Refactored others type of subotimal asserts (#7672)

### Description

As discussed in PR #7609, I tried to break the suboptimal test refactors
to smaller pieces. In this PR all asserts are checking textual content
or instance of a parameter.

Suboptimal Assert: Instead of using statements such as assertIsNone,
assertIsInstance, always simply use assertTrue or assertFalse. This will
decrease the code overall readability and increase the execution time as
extra logic needed.

### 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`.
- [ ] 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>
Signed-off-by: Ben Murray <ben.murray@gmail.com>
Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>
Co-authored-by: Ben Murray <ben.murray@gmail.com>

tests/test_bundle_ckpt_export.py

@@ -72,9 +72,9 @@ def test_export(self, key_in_ckpt, use_trace):
             _, metadata, extra_files = load_net_with_metadata(
                 ts_file, more_extra_files=["inference.json", "def_args.json"]
             )
-            self.assertTrue("schema" in metadata)
-            self.assertTrue("meta_file" in json.loads(extra_files["def_args.json"]))
-            self.assertTrue("network_def" in json.loads(extra_files["inference.json"]))
+            self.assertIn("schema", metadata)
+            self.assertIn("meta_file", json.loads(extra_files["def_args.json"]))
+            self.assertIn("network_def", json.loads(extra_files["inference.json"]))
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
     def test_default_value(self, key_in_ckpt, use_trace):

tests/test_bundle_get_data.py

@@ -51,25 +51,26 @@ class TestGetBundleData(unittest.TestCase):
     def test_get_all_bundles_list(self, params):
         with skip_if_downloading_fails():
             output = get_all_bundles_list(**params)
-            self.assertTrue(isinstance(output, list))
-            self.assertTrue(isinstance(output[0], tuple))
-            self.assertEqual(len(output[0]), 2)
+            self.assertIsInstance(output, list)
+            self.assertIsInstance(output[0], tuple)
+            self.assertTrue(len(output[0]) == 2)
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_5])
     @skip_if_quick
     def test_get_bundle_versions(self, params):
         with skip_if_downloading_fails():
             output = get_bundle_versions(**params)
-            self.assertTrue(isinstance(output, dict))
-            self.assertTrue("latest_version" in output and "all_versions" in output)
-            self.assertTrue("0.1.0" in output["all_versions"])
+            self.assertIsInstance(output, dict)
+            self.assertIn("latest_version", output)
+            self.assertIn("all_versions", output)
+            self.assertIn("0.1.0", output["all_versions"])
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2])
     @skip_if_quick
     def test_get_bundle_info(self, params):
         with skip_if_downloading_fails():
             output = get_bundle_info(**params)
-            self.assertTrue(isinstance(output, dict))
+            self.assertIsInstance(output, dict)
             for key in ["id", "name", "size", "download_count", "browser_download_url"]:
                 self.assertTrue(key in output)
 
@@ -78,7 +79,7 @@ def test_get_bundle_info(self, params):
     def test_get_bundle_info_monaihosting(self, params):
         with skip_if_downloading_fails():
             output = get_bundle_info(**params)
-            self.assertTrue(isinstance(output, dict))
+            self.assertIsInstance(output, dict)
             for key in ["name", "browser_download_url"]:
                 self.assertTrue(key in output)
 

tests/test_bundle_trt_export.py

@@ -91,9 +91,9 @@ def test_trt_export(self, convert_precision, input_shape, dynamic_batch):
             _, metadata, extra_files = load_net_with_metadata(
                 ts_file, more_extra_files=["inference.json", "def_args.json"]
             )
-            self.assertTrue("schema" in metadata)
-            self.assertTrue("meta_file" in json.loads(extra_files["def_args.json"]))
-            self.assertTrue("network_def" in json.loads(extra_files["inference.json"]))
+            self.assertIn("schema", metadata)
+            self.assertIn("meta_file", json.loads(extra_files["def_args.json"]))
+            self.assertIn("network_def", json.loads(extra_files["inference.json"]))
 
