67 lines
2.8 KiB
Python
67 lines
2.8 KiB
Python
"""create dataset and dataloader"""
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import logging
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import torch
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import torch.utils.data
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from utils.util import opt_get
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def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
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phase = dataset_opt['phase']
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if phase == 'train':
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if opt_get(opt, ['dist'], False):
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world_size = torch.distributed.get_world_size()
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num_workers = dataset_opt['n_workers']
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assert dataset_opt['batch_size'] % world_size == 0
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batch_size = dataset_opt['batch_size'] // world_size
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shuffle = False
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else:
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num_workers = dataset_opt['n_workers']
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batch_size = dataset_opt['batch_size']
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shuffle = True
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return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle,
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num_workers=num_workers, sampler=sampler, drop_last=True,
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pin_memory=True)
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else:
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batch_size = dataset_opt['batch_size'] or 1
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return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0,
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pin_memory=True)
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def create_dataset(dataset_opt):
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mode = dataset_opt['mode']
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# datasets for image restoration
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if mode == 'fullimage':
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from data.full_image_dataset import FullImageDataset as D
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elif mode == 'single_image_extensible':
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from data.single_image_dataset import SingleImageDataset as D
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elif mode == 'multi_frame_extensible':
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from data.multi_frame_dataset import MultiFrameDataset as D
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elif mode == 'combined':
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from data.combined_dataset import CombinedDataset as D
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elif mode == 'multiscale':
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from data.multiscale_dataset import MultiScaleDataset as D
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elif mode == 'paired_frame':
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from data.paired_frame_dataset import PairedFrameDataset as D
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elif mode == 'stylegan2':
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from data.stylegan2_dataset import Stylegan2Dataset as D
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elif mode == 'imagefolder':
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from data.image_folder_dataset import ImageFolderDataset as D
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elif mode == 'torch_dataset':
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from data.torch_dataset import TorchDataset as D
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elif mode == 'byol_dataset':
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from data.byol_attachment import ByolDatasetWrapper as D
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elif mode == 'byol_structured_dataset':
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from data.byol_attachment import StructuredCropDatasetWrapper as D
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elif mode == 'random_aug_wrapper':
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from data.byol_attachment import DatasetRandomAugWrapper as D
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elif mode == 'random_dataset':
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from data.random_dataset import RandomDataset as D
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elif mode == 'zipfile':
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from data.zip_file_dataset import ZipFileDataset as D
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else:
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raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
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dataset = D(dataset_opt)
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return dataset
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