Add a recursive import before running doctest.
This allows all code files are checked, including things that are not implicitly imported by `import tensorflow` This also: - Fixes some broken doctests that were hiding in the docs - switches StructuredTensor's __repr__ to print one item per line. PiperOrigin-RevId: 314261531 Change-Id: I9ef7c344e40c5e03fd441105327e70a0ab4fdedc
This commit is contained in:
Mark Daoust
TensorFlower Gardener
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@ -60,7 +60,6 @@ class StructuredTensor(composite_tensor.CompositeTensor):
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### Examples
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```python
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>>> # A scalar StructuredTensor describing a single person.
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>>> s1 = StructuredTensor.from_pyval(
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... {"age": 82, "nicknames": ["Bob", "Bobby"]})
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@ -78,7 +77,7 @@ class StructuredTensor(composite_tensor.CompositeTensor):
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TensorShape([3])
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>>> s2[0]["age"]
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<tf.Tensor: shape=(), dtype=int32, numpy=12>
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```
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### Field Paths
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@ -156,18 +155,19 @@ class StructuredTensor(composite_tensor.CompositeTensor):
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Examples:
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>>> StructuredTensor.from_fields({'x': 1, 'y': [1, 2, 3]})
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<StructuredTensor(fields={
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x: tf.Tensor(1, shape=(), dtype=int32),
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y: tf.Tensor([1 2 3], shape=(3,), dtype=int32)},
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shape=())>
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<StructuredTensor(
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fields={
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"x": tf.Tensor(1, shape=(), dtype=int32),
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"y": tf.Tensor([1 2 3], shape=(3,), dtype=int32)},
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shape=())>
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>>> StructuredTensor.from_fields({'foo': [1, 2], 'bar': [3, 4]},
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... shape=[2])
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<StructuredTensor(fields={
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bar: tf.Tensor([3 4], shape=(2,), dtype=int32),
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foo: tf.Tensor([1 2], shape=(2,), dtype=int32)},
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shape=(2,))>
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<StructuredTensor(
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fields={
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"bar": tf.Tensor([3 4], shape=(2,), dtype=int32),
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"foo": tf.Tensor([1 2], shape=(2,), dtype=int32)},
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shape=(2,))>
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"""
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shape = tensor_shape.as_shape(shape)
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rank = shape.rank
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@ -437,9 +437,15 @@ class StructuredTensor(composite_tensor.CompositeTensor):
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return self._fields[key[rank]].__getitem__(key[:rank] + key[rank + 1:])
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def __repr__(self):
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return '<StructuredTensor(fields={%s}, shape=%s)>' % (', '.join(
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'"%s": %s' % (k, v)
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for k, v in sorted(self._fields.items())), self._shape)
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fields = sorted(self._fields.items())
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fields = ((k, str(v).replace('\n', '\n ')) for k, v in fields)
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fields = ('"{}": {}'.format(k, v) for k, v in fields)
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dict_repr = ',\n '.join(fields)
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return (
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'<StructuredTensor(\n'
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' fields={\n'
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' %s},\n'
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' shape=%s)>' % (dict_repr, self._shape))
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#=============================================================================
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# Conversion
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@ -506,10 +512,11 @@ class StructuredTensor(composite_tensor.CompositeTensor):
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>>> StructuredTensor.from_pyval(
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... {'a': [1, 2, 3], 'b': [[4, 5], [6, 7]]})
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<StructuredTensor(fields={
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a: tf.Tensor([1 2 3], shape=(3,), dtype=int32),
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b: <tf.RaggedTensor [[4, 5], [6, 7]]>},
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shape=())>
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<StructuredTensor(
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fields={
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"a": tf.Tensor([1 2 3], shape=(3,), dtype=int32),
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"b": <tf.RaggedTensor [[4, 5], [6, 7]]>},
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shape=())>
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Note that `StructuredTensor.from_pyval(pyval).to_pyval() == pyval`.
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@ -639,9 +646,10 @@ class StructuredTensor(composite_tensor.CompositeTensor):
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... [{'foo': 12}, {'foo': 33}, {'foo': 99}])
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>>> partition = RowPartition.from_row_lengths([2, 0, 1])
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>>> st.partition_outer_dimension(partition)
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<StructuredTensor(fields={
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foo: <tf.RaggedTensor [[12, 33], [], [99]]>},
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shape=(3, None))>
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<StructuredTensor(
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fields={
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"foo": <tf.RaggedTensor [[12, 33], [], [99]]>},
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shape=(3, None))>
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Args:
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row_partition: A `RowPartition`.
