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tests: remove byte-identical duplicate test cases #9051

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tests: remove byte-identical duplicate test cases
aymuos15 Aug 9, 2026
1cea0bd
Merge branch 'dev' into test/remove-duplicate-test-cases
aymuos15 Aug 9, 2026
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14 changes: 1 addition & 13 deletions tests/fl/monai_algo/test_fl_monai_algo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,18 +53,6 @@
"config_filters_filename": None,
}
]
TEST_TRAIN_3 = [
{
"bundle_root": _data_dir,
"train_workflow": ConfigWorkflow(
config_file=os.path.join(_data_dir, "config_fl_train.json"),
workflow_type="train",
logging_file=_logging_file,
),
"config_evaluate_filename": None,
"config_filters_filename": os.path.join(_data_dir, "config_fl_filters.json"),
}
]

TEST_TRAIN_4 = [
{
Expand Down Expand Up @@ -182,7 +170,7 @@
@SkipIfNoModule("ignite")
@SkipIfNoModule("mlflow")
class TestFLMonaiAlgo(unittest.TestCase):
@parameterized.expand([TEST_TRAIN_1, TEST_TRAIN_2, TEST_TRAIN_3, TEST_TRAIN_4])
@parameterized.expand([TEST_TRAIN_1, TEST_TRAIN_2, TEST_TRAIN_4])
def test_train(self, input_params):
# initialize algo
algo = MonaiAlgo(**input_params)
Expand Down
11 changes: 0 additions & 11 deletions tests/inferers/test_patch_inferer.py
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Expand Up @@ -231,17 +231,6 @@
TEST_CASE_ERROR_8 = [torch.zeros(2, 2), dict(splitter=None), ValueError]
# invalid inputs: split patches MetaTensor without location metadata
TEST_CASE_ERROR_9 = [MetaTensor(torch.zeros(2, 2)), dict(splitter=None), ValueError]
# merged_shape is not provided for the merger
TEST_CASE_ERROR_10 = [
[
(TENSOR_4x4[..., :2, :2], (0, 0)),
(TENSOR_4x4[..., :2, 2:], (0, 2)),
(TENSOR_4x4[..., 2:, :2], (2, 0)),
(TENSOR_4x4[..., 2:, 2:], (2, 2)),
],
dict(splitter=SlidingWindowSplitter(patch_size=(2, 2))),
ValueError,
]


