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Graduate tf.data.experimental.dense_to_sparse_batch to tf.data.Dataset.sparse_batch.
PiperOrigin-RevId: 485168714
This commit is contained in:
Matt Callanan
TensorFlower Gardener
parent
4fea1e92bb
commit
29e6955939
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@ -164,6 +164,7 @@ This release contains contributions from many people at Google, as well as:
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* `tf.data`:
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* Graduated experimental APIs:
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* [`tf.data.Dataset.ragged_batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#ragged_batch), which batches elements of `tf.data.Dataset`s into `tf.RaggedTensor`s.
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* [`tf.data.Dataset.sparse_batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#sparse_batch), which batches elements of `tf.data.Dataset`s into `tf.sparse.SparseTensor`s.
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## Bug Fixes and Other Changes
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@ -136,7 +136,7 @@ def dense_to_sparse_batch(batch_size, row_shape):
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"""
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def _apply_fn(dataset):
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return _DenseToSparseBatchDataset(dataset, batch_size, row_shape)
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return dataset.sparse_batch(batch_size, row_shape)
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return _apply_fn
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@ -770,6 +770,22 @@ tf_py_test(
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],
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)
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tf_py_test(
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name = "sparse_batch_test",
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size = "medium",
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srcs = ["sparse_batch_test.py"],
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deps = [
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:errors",
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"//tensorflow/python/data/kernel_tests:checkpoint_test_base",
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"//tensorflow/python/data/kernel_tests:test_base",
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"//tensorflow/python/data/ops:dataset_ops",
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"//third_party/py/numpy",
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],
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)
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tf_py_test(
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name = "random_test",
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size = "small",
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123
tensorflow/python/data/kernel_tests/sparse_batch_test.py
Normal file
123
tensorflow/python/data/kernel_tests/sparse_batch_test.py
Normal file
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@ -0,0 +1,123 @@
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# Copyright 2017 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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"""Tests for `tf.data.Dataset.sparse_batch`."""
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python.data.kernel_tests import checkpoint_test_base
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from tensorflow.python.data.kernel_tests import test_base
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.framework import combinations
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from tensorflow.python.framework import errors
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from tensorflow.python.ops import array_ops
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from tensorflow.python.platform import test
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class DenseToSparseBatchTest(test_base.DatasetTestBase, parameterized.TestCase):
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@combinations.generate(test_base.default_test_combinations())
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def testBasic(self):
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components = np.random.randint(12, size=(100,)).astype(np.int32)
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dataset = dataset_ops.Dataset.from_tensor_slices(components).map(
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lambda x: array_ops.fill([x], x)).sparse_batch(4, [12])
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get_next = self.getNext(dataset)
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for start in range(0, len(components), 4):
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results = self.evaluate(get_next())
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self.assertAllEqual([[i, j]
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for i, c in enumerate(components[start:start + 4])
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for j in range(c)], results.indices)
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self.assertAllEqual(
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[c for c in components[start:start + 4] for _ in range(c)],
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results.values)
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self.assertAllEqual([min(4,
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len(components) - start), 12],
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results.dense_shape)
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with self.assertRaises(errors.OutOfRangeError):
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self.evaluate(get_next())
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@combinations.generate(test_base.default_test_combinations())
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def testWithUnknownShape(self):
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components = np.random.randint(5, size=(40,)).astype(np.int32)
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dataset = dataset_ops.Dataset.from_tensor_slices(components).map(
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lambda x: array_ops.fill([x, x], x)).sparse_batch(4, [5, None])
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get_next = self.getNext(dataset)
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for start in range(0, len(components), 4):
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results = self.evaluate(get_next())
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self.assertAllEqual([[i, j, z]
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for i, c in enumerate(components[start:start + 4])
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for j in range(c)
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for z in range(c)], results.indices)
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self.assertAllEqual([
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c for c in components[start:start + 4] for _ in range(c)
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for _ in range(c)
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], results.values)
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self.assertAllEqual([
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min(4,
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len(components) - start), 5,
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np.max(components[start:start + 4])
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], results.dense_shape)
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with self.assertRaises(errors.OutOfRangeError):
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self.evaluate(get_next())
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@combinations.generate(test_base.default_test_combinations())
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def testWithInvalidShape(self):
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input_tensor = array_ops.constant([[1]])
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with self.assertRaisesRegex(ValueError, "Dimension -2 must be >= 0"):
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dataset_ops.Dataset.from_tensors(input_tensor).sparse_batch(4, [-2])
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@combinations.generate(test_base.default_test_combinations())
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def testShapeErrors(self):
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def dataset_fn(input_tensor):
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return dataset_ops.Dataset.from_tensors(input_tensor).sparse_batch(
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4, [12])
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# Initialize with an input tensor of incompatible rank.