     @parameterized.expand([TEST_CASE_3, TEST_CASE_4])
     @unittest.skipUnless(
@@ -129,9 +129,9 @@ def test_onnx_trt_export(self, convert_precision, input_shape, dynamic_batch):
             _, metadata, extra_files = load_net_with_metadata(
                 ts_file, more_extra_files=["inference.json", "def_args.json"]
             )
-            self.assertTrue("schema" in metadata)
-            self.assertTrue("meta_file" in json.loads(extra_files["def_args.json"]))
-            self.assertTrue("network_def" in json.loads(extra_files["inference.json"]))
+            self.assertIn("schema", metadata)
+            self.assertIn("meta_file", json.loads(extra_files["def_args.json"]))
+            self.assertIn("network_def", json.loads(extra_files["inference.json"]))
 
 
 if __name__ == "__main__":

tests/test_bundle_workflow.py

@@ -138,11 +138,11 @@ def test_train_config(self, config_file):
         self.assertListEqual(trainer.check_properties(), [])
         # test read / write the properties
         dataset = trainer.train_dataset
-        self.assertTrue(isinstance(dataset, Dataset))
+        self.assertIsInstance(dataset, Dataset)
         inferer = trainer.train_inferer
-        self.assertTrue(isinstance(inferer, SimpleInferer))
+        self.assertIsInstance(inferer, SimpleInferer)
         # test optional properties get
-        self.assertTrue(trainer.train_key_metric is None)
+        self.assertIsNone(trainer.train_key_metric)
         trainer.train_dataset = deepcopy(dataset)
         trainer.train_inferer = deepcopy(inferer)
         # test optional properties set

tests/test_component_store.py

@@ -48,17 +48,17 @@ def test_add2(self):
         self.cs.add("test_obj2", "Test object", test_obj2)
 
         self.assertEqual(len(self.cs), 2)
-        self.assertTrue("test_obj1" in self.cs)
-        self.assertTrue("test_obj2" in self.cs)
+        self.assertIn("test_obj1", self.cs)
+        self.assertIn("test_obj2", self.cs)
 
     def test_add_def(self):
-        self.assertFalse("test_func" in self.cs)
+        self.assertNotIn("test_func", self.cs)
 
         @self.cs.add_def("test_func", "Test function")
         def test_func():
             return 123
 
-        self.assertTrue("test_func" in self.cs)
+        self.assertIn("test_func", self.cs)
 
         self.assertEqual(len(self.cs), 1)
         self.assertEqual(list(self.cs), [("test_func", test_func)])

tests/test_compute_ho_ver_maps.py

@@ -67,8 +67,8 @@ class ComputeHoVerMapsTests(unittest.TestCase):
     def test_horizontal_certical_maps(self, in_type, arguments, mask, hv_mask):
         input_image = in_type(mask)
         result = ComputeHoVerMaps(**arguments)(input_image)
-        self.assertTrue(isinstance(result, torch.Tensor))
-        self.assertTrue(str(result.dtype).split(".")[1] == arguments.get("dtype", "float32"))
+        self.assertIsInstance(result, torch.Tensor)
+        self.assertEqual(str(result.dtype).split(".")[1], arguments.get("dtype", "float32"))
         assert_allclose(result, hv_mask, type_test="tensor")
 
 

tests/test_compute_ho_ver_maps_d.py

@@ -71,8 +71,8 @@ def test_horizontal_certical_maps(self, in_type, arguments, mask, hv_mask):
         for k in mask.keys():
             input_image[k] = in_type(mask[k])
         result = ComputeHoVerMapsd(keys="mask", **arguments)(input_image)[hv_key]
-        self.assertTrue(isinstance(result, torch.Tensor))
-        self.assertTrue(str(result.dtype).split(".")[1] == arguments.get("dtype", "float32"))
+        self.assertIsInstance(result, torch.Tensor)
+        self.assertEqual(str(result.dtype).split(".")[1], arguments.get("dtype", "float32"))
         assert_allclose(result, hv_mask[hv_key], type_test="tensor")
 
