def get_Data(is_train, dataset_name, batch_size):
val_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
voc_helen = ['bird', 'cat', 'cow', 'dog', 'horse', 'sheep', 'helen', 'voc_multi']
##cub dataset###
label = None if is_train else 0
if not is_train:
batch_size = 1
if dataset_name == 'cub':
trainset = CUB_VOC(cub_file, dataset_name, 'iccnn', train=True, transform=val_transform, is_frac=label)
testset = CUB_VOC(cub_file, dataset_name, 'iccnn', train=False, transform=val_transform, is_frac=label)
###cropped voc dataset###
elif dataset_name in voc_helen:
trainset = CUB_VOC(voc_file, dataset_name, 'iccnn', train=True, transform=val_transform, is_frac=label)
testset = CUB_VOC(voc_file, dataset_name, 'iccnn', train=False, transform=val_transform, is_frac=label)
###celeb dataset###
#elif dataset_name == 'celeb':
# trainset = Celeb(training = True, transform=None)
# testset = Celeb(training = False, transform=None)
train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(testset, batch_size=batch_size, shuffle=False)
return train_loader, test_loader
def get_Data(is_train, dataset_name, batch_size):
transform = transforms.Compose([
transforms.RandomResizedCrop((224, 224), scale=(0.5, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
val_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
voc_helen_name = [
'bird', 'cat', 'cow', 'dog', 'horse', 'sheep', 'helen', 'voc_multi'
]
##cub dataset###
label = None if is_train else 0
if dataset_name == 'cub':
trainset = CUB_VOC(cub_file,
dataset_name,
'ori',
train=True,
transform=transform,
is_frac=label)
testset = CUB_VOC(cub_file,
dataset_name,
'ori',
train=False,
transform=val_transform,
is_frac=label)
###cropped voc dataset###
elif dataset_name in voc_helen_name:
trainset = CUB_VOC(voc_file,
dataset_name,
'ori',
train=True,
transform=transform,
is_frac=label)
testset = CUB_VOC(voc_file,
dataset_name,
'ori',
train=False,
transform=val_transform,
is_frac=label)
###celeb dataset###
elif dataset_name == 'celeb':
trainset = Celeb(celeb_file,
training=True,
transform=None,
train_num=162770)
testset = Celeb(celeb_file,
training=False,
transform=None,
train_num=19962)
train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(testset,
batch_size=batch_size,
shuffle=False,
drop_last=False)
return train_loader, test_loader