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Source code for mmedit.datasets.builder

# Copyright (c) OpenMMLab. All rights reserved.
import copy
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 build_from_cfg
from packaging import version
from torch.utils.data import ConcatDataset, DataLoader

from .dataset_wrappers import RepeatDataset
from .registry import DATASETS
from .samplers import DistributedSampler

if platform.system() != 'Windows':
    # https://github.com/pytorch/pytorch/issues/973
    import resource
    rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
    base_soft_limit = rlimit[0]
    hard_limit = rlimit[1]
    soft_limit = min(max(4096, base_soft_limit), hard_limit)
    resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))


def _concat_dataset(cfg, default_args=None):
    """Concat datasets with different ann_file but the same type.

    Args:
        cfg (dict): The config of dataset.
        default_args (dict, optional): Default initialization arguments.
            Default: None.

    Returns:
        Dataset: The concatenated dataset.
    """
    ann_files = cfg['ann_file']

    datasets = []
    num_dset = len(ann_files)
    for i in range(num_dset):
        data_cfg = copy.deepcopy(cfg)
        data_cfg['ann_file'] = ann_files[i]
        datasets.append(build_dataset(data_cfg, default_args))

    return ConcatDataset(datasets)


[docs]def build_dataset(cfg, default_args=None): """Build a dataset from config dict. It supports a variety of dataset config. If ``cfg`` is a Sequential (list or dict), it will be a concatenated dataset of the datasets specified by the Sequential. If it is a ``RepeatDataset``, then it will repeat the dataset ``cfg['dataset']`` for ``cfg['times']`` times. If the ``ann_file`` of the dataset is a Sequential, then it will build a concatenated dataset with the same dataset type but different ``ann_file``. Args: cfg (dict): Config dict. It should at least contain the key "type". default_args (dict, optional): Default initialization arguments. Default: None. Returns: Dataset: The constructed dataset. """ 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('ann_file'), (list, tuple)): dataset = _concat_dataset(cfg, default_args) else: dataset = build_from_cfg(cfg, DATASETS, default_args) return dataset
[docs]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, persistent_workers=True, **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. samples_per_gpu (int): Number of 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. 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>=1.7.0. Default: True kwargs (dict, optional): 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, samples_per_gpu=samples_per_gpu) 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 if version.parse(torch.__version__) >= version.parse('1.7.0'): 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=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): """Function to initialize each worker. The seed of each worker equals to ``num_worker * rank + worker_id + user_seed``. Args: worker_id (int): Id for each worker. num_workers (int): Number of workers. rank (int): Rank in distributed training. seed (int): Random seed. """ worker_seed = num_workers * rank + worker_id + seed np.random.seed(worker_seed) random.seed(worker_seed)
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