 

tests/test_concat_itemsd.py

@@ -30,7 +30,7 @@ def test_tensor_values(self):
             "img2": torch.tensor([[0, 1], [1, 2]], device=device),
         }
         result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data)
-        self.assertTrue("cat_img" in result)
+        self.assertIn("cat_img", result)
         result["cat_img"] += 1
         assert_allclose(result["img1"], torch.tensor([[0, 1], [1, 2]], device=device))
         assert_allclose(result["cat_img"], torch.tensor([[1, 2], [2, 3], [1, 2], [2, 3]], device=device))
@@ -42,8 +42,8 @@ def test_metatensor_values(self):
             "img2": MetaTensor([[0, 1], [1, 2]], device=device),
         }
         result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data)
-        self.assertTrue("cat_img" in result)
-        self.assertTrue(isinstance(result["cat_img"], MetaTensor))
+        self.assertIn("cat_img", result)
+        self.assertIsInstance(result["cat_img"], MetaTensor)
         self.assertEqual(result["img1"].meta, result["cat_img"].meta)
         result["cat_img"] += 1
         assert_allclose(result["img1"], torch.tensor([[0, 1], [1, 2]], device=device))
@@ -52,7 +52,7 @@ def test_metatensor_values(self):
     def test_numpy_values(self):
         input_data = {"img1": np.array([[0, 1], [1, 2]]), "img2": np.array([[0, 1], [1, 2]])}
         result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data)
-        self.assertTrue("cat_img" in result)
+        self.assertIn("cat_img", result)
         result["cat_img"] += 1
         np.testing.assert_allclose(result["img1"], np.array([[0, 1], [1, 2]]))
         np.testing.assert_allclose(result["cat_img"], np.array([[1, 2], [2, 3], [1, 2], [2, 3]]))

tests/test_config_parser.py

@@ -185,7 +185,7 @@ def test_function(self, config):
             if id in ("compute", "cls_compute"):
                 parser[f"{id}#_mode_"] = "callable"
             func = parser.get_parsed_content(id=id)
-            self.assertTrue(id in parser.ref_resolver.resolved_content)
+            self.assertIn(id, parser.ref_resolver.resolved_content)
             if id == "error_func":
                 with self.assertRaises(TypeError):
                     func(1, 2)

tests/test_cucim_dict_transform.py

@@ -80,8 +80,8 @@ class TestCuCIMDict(unittest.TestCase):
     def test_tramsforms_numpy_single(self, params, input, expected):
         input = {"image": input}
         output = CuCIMd(keys="image", **params)(input)["image"]
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, np.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, np.ndarray)
         cp.testing.assert_allclose(output, expected)
 
     @parameterized.expand(
@@ -98,8 +98,8 @@ def test_tramsforms_numpy_batch(self, params, input, expected):
         input = {"image": input[cp.newaxis, ...]}
         expected = expected[cp.newaxis, ...]
         output = CuCIMd(keys="image", **params)(input)["image"]
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, np.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, np.ndarray)
         cp.testing.assert_allclose(output, expected)
 
     @parameterized.expand(
@@ -116,8 +116,8 @@ def test_tramsforms_cupy_single(self, params, input, expected):
         input = {"image": cp.asarray(input)}
         expected = cp.asarray(expected)
         output = CuCIMd(keys="image", **params)(input)["image"]
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, cp.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, cp.ndarray)
         cp.testing.assert_allclose(output, expected)
 
     @parameterized.expand(
@@ -134,8 +134,8 @@ def test_tramsforms_cupy_batch(self, params, input, expected):
         input = {"image": cp.asarray(input)[cp.newaxis, ...]}
         expected = cp.asarray(expected)[cp.newaxis, ...]
         output = CuCIMd(keys="image", **params)(input)["image"]
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, cp.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, cp.ndarray)
         cp.testing.assert_allclose(output, expected)
 
 

tests/test_cucim_transform.py

@@ -79,8 +79,8 @@ class TestCuCIM(unittest.TestCase):
     )
     def test_tramsforms_numpy_single(self, params, input, expected):
         output = CuCIM(**params)(input)
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, np.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, np.ndarray)
         cp.testing.assert_allclose(output, expected)
 
