def test_v1_5_metrics_utils():
x = torch.tensor([1, 2, 3])
with pytest.deprecated_call(match="It will be removed in v1.5.0"):
assert torch.equal(to_onehot(x), torch.Tensor([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]).to(int))
with pytest.deprecated_call(match="It will be removed in v1.5.0"):
assert get_num_classes(torch.tensor([1, 2, 3]), torch.tensor([1, 2, 0])) == 4
x = torch.tensor([[1.1, 2.0, 3.0], [2.0, 1.0, 0.5]])
with pytest.deprecated_call(match="It will be removed in v1.5.0"):
assert torch.equal(select_topk(x, topk=2), torch.Tensor([[0, 1, 1], [1, 1, 0]]).to(torch.int32))
x = torch.tensor([[0.2, 0.5], [0.9, 0.1]])
with pytest.deprecated_call(match="It will be removed in v1.5.0"):
assert torch.equal(to_categorical(x), torch.Tensor([1, 0]).to(int))
def _top1(x):
return select_topk(x, 1)
def _top2(x):
return select_topk(x, 2)
case = _check_classification_inputs(
preds,
target,
threshold=threshold,
num_classes=num_classes,
is_multiclass=is_multiclass,
top_k=top_k,
)
if case in (DataType.BINARY, DataType.MULTILABEL) and not top_k:
preds = (preds >= threshold).int()
num_classes = num_classes if not is_multiclass else 2
if case == DataType.MULTILABEL and top_k:
preds = select_topk(preds, top_k)
if case in (DataType.MULTICLASS, DataType.MULTIDIM_MULTICLASS) or is_multiclass:
if preds.is_floating_point():
num_classes = preds.shape[1]
preds = select_topk(preds, top_k or 1)
else:
num_classes = num_classes if num_classes else max(preds.max(), target.max()) + 1
preds = to_onehot(preds, max(2, num_classes))
target = to_onehot(target, max(2, num_classes))
if is_multiclass is False:
preds, target = preds[:, 1, ...], target[:, 1, ...]
if (case in (DataType.MULTICLASS, DataType.MULTIDIM_MULTICLASS) and is_multiclass is not False) or is_multiclass: