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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.
# ==============================================================================
"""Test utilities for tf.data functionality."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import nest
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class DatasetTestBase(test.TestCase):
"""Base class for dataset tests."""
def assertSparseValuesEqual(self, a, b):
"""Asserts that two SparseTensors/SparseTensorValues are equal."""
self.assertAllEqual(a.indices, b.indices)
self.assertAllEqual(a.values, b.values)
self.assertAllEqual(a.dense_shape, b.dense_shape)
def getNext(self, dataset):
"""Returns a callable that returns the next element of the dataset.
Example use:
```python
# In both graph and eager modes
dataset = ...
nxt = self.getNext(dataset)
result = self.evaluate(nxt())
```
Args:
dataset: A dataset whose next element is returned
Returns:
A callable that returns the next element of `dataset`
"""
it = dataset.make_one_shot_iterator()
if context.executing_eagerly():
return it.get_next
else:
nxt = it.get_next()
return lambda: nxt
def assertDatasetsEqual(self, dataset1, dataset2):
"""Checks that datasets are equal. Supports both graph and eager mode."""
self.assertEqual(dataset1.output_types, dataset2.output_types)
self.assertEqual(dataset1.output_classes, dataset2.output_classes)
next1 = self.getNext(dataset1)
next2 = self.getNext(dataset2)
while True:
try:
op1 = self.evaluate(next1())
except errors.OutOfRangeError:
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(next2())
break
op2 = self.evaluate(next2())
op1 = nest.flatten(op1)
op2 = nest.flatten(op2)
assert len(op1) == len(op2)
for i in range(len(op1)):
if isinstance(
op1[i],
(sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
self.assertSparseValuesEqual(op1[i], op2[i])
else:
self.assertAllEqual(op1[i], op2[i])
def assertDatasetsRaiseSameError(self,
dataset1,
dataset2,
exception_class,
replacements=None):
"""Checks that datasets raise the same error on the first get_next call."""
next1 = self.getNext(dataset1)
next2 = self.getNext(dataset2)
try:
self.evaluate(next1())
raise ValueError(
'Expected dataset to raise an error of type %s, but it did not.' %
repr(exception_class))
except exception_class as e:
expected_message = e.message
for old, new, count in replacements:
expected_message = expected_message.replace(old, new, count)
# Check that the first segment of the error messages are the same.
with self.assertRaisesRegexp(exception_class,
re.escape(expected_message)):
self.evaluate(next2())
def structuredDataset(self, structure, shape=None, dtype=dtypes.int64):
"""Returns a singleton dataset with the given structure."""
if shape is None:
shape = []
if structure is None:
return dataset_ops.Dataset.from_tensors(
array_ops.zeros(shape, dtype=dtype))
else:
return dataset_ops.Dataset.zip(
tuple([
self.structuredDataset(substructure, shape, dtype)
for substructure in structure
]))
def structuredElement(self, structure, shape=None, dtype=dtypes.int64):
"""Returns an element with the given structure."""
if shape is None:
shape = []
if structure is None:
return array_ops.zeros(shape, dtype=dtype)
else:
return tuple([
self.structuredElement(substructure, shape, dtype)
for substructure in structure
])
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