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('de_NTIRE2019') < 0 else 'NTIRE2019'
m = import_module('data.' + module_name.lower())
datasets.append(getattr(m, module_name)(args, name=d))
self.loader_train = MSDataLoader(args,
MyConcatDataset(datasets),
batch_size=args.batch_size,
shuffle=True,
pin_memory=not args.cpu)
self.loader_test = []
for d in args.data_test:
if d in [
'Set5', 'Set14', 'B100', 'Urban100', 'Manga109',
'NTIRE2019', 'de_NTIRE2019'
]:
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(
MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
pin_memory=not args.cpu))
def get_loader(self):
self.module_train = import_module('data.' + self.args.data_train)
self.module_test = import_module('data.' + self.args.data_test)
kwargs = {}
if self.args.no_cuda:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = False
else:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = True
loader_train = None
if not self.args.test_only:
trainset = getattr(
self.module_train, self.args.data_train)(self.args)
loader_train = MSDataLoader(
self.args,
trainset,
batch_size=self.args.batch_size,
shuffle=True,
**kwargs)
testset = getattr(self.module_test, self.args.data_test)(
self.args, train=False)
loader_test = MSDataLoader(
self.args,
testset,
batch_size=1,
shuffle=False,
**kwargs)
return loader_train, loader_test
def __init__(self, args):
self.loader_train = None
if not args.test_only:
module_train = import_module(
'data.' + args.data_train.lower()
) ## load the right dataset loader module
trainset = getattr(module_train, args.data_train)(
args
) ### load the dataset, args.data_train is the dataset name
self.loader_train = MSDataLoader(args,
trainset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=default_collate,
pin_memory=not args.cpu)
if args.data_test in ['Set5', 'Set14', 'B100', 'Manga109', 'Urban100']:
module_test = import_module('data.benchmark')
testset = getattr(module_test, 'Benchmark')(args,
name=args.data_test,
train=False)
else:
module_test = import_module('data.' + args.data_test.lower())
testset = getattr(module_test, args.data_test)(args, train=False)
self.loader_test = MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
collate_fn=default_collate,
pin_memory=not args.cpu)
def __init__(self, args):
self.loader_train = None
if not args.test_only:
module_train = import_module('data.' + args.data_train.lower())
trainset = getattr(module_train, args.data_train)(args)
self.loader_train = MSDataLoader(
args,
trainset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=not args.cpu
)
if args.data_test in ['Set5', 'Set14', 'DPED', 'Urban100']:
module_test = import_module('data.benchmark')
testset = getattr(module_test, 'Benchmark')(args, train=False)
else:
module_test = import_module('data.' + args.data_test.lower())
testset = getattr(module_test, args.data_test)(args, train=False)
self.loader_test = MSDataLoader(
args,
testset,
batch_size=1,
shuffle=False,
pin_memory=not args.cpu
)
def __init__(self, args):
kwargs = {}
if not args.cpu:
kwargs["collate_fn"] = default_collate
kwargs["pin_memory"] = True
else:
kwargs["collate_fn"] = default_collate
kwargs["pin_memory"] = False
self.loader_train = None
if not args.test_only:
module_train = import_module("data." + args.data_train.lower())
trainset = getattr(module_train, args.data_train)(args)
self.loader_train = MSDataLoader(
args, trainset, batch_size=args.batch_size, shuffle=True, **kwargs
)
if args.data_test in ["dehaze_test"]:
module_test = import_module("data.benchmark")
testset = getattr(module_test, "Benchmark")(args, train=False)
else:
module_test = import_module("data." + args.data_test.lower())
testset = getattr(module_test, args.data_test)(args, train=False)
self.loader_test = MSDataLoader(
args, testset, batch_size=1, shuffle=False, **kwargs
)
def __init__(self, args, model):
kwargs = {}
if not args.cpu:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = True
else:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = False
self.loader_train = None
if not args.test_only:
if args.data_train.lower() != 'rrl':
module_train = import_module('data.' + args.data_train.lower())
trainset = getattr(module_train, args.data_train)(args)
else:
module_train = import_module('data.' + args.rrl_data.lower())
trainclass = getattr(module_train, args.rrl_data)
module_train = import_module('data.rrl')
trainset = getattr(module_train, 'RRL')(trainclass, args,
model)
self.loader_train = MSDataLoader(args,
trainset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
if args.data_test in ['Set5', 'Set14', 'B100', 'Urban100']:
if not args.benchmark_noise:
module_test = import_module('data.benchmark')
testset = getattr(module_test, 'Benchmark')(args, train=False)
else:
module_test = import_module('data.benchmark_noise')
testset = getattr(module_test, 'BenchmarkNoise')(args,
train=False)
else:
if args.data_test.lower() != 'rrl':
module_test = import_module('data.' + args.data_test.lower())
testset = getattr(module_test, args.data_test)(args,
train=False)
else:
module_test = import_module('data.' + args.rrl_data.lower())
testclass = getattr(module_test, args.rrl_data)
module_test = import_module('data.rrl')
testset = getattr(module_test, 'RRL')(testclass, args, model,
False)
self.loader_test = MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
**kwargs)
def __init__(self, args):
kwargs = {}
if not args.cpu:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = True
else:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = False
self.loader_train = None
if not args.test_only:
module_train = import_module('data.' + args.data_train.lower())
trainset = getattr(module_train, args.data_train)(args)
self.loader_train = MSDataLoader(
args,
trainset,
batch_size=args.batch_size,
shuffle=True,
**kwargs
)
print(args.data_test)
args.data_test = 'Set5'
if args.data_test in ['Set5', 'Set14', 'B100', 'Urban100']:
if not args.benchmark_noise:
module_test = import_module('data.benchmark')
testset = getattr(module_test, 'Benchmark')(args, train=False)
else:
module_test = import_module('data.benchmark_noise')
testset = getattr(module_test, 'BenchmarkNoise')(
args,
train=False
)
else:
print('\n')
print('liuzhe======================')
module_test = import_module('data.' + args.data_test.lower())
testset = getattr(module_test, args.data_test)(args, train=False)
self.loader_test = MSDataLoader(
args,
testset,
batch_size=1,
shuffle=False,
**kwargs
)
def __init__(self, args):
self.loader_train = None
if not args.test_only:
datasets = []
for d in args.data_train:
if d.find('300W') == 0:
module_name = "W300"
else:
module_name = d
m = import_module('data.' + module_name.lower())
datasets.append(getattr(m, module_name)(args, name=d))
self.loader_train = MSDataLoader(args,
MyConcatDataset(datasets),
batch_size=args.batch_size,
shuffle=True,
pin_memory=not args.cpu)
self.loader_test = []
for d in args.data_test:
print(d)
if d.find('MultiPIE') == 0:
module_name = d.split('_')[0]
testPose = [int(x) for x in d.split('_')[1:]]
m = import_module('data.' + module_name.lower())
testset = getattr(m, module_name)(args,
train=False,
name=d,
testPose=testPose)
else:
if d.find('300W') == 0:
module_name = "W300"
else:
module_name = d
m = import_module('data.' + module_name.lower())
testset = getattr(m, module_name)(args, train=False, name=d)
self.loader_test.append(
MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
pin_memory=not args.cpu))
def __init__(self, args):
kwargs = {} # kwargs就是当你传入key=value是存储的字典，成对键值对
if not args.cpu:
kwargs[
'collate_fn'] = default_collate #输入batch_size张图片，组成一个batch张量
kwargs['pin_memory'] = True #若不是只用CPU（还用GPU），则设置锁页内存
else:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = False #若只用CPU，则设置不锁页内存
self.loader_train = None
if not args.test_only: #如果不是测试，即为训练
module_train = import_module('data.' + args.data_train.lower())
# 导入模块data.DIV2K
trainset = getattr(module_train,
args.data_train)(args) #等效于调用module_train.DIV2K
#即trainset=div2k中DIV2K类的一个例子
self.loader_train = MSDataLoader(args,
trainset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
#将trainset中数据分块为batch
if args.data_test in ['Set5', 'Set14', 'B100', 'Urban100']:
if not args.benchmark_noise:
module_test = import_module('data.benchmark')
testset = getattr(module_test, 'Benchmark')(args, train=False)
# testset=Benchmark(args, train=False)返回测试文件路径列表list_hr, list_lr
else:
module_test = import_module('data.benchmark_noise')
testset = getattr(module_test, 'BenchmarkNoise')(args,
train=False)
else: #如果是DIV2K，就不能用benchmark了，用它特定的函数
module_test = import_module('data.' +
args.data_test.lower()) #data.div2k
testset = getattr(module_test, args.data_test)(args, train=False)
#testset=DIV2K(args, train=False)
self.loader_test = MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
**kwargs)
def __init__(self, args):
print('Initializing Data Class...')
