Merge similar test components with parameterized (#7663)

### Description

I noticed some test cases contain same duplicated asserts. Having
multiple asserts in one test cases can cause potential issues like when
the first assert fails, the test case stops and won't check the second
assert. By using @parameterized.expand, this issue can be resolved and
the caching also saves execution time.

Added sign-offs from  #7648 

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [x] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Han Wang <freddie.wanah@gmail.com>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>

Author: freddiewanah

tests/test_affine_transform.py

@@ -133,28 +133,17 @@ def test_to_norm_affine_ill(self, affine, src_size, dst_size, align_corners):
 
 class TestAffineTransform(unittest.TestCase):
 
-    def test_affine_shift(self):
-        affine = torch.as_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, -1.0]])
-        image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]])
-        out = AffineTransform(align_corners=False)(image, affine)
-        out = out.detach().cpu().numpy()
-        expected = [[[[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]]]
-        np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)
-
-    def test_affine_shift_1(self):
-        affine = torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, -1.0]])
-        image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]])
-        out = AffineTransform(align_corners=False)(image, affine)
-        out = out.detach().cpu().numpy()
-        expected = [[[[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]]]
-        np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)
-
-    def test_affine_shift_2(self):
-        affine = torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, 0.0]])
+    @parameterized.expand(
+        [
+            (torch.as_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, -1.0]]), [[[[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]]]),
+            (torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, -1.0]]), [[[[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]]]),
+            (torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, 0.0]]), [[[[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]]]),
+        ]
+    )
+    def test_affine_transforms(self, affine, expected):
         image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]])
         out = AffineTransform(align_corners=False)(image, affine)
         out = out.detach().cpu().numpy()
-        expected = [[[[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]]]
         np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)
 
     def test_zoom(self):

tests/test_compute_f_beta.py

@@ -15,6 +15,7 @@
 
 import numpy as np
 import torch
+from parameterized import parameterized
 
 from monai.metrics import FBetaScore
 from tests.utils import assert_allclose
@@ -33,26 +34,21 @@ def test_expecting_success_and_device(self):
         assert_allclose(result, torch.Tensor([0.714286]), atol=1e-6, rtol=1e-6)
         np.testing.assert_equal(result.device, y_pred.device)
 
-    def test_expecting_success2(self):
-        metric = FBetaScore(beta=0.5)
-        metric(
-            y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
-        )
-        assert_allclose(metric.aggregate()[0], torch.Tensor([0.609756]), atol=1e-6, rtol=1e-6)
-
-    def test_expecting_success3(self):
-        metric = FBetaScore(beta=2)
-        metric(
-            y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
-        )
-        assert_allclose(metric.aggregate()[0], torch.Tensor([0.862069]), atol=1e-6, rtol=1e-6)
-
-    def test_denominator_is_zero(self):
-        metric = FBetaScore(beta=2)
-        metric(
-            y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=torch.Tensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
-        )
-        assert_allclose(metric.aggregate()[0], torch.Tensor([0.0]), atol=1e-6, rtol=1e-6)
+    @parameterized.expand(
+        [
+            (0.5, torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]]), torch.Tensor([0.609756])),  # success_beta_0_5
+            (2, torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]]), torch.Tensor([0.862069])),  # success_beta_2
+            (
+                2,  # success_beta_2, denominator_zero
+                torch.Tensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]]),
+                torch.Tensor([0.0]),
+            ),
+        ]
+    )
+    def test_success_and_zero(self, beta, y, expected_score):
+        metric = FBetaScore(beta=beta)
+        metric(y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=y)
+        assert_allclose(metric.aggregate()[0], expected_score, atol=1e-6, rtol=1e-6)
 
     def test_number_of_dimensions_less_than_2_should_raise_error(self):
         metric = FBetaScore()

tests/test_global_mutual_information_loss.py

@@ -15,6 +15,7 @@
 
 import numpy as np
 import torch
+from parameterized import parameterized
 
 from monai import transforms
 from monai.losses.image_dissimilarity import GlobalMutualInformationLoss
@@ -116,24 +117,33 @@ def transformation(translate_params=(0.0, 0.0, 0.0), rotate_params=(0.0, 0.0, 0.
 
 class TestGlobalMutualInformationLossIll(unittest.TestCase):
 
