def infer(self, args, dataloader, json_metas, subset_size, log_every=1, verbose=True):
    self.model.eval()
    with torch.no_grad():
        (logits, filename_ids) = self._infer(args, dataloader, json_metas, log_every, verbose)
    if (subset_size > logits.shape[0]):
        subset_size = logits.shape[0]
    (scores, ids) = logits.topk(subset_size, sorted=True)
    return (scores, ids, filename_ids)
def infer(self, args, dataloader, json_metas, subset_size, log_every=1, verbose=True):
    self.model.eval()
    with torch.no_grad():
        (logits, filename_ids) = self._infer(args, dataloader, json_metas, log_every, verbose)
    if (subset_size > logits.shape[0]):
        subset_size = logits.shape[0]
    (scores, ids) = logits.topk(subset_size, sorted=True)
    return (scores, ids, filename_ids)