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@ -664,9 +672,10 @@ class StructuredTensor(composite_tensor.CompositeTensor):
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>>> st = StructuredTensor.from_pyval(
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... [[{'foo': 12}, {'foo': 33}], [], [{'foo': 99}]])
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>>> st.merge_dims(0, 1)
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<StructuredTensor(fields={
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foo: tf.Tensor([12 33 99], shape=(3,), dtype=int32)},
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shape=(3,))>
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<StructuredTensor(
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fields={
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"foo": tf.Tensor([12 33 99], shape=(3,), dtype=int32)},
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shape=(3,))>
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Args:
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outer_axis: `int`: The first dimension in the range of dimensions to
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@ -1071,16 +1080,17 @@ def _partition_outer_dimension(value, row_partition):
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Examples:
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>>> partition = row_partition.RowPartition.from_row_lengths([2, 0, 1])
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>>> partition = RowPartition.from_row_lengths([2, 0, 1])
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>>> _partition_outer_dimension(tf.constant([1, 2, 3]), partition)
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<tf.RaggedTensor [[1, 2], [], [3]]>
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>>> struct_value = StructuredTensor.from_pyval(
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... [{'x': 1}, {'x': 2}, {'x': 3}])
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>>> _partition_outer_dimension(struct_value, partition)
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<StructuredTensor(fields={
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x: <tf.RaggedTensor [[1, 2], [], [3]]>},
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shape=(3, None))>
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<StructuredTensor(
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fields={
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"x": <tf.RaggedTensor [[1, 2], [], [3]]>},
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shape=(3, None))>
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Args:
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value: Tensor, RaggedTensor, or StructuredTensor
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@ -18,6 +18,8 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import textwrap
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from absl.testing import parameterized
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import numpy as np
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@ -929,17 +931,25 @@ class StructuredTensorTest(test_util.TensorFlowTestCase,
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def testRepr(self):
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st = StructuredTensor.from_pyval({"a": 5, "b": {"c": [1, 2, 3]}})
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if context.executing_eagerly():
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expected = ("<StructuredTensor(fields={"
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'"a": tf.Tensor(5, shape=(), dtype=int32), '
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'"b": <StructuredTensor(fields={'
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'"c": tf.Tensor([1 2 3], shape=(3,), dtype=int32)}, '
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"shape=())>}, shape=())>")
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expected = textwrap.dedent("""
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<StructuredTensor(
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fields={
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"a": tf.Tensor(5, shape=(), dtype=int32),
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"b": <StructuredTensor(
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fields={
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"c": tf.Tensor([1 2 3], shape=(3,), dtype=int32)},
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shape=())>},
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shape=())>""")[1:]
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else:
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expected = ("<StructuredTensor(fields={"
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'"a": Tensor("Const:0", shape=(), dtype=int32), '
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'"b": <StructuredTensor(fields={'
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'"c": Tensor("RaggedConstant/Const:0", shape=(3,), '
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"dtype=int32)}, shape=())>}, shape=())>")
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expected = textwrap.dedent("""
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<StructuredTensor(
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fields={
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"a": Tensor("Const:0", shape=(), dtype=int32),
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"b": <StructuredTensor(
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fields={
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"c": Tensor("RaggedConstant/Const:0", shape=(3,), dtype=int32)},
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shape=())>},
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shape=())>""")[1:]
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self.assertEqual(repr(st), expected)
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def testPartitionOuterDimension2DDenseField(self):
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@ -21,9 +21,11 @@ or that should be permanently associated with the training session.
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You can use this just like the logging module:
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>>> tensorboard_logging.set_summary_writer(summary_writer)
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>>> tensorboard_logging.info("my %s", "message")
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>>> tensorboard_logging.log(tensorboard_logging.WARN, "something")
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```
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tensorboard_logging.set_summary_writer(summary_writer)
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tensorboard_logging.info("my %s", "message")
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tensorboard_logging.log(tensorboard_logging.WARN, "something")
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```
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"""
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from __future__ import absolute_import
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@ -39,7 +39,6 @@ py_test(
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deps = [
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":tf_doctest_lib",
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"//tensorflow:tensorflow_py",
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"//tensorflow/python/keras/preprocessing",
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"//third_party/py/numpy",
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],
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)
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@ -19,16 +19,17 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import importlib
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import os
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import pkgutil
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import sys
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from absl import flags
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from absl.testing import absltest
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import numpy as np
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import tensorflow.compat.v2 as tf
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from tensorflow.python.keras import preprocessing
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import tensorflow.python as tf_root
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from tensorflow.tools.docs import tf_doctest_lib
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# We put doctest after absltest so that it picks up the unittest monkeypatch.
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@ -37,9 +38,6 @@ import doctest # pylint: disable=g-bad-import-order
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tf.compat.v1.enable_v2_behavior()
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# Inject keras.preprocessing files into `tf.keras.preprocessing` namespace.
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tf.keras.preprocessing = preprocessing
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FLAGS = flags.FLAGS
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flags.DEFINE_list('module', [], 'A list of specific module to run doctest on.')
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@ -56,6 +54,20 @@ flags.mark_flags_as_mutual_exclusive(['list', 'file'])
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PACKAGE = 'tensorflow.python.'
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def recursive_import(root):
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"""Recursively imports all the sub-modules under a root package.
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Args:
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root: A python package.
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"""
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for _, name, _ in pkgutil.walk_packages(
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root.__path__, prefix=root.__name__ + '.'):
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try:
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importlib.import_module(name)
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except (AttributeError, ImportError):
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pass
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def find_modules():
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"""Finds all the modules in the core package imported.
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@ -166,6 +178,6 @@ def load_tests(unused_loader, tests, unused_ignore):
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))
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return tests
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if __name__ == '__main__':
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recursive_import(tf_root)
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absltest.main()
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