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
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
|
# Copyright 2015 Google Inc. 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.
# ==============================================================================
"""Integration tests for TensorBoard.
These tests start up a full-fledged TensorBoard server.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import base64
import gzip
import json
import os
import shutil
import threading
import zlib
from six import BytesIO
from six.moves import http_client
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.summary import event_multiplexer
from tensorflow.tensorboard.backend import server
class TensorboardServerTest(tf.test.TestCase):
# Number of scalar-containing events to make.
_SCALAR_COUNT = 99
def setUp(self):
self._GenerateTestData()
self._multiplexer = event_multiplexer.EventMultiplexer(
size_guidance=server.TENSORBOARD_SIZE_GUIDANCE)
server.ReloadMultiplexer(self._multiplexer, {self.get_temp_dir(): None})
# 0 to pick an unused port.
self._server = server.BuildServer(self._multiplexer, 'localhost', 0)
self._server_thread = threading.Thread(target=self._server.serve_forever)
self._server_thread.daemon = True
self._server_thread.start()
self._connection = http_client.HTTPConnection(
'localhost', self._server.server_address[1])
def tearDown(self):
self._connection.close()
self._server.shutdown()
self._server.server_close()
def _get(self, path):
"""Perform a GET request for the given path."""
self._connection.request('GET', path)
return self._connection.getresponse()
def _getJson(self, path):
"""Perform a GET request and decode the result as JSON."""
self._connection.request('GET', path)
response = self._connection.getresponse()
self.assertEqual(response.status, 200)
return json.loads(response.read().decode('utf-8'))
def _decodeResponse(self, response):
"""Decompresses (if necessary) the response from the server."""
encoding = response.getheader('Content-Encoding')
content = response.read()
if encoding in ('gzip', 'x-gzip', 'deflate'):
if encoding == 'deflate':
data = BytesIO(zlib.decompress(content))
else:
data = gzip.GzipFile('', 'rb', 9, BytesIO(content))
content = data.read()
return content
def testBasicStartup(self):
"""Start the server up and then shut it down immediately."""
pass
def testRequestMainPage(self):
"""Navigate to the main page and verify that it returns a 200."""
response = self._get('/')
self.assertEqual(response.status, 200)
def testRequestNonexistentPage(self):
"""Request a page that doesn't exist; it should 404."""
response = self._get('/asdf')
self.assertEqual(response.status, 404)
def testDirectoryTraversal(self):
"""Attempt a directory traversal attack."""
response = self._get('/..' * 30 + '/etc/passwd')
self.assertEqual(response.status, 404)
def testRuns(self):
"""Test the format of the /data/runs endpoint."""
self.assertEqual(
self._getJson('/data/runs'),
{'run1': {'compressedHistograms': ['histogram'],
'scalars': ['simple_values'],
'histograms': ['histogram'],
'images': ['image'],
'graph': True}})
def testHistograms(self):
"""Test the format of /data/histograms."""
self.assertEqual(
self._getJson('/data/histograms?tag=histogram&run=run1'),
[[0, 0, [0, 2.0, 3.0, 6.0, 5.0, [0.0, 1.0, 2.0], [1.0, 1.0, 1.0]]]])
def testSampleScalars(self):
"""Test the sample_count parameter of /data/scalars."""
for i in xrange(10, self._SCALAR_COUNT, 10):
samples = self._getJson('/data/scalars?sample_count=%d' % i)
values = samples['run1']['simple_values']
# Verify that we got the right amount of values and that we got the
# endpoints.
self.assertEqual(len(values), i)
self.assertEqual(values[0], [100, 10, 1])
self.assertEqual(values[-1], [9900, 990, 99])
def testSampleScalarsWithLargeSampleCount(self):
"""Test using a large sample_count."""
samples = self._getJson('/data/scalars?sample_count=999999')
values = samples['run1']['simple_values']
self.assertEqual(len(values), self._SCALAR_COUNT)
def testImages(self):
"""Test listing images and retrieving an individual image."""
image_json = self._getJson('/data/images?tag=image&run=run1')
image_query = image_json[0]['query']
# We don't care about the format of the image query.
del image_json[0]['query']
self.assertEqual(image_json, [{
'wall_time': 0,
'step': 0,
'height': 1,
'width': 1
}])
response = self._get('/data/individualImage?%s' % image_query)
self.assertEqual(response.status, 200)
def testGraph(self):
"""Test retrieving the graph definition."""
response = self._get('/data/graph?run=run1&limit_attr_size=1024'
'&large_attrs_key=_very_large_attrs')
self.assertEqual(response.status, 200)
# Decompress (unzip) the response, since graphs come gzipped.
graph_pbtxt = self._decodeResponse(response)
# Parse the graph from pbtxt into a graph message.
graph = tf.GraphDef()
graph = text_format.Parse(graph_pbtxt, graph)
self.assertEqual(len(graph.node), 2)
self.assertEqual(graph.node[0].name, 'a')
self.assertEqual(graph.node[1].name, 'b')
# Make sure the second node has an attribute that was filtered out because
# it was too large and was added to the "too large" attributes list.
self.assertEqual(graph.node[1].attr.keys(), ['_very_large_attrs'])
self.assertEqual(graph.node[1].attr['_very_large_attrs'].list.s,
['very_large_attr'])
def _GenerateTestData(self):
"""Generates the test data directory.
The test data has a single run named run1 which contains:
- a histogram
- an image at timestamp and step 0
- scalar events containing the value i at step 10 * i and wall time
100 * i, for i in [1, _SCALAR_COUNT).
- a graph definition
"""
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir)
run1_path = os.path.join(temp_dir, 'run1')
os.makedirs(run1_path)
writer = tf.train.SummaryWriter(run1_path)
histogram_value = tf.HistogramProto(min=0,
max=2,
num=3,
sum=6,
sum_squares=5,
bucket_limit=[0, 1, 2],
bucket=[1, 1, 1])
# Add a simple graph event.
graph_def = tf.GraphDef()
node1 = graph_def.node.add()
node1.name = 'a'
node2 = graph_def.node.add()
node2.name = 'b'
node2.attr['very_large_attr'].s = b'a' * 2048 # 2 KB attribute
writer.add_event(tf.Event(graph_def=graph_def.SerializeToString()))
# 1x1 transparent GIF.
encoded_image = base64.b64decode(
'R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7')
image_value = tf.Summary.Image(height=1,
width=1,
colorspace=1,
encoded_image_string=encoded_image)
writer.add_event(tf.Event(wall_time=0,
step=0,
summary=tf.Summary(value=[tf.Summary.Value(
tag='histogram',
histo=histogram_value), tf.Summary.Value(
tag='image',
image=image_value)])))
# Write 100 simple values.
for i in xrange(1, self._SCALAR_COUNT + 1):
writer.add_event(tf.Event(
# We use different values for wall time, step, and the value so we can
# tell them apart.
wall_time=100 * i,
step=10 * i,
summary=tf.Summary(value=[tf.Summary.Value(tag='simple_values',
simple_value=i)])))
writer.flush()
writer.close()
class ParseEventFilesSpecTest(tf.test.TestCase):
def testRespectsGCSPath(self):
logdir_string = 'gs://foo/path'
expected = {'gs://foo/path': None}
self.assertEqual(server.ParseEventFilesSpec(logdir_string), expected)
if __name__ == '__main__':
tf.test.main()
|
