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ragged_factory_ops.py
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/
ragged_factory_ops.py
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Operations for constructing RaggedTensors."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.ops.ragged import ragged_tensor_value
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
#===============================================================================
# Op to construct a constant RaggedTensor from a nested Python list.
#===============================================================================
@tf_export("ragged.constant")
@dispatch.add_dispatch_support
def constant(pylist, dtype=None, ragged_rank=None, inner_shape=None,
name=None, row_splits_dtype=dtypes.int64):
"""Constructs a constant RaggedTensor from a nested Python list.
Example:
>>> tf.ragged.constant([[1, 2], [3], [4, 5, 6]])
<tf.RaggedTensor [[1, 2], [3], [4, 5, 6]]>
All scalar values in `pylist` must have the same nesting depth `K`, and the
returned `RaggedTensor` will have rank `K`. If `pylist` contains no scalar
values, then `K` is one greater than the maximum depth of empty lists in
`pylist`. All scalar values in `pylist` must be compatible with `dtype`.
Args:
pylist: A nested `list`, `tuple` or `np.ndarray`. Any nested element that
is not a `list`, `tuple` or `np.ndarray` must be a scalar value
compatible with `dtype`.
dtype: The type of elements for the returned `RaggedTensor`. If not
specified, then a default is chosen based on the scalar values in
`pylist`.
ragged_rank: An integer specifying the ragged rank of the returned
`RaggedTensor`. Must be nonnegative and less than `K`. Defaults to
`max(0, K - 1)` if `inner_shape` is not specified. Defaults to
`max(0, K - 1 - len(inner_shape))` if `inner_shape` is specified.
inner_shape: A tuple of integers specifying the shape for individual inner
values in the returned `RaggedTensor`. Defaults to `()` if `ragged_rank`
is not specified. If `ragged_rank` is specified, then a default is chosen
based on the contents of `pylist`.
name: A name prefix for the returned tensor (optional).
row_splits_dtype: data type for the constructed `RaggedTensor`'s row_splits.
One of `tf.int32` or `tf.int64`.
Returns:
A potentially ragged tensor with rank `K` and the specified `ragged_rank`,
containing the values from `pylist`.
Raises:
ValueError: If the scalar values in `pylist` have inconsistent nesting
depth; or if ragged_rank or inner_shape are incompatible with `pylist`.
"""
def ragged_factory(values, row_splits):
row_splits = constant_op.constant(row_splits, dtype=row_splits_dtype)
return ragged_tensor.RaggedTensor.from_row_splits(values, row_splits,
validate=False)
with ops.name_scope(name, "RaggedConstant"):
return _constant_value(ragged_factory, constant_op.constant, pylist, dtype,
ragged_rank, inner_shape)
@tf_export(v1=["ragged.constant_value"])
@dispatch.add_dispatch_support
def constant_value(pylist, dtype=None, ragged_rank=None, inner_shape=None,
row_splits_dtype="int64"):
"""Constructs a RaggedTensorValue from a nested Python list.
Warning: This function returns a `RaggedTensorValue`, not a `RaggedTensor`.
If you wish to construct a constant `RaggedTensor`, use
[`ragged.constant(...)`](constant.md) instead.
Example:
>>> tf.compat.v1.ragged.constant_value([[1, 2], [3], [4, 5, 6]])
tf.RaggedTensorValue(values=array([1, 2, 3, 4, 5, 6]),
row_splits=array([0, 2, 3, 6]))
All scalar values in `pylist` must have the same nesting depth `K`, and the
returned `RaggedTensorValue` will have rank `K`. If `pylist` contains no
scalar values, then `K` is one greater than the maximum depth of empty lists
in `pylist`. All scalar values in `pylist` must be compatible with `dtype`.
Args:
pylist: A nested `list`, `tuple` or `np.ndarray`. Any nested element that
is not a `list` or `tuple` must be a scalar value compatible with `dtype`.
dtype: `numpy.dtype`. The type of elements for the returned `RaggedTensor`.
If not specified, then a default is chosen based on the scalar values in
`pylist`.
ragged_rank: An integer specifying the ragged rank of the returned
`RaggedTensorValue`. Must be nonnegative and less than `K`. Defaults to
`max(0, K - 1)` if `inner_shape` is not specified. Defaults to `max(0, K
- 1 - len(inner_shape))` if `inner_shape` is specified.
inner_shape: A tuple of integers specifying the shape for individual inner
values in the returned `RaggedTensorValue`. Defaults to `()` if
`ragged_rank` is not specified. If `ragged_rank` is specified, then a
default is chosen based on the contents of `pylist`.
row_splits_dtype: data type for the constructed `RaggedTensorValue`'s
row_splits. One of `numpy.int32` or `numpy.int64`.
