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adds available_device to test_multilabel_confusion_matrix #3335 #3366

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Merged
vfdev-5 merged 3 commits into from
Mar 28, 2025
Merged

adds available_device to test_multilabel_confusion_matrix #3335 #3366

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2b29873
adds available_device to test_multilabel_confusion_matrix #3335
BanzaiTokyo Mar 28, 2025
d75a074
moves tensors to cpu
BanzaiTokyo Mar 28, 2025
4663000
Merge branch 'master' into test_multilabel_confusion_matrix_available…
vfdev-5 Mar 28, 2025
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75 changes: 43 additions & 32 deletions tests/ignite/metrics/test_multilabel_confusion_matrix.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,9 +87,10 @@ def get_y_true_y_pred():
return y_true, y_pred


def test_multiclass_images():
def test_multiclass_images(available_device):
num_classes = 3
cm = MultiLabelConfusionMatrix(num_classes=num_classes)
cm = MultiLabelConfusionMatrix(num_classes=num_classes, device=available_device)
assert cm._device == torch.device(available_device)

y_true, y_pred = get_y_true_y_pred()

Expand All @@ -107,7 +108,8 @@ def test_multiclass_images():
assert np.all(ignite_CM == sklearn_CM)

# Another test on batch of 2 images
cm = MultiLabelConfusionMatrix(num_classes=num_classes)
cm = MultiLabelConfusionMatrix(num_classes=num_classes, device=available_device)
assert cm._device == torch.device(available_device)

# Create a batch of two images:
th_y_true1 = torch.tensor(y_true)
Expand Down Expand Up @@ -208,7 +210,7 @@ def _test_distrib_accumulator_device(device):
), f"{type(cm.confusion_matrix.device)}:{cm._num_correct.device} vs {type(metric_device)}:{metric_device}"


def test_simple_2D_input():
def test_simple_2D_input(available_device):
# Tests for 2D inputs with normalized = True and False

num_iters = 5
Expand All @@ -218,19 +220,21 @@ def test_simple_2D_input():
for _ in range(num_iters):
target = torch.randint(0, 2, size=(num_samples, num_classes))
prediction = torch.randint(0, 2, size=(num_samples, num_classes))
sklearn_CM = multilabel_confusion_matrix(target.numpy(), prediction.numpy())
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
sklearn_CM = multilabel_confusion_matrix(target.cpu().numpy(), prediction.cpu().numpy())
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
mlcm.update([prediction, target])
ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=True)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=True, device=available_device)
assert mlcm._device == torch.device(available_device)
mlcm.update([prediction, target])
ignite_CM_normalized = mlcm.compute().numpy()
ignite_CM_normalized = mlcm.compute().cpu().numpy()
sklearn_CM_normalized = sklearn_CM / sklearn_CM.sum(axis=(1, 2))[:, None, None]
assert np.allclose(sklearn_CM_normalized, ignite_CM_normalized)


def test_simple_ND_input():
def test_simple_ND_input(available_device):
num_iters = 5
num_samples = 100
num_classes = 10
Expand All @@ -240,82 +244,88 @@ def test_simple_ND_input():
for _ in range(num_iters): # 3D tests
target = torch.randint(0, 2, size=(num_samples, num_classes, size_3d))
prediction = torch.randint(0, 2, size=(num_samples, num_classes, size_3d))
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
mlcm.update([prediction, target])
ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
target_reshaped = target.permute(0, 2, 1).reshape(size_3d * num_samples, num_classes)
prediction_reshaped = prediction.permute(0, 2, 1).reshape(size_3d * num_samples, num_classes)
sklearn_CM = multilabel_confusion_matrix(target_reshaped.numpy(), prediction_reshaped.numpy())
sklearn_CM = multilabel_confusion_matrix(target_reshaped.cpu().numpy(), prediction_reshaped.cpu().numpy())
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))