-    def test_ill_shape(self):
+    @parameterized.expand(
+        [
+            (torch.ones((1, 2), dtype=torch.float), torch.ones((1, 3), dtype=torch.float)),  # mismatched_simple_dims
+            (
+                torch.ones((1, 3, 3), dtype=torch.float),
+                torch.ones((1, 3), dtype=torch.float),
+            ),  # mismatched_advanced_dims
+        ]
+    )
+    def test_ill_shape(self, input1, input2):
         loss = GlobalMutualInformationLoss()
-        with self.assertRaisesRegex(ValueError, ""):
-            loss.forward(torch.ones((1, 2), dtype=torch.float), torch.ones((1, 3), dtype=torch.float, device=device))
-        with self.assertRaisesRegex(ValueError, ""):
-            loss.forward(torch.ones((1, 3, 3), dtype=torch.float), torch.ones((1, 3), dtype=torch.float, device=device))
-
-    def test_ill_opts(self):
+        with self.assertRaises(ValueError):
+            loss.forward(input1, input2)
+
+    @parameterized.expand(
+        [
+            (0, "mean", ValueError, ""),  # num_bins_zero
+            (-1, "mean", ValueError, ""),  # num_bins_negative
+            (64, "unknown", ValueError, ""),  # reduction_unknown
+            (64, None, ValueError, ""),  # reduction_none
+        ]
+    )
+    def test_ill_opts(self, num_bins, reduction, expected_exception, expected_message):
         pred = torch.ones((1, 3, 3, 3, 3), dtype=torch.float, device=device)
         target = torch.ones((1, 3, 3, 3, 3), dtype=torch.float, device=device)
-        with self.assertRaisesRegex(ValueError, ""):
-            GlobalMutualInformationLoss(num_bins=0)(pred, target)
-        with self.assertRaisesRegex(ValueError, ""):
-            GlobalMutualInformationLoss(num_bins=-1)(pred, target)
-        with self.assertRaisesRegex(ValueError, ""):
-            GlobalMutualInformationLoss(reduction="unknown")(pred, target)
-        with self.assertRaisesRegex(ValueError, ""):
-            GlobalMutualInformationLoss(reduction=None)(pred, target)
+        with self.assertRaisesRegex(expected_exception, expected_message):
+            GlobalMutualInformationLoss(num_bins=num_bins, reduction=reduction)(pred, target)
 
 
 if __name__ == "__main__":

tests/test_hausdorff_loss.py

@@ -219,17 +219,12 @@ def test_ill_opts(self):
         with self.assertRaisesRegex(ValueError, ""):
             HausdorffDTLoss(reduction=None)(chn_input, chn_target)
 
-    def test_input_warnings(self):
+    @parameterized.expand([(False, False, False), (False, True, False), (False, False, True)])
+    def test_input_warnings(self, include_background, softmax, to_onehot_y):
         chn_input = torch.ones((1, 1, 1, 3))
         chn_target = torch.ones((1, 1, 1, 3))
         with self.assertWarns(Warning):
-            loss = HausdorffDTLoss(include_background=False)
-            loss.forward(chn_input, chn_target)
-        with self.assertWarns(Warning):
-            loss = HausdorffDTLoss(softmax=True)
-            loss.forward(chn_input, chn_target)
-        with self.assertWarns(Warning):
-            loss = HausdorffDTLoss(to_onehot_y=True)
+            loss = HausdorffDTLoss(include_background=include_background, softmax=softmax, to_onehot_y=to_onehot_y)
             loss.forward(chn_input, chn_target)
 
 
@@ -256,17 +251,12 @@ def test_ill_opts(self):
         with self.assertRaisesRegex(ValueError, ""):
             LogHausdorffDTLoss(reduction=None)(chn_input, chn_target)
 
-    def test_input_warnings(self):
+    @parameterized.expand([(False, False, False), (False, True, False), (False, False, True)])
+    def test_input_warnings(self, include_background, softmax, to_onehot_y):
         chn_input = torch.ones((1, 1, 1, 3))
         chn_target = torch.ones((1, 1, 1, 3))
         with self.assertWarns(Warning):
-            loss = LogHausdorffDTLoss(include_background=False)
-            loss.forward(chn_input, chn_target)
-        with self.assertWarns(Warning):
-            loss = LogHausdorffDTLoss(softmax=True)
-            loss.forward(chn_input, chn_target)
-        with self.assertWarns(Warning):
-            loss = LogHausdorffDTLoss(to_onehot_y=True)
+            loss = LogHausdorffDTLoss(include_background=include_background, softmax=softmax, to_onehot_y=to_onehot_y)
             loss.forward(chn_input, chn_target)
 
 

tests/test_median_filter.py

@@ -15,27 +15,20 @@
 
 import numpy as np
 import torch
+from parameterized import parameterized
 
 from monai.networks.layers import MedianFilter
 
 
 class MedianFilterTestCase(unittest.TestCase):
+    @parameterized.expand([(torch.ones(1, 1, 2, 3, 5), [1, 2, 4]), (torch.ones(1, 1, 4, 3, 4), 1)])  # 3d_big  # 3d
+    def test_3d(self, input_tensor, radius):
+        filter = MedianFilter(radius).to(torch.device("cpu:0"))
 
-    def test_3d_big(self):
-        a = torch.ones(1, 1, 2, 3, 5)
-        g = MedianFilter([1, 2, 4]).to(torch.device("cpu:0"))
+        expected = input_tensor.numpy()
+        output = filter(input_tensor).cpu().numpy()
 
