def test_Caltech101():
dset = caltech.Caltech101()
task = dset.img_classification_task(dtype='float32')
tasks.assert_img_classification(*task, N=9144)
X, y = task
assert X[0].shape == (144, 145, 3)
assert X[1].shape == (817, 656, 3)
assert X[100].shape == (502, 388, 3)
assert len(np.unique(y)) == 102 # number of categories
ylist = y.tolist()
counts = [ylist.count(z) for z in np.unique(ylist)]
assert counts == counts_101
z = y.copy()
z.sort()
assert (y == z).all()
assert (y[1:] != y[:-1]).nonzero()[0].tolist() == caltech_101_breaks
def test_Caltech101():
dset = caltech.Caltech101()
task = dset.img_classification_task(dtype="float32")
tasks.assert_img_classification(*task, N=9144)
X, y = task
assert X[0].shape == (144, 145, 3)
assert X[1].shape == (817, 656, 3)
assert X[100].shape == (502, 388, 3)
assert len(np.unique(y)) == 102 # number of categories
ylist = y.tolist()
counts = [ylist.count(z) for z in np.unique(ylist)]
assert counts == counts_101
z = y.copy()
z.sort()
assert (y == z).all()
assert (y[1:] != y[:-1]).nonzero()[0].tolist() == caltech_101_breaks
def test_Caltech256():
dset = caltech.Caltech256()
task = dset.img_classification_task(dtype='float32')
tasks.assert_img_classification(*task)
def test_img_classification_task():
dset = lfw.Original()
X, y = dset.img_classification_task(dtype='float32')
tasks.assert_img_classification(X, y)
def test_Caltech256():
dset = caltech.Caltech256()
task = dset.img_classification_task(dtype="float32")
tasks.assert_img_classification(*task)