Returns:
A `tf.RaggedTensorValue` or `numpy.array` with rank `K` and the specified
`ragged_rank`, containing the values from `pylist`.
Raises:
ValueError: If the scalar values in `pylist` have inconsistent nesting
depth; or if ragged_rank or inner_shape are incompatible with `pylist`.
"""
if dtype is not None and isinstance(dtype, dtypes.DType):
dtype = dtype.as_numpy_dtype
row_splits_dtype = dtypes.as_dtype(row_splits_dtype).as_numpy_dtype
def _ragged_factory(values, row_splits):
row_splits = np.array(row_splits, dtype=row_splits_dtype)
return ragged_tensor_value.RaggedTensorValue(values, row_splits)
def _inner_factory(pylist, dtype, shape, name=None): # pylint: disable=unused-argument
return np.reshape(np.array(pylist, dtype=dtype), shape)
return _constant_value(_ragged_factory, _inner_factory, pylist, dtype,
ragged_rank, inner_shape)
def _constant_value(ragged_factory, inner_factory, pylist, dtype, ragged_rank,
inner_shape):
"""Constructs a constant RaggedTensor or RaggedTensorValue.
Args:
ragged_factory: A factory function with the signature:
`ragged_factory(values, row_splits)`
inner_factory: A factory function with the signature: `inner_factory(pylist,
dtype, shape, name)`
pylist: A nested `list`, `tuple` or `np.ndarray`.
dtype: Data type for returned value.
ragged_rank: Ragged rank for returned value.
inner_shape: Inner value shape for returned value.
Returns:
A value returned by `ragged_factory` or `inner_factory`.
Raises:
ValueError: If the scalar values in `pylist` have inconsistent nesting
depth; or if ragged_rank or inner_shape are incompatible with `pylist`.
"""
if ragged_tensor.is_ragged(pylist):
raise TypeError("pylist may not be a RaggedTensor or RaggedTensorValue.")
# np.ndim builds an array, so we short-circuit lists and tuples.
if not isinstance(pylist, (list, tuple)) and np.ndim(pylist) == 0:
# Scalar value
if ragged_rank is not None and ragged_rank != 0:
raise ValueError("Invalid pylist=%r: incompatible with ragged_rank=%d" %
(pylist, ragged_rank))
if inner_shape is not None and inner_shape:
raise ValueError(
"Invalid pylist=%r: incompatible with dim(inner_shape)=%d" %
(pylist, len(inner_shape)))
return inner_factory(pylist, dtype, ())
if ragged_rank is not None and ragged_rank < 0:
raise ValueError(
"Invalid ragged_rank=%r: must be nonnegative" % ragged_rank)
# Find the depth of scalar values in `pylist`.
scalar_depth, max_depth = _find_scalar_and_max_depth(pylist)
if scalar_depth is not None:
if max_depth > scalar_depth:
raise ValueError("Invalid pylist=%r: empty list nesting is greater "
"than scalar value nesting" % pylist)
if ragged_rank is not None and max_depth < ragged_rank:
raise ValueError(f"Invalid pylist={pylist}, max depth smaller than "
f"ragged_rank={ragged_rank}")
# If both inner_shape and ragged_rank were specified, then check that
# they are compatible with pylist.
if inner_shape is not None and ragged_rank is not None:
expected_depth = ragged_rank + len(inner_shape) + 1
if ((scalar_depth is not None and expected_depth != scalar_depth) or
(scalar_depth is None and expected_depth < max_depth)):
raise ValueError(
"Invalid pylist=%r: incompatible with ragged_rank=%d "
"and dim(inner_shape)=%d" % (pylist, ragged_rank, len(inner_shape)))
# Check if the result is a `Tensor`.
if (ragged_rank == 0 or
(ragged_rank is None and
((max_depth < 2) or
(inner_shape is not None and max_depth - len(inner_shape) < 2)))):
return inner_factory(pylist, dtype, inner_shape)
# Compute default value for inner_shape.
if inner_shape is None:
if ragged_rank is None:
inner_shape = ()
else:
inner_shape = _default_inner_shape_for_pylist(pylist, ragged_rank)
# Compute default value for ragged_rank.
if ragged_rank is None:
if scalar_depth is None:
ragged_rank = max(1, max_depth - 1)
else:
ragged_rank = max(1, scalar_depth - 1 - len(inner_shape))
# Build the splits for each ragged rank, and concatenate the inner values
# into a single list.
nested_splits = []
values = pylist
for dim in range(ragged_rank):
nested_splits.append([0])
concatenated_values = []
for row in values:
nested_splits[dim].append(nested_splits[dim][-1] + len(row))
concatenated_values.extend(row)
values = concatenated_values
values = inner_factory(
values, dtype=dtype, shape=(len(values),) + inner_shape, name="values")
for row_splits in reversed(nested_splits):
values = ragged_factory(values, row_splits)
return values
def _find_scalar_and_max_depth(pylist):
"""Finds nesting depth of scalar values in pylist.
