def test_val_0():
from batchup.datasets import stl
ds = stl.STL(n_val_folds=0)
assert ds.train_X_u8.shape == (5000, 3, 96, 96)
assert ds.train_X_u8.dtype == np.uint8
assert ds.train_y.shape == (5000, )
assert ds.train_y.dtype == np.int32
assert ds.val_X_u8.shape == (0, 3, 96, 96)
assert ds.val_X_u8.dtype == np.uint8
assert ds.val_y.shape == (0, )
assert ds.val_y.dtype == np.int32
assert ds.test_X_u8.shape == (8000, 3, 96, 96)
assert ds.test_X_u8.dtype == np.uint8
assert ds.test_y.shape == (8000, )
assert ds.test_y.dtype == np.int32
assert ds.class_names == [
'airplane', 'bird', 'car', 'cat', 'deer', 'dog', 'horse', 'monkey',
'ship', 'truck'
]
def test_train_test_split():
from batchup.datasets import stl
from batchup.tests.dataset_test_helpers import sample_hashes
ds = stl.STL(n_val_folds=1)
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()
def load_stl(zero_centre=False, val=False):
#
#
# Load STL for adaptation with CIFAR-10
#
#
print('Loading STL...')
if val:
d_stl = stl.STL()
else:
d_stl = stl.STL(n_val_folds=0)
d_stl.train_X = d_stl.train_X[:]
d_stl.val_X = d_stl.val_X[:]
d_stl.test_X = d_stl.test_X[:]
d_stl.train_y = d_stl.train_y[:]
d_stl.val_y = d_stl.val_y[:]
d_stl.test_y = d_stl.test_y[:]
# Remap class indices to match CIFAR-10:
cls_mapping = np.array([0, 2, 1, 3, 4, 5, 6, -1, 7, 8])
d_stl.train_y = cls_mapping[d_stl.train_y]
d_stl.val_y = cls_mapping[d_stl.val_y]
d_stl.test_y = cls_mapping[d_stl.test_y]
d_stl.train_X = d_stl.train_X[:]
d_stl.val_X = d_stl.val_X[:]
d_stl.test_X = d_stl.test_X[:]
# Remove all samples from class -1 (monkey) as it does not appear int the CIFAR-10 dataset
train_mask = d_stl.train_y != -1
val_mask = d_stl.val_y != -1
test_mask = d_stl.test_y != -1
d_stl.train_X = d_stl.train_X[train_mask]
d_stl.train_y = d_stl.train_y[train_mask]
d_stl.val_X = d_stl.val_X[val_mask]
d_stl.val_y = d_stl.val_y[val_mask]
d_stl.test_X = d_stl.test_X[test_mask]
d_stl.test_y = d_stl.test_y[test_mask]
# Downsample images from 96x96 to 32x32
d_stl.train_X = downscale_local_mean(d_stl.train_X, (1, 1, 3, 3))
d_stl.val_X = downscale_local_mean(d_stl.val_X, (1, 1, 3, 3))
d_stl.test_X = downscale_local_mean(d_stl.test_X, (1, 1, 3, 3))
if zero_centre:
d_stl.train_X = d_stl.train_X * 2.0 - 1.0
d_stl.val_X = d_stl.val_X * 2.0 - 1.0
d_stl.test_X = d_stl.test_X * 2.0 - 1.0
print(
'STL: train: X.shape={}, y.shape={}, val: X.shape={}, y.shape={}, test: X.shape={}, y.shape={}'
.format(d_stl.train_X.shape, d_stl.train_y.shape, d_stl.val_X.shape,
d_stl.val_y.shape, d_stl.test_X.shape, d_stl.test_y.shape))
print('STL: train: X.min={}, X.max={}'.format(d_stl.train_X.min(),
d_stl.train_X.max()))
d_stl.n_classes = 9
return d_stl