kwargs = {}
if not args.cpu:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = True
else:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = False
self.loader_train = None
if not args.test_only:
print('Importing data module...')
module_train = import_module('data.' + args.data_train.lower())
print('Data module ' + args.data_train.lower() + ' imported.')
trainset = getattr(module_train, args.data_train)(args)
print('Importing data module... : trainset acquired')
self.loader_train = MSDataLoader(args,
trainset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
print('dataset: ' + args.data_test)
if args.data_test in ['Set5', 'Set14', 'B100', 'Urban100']:
if not args.benchmark_noise:
module_test = import_module('data.benchmark')
testset = getattr(module_test, 'Benchmark')(args, train=False)
else:
module_test = import_module('data.benchmark_noise')
testset = getattr(module_test, 'BenchmarkNoise')(args,
train=False)
else:
print('Importing data module...')
module_test = import_module('data.' + args.data_test.lower())
print('Data module ' + args.data_train.lower() + ' imported.')
testset = getattr(module_test, args.data_test)(args, train=False)
print('Importing data module... : testset acquired')
self.loader_test = MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
**kwargs)
def __init__(self, args):
self.loader_train = None
if not args.test_only:
datasets = []
for d in args.data_train:
if d.find('300W') == 0:
module_name = "W300"
else:
module_name = d
if d.find('LS3DW') == 0:
name = "LS3D-W-balanced"
else:
name = d
m = import_module('data.' + module_name.lower())
datasets.append(getattr(m, module_name)(args, name=name))
self.loader_train = MSDataLoader(args,
MyConcatDataset(datasets),
batch_size=args.batch_size,
shuffle=True,
pin_memory=not args.cpu)
self.loader_test = []
for d in args.data_test:
if d.find('300W') == 0:
module_name = "W300"
else:
module_name = d
if d.find('LS3DW') == 0:
name = "LS3D-W-balanced"
else:
name = d
m = import_module('data.' + module_name.lower())
testset = getattr(m, module_name)(args, train=False, name=name)
self.loader_test.append(
MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
pin_memory=not args.cpu))
def __init__(self, args):
self.loader_train = None
if not args.test_only:
datasets = []
for d in args.data_train: #是可以有多个数据集的,将对应数据集的dataset类拼接起来作为最后的训练数据
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)) #添加对应数据集的类
self.loader_train = MSDataLoader(
args,
MyConcatDataset(datasets), #合并
batch_size=args.batch_size,
shuffle=True,
pin_memory=not args.cpu)
self.loader_test = []
if not args.not_test:
for d in args.data_test: #同上 可以输入多个数据集,最后拼接起来
if d in ['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())
print(module_name)
testset = getattr(m, module_name)(args,
train=False,
name=d)
self.loader_test.append(
MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
pin_memory=not args.cpu))
def get_loader(self, args):
module_train = import_module("data." + args.data_train.lower())
if args.data_test in ["Set5", "Set14", "B100", "Urban100"]:
module_test = import_module("data.benchmark")
benchmark = True
else:
module_test = import_module("data." + args.data_test.lower())
benchmark = False
kwargs = {}
if args.no_cuda:
kwargs["collate_fn"] = default_collate
kwargs["pin_memory"] = False
else:
kwargs["collate_fn"] = default_collate
kwargs["pin_memory"] = True
loader_train = None
if not args.test_only:
trainset = getattr(module_train, args.data_train)(args)
loader_train = MSDataLoader(args,
trainset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
if benchmark:
testset = getattr(module_test, "Benchmark")(args, train=False)
else:
testset = getattr(module_test, args.data_test)(args, train=False)
loader_test = MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
**kwargs)
return loader_train, loader_test
def get_loader(self, args):
module_train = import_module('data.' + args.data_train.lower())
if args.data_test in ['Set5', 'Set14', 'B100', 'Urban100']:
module_test = import_module('data.benchmark')
benchmark = True
else:
module_test = import_module('data.' + args.data_test.lower())
benchmark = False
kwargs = {}
if args.no_cuda:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = False
else:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = True
loader_train = None
if not args.test_only:
trainset = getattr(module_train, args.data_train)(args)
loader_train = MSDataLoader(args,
trainset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
if benchmark:
testset = getattr(module_test, 'Benchmark')(args, train=False)
else:
testset = getattr(module_test, args.data_test)(args, train=False)
loader_test = MSDataLoader(args,
testset,
batch_size=1,
shuffle=False,
**kwargs)
return loader_train, loader_test