class PatchInfererTests(unittest.TestCase):
Expand Down
2 changes: 0 additions & 2 deletions tests/transforms/spatial/test_grid_patch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,6 @@
TEST_CASE_1 = [{"patch_size": (2, 2), "num_patches": 3}, A, [A11, A12, A21]]
TEST_CASE_2 = [{"patch_size": (2, 2), "num_patches": 5}, A, [A11, A12, A21, A22, np.zeros((3, 2, 2))]]
TEST_CASE_3 = [{"patch_size": (2, 2), "offset": (0, 0)}, A, [A11, A12, A21, A22]]
TEST_CASE_4 = [{"patch_size": (2, 2), "offset": (0, 0)}, A, [A11, A12, A21, A22]]
TEST_CASE_5 = [{"patch_size": (2, 2), "offset": (2, 2)}, A, [A22]]
TEST_CASE_6 = [{"patch_size": (2, 2), "offset": (0, 2)}, A, [A12, A22]]
TEST_CASE_7 = [{"patch_size": (2, 2), "offset": (2, 0)}, A, [A21, A22]]
Expand Down Expand Up @@ -81,7 +80,6 @@
TEST_CASES.append([p, *TEST_CASE_1])
TEST_CASES.append([p, *TEST_CASE_2])
TEST_CASES.append([p, *TEST_CASE_3])
TEST_CASES.append([p, *TEST_CASE_4])
TEST_CASES.append([p, *TEST_CASE_5])
TEST_CASES.append([p, *TEST_CASE_6])
TEST_CASES.append([p, *TEST_CASE_7])
Expand Down
2 changes: 0 additions & 2 deletions tests/transforms/spatial/test_grid_patchd.py
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Expand Up @@ -30,7 +30,6 @@
TEST_CASE_1 = [{"patch_size": (2, 2), "num_patches": 3}, {"image": A}, [A11, A12, A21]]
TEST_CASE_2 = [{"patch_size": (2, 2), "num_patches": 5}, {"image": A}, [A11, A12, A21, A22, np.zeros((3, 2, 2))]]
TEST_CASE_3 = [{"patch_size": (2, 2), "offset": (0, 0)}, {"image": A}, [A11, A12, A21, A22]]
TEST_CASE_4 = [{"patch_size": (2, 2), "offset": (0, 0)}, {"image": A}, [A11, A12, A21, A22]]
TEST_CASE_5 = [{"patch_size": (2, 2), "offset": (2, 2)}, {"image": A}, [A22]]
TEST_CASE_6 = [{"patch_size": (2, 2), "offset": (0, 2)}, {"image": A}, [A12, A22]]
TEST_CASE_7 = [{"patch_size": (2, 2), "offset": (2, 0)}, {"image": A}, [A21, A22]]
Expand Down Expand Up @@ -61,7 +60,6 @@
TEST_SINGLE.append([p, *TEST_CASE_1])
TEST_SINGLE.append([p, *TEST_CASE_2])
TEST_SINGLE.append([p, *TEST_CASE_3])
TEST_SINGLE.append([p, *TEST_CASE_4])
TEST_SINGLE.append([p, *TEST_CASE_5])
TEST_SINGLE.append([p, *TEST_CASE_6])
TEST_SINGLE.append([p, *TEST_CASE_7])
Expand Down
2 changes: 0 additions & 2 deletions tests/transforms/spatial/test_rand_grid_patch.py
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Expand Up @@ -35,7 +35,6 @@
A,
[A11, A12, A21, A22, np.zeros((3, 2, 2))],
]
TEST_CASE_3 = [{"patch_size": (2, 2), "min_offset": 0, "max_offset": 0}, A, [A11, A12, A21, A22]]
TEST_CASE_4 = [{"patch_size": (2, 2)}, A, [A11, A12, A21, A22]]
TEST_CASE_5 = [{"patch_size": (2, 2), "min_offset": 2, "max_offset": 2}, A, [A22]]
TEST_CASE_6 = [{"patch_size": (2, 2), "min_offset": (0, 2), "max_offset": (0, 2)}, A, [A12, A22]]
Expand Down Expand Up @@ -91,7 +90,6 @@
TEST_SINGLE.append([p, *TEST_CASE_0])
TEST_SINGLE.append([p, *TEST_CASE_1])
TEST_SINGLE.append([p, *TEST_CASE_2])
TEST_SINGLE.append([p, *TEST_CASE_3])
TEST_SINGLE.append([p, *TEST_CASE_4])
TEST_SINGLE.append([p, *TEST_CASE_5])
TEST_SINGLE.append([p, *TEST_CASE_6])
Expand Down
2 changes: 0 additions & 2 deletions tests/transforms/spatial/test_rand_grid_patchd.py
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Expand Up @@ -34,7 +34,6 @@
{"image": A},
[A11, A12, A21, A22, np.zeros((3, 2, 2))],
]
TEST_CASE_3 = [{"patch_size": (2, 2), "min_offset": 0, "max_offset": 0}, {"image": A}, [A11, A12, A21, A22]]
TEST_CASE_4 = [{"patch_size": (2, 2)}, {"image": A}, [A11, A12, A21, A22]]
TEST_CASE_5 = [{"patch_size": (2, 2), "min_offset": 2, "max_offset": 2}, {"image": A}, [A22]]
TEST_CASE_6 = [{"patch_size": (2, 2), "min_offset": (0, 2), "max_offset": (0, 2)}, {"image": A}, [A12, A22]]
Expand Down Expand Up @@ -71,7 +70,6 @@
TEST_SINGLE.append([p, *TEST_CASE_0])
TEST_SINGLE.append([p, *TEST_CASE_1])
TEST_SINGLE.append([p, *TEST_CASE_2])
TEST_SINGLE.append([p, *TEST_CASE_3])
TEST_SINGLE.append([p, *TEST_CASE_4])
TEST_SINGLE.append([p, *TEST_CASE_5])
TEST_SINGLE.append([p, *TEST_CASE_6])
Expand Down
18 changes: 2 additions & 16 deletions tests/transforms/test_load_image.py
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Expand Up @@ -54,8 +54,6 @@ def get_data(self, _obj):

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

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

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

TEST_CASE_3_1 = [ # .mgz format
Expand All @@ -64,8 +62,6 @@ def get_data(self, _obj):
(3, 128, 128, 128),
]

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

TEST_CASE_4_1 = [ # additional parameter
{"mmap": False},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
Expand All @@ -92,8 +88,6 @@ def get_data(self, _obj):

TEST_CASE_6 = [{"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"], (128, 128, 128)]

TEST_CASE_8 = [
{"reader": ITKReader() if has_itk else "itkreader"},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
Expand All @@ -106,12 +100,6 @@ def get_data(self, _obj):
(384, 128, 128),
]

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),
]

TEST_CASE_10 = [
{"reader": ITKReader(pixel_type=itk_uc) if has_itk else "itkreader"},
"tests/testing_data/CT_DICOM",
Expand Down Expand Up @@ -205,9 +193,7 @@ def tearDownClass(cls):
shutil.rmtree(cls.tmpdir)
super().tearDownClass()

@parameterized.expand(
[TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_3_1, TEST_CASE_4, TEST_CASE_4_1, TEST_CASE_5]
)
@parameterized.expand([TEST_CASE_1, TEST_CASE_3, TEST_CASE_3_1, 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)
with tempfile.TemporaryDirectory() as tempdir:
Expand Down Expand Up @@ -249,7 +235,7 @@ def test_nibabel_reader_gpu(self, input_param, filenames, expected_shape):
result_cpu = LoadImage(image_only=True, **input_param_cpu)(filenames)
assert_allclose(result_cpu, result.cpu(), atol=1e-6)

@parameterized.expand([TEST_CASE_6, TEST_CASE_7, TEST_CASE_8, TEST_CASE_8_1, TEST_CASE_9])
@parameterized.expand([TEST_CASE_6, TEST_CASE_8, TEST_CASE_8_1])
def test_itk_reader(self, input_param, filenames, expected_shape):
test_image = torch.randint(0, 256, (128, 128, 128), dtype=torch.uint8).numpy()
print("Test image value range:", test_image.min(), test_image.max())
Expand Down
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