def test_eq(self): x1 = provider.BlobSequenceTimeSeries(77, 1234.5, 6, b"\x12", "one", "two") x2 = provider.BlobSequenceTimeSeries(77, 1234.5, 6, b"\x12", "one", "two") x3 = provider.BlobSequenceTimeSeries(66, 4321.0, 7, b"\x7F", "hmm", "hum") self.assertEqual(x1, x2) self.assertNotEqual(x1, x3) self.assertNotEqual(x1, object())
def test_hash(self): x1 = provider.BlobSequenceTimeSeries(77, 1234.5, 6, b"\x12", "one", "two") x2 = provider.BlobSequenceTimeSeries(77, 1234.5, 6, b"\x12", "one", "two") x3 = provider.BlobSequenceTimeSeries(66, 4321.0, 7, b"\x7F", "hmm", "hum") self.assertEqual(hash(x1), hash(x2)) # The next check is technically not required by the `__hash__` # contract, but _should_ pass; failure on this assertion would at # least warrant some scrutiny. self.assertNotEqual(hash(x1), hash(x3))
def list_blob_sequences(
self, ctx, experiment_id, plugin_name, run_tag_filter=None
):
with timing.log_latency("build request"):
req = data_provider_pb2.ListBlobSequencesRequest()
req.experiment_id = experiment_id
req.plugin_filter.plugin_name = plugin_name
_populate_rtf(run_tag_filter, req.run_tag_filter)
with timing.log_latency("_stub.ListBlobSequences"):
with _translate_grpc_error():
res = self._stub.ListBlobSequences(req)
with timing.log_latency("build result"):
result = {}
for run_entry in res.runs:
tags = {}
result[run_entry.run_name] = tags
for tag_entry in run_entry.tags:
time_series = tag_entry.metadata
tags[tag_entry.tag_name] = provider.BlobSequenceTimeSeries(
max_step=time_series.max_step,
max_wall_time=time_series.max_wall_time,
max_length=time_series.max_length,
plugin_content=time_series.summary_metadata.plugin_data.content,
description=time_series.summary_metadata.summary_description,
display_name=time_series.summary_metadata.display_name,
)
return result
def list_blob_sequences(self,
experiment_id,
plugin_name,
run_tag_filter=None):
self._validate_experiment_id(experiment_id)
if run_tag_filter is None:
run_tag_filter = provider.RunTagFilter(runs=None, tags=None)
# TODO(davidsoergel, wchargin): consider images, etc.
# Note this plugin_name can really just be 'graphs' for now; the
# v2 cases are not handled yet.
if plugin_name != graphs_metadata.PLUGIN_NAME:
logger.warn("Directory has no blob data for plugin %r",
plugin_name)
return {}
result = collections.defaultdict(lambda: {})
for (run, run_info) in six.iteritems(self._multiplexer.Runs()):
tag = None
if not self._test_run_tag(run_tag_filter, run, tag):
continue
if not run_info[plugin_event_accumulator.GRAPH]:
continue
result[run][tag] = provider.BlobSequenceTimeSeries(
max_step=0,
max_wall_time=0,
latest_max_index=0, # Graphs are always one blob at a time
plugin_content=None,
description=None,
display_name=None,
)
return result
def list_blob_sequences(
self, ctx=None, *, experiment_id, plugin_name, run_tag_filter=None
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
index = self._index(
plugin_name, run_tag_filter, summary_pb2.DATA_CLASS_BLOB_SEQUENCE
)
result = {}
for (run, tag_to_metadata) in index.items():
result_for_run = {}
result[run] = result_for_run
for (tag, metadata) in tag_to_metadata.items():
max_step = None
max_wall_time = None
max_length = None
for event in self._multiplexer.Tensors(run, tag):
if max_step is None or max_step < event.step:
max_step = event.step
if max_wall_time is None or max_wall_time < event.wall_time:
max_wall_time = event.wall_time
length = _tensor_size(event.tensor_proto)
if max_length is None or length > max_length:
max_length = length
result_for_run[tag] = provider.BlobSequenceTimeSeries(
max_step=max_step,
max_wall_time=max_wall_time,
max_length=max_length,
plugin_content=metadata.plugin_data.content,
description=metadata.summary_description,
