def benchmark_workers(self, num_iter, warmup=10):
        """
        Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers].
        """
        candidates = [0, 1]
        if self.num_workers not in candidates:
            candidates.append(self.num_workers)

        dataset = MapDataset(self.dataset, self.mapper)
        for n in candidates:
            loader = build_batch_data_loader(
                dataset,
                self.sampler,
                self.total_batch_size,
                num_workers=n,
            )
            self._benchmark(
                iter(loader),
                num_iter * max(n, 1),
                warmup * max(n, 1),
                f"DataLoader ({n} workers, bs={self.per_gpu_batch_size})",
            )
            del loader

        Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers].
        
video_chat_text/video_chat_with_ChatGPT/models/grit_src/third_party/CenterNet2/detectron2/data/benchmark.py