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# Source code for mmseg.datasets.builder

    import copy
    import platform
    import random
    from functools import partial

    import numpy as np
    from mmcv.parallel import collate
    from mmcv.runner import get_dist_info
    from mmcv.utils import Registry, build_from_cfg
    from mmcv.utils.parrots_wrapper import DataLoader, PoolDataLoader
    from torch.utils.data import DistributedSampler

    if platform.system() != 'Windows':
        # https://github.com/pytorch/pytorch/issues/973
        import resource
        rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
        hard_limit = rlimit[1]
        soft_limit = min(4096, hard_limit)
        resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))

    DATASETS = Registry('dataset')
    PIPELINES = Registry('pipeline')

    def _concat_dataset(cfg, default_args=None):
        """Build :obj:`ConcatDataset by."""
        from .dataset_wrappers import ConcatDataset
        img_dir = cfg['img_dir']
        ann_dir = cfg.get('ann_dir', None)
        split = cfg.get('split', None)
        num_img_dir = len(img_dir) if isinstance(img_dir, (list, tuple)) else 1
        if ann_dir is not None:
            num_ann_dir = len(ann_dir) if isinstance(ann_dir, (list, tuple)) else 1
        else:
            num_ann_dir = 0
        if split is not None:
            num_split = len(split) if isinstance(split, (list, tuple)) else 1
        else:
            num_split = 0
        if num_img_dir > 1:
            assert num_img_dir == num_ann_dir or num_ann_dir == 0
            assert num_img_dir == num_split or num_split == 0
        else:
            assert num_split == num_ann_dir or num_ann_dir <= 1
        num_dset = max(num_split, num_img_dir)

        datasets = []
        for i in range(num_dset):
            data_cfg = copy.deepcopy(cfg)
            if isinstance(img_dir, (list, tuple)):
                data_cfg['img_dir'] = img_dir[i]
            if isinstance(ann_dir, (list, tuple)):
                data_cfg['ann_dir'] = ann_dir[i]
            if isinstance(split, (list, tuple)):
                data_cfg['split'] = split[i]
            datasets.append(build_dataset(data_cfg, default_args))

        return ConcatDataset(datasets)

    def build_dataset(cfg, default_args=None):
        """Build datasets."""
        from .dataset_wrappers import ConcatDataset, RepeatDataset
        if isinstance(cfg, (list, tuple)):
            dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
        elif cfg['type'] == 'RepeatDataset':
            dataset = RepeatDataset(
                build_dataset(cfg['dataset'], default_args), cfg['times'])
        elif isinstance(cfg.get('img_dir'), (list, tuple)) or isinstance(
                cfg.get('split', None), (list, tuple)):
            dataset = _concat_dataset(cfg, default_args)
        else:
            dataset = build_from_cfg(cfg, DATASETS, default_args)

        return dataset

    def build_dataloader(dataset,
                         samples_per_gpu,
                         workers_per_gpu,
                         num_gpus=1,
                         dist=True,
                         shuffle=True,
                         seed=None,
                         drop_last=False,
                         pin_memory=True,
                         dataloader_type='PoolDataLoader',
                         **kwargs):
        """Build PyTorch DataLoader.

        In distributed training, each GPU/process has a dataloader.
        In non-distributed training, there is only one dataloader for all GPUs.

        Args:
            dataset (Dataset): A PyTorch dataset.
            samples_per_gpu (int): Number of training samples on each GPU, i.e.,
                batch size of each GPU.
            workers_per_gpu (int): How many subprocesses to use for data loading
                for each GPU.
            num_gpus (int): Number of GPUs. Only used in non-distributed training.
            dist (bool): Distributed training/test or not. Default: True.
            shuffle (bool): Whether to shuffle the data at every epoch.
                Default: True.
            seed (int | None): Seed to be used. Default: None.
            drop_last (bool): Whether to drop the last incomplete batch in epoch.
                Default: False
            pin_memory (bool): Whether to use pin_memory in DataLoader.
                Default: True
            dataloader_type (str): Type of dataloader. Default: 'PoolDataLoader'
            kwargs: any keyword argument to be used to initialize DataLoader

        Returns:
            DataLoader: A PyTorch dataloader.
        """
        rank, world_size = get_dist_info()
        if dist:
            sampler = DistributedSampler(
                dataset, world_size, rank, shuffle=shuffle)
            shuffle = False
            batch_size = samples_per_gpu
            num_workers = workers_per_gpu
        else:
            sampler = None
            batch_size = num_gpus * samples_per_gpu
            num_workers = num_gpus * workers_per_gpu

        init_fn = partial(
            worker_init_fn, num_workers=num_workers, rank=rank,
            seed=seed) if seed is not None else None

        assert dataloader_type in (
            'DataLoader',
            'PoolDataLoader'), f'unsupported dataloader {dataloader_type}'

        if dataloader_type == 'PoolDataLoader':
            dataloader = PoolDataLoader
        elif dataloader_type == 'DataLoader':
            dataloader = DataLoader

        data_loader = dataloader(
            dataset,
            batch_size=batch_size,
            sampler=sampler,
            num_workers=num_workers,
            collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
            pin_memory=pin_memory,
            shuffle=shuffle,
            worker_init_fn=init_fn,
            drop_last=drop_last,
            **kwargs)

        return data_loader

    def worker_init_fn(worker_id, num_workers, rank, seed):
        """Worker init func for dataloader.

        The seed of each worker equals to num_worker * rank + worker_id + user_seed

        Args:
            worker_id (int): Worker id.
            num_workers (int): Number of workers.
            rank (int): The rank of current process.
            seed (int): The random seed to use.
        """

        worker_seed = num_workers * rank + worker_id + seed
        np.random.seed(worker_seed)
        random.seed(worker_seed)