def create_dataset(dataset_opt):
'''create dataset'''
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHROTF':
from data.LRHROTF_dataset import LRHRDataset as D
elif mode == 'LRHRC':
from data.LRHRC_dataset import LRHRDataset as D
elif mode == 'LRHRseg_bg':
from data.LRHR_seg_bg_dataset import LRHRSeg_BG_Dataset as D
elif mode == 'VLRHR':
from data.Vid_dataset import VidTrainsetLoader as D
elif mode == 'VLR':
from data.Vid_dataset import VidTestsetLoader as D
elif mode == 'LRHRPBR':
from data.LRHRPBR_dataset import LRHRDataset as D
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
def create_dataset(dataset_opt):
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
dataset = D(dataset_opt)
elif mode == 'LQGT':
from data.LQGT_dataset import LQGTDataset as D
dataset = D(dataset_opt)
# elif mode == 'LQGTseg_bg':
# from data.LQGT_seg_bg_dataset import LQGTSeg_BG_Dataset as D
elif mode == 'yoon':
if dataset_opt["phase"] == "train":
from degradation_pair_data import DegradationParing as D
hr_folder = dataset_opt["dataroot_GT"]
kern_folder = dataset_opt["kernel_folder"]
noise_folder = dataset_opt["noise_folder"]
gt_patch_size = dataset_opt["GT_size"]
scale_factor = 1 / dataset_opt["scale"]
use_shuffle = dataset_opt["use_shuffle"]
rgb = dataset_opt["color"] == "RGB"
dataset = D(hr_folder,
kern_folder,
noise_folder,
scale_factor,
gt_patch_size,
permute=use_shuffle,
bgr2rgb=rgb)
else:
from degradation_pair_data import TestDataSR as D
lr_folder = dataset_opt["dataroot_LQ"]
gt_folder = dataset_opt["dataroot_GT"]
use_shuffle = dataset_opt["use_shuffle"]
rgb = dataset_opt["color"] == "RGB"
dataset = D(lr_folder,
gt_folder,
"/mnt/data/NTIRE2020/realSR/track1/Corrupted-te-x",
permute=use_shuffle,
bgr2rgb=rgb)
else:
raise NotImplementedError(
'Dataset [{:s}] is not recognized.'.format(mode))
logger = logging.getLogger('base')
logger.info('Dataset [{:s} - {:s}] is created.'.format(
dataset.__class__.__name__, dataset_opt['name']))
return dataset
def create_dataset(dataset_opt, is_train=True):
'''create dataset'''
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'RANK_IMIM_Pair':
from data.Rank_IMIM_Pair_dataset import RANK_IMIM_Pair_Dataset as D
else:
raise NotImplementedError(
'Dataset [{:s}] is not recognized.'.format(mode))
if 'RANK_IMIM_Pair' in mode:
dataset = D(dataset_opt, is_train=is_train)
else:
dataset = D(dataset_opt)
logger = logging.getLogger('base')
logger.info('Dataset [{:s} - {:s}] is created.'.format(
dataset.__class__.__name__, dataset_opt['name']))
return dataset
def create_dataset(dataset_opt):
mode = dataset_opt['mode'].upper()
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
else:
raise NotImplementedError("Dataset [%s] is not recognized." % mode)
dataset = D(dataset_opt)
print('===> [%s] Dataset is created.' % (mode))
return dataset
def create_dataset(dataset_opt):
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LQGT':
from data.LQGT_dataset import LQGTDataset as D
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
def create_dataset(dataset_opt):
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHRseg_bg':
from data.LRHR_seg_bg_dataset import LRHRSeg_BG_Dataset as D
else:
raise NotImplementedError("Dataset [%s] is not recognized." % mode)
dataset = D(dataset_opt)
print('Dataset [%s - %s] is created.' %
(dataset.__class__.__name__, dataset_opt['name']))
return dataset
def create_dataset(dataset_opt):
mode = dataset_opt['mode']
if mode == 'LR': # Only LR images are provided
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR': # LR and target images are provided
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHR_four_levels': # LR, target with intermediate resolution images are provided
from data.LRHR_four_levels_dataset import LRHRFourLevelsDataset as D
