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)