def load_cifar10(range_01=False, val=False):
#
#
# Load CIFAR-10 for adaptation with STL
#
#
print('Loading CIFAR-10...')
if val:
d_cifar = cifar10.CIFAR10(n_val=5000)
else:
d_cifar = cifar10.CIFAR10(n_val=0)
d_cifar.train_X = d_cifar.train_X[:]
d_cifar.val_X = d_cifar.val_X[:]
d_cifar.test_X = d_cifar.test_X[:]
d_cifar.train_y = d_cifar.train_y[:]
d_cifar.val_y = d_cifar.val_y[:]
d_cifar.test_y = d_cifar.test_y[:]
# Remap class indices so that the frog class (6) has an index of -1 as it does not appear int the STL dataset
cls_mapping = np.array([0, 1, 2, 3, 4, 5, -1, 6, 7, 8])
d_cifar.train_y = cls_mapping[d_cifar.train_y]
d_cifar.val_y = cls_mapping[d_cifar.val_y]
d_cifar.test_y = cls_mapping[d_cifar.test_y]
# Remove all samples from skipped classes
train_mask = d_cifar.train_y != -1
val_mask = d_cifar.val_y != -1
test_mask = d_cifar.test_y != -1
d_cifar.train_X = d_cifar.train_X[train_mask]
d_cifar.train_y = d_cifar.train_y[train_mask]
d_cifar.val_X = d_cifar.val_X[val_mask]
d_cifar.val_y = d_cifar.val_y[val_mask]
d_cifar.test_X = d_cifar.test_X[test_mask]
d_cifar.test_y = d_cifar.test_y[test_mask]
if range_01:
d_cifar.train_X = d_cifar.train_X * 2.0 - 1.0
d_cifar.val_X = d_cifar.val_X * 2.0 - 1.0
d_cifar.test_X = d_cifar.test_X * 2.0 - 1.0
print(
'CIFAR-10: train: X.shape={}, y.shape={}, val: X.shape={}, y.shape={}, test: X.shape={}, y.shape={}'
.format(d_cifar.train_X.shape, d_cifar.train_y.shape,
d_cifar.val_X.shape, d_cifar.val_y.shape, d_cifar.test_X.shape,
d_cifar.test_y.shape))
print('CIFAR-10: train: X.min={}, X.max={}'.format(d_cifar.train_X.min(),
d_cifar.train_X.max()))
d_cifar.n_classes = 9
return d_cifar
def test_val_0():
from batchup.datasets import cifar10
ds = cifar10.CIFAR10(n_val=0)
assert ds.train_X.shape == (50000, 3, 32, 32)
assert ds.train_X.dtype == np.float32
assert ds.train_y.shape == (50000, )
assert ds.train_y.dtype == np.int32
assert ds.val_X.shape == (0, 3, 32, 32)
assert ds.val_X.dtype == np.float32
assert ds.val_y.shape == (0, )
assert ds.val_y.dtype == np.int32
assert ds.test_X.shape == (10000, 3, 32, 32)
assert ds.test_X.dtype == np.float32
assert ds.test_y.shape == (10000, )
assert ds.test_y.dtype == np.int32
assert ds.class_names == [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
def test_train_test_split():
from batchup.datasets import cifar10
from batchup.tests.dataset_test_helpers import sample_hashes
ds = cifar10.CIFAR10(n_val=0)
train_h = sample_hashes(ds.train_X_u8)
test_h = sample_hashes(ds.test_X_u8)
assert set(train_h).intersection(set(test_h)) == set()