Merge similar test components with parameterized

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__":