def testMismatchRaggedRank(self):
elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
with self.assertRaisesRegex(
ValueError, r'(?s)Expected `fn` to return.*But it returned.*'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=23))
def testMismatchRaggedRank(self):
elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
with self.assertRaisesWithLiteralMatch(
ValueError,
r'The declared ragged rank (23) mismatches the result (1)'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=23))
def testMismatchRaggedRank(self):
elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
with self.assertRaisesWithLiteralMatch(
ValueError, r'The declared ragged rank (23) mismatches the result (1)'):
_ = ragged_map_ops.map_fn(
fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=23))
def boolean_mask(data, mask, name=None):
"""Applies a boolean mask to `data` without flattening the mask dimensions.
Returns a potentially ragged tensor that is formed by retaining the elements
in `data` where the corresponding value in `mask` is `True`.
* `output[a1...aA, i, b1...bB] = data[a1...aA, j, b1...bB]`
Where `j` is the `i`th `True` entry of `mask[a1...aA]`.
Note that `output` preserves the mask dimensions `a1...aA`; this differs
from `tf.boolean_mask`, which flattens those dimensions.
Args:
data: A potentially ragged tensor.
mask: A potentially ragged boolean tensor. `mask`'s shape must be a prefix
of `data`'s shape. `rank(mask)` must be known statically.
name: A name prefix for the returned tensor (optional).
Returns:
A potentially ragged tensor that is formed by retaining the elements in
`data` where the corresponding value in `mask` is `True`.
* `rank(output) = rank(data)`.
* `output.ragged_rank = max(data.ragged_rank, rank(mask) - 1)`.
Raises:
ValueError: if `rank(mask)` is not known statically; or if `mask.shape` is
not a prefix of `data.shape`.
#### Examples:
>>> # Aliases for True & False so data and mask line up.
>>> T, F = (True, False)
>>> tf.ragged.boolean_mask( # Mask a 2D Tensor.
... data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
... mask=[[T, F, T], [F, F, F], [T, F, F]]).to_list()
[[1, 3], [], [7]]
>>> tf.ragged.boolean_mask( # Mask a 2D RaggedTensor.
... tf.ragged.constant([[1, 2, 3], [4], [5, 6]]),
... tf.ragged.constant([[F, F, T], [F], [T, T]])).to_list()
[[3], [], [5, 6]]
>>> tf.ragged.boolean_mask( # Mask rows of a 2D RaggedTensor.
... tf.ragged.constant([[1, 2, 3], [4], [5, 6]]),
... tf.ragged.constant([True, False, True])).to_list()
[[1, 2, 3], [5, 6]]
"""
with ops.name_scope(name, 'RaggedMask', [data, mask]):
# Convert inputs to tensors.
data = ragged_tensor.convert_to_tensor_or_ragged_tensor(data, name='data')
mask = ragged_tensor.convert_to_tensor_or_ragged_tensor(
mask, dtypes.bool, name='mask')
row_splits_dtype, (data, mask) = ragged_tensor.match_row_splits_dtypes(
data, mask, return_dtype=True)
# Get static rank of mask.
if mask.shape.ndims is None:
raise ValueError('mask.shape.ndims must be known statically.')
elif mask.shape.ndims == 0:
raise ValueError('mask cannot be scalar.')
# If mask is ragged, then recurse with a non-ragged mask.
if ragged_tensor.is_ragged(mask):
if not ragged_tensor.is_ragged(data):
data = ragged_tensor.RaggedTensor.from_tensor(
data,
ragged_rank=mask.ragged_rank,
row_splits_dtype=mask.row_splits.dtype)
# Check that mask.nested_row_splits is a prefix of
# data.nested_row_splits.
splits_list = [
mask.nested_row_splits, data.nested_row_splits[:mask.ragged_rank]
]
with ops.control_dependencies(
ragged_util.assert_splits_match(splits_list)):