     @parameterized.expand(
@@ -97,8 +97,8 @@ def test_tramsforms_numpy_batch(self, params, input, expected):
         input = input[cp.newaxis, ...]
         expected = expected[cp.newaxis, ...]
         output = CuCIM(**params)(input)
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, np.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, np.ndarray)
         cp.testing.assert_allclose(output, expected)
 
     @parameterized.expand(
@@ -115,8 +115,8 @@ def test_tramsforms_cupy_single(self, params, input, expected):
         input = cp.asarray(input)
         expected = cp.asarray(expected)
         output = CuCIM(**params)(input)
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, cp.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, cp.ndarray)
         cp.testing.assert_allclose(output, expected)
 
     @parameterized.expand(
@@ -133,8 +133,8 @@ def test_tramsforms_cupy_batch(self, params, input, expected):
         input = cp.asarray(input)[cp.newaxis, ...]
         expected = cp.asarray(expected)[cp.newaxis, ...]
         output = CuCIM(**params)(input)
-        self.assertTrue(output.dtype == expected.dtype)
-        self.assertTrue(isinstance(output, cp.ndarray))
+        self.assertEqual(output.dtype, expected.dtype)
+        self.assertIsInstance(output, cp.ndarray)
         cp.testing.assert_allclose(output, expected)
 
 

tests/test_detect_envelope.py

@@ -147,7 +147,7 @@ def test_value_error(self, arguments, image, method):
         elif method == "__call__":
             self.assertRaises(ValueError, DetectEnvelope(**arguments), image)
         else:
-            raise ValueError("Expected raising method invalid. Should be __init__ or __call__.")
+            self.fail("Expected raising method invalid. Should be __init__ or __call__.")
 
 
 @SkipIfModule("torch.fft")

tests/test_ensure_typed.py

@@ -33,8 +33,8 @@ def test_array_input(self):
                     keys="data", data_type=dtype, dtype=np.float32 if dtype == "NUMPY" else None, device="cpu"
                 )({"data": test_data})["data"]
                 if dtype == "NUMPY":
-                    self.assertTrue(result.dtype == np.float32)
-                self.assertTrue(isinstance(result, torch.Tensor if dtype == "tensor" else np.ndarray))
+                    self.assertEqual(result.dtype, np.float32)
+                self.assertIsInstance(result, torch.Tensor if dtype == "tensor" else np.ndarray)
                 assert_allclose(result, test_data, type_test=False)
                 self.assertTupleEqual(result.shape, (2, 2))
 
@@ -45,7 +45,7 @@ def test_single_input(self):
         for test_data in test_datas:
             for dtype in ("tensor", "numpy"):
                 result = EnsureTyped(keys="data", data_type=dtype)({"data": test_data})["data"]
-                self.assertTrue(isinstance(result, torch.Tensor if dtype == "tensor" else np.ndarray))
+                self.assertIsInstance(result, torch.Tensor if dtype == "tensor" else np.ndarray)
                 if isinstance(test_data, bool):
                     self.assertFalse(result)
                 else:
@@ -56,27 +56,27 @@ def test_string(self):
         for dtype in ("tensor", "numpy"):
             # string input
             result = EnsureTyped(keys="data", data_type=dtype)({"data": "test_string"})["data"]
-            self.assertTrue(isinstance(result, str))
+            self.assertIsInstance(result, str)
             self.assertEqual(result, "test_string")
             # numpy array of string
             result = EnsureTyped(keys="data", data_type=dtype)({"data": np.array(["test_string"])})["data"]
-            self.assertTrue(isinstance(result, np.ndarray))
+            self.assertIsInstance(result, np.ndarray)
             self.assertEqual(result[0], "test_string")
 
