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| import importlib | |
| import numpy as np | |
| import random | |
| import torch | |
| import torch.utils.data | |
| from copy import deepcopy | |
| from functools import partial | |
| from os import path as osp | |
| from basicsr.data.prefetch_dataloader import PrefetchDataLoader | |
| from basicsr.utils import get_root_logger, scandir | |
| from basicsr.utils.dist_util import get_dist_info | |
| from basicsr.utils.registry import DATASET_REGISTRY | |
| __all__ = ['build_dataset', 'build_dataloader'] | |
| # automatically scan and import dataset modules for registry | |
| # scan all the files under the data folder with '_dataset' in file names | |
| data_folder = osp.dirname(osp.abspath(__file__)) | |
| dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] | |
| # import all the dataset modules | |
| _dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames] | |
| def build_dataset(dataset_opt): | |
| """Build dataset from options. | |
| Args: | |
| dataset_opt (dict): Configuration for dataset. It must contain: | |
| name (str): Dataset name. | |
| type (str): Dataset type. | |
| """ | |
| dataset_opt = deepcopy(dataset_opt) | |
| dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt) | |
| logger = get_root_logger() | |
| logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} is built.') | |
| return dataset | |
| def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None): | |
| """Build dataloader. | |
| Args: | |
| dataset (torch.utils.data.Dataset): Dataset. | |
| dataset_opt (dict): Dataset options. It contains the following keys: | |
| phase (str): 'train' or 'val'. | |
| num_worker_per_gpu (int): Number of workers for each GPU. | |
| batch_size_per_gpu (int): Training batch size for each GPU. | |
| num_gpu (int): Number of GPUs. Used only in the train phase. | |
| Default: 1. | |
| dist (bool): Whether in distributed training. Used only in the train | |
| phase. Default: False. | |
| sampler (torch.utils.data.sampler): Data sampler. Default: None. | |
| seed (int | None): Seed. Default: None | |
| """ | |
| phase = dataset_opt['phase'] | |
| rank, _ = get_dist_info() | |
| if phase == 'train': | |
| if dist: # distributed training | |
| batch_size = dataset_opt['batch_size_per_gpu'] | |
| num_workers = dataset_opt['num_worker_per_gpu'] | |
| else: # non-distributed training | |
| multiplier = 1 if num_gpu == 0 else num_gpu | |
| batch_size = dataset_opt['batch_size_per_gpu'] * multiplier | |
| num_workers = dataset_opt['num_worker_per_gpu'] * multiplier | |
| dataloader_args = dict( | |
| dataset=dataset, | |
| batch_size=batch_size, | |
| shuffle=False, | |
| num_workers=num_workers, | |
| sampler=sampler, | |
| drop_last=True) | |
| if sampler is None: | |
| dataloader_args['shuffle'] = True | |
| dataloader_args['worker_init_fn'] = partial( | |
| worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None | |
| elif phase in ['val', 'test']: # validation | |
| dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0) | |
| else: | |
| raise ValueError(f"Wrong dataset phase: {phase}. Supported ones are 'train', 'val' and 'test'.") | |
| dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False) | |
| dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', False) | |
| prefetch_mode = dataset_opt.get('prefetch_mode') | |
| if prefetch_mode == 'cpu': # CPUPrefetcher | |
| num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1) | |
| logger = get_root_logger() | |
| logger.info(f'Use {prefetch_mode} prefetch dataloader: num_prefetch_queue = {num_prefetch_queue}') | |
| return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args) | |
| else: | |
| # prefetch_mode=None: Normal dataloader | |
| # prefetch_mode='cuda': dataloader for CUDAPrefetcher | |
| return torch.utils.data.DataLoader(**dataloader_args) | |
| def worker_init_fn(worker_id, num_workers, rank, seed): | |
| # Set the worker seed to num_workers * rank + worker_id + seed | |
| worker_seed = num_workers * rank + worker_id + seed | |
| np.random.seed(worker_seed) | |
| random.seed(worker_seed) | |