def test_get_tensor_signal():
signals = SignalDict(tf.float32, 3)
# check that tensor_signal is created correctly
key = object()
tensor_signal = signals.get_tensor_signal(
(0,), key, np.float64, (3, 4), True)
assert isinstance(tensor_signal, TensorSignal)
assert np.array_equal(tensor_signal.indices, (0,))
assert tensor_signal.key == key
assert tensor_signal.dtype == np.float64
assert tensor_signal.shape == (3, 4)
assert tensor_signal.minibatch_size == 3
assert tensor_signal.constant == signals.constant
assert len(signals) == 0
# check adding signal to sig_map
sig = Signal(np.zeros(4))
sig.minibatched = True
tensor_signal = signals.get_tensor_signal(
np.arange(4), key, np.float64, (2, 2), True, signal=sig)
assert len(signals) == 1
assert signals[sig] is tensor_signal
assert next(iter(signals)) is sig
assert next(iter(signals.values())) is tensor_signal
# error if sig shape doesn't match indices
with pytest.raises(AssertionError):
sig = Signal(np.zeros((2, 2)))
sig.minibatched = True
signals.get_tensor_signal(
np.arange(4), key, np.float64, (2, 2), True, signal=sig)
# error if sig size doesn't match given shape
with pytest.raises(AssertionError):
sig = Signal(np.zeros(4))
sig.minibatched = True
signals.get_tensor_signal(
np.arange(4), key, np.float64, (2, 3), True, signal=sig)
# error if minibatched doesn't match
with pytest.raises(AssertionError):
sig = Signal(np.zeros(4))
sig.minibatched = False
signals.get_tensor_signal(
np.arange(4), key, np.float64, (2, 2), True, signal=sig)
