adds available_device to test_precision_recall_curve #3335

tests/ignite/metrics/test_precision_recall_curve.py

@@ -14,6 +14,16 @@
 from ignite.metrics.precision_recall_curve import PrecisionRecallCurve
 
 
+def to_numpy_float32(x):
+    if isinstance(x, torch.Tensor):
+        if x.device.type == "mps":
+            x = x.to("cpu")  # Explicitly move from MPS to CPU
+        return x.detach().to(dtype=torch.float32).numpy()
+    elif isinstance(x, np.ndarray):
+        return x.astype(np.float32)
+    return x
+
+
 @pytest.fixture()
 def mock_no_sklearn():
     with patch.dict("sys.modules", {"sklearn.metrics": None}):
@@ -28,112 +38,144 @@ def test_no_sklearn(mock_no_sklearn):
         pr_curve.compute()
 
 
-def test_precision_recall_curve():
+def test_precision_recall_curve(available_device):
     size = 100
-    np_y_pred = np.random.rand(size, 1)
-    np_y = np.zeros((size,))
-    np_y[size // 2 :] = 1
-    sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred)
+    y_pred = torch.rand(size, 1, dtype=torch.float32, device=available_device)
+    y_true = torch.zeros(size, dtype=torch.float32, device=available_device)
+    y_true[size // 2 :] = 1.0
+    sk_precision, sk_recall, sk_thresholds = precision_recall_curve(to_numpy_float32(y_true), to_numpy_float32(y_pred))
 
-    precision_recall_curve_metric = PrecisionRecallCurve()
-    y_pred = torch.from_numpy(np_y_pred)
-    y = torch.from_numpy(np_y)
+    sk_precision = to_numpy_float32(sk_precision)
+    sk_recall = to_numpy_float32(sk_recall)
+    sk_thresholds = to_numpy_float32(sk_thresholds)
 
-    precision_recall_curve_metric.update((y_pred, y))
+    precision_recall_curve_metric = PrecisionRecallCurve(device=available_device)
+    assert precision_recall_curve_metric._device == torch.device(available_device)
+
+    precision_recall_curve_metric.update((y_pred, y_true))
     precision, recall, thresholds = precision_recall_curve_metric.compute()
-    precision = precision.numpy()
-    recall = recall.numpy()
-    thresholds = thresholds.numpy()
 
-    assert pytest.approx(precision) == sk_precision
-    assert pytest.approx(recall) == sk_recall
+    precision = to_numpy_float32(precision)
+    recall = to_numpy_float32(recall)
+    thresholds = to_numpy_float32(thresholds)
+
+    sk_precision = to_numpy_float32(sk_precision)
+    sk_recall = to_numpy_float32(sk_recall)
+    sk_thresholds = to_numpy_float32(sk_thresholds)
+
+    assert np.allclose(precision, sk_precision, rtol=1e-6)
+    assert np.allclose(recall, sk_recall, rtol=1e-6)
     # assert thresholds almost equal, due to numpy->torch->numpy conversion
     np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
 
 
-def test_integration_precision_recall_curve_with_output_transform():
-    np.random.seed(1)
+def test_integration_precision_recall_curve_with_output_transform(available_device):
+    torch.manual_seed(1)
     size = 100
-    np_y_pred = np.random.rand(size, 1)
-    np_y = np.zeros((size,))
-    np_y[size // 2 :] = 1
-    np.random.shuffle(np_y)
+    y_pred = torch.rand(size, 1, dtype=torch.float32, device=available_device)
+    y_true = torch.zeros(size, dtype=torch.float32, device=available_device)
+    y_true[size // 2 :] = 1.0
+    perm = torch.randperm(size)
+    y_pred = y_pred[perm]
+    y_true = y_true[perm]
 