# Strip off ragged `splits` until `mask` is non-ragged. Keep the splits
# that we strip off in `splits`, so we can add them back on after
# we recursively mask the non-ragged data.
splits = []
while ragged_tensor.is_ragged(mask):
if mask.shape.ndims > 2:
splits.append(mask.row_splits)
else:
# Count the number of True mask values in each row to find the
# lengths of the filtered rows; then convert to splits.
int_mask = ragged_functional_ops.map_flat_values(
math_ops.cast, mask, dtype=row_splits_dtype)
masked_row_lengths = ragged_math_ops.reduce_sum(int_mask, axis=1)
splits.append(ragged_util.lengths_to_splits(masked_row_lengths))
mask = mask.values
data = data.values
# Recursively apply the nested non-ragged mask to the nested data.
masked_values = boolean_mask(data, mask)
# Add the ragged `splits` back to the result.
masked_values = ragged_tensor.RaggedTensor.from_nested_row_splits(
masked_values, splits, validate=False)
return masked_values
# If mask is non-ragged and has rank 1, and data is ragged, then build a
# ragged tensor with the indicated rows.
elif ragged_tensor.is_ragged(data) and mask.shape.ndims == 1:
# Get the masked splits: first get the length of each row, then filter
# out the rows that we are deleting, and convert that filtered set of
# masks back to a splits tensor.
lengths = data.row_lengths()
masked_lengths = array_ops.boolean_mask(lengths, mask)
masked_splits = ragged_util.lengths_to_splits(masked_lengths)
# Get the masked values: first get row ids corresponding to each
# value, then use tf.gather to build a boolean mask that's false for
# values that come from rows that we are deleting, and use that mask to
# construct the masked values tensor.
segment_ids = segment_id_ops.row_splits_to_segment_ids(data.row_splits)
segment_mask = array_ops.gather(mask, segment_ids)
masked_values = boolean_mask(data.values, segment_mask)
return ragged_tensor.RaggedTensor.from_row_splits(
masked_values, masked_splits, validate=False)
# If mask is non-ragged and has rank>1, then convert it to be ragged,
# with a ragged rank matching data.
if ragged_tensor.is_ragged(data):
mask = ragged_tensor.RaggedTensor.from_tensor(
mask,
ragged_rank=min(data.ragged_rank, mask.shape.ndims - 1),
row_splits_dtype=data.row_splits.dtype)
return boolean_mask(data, mask)
# Otherwise, data and mask are both `Tensor`s.
else:
# Apply `boolean_mask` to get the masked values.
masked_values = array_ops.boolean_mask(data, mask)
if mask.shape.ndims >= 2:
# Add the innermost ragged dimension. For each innermost cell, get the
# number of values it contains. Then flatten that to get a list of
# cell lengths, and convert it to splits. Finally, combine the splits
# and values to get the innermost ragged tensor.
masked_lengths = math_ops.count_nonzero(
mask, axis=-1, dtype=row_splits_dtype)
flattened_masked_lengths = array_ops.reshape(masked_lengths, [-1])
masked_values = ragged_tensor.RaggedTensor.from_row_lengths(
masked_values, flattened_masked_lengths, validate=False)
