115 lines
4.3 KiB
Python
115 lines
4.3 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Value for RaggedTensor."""
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import numpy as np
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from tensorflow.python.ops.ragged.row_partition import RowPartition
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from tensorflow.python.util import dispatch
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from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=["ragged.RaggedTensorValue"])
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@dispatch.register_dispatchable_type
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class RaggedTensorValue:
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"""Represents the value of a `RaggedTensor`.
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Warning: `RaggedTensorValue` should only be used in graph mode; in
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eager mode, the `tf.RaggedTensor` class contains its value directly.
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See `tf.RaggedTensor` for a description of ragged tensors.
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"""
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def __init__(self, values, row_splits):
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"""Creates a `RaggedTensorValue`.
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Args:
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values: A numpy array of any type and shape; or a RaggedTensorValue.
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row_splits: A 1-D int32 or int64 numpy array.
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"""
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if not (isinstance(row_splits, (np.ndarray, np.generic)) and
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row_splits.dtype in (np.int64, np.int32) and row_splits.ndim == 1):
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raise TypeError("row_splits must be a 1D int32 or int64 numpy array")
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if not isinstance(values, (np.ndarray, np.generic, RaggedTensorValue)):
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raise TypeError("values must be a numpy array or a RaggedTensorValue")
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if (isinstance(values, RaggedTensorValue) and
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row_splits.dtype != values.row_splits.dtype):
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raise ValueError("row_splits and values.row_splits must have "
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"the same dtype")
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self._values = values
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self._row_splits = row_splits
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row_splits = property(
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lambda self: self._row_splits,
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doc="""The split indices for the ragged tensor value.""")
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values = property(
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lambda self: self._values,
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doc="""The concatenated values for all rows in this tensor.""")
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dtype = property(
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lambda self: self._values.dtype,
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doc="""The numpy dtype of values in this tensor.""")
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@property
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def flat_values(self):
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"""The innermost `values` array for this ragged tensor value."""
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rt_values = self.values
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while isinstance(rt_values, RaggedTensorValue):
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rt_values = rt_values.values
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return rt_values
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@property
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def nested_row_splits(self):
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"""The row_splits for all ragged dimensions in this ragged tensor value."""
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rt_nested_splits = [self.row_splits]
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rt_values = self.values
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while isinstance(rt_values, RaggedTensorValue):
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rt_nested_splits.append(rt_values.row_splits)
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rt_values = rt_values.values
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return tuple(rt_nested_splits)
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@property
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def ragged_rank(self):
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"""The number of ragged dimensions in this ragged tensor value."""
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values_is_ragged = isinstance(self._values, RaggedTensorValue)
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return self._values.ragged_rank + 1 if values_is_ragged else 1
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@property
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def shape(self):
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"""A tuple indicating the shape of this RaggedTensorValue."""
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return (self._row_splits.shape[0] - 1,) + (None,) + self._values.shape[1:]
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@property
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def _nested_row_partitions(self):
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"""The row_partitions representing this shape."""
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return [RowPartition.from_row_splits(rs) for rs in self.nested_row_splits]
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def __str__(self):
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return "<tf.RaggedTensorValue %s>" % self.to_list()
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def __repr__(self):
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return "tf.RaggedTensorValue(values=%r, row_splits=%r)" % (self._values,
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self._row_splits)
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def to_list(self):
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"""Returns this ragged tensor value as a nested Python list."""
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if isinstance(self._values, RaggedTensorValue):
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values_as_list = self._values.to_list()
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else:
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values_as_list = self._values.tolist()
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return [
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values_as_list[self._row_splits[i]:self._row_splits[i + 1]]
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for i in range(len(self._row_splits) - 1)
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]
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