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get_next = self.getNext(dataset_fn([[1]]))
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with self.assertRaisesRegex(errors.InvalidArgumentError,
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"incompatible with the row shape"):
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self.evaluate(get_next())
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# Initialize with an input tensor that is larger than `row_shape`.
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get_next = self.getNext(dataset_fn(np.int32(range(13))))
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with self.assertRaisesRegex(errors.DataLossError,
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"larger than the row shape"):
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self.evaluate(get_next())
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class DenseToSparseBatchCheckpointTest(checkpoint_test_base.CheckpointTestBase,
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parameterized.TestCase):
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def _build_dataset(self, components):
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return dataset_ops.Dataset.from_tensor_slices(components).map(
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lambda x: array_ops.fill([x], x)).sparse_batch(4, [12])
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@combinations.generate(
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combinations.times(test_base.default_test_combinations(),
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checkpoint_test_base.default_test_combinations()))
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def test(self, verify_fn):
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components = np.random.randint(5, size=(40,)).astype(np.int32)
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num_outputs = len(components) // 4
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verify_fn(self, lambda: self._build_dataset(components), num_outputs)
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if __name__ == "__main__":
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test.main()
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@ -62,6 +62,7 @@ py_library(
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":ragged_batch_op",
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":rebatch_op",
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":save_ops",
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":sparse_batch_op",
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":structured_function",
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":zip_op",
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"//tensorflow/python:constant_op",
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@ -162,6 +163,14 @@ py_library(
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],
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)
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py_library(
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name = "sparse_batch_op",
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srcs = ["sparse_batch_op.py"],
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srcs_version = "PY3",
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deps = [
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],
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)
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py_library(
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name = "save_ops",
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srcs = ["save_op.py"],
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@ -2180,6 +2180,53 @@ class DatasetV2(
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return ragged_batch_op.ragged_batch(self, batch_size, drop_remainder,
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row_splits_dtype, name)
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def sparse_batch(self, batch_size, row_shape, name=None):
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"""Combines consecutive elements into `tf.sparse.SparseTensor`s.
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Like `Dataset.padded_batch()`, this transformation combines multiple
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consecutive elements of the dataset, which might have different
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shapes, into a single element. The resulting element has three
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components (`indices`, `values`, and `dense_shape`), which
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comprise a `tf.sparse.SparseTensor` that represents the same data. The
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`row_shape` represents the dense shape of each row in the
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resulting `tf.sparse.SparseTensor`, to which the effective batch size is
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prepended. For example:
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```python
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# NOTE: The following examples use `{ ... }` to represent the
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# contents of a dataset.
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a = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] }
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a.apply(tf.data.experimental.dense_to_sparse_batch(
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batch_size=2, row_shape=[6])) ==
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{
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([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]], # indices
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['a', 'b', 'c', 'a', 'b'], # values
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[2, 6]), # dense_shape
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([[0, 0], [0, 1], [0, 2], [0, 3]],
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['a', 'b', 'c', 'd'],
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[1, 6])
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}
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```
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Args:
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batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
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consecutive elements of this dataset to combine in a single batch.
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row_shape: A `tf.TensorShape` or `tf.int64` vector tensor-like object
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representing the equivalent dense shape of a row in the resulting
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`tf.sparse.SparseTensor`. Each element of this dataset must have the
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same rank as `row_shape`, and must have size less than or equal to
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`row_shape` in each dimension.
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name: (Optional.) A string indicating a name for the `tf.data` operation.
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Returns:
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A new `Dataset` with the transformation applied as described above.
