def testScalars(self):
gen = _EventGenerator()
acc = ea.EventAccumulator(gen)
s1 = ea.ScalarEvent(wall_time=1, step=10, value=32)
s2 = ea.ScalarEvent(wall_time=2, step=12, value=64)
gen.AddScalar('s1', wall_time=1, step=10, value=32)
gen.AddScalar('s2', wall_time=2, step=12, value=64)
acc.Reload()
self.assertEqual(acc.Scalars('s1'), [s1])
self.assertEqual(acc.Scalars('s2'), [s2])
def testTFSummaryScalar(self):
"""Verify processing of tf.summary.scalar, which uses TensorSummary op."""
event_sink = _EventGenerator(zero_out_timestamps=True)
writer = SummaryToEventTransformer(event_sink)
with self.test_session() as sess:
ipt = tf.placeholder(tf.float32)
tf.summary.scalar('scalar1', ipt)
tf.summary.scalar('scalar2', ipt * ipt)
merged = tf.contrib.deprecated.merge_all_summaries()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged, feed_dict={ipt: i})
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
seq1 = [
ea.ScalarEvent(wall_time=0, step=i, value=i) for i in xrange(10)
]
seq2 = [
ea.ScalarEvent(wall_time=0, step=i, value=i * i)
for i in xrange(10)
]
self.assertTagsEqual(
accumulator.Tags(), {
ea.IMAGES: [],
ea.AUDIO: [],
ea.SCALARS: ['scalar1', 'scalar2'],
ea.HISTOGRAMS: [],
ea.COMPRESSED_HISTOGRAMS: [],
ea.GRAPH: True,
ea.META_GRAPH: False,
ea.RUN_METADATA: []
})
self.assertEqual(accumulator.Scalars('scalar1'), seq1)
self.assertEqual(accumulator.Scalars('scalar2'), seq2)
first_value = accumulator.Scalars('scalar1')[0].value
self.assertTrue(isinstance(first_value, float))
def testTFSummaryScalar(self):
"""Verify processing of tf.summary.scalar."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = SummaryToEventTransformer(event_sink)
with self.test_session() as sess:
ipt = array_ops.placeholder(dtypes.float32)
summary_lib.scalar('scalar1', ipt)
summary_lib.scalar('scalar2', ipt * ipt)
merged = summary_lib.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged, feed_dict={ipt: i})
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
seq1 = [
ea.ScalarEvent(wall_time=0, step=i, value=i) for i in xrange(10)
]
seq2 = [
ea.ScalarEvent(wall_time=0, step=i, value=i * i)
for i in xrange(10)
]
self.assertTagsEqual(
accumulator.Tags(), {
ea.SCALARS: ['scalar1', 'scalar2'],
ea.GRAPH: True,
ea.META_GRAPH: False,
})
self.assertEqual(accumulator.Scalars('scalar1'), seq1)
self.assertEqual(accumulator.Scalars('scalar2'), seq2)
first_value = accumulator.Scalars('scalar1')[0].value
self.assertTrue(isinstance(first_value, float))
