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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg, is_seq_of
from mmcv.utils.parrots_wrapper import _get_dataloader
from torch.utils.data.dataset import ConcatDataset
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))
DATASETS = Registry('dataset')
PIPELINES = Registry('pipeline')
def _concat_dataset(cfg, default_args=None):
types = cfg['type']
ann_files = cfg['ann_file']
img_prefixes = cfg.get('img_prefix', None)
dataset_infos = cfg.get('dataset_info', None)
num_joints = cfg['data_cfg'].get('num_joints', None)
dataset_channel = cfg['data_cfg'].get('dataset_channel', None)
datasets = []
num_dset = len(ann_files)
for i in range(num_dset):
cfg_copy = copy.deepcopy(cfg)
cfg_copy['ann_file'] = ann_files[i]
if isinstance(types, (list, tuple)):
cfg_copy['type'] = types[i]
if isinstance(img_prefixes, (list, tuple)):
cfg_copy['img_prefix'] = img_prefixes[i]
if isinstance(dataset_infos, (list, tuple)):
cfg_copy['dataset_info'] = dataset_infos[i]
if isinstance(num_joints, (list, tuple)):
cfg_copy['data_cfg']['num_joints'] = num_joints[i]
if is_seq_of(dataset_channel, list):
cfg_copy['data_cfg']['dataset_channel'] = dataset_channel[i]
datasets.append(build_dataset(cfg_copy, default_args))
return ConcatDataset(datasets)
def build_dataset(cfg, default_args=None):
"""Build a dataset from config dict.
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.
"""
from .dataset_wrappers import 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']])
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
def build_dataloader(dataset,
samples_per_gpu,
workers_per_gpu,
num_gpus=1,
dist=True,
shuffle=True,
seed=None,
drop_last=True,
pin_memory=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 (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.
drop_last (bool): Whether to drop the last incomplete batch in epoch.
Default: True
pin_memory (bool): Whether to use pin_memory in DataLoader.
Default: True
kwargs: 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, seed=seed)
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
_, DataLoader = _get_dataloader()
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):
"""Init the random seed for various workers."""
# 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)
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