Fix GPU tests because of missing .cpu()

examples/contrib/cifar10/utils.py

@@ -18,8 +18,9 @@
 
 
 def get_train_test_datasets(path):
-    if not Path(path).exists():
-        Path.mkdir(path, parents=True)
+    path = Path(path)
+    if not path.exists():
+        path.mkdir(parents=True)
         download = True
     else:
         download = True if len(os.listdir(path)) < 1 else False

ignite/contrib/metrics/precision_recall_curve.py

@@ -13,8 +13,8 @@ def precision_recall_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Te
     except ImportError:
         raise RuntimeError("This contrib module requires sklearn to be installed.")
 
-    y_true = y_targets.numpy()
-    y_pred = y_preds.numpy()
+    y_true = y_targets.cpu().numpy()
+    y_pred = y_preds.cpu().numpy()
     return precision_recall_curve(y_true, y_pred)
 
 

tests/ignite/contrib/metrics/test_average_precision.py

@@ -205,6 +205,8 @@ def get_test_cases():
     for _ in range(3):
         test_cases = get_test_cases()
         for y_pred, y, batch_size in test_cases:
+            y_pred = y_pred.to(device)
+            y = y.to(device)
             _test(y_pred, y, batch_size, "cpu")
             if device.type != "xla":
                 _test(y_pred, y, batch_size, idist.device())

tests/ignite/contrib/metrics/test_precision_recall_curve.py

@@ -111,9 +111,9 @@ def update_fn(engine, batch):
 
     data = list(range(size // batch_size))
     precision, recall, thresholds = engine.run(data, max_epochs=1).metrics["precision_recall_curve"]
-    precision = precision.numpy()
-    recall = recall.numpy()
-    thresholds = thresholds.numpy()
+    precision = precision.cpu().numpy()
+    recall = recall.cpu().numpy()
+    thresholds = thresholds.cpu().numpy()
 
     assert pytest.approx(precision) == sk_precision
     assert pytest.approx(recall) == sk_recall
@@ -168,9 +168,9 @@ def _test(y_pred, y, batch_size, metric_device):
         res = prc.compute()
 
         assert isinstance(res, Tuple)
-        assert precision_recall_curve(np_y, np_y_pred)[0] == pytest.approx(res[0])
-        assert precision_recall_curve(np_y, np_y_pred)[1] == pytest.approx(res[1])
-        assert precision_recall_curve(np_y, np_y_pred)[2] == pytest.approx(res[2])
+        assert precision_recall_curve(np_y, np_y_pred)[0] == pytest.approx(res[0].cpu().numpy())
+        assert precision_recall_curve(np_y, np_y_pred)[1] == pytest.approx(res[1].cpu().numpy())
+        assert precision_recall_curve(np_y, np_y_pred)[2] == pytest.approx(res[2].cpu().numpy())
 
     def get_test_cases():
         test_cases = [
@@ -183,9 +183,11 @@ def get_test_cases():
         ]
         return test_cases
 
-    for _ in range(5):
+    for _ in range(3):
         test_cases = get_test_cases()
         for y_pred, y, batch_size in test_cases:
+            y_pred = y_pred.to(device)
+            y = y.to(device)
             _test(y_pred, y, batch_size, "cpu")
             if device.type != "xla":
                 _test(y_pred, y, batch_size, idist.device())
@@ -229,9 +231,9 @@ def update(engine, i):
         assert precision.shape == sk_precision.shape
         assert recall.shape == sk_recall.shape
         assert thresholds.shape == sk_thresholds.shape
-        assert pytest.approx(precision) == sk_precision
-        assert pytest.approx(recall) == sk_recall
-        assert pytest.approx(thresholds) == sk_thresholds
+        assert pytest.approx(precision.cpu().numpy()) == sk_precision
+        assert pytest.approx(recall.cpu().numpy()) == sk_recall
+        assert pytest.approx(thresholds.cpu().numpy()) == sk_thresholds
 
     metric_devices = ["cpu"]
     if device.type != "xla":