display_name=metadata.display_name,
)
return result
def test_repr(self):
x = provider.BlobSequenceTimeSeries(
max_step=77,
max_wall_time=1234.5,
latest_max_index=6,
plugin_content=b"AB\xCD\xEF!\x00",
description="test test",
display_name="one two",
)
repr_ = repr(x)
self.assertIn(repr(x.max_step), repr_)
self.assertIn(repr(x.max_wall_time), repr_)
self.assertIn(repr(x.latest_max_index), repr_)
self.assertIn(repr(x.plugin_content), repr_)
self.assertIn(repr(x.description), repr_)
self.assertIn(repr(x.display_name), repr_)
def list_blob_sequences(
self, ctx, *, experiment_id, plugin_name, run_tag_filter=None
):
self._validate_eid(experiment_id)
run_name = "%s/test" % experiment_id
tag_name = "input.%s" % plugin_name
return {
run_name: {
tag_name: provider.BlobSequenceTimeSeries(
max_step=0,
max_wall_time=0.0,
max_length=2,
plugin_content=b"",
description="Greetings via %s" % self._name,
display_name="input",
)
}
}
def _blob_sequence_time_series(
self,
max_step,
max_wall_time,
max_length,
plugin_content,
description,
display_name,
):
# Helper to use explicit kwargs.
return provider.BlobSequenceTimeSeries(
max_step=max_step,
max_wall_time=max_wall_time,
max_length=max_length,
plugin_content=plugin_content,
description=description,
display_name=display_name,
)
def list_blob_sequences(
self, experiment_id, plugin_name, run_tag_filter=None
):
self._validate_experiment_id(experiment_id)
if run_tag_filter is None:
run_tag_filter = provider.RunTagFilter(runs=None, tags=None)
result = {}
run_tag_content = self._multiplexer.PluginRunToTagToContent(plugin_name)
for (run, tag_to_content) in six.iteritems(run_tag_content):
result_for_run = {}
for tag in tag_to_content:
if not self._test_run_tag(run_tag_filter, run, tag):
continue
summary_metadata = self._multiplexer.SummaryMetadata(run, tag)
if (
summary_metadata.data_class
!= summary_pb2.DATA_CLASS_BLOB_SEQUENCE
):
continue
result[run] = result_for_run
max_step = None
max_wall_time = None
max_length = None
for event in self._multiplexer.Tensors(run, tag):
if max_step is None or max_step < event.step:
max_step = event.step
if max_wall_time is None or max_wall_time < event.wall_time:
max_wall_time = event.wall_time
length = _tensor_size(event.tensor_proto)
if max_length is None or length > max_length:
max_length = length
result_for_run[tag] = provider.BlobSequenceTimeSeries(
max_step=max_step,
max_wall_time=max_wall_time,
max_length=max_length,
plugin_content=summary_metadata.plugin_data.content,
description=summary_metadata.summary_description,
display_name=summary_metadata.display_name,
)
return result
def test_list_blob_sequences(self):
res = data_provider_pb2.ListBlobSequencesResponse()
run1 = res.runs.add(run_name="train")
tag11 = run1.tags.add(tag_name="input_image")
tag11.metadata.max_step = 7
tag11.metadata.max_wall_time = 7.77
tag11.metadata.max_length = 3
tag11.metadata.summary_metadata.plugin_data.content = b"PNG"
tag11.metadata.summary_metadata.display_name = "Input image"
tag11.metadata.summary_metadata.summary_description = "img"
self.stub.ListBlobSequences.return_value = res
actual = self.provider.list_blob_sequences(
self.ctx,
experiment_id="123",
plugin_name="images",
run_tag_filter=provider.RunTagFilter(runs=["val", "train"]),
)
expected = {
"train": {
"input_image":
provider.BlobSequenceTimeSeries(
max_step=7,
max_wall_time=7.77,
max_length=3,
plugin_content=b"PNG",
description="img",
display_name="Input image",
),
},
}
self.assertEqual(actual, expected)
req = data_provider_pb2.ListBlobSequencesRequest()
req.experiment_id = "123"
req.plugin_filter.plugin_name = "images"
req.run_tag_filter.runs.names.extend(["train", "val"]) # sorted
self.stub.ListBlobSequences.assert_called_once_with(req)
