def testTFSummaryTensor(self):
"""Verify processing of tf.summary.tensor."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = SummaryToEventTransformer(event_sink)
with self.test_session() as sess:
tf.summary.tensor_summary('scalar', tf.constant(1.0))
tf.summary.tensor_summary('vector', tf.constant([1.0, 2.0, 3.0]))
tf.summary.tensor_summary('string', tf.constant(six.b('foobar')))
merged = tf.summary.merge_all()
summ = sess.run(merged)
writer.add_summary(summ, 0)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: ['scalar', 'vector', 'string'],
})
scalar_proto = accumulator.Tensors('scalar')[0].tensor_proto
scalar = tensor_util.MakeNdarray(scalar_proto)
vector_proto = accumulator.Tensors('vector')[0].tensor_proto
vector = tensor_util.MakeNdarray(vector_proto)
string_proto = accumulator.Tensors('string')[0].tensor_proto
string = tensor_util.MakeNdarray(string_proto)
self.assertTrue(np.array_equal(scalar, 1.0))
self.assertTrue(np.array_equal(vector, [1.0, 2.0, 3.0]))
self.assertTrue(np.array_equal(string, six.b('foobar')))
def testTFSummaryTensor(self):
"""Verify processing of tf.summary.tensor."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = SummaryToEventTransformer(event_sink)
with self.test_session() as sess:
summary_lib.tensor_summary('scalar', constant_op.constant(1.0))
summary_lib.tensor_summary('vector', constant_op.constant(
[1.0, 2.0, 3.0]))
summary_lib.tensor_summary('string',
constant_op.constant(six.b('foobar')))
merged = summary_lib.merge_all()
summ = sess.run(merged)
writer.add_summary(summ, 0)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: ['scalar', 'vector', 'string'],
})
scalar_proto = accumulator.Tensors('scalar')[0].tensor_proto
scalar = tensor_util.MakeNdarray(scalar_proto)
vector_proto = accumulator.Tensors('vector')[0].tensor_proto
vector = tensor_util.MakeNdarray(vector_proto)
string_proto = accumulator.Tensors('string')[0].tensor_proto
string = tensor_util.MakeNdarray(string_proto)
self.assertTrue(np.array_equal(scalar, 1.0))
self.assertTrue(np.array_equal(vector, [1.0, 2.0, 3.0]))
self.assertTrue(np.array_equal(string, six.b('foobar')))
def testTFSummaryImage(self):
"""Verify processing of tf.summary.image."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = SummaryToEventTransformer(event_sink)
with self.test_session() as sess:
ipt = array_ops.ones([10, 4, 4, 3], dtypes.uint8)
# This is an interesting example, because the old tf.image_summary op
# would throw an error here, because it would be tag reuse.
# Using the tf node name instead allows argument re-use to the image
# summary.
with ops.name_scope('1'):
summary_lib.image('images', ipt, max_outputs=1)
with ops.name_scope('2'):
summary_lib.image('images', ipt, max_outputs=2)
with ops.name_scope('3'):
summary_lib.image('images', ipt, max_outputs=3)
merged = summary_lib.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
u'1/images/image', u'2/images/image/0', u'2/images/image/1',
u'3/images/image/0', u'3/images/image/1', u'3/images/image/2'
]
self.assertTagsEqual(accumulator.Tags(), {
ea.IMAGES: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
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))
def testTFSummaryImage(self):
"""Verify processing of tf.summary.image."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = SummaryToEventTransformer(event_sink)
with self.test_session() as sess:
ipt = tf.ones([10, 4, 4, 3], tf.uint8)
# This is an interesting example, because the old tf.image_summary op
# would throw an error here, because it would be tag reuse.
# Using the tf node name instead allows argument re-use to the image
# summary.
with tf.name_scope('1'):
tf.summary.image('images', ipt, max_outputs=1)
with tf.name_scope('2'):
tf.summary.image('images', ipt, max_outputs=2)
with tf.name_scope('3'):
tf.summary.image('images', ipt, max_outputs=3)
merged = tf.summary.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
u'1/images/image', u'2/images/image/0', u'2/images/image/1',
u'3/images/image/0', u'3/images/image/1', u'3/images/image/2'
]
self.assertTagsEqual(accumulator.Tags(), {
ea.IMAGES: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
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 = tf.placeholder(tf.float32)
tf.summary.scalar('scalar1', ipt)
tf.summary.scalar('scalar2', ipt * ipt)
merged = tf.summary.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))
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.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, which uses TensorSummary op."""
event_sink = _EventGenerator(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.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))
