def test_raises_when_zero(self):
with self.test_session():
claire = constant_op.constant([0], name="claire")
with ops.control_dependencies([check_ops.assert_negative(claire)]):
out = array_ops.identity(claire)
with self.assertRaisesOpError("claire"):
out.eval()
def test_raises_when_positive(self):
with self.test_session():
doug = constant_op.constant([1, 2], name="doug")
with ops.control_dependencies(
[check_ops.assert_negative(doug, message="fail")]):
out = array_ops.identity(doug)
with self.assertRaisesOpError("fail.*doug"):
out.eval()
def test_raises_when_positive(self):
doug = constant_op.constant([1, 2], name="doug")
with self.assertRaisesOpError("fail"):
with ops.control_dependencies(
[check_ops.assert_negative(
doug, message="fail")]):
out = array_ops.identity(doug)
self.evaluate(out)
def test_empty_tensor_doesnt_raise(self):
# A tensor is negative when it satisfies:
# For every element x_i in x, x_i < 0
# and an empty tensor has no elements, so this is trivially satisfied.
# This is standard set theory.
empty = constant_op.constant([], name="empty")
with ops.control_dependencies([check_ops.assert_negative(empty)]):
out = array_ops.identity(empty)
self.evaluate(out)
def test_empty_tensor_doesnt_raise(self):
# A tensor is negative when it satisfies:
# For every element x_i in x, x_i < 0
# and an empty tensor has no elements, so this is trivially satisfied.
# This is standard set theory.
with self.test_session():
empty = constant_op.constant([], name="empty")
with ops.control_dependencies([check_ops.assert_negative(empty)]):
out = array_ops.identity(empty)
out.eval()
def test_doesnt_raise_when_negative(self):
with self.test_session():
frank = constant_op.constant([-1, -2], name="frank")
with ops.control_dependencies([check_ops.assert_negative(frank)]):
out = array_ops.identity(frank)
out.eval()
def stack_dynamic_partitions(data, partitions, num_partitions, name=None):
"""Stacks dynamic partitions of a Tensor or RaggedTensor.
Returns a RaggedTensor `output` with `num_partitions` rows, where the row
`output[i]` is formed by stacking all slices `data[j1...jN]` such that
`partitions[j1...jN] = i`. Slices of `data` are stacked in row-major
order.
If `num_partitions` is an `int` (not a `Tensor`), then this is equivalent to
`tf.ragged.stack(tf.dynamic_partition(data, partitions, num_partitions))`.
#### Example:
>>> data = ['a', 'b', 'c', 'd', 'e']
>>> partitions = [ 3, 0, 2, 2, 3]
>>> num_partitions = 5
>>> tf.ragged.stack_dynamic_partitions(data, partitions, num_partitions)
<tf.RaggedTensor [[b'b'], [], [b'c', b'd'], [b'a', b'e'], []]>
Args:
data: A `Tensor` or `RaggedTensor` containing the values to stack.
partitions: An `int32` or `int64` `Tensor` or `RaggedTensor` specifying the
partition that each slice of `data` should be added to.
`partitions.shape` must be a prefix of `data.shape`. Values must be
greater than or equal to zero, and less than `num_partitions`.
`partitions` is not required to be sorted.
num_partitions: An `int32` or `int64` scalar specifying the number of
partitions to output. This determines the number of rows in `output`.
name: A name prefix for the returned tensor (optional).
Returns:
A `RaggedTensor` containing the stacked partitions. The returned tensor
has the same dtype as `data`, and its shape is
`[num_partitions, (D)] + data.shape[partitions.rank:]`, where `(D)` is a
ragged dimension whose length is the number of data slices stacked for
each `partition`.
"""
with ops.name_scope(name, 'SegmentStack',
[data, partitions, num_partitions]):
# Convert inputs to tensors.
data = ragged_tensor.convert_to_tensor_or_ragged_tensor(data,
name='data')
row_splits_dtype = (data.row_splits.dtype if isinstance(
data, ragged_tensor.RaggedTensor) else None)
partitions = ragged_tensor.convert_to_tensor_or_ragged_tensor(
partitions, name='partitions', preferred_dtype=row_splits_dtype)
num_partitions = ops.convert_to_tensor(
num_partitions,
name='num_partitions',
preferred_dtype=partitions.dtype)
if row_splits_dtype is not None:
partitions = math_ops.cast(partitions, row_splits_dtype)
num_partitions = math_ops.cast(num_partitions, partitions.dtype)
# Sanity-checks for shapes.
partitions_rank = partitions.shape.ndims
if partitions_rank is None:
raise ValueError('partitions must have known rank.')
num_partitions.shape.assert_has_rank(0)
partitions.shape.assert_is_compatible_with(
data.shape[:partitions_rank])
if partitions_rank == 0:
# If partitions is a scalar, then just create a RaggedTensor containing
# that single the complete `data` value in the specified row.
return ragged_tensor.RaggedTensor.from_value_rowids(
values=array_ops.stack([data]),
value_rowids=array_ops.stack([partitions]),
nrows=num_partitions,
validate=False)
elif partitions_rank == 1:
# If partitions is a vector (the typical case): we can just use data and
# partitions as the `values` and `value_rowids` for `from_value_rowids`,
# as long as we sort them first.
permutation = sort_ops.argsort(partitions, stable=True)
value_rowids = array_ops.gather(partitions, permutation)
values = array_ops.gather(data, permutation)
check = check_ops.assert_negative(
value_rowids[-1:] - num_partitions,
message='partitions must be less than num_partitions')
with ops.control_dependencies([check]):
return ragged_tensor.RaggedTensor.from_value_rowids(
values, value_rowids, nrows=num_partitions, validate=False)
else:
# Handle higher-dimensional partitions via recursion.
if not isinstance(data, ragged_tensor.RaggedTensor):
data = ragged_tensor.RaggedTensor.from_tensor(
data, row_splits_dtype=partitions.dtype, ragged_rank=1)
if not isinstance(partitions, ragged_tensor.RaggedTensor):
partitions = ragged_tensor.RaggedTensor.from_tensor(
partitions,
row_splits_dtype=partitions.dtype,
ragged_rank=max(data.ragged_rank, partitions_rank - 1))
check = check_ops.assert_equal(
data.row_splits,
partitions.row_splits,
message='data and partitions have incompatible ragged shapes')
with ops.control_dependencies([check]):
return stack_dynamic_partitions(data.values, partitions.values,
num_partitions)
def test_raises_when_zero(self):
claire = constant_op.constant([0], name="claire")
with self.assertRaisesOpError("x < 0 did not hold"):
with ops.control_dependencies([check_ops.assert_negative(claire)]):
out = array_ops.identity(claire)
self.evaluate(out)