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from importlib import import_module
#from dataloader import MSDataLoader
from torch.utils.data import dataloader
from torch.utils.data import ConcatDataset
# This is a simple wrapper function for ConcatDataset
class MyConcatDataset(ConcatDataset):
def __init__(self, datasets):
super(MyConcatDataset, self).__init__(datasets)
self.train = datasets[0].train
def set_scale(self, idx_scale):
for d in self.datasets:
if hasattr(d, 'set_scale'): d.set_scale(idx_scale)
class Data:
def __init__(self, args):
self.loader_train = None
if not args.test_only:
datasets = []
for d in args.data_train:
module_name = d if d.find('DIV2K-Q') < 0 else 'DIV2KJPEG'
m = import_module('data.' + module_name.lower())
datasets.append(getattr(m, module_name)(args, name=d))
self.loader_train = dataloader.DataLoader(
MyConcatDataset(datasets),
batch_size=args.batch_size,
shuffle=True,
pin_memory=not args.cpu,
num_workers=args.n_threads,
)
self.loader_test = []
for d in args.data_test:
if d in ['CBSD68','Kodak24','McM','Set5', 'Set14', 'B100', 'Urban100']:
m = import_module('data.benchmark')
testset = getattr(m, 'Benchmark')(args, train=False, name=d)
else:
module_name = d if d.find('DIV2K-Q') < 0 else 'DIV2KJPEG'
m = import_module('data.' + module_name.lower())
testset = getattr(m, module_name)(args, train=False, name=d)
self.loader_test.append(
dataloader.DataLoader(
testset,
batch_size=1,
shuffle=False,
pin_memory=not args.cpu,
num_workers=args.n_threads,
)
)
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