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# Copyright 2017 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.
# ==============================================================================
"""Tests for `tf.data.experimental.dense_to_sparse_batch()."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.data.experimental.ops import batching
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class DenseToSparseBatchTest(test_base.DatasetTestBase):
def testDenseToSparseBatchDataset(self):
components = np.random.randint(12, size=(100,)).astype(np.int32)
iterator = (
dataset_ops.Dataset.from_tensor_slices(components)
.map(lambda x: array_ops.fill([x], x)).apply(
batching.dense_to_sparse_batch(4, [12]))
.make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
for start in range(0, len(components), 4):
results = sess.run(get_next)
self.assertAllEqual([[i, j]
for i, c in enumerate(components[start:start + 4])
for j in range(c)], results.indices)
self.assertAllEqual(
[c for c in components[start:start + 4] for _ in range(c)],
results.values)
self.assertAllEqual([min(4,
len(components) - start), 12],
results.dense_shape)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testDenseToSparseBatchDatasetWithUnknownShape(self):
components = np.random.randint(5, size=(40,)).astype(np.int32)
iterator = (
dataset_ops.Dataset.from_tensor_slices(components)
.map(lambda x: array_ops.fill([x, x], x)).apply(
batching.dense_to_sparse_batch(
4, [5, None])).make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
for start in range(0, len(components), 4):
results = sess.run(get_next)
self.assertAllEqual([[i, j, z]
for i, c in enumerate(components[start:start + 4])
for j in range(c)
for z in range(c)], results.indices)
self.assertAllEqual([
c
for c in components[start:start + 4] for _ in range(c)
for _ in range(c)
], results.values)
self.assertAllEqual([
min(4,
len(components) - start), 5,
np.max(components[start:start + 4])
], results.dense_shape)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testDenseToSparseBatchDatasetWithInvalidShape(self):
input_tensor = array_ops.constant([[1]])
with self.assertRaisesRegexp(ValueError, "Dimension -2 must be >= 0"):
dataset_ops.Dataset.from_tensors(input_tensor).apply(
batching.dense_to_sparse_batch(4, [-2])).make_initializable_iterator()
def testDenseToSparseBatchDatasetShapeErrors(self):
input_tensor = array_ops.placeholder(dtypes.int32)
iterator = (
dataset_ops.Dataset.from_tensors(input_tensor).apply(
batching.dense_to_sparse_batch(4, [12]))
.make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
# Initialize with an input tensor of incompatible rank.
sess.run(init_op, feed_dict={input_tensor: [[1]]})
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"incompatible with the row shape"):
sess.run(get_next)
# Initialize with an input tensor that is larger than `row_shape`.
sess.run(init_op, feed_dict={input_tensor: range(13)})
with self.assertRaisesRegexp(errors.DataLossError,
"larger than the row shape"):
sess.run(get_next)
if __name__ == "__main__":
test.main()
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