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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
import warnings
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 TORCH_VERSION, Registry, build_from_cfg, digit_version
from torch.utils.data import DataLoader
from .samplers import (ClassAwareSampler, 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 (ClassBalancedDataset, ConcatDataset,
MultiImageMixDataset, RepeatDataset)
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
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,
class_aware_sampler=None,
**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.
class_aware_sampler (dict): Whether to use `ClassAwareSampler`
during training. Default: None.
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 class_aware_sampler is not None:
# ClassAwareSampler can be used in both distributed and
# non-distributed training.
num_sample_class = class_aware_sampler.get('num_sample_class', 1)
sampler = ClassAwareSampler(
dataset,
samples_per_gpu,
world_size,
rank,
seed=seed,
num_sample_class=num_sample_class)
elif 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=kwargs.pop('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)
torch.manual_seed(worker_seed)
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