Shortcuts

Source code for mmdet.datasets.builder

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
import warnings
from functools import partial

import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import TORCH_VERSION, Registry, build_from_cfg, digit_version
from torch.utils.data import DataLoader

from .samplers import (DistributedGroupSampler, DistributedSampler,
                       GroupSampler, InfiniteBatchSampler,
                       InfiniteGroupBatchSampler)

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))

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


def _concat_dataset(cfg, default_args=None):
    from .dataset_wrappers import ConcatDataset
    ann_files = cfg['ann_file']
    img_prefixes = cfg.get('img_prefix', None)
    seg_prefixes = cfg.get('seg_prefix', None)
    proposal_files = cfg.get('proposal_file', None)
    separate_eval = cfg.get('separate_eval', True)

    datasets = []
    num_dset = len(ann_files)
    for i in range(num_dset):
        data_cfg = copy.deepcopy(cfg)
        # pop 'separate_eval' since it is not a valid key for common datasets.
        if 'separate_eval' in data_cfg:
            data_cfg.pop('separate_eval')
        data_cfg['ann_file'] = ann_files[i]
        if isinstance(img_prefixes, (list, tuple)):
            data_cfg['img_prefix'] = img_prefixes[i]
        if isinstance(seg_prefixes, (list, tuple)):
            data_cfg['seg_prefix'] = seg_prefixes[i]
        if isinstance(proposal_files, (list, tuple)):
            data_cfg['proposal_file'] = proposal_files[i]
        datasets.append(build_dataset(data_cfg, default_args))

    return ConcatDataset(datasets, separate_eval)


def build_dataset(cfg, default_args=None):
    from .dataset_wrappers import (ConcatDataset, RepeatDataset,
                                   ClassBalancedDataset, MultiImageMixDataset)
    if isinstance(cfg, (list, tuple)):
        dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
    elif cfg['type'] == 'ConcatDataset':
        dataset = ConcatDataset(
            [build_dataset(c, default_args) for c in cfg['datasets']],
            cfg.get('separate_eval', True))
    elif cfg['type'] == 'RepeatDataset':
        dataset = RepeatDataset(
            build_dataset(cfg['dataset'], default_args), cfg['times'])
    elif cfg['type'] == 'ClassBalancedDataset':
        dataset = ClassBalancedDataset(
            build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])
    elif cfg['type'] == 'MultiImageMixDataset':
        cp_cfg = copy.deepcopy(cfg)
        cp_cfg['dataset'] = build_dataset(cp_cfg['dataset'])
        cp_cfg.pop('type')
        dataset = MultiImageMixDataset(**cp_cfg)
    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, runner_type='EpochBasedRunner', 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 (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, Optional): Seed to be used. Default: None. runner_type (str): Type of runner. Default: `EpochBasedRunner` 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. This argument is only valid when PyTorch>=1.7.0. Default: False. kwargs: any keyword argument to be used to initialize DataLoader Returns: DataLoader: A PyTorch dataloader. """ rank, world_size = get_dist_info() if dist: # When model is :obj:`DistributedDataParallel`, # `batch_size` of :obj:`dataloader` is the # number of training samples on each GPU. batch_size = samples_per_gpu num_workers = workers_per_gpu else: # When model is obj:`DataParallel` # the batch size is samples on all the GPUS batch_size = num_gpus * samples_per_gpu num_workers = num_gpus * workers_per_gpu if runner_type == 'IterBasedRunner': # this is a batch sampler, which can yield # a mini-batch indices each time. # it can be used in both `DataParallel` and # `DistributedDataParallel` if shuffle: batch_sampler = InfiniteGroupBatchSampler( dataset, batch_size, world_size, rank, seed=seed) else: batch_sampler = InfiniteBatchSampler( dataset, batch_size, world_size, rank, seed=seed, shuffle=False) batch_size = 1 sampler = None else: if dist: # DistributedGroupSampler will definitely shuffle the data to # satisfy that images on each GPU are in the same group if shuffle: sampler = DistributedGroupSampler( dataset, samples_per_gpu, world_size, rank, seed=seed) else: sampler = DistributedSampler( dataset, world_size, rank, shuffle=False, seed=seed) else: sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None batch_sampler = None init_fn = partial( worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None if (TORCH_VERSION != 'parrots' and digit_version(TORCH_VERSION) >= digit_version('1.7.0')): kwargs['persistent_workers'] = persistent_workers elif persistent_workers is True: warnings.warn('persistent_workers is invalid because your pytorch ' 'version is lower than 1.7.0') data_loader = DataLoader( dataset, batch_size=batch_size, sampler=sampler, num_workers=num_workers, batch_sampler=batch_sampler, collate_fn=partial(collate, samples_per_gpu=samples_per_gpu), pin_memory=False, worker_init_fn=init_fn, **kwargs) return data_loader
def worker_init_fn(worker_id, num_workers, rank, seed): # 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)
Read the Docs v: v2.19.1
Versions
latest
stable
v2.19.1
v2.19.0
v2.18.1
v2.18.0
v2.17.0
v2.16.0
v2.15.1
v2.15.0
v2.14.0
v2.13.0
v2.12.0
v2.11.0
v2.10.0
v2.9.0
v2.8.0
v2.7.0
v2.6.0
v2.5.0
v2.4.0
v2.3.0
v2.2.1
v2.2.0
v2.1.0
v2.0.0
v1.2.0
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.