def test_pad_arrays_axis(axis: int, is_right_pad: bool):
arrays = [d["target"] for d in list(iter(get_dataset()))]
if axis == 0:
arrays = [x.T for x in arrays]
padded_arrays = _pad_arrays(arrays, axis, is_right_pad=is_right_pad)
assert all(a.shape[axis] == 8 for a in padded_arrays)
assert all(a.shape[1 - axis] == 2 for a in padded_arrays)
def test_pad_arrays_axis(array_type, multi_processing, axis: int):
arrays = [
d["target"] if array_type == "np" else mx.nd.array(d["target"])
for d in list(iter(get_dataset()))
]
if axis == 0:
arrays = [x.T for x in arrays]
padded_arrays = _pad_arrays(arrays, axis)
assert all(a.shape[axis] == 8 for a in padded_arrays)
assert all(a.shape[1 - axis] == 2 for a in padded_arrays)
def test_pad_arrays_pad_left():
arrays = [d["target"] for d in list(iter(get_dataset()))]
padded_arrays = _pad_arrays(arrays, 1, is_right_pad=False)
for padded_array in padded_arrays[1:]:
assert np.allclose(padded_array[:, 0], 0)
