def update(self, preds, target):
assert preds.shape == target.shape
binary_topk_preds = select_topk(preds, self.top_k)
target = target.to(dtype=torch.int)
num_relevant = torch.sum(binary_topk_preds & target, dim=-1)
top_ks = torch.tensor([self.top_k] * preds.shape[0]).to(preds.device)
self.score += torch.nan_to_num(num_relevant /
torch.min(top_ks, target.sum(dim=-1)),
posinf=0.).sum()
self.num_sample += len(preds)
def _top2(x):
return select_topk(x, 2)
def _top1(x):
return select_topk(x, 1)
case = _check_classification_inputs(
preds,
target,
threshold=threshold,
num_classes=num_classes,
multiclass=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 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 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 multiclass is False:
preds, target = preds[:, 1, ...], target[:, 1, ...]
if (case in (DataType.MULTICLASS, DataType.MULTIDIM_MULTICLASS) and multiclass is not False) or multiclass:
