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=None,
teacher_idx=None,
seed=8888):
self.batch_size = batch_size
self.num_workers = num_workers
self.num_batches = num_batches
self.training = training
self.transform_train = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.6959, 0.6537, 0.6371),
(0.3113, 0.3192, 0.3214)),
])
self.transform_val = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.6959, 0.6537, 0.6371),
(0.3113, 0.3192, 0.3214)),
])
self.data_dir = data_dir
if config == None:
config = ConfigParser.get_instance()
cfg_trainer = config['trainer']
self.train_dataset, self.val_dataset = get_clothing1m(
config['data_loader']['args']['data_dir'],
cfg_trainer,
num_samples=self.num_batches * self.batch_size,
train=training,
# self.train_dataset, self.val_dataset = get_clothing1m(config['data_loader']['args']['data_dir'], cfg_trainer, num_samples=260000, train=training,
transform_train=self.transform_train,
transform_val=self.transform_val,
teacher_idx=teacher_idx,
seed=seed)
super().__init__(self.train_dataset,
batch_size,
shuffle,
validation_split,
num_workers,
pin_memory,
val_dataset=self.val_dataset)
def __init__(self, num_examp, num_classes=10, beta=0.3):
super().__init__()
self.num_classes = num_classes
self.config = ConfigParser.get_instance()
self.USE_CUDA = torch.cuda.is_available()
self.target = torch.zeros(
num_examp,
self.num_classes).cuda() if self.USE_CUDA else torch.zeros(
num_examp, self.num_classes)
self.beta = beta
def __init__(self,
data_dir,
batch_size,
shuffle=True,
validation_split=0.0,
num_batches=0,
training=True,
num_workers=4,
pin_memory=True,
num_class=50,
teacher_idx=None):
self.batch_size = batch_size
self.num_workers = num_workers
self.num_batches = num_batches
self.training = training
self.transform_train = transforms.Compose([
transforms.RandomCrop(227),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
self.transform_val = transforms.Compose([
transforms.CenterCrop(227),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
self.transform_imagenet = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(227),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
self.data_dir = data_dir
config = ConfigParser.get_instance()
cfg_trainer = config['trainer']
self.train_dataset, self.val_dataset = get_webvision(
config['data_loader']['args']['data_dir'],
cfg_trainer,
num_samples=self.num_batches * self.batch_size,
train=training,
transform_train=self.transform_train,
transform_val=self.transform_val,
num_class=num_class,
teacher_idx=teacher_idx)
super().__init__(self.train_dataset,
batch_size,
shuffle,
validation_split,
num_workers,
pin_memory,
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=None,
teacher_idx=None,
seed=888):
if config is None:
config = ConfigParser.get_instance()
cfg_trainer = config['trainer']
transform_train = transforms.Compose([
#transforms.ColorJitter(brightness= 0.4, contrast= 0.4, saturation= 0.4, hue= 0.1),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408),
(0.2675, 0.2565, 0.2761)),
])
transform_val = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408),
(0.2675, 0.2565, 0.2761)),
])
self.data_dir = data_dir
# cfg_trainer = config['trainer']
noise_file = '%sCIFAR100_%.1f_Asym_%s.json' % (
config['data_loader']['args']['data_dir'], cfg_trainer['percent'],
cfg_trainer['asym'])
self.train_dataset, self.val_dataset = get_cifar100(
config['data_loader']['args']['data_dir'],
cfg_trainer,
train=training,
transform_train=transform_train,
transform_val=transform_val,
noise_file=noise_file,
teacher_idx=teacher_idx,
seed=seed)
super().__init__(self.train_dataset,
batch_size,
shuffle,
validation_split,
num_workers,
pin_memory,
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=None,
teacher_idx=None,
seed=888):
if config == None:
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)),
])
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,
teacher_idx=teacher_idx,
seed=seed)
super().__init__(self.train_dataset,
batch_size,
shuffle,
validation_split,
num_workers,
pin_memory,
val_dataset=self.val_dataset)