Refactored test assertions that have suboptimal tests with numbers (#7671)

### Description

As discussed in PR #7609, I tried to break the suboptimal test refactors
to smaller pieces.

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>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>

Author: freddiewanah

tests/test_bundle_get_data.py

@@ -53,7 +53,7 @@ def test_get_all_bundles_list(self, params):
             output = get_all_bundles_list(**params)
             self.assertTrue(isinstance(output, list))
             self.assertTrue(isinstance(output[0], tuple))
-            self.assertTrue(len(output[0]) == 2)
+            self.assertEqual(len(output[0]), 2)
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_5])
     @skip_if_quick

tests/test_compute_regression_metrics.py

@@ -70,22 +70,24 @@ def test_shape_reduction(self):
                         mt = mt_fn(reduction="mean")
                         mt(in_tensor, in_tensor)
                         out_tensor = mt.aggregate()
-                        self.assertTrue(len(out_tensor.shape) == 1)
+                        self.assertEqual(len(out_tensor.shape), 1)
 
                         mt = mt_fn(reduction="sum")
                         mt(in_tensor, in_tensor)
                         out_tensor = mt.aggregate()
-                        self.assertTrue(len(out_tensor.shape) == 0)
+                        self.assertEqual(len(out_tensor.shape), 0)
 
                         mt = mt_fn(reduction="sum")  # test reduction arg overriding
                         mt(in_tensor, in_tensor)
                         out_tensor = mt.aggregate(reduction="mean_channel")
-                        self.assertTrue(len(out_tensor.shape) == 1 and out_tensor.shape[0] == batch)
+                        self.assertEqual(len(out_tensor.shape), 1)
+                        self.assertEqual(out_tensor.shape[0], batch)
 
                         mt = mt_fn(reduction="sum_channel")
                         mt(in_tensor, in_tensor)
                         out_tensor = mt.aggregate()
-                        self.assertTrue(len(out_tensor.shape) == 1 and out_tensor.shape[0] == batch)
+                        self.assertEqual(len(out_tensor.shape), 1)
+                        self.assertEqual(out_tensor.shape[0], batch)
 
     def test_compare_numpy(self):
         set_determinism(seed=123)

tests/test_handler_stats.py

@@ -76,9 +76,9 @@ def _update_metric(engine):
             if has_key_word.match(line):
                 content_count += 1
         if epoch_log is True:
-            self.assertTrue(content_count == max_epochs)
+            self.assertEqual(content_count, max_epochs)
         else:
-            self.assertTrue(content_count == 2)  # 2 = len([1, 2]) from event_filter
+            self.assertEqual(content_count, 2)  # 2 = len([1, 2]) from event_filter
 
     @parameterized.expand([[True], [get_event_filter([1, 3])]])
     def test_loss_print(self, iteration_log):
@@ -116,9 +116,9 @@ def _train_func(engine, batch):
             if has_key_word.match(line):
                 content_count += 1
         if iteration_log is True:
-            self.assertTrue(content_count == num_iters * max_epochs)
+            self.assertEqual(content_count, num_iters * max_epochs)
         else:
-            self.assertTrue(content_count == 2)  # 2 = len([1, 3]) from event_filter
+            self.assertEqual(content_count, 2)  # 2 = len([1, 3]) from event_filter
 
     def test_loss_dict(self):
         log_stream = StringIO()
@@ -150,7 +150,7 @@ def _train_func(engine, batch):
         for line in output_str.split("\n"):
             if has_key_word.match(line):
                 content_count += 1
-        self.assertTrue(content_count > 0)
+        self.assertGreater(content_count, 0)
 
     def test_loss_file(self):
         key_to_handler = "test_logging"
@@ -184,7 +184,7 @@ def _train_func(engine, batch):
                 for line in output_str.split("\n"):
                     if has_key_word.match(line):
                         content_count += 1
-                self.assertTrue(content_count > 0)
+                self.assertGreater(content_count, 0)
 
     def test_exception(self):
         # set up engine
@@ -239,7 +239,7 @@ def _update_metric(engine):
         for line in output_str.split("\n"):
             if has_key_word.match(line):
                 content_count += 1
-        self.assertTrue(content_count > 0)
+        self.assertGreater(content_count, 0)
 
     def test_default_logger(self):
         log_stream = StringIO()
@@ -274,7 +274,7 @@ def _train_func(engine, batch):
         for line in output_str.split("\n"):
             if has_key_word.match(line):
                 content_count += 1
-        self.assertTrue(content_count > 0)
+        self.assertGreater(content_count, 0)
 
 
 if __name__ == "__main__":

