--- a
+++ b/mmaction/datasets/builder.py
@@ -0,0 +1,138 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import platform
+import random
+from functools import partial
+
+import numpy as np
+import torch
+from mmcv.parallel import collate
+from mmcv.runner import get_dist_info
+from mmcv.utils import Registry, build_from_cfg, digit_version
+from torch.utils.data import DataLoader
+
+from .samplers import ClassSpecificDistributedSampler, 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')
+BLENDINGS = Registry('blending')
+
+
+def build_dataset(cfg, default_args=None):
+    """Build a dataset from config dict.
+
+    Args:
+        cfg (dict): Config dict. It should at least contain the key "type".
+        default_args (dict | None, optional): Default initialization arguments.
+            Default: None.
+
+    Returns:
+        Dataset: The constructed dataset.
+    """
+    dataset = build_from_cfg(cfg, DATASETS, default_args)
+    return dataset
+
+
+def build_dataloader(dataset,
+                     videos_per_gpu,
+                     workers_per_gpu,
+                     num_gpus=1,
+                     dist=True,
+                     shuffle=True,
+                     seed=None,
+                     drop_last=False,
+                     pin_memory=True,
+                     persistent_workers=False,
+                     **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 (:obj:`Dataset`): A PyTorch dataset.
+        videos_per_gpu (int): Number of videos 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. Default: 1.
+        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
+        persistent_workers (bool): If True, the data loader will not shutdown
+            the worker processes after a dataset has been consumed once.
+            This allows to maintain the workers Dataset instances alive.
+            The argument also has effect in PyTorch>=1.8.0.
+            Default: False
+        kwargs (dict, optional): Any keyword argument to be used to initialize
+            DataLoader.
+
+    Returns:
+        DataLoader: A PyTorch dataloader.
+    """
+    rank, world_size = get_dist_info()
+    sample_by_class = getattr(dataset, 'sample_by_class', False)
+
+    if dist:
+        if sample_by_class:
+            dynamic_length = getattr(dataset, 'dynamic_length', True)
+            sampler = ClassSpecificDistributedSampler(
+                dataset,
+                world_size,
+                rank,
+                dynamic_length=dynamic_length,
+                shuffle=shuffle,
+                seed=seed)
+        else:
+            sampler = DistributedSampler(
+                dataset, world_size, rank, shuffle=shuffle, seed=seed)
+        shuffle = False
+        batch_size = videos_per_gpu
+        num_workers = workers_per_gpu
+    else:
+        sampler = None
+        batch_size = num_gpus * videos_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
+
+    if digit_version(torch.__version__) >= digit_version('1.8.0'):
+        kwargs['persistent_workers'] = persistent_workers
+
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        collate_fn=partial(collate, samples_per_gpu=videos_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):
+    """Init the random seed for various workers."""
+    # The seed of each worker equals to
+    # num_worker * rank + worker_id + user_seed
+    worker_seed = num_workers * rank + worker_id + seed
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)