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# Copyright (c) OpenMMLab. All rights reserved.
# ---------------------------------------------
# Modified by Shihao Wang
# ---------------------------------------------
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
from torch.utils.data import DataLoader
from mmdet.datasets.samplers import GroupSampler
from projects.mmdet3d_plugin.datasets.samplers.group_sampler import DistributedGroupSampler
from projects.mmdet3d_plugin.datasets.samplers.distributed_sampler import DistributedSampler
from projects.mmdet3d_plugin.datasets.samplers.group_sampler import InfiniteGroupEachSampleInBatchSampler
from projects.mmdet3d_plugin.datasets.samplers.sampler import build_sampler
def build_dataloader(dataset,
samples_per_gpu,
workers_per_gpu,
num_gpus=1,
dist=True,
shuffle=True,
seed=None,
shuffler_sampler=None,
nonshuffler_sampler=None,
runner_type=dict(type='EpochBasedRunner'),
**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.
kwargs: any keyword argument to be used to initialize DataLoader
Returns:
DataLoader: A PyTorch dataloader.
"""
rank, world_size = get_dist_info()
if dist:
# DistributedGroupSampler will definitely shuffle the data to satisfy
# that images on each GPU are in the same group
if shuffle:
sampler = build_sampler(shuffler_sampler if shuffler_sampler is not None else dict(type='DistributedGroupSampler'),
dict(
dataset=dataset,
samples_per_gpu=samples_per_gpu,
num_replicas=world_size,
rank=rank,
seed=seed)
)
else:
sampler = build_sampler(nonshuffler_sampler if nonshuffler_sampler is not None else dict(type='DistributedSampler'),
dict(
dataset=dataset,
num_replicas=world_size,
rank=rank,
shuffle=shuffle,
seed=seed)
)
batch_size = samples_per_gpu
num_workers = workers_per_gpu
batch_sampler = None
else:
# assert False, 'not support in bevformer'
print('WARNING!!!!, Only can be used for obtain inference speed!!!!')
sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None
batch_size = num_gpus * samples_per_gpu
num_workers = num_gpus * workers_per_gpu
batch_sampler = None
if runner_type['type'] == 'IterBasedRunner' and shuffler_sampler['type'] =='InfiniteGroupEachSampleInBatchSampler':
# TODO: original has more options, but I'm not using them
# https://github.com/open-mmlab/mmdetection/blob/3b72b12fe9b14de906d1363982b9fba05e7d47c1/mmdet/datasets/builder.py#L145-L157
batch_sampler = InfiniteGroupEachSampleInBatchSampler(
dataset,
samples_per_gpu,
world_size,
rank,
seed=seed)
batch_size = 1
sampler = None
init_fn = partial(
worker_init_fn, num_workers=num_workers, rank=rank,
seed=seed) if seed is not None else None
data_loader = DataLoader(
dataset,
batch_size=batch_size,
sampler=sampler,
batch_sampler=batch_sampler,
num_workers=num_workers,
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)
# Copyright (c) OpenMMLab. All rights reserved.
import platform
from mmcv.utils import Registry, build_from_cfg
from mmdet.datasets import DATASETS
from mmdet.datasets.builder import _concat_dataset
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))
OBJECTSAMPLERS = Registry('Object sampler')
def custom_build_dataset(cfg, default_args=None):
from mmdet3d.datasets.dataset_wrappers import CBGSDataset
from mmdet.datasets.dataset_wrappers import (ClassBalancedDataset,
ConcatDataset, RepeatDataset)
if isinstance(cfg, (list, tuple)):
dataset = ConcatDataset([custom_build_dataset(c, default_args) for c in cfg])
elif cfg['type'] == 'ConcatDataset':
dataset = ConcatDataset(
[custom_build_dataset(c, default_args) for c in cfg['datasets']],
cfg.get('separate_eval', True))
elif cfg['type'] == 'RepeatDataset':
dataset = RepeatDataset(
custom_build_dataset(cfg['dataset'], default_args), cfg['times'])
elif cfg['type'] == 'ClassBalancedDataset':
dataset = ClassBalancedDataset(
custom_build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])
elif cfg['type'] == 'CBGSDataset':
dataset = CBGSDataset(custom_build_dataset(cfg['dataset'], default_args))
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 |