-    sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred)
+    sk_precision, sk_recall, sk_thresholds = precision_recall_curve(to_numpy_float32(y_true), to_numpy_float32(y_pred))
 
     batch_size = 10
 
     def update_fn(engine, batch):
         idx = (engine.state.iteration - 1) * batch_size
-        y_true_batch = np_y[idx : idx + batch_size]
-        y_pred_batch = np_y_pred[idx : idx + batch_size]
-        return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
+        y_true_batch = y_true[idx : idx + batch_size]
+        y_pred_batch = y_pred[idx : idx + batch_size]
+        return idx, y_pred_batch, y_true_batch
 
     engine = Engine(update_fn)
 
-    precision_recall_curve_metric = PrecisionRecallCurve(output_transform=lambda x: (x[1], x[2]))
+    precision_recall_curve_metric = PrecisionRecallCurve(
+        output_transform=lambda x: (x[1], x[2]), device=available_device
+    )
+    assert precision_recall_curve_metric._device == torch.device(available_device)
     precision_recall_curve_metric.attach(engine, "precision_recall_curve")
 
     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()
-    assert pytest.approx(precision) == sk_precision
-    assert pytest.approx(recall) == sk_recall
+
+    precision = to_numpy_float32(precision)
+    recall = to_numpy_float32(recall)
+    thresholds = to_numpy_float32(thresholds)
+
+    sk_precision = to_numpy_float32(sk_precision)
+    sk_recall = to_numpy_float32(sk_recall)
+    sk_thresholds = to_numpy_float32(sk_thresholds)
+
+    assert np.allclose(precision, sk_precision, rtol=1e-6)
+    assert np.allclose(recall, sk_recall, rtol=1e-6)
     # assert thresholds almost equal, due to numpy->torch->numpy conversion
     np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
 
 
-def test_integration_precision_recall_curve_with_activated_output_transform():
+def test_integration_precision_recall_curve_with_activated_output_transform(available_device):
     np.random.seed(1)
     size = 100
-    np_y_pred = np.random.rand(size, 1)
-    np_y_pred_sigmoid = torch.sigmoid(torch.from_numpy(np_y_pred)).numpy()
-    np_y = np.zeros((size,))
-    np_y[size // 2 :] = 1
-    np.random.shuffle(np_y)
-
-    sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred_sigmoid)
+    y_pred = torch.rand(size, 1, dtype=torch.float32, device=available_device)
+    y_true = torch.zeros(size, dtype=torch.float32, device=available_device)
+    y_true[size // 2 :] = 1.0
+    perm = torch.randperm(size)
+    y_pred = y_pred[perm]
+    y_true = y_true[perm]
+
+    sigmoid_y_pred = torch.sigmoid(y_pred).cpu().numpy()
+    sk_precision, sk_recall, sk_thresholds = precision_recall_curve(
+        to_numpy_float32(y_true), to_numpy_float32(sigmoid_y_pred)
+    )
 
     batch_size = 10
 
     def update_fn(engine, batch):
         idx = (engine.state.iteration - 1) * batch_size
-        y_true_batch = np_y[idx : idx + batch_size]
-        y_pred_batch = np_y_pred[idx : idx + batch_size]
-        return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
+        y_true_batch = y_true[idx : idx + batch_size]
+        y_pred_batch = y_pred[idx : idx + batch_size]
+        return idx, y_pred_batch, y_true_batch
 
     engine = Engine(update_fn)
 
-    precision_recall_curve_metric = PrecisionRecallCurve(output_transform=lambda x: (torch.sigmoid(x[1]), x[2]))
+    precision_recall_curve_metric = PrecisionRecallCurve(
+        output_transform=lambda x: (torch.sigmoid(x[1]), x[2]), device=available_device
+    )
+    assert precision_recall_curve_metric._device == torch.device(available_device)
     precision_recall_curve_metric.attach(engine, "precision_recall_curve")
 
     data = list(range(size // batch_size))
     precision, recall, thresholds = engine.run(data, max_epochs=1).metrics["precision_recall_curve"]
-    precision = precision.cpu().numpy()
-    recall = recall.cpu().numpy()
-    thresholds = thresholds.cpu().numpy()
+    precision = to_numpy_float32(precision)
+    recall = to_numpy_float32(recall)
+    thresholds = to_numpy_float32(thresholds)
+
+    sk_precision = to_numpy_float32(sk_precision)
+    sk_recall = to_numpy_float32(sk_recall)
+    sk_thresholds = to_numpy_float32(sk_thresholds)
 
-    assert pytest.approx(precision) == sk_precision
-    assert pytest.approx(recall) == sk_recall
+    assert np.allclose(precision, sk_precision, rtol=1e-6)
+    assert np.allclose(recall, sk_recall, rtol=1e-6)
     # assert thresholds almost equal, due to numpy->torch->numpy conversion
     np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
 
 
-def test_check_compute_fn():
+def test_check_compute_fn(available_device):
     y_pred = torch.zeros((8, 13))
     y_pred[:, 1] = 1
     y_true = torch.zeros_like(y_pred)
     output = (y_pred, y_true)
 
-    em = PrecisionRecallCurve(check_compute_fn=True)
+    em = PrecisionRecallCurve(check_compute_fn=True, device=available_device)
+    assert em._device == torch.device(available_device)
 
     em.reset()
     with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
         em.update(output)
 
-    em = PrecisionRecallCurve(check_compute_fn=False)
+    em = PrecisionRecallCurve(check_compute_fn=False, device=available_device)
+    assert em._device == torch.device(available_device)
     em.update(output)
 
 
@@ -157,15 +199,29 @@ def _test(y_pred, y, batch_size, metric_device):
         y_pred = idist.all_gather(y_pred)
         y = idist.all_gather(y)
 
-        np_y = y.cpu().numpy()
-        np_y_pred = y_pred.cpu().numpy()
+        np_y = to_numpy_float32(y)
+        np_y_pred = to_numpy_float32(y_pred)
 
         res = prc.compute()
 
         assert isinstance(res, Tuple)
-        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())
+        sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred)
+
+        assert np.allclose(
+            to_numpy_float32(res[0]),
+            to_numpy_float32(sk_precision),
+            rtol=1e-6,
+        )
+        assert np.allclose(
+            to_numpy_float32(res[1]),
+            to_numpy_float32(sk_recall),
+            rtol=1e-6,
+        )
+        assert np.allclose(
+            to_numpy_float32(res[2]),
+            to_numpy_float32(sk_thresholds),
+            rtol=1e-6,
+        )
 
     def get_test_cases():
         test_cases = [
@@ -222,17 +278,26 @@ def update(engine, i):
 
         precision, recall, thresholds = engine.state.metrics["prc"]
 
-        np_y_true = y_true.cpu().numpy().ravel()
-        np_y_preds = y_preds.cpu().numpy().ravel()
+        np_y_true = to_numpy_float32(y_true).ravel()
+        np_y_preds = to_numpy_float32(y_preds).ravel()
 
         sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y_true, np_y_preds)
 
+        sk_precision = sk_precision.astype(np.float32)
+        sk_recall = sk_recall.astype(np.float32)
+        sk_thresholds = sk_thresholds.astype(np.float32)
+
+        precision = to_numpy_float32(precision)
+        recall = to_numpy_float32(recall)
+        thresholds = to_numpy_float32(thresholds)
+
         assert precision.shape == sk_precision.shape
         assert recall.shape == sk_recall.shape
         assert thresholds.shape == sk_thresholds.shape
-        assert pytest.approx(precision.cpu().numpy()) == sk_precision
-        assert pytest.approx(recall.cpu().numpy()) == sk_recall
-        assert pytest.approx(thresholds.cpu().numpy()) == sk_thresholds
+
+        assert np.allclose(precision, sk_precision, rtol=1e-6)
+        assert np.allclose(recall, sk_recall, rtol=1e-6)
+        np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
 
     metric_devices = ["cpu"]
     if device.type != "xla":