np.random.rand(32, 32, 32), np.eye(4))
test_data = {}
with tempfile.TemporaryDirectory() as tempdir:
for key in KEYS:
Expand Down Expand Up @@ -159,7 +159,7 @@ class TestLoadImagedMeta(unittest.TestCase):
def setUpClass(cls):
super().setUpClass()
cls.tmpdir = tempfile.mkdtemp()
test_image = nib.Nifti1Image(np.random.rand(128, 128, 128), np.eye(4))
test_image = nib.Nifti1Image(np.random.rand(32, 32, 32), np.eye(4))
cls.test_data = {}
for key in KEYS:
nib.save(test_image, os.path.join(cls.tmpdir, key + ".nii.gz"))
Expand Down
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