def testUsesSeed(self):
"""Tests that reservoirs with different seeds keep different samples."""
key = 'key'
r1 = reservoir.Reservoir(10, seed=0)
r2 = reservoir.Reservoir(10, seed=1)
for i in xrange(100):
r1.AddItem('key', i)
r2.AddItem('key', i)
self.assertNotEqual(r1.Items(key), r2.Items(key))
def testExceptions(self):
with self.assertRaises(ValueError):
reservoir.Reservoir(-1)
with self.assertRaises(ValueError):
reservoir.Reservoir(13.3)
r = reservoir.Reservoir(12)
with self.assertRaises(KeyError):
r.Items("missing key")
def testDeterminism(self):
"""Tests that the reservoir is deterministic."""
key = "key"
r1 = reservoir.Reservoir(10)
r2 = reservoir.Reservoir(10)
for i in xrange(100):
r1.AddItem("key", i)
r2.AddItem("key", i)
self.assertEqual(r1.Items(key), r2.Items(key))
def _ProcessTensor(self, tag, wall_time, step, tensor):
tv = TensorEvent(wall_time=wall_time, step=step, tensor_proto=tensor)
with self._tensors_by_tag_lock:
if tag not in self.tensors_by_tag:
reservoir_size = self._GetTensorReservoirSize(tag)
self.tensors_by_tag[tag] = reservoir.Reservoir(reservoir_size)
self.tensors_by_tag[tag].AddItem(_TENSOR_RESERVOIR_KEY, tv)
def testItemsAndKeys(self):
r = reservoir.Reservoir(42)
r.AddItem("foo", 4)
r.AddItem("bar", 9)
r.AddItem("foo", 19)
self.assertItemsEqual(r.Keys(), ["foo", "bar"])
self.assertEqual(r.Items("foo"), [4, 19])
self.assertEqual(r.Items("bar"), [9])
def testItemsAndKeys(self):
r = reservoir.Reservoir(42)
r.AddItem('foo', 4)
r.AddItem('bar', 9)
r.AddItem('foo', 19)
self.assertItemsEqual(r.Keys(), ['foo', 'bar'])
self.assertEqual(r.Items('foo'), [4, 19])
self.assertEqual(r.Items('bar'), [9])
def testBucketDeterminism(self):
"""Tests that reservoirs are deterministic at a bucket level.
This means that only the order elements are added within a bucket matters.
"""
separate_reservoir = reservoir.Reservoir(10)
interleaved_reservoir = reservoir.Reservoir(10)
for i in xrange(100):
separate_reservoir.AddItem('key1', i)
for i in xrange(100):
separate_reservoir.AddItem('key2', i)
for i in xrange(100):
interleaved_reservoir.AddItem('key1', i)
interleaved_reservoir.AddItem('key2', i)
for key in ['key1', 'key2']:
self.assertEqual(separate_reservoir.Items(key),
interleaved_reservoir.Items(key))
def testFilterItemsByKey(self):
r = reservoir.Reservoir(100, seed=0)
for i in xrange(10):
r.AddItem("key1", i)
r.AddItem("key2", i)
self.assertEqual(len(r.Items("key1")), 10)
self.assertEqual(len(r.Items("key2")), 10)
self.assertEqual(r.FilterItems(lambda x: x <= