def get_loader_mnist_rgb(batchsize):
transform_train = augment_mnist_rgb()
trainset_m = MNIST(root='./data', train=True, download=True, transform=transform_train)
testset_m = MNIST(root='./data', train=False, download=True, transform=no_augment_mnist_rgb())
# SVHN object accepts NUMPY:
train_data = trainset_m.train_data.numpy()
test_data = testset_m.test_data.numpy()
train_labels = trainset_m.train_labels.numpy()
test_labels = testset_m.test_labels.numpy()
print("Original MNIST")
print(train_data.shape, len(train_labels))
print(test_data.shape, len(test_labels))
### use SVHN object to load MNIST RGB data
trainset = SVHN(root='./data', split='train', download=True, transform=transform_train)
testset = SVHN(root='./data', split='test', download=True, transform=no_augment_mnist_rgb())
trainset.data = convert_mnist_images(trainset_m.train_data)
testset.data = convert_mnist_images(testset_m.test_data)
trainset.labels = trainset_m.train_labels
testset.labels = testset_m.test_labels
print("RGB MNIST")
print(trainset.data.shape, len(trainset.labels))
print(testset.data.shape, len(testset.labels))
trainloader = DataLoader(trainset, batch_size=batchsize, shuffle=True, num_workers=0)
testloader = DataLoader(testset, batch_size=batchsize, shuffle=False, num_workers=0)
print("MNIST train min=%f, max=%f" % (trainset.data.min(), trainset.data.max()))
print("MNIST test min=%f, max=%f" % (testset.data.min(), testset.data.max()))
return trainloader, testloader
def get_loader_digit_rgb(batchsize):
transform_train = no_augment_mnist_rgb()
########## download synth data from Ganin's Google Drive ####################
gdd.download_file_from_google_drive(file_id='0B9Z4d7lAwbnTSVR1dEFSRUFxOUU', dest_path='data/SynthDigits.zip', unzip=True)
folder_name = "data/"
file_train = 'synth_train_32x32.mat'
train_data = loadmat(folder_name+file_train)
train_x = train_data["X"]
train_x = np.rollaxis(train_x, 3, 0)
train_x = np.rollaxis(train_x, 3, 1)
train_y = train_data["y"]
print(train_x.shape)
print(train_y.shape)
file_test = 'synth_test_32x32.mat'
test_data = loadmat(folder_name+file_test)
test_x = test_data["X"]
test_x = np.rollaxis(test_x, 3, 0)
test_x = np.rollaxis(test_x, 3, 1)
test_y = test_data["y"]
print(test_x.shape)
print(test_y.shape)
trainset = SVHN(root='./data', split='train', download=True, transform=transform_train)
testset = SVHN(root='./data', split='test', download=True, transform=no_augment_mnist_rgb())
trainset.data = train_x
testset.data = test_x
trainset.labels = train_y
testset.labels = test_y
print (trainset.data.shape, len(trainset.labels))
print (testset.data.shape, len(testset.labels))
trainloader = DataLoader(trainset, batch_size=batchsize, shuffle=True, num_workers=0)
testloader = DataLoader(testset, batch_size=batchsize, shuffle=False, num_workers=0)
print("synth train min=%f, max=%f" % (trainset.data.min(), trainset.data.max()))
print("synth test min=%f, max=%f" % (testset.data.min(), testset.data.max()))
return trainloader, testloader
def get_loaders(nb_labelled,
batch_size,
unlab_rat,
augment_type,
lab_inds=[],
is_balanced=True):
if augment_type == "affine":
transform_train, transform_test = augment_affine_svhn()
elif augment_type == "mean":
transform_train, transform_test = augment_mean_svhn()
elif augment_type == "no":
transform_train, transform_test = noaug_SVHN()
trainset_l = SVHN(root='./data',
split='train',
download=True,
transform=transform_train)
test_set = SVHN(root='./data',
split='test',
download=True,
transform=transform_test)
print(trainset_l.data.shape, len(trainset_l.labels))
if len(lab_inds) == 0:
if is_balanced:
lab_inds = []
for i in range(10):
labels = np.array(trainset_l.labels)
inds_i = np.where(labels == i)[0]
inds_i = np.random.permutation(inds_i)
lab_inds.extend(inds_i[0:int(nb_labelled / 10)].tolist())
lab_inds = np.array(lab_inds)
else:
lab_inds = np.arange(0, nb_labelled)
all_inds = np.arange(len(trainset_l.labels))
unlab_inds = np.setdiff1d(all_inds, lab_inds)
trainset_u = copy.deepcopy(trainset_l)
unlab_inds = unlab_inds[0:int(unlab_rat * len(unlab_inds))]
trainset_u.data = np.array(trainset_u.data)[unlab_inds]
trainset_u.labels = np.array(trainset_u.labels)[unlab_inds]
trainloader_u = DataLoader(trainset_u,
batch_size=batch_size,
shuffle=False,
num_workers=1)
print(trainset_u.data.shape, len(trainset_u.labels))
trainset_l.data = np.array(trainset_l.data)[lab_inds]
trainset_l.labels = np.array(trainset_l.labels)[lab_inds]
print(trainset_l.data.shape, len(trainset_l.labels))
trainloader_l = DataLoader(trainset_l,
batch_size=batch_size,
shuffle=True,
num_workers=1)
testloader = DataLoader(test_set,
batch_size=batch_size,
shuffle=False,
num_workers=1)
loaders = {
"trainloader_l": trainloader_l,
"testloader": testloader,
"trainloader_u": trainloader_u,
"trainset_l": trainset_l,
"test_set": test_set,
"trainset_u": trainset_u,
"lab_inds": lab_inds
}
return loaders
def load_data(opt):
""" Load Data
Args:
opt ([type]): Argument Parser
Raises:
IOError: Cannot Load Dataset
Returns:
[type]: dataloader
"""
##
# LOAD DATA SET
if opt.dataroot == '':
opt.dataroot = './data/{}'.format(opt.dataset)
if opt.dataset in ['cifar10']:
splits = ['train', 'test']
drop_last_batch = {'train': True, 'test': False}
shuffle = {'train': True, 'test': False}
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
classes = {
'plane': 0,
'car': 1,
'bird': 2,
'cat': 3,
'deer': 4,
'dog': 5,
'frog': 6,
'horse': 7,
'ship': 8,
'truck': 9
}
dataset = {}
dataset['train'] = CIFAR10(root='./data',
train=True,
download=True,
transform=transform)
dataset['test'] = CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
dataset['train'].data, dataset['train'].targets, \
dataset['test'].data, dataset['test'].targets = get_cifar_anomaly_dataset(
trn_img=dataset['train'].data,
trn_lbl=dataset['train'].targets,
tst_img=dataset['test'].data,
tst_lbl=dataset['test'].targets,
abn_cls_idx=classes[opt.abnormal_class],
manualseed=opt.manualseed,
perc_outlier=opt.perc_outlier
)
dataloader = {
x: torch.utils.data.DataLoader(
dataset=dataset[x],
batch_size=opt.batchsize,
shuffle=shuffle[x],
num_workers=int(opt.workers),
drop_last=drop_last_batch[x],
worker_init_fn=(None if opt.manualseed == -1 else
lambda x: np.random.seed(opt.manualseed)))
for x in splits
}
return dataloader
elif opt.dataset in ['svhn']:
splits = ['train', 'test']
drop_last_batch = {'train': True, 'test': False}
shuffle = {'train': True, 'test': False}
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.ToTensor(),
#transforms.Normalize((0.45141584, 0.45141453, 0.45142587), (0.19929032, 0.1992932, 0.19929022))
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = {}
dataset['train'] = SVHN(root='./data',
split='train',
download=True,
transform=transform)
dataset['test'] = SVHN(root='./data',
split='test',
download=True,
transform=transform)
dataset['train'].data = dataset['train'].data.reshape(-1, 32, 32, 3)
dataset['test'].data = dataset['test'].data.reshape(-1, 32, 32, 3)
dataset['train'].data, dataset['train'].labels, \
dataset['test'].data, dataset['test'].labels = get_cifar_anomaly_dataset(
trn_img=dataset['train'].data,
trn_lbl=dataset['train'].labels,
tst_img=dataset['test'].data,
tst_lbl=dataset['test'].labels,
abn_cls_idx=int(opt.abnormal_class),
manualseed=opt.manualseed
)
dataset['train'].data = dataset['train'].data.reshape(-1, 3, 32, 32)
dataset['test'].data = dataset['test'].data.reshape(-1, 3, 32, 32)
dataloader = {
x: torch.utils.data.DataLoader(
dataset=dataset[x],
batch_size=opt.batchsize,
shuffle=shuffle[x],
num_workers=int(opt.workers),
drop_last=drop_last_batch[x],
worker_init_fn=(None if opt.manualseed == -1 else
lambda x: np.random.seed(opt.manualseed)))
for x in splits
}
return dataloader
elif opt.dataset in ['fashionmnist']:
splits = ['train', 'test']
drop_last_batch = {'train': True, 'test': False}
shuffle = {'train': True, 'test': False}
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.2860402, ), (0.3530239, ))
])
classes = {
'tshirt': 0,
'trouser': 1,
'pullover': 2,
'dress': 3,
'coat': 4,
'sandal': 5,
'shirt': 6,
'sneacker': 7,
'bag': 8,
'boot': 9
}
dataset = {}
dataset['train'] = FashionMNIST(root='./data',
train=True,
download=True,
transform=transform)
dataset['test'] = FashionMNIST(root='./data',
train=False,
download=True,
transform=transform)
dataset['train'].data, dataset['train'].targets, \
dataset['test'].data, dataset['test'].targets = get_mnist_anomaly_dataset(
trn_img=dataset['train'].data,
trn_lbl=dataset['train'].targets,
tst_img=dataset['test'].data,
tst_lbl=dataset['test'].targets,
abn_cls_idx=classes[opt.abnormal_class],
manualseed=opt.manualseed,
perc_outlier=opt.perc_outlier
)
dataloader = {
x: torch.utils.data.DataLoader(
dataset=dataset[x],
batch_size=opt.batchsize,
shuffle=shuffle[x],
num_workers=int(opt.workers),
drop_last=drop_last_batch[x],
worker_init_fn=(None if opt.manualseed == -1 else
lambda x: np.random.seed(opt.manualseed)))
for x in splits
}
return dataloader
elif opt.dataset in ['mnist']:
opt.abnormal_class = int(opt.abnormal_class)
splits = ['train', 'test']
drop_last_batch = {'train': True, 'test': False}
shuffle = {'train': True, 'test': True}
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
dataset = {}
dataset['train'] = MNIST(root='./data',
train=True,
download=True,
transform=transform)
dataset['test'] = MNIST(root='./data',
train=False,
download=True,
transform=transform)
dataset['train'].data, dataset['train'].targets, \
dataset['test'].data, dataset['test'].targets = get_mnist_anomaly_dataset(
trn_img=dataset['train'].data,
trn_lbl=dataset['train'].targets,
tst_img=dataset['test'].data,
tst_lbl=dataset['test'].targets,
abn_cls_idx=opt.abnormal_class,
manualseed=opt.manualseed,
perc_outlier=opt.perc_outlier
)
dataloader = {
x: torch.utils.data.DataLoader(
dataset=dataset[x],
batch_size=opt.batchsize,
shuffle=shuffle[x],
num_workers=int(opt.workers),
drop_last=drop_last_batch[x],
worker_init_fn=(None if opt.manualseed == -1 else
lambda x: np.random.seed(opt.manualseed)))
for x in splits
}
return dataloader
elif opt.dataset in ['mnist2']:
opt.abnormal_class = int(opt.abnormal_class)
splits = ['train', 'test']
drop_last_batch = {'train': True, 'test': False}
shuffle = {'train': True, 'test': True}
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
dataset = {}
dataset['train'] = MNIST(root='./data',
train=True,
download=True,
transform=transform)
dataset['test'] = MNIST(root='./data',
train=False,
download=True,
transform=transform)
dataset['train'].data, dataset['train'].targets, \
dataset['test'].data, dataset['test'].targets = get_mnist2_anomaly_dataset(
trn_img=dataset['train'].data,
trn_lbl=dataset['train'].targets,
tst_img=dataset['test'].data,
tst_lbl=dataset['test'].targets,
nrm_cls_idx=opt.abnormal_class,
proportion=opt.proportion,
manualseed=opt.manualseed
)
dataloader = {
x: torch.utils.data.DataLoader(
dataset=dataset[x],
batch_size=opt.batchsize,
shuffle=shuffle[x],
num_workers=int(opt.workers),
drop_last=drop_last_batch[x],
worker_init_fn=(None if opt.manualseed == -1 else
lambda x: np.random.seed(opt.manualseed)))
for x in splits
}
return dataloader
else:
splits = ['train', 'test']
drop_last_batch = {'train': True, 'test': False}
shuffle = {'train': True, 'test': True}
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.CenterCrop(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
dataset = {
x: ImageFolder(os.path.join(opt.dataroot, x), transform)
for x in splits
}
dataloader = {
x: torch.utils.data.DataLoader(
dataset=dataset[x],
batch_size=opt.batchsize,
shuffle=shuffle[x],
num_workers=int(opt.workers),
drop_last=drop_last_batch[x],
worker_init_fn=(None if opt.manualseed == -1 else
lambda x: np.random.seed(opt.manualseed)))
for x in splits
}
return dataloader