tests/test_invertd.py

@@ -134,7 +134,7 @@ def test_invert(self):
         # 25300: 2 workers (cpu, non-macos)
         # 1812: 0 workers (gpu or macos)
         # 1821: windows torch 1.10.0
-        self.assertTrue((reverted.size - n_good) < 40000, f"diff.  {reverted.size - n_good}")
+        self.assertLess((reverted.size - n_good), 40000, f"diff.  {reverted.size - n_good}")
 
         set_determinism(seed=None)
 

tests/test_load_spacing_orientation.py

@@ -48,7 +48,7 @@ def test_load_spacingd(self, filename):
         ref = resample_to_output(anat, (1, 0.2, 1), order=1)
         t2 = time.time()
         print(f"time scipy: {t2 - t1}")
-        self.assertTrue(t2 >= t1)
+        self.assertGreaterEqual(t2, t1)
         np.testing.assert_allclose(res_dict["image"].affine, ref.affine)
         np.testing.assert_allclose(res_dict["image"].shape[1:], ref.shape)
         np.testing.assert_allclose(ref.get_fdata(), res_dict["image"][0], atol=0.05)
@@ -68,7 +68,7 @@ def test_load_spacingd_rotate(self, filename):
         ref = resample_to_output(anat, (1, 2, 3), order=1)
         t2 = time.time()
         print(f"time scipy: {t2 - t1}")
-        self.assertTrue(t2 >= t1)
+        self.assertGreaterEqual(t2, t1)
         np.testing.assert_allclose(res_dict["image"].affine, ref.affine)
         if "anatomical" not in filename:
             np.testing.assert_allclose(res_dict["image"].shape[1:], ref.shape)

tests/test_meta_affine.py

@@ -160,7 +160,7 @@ def test_linear_consistent(self, xform_cls, input_dict, atol):
         diff = np.abs(itk.GetArrayFromImage(ref_2) - itk.GetArrayFromImage(expected))
         avg_diff = np.mean(diff)
 
-        self.assertTrue(avg_diff < atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}")
+        self.assertLess(avg_diff, atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}")
 
     @parameterized.expand(TEST_CASES_DICT)
     def test_linear_consistent_dict(self, xform_cls, input_dict, atol):
@@ -175,7 +175,7 @@ def test_linear_consistent_dict(self, xform_cls, input_dict, atol):
         diff = {k: np.abs(itk.GetArrayFromImage(ref_2[k]) - itk.GetArrayFromImage(expected[k])) for k in keys}
         avg_diff = {k: np.mean(diff[k]) for k in keys}
         for k in keys:
-            self.assertTrue(avg_diff[k] < atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}")
+            self.assertLess(avg_diff[k], atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}")
 
 
 if __name__ == "__main__":

tests/test_persistentdataset.py

@@ -165,7 +165,7 @@ def test_different_transforms(self):
             im1 = PersistentDataset([im], Identity(), cache_dir=path, hash_transform=json_hashing)[0]
             im2 = PersistentDataset([im], Flip(1), cache_dir=path, hash_transform=json_hashing)[0]
             l2 = ((im1 - im2) ** 2).sum() ** 0.5
-            self.assertTrue(l2 > 1)
+            self.assertGreater(l2, 1)
 
 
 if __name__ == "__main__":

tests/test_rand_weighted_cropd.py

@@ -154,7 +154,7 @@ def test_rand_weighted_cropd(self, _, init_params, input_data, expected_shape, e
         crop = RandWeightedCropd(**init_params)
         crop.set_random_state(10)
         result = crop(input_data)
-        self.assertTrue(len(result) == init_params["num_samples"])
+        self.assertEqual(len(result), init_params["num_samples"])
         _len = len(tuple(input_data.keys()))
         self.assertTupleEqual(tuple(result[0].keys())[:_len], tuple(input_data.keys()))
 

tests/test_recon_net_utils.py

@@ -64,7 +64,7 @@ def test_reshape_channel_complex(self, test_data):
     def test_complex_normalize(self, test_data):
         result, mean, std = complex_normalize(test_data)
         result = result * std + mean
-        self.assertTrue((((result - test_data) ** 2).mean() ** 0.5).item() < 1e-5)
+        self.assertLess((((result - test_data) ** 2).mean() ** 0.5).item(), 1e-5)
 
     @parameterized.expand(TEST_PAD)
     def test_pad(self, test_data):

tests/test_reg_loss_integration.py

@@ -99,7 +99,7 @@ def forward(self, x):
             # backward pass
             loss_val.backward()
             optimizer.step()
-        self.assertTrue(init_loss > loss_val, "loss did not decrease")
+        self.assertGreater(init_loss, loss_val, "loss did not decrease")
 
 
 if __name__ == "__main__":