def test_tensor_signal_load_indices():
sess = tf.InteractiveSession()
sig = TensorSignal([2, 3, 4, 5], object(), None, (4,), None)
sig.load_indices()
assert np.all(sig.tf_indices.eval() == sig.indices)
start, stop, step = sess.run(sig.as_slice)
assert start == 2
assert stop == 6
assert step == 1
sig = TensorSignal([2, 4, 6, 8], object(), None, (4,), None)
sig.load_indices()
assert np.all(sig.tf_indices.eval() == sig.indices)
start, stop, step = sess.run(sig.as_slice)
assert start == 2
assert stop == 9
assert step == 2
sig = TensorSignal([2, 2, 3, 3], object(), None, (4,), None)
sig.load_indices()
assert np.all(sig.tf_indices.eval() == sig.indices)
assert sig.as_slice is None
sess.close()
def test_signal_dict_scatter():
minibatch_size = 1
var_size = 19
signals = SignalDict(None, tf.float32, minibatch_size)
sess = tf.InteractiveSession()
key = object()
val = np.random.randn(var_size, minibatch_size)
signals.bases = {key: tf.assign(tf.Variable(val, dtype=tf.float32),
val)}
x = TensorSignal([0, 1, 2, 3], key, tf.float32, (4,), None)
with pytest.raises(BuildError):
# assigning to trainable variable
signals.scatter(x, None)
x.minibatch_size = 1
with pytest.raises(BuildError):
# indices not loaded
signals.scatter(x, None)
x.load_indices()
with pytest.raises(BuildError):
# wrong dtype
signals.scatter(x, tf.ones((4,), dtype=tf.float64))
# update
signals.scatter(x, tf.ones((4,)))
y = sess.run(signals.bases[key])
assert np.allclose(y[:4], 1)
assert np.allclose(y[4:], val[4:])
# increment, and reshaping val
signals.scatter(x, tf.ones((2, 2)), mode="inc")
y = sess.run(signals.bases[key])
assert np.allclose(y[:4], 2)
assert np.allclose(y[4:], val[4:])
# recognize assignment to full array
x = TensorSignal(np.arange(var_size), key, tf.float32, (var_size,), 1)
x.load_indices()
y = tf.ones((var_size, 1))
signals.scatter(x, y)
assert signals.bases[key].op.type == "Assign"
# recognize assignment to strided full array
x = TensorSignal(np.arange(0, var_size, 2), key, tf.float32,
(var_size // 2 + 1,), True)
x.load_indices()
y = tf.ones((var_size // 2 + 1, 1))
signals.scatter(x, y)
assert signals.bases[key].op.type == "ScatterUpdate"
sess.close()
def test_signal_dict_gather():
minibatch_size = 1
var_size = 19
signals = SignalDict(None, tf.float32, minibatch_size)
sess = tf.InteractiveSession()
key = object()
val = np.random.randn(var_size, minibatch_size)
signals.bases = {key: tf.constant(val, dtype=tf.float32)}
x = TensorSignal([0, 1, 2, 3], key, tf.float32, (4,), 1)
with pytest.raises(BuildError):
# indices not loaded
signals.gather(x)
# sliced read
x.load_indices()
assert np.allclose(sess.run(signals.gather(x)), val[:4])
# read with reshape
x = TensorSignal([0, 1, 2, 3], key, tf.float32, (2, 2), 1)
x.load_indices()
assert np.allclose(sess.run(signals.gather(x)),
val[:4].reshape((2, 2, minibatch_size)))
# gather read
x = TensorSignal([0, 1, 2, 3], key, tf.float32, (4,), 1)
x.load_indices()
y = signals.gather(x, force_copy=True)
assert "Gather" in y.op.type
x = TensorSignal([0, 0, 3, 3], key, tf.float32, (4,), 1)
x.load_indices()
assert np.allclose(sess.run(signals.gather(x)),
val[[0, 0, 3, 3]])
assert "Gather" in y.op.type
# reading from full array
x = TensorSignal(np.arange(var_size), key, tf.float32, (var_size,), 1)
x.load_indices()
y = signals.gather(x)
assert y is signals.bases[key]
# reading from strided full array
x = TensorSignal(np.arange(0, var_size, 2), key, tf.float32,
(var_size // 2 + 1,), 1)
x.load_indices()
y = signals.gather(x)
assert y.op.type == "StridedSlice"
assert y.op.inputs[0] is signals.bases[key]
# minibatch dimension
x = TensorSignal([0, 1, 2, 3], key, tf.float32, (4,), 1)
x.load_indices()
assert signals.gather(x).get_shape() == (4, 1)
x = TensorSignal([0, 1, 2, 3], key, tf.float32, (4,), None)
x.load_indices()
assert signals.gather(x).get_shape() == (4,)
sess.close()