# Wrap remaining ragged dimensions.
if mask.shape.ndims > 2:
mask_shape = array_ops.shape(mask, out_type=row_splits_dtype)
split_size = math_ops.cumprod(mask_shape) + 1
for dim in range(mask.shape.ndims - 3, -1, -1):
elt_size = mask_shape[dim + 1]
masked_splits = math_ops.range(split_size[dim]) * elt_size
masked_values = ragged_tensor.RaggedTensor.from_row_splits(
masked_values, masked_splits, validate=False)
return masked_values
def testReduceKeepsInnerDimensionShape(self): # Test for bug [b/139823356]. rt = ragged_factory_ops.constant([[[[1, 1]]]], ragged_rank=2) self.assertEqual(rt.shape.as_list(), [1, None, None, 2]) reduced = ragged_math_ops.reduce_sum(rt, axis=2) self.assertEqual(reduced.shape.as_list(), [1, None, 2])
class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
@parameterized.parameters([
# The following test sets map over a RaggedTensor and apply a
# transformation that returns with shape:
# [d1, (d2)] -> [d1]
dict(
fn=mo.reduce_mean,
elems=[[1, 2, 3], [4, 5], [6, 7]],
elems_dtype=dtypes.int32,
expected_output=[2, 4, 6],
result_dtype=dtypes.int32,
),
dict(
fn=string_ops.reduce_join,
elems=[['foo', 'bar', 'baz'], ['a'], ['b', 'c']],
expected_output=[b'foobarbaz', b'a', b'bc'],
elems_dtype=dtypes.string,
result_dtype=dtypes.string,
),
# [d1, (d2)] -> [d1, 2]
dict(
fn=lambda x: array_ops.stack([mo.reduce_mean(x),
mo.reduce_sum(x)]),
# fn=self.stack_mean_and_sum,
elems=[[1, 2, 3], [4, 5], [6, 7]],
expected_output=[[2, 6], [4.5, 9], [6.5, 13]],
elems_dtype=dtypes.float32,
result_dtype=dtypes.float32,
expected_ragged_rank=0,
),
# [d1, (d2)] -> [d1, (d2)]
dict(
fn=lambda x: x + np.int64(1),
elems=[[1, 2, 3], [4, 5], [6, 7]],
expected_output=[[2, 3, 4], [5, 6], [7, 8]],
elems_dtype=dtypes.int64,
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), d3] -> [d1, (d2), d3]
dict(
fn=lambda x: x + np.int64(1),
elems=[[[1, 2], [3, 4]], [], [[5, 6], [7, 8], [9, 0]]],
elems_ragged_rank=1,
expected_ragged_rank=1,
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
expected_output=[[[2, 3], [4, 5]], [], [[6, 7], [8, 9], [10, 1]]],
),
# [d1, (d2)] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0]),
elems=[[1, 2, 3], [4, 5], [6, 7]],
expected_output=[[[1, 2, 3]], [[4, 5]], [[6, 7]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3)] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged_functional_ops.map_flat_values(mo.add, x, 1),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[[2, 3, 4]], [[5, 6], [7, 8]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3)] -> [d1, (d2)]
dict(
fn=lambda x: ragged_math_ops.reduce_sum(x, axis=1),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[6], [9, 13]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), (d3)] -> [d1, (d3)]
dict(
fn=lambda x: ragged_math_ops.reduce_sum(x, axis=0),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[1, 2, 3], [10, 12]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), (d3)] -> [d1]
dict(
fn=ragged_math_ops.reduce_sum,
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[6, 22],
result_dtype=dtypes.int64,
),
# [d1] -> [d1, (d2)]
dict(
fn=mo.range,
elems=[4, 0, 2],
expected_output=[[0, 1, 2, 3], [], [0, 1]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged_math_ops.range(mo.range(x)),
elems=[5, 0, 3],
expected_output=[[[], [0], [0, 1], [0, 1, 2], [0, 1, 2, 3]], [],
[[], [0], [0, 1]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3), (d4a), (d5)] -> [d1, (d2), (d3), (d4b), (d5)]
dict(
fn=lambda x: x + np.int64(1),
elems=[[[[[1, 2, 3]], [[4], [5]]]], [[[[6, 7]]], [[[8], []]]]],
expected_output=[[[[[2, 3, 4]], [[5], [6]]]],
[[[[7, 8]]], [[[9], []]]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=4),
),
])
def testRaggedMap(
self,
fn,
elems,
expected_output,
expected_ragged_rank=None,
result_ragged_rank=None,
elems_ragged_rank=None,
elems_dtype=dtypes.int64,
result_dtype=None,
infer_shape=True,
):
elems = ragged_factory_ops.constant(elems, elems_dtype,
elems_ragged_rank)
output = ragged_map_ops.map_fn(fn=fn,
elems=elems,
dtype=result_dtype,
infer_shape=infer_shape)
expected_rt = ragged_factory_ops.constant(
expected_output, ragged_rank=expected_ragged_rank)
self.assertAllEqual(expected_rt, output)
def testRaggedMapOnStructure(self):
batman = ragged_factory_ops.constant([[1, 2, 3], [4], [5, 6, 7]])
# [[10, 20, 30], [40], [50, 60, 70]]
robin = ragged_functional_ops.map_flat_values(mo.multiply, batman, 10)
features = {'batman': batman, 'robin': robin}
def _reduce_sum_from_all(f):
return mo.reduce_sum(f['batman']) + mo.reduce_sum(f['robin'])
output = ragged_map_ops.map_fn(
fn=_reduce_sum_from_all,
elems=features,
dtype=dtypes.int32,
)
self.assertAllEqual(output, [66, 44, 198])
