def testSummaryDescriptionAndDisplayName(self):
with self.cached_session() as sess:
def get_description(summary_op):
summ_str = sess.run(summary_op)
summ = summary_pb2.Summary()
summ.ParseFromString(summ_str)
return summ.value[0].metadata
const = constant_op.constant(1)
# Default case; no description or display name
simple_summary = summary_lib.tensor_summary("simple", const)
descr = get_description(simple_summary)
self.assertEqual(descr.display_name, "")
self.assertEqual(descr.summary_description, "")
# Values are provided via function args
with_values = summary_lib.tensor_summary(
"simple",
const,
display_name="my name",
summary_description="my description")
descr = get_description(with_values)
self.assertEqual(descr.display_name, "my name")
self.assertEqual(descr.summary_description, "my description")
# Values are provided via the SummaryMetadata arg
metadata = summary_pb2.SummaryMetadata()
metadata.display_name = "my name"
metadata.summary_description = "my description"
with_metadata = summary_lib.tensor_summary(
"simple", const, summary_metadata=metadata)
descr = get_description(with_metadata)
self.assertEqual(descr.display_name, "my name")
self.assertEqual(descr.summary_description, "my description")
# If both SummaryMetadata and explicit args are provided, the args win
overwrite = summary_lib.tensor_summary(
"simple",
const,
summary_metadata=metadata,
display_name="overwritten",
summary_description="overwritten")
descr = get_description(overwrite)
self.assertEqual(descr.display_name, "overwritten")
self.assertEqual(descr.summary_description, "overwritten")
def testManyScalarSummary(self):
with self.cached_session() as sess:
const = array_ops.ones([5, 5, 5])
summ = summary_lib.tensor_summary("foo", const)
result = sess.run(summ)
value = self._SummarySingleValue(result)
n = tensor_util.MakeNdarray(value.tensor)
self._AssertNumpyEq(n, np.ones([5, 5, 5]))
def testManyStringSummary(self):
strings = [[six.b("foo bar"), six.b("baz")], [six.b("zoink"), six.b("zod")]]
with self.cached_session() as sess:
const = constant_op.constant(strings)
summ = summary_lib.tensor_summary("foo", const)
result = sess.run(summ)
value = self._SummarySingleValue(result)
n = tensor_util.MakeNdarray(value.tensor)
self._AssertNumpyEq(n, strings)
def testScalarSummary(self):
with self.cached_session() as sess:
const = constant_op.constant(10.0)
summ = summary_lib.tensor_summary("foo", const)
result = sess.run(summ)
value = self._SummarySingleValue(result)
n = tensor_util.MakeNdarray(value.tensor)
self._AssertNumpyEq(n, 10)
def testStringSummary(self):
s = six.b("foobar")
with self.cached_session() as sess:
const = constant_op.constant(s)
summ = summary_lib.tensor_summary("foo", const)
result = sess.run(summ)
value = self._SummarySingleValue(result)
n = tensor_util.MakeNdarray(value.tensor)
self._AssertNumpyEq(n, s)
def testManyBools(self):
bools = [True, True, True, False, False, False]
with self.cached_session() as sess:
const = constant_op.constant(bools)
summ = summary_lib.tensor_summary("foo", const)
result = sess.run(summ)
value = self._SummarySingleValue(result)
n = tensor_util.MakeNdarray(value.tensor)
self._AssertNumpyEq(n, bools)
def testStringSummary(self):
s = six.b("foobar")
with self.cached_session() as sess:
const = constant_op.constant(s)
summ = summary_lib.tensor_summary("foo", const)
result = sess.run(summ)
value = self._SummarySingleValue(result)
n = tensor_util.MakeNdarray(value.tensor)
self._AssertNumpyEq(n, s)
def testManyBools(self):
bools = [True, True, True, False, False, False]
with self.cached_session() as sess:
const = constant_op.constant(bools)
summ = summary_lib.tensor_summary("foo", const)
result = sess.run(summ)
value = self._SummarySingleValue(result)
n = tensor_util.MakeNdarray(value.tensor)
self._AssertNumpyEq(n, bools)
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 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 testTags(self):
with self.cached_session() as sess:
c = constant_op.constant(1)
s1 = summary_lib.tensor_summary("s1", c)
with ops.name_scope("foo", skip_on_eager=False):
s2 = summary_lib.tensor_summary("s2", c)
with ops.name_scope("zod", skip_on_eager=False):
s3 = summary_lib.tensor_summary("s3", c)
s4 = summary_lib.tensor_summary("TensorSummary", c)
summ1, summ2, summ3, summ4 = self.evaluate([s1, s2, s3, s4])
v1 = self._SummarySingleValue(summ1)
self.assertEqual(v1.tag, "s1")
v2 = self._SummarySingleValue(summ2)
self.assertEqual(v2.tag, "foo/s2")
v3 = self._SummarySingleValue(summ3)
self.assertEqual(v3.tag, "foo/zod/s3")
v4 = self._SummarySingleValue(summ4)
self.assertEqual(v4.tag, "foo/zod/TensorSummary")
def test_fully_populated_tensor(self):
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.add(plugin_name='font_of_wisdom',
content='adobe_garamond')
op = summary_lib.tensor_summary(
name='tensorpocalypse',
tensor=constant_op.constant([[0.0, 2.0], [float('inf'), float('nan')]]),
display_name='TENSORPOCALYPSE',
summary_description='look on my works ye mighty and despair',
summary_metadata=metadata)
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def testTags(self):
with self.cached_session() as sess:
c = constant_op.constant(1)
s1 = summary_lib.tensor_summary("s1", c)
with ops.name_scope("foo"):
s2 = summary_lib.tensor_summary("s2", c)
with ops.name_scope("zod"):
s3 = summary_lib.tensor_summary("s3", c)
s4 = summary_lib.tensor_summary("TensorSummary", c)
summ1, summ2, summ3, summ4 = sess.run([s1, s2, s3, s4])
v1 = self._SummarySingleValue(summ1)
self.assertEqual(v1.tag, "s1")
v2 = self._SummarySingleValue(summ2)
self.assertEqual(v2.tag, "foo/s2")
v3 = self._SummarySingleValue(summ3)
self.assertEqual(v3.tag, "foo/zod/s3")
v4 = self._SummarySingleValue(summ4)
self.assertEqual(v4.tag, "foo/zod/TensorSummary")
def ipu_compile_summary(name, op_list, collections=None):
with ops.device("cpu"):
with ops.control_dependencies(op_list):
reports = gen_ipu_ops.ipu_event_trace()
summary_metadata = summary_pb2.SummaryMetadata(
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name="ipu"))
t_summary = tensor_summary(
name=name,
tensor=reports,
summary_metadata=summary_metadata,
collections=collections)
return t_summary