-        expected = a.numpy()
-        out = g(a).cpu().numpy()
-        np.testing.assert_allclose(out, expected, rtol=1e-5)
-
-    def test_3d(self):
-        a = torch.ones(1, 1, 4, 3, 4)
-        g = MedianFilter(1).to(torch.device("cpu:0"))
-
-        expected = a.numpy()
-        out = g(a).cpu().numpy()
-        np.testing.assert_allclose(out, expected, rtol=1e-5)
+        np.testing.assert_allclose(output, expected, rtol=1e-5)
 
     def test_3d_radii(self):
         a = torch.ones(1, 1, 4, 3, 2)

tests/test_multi_scale.py

@@ -58,17 +58,24 @@ def test_shape(self, input_param, input_data, expected_val):
         result = MultiScaleLoss(**input_param).forward(**input_data)
         np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)
 
-    def test_ill_opts(self):
-        with self.assertRaisesRegex(ValueError, ""):
-            MultiScaleLoss(loss=dice_loss, kernel="none")
-        with self.assertRaisesRegex(ValueError, ""):
-            MultiScaleLoss(loss=dice_loss, scales=[-1])(
-                torch.ones((1, 1, 3), device=device), torch.ones((1, 1, 3), device=device)
-            )
-        with self.assertRaisesRegex(ValueError, ""):
-            MultiScaleLoss(loss=dice_loss, scales=[-1], reduction="none")(
-                torch.ones((1, 1, 3), device=device), torch.ones((1, 1, 3), device=device)
-            )
+    @parameterized.expand(
+        [
+            ({"loss": dice_loss, "kernel": "none"}, None, None),  # kernel_none
+            ({"loss": dice_loss, "scales": [-1]}, torch.ones((1, 1, 3)), torch.ones((1, 1, 3))),  # scales_negative
+            (
+                {"loss": dice_loss, "scales": [-1], "reduction": "none"},
+                torch.ones((1, 1, 3)),
+                torch.ones((1, 1, 3)),
+            ),  # scales_negative_reduction_none
+        ]
+    )
+    def test_ill_opts(self, kwargs, input, target):
+        if input is None and target is None:
+            with self.assertRaisesRegex(ValueError, ""):
+                MultiScaleLoss(**kwargs)
+        else:
+            with self.assertRaisesRegex(ValueError, ""):
+                MultiScaleLoss(**kwargs)(input, target)
 
     def test_script(self):
         input_param, input_data, expected_val = TEST_CASES[0]

tests/test_optional_import.py

@@ -13,22 +13,20 @@
 
 import unittest
 
+from parameterized import parameterized
+
 from monai.utils import OptionalImportError, exact_version, optional_import
 
 
 class TestOptionalImport(unittest.TestCase):
 
-    def test_default(self):
-        my_module, flag = optional_import("not_a_module")
+    @parameterized.expand(["not_a_module", "torch.randint"])
+    def test_default(self, import_module):
+        my_module, flag = optional_import(import_module)
         self.assertFalse(flag)
         with self.assertRaises(OptionalImportError):
             my_module.test
 
-        my_module, flag = optional_import("torch.randint")
-        with self.assertRaises(OptionalImportError):
-            self.assertFalse(flag)
-            print(my_module.test)
-
     def test_import_valid(self):
         my_module, flag = optional_import("torch")
         self.assertTrue(flag)
@@ -47,18 +45,9 @@ def test_import_wrong_number(self):
             self.assertTrue(flag)
             print(my_module.randint(1, 2, (1, 2)))
 
-    def test_import_good_number(self):
-        my_module, flag = optional_import("torch", "0")
-        my_module.nn
-        self.assertTrue(flag)
-        print(my_module.randint(1, 2, (1, 2)))
-
-        my_module, flag = optional_import("torch", "0.0.0.1")
-        my_module.nn
-        self.assertTrue(flag)
-        print(my_module.randint(1, 2, (1, 2)))
-
-        my_module, flag = optional_import("torch", "1.1.0")
+    @parameterized.expand(["0", "0.0.0.1", "1.1.0"])
+    def test_import_good_number(self, version_number):
+        my_module, flag = optional_import("torch", version_number)
         my_module.nn
         self.assertTrue(flag)
         print(my_module.randint(1, 2, (1, 2)))

tests/test_perceptual_loss.py

@@ -113,12 +113,10 @@ def test_1d(self):
         with self.assertRaises(NotImplementedError):
             PerceptualLoss(spatial_dims=1)
 
-    def test_medicalnet_on_2d_data(self):
+    @parameterized.expand(["medicalnet_resnet10_23datasets", "medicalnet_resnet50_23datasets"])
+    def test_medicalnet_on_2d_data(self, network_type):
         with self.assertRaises(ValueError):
-            PerceptualLoss(spatial_dims=2, network_type="medicalnet_resnet10_23datasets")
-
-        with self.assertRaises(ValueError):
-            PerceptualLoss(spatial_dims=2, network_type="medicalnet_resnet50_23datasets")
+            PerceptualLoss(spatial_dims=2, network_type=network_type)
 
 
 if __name__ == "__main__":