Added: Improved tests using pytest parametrize

tests/ignite/metrics/test_root_mean_squared_error.py

@@ -17,46 +17,40 @@ def test_zero_sample():
         rmse.compute()
 
 
-def test_compute():
+@pytest.fixture(params=[0, 1, 2, 3])
+def test_data(request):
+    return [
+        (torch.empty(10).uniform_(0, 10), torch.empty(10).uniform_(0, 10), 1),
+        (torch.empty(10, 1).uniform_(-10, 10), torch.empty(10, 1).uniform_(-10, 10), 1),
+        # updated batches
+        (torch.empty(50).uniform_(0, 10), torch.empty(50).uniform_(0, 10), 16),
+        (torch.empty(50, 1).uniform_(-10, 10), torch.empty(50, 1).uniform_(-10, 10), 16),
+    ][request.param]
+
+
+@pytest.mark.parametrize("n_times", range(3))
+def test_compute(n_times, test_data):
 
     rmse = RootMeanSquaredError()
 
-    def _test(y_pred, y, batch_size):
-        rmse.reset()
-        if batch_size > 1:
-            n_iters = y.shape[0] // batch_size + 1
-            for i in range(n_iters):
-                idx = i * batch_size
-                rmse.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
-        else:
-            rmse.update((y_pred, y))
-
-        np_y = y.numpy().ravel()
-        np_y_pred = y_pred.numpy().ravel()
-
-        np_res = np.sqrt(np.power((np_y - np_y_pred), 2.0).sum() / np_y.shape[0])
-        res = rmse.compute()
-
-        assert isinstance(res, float)
-        assert pytest.approx(res) == np_res
-
-    def get_test_cases():
-
-        test_cases = [
-            (torch.empty(10).uniform_(0, 10), torch.empty(10).uniform_(0, 10), 1),
-            (torch.empty(10, 1).uniform_(-10, 10), torch.empty(10, 1).uniform_(-10, 10), 1),
-            # updated batches
-            (torch.empty(50).uniform_(0, 10), torch.empty(50).uniform_(0, 10), 16),
-            (torch.empty(50, 1).uniform_(-10, 10), torch.empty(50, 1).uniform_(-10, 10), 16),
-        ]
-
-        return test_cases
-
-    for _ in range(5):
-        # check multiple random inputs as random exact occurencies are rare
-        test_cases = get_test_cases()
-        for y_pred, y, batch_size in test_cases:
-            _test(y_pred, y, batch_size)
+    y_pred, y, batch_size = test_data
+    rmse.reset()
+    if batch_size > 1:
+        n_iters = y.shape[0] // batch_size + 1
+        for i in range(n_iters):
+            idx = i * batch_size
+            rmse.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
+    else:
+        rmse.update((y_pred, y))
+
+    np_y = y.numpy().ravel()
+    np_y_pred = y_pred.numpy().ravel()
+
+    np_res = np.sqrt(np.power((np_y - np_y_pred), 2.0).sum() / np_y.shape[0])
+    res = rmse.compute()
+
+    assert isinstance(res, float)
+    assert pytest.approx(res) == np_res
 
 
 def _test_distrib_integration(device, tol=1e-6):