tests: remove byte-identical duplicate test cases

Signed-off-by: Soumya Snigdha Kundu <soumya_snigdha.kundu@kcl.ac.uk>

Author: aymuos15

tests/fl/monai_algo/test_fl_monai_algo.py

@@ -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 = [
     {
@@ -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)

tests/inferers/test_patch_inferer.py

@@ -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):

tests/transforms/spatial/test_grid_patch.py

@@ -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]]
@@ -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])

tests/transforms/spatial/test_grid_patchd.py

@@ -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]]
@@ -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])

tests/transforms/spatial/test_rand_grid_patch.py

@@ -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]]
@@ -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])

tests/transforms/spatial/test_rand_grid_patchd.py

@@ -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]]
@@ -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])

tests/transforms/test_load_image.py

@@ -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
@@ -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"],
@@ -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"],
@@ -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",
@@ -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:
@@ -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())