1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
|
# 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.group_by_reducer()`."""
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 grouping
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class GroupByReducerTest(test_base.DatasetTestBase):
def checkResults(self, dataset, shapes, values):
self.assertEqual(shapes, dataset.output_shapes)
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
for expected in values:
got = sess.run(get_next)
self.assertEqual(got, expected)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testSum(self):
reducer = grouping.Reducer(
init_func=lambda _: np.int64(0),
reduce_func=lambda x, y: x + y,
finalize_func=lambda x: x)
for i in range(1, 11):
dataset = dataset_ops.Dataset.range(2 * i).apply(
grouping.group_by_reducer(lambda x: x % 2, reducer))
self.checkResults(
dataset, shapes=tensor_shape.scalar(), values=[(i - 1) * i, i * i])
def testAverage(self):
def reduce_fn(x, y):
return (x[0] * x[1] + math_ops.cast(y, dtypes.float32)) / (
x[1] + 1), x[1] + 1
reducer = grouping.Reducer(
init_func=lambda _: (0.0, 0.0),
reduce_func=reduce_fn,
finalize_func=lambda x, _: x)
for i in range(1, 11):
dataset = dataset_ops.Dataset.range(2 * i).apply(
grouping.group_by_reducer(
lambda x: math_ops.cast(x, dtypes.int64) % 2, reducer))
self.checkResults(
dataset, shapes=tensor_shape.scalar(), values=[i - 1, i])
def testConcat(self):
components = np.array(list("abcdefghijklmnopqrst")).view(np.chararray)
reducer = grouping.Reducer(
init_func=lambda x: "",
reduce_func=lambda x, y: x + y[0],
finalize_func=lambda x: x)
for i in range(1, 11):
dataset = dataset_ops.Dataset.zip(
(dataset_ops.Dataset.from_tensor_slices(components),
dataset_ops.Dataset.range(2 * i))).apply(
grouping.group_by_reducer(lambda x, y: y % 2, reducer))
self.checkResults(
dataset,
shapes=tensor_shape.scalar(),
values=[b"acegikmoqs" [:i], b"bdfhjlnprt" [:i]])
def testSparseSum(self):
def _sparse(i):
return sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0]]),
values=(i * np.array([1], dtype=np.int64)),
dense_shape=np.array([1, 1]))
reducer = grouping.Reducer(
init_func=lambda _: _sparse(np.int64(0)),
reduce_func=lambda x, y: _sparse(x.values[0] + y.values[0]),
finalize_func=lambda x: x.values[0])
for i in range(1, 11):
dataset = dataset_ops.Dataset.range(2 * i).map(_sparse).apply(
grouping.group_by_reducer(lambda x: x.values[0] % 2, reducer))
self.checkResults(
dataset, shapes=tensor_shape.scalar(), values=[(i - 1) * i, i * i])
def testChangingStateShape(self):
def reduce_fn(x, _):
# Statically known rank, but dynamic length.
larger_dim = array_ops.concat([x[0], x[0]], 0)
# Statically unknown rank.
larger_rank = array_ops.expand_dims(x[1], 0)
return larger_dim, larger_rank
reducer = grouping.Reducer(
init_func=lambda x: ([0], 1),
reduce_func=reduce_fn,
finalize_func=lambda x, y: (x, y))
for i in range(1, 11):
dataset = dataset_ops.Dataset.from_tensors(np.int64(0)).repeat(i).apply(
grouping.group_by_reducer(lambda x: x, reducer))
self.assertEqual([None], dataset.output_shapes[0].as_list())
self.assertIs(None, dataset.output_shapes[1].ndims)
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
with self.cached_session() as sess:
x, y = sess.run(get_next)
self.assertAllEqual([0] * (2**i), x)
self.assertAllEqual(np.array(1, ndmin=i), y)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testTypeMismatch(self):
reducer = grouping.Reducer(
init_func=lambda x: constant_op.constant(1, dtype=dtypes.int32),
reduce_func=lambda x, y: constant_op.constant(1, dtype=dtypes.int64),
finalize_func=lambda x: x)
dataset = dataset_ops.Dataset.range(10)
with self.assertRaisesRegexp(
TypeError,
"The element types for the new state must match the initial state."):
dataset.apply(
grouping.group_by_reducer(lambda _: np.int64(0), reducer))
# TODO(b/78665031): Remove once non-scalar keys are supported.
def testInvalidKeyShape(self):
reducer = grouping.Reducer(
init_func=lambda x: np.int64(0),
reduce_func=lambda x, y: x + y,
finalize_func=lambda x: x)
dataset = dataset_ops.Dataset.range(10)
with self.assertRaisesRegexp(
ValueError, "`key_func` must return a single tf.int64 tensor."):
dataset.apply(
grouping.group_by_reducer(lambda _: np.int64((0, 0)), reducer))
# TODO(b/78665031): Remove once non-int64 keys are supported.
def testInvalidKeyType(self):
reducer = grouping.Reducer(
init_func=lambda x: np.int64(0),
reduce_func=lambda x, y: x + y,
finalize_func=lambda x: x)
dataset = dataset_ops.Dataset.range(10)
with self.assertRaisesRegexp(
ValueError, "`key_func` must return a single tf.int64 tensor."):
dataset.apply(
grouping.group_by_reducer(lambda _: "wrong", reducer))
def testTuple(self):
def init_fn(_):
return np.array([], dtype=np.int64), np.int64(0)
def reduce_fn(state, value):
s1, s2 = state
v1, v2 = value
return array_ops.concat([s1, [v1]], 0), s2 + v2
def finalize_fn(s1, s2):
return s1, s2
reducer = grouping.Reducer(init_fn, reduce_fn, finalize_fn)
dataset = dataset_ops.Dataset.zip(
(dataset_ops.Dataset.range(10), dataset_ops.Dataset.range(10))).apply(
grouping.group_by_reducer(lambda x, y: np.int64(0), reducer))
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
x, y = sess.run(get_next)
self.assertAllEqual(x, np.asarray([x for x in range(10)]))
self.assertEqual(y, 45)
if __name__ == "__main__":
test.main()
|
