DL-Art-School/codes/data/__init__.py

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"""create dataset and dataloader"""
import logging
import torch
import torch.utils.data
def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
phase = dataset_opt['phase']
if phase == 'train':
if opt['dist']:
world_size = torch.distributed.get_world_size()
num_workers = dataset_opt['n_workers']
assert dataset_opt['batch_size'] % world_size == 0
batch_size = dataset_opt['batch_size'] // world_size
shuffle = False
else:
num_workers = dataset_opt['n_workers'] * len(opt['gpu_ids'])
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batch_size = dataset_opt['batch_size']
shuffle = True
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle,
num_workers=num_workers, sampler=sampler, drop_last=True,
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pin_memory=True)
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else:
batch_size = dataset_opt['batch_size'] or 1
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=max(int(batch_size/2), 1),
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pin_memory=True)
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def create_dataset(dataset_opt):
mode = dataset_opt['mode']
# datasets for image restoration
if mode == 'LQ':
from data.LQ_dataset import LQDataset as D
elif mode == 'LQGT':
from data.LQGT_dataset import LQGTDataset as D
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# datasets for image corruption
elif mode == 'downsample':
from data.Downsample_dataset import DownsampleDataset as D
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elif mode == 'fullimage':
from data.full_image_dataset import FullImageDataset as D
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elif mode == 'single_image_extensible':
from data.single_image_dataset import SingleImageDataset as D
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elif mode == 'multi_frame_extensible':
from data.multi_frame_dataset import MultiFrameDataset as D
elif mode == 'combined':
from data.combined_dataset import CombinedDataset as D
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else:
raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
dataset = D(dataset_opt)
logger = logging.getLogger('base')
logger.info('Dataset [{:s} - {:s}] is created.'.format(dataset.__class__.__name__,
dataset_opt['name']))
return dataset