def __init__(self,
data_dir,
batch_size,
shuffle=True,
validation_split=0.0,
num_workers=4):
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=8),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_val = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
self.data_dir = data_dir
config = ConfigParser.get_instance()
cfg_trainer = config['trainer']
self.train_dataset, self.val_dataset = get_cifar10(
config['data_loader']['args']['data_dir'],
cfg_trainer,
transform_train=transform_train,
transform_val=transform_val)
super().__init__(self.train_dataset,
batch_size,
shuffle,
validation_split,
num_workers,
val_dataset=self.val_dataset)
def __init__(self,
data_dir,
batch_size,
shuffle=True,
validation_split=0.0,
num_batches=0,
training=True,
num_workers=4,
pin_memory=True):
config = ConfigParser.get_instance()
cfg_trainer = config['trainer']
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_val = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
# transform_train = transforms.Compose([
# transforms.RandomCrop(32, padding=4),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# ])
# transform_val = transforms.Compose([
# transforms.ToTensor(),
# ])
self.data_dir = data_dir
# noise_file='%sCIFAR10_%.1f_Asym_%s.json'%(config['data_loader']['args']['data_dir'],cfg_trainer['percent'],cfg_trainer['asym'])
self.train_dataset, self.val_dataset = get_cifar10(
config['data_loader']['args']['data_dir'],
cfg_trainer,
train=training,
transform_train=transform_train,
transform_val=transform_val) #, noise_file = noise_file)
super().__init__(self.train_dataset,
batch_size,
shuffle,
validation_split,
num_workers,
pin_memory,
val_dataset=self.val_dataset)