105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
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 .prefetch_dataloader import PrefetchDataLoader
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from ..utils import get_root_logger
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from ..utils.dist_util import get_dist_info
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from ..utils.registry import DATASET_REGISTRY
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__all__ = ['build_dataset', 'build_dataloader']
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# P2.4 - explicit import list; the eager folder scan is gone
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from . import (
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ffhq_dataset,
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paired_image_dataset,
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realesrgan_dataset,
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realesrgan_paired_dataset,
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reds_dataset,
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single_image_dataset,
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video_test_dataset,
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vimeo90k_dataset,
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)
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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: # distributed training
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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: # non-distributed training
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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']: # validation
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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': # CPUPrefetcher
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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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# prefetch_mode=None: Normal dataloader
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# prefetch_mode='cuda': dataloader for CUDAPrefetcher
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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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# Set the worker seed to num_workers * rank + worker_id + 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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