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"""
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# Loaded lazily due to a circular dependency (dataset_ops ->
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# sparse_batch_op -> dataset_ops).
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from tensorflow.python.data.ops import sparse_batch_op # pylint: disable=g-import-not-at-top
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return sparse_batch_op.sparse_batch(self, batch_size, row_shape, name)
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def map(self,
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map_func,
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num_parallel_calls=None,
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56
tensorflow/python/data/ops/sparse_batch_op.py
Normal file
56
tensorflow/python/data/ops/sparse_batch_op.py
Normal file
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@ -0,0 +1,56 @@
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# Copyright 2017 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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"""The implementation of `tf.data.Dataset.sparse_batch`."""
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.util import convert
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
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def sparse_batch(self, batch_size, row_shape, name=None):
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return _DenseToSparseBatchDataset(self, batch_size, row_shape, name)
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class _DenseToSparseBatchDataset(dataset_ops.UnaryDataset):
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"""A `Dataset` that batches ragged dense elements into `tf.sparse.SparseTensor`s."""
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def __init__(self, input_dataset, batch_size, row_shape, name=None):
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"""See `Dataset.dense_to_sparse_batch()` for more details."""
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if not isinstance(
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dataset_ops.get_legacy_output_types(input_dataset), dtypes.DType):
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raise TypeError("`dense_to_sparse_batch` requires an input dataset whose "
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"elements have a single component, but the given dataset "
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"has the following component types: "
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f"{dataset_ops.get_legacy_output_types(input_dataset)}.")
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self._input_dataset = input_dataset
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self._batch_size = batch_size
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self._row_shape = row_shape
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self._element_spec = sparse_tensor.SparseTensorSpec(
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tensor_shape.TensorShape([None]).concatenate(self._row_shape),
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dataset_ops.get_legacy_output_types(input_dataset))
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self._name = name
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variant_tensor = ged_ops.dense_to_sparse_batch_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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self._batch_size,
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row_shape=convert.partial_shape_to_tensor(self._row_shape),
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**self._flat_structure)
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super(_DenseToSparseBatchDataset, self).__init__(input_dataset,
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variant_tensor)
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@property
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def element_spec(self):
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return self._element_spec
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@ -199,6 +199,10 @@ tf_class {
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name: "snapshot"
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argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
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}
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member_method {
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name: "sparse_batch"
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argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "take"
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argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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@ -201,6 +201,10 @@ tf_class {
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name: "snapshot"
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argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
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}
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member_method {
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name: "sparse_batch"
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argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "take"
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argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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|
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@ -201,6 +201,10 @@ tf_class {
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name: "snapshot"
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argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
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}
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member_method {
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name: "sparse_batch"
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argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "take"
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argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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|
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@ -201,6 +201,10 @@ tf_class {
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name: "snapshot"
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argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
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}
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member_method {
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name: "sparse_batch"
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argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "take"
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argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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|
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@ -201,6 +201,10 @@ tf_class {
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name: "snapshot"
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argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
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}
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member_method {
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name: "sparse_batch"
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argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "take"
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argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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|
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@ -201,6 +201,10 @@ tf_class {
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name: "snapshot"
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argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
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}
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member_method {
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name: "sparse_batch"
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argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "take"
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argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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|
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@ -201,6 +201,10 @@ tf_class {
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name: "snapshot"
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argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
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}
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member_method {
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name: "sparse_batch"
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argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "take"
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argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
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|
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|
|
@ -166,6 +166,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
|
|
@ -168,6 +168,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
|
|
@ -167,6 +167,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
|
|
@ -168,6 +168,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
|
|
@ -168,6 +168,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
|
|
@ -169,6 +169,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
|
|
@ -168,6 +168,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
|
|
@ -169,6 +169,10 @@ tf_class {
|
|||
name: "snapshot"
|
||||
argspec: "args=[\'self\', \'path\', \'compression\', \'reader_func\', \'shard_func\', \'name\'], varargs=None, keywords=None, defaults=[\'AUTO\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "sparse_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'row_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "take"
|
||||
argspec: "args=[\'self\', \'count\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user