else:
raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
dataset = D(dataset_opt)
print('Dataset [{:s} - {:s}] is created.'.format(dataset.__class__.__name__,
dataset_opt['name']))
return dataset
def create_dataset(dataset_opt):
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHR_mid':
from data.LRHR_mid_dataset import LRHRMidDataset as D
else:
raise NotImplementedError(
'Dataset [{:s}] is not recognized.'.format(mode))
dataset = D(dataset_opt)
print('Dataset [{:s} - {:s}] is created.'.format(
dataset.__class__.__name__, dataset_opt['name']))
return dataset
def create_dataset(dataset_opt, **kwargs):
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHRseg_bg':
from data.LRHR_seg_bg_dataset import LRHRSeg_BG_Dataset as D
elif 'JPEG' in mode:
from data.JPEG_dataset import JpegDataset as D
else:
raise NotImplementedError(
'Dataset [{:s}] is not recognized.'.format(mode))
dataset = D(dataset_opt, **kwargs)
print('Dataset [{:s} - {:s}] is created.'.format(
dataset.__class__.__name__, dataset_opt['name']))
return dataset
def _create_dataset(dataset_opt):
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'AB':
from data.AB_dataset import ABDataset as D
elif mode == 'ImgSyn_five_levels':
from data.ImgSyn_five_levels_dataset import ImageLabelDatasetFiveLevels as D
else:
raise NotImplementedError(
'Dataset [{:s}] is not recognized.'.format(mode))
dataset = D(dataset_opt)
print('Dataset [{:s} - {:s}] is created.'.format(
dataset.__class__.__name__, dataset_opt['name']))
return dataset
def create_dataset(dataset_opt):
'''create dataset'''
mode = dataset_opt['mode']
if mode == 'LR': # for testing
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR': # for training or validation
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'MLRHR': # for training or validation
from data.MLRHR_dataset import MLRHRDataset as D
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
def create_dataset(dataset_opt):
'''create dataset'''
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHRseg_bg':
from data.LRHR_seg_bg_dataset import LRHRSeg_BG_Dataset as D
elif mode == 'dstl':
from data.dstl_dataset.dataset_png import DstlDataset as D
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
def create_dataset(dataset_opt):
mode = dataset_opt['mode'].upper()
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHR_LMDB':
from data.LRHR_dataset import LRHRlmdbDatasetwithcoeff as D
elif mode == 'LRHRSEG':
from data.LRHR_seg_dataset import LRHRSegDataset as D
elif mode == 'LRHR_H5':
from data.LRHR_H5dataset import LRHRH5Dataset as D
elif mode == 'LRHR_H5_M':
from data.LRHR_H5dataset4memory import LRHRH5Dataset as D
else:
raise NotImplementedError("Dataset [%s] is not recognized." % mode)
dataset = D(dataset_opt)
print('Dataset [%s - %s] is created.' %
(dataset.name(), dataset_opt['name']))
return dataset
def create_dataset(dataset_opt):
'''create dataset'''
mode = dataset_opt['mode']
if mode == 'LR':
from data.LR_dataset import LRDataset as D
elif mode == 'LRHR':
from data.LRHR_dataset import LRHRDataset as D
elif mode == 'LRHR_Trans_Wavelet_GAN':
from data.LRHR_Trans_Wavelet_GAN import LRHRTransWaveletGAN as D
elif mode == 'LRHR_wavelet_unpair':
from data.LRHR_wavelet_unpairMix_dataset import LRHR_wavelet_Mixunpair_Dataset as D
elif mode == 'LRHR_wavelet_unpair_fake_real_w_EQ':
from data.LRHR_wavelet_unpairEq_dataset import LRHR_wavelet_Equnpair_Dataset as D
elif mode == 'LRHR_wavelet_unpair_fake_weights_EQ':
from data.LRHR_wavelet_unpairEq_fake_w_dataset import LRHR_wavelet_Equnpair_Dataset as D
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
def create_dataset(dataset_opt):
return D(dataset_opt)
def create_dataset(dataset_opt):
'''create dataset'''
dataset = D(dataset_opt)
return dataset