def test_warning_on_difference_in_number_of_classes():
"""Test that warning is thrown if the detected number of classes are different from the the specified number of
classes."""
preds = torch.randint(3, (10, ))
target = torch.randint(3, (10, ))
with pytest.warns(RuntimeWarning, ):
jaccard_index(preds, target, num_classes=4)
def test(loader):
model.eval()
ious, categories = [], []
y_map = torch.empty(loader.dataset.num_classes, device=device).long()
for data in loader:
data = data.to(device)
outs = model(data)
sizes = (data.ptr[1:] - data.ptr[:-1]).tolist()
for out, y, category in zip(outs.split(sizes), data.y.split(sizes),
data.category.tolist()):
category = list(ShapeNet.seg_classes.keys())[category]
part = ShapeNet.seg_classes[category]
part = torch.tensor(part, device=device)
y_map[part] = torch.arange(part.size(0), device=device)
iou = jaccard_index(out[:, part].argmax(dim=-1), y_map[y],
num_classes=part.size(0), absent_score=1.0)
ious.append(iou)
categories.append(data.category)
iou = torch.tensor(ious, device=device)
category = torch.cat(categories, dim=0)
mean_iou = scatter(iou, category, reduce='mean') # Per-category IoU.
return float(mean_iou.mean()) # Global IoU.
def test_jaccard_ignore_index(pred, target, ignore_index, num_classes, reduction, expected):
jaccard_val = jaccard_index(
preds=tensor(pred),
target=tensor(target),
ignore_index=ignore_index,
num_classes=num_classes,
reduction=reduction,
)
assert torch.allclose(jaccard_val, tensor(expected).to(jaccard_val))
def test_jaccard_absent_score(pred, target, ignore_index, absent_score, num_classes, expected):
jaccard_val = jaccard_index(
preds=tensor(pred),
target=tensor(target),
ignore_index=ignore_index,
absent_score=absent_score,
num_classes=num_classes,
reduction="none",
)
assert torch.allclose(jaccard_val, tensor(expected).to(jaccard_val))
def test_jaccard(half_ones, reduction, ignore_index, expected):
preds = (torch.arange(120) % 3).view(-1, 1)
target = (torch.arange(120) % 3).view(-1, 1)
if half_ones:
preds[:60] = 1
jaccard_val = jaccard_index(
preds=preds,
target=target,
num_classes=3,
ignore_index=ignore_index,
reduction=reduction,
)
assert torch.allclose(jaccard_val, expected, atol=1e-9)