# Test mapping over a dict of RTs can produce a dict of RTs.
def testRaggedMapOnStructure_RaggedOutputs(self):
batman = ragged_factory_ops.constant([[1, 2, 3], [4], [5, 6, 7]])
# [[10, 20, 30], [40], [50, 60, 70]]
robin = ragged_functional_ops.map_flat_values(mo.multiply, batman, 10)
features = {'batman': batman, 'robin': robin}
def _increment(f):
return {
'batman': f['batman'] + 1,
'robin': f['robin'] + 1,
}
output = ragged_map_ops.map_fn(
fn=_increment,
elems=features,
infer_shape=False,
dtype={
'batman':
ragged_tensor.RaggedTensorType(dtype=dtypes.int32,
ragged_rank=1),
'robin':
ragged_tensor.RaggedTensorType(dtype=dtypes.int32,
ragged_rank=1)
},
)
self.assertAllEqual(output['batman'], [[2, 3, 4], [5], [6, 7, 8]])
self.assertAllEqual(output['robin'],
[[11, 21, 31], [41], [51, 61, 71]])
def testZip(self):
x = ragged_factory_ops.constant(
[[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]], dtypes.int64)
y = array_ops.expand_dims(mo.range(x.nrows(out_type=dtypes.int64)),
axis=1)
def _zip(foo):
y_val, x_val = foo
bar = array_ops.tile(y_val, array_ops.shape(x_val))
return array_ops.stack([bar, x_val], axis=1)
output = ragged_map_ops.map_fn(_zip, (y, x),
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=1),
infer_shape=False)
self.assertAllEqual(
output,
[[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]],
[[3, 70]], [[4, 80], [4, 90], [4, 100]]])
def testBatchGather(self):
tokens = ragged_factory_ops.constant([['hello', '.', 'there'],
['merhaba'],
['bonjour', '.', 'ca va', '?']])
indices = ragged_factory_ops.constant([[0, 2], [0], [0, 2]])
def gather(x):
tokens_val, indices_val = x
return array_ops.gather(tokens_val, indices_val)
data = tokens, indices
out = ragged_map_ops.map_fn(gather,
data,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.string, ragged_rank=1),
infer_shape=False)
self.assertAllEqual(
out, [[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']])
def testMismatchRaggedRank(self):
elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
with self.assertRaisesRegex(
ValueError, r'(?s)Expected `fn` to return.*But it returned.*'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=23))
def testMismatchRaggedRank2(self):
elems = ragged_factory_ops.constant([[1, 2, 3], [4, 5], [6, 7]])
fn = lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0])
with self.assertRaisesRegex(
ValueError, r'(?s)Expected `fn` to return.*But it returned.*'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=10))
def testMapOnSparseTensor(self):
s = sparse_tensor.SparseTensor(
indices=[[0, 0], [0, 1], [1, 0], [1, 1]],
values=[0, 5, 0, 4],
dense_shape=[2, 2],
)
t2 = ragged_tensor.RaggedTensor.from_sparse(s)
id_t2 = ragged_map_ops.map_fn(
lambda x: x,
t2,
)
self.assertAllEqual(id_t2, [[0, 5], [0, 4]])
def boolean_mask(data, mask, keepdims=False, name=None):
"""Applies a boolean mask to `data`.
Returns a potentially ragged tensor that is formed by retaining the elements
in `data` where the corresponding value in `mask` is `True`.
