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