def test_with_rank_ndarray(self):
x = np.array([2], dtype=np.int32)
with self.assertRaises(ValueError):
tensorshape_util.with_rank(x, 2)
x = np.array([2, 3, 4], dtype=np.int32)
y = tensorshape_util.with_rank(x, 3)
self.assertAllEqual(x, y)
x = np.array([2, 3, 4, 5], dtype=np.int32)
y = tensorshape_util.with_rank_at_least(x, 3)
self.assertAllEqual(x, y)
def test_with_rank_list_tuple(self):
with self.assertRaises(ValueError):
tensorshape_util.with_rank([2], 2)
with self.assertRaises(ValueError):
tensorshape_util.with_rank((2, ), 2)
self.assertAllEqual((2, 1), tensorshape_util.with_rank((2, 1), 2))
self.assertAllEqual([2, 1], tensorshape_util.with_rank([2, 1], 2))
self.assertAllEqual((2, 3, 4),
tensorshape_util.with_rank_at_least((2, 3, 4), 2))
self.assertAllEqual([2, 3, 4],
tensorshape_util.with_rank_at_least([2, 3, 4], 2))
def _event_shape(self):
# We will never broadcast the num_categories with total_count.
return tensorshape_util.with_rank(
(self._probs if self._logits is None else self._logits).shape[-1:],
rank=1)
def _event_shape(self):
return tensorshape_util.with_rank(self.concentration.shape[-1:],
rank=1)
def _event_shape(self):
param = self._logits if self._logits is not None else self._probs
return tensorshape_util.with_rank(param.shape[-1:], rank=1)
def _event_shape(self): return tensorshape_util.with_rank(self.mean_direction.shape[-1:], rank=1)
def _event_shape(self):
# Event shape depends only on concentration, not total_count.
return tensorshape_util.with_rank(self.concentration.shape[-1:],
rank=1)
def _sample_n(self, n, seed=None):
stream = SeedStream(seed, salt="VectorDiffeomixture")
x = self.distribution.sample(sample_shape=concat_vectors(
[n], self.batch_shape_tensor(), self.event_shape_tensor()),
seed=stream()) # shape: [n, B, e]
x = [aff.forward(x) for aff in self.endpoint_affine]
# Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
# ids as a [n]-shaped vector.
batch_size = tensorshape_util.num_elements(self.batch_shape)
if batch_size is None:
batch_size = tf.reduce_prod(self.batch_shape_tensor())
mix_batch_size = tensorshape_util.num_elements(
self.mixture_distribution.batch_shape)
if mix_batch_size is None:
mix_batch_size = tf.reduce_prod(
self.mixture_distribution.batch_shape_tensor())
ids = self.mixture_distribution.sample(sample_shape=concat_vectors(
[n],
distribution_util.pick_vector(self.is_scalar_batch(), np.int32([]),
[batch_size // mix_batch_size])),
seed=stream())
# We need to flatten batch dims in case mixture_distribution has its own
# batch dims.
ids = tf.reshape(ids,
shape=concat_vectors([n],
distribution_util.pick_vector(
self.is_scalar_batch(),
np.int32([]),
np.int32([-1]))))
# Stride `components * quadrature_size` for `batch_size` number of times.
stride = tensorshape_util.num_elements(
tensorshape_util.with_rank(self.grid.shape[-2:], rank=2))
if stride is None:
stride = tf.reduce_prod(tf.shape(self.grid)[-2:])
offset = tf.range(start=0,
limit=batch_size * stride,
delta=stride,
dtype=ids.dtype)
weight = tf.gather(tf.reshape(self.grid, shape=[-1]), ids + offset)
# At this point, weight flattened all batch dims into one.
# We also need to append a singleton to broadcast with event dims.
if tensorshape_util.is_fully_defined(self.batch_shape):
new_shape = [-1] + tensorshape_util.as_list(self.batch_shape) + [1]
else:
new_shape = tf.concat(([-1], self.batch_shape_tensor(), [1]),
axis=0)
weight = tf.reshape(weight, shape=new_shape)
if len(x) != 2:
# We actually should have already triggered this exception. However as a
# policy we're putting this exception wherever we exploit the bimixture
# assumption.
raise NotImplementedError(
"Currently only bimixtures are supported; "
"len(scale)={} is not 2.".format(len(x)))
# Alternatively:
# x = weight * x[0] + (1. - weight) * x[1]
x = weight * (x[0] - x[1]) + x[1]
return x