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mmedit.datasets.builder 源代码
# 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 build_from_cfg
from packaging import version
from torch.utils.data import ConcatDataset, DataLoader
from .dataset_wrappers import RepeatDataset
from .registry import DATASETS
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))
def _concat_dataset(cfg, default_args=None):
"""Concat datasets with different ann_file but the same type.
Args:
cfg (dict): The config of dataset.
default_args (dict, optional): Default initialization arguments.
Default: None.
Returns:
Dataset: The concatenated dataset.
"""
ann_files = cfg['ann_file']
datasets = []
num_dset = len(ann_files)
for i in range(num_dset):
data_cfg = copy.deepcopy(cfg)
data_cfg['ann_file'] = ann_files[i]
datasets.append(build_dataset(data_cfg, default_args))
return ConcatDataset(datasets)
[文档]def build_dataset(cfg, default_args=None):
"""Build a dataset from config dict.
It supports a variety of dataset config. If ``cfg`` is a Sequential (list
or dict), it will be a concatenated dataset of the datasets specified by
the Sequential. If it is a ``RepeatDataset``, then it will repeat the
dataset ``cfg['dataset']`` for ``cfg['times']`` times. If the ``ann_file``
of the dataset is a Sequential, then it will build a concatenated dataset
with the same dataset type but different ``ann_file``.
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.
"""
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 isinstance(cfg.get('ann_file'), (list, tuple)):
dataset = _concat_dataset(cfg, default_args)
else:
dataset = build_from_cfg(cfg, DATASETS, default_args)
return dataset
[文档]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 (:obj:`Dataset`): A PyTorch dataset.
samples_per_gpu (int): Number of 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. Default: 1.
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 (dict, optional): 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,
samples_per_gpu=samples_per_gpu,
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
if version.parse(torch.__version__) >= version.parse('1.7.0'):
kwargs['persistent_workers'] = persistent_workers
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):
"""Function to initialize each worker.
The seed of each worker equals to
``num_worker * rank + worker_id + user_seed``.
Args:
worker_id (int): Id for each worker.
num_workers (int): Number of workers.
rank (int): Rank in distributed training.
seed (int): Random seed.
"""
worker_seed = num_workers * rank + worker_id + seed
np.random.seed(worker_seed)
random.seed(worker_seed)
torch.manual_seed(worker_seed)