113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
import platform
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import random
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from functools import partial
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from typing import Optional, Union
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import numpy as np
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from custom_mmpkg.custom_mmcv.parallel import collate
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from custom_mmpkg.custom_mmcv.runner import get_dist_info
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from custom_mmpkg.custom_mmcv.utils import Registry, build_from_cfg
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from torch.utils.data import DataLoader
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from torch.utils.data.dataset import Dataset
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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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base_soft_limit = rlimit[0]
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hard_limit = rlimit[1]
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soft_limit = min(max(4096, base_soft_limit), 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: Union[dict, list, tuple],
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default_args: Optional[Union[dict, None]] = None):
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""""Build dataset by the given config."""
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from .dataset_wrappers import (
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ConcatDataset,
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RepeatDataset,
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)
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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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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: Dataset,
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samples_per_gpu: int,
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workers_per_gpu: int,
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num_gpus: Optional[int] = 1,
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dist: Optional[bool] = True,
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shuffle: Optional[bool] = True,
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round_up: Optional[bool] = True,
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seed: Optional[Union[int, None]] = None,
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persistent_workers: Optional[bool] = 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 (:obj:`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, optional): Number of GPUs. Only used in non-distributed
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training.
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dist (bool, optional): Distributed training/test or not. Default: True.
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shuffle (bool, optional): Whether to shuffle the data at every epoch.
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Default: True.
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round_up (bool, optional): Whether to round up the length of dataset by
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adding extra samples to make it evenly divisible. 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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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=False,
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shuffle=shuffle,
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worker_init_fn=init_fn,
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persistent_workers=persistent_workers,
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**kwargs)
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return data_loader
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def worker_init_fn(worker_id: int, num_workers: int, rank: int, seed: int):
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"""Init random seed for each worker."""
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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) |