If `keepdims` is true then outer dimensions (corresponding to the `mask`
dimensions) are preserved, and:
* `output[a1...aA, i, b1...bB] = data[a1...aA, j, b1...bB]`
Where `j` is the `i`th `True` entry of `mask[a1...aA]`.
If `keepdims` is false, then the outer dimensions are collapsed (similar to
the behavior of `tf.boolean_mask`), and:
* `output[i, b1...bB] = data[a1...aA, b1...bB]`
Where `(a1...aA)` is the `i`th `True` entry of `mask`
(in row-major order).
Args:
data: A potentially ragged tensor.
mask: A potentially ragged boolean tensor. `mask`'s shape must be a prefix
of `data`'s shape. `rank(mask)` must be known statically.
keepdims: Whether to preserve the outer dimensions (`keepdims=True`) or
flatten them (`keepdims=False`).
name: A name prefix for the returned tensor (optional).
Returns:
A potentially ragged tensor that is formed by retaining the elements in
`data` where the corresponding value in `mask` is `True`.
If `keepdims` is false:
* `rank(output) = rank(data) - rank(mask) + 1`.
* `output.ragged_rank = max(data.ragged_rank - rank(mask) + 1, 0)`.
If `keepdims` is true:
* `rank(output) = rank(data)`.
* `output.ragged_rank = max(data.ragged_rank, rank(mask) - 1)`.
Raises:
ValueError: if `rank(mask)` is not known statically; or if `mask.shape` is
not a prefix of `data.shape`.
#### Examples:
```python
>>> # Aliases for True & False so data and mask line up.
>>> T, F = (True, False)
>>> tf.ragged.boolean_mask( # Mask a 2D Tensor. Flatten outer dims.
... data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
... mask=[[T, F, T], [F, F, F], [T, F, F]],
... keepdims=False).tolist()
[1, 3, 7]
>>> tf.ragged.boolean_mask( # Mask a 2D Tensor. Preserve outer dims.
... data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
... mask=[[T, F, T], [F, F, F], [T, F, F]],
... keepdims=True).tolist()
[[1, 3], [], [7]]
>>> tf.ragged.boolean_mask( # Mask a 2D RaggedTensor. Flatten outer dims.
... tf.ragged.constant([[1, 2, 3], [4], [5, 6]]),
... tf.ragged.constant([[F, F, T], [F], [T, T]]),
... keepdims=False).tolist()
[3, 5, 6]
>>> tf.ragged.boolean_mask( # Mask a 2D RaggedTensor. Preserve outer dims.
... tf.ragged.constant([[1, 2, 3], [4], [5, 6]]),
... tf.ragged.constant([[F, F, T], [F], [T, T]]),
... keepdims=True).tolist()
[[3], [], [5, 6]]
>>> tf.ragged.boolean_mask( # Mask rows of a 2D RaggedTensor.
... tf.ragged.constant([[1, 2, 3], [4], [5, 6]]),
... tf.ragged.constant([True, False, True]),
... keepdims=True).tolist()
[[1, 2, 3], [5, 6]]
```
"""
with ops.name_scope(name, 'RaggedMask', [data, mask]):
# Convert inputs to tensors.
data = ragged_tensor.convert_to_tensor_or_ragged_tensor(data, name='data')
mask = ragged_tensor.convert_to_tensor_or_ragged_tensor(
mask, dtypes.bool, name='mask')
row_splits_dtype, (data, mask) = ragged_tensor.match_row_splits_dtypes(
data, mask, return_dtype=True)
# Get static rank of mask.
if mask.shape.ndims is None:
raise ValueError('mask.shape.ndims must be known statically.')
elif mask.shape.ndims == 0:
raise ValueError('mask cannot be scalar.')
