def test_single_param_multi_ellipsis(self):
with self.assertRaisesRegexp(ValueError, 'Found multiple `...`'):
slicing._slice_single_param(tf.zeros([7, 6, 5, 4, 3]),
param_event_ndims=2,
slices=make_slices[:, ..., 2, ...],
dist_batch_shape=tf.constant([7, 6,
5]))
def test_single_param_too_many_slices(self):
with self.assertRaises((ValueError, tf.errors.InvalidArgumentError)):
slicing._slice_single_param(
tf.zeros([7, 6, 5, 4, 3]),
param_event_ndims=2,
slices=make_slices[:, :3, ..., -2:, :],
dist_batch_shape=tf.constant([7, 6, 5]))
def test_single_param_slice_start_broadcastdim(self):
sliced = slicing._slice_single_param(tf.zeros([7, 1, 5, 4, 3]),
param_event_ndims=2,
slices=make_slices[:, 2:],
dist_batch_shape=tf.constant(
[7, 6, 5]))
self.assertAllEqual((7, 1, 5, 4, 3), self.evaluate(sliced).shape)
def test_single_param_slice_newaxis_trailing(self):
sliced = slicing._slice_single_param(
tf.zeros([7, 6, 5, 4, 3]),
param_event_ndims=2,
slices=make_slices[..., tf.newaxis, :],
dist_batch_shape=tf.constant([7, 6, 5]))
self.assertAllEqual((7, 6, 1, 5, 4, 3), self.evaluate(sliced).shape)
def test_single_param_slice_stop_leadingdim(self):
sliced = slicing._slice_single_param(tf.zeros([7, 6, 5, 4, 3]),
param_event_ndims=2,
slices=make_slices[:2],
dist_batch_shape=tf.constant(
[7, 6, 5], dtype=tf.int32))
self.assertAllEqual((2, 6, 5, 4, 3), self.evaluate(sliced).shape)
def test_single_param_slice_withstep_broadcastdim(self):
event_dim = 3
sliced = slicing._slice_single_param(
tf.zeros([1, 1, event_dim]),
param_event_ndims=1,
slices=make_slices[44:-52:-3, -94::],
dist_batch_shape=tf.constant([2, 7], dtype=tf.int32))
self.assertAllEqual((1, 1, event_dim), self.evaluate(sliced).shape)
def test_single_param_slice_tensor_broadcastdim(self):
param = tf1.placeholder_with_default(tf.zeros([7, 1, 5, 4, 3]),
shape=None)
idx = tf1.placeholder_with_default(tf.constant(2, dtype=tf.int32),
shape=[])
sliced = slicing._slice_single_param(param,
param_event_ndims=2,
slices=make_slices[:, idx],
dist_batch_shape=tf.constant(
[7, 6, 5]))
self.assertAllEqual((7, 5, 4, 3), self.evaluate(sliced).shape)
def test_single_param_slice_broadcast_batch(self):
if not tf.executing_eagerly():
return
sliced = slicing._slice_single_param(
tf.zeros([4, 3, 1]), # batch = [4, 3], event = [1]
param_event_ndims=1,
slices=make_slices[..., tf.newaxis, 2:, tf.newaxis],
dist_batch_shape=tf.constant([7, 4, 3]))
self.assertAllEqual(
list(tf.zeros([1, 4, 3])[..., tf.newaxis, 2:, tf.newaxis].shape) + [1],
self.evaluate(sliced).shape)
def test_single_param_slice_broadcast_batch_leading_newaxis(self):
if not tf.executing_eagerly():
return
sliced = slicing._slice_single_param(
tf.zeros([4, 3, 1]), # batch = [4, 3], event = [1]
param_event_ndims=1,
slices=make_slices[tf.newaxis, ..., tf.newaxis, 2:, tf.newaxis],
dist_batch_shape=tf.constant([7, 4, 3]))
expected = tensorshape_util.as_list(
(tf.zeros([1, 4, 3])[tf.newaxis, ..., tf.newaxis, 2:,
tf.newaxis]).shape) + [1]
self.assertAllEqual(expected, self.evaluate(sliced).shape)
