Source code for mmseg.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 Registry, build_from_cfg, digit_version
from torch.utils.data import DataLoader, 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):
"""Build :obj:`ConcatDataset by."""
from .dataset_wrappers import ConcatDataset
img_dir = cfg['img_dir']
ann_dir = cfg.get('ann_dir', None)
split = cfg.get('split', None)
# pop 'separate_eval' since it is not a valid key for common datasets.
separate_eval = cfg.pop('separate_eval', True)
num_img_dir = len(img_dir) if isinstance(img_dir, (list, tuple)) else 1
if ann_dir is not None:
num_ann_dir = len(ann_dir) if isinstance(ann_dir, (list, tuple)) else 1
else:
num_ann_dir = 0
if split is not None:
num_split = len(split) if isinstance(split, (list, tuple)) else 1
else:
num_split = 0
if num_img_dir > 1:
assert num_img_dir == num_ann_dir or num_ann_dir == 0
assert num_img_dir == num_split or num_split == 0
else:
assert num_split == num_ann_dir or num_ann_dir <= 1
num_dset = max(num_split, num_img_dir)
datasets = []
for i in range(num_dset):
data_cfg = copy.deepcopy(cfg)
if isinstance(img_dir, (list, tuple)):
data_cfg['img_dir'] = img_dir[i]
if isinstance(ann_dir, (list, tuple)):
data_cfg['ann_dir'] = ann_dir[i]
if isinstance(split, (list, tuple)):
data_cfg['split'] = split[i]
datasets.append(build_dataset(data_cfg, default_args))
return ConcatDataset(datasets, separate_eval)
[docs]def build_dataset(cfg, default_args=None):
"""Build datasets."""
from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset,
RepeatDataset)
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 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('img_dir'), (list, tuple)) or isinstance(
cfg.get('split', None), (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 (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 | 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: 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)
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 digit_version(torch.__version__) >= digit_version('1.8.0'):
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,
persistent_workers=persistent_workers,
**kwargs)
else:
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):
"""Worker init func for dataloader.
The seed of each worker equals to num_worker * rank + worker_id + user_seed
Args:
worker_id (int): Worker id.
num_workers (int): Number of workers.
rank (int): The rank of current process.
seed (int): The random seed to use.
"""
worker_seed = num_workers * rank + worker_id + seed
np.random.seed(worker_seed)
random.seed(worker_seed)
torch.manual_seed(worker_seed)