     def test_list_tuple(self):
         for dtype in ("tensor", "numpy"):
             result = EnsureTyped(keys="data", data_type=dtype, wrap_sequence=False, track_meta=True)(
                 {"data": [[1, 2], [3, 4]]}
             )["data"]
-            self.assertTrue(isinstance(result, list))
-            self.assertTrue(isinstance(result[0][1], MetaTensor if dtype == "tensor" else np.ndarray))
+            self.assertIsInstance(result, list)
+            self.assertIsInstance(result[0][1], MetaTensor if dtype == "tensor" else np.ndarray)
             assert_allclose(result[1][0], torch.as_tensor(3), type_test=False)
             # tuple of numpy arrays
             result = EnsureTyped(keys="data", data_type=dtype, wrap_sequence=False)(
                 {"data": (np.array([1, 2]), np.array([3, 4]))}
             )["data"]
-            self.assertTrue(isinstance(result, tuple))
-            self.assertTrue(isinstance(result[0], torch.Tensor if dtype == "tensor" else np.ndarray))
+            self.assertIsInstance(result, tuple)
+            self.assertIsInstance(result[0], torch.Tensor if dtype == "tensor" else np.ndarray)
             assert_allclose(result[1], torch.as_tensor([3, 4]), type_test=False)
 
     def test_dict(self):
@@ -92,19 +92,19 @@ def test_dict(self):
             )
             for key in ("data", "label"):
                 result = trans[key]
-                self.assertTrue(isinstance(result, dict))
-                self.assertTrue(isinstance(result["img"], torch.Tensor if dtype == "tensor" else np.ndarray))
-                self.assertTrue(isinstance(result["meta"]["size"], torch.Tensor if dtype == "tensor" else np.ndarray))
+                self.assertIsInstance(result, dict)
+                self.assertIsInstance(result["img"], torch.Tensor if dtype == "tensor" else np.ndarray)
+                self.assertIsInstance(result["meta"]["size"], torch.Tensor if dtype == "tensor" else np.ndarray)
                 self.assertEqual(result["meta"]["path"], "temp/test")
                 self.assertEqual(result["extra"], None)
                 assert_allclose(result["img"], torch.as_tensor([1.0, 2.0]), type_test=False)
                 assert_allclose(result["meta"]["size"], torch.as_tensor([1, 2, 3]), type_test=False)
             if dtype == "numpy":
-                self.assertTrue(trans["data"]["img"].dtype == np.float32)
-                self.assertTrue(trans["label"]["img"].dtype == np.int8)
+                self.assertEqual(trans["data"]["img"].dtype, np.float32)
+                self.assertEqual(trans["label"]["img"].dtype, np.int8)
             else:
-                self.assertTrue(trans["data"]["img"].dtype == torch.float32)
-                self.assertTrue(trans["label"]["img"].dtype == torch.int8)
+                self.assertEqual(trans["data"]["img"].dtype, torch.float32)
+                self.assertEqual(trans["label"]["img"].dtype, torch.int8)
 
 
 if __name__ == "__main__":

tests/test_flipd.py

@@ -78,7 +78,7 @@ def test_torch(self, spatial_axis, img: torch.Tensor, track_meta: bool, device):
     def test_meta_dict(self):
         xform = Flipd("image", [0, 1])
         res = xform({"image": torch.zeros(1, 3, 4)})
-        self.assertTrue(res["image"].applied_operations == res["image_transforms"])
+        self.assertEqual(res["image"].applied_operations, res["image_transforms"])
 
 
 if __name__ == "__main__":

tests/test_freeze_layers.py

@@ -40,9 +40,9 @@ def test_freeze_vars(self, device):
 
         for name, param in model.named_parameters():
             if "class_layer" in name:
-                self.assertEqual(param.requires_grad, False)
+                self.assertFalse(param.requires_grad)
             else:
-                self.assertEqual(param.requires_grad, True)
+                self.assertTrue(param.requires_grad)
 
     @parameterized.expand(TEST_CASES)
     def test_exclude_vars(self, device):
@@ -53,9 +53,9 @@ def test_exclude_vars(self, device):
 
         for name, param in model.named_parameters():
             if "class_layer" in name:
-                self.assertEqual(param.requires_grad, True)
+                self.assertTrue(param.requires_grad)
             else:
-                self.assertEqual(param.requires_grad, False)
+                self.assertFalse(param.requires_grad)
 
 
 if __name__ == "__main__":