Args:
pylist: A nested python `list` or `tuple`.
Returns:
A tuple `(scalar_depth, max_depth)`. `scalar_depth` is the nesting
depth of scalar values in `pylist`, or `None` if `pylist` contains no
scalars. `max_depth` is the maximum depth of `pylist` (including
empty lists).
Raises:
ValueError: If pylist has inconsistent nesting depths for scalars.
"""
# Check if pylist is not scalar. np.ndim builds an array, so we
# short-circuit lists and tuples.
if isinstance(pylist, (list, tuple)) or np.ndim(pylist) != 0:
scalar_depth = None
max_depth = 1
for child in pylist:
child_scalar_depth, child_max_depth = _find_scalar_and_max_depth(child)
if child_scalar_depth is not None:
if scalar_depth is not None and scalar_depth != child_scalar_depth + 1:
raise ValueError("all scalar values must have the same nesting depth")
scalar_depth = child_scalar_depth + 1
max_depth = max(max_depth, child_max_depth + 1)
return (scalar_depth, max_depth)
return (0, 0)
def _default_inner_shape_for_pylist(pylist, ragged_rank):
"""Computes a default inner shape for the given python list."""
def get_inner_shape(item):
"""Returns the inner shape for a python list `item`."""
if not isinstance(item, (list, tuple)) and np.ndim(item) == 0:
return ()
# Note that we need this check here in case `item` is not a Python list but
# fakes as being one (pylist). For a scenario of this, see test added in
# https://github.com/tensorflow/tensorflow/pull/48945
elif len(item) > 0: # pylint: disable=g-explicit-length-test
return (len(item),) + get_inner_shape(item[0])
return (0,)
def check_inner_shape(item, shape):
"""Checks that `item` has a consistent shape matching `shape`."""
is_nested = isinstance(item, (list, tuple)) or np.ndim(item) != 0
if is_nested != bool(shape):
raise ValueError("inner values have inconsistent shape")
if is_nested:
if shape[0] != len(item):
raise ValueError("inner values have inconsistent shape")
for child in item:
check_inner_shape(child, shape[1:])
# Collapse the ragged layers to get the list of inner values.
flat_values = pylist
for dim in range(ragged_rank):
if not all(
isinstance(v, (list, tuple)) or np.ndim(v) != 0 for v in flat_values):
raise ValueError("pylist has scalar values depth %d, but ragged_rank=%d "
"requires scalar value depth greater than %d" %
(dim + 1, ragged_rank, ragged_rank))
flat_values = sum((list(v) for v in flat_values), [])
# Compute the inner shape looking only at the leftmost elements; and then
# use check_inner_shape to verify that other elements have the same shape.
inner_shape = get_inner_shape(flat_values)
check_inner_shape(flat_values, inner_shape)
return inner_shape[1:]
@tf_export(v1=["ragged.placeholder"])
@dispatch.add_dispatch_support
def placeholder(dtype, ragged_rank, value_shape=None, name=None):
"""Creates a placeholder for a `tf.RaggedTensor` that will always be fed.
**Important**: This ragged tensor will produce an error if evaluated.
Its value must be fed using the `feed_dict` optional argument to
`Session.run()`, `Tensor.eval()`, or `Operation.run()`.
@compatibility{eager} Placeholders are not compatible with eager execution.
Args:
dtype: The data type for the `RaggedTensor`.
ragged_rank: The ragged rank for the `RaggedTensor`
value_shape: The shape for individual flat values in the `RaggedTensor`.
name: A name for the operation (optional).
Returns:
A `RaggedTensor` that may be used as a handle for feeding a value, but
not evaluated directly.
Raises:
RuntimeError: if eager execution is enabled
"""
if ragged_rank == 0:
return array_ops.placeholder(dtype, value_shape, name)
with ops.name_scope(name, "RaggedPlaceholder", []):
flat_shape = tensor_shape.TensorShape([None]).concatenate(value_shape)
result = array_ops.placeholder(dtype, flat_shape, "flat_values")
for i in reversed(range(ragged_rank)):
row_splits = array_ops.placeholder(dtypes.int64, [None],
"row_splits_%d" % i)
result = ragged_tensor.RaggedTensor.from_row_splits(result, row_splits,
validate=False)
return result