123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
import platform
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import random
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from distutils.version import LooseVersion
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from functools import partial
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import numpy as np
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import torch
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from mmcv.parallel import collate
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from mmcv.runner import get_dist_info
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from mmcv.utils import Registry, build_from_cfg
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from torch.utils.data import DataLoader
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from .samplers import DistributedSampler
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if platform.system() != 'Windows':
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# https://github.com/pytorch/pytorch/issues/973
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import resource
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rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
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hard_limit = rlimit[1]
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soft_limit = min(4096, hard_limit)
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resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
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DATASETS = Registry('dataset')
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PIPELINES = Registry('pipeline')
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def build_dataset(cfg, default_args=None):
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from .dataset_wrappers import (ConcatDataset, RepeatDataset,
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ClassBalancedDataset)
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if isinstance(cfg, (list, tuple)):
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dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
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elif cfg['type'] == 'RepeatDataset':
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dataset = RepeatDataset(
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build_dataset(cfg['dataset'], default_args), cfg['times'])
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elif cfg['type'] == 'ClassBalancedDataset':
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dataset = ClassBalancedDataset(
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build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])
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else:
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dataset = build_from_cfg(cfg, DATASETS, default_args)
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return dataset
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def build_dataloader(dataset,
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samples_per_gpu,
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workers_per_gpu,
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num_gpus=1,
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dist=True,
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shuffle=True,
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round_up=True,
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seed=None,
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pin_memory=True,
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persistent_workers=True,
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**kwargs):
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"""Build PyTorch DataLoader.
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In distributed training, each GPU/process has a dataloader.
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In non-distributed training, there is only one dataloader for all GPUs.
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Args:
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dataset (Dataset): A PyTorch dataset.
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samples_per_gpu (int): Number of training samples on each GPU, i.e.,
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batch size of each GPU.
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workers_per_gpu (int): How many subprocesses to use for data loading
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for each GPU.
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num_gpus (int): Number of GPUs. Only used in non-distributed training.
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dist (bool): Distributed training/test or not. Default: True.
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shuffle (bool): Whether to shuffle the data at every epoch.
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Default: True.
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round_up (bool): Whether to round up the length of dataset by adding
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extra samples to make it evenly divisible. Default: True.
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pin_memory (bool): Whether to use pin_memory in DataLoader.
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Default: True
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persistent_workers (bool): If True, the data loader will not shutdown
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the worker processes after a dataset has been consumed once.
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This allows to maintain the workers Dataset instances alive.
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The argument also has effect in PyTorch>=1.7.0.
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Default: True
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kwargs: any keyword argument to be used to initialize DataLoader
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Returns:
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DataLoader: A PyTorch dataloader.
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"""
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rank, world_size = get_dist_info()
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if dist:
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sampler = DistributedSampler(
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dataset, world_size, rank, shuffle=shuffle, round_up=round_up)
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shuffle = False
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batch_size = samples_per_gpu
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num_workers = workers_per_gpu
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else:
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sampler = None
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batch_size = num_gpus * samples_per_gpu
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num_workers = num_gpus * workers_per_gpu
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init_fn = partial(
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worker_init_fn, num_workers=num_workers, rank=rank,
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seed=seed) if seed is not None else None
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if LooseVersion(torch.__version__) >= LooseVersion('1.7.0'):
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kwargs['persistent_workers'] = persistent_workers
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data_loader = DataLoader(
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dataset,
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batch_size=batch_size,
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sampler=sampler,
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num_workers=num_workers,
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collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
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pin_memory=pin_memory,
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shuffle=shuffle,
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worker_init_fn=init_fn,
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**kwargs)
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return data_loader
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def worker_init_fn(worker_id, num_workers, rank, seed):
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# The seed of each worker equals to
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# num_worker * rank + worker_id + user_seed
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worker_seed = num_workers * rank + worker_id + seed
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np.random.seed(worker_seed)
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random.seed(worker_seed)
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