def compute_and_check_summary_pb(self,
name='nemo',
data=None,
bucket_count=None,
display_name=None,
description=None,
data_tensor=None,
bucket_count_tensor=None,
feed_dict=None):
"""Use both `op` and `pb` to get a summary, asserting equality.
Returns:
a `Summary` protocol buffer
"""
if data is None:
data = self.gaussian
if data_tensor is None:
data_tensor = tf.constant(data)
if bucket_count_tensor is None:
bucket_count_tensor = bucket_count
op = summary.op(name,
data_tensor,
bucket_count=bucket_count_tensor,
display_name=display_name,
description=description)
pb = summary.pb(name,
data,
bucket_count=bucket_count,
display_name=display_name,
description=description)
pb_via_op = self.pb_via_op(op, feed_dict=feed_dict)
self.assertProtoEquals(pb, pb_via_op)
return pb
def test_histogram(self):
old_event = event_pb2.Event()
old_event.step = 123
old_event.wall_time = 456.75
histogram_pb = histogram_summary.pb(
"foo",
[1.0, 2.0, 3.0, 4.0],
bucket_count=12,
display_name="bar",
description="baz",
)
old_event.summary.ParseFromString(histogram_pb.SerializeToString())
new_events = self._migrate_event(old_event)
self.assertLen(new_events, 1)
self.assertLen(new_events[0].summary.value, 1)
value = new_events[0].summary.value[0]
tensor = tensor_util.make_ndarray(value.tensor)
self.assertEqual(tensor.shape, (12, 3))
np.testing.assert_array_equal(
tensor, tensor_util.make_ndarray(histogram_pb.value[0].tensor))
self.assertEqual(value.metadata.data_class,
summary_pb2.DATA_CLASS_TENSOR)
self.assertEqual(
value.metadata.plugin_data.plugin_name,
histogram_metadata.PLUGIN_NAME,
)
def histogram(self, *args, **kwargs):
# Map new name to the old name.
if "buckets" in kwargs:
kwargs["bucket_count"] = kwargs.pop("buckets")
return summary.pb(*args, **kwargs)
def histogram(self, *args, **kwargs):
# Map new name to the old name.
if 'buckets' in kwargs:
kwargs['bucket_count'] = kwargs.pop('buckets')
return summary.pb(*args, **kwargs)
