Add dict_product utility for test case generation

- Introduced a new `dict_product` function to create combinations of input parameters.
- Updated test cases to use the new utility for generating shape and input type combinations.
- Cleaned up code by removing old nested loops in favor of the new function.

Author: garciadias

tests/test_utils.py

@@ -30,9 +30,10 @@
 import warnings
 from contextlib import contextmanager
 from functools import partial, reduce
+from itertools import product
 from pathlib import Path
 from subprocess import PIPE, Popen
-from typing import Callable
+from typing import Callable, Literal
 from urllib.error import ContentTooShortError, HTTPError
 
 import numpy as np
@@ -862,6 +863,21 @@ def equal_state_dict(st_1, st_2):
 if torch.cuda.is_available():
     TEST_DEVICES.append([torch.device("cuda")])
 
+
+def dict_product(trailing=False, format: Literal["list", "dict"] = "dict", **items):
+    keys = items.keys()
+    values = items.values()
+    for pvalues in product(*values):
+        dict_comb = dict(zip(keys, pvalues))
+        if format == "dict":
+            if trailing:
+                yield [dict_comb] + list(pvalues)
+            else:
+                yield dict_comb
+        else:
+            yield pvalues
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(prog="util")
     parser.add_argument("-c", "--count", default=2, help="max number of gpus")

tests/transforms/test_gibbs_noise.py

@@ -21,14 +21,12 @@
 from monai.transforms import GibbsNoise
 from monai.utils.misc import set_determinism
 from monai.utils.module import optional_import
-from tests.test_utils import TEST_NDARRAYS, assert_allclose
+from tests.test_utils import TEST_NDARRAYS, assert_allclose, dict_product
 
 _, has_torch_fft = optional_import("torch.fft", name="fftshift")
 
-TEST_CASES = []
-for shape in ((128, 64), (64, 48, 80)):
-    for input_type in TEST_NDARRAYS if has_torch_fft else [np.array]:
-        TEST_CASES.append((shape, input_type))
+params = {"shape": ((128, 64), (64, 48, 80)), "input_type": TEST_NDARRAYS if has_torch_fft else [np.array]}
+TEST_CASES = list(dict_product(format="list", **params))
 
 
 class TestGibbsNoise(unittest.TestCase):