# If mask is ragged, then recurse with a non-ragged mask.
if ragged_tensor.is_ragged(mask):
if not ragged_tensor.is_ragged(data):
data = ragged_tensor.RaggedTensor.from_tensor(
data, ragged_rank=mask.ragged_rank,
row_splits_dtype=mask.row_splits.dtype)
# Check that mask.nested_row_splits is a prefix of
# data.nested_row_splits.
splits_list = [
mask.nested_row_splits, data.nested_row_splits[:mask.ragged_rank]
]
with ops.control_dependencies(
ragged_util.assert_splits_match(splits_list)):
# Strip off ragged `splits` until `mask` is non-ragged. Keep the splits
# that we strip off in `splits`, so we can add them back on after
# we recursively mask the non-ragged data.
splits = []
while ragged_tensor.is_ragged(mask):
if mask.shape.ndims > 2:
splits.append(mask.row_splits)
else:
# Count the number of True mask values in each row to find the
# lengths of the filtered rows; then convert to splits.
int_mask = ragged_functional_ops.map_flat_values(
math_ops.cast, mask, dtype=row_splits_dtype)
masked_row_lengths = ragged_math_ops.reduce_sum(int_mask, axis=1)
splits.append(ragged_util.lengths_to_splits(masked_row_lengths))
mask = mask.values
data = data.values
# Recursively apply the nested non-ragged mask to the nested data.
masked_values = boolean_mask(data, mask, keepdims)
# Add the ragged `splits` back to the result.
if keepdims:
masked_values = ragged_tensor.RaggedTensor.from_nested_row_splits(
masked_values, splits, validate=False)
return masked_values
# If mask is non-ragged and has rank 1, and data is ragged, then build a
# ragged tensor with the indicated rows.
elif ragged_tensor.is_ragged(data) and mask.shape.ndims == 1:
# Get the masked splits: first get the length of each row, then filter
# out the rows that we are deleting, and convert that filtered set of
# masks back to a splits tensor.
lengths = data.row_lengths()
masked_lengths = array_ops.boolean_mask(lengths, mask)
masked_splits = ragged_util.lengths_to_splits(masked_lengths)
# Get the masked values: first get row ids corresponding to each
# value, then use tf.gather to build a boolean mask that's false for
# values that come from rows that we are deleting, and use that mask to
# construct the masked values tensor.
segment_ids = segment_id_ops.row_splits_to_segment_ids(data.row_splits)
segment_mask = array_ops.gather(mask, segment_ids)
masked_values = boolean_mask(data.values, segment_mask, keepdims=False)
return ragged_tensor.RaggedTensor.from_row_splits(masked_values,
masked_splits,
validate=False)
# If mask is non-ragged and has rank>1, then convert it to be ragged,
# with a ragged rank matching data.
if ragged_tensor.is_ragged(data):
mask = ragged_tensor.RaggedTensor.from_tensor(
mask, ragged_rank=min(data.ragged_rank, mask.shape.ndims - 1),
row_splits_dtype=data.row_splits.dtype)
return boolean_mask(data, mask, keepdims)
# Otherwise, data and mask are both `Tensor`s.
else:
# Apply `boolean_mask` to get the masked values.
masked_values = array_ops.boolean_mask(data, mask)
if mask.shape.ndims >= 2 and keepdims:
# Add the innermost ragged dimension. For each innermost cell, get the
# number of values it contains. Then flatten that to get a list of
# cell lengths, and convert it to splits. Finally, combine the splits
# and values to get the innermost ragged tensor.
masked_lengths = math_ops.count_nonzero(mask, axis=-1,
dtype=row_splits_dtype)
flattened_masked_lengths = array_ops.reshape(masked_lengths, [-1])
masked_values = ragged_tensor.RaggedTensor.from_row_lengths(
masked_values, flattened_masked_lengths, validate=False)
# Wrap remaining ragged dimensions.
if mask.shape.ndims > 2 and keepdims:
mask_shape = array_ops.shape(mask, out_type=row_splits_dtype)
split_size = math_ops.cumprod(mask_shape) + 1
for dim in range(mask.shape.ndims - 3, -1, -1):
elt_size = mask_shape[dim + 1]
masked_splits = math_ops.range(split_size[dim]) * elt_size
masked_values = ragged_tensor.RaggedTensor.from_row_splits(
masked_values, masked_splits, validate=False)
return masked_values
