def _get_graph(self, *args, **kwargs):
"""Set up runs, then fetch and return the graph as a proto."""
self.set_up_with_runs()
(graph_pbtxt,
mime_type) = self.plugin.graph_impl(self._RUN_WITH_GRAPH, *args,
**kwargs)
self.assertEqual(mime_type, 'text/x-protobuf')
return text_format.Parse(graph_pbtxt, tf.GraphDef())
def testOnlySummaryEventsTriggerDiscards(self):
"""Test that file version event does not trigger data purge."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddScalar('s1', wall_time=1, step=100, value=20)
ev1 = tf.Event(wall_time=2, step=0, file_version='brain.Event:1')
graph_bytes = tf.GraphDef().SerializeToString()
ev2 = tf.Event(wall_time=3, step=0, graph_def=graph_bytes)
gen.AddEvent(ev1)
gen.AddEvent(ev2)
acc.Reload()
self.assertEqual([x.step for x in acc.Scalars('s1')], [100])
def Graph(self):
"""Return the graph definition, if there is one.
If the graph is stored directly, return that. If no graph is stored
directly but a metagraph is stored containing a graph, return that.
Raises:
ValueError: If there is no graph for this run.
Returns:
The `graph_def` proto.
"""
graph = tf.GraphDef()
if self._graph is not None:
graph.ParseFromString(self._graph)
return graph
raise ValueError('There is no graph in this EventAccumulator')
def _generate_test_data(self, run_name, experiment_name):
"""Generates the test data directory.
The test data has a single run of the given name, containing:
- a graph definition and metagraph definition
Arguments:
run_name: The directory under self.logdir into which to write
events.
"""
run_path = os.path.join(self.logdir, run_name)
writer = tf.summary.FileWriter(run_path)
# 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
meta_graph_def = tf.MetaGraphDef(graph_def=graph_def)
if self._only_use_meta_graph:
writer.add_meta_graph(meta_graph_def)
else:
writer.add_graph(graph_def)
writer.flush()
writer.close()
# Write data for the run to the database.
# TODO(nickfelt): Figure out why reseting the graph is necessary.
tf.reset_default_graph()
db_writer = tf.contrib.summary.create_db_writer(
db_uri=self.db_path,
experiment_name=experiment_name,
run_name=run_name,
user_name='user')
with db_writer.as_default(), tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar('mytag', 1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.contrib.summary.summary_writer_initializer_op())
sess.run(tf.contrib.summary.all_summary_ops())
def GenerateTestData(path):
"""Generates the test data directory."""
run1_path = os.path.join(path, "run1")
os.makedirs(run1_path)
writer1 = tf.summary.FileWriter(run1_path)
WriteScalarSeries(writer1, "foo/square", lambda x: x * x)
WriteScalarSeries(writer1, "bar/square", lambda x: x * x)
WriteScalarSeries(writer1, "foo/sin", math.sin)
WriteScalarSeries(writer1, "foo/cos", math.cos)
WriteHistogramSeries(writer1, "histo1",
[[0, 1], [0.3, 1], [0.5, 1], [0.7, 1], [1, 1]])
WriteImageSeries(writer1, "im1")
WriteImageSeries(writer1, "im2")
WriteAudioSeries(writer1, "au1")
run2_path = os.path.join(path, "run2")
os.makedirs(run2_path)
writer2 = tf.summary.FileWriter(run2_path)
WriteScalarSeries(writer2, "foo/square", lambda x: x * x * 2)
WriteScalarSeries(writer2, "bar/square", lambda x: x * x * 3)
WriteScalarSeries(writer2, "foo/cos", lambda x: math.cos(x) * 2)
WriteHistogramSeries(writer2, "histo1",
[[0, 2], [0.3, 2], [0.5, 2], [0.7, 2], [1, 2]])
WriteHistogramSeries(writer2, "histo2",
[[0, 1], [0.3, 1], [0.5, 1], [0.7, 1], [1, 1]])
WriteImageSeries(writer2, "im1")
WriteAudioSeries(writer2, "au2")
graph_def = tf.GraphDef()
node1 = graph_def.node.add()
node1.name = "a"
node1.op = "matmul"
node2 = graph_def.node.add()
node2.name = "b"
node2.op = "matmul"
node2.input.extend(["a:0"])
writer1.add_graph(graph_def)
node3 = graph_def.node.add()
node3.name = "c"
node3.op = "matmul"
node3.input.extend(["a:0", "b:0"])
writer2.add_graph(graph_def)
writer1.close()
writer2.close()