size_4d = 4
for _ in range(num_iters): # 4D tests
target = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d))
prediction = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d))
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
mlcm.update([prediction, target])
ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
target_reshaped = target.permute(0, 2, 3, 1).reshape(size_3d * size_4d * num_samples, num_classes)
prediction_reshaped = prediction.permute(0, 2, 3, 1).reshape(size_3d * size_4d * num_samples, num_classes)
sklearn_CM = multilabel_confusion_matrix(target_reshaped.numpy(), prediction_reshaped.numpy())
sklearn_CM = multilabel_confusion_matrix(target_reshaped.cpu().numpy(), prediction_reshaped.cpu().numpy())
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))

size_5d = 4
for _ in range(num_iters): # 5D tests
target = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d, size_5d))
prediction = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d, size_5d))
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
mlcm.update([prediction, target])
ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
target_reshaped = target.permute(0, 2, 3, 4, 1).reshape(size_3d * size_4d * size_5d * num_samples, num_classes)
prediction_reshaped = prediction.permute(0, 2, 3, 4, 1).reshape(
size_3d * size_4d * size_5d * num_samples, num_classes
)
sklearn_CM = multilabel_confusion_matrix(target_reshaped.numpy(), prediction_reshaped.numpy())
sklearn_CM = multilabel_confusion_matrix(target_reshaped.cpu().numpy(), prediction_reshaped.cpu().numpy())
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))


def test_simple_batched():
def test_simple_batched(available_device):
num_iters = 5
num_samples = 100
num_classes = 10
batch_size = 1
torch.manual_seed(0)

for _ in range(num_iters): # 2D tests
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
targets = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes))
predictions = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes))
for i in range(int(num_samples / batch_size)):
target_sample = targets[i]
prediction_sample = predictions[i]
mlcm.update([prediction_sample, target_sample])

ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
targets_reshaped = targets.reshape(-1, num_classes)
predictions_reshaped = predictions.reshape(-1, num_classes)
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy())
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.cpu().numpy(), predictions_reshaped.cpu().numpy())
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))

size_3d = 4
for _ in range(num_iters): # 3D tests
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
targets = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d))
predictions = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d))
for i in range(int(num_samples / batch_size)):
target_sample = targets[i]
prediction_sample = predictions[i]
mlcm.update([prediction_sample, target_sample])

ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
targets_reshaped = targets.permute(0, 1, 3, 2).reshape(-1, num_classes)
predictions_reshaped = predictions.permute(0, 1, 3, 2).reshape(-1, num_classes)
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy())
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.cpu().numpy(), predictions_reshaped.cpu().numpy())
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))

size_4d = 4
for _ in range(num_iters): # 4D tests
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
targets = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d, size_4d))
predictions = torch.randint(
0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d, size_4d)
Expand All @@ -325,15 +335,16 @@ def test_simple_batched():
prediction_sample = predictions[i]
mlcm.update([prediction_sample, target_sample])

ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
targets_reshaped = targets.permute(0, 1, 3, 4, 2).reshape(-1, num_classes)
predictions_reshaped = predictions.permute(0, 1, 3, 4, 2).reshape(-1, num_classes)
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy())
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.cpu().numpy(), predictions_reshaped.cpu().numpy())
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))

size_5d = 4
for _ in range(num_iters): # 5D tests
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False)
mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False, device=available_device)
assert mlcm._device == torch.device(available_device)
targets = torch.randint(
0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d, size_4d, size_5d)
)
Expand All @@ -345,10 +356,10 @@ def test_simple_batched():
prediction_sample = predictions[i]
mlcm.update([prediction_sample, target_sample])

ignite_CM = mlcm.compute().numpy()
ignite_CM = mlcm.compute().cpu().numpy()
targets_reshaped = targets.permute(0, 1, 3, 4, 5, 2).reshape(-1, num_classes)
predictions_reshaped = predictions.permute(0, 1, 3, 4, 5, 2).reshape(-1, num_classes)
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy())
sklearn_CM = multilabel_confusion_matrix(targets_reshaped.cpu().numpy(), predictions_reshaped.cpu().numpy())
assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64))


Expand Down
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