Shortcuts

Source code for mmpose.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, 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)


[docs]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
[docs]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) torch.manual_seed(worker_seed)
Read the Docs v: latest
Versions
latest
1.x
v0.14.0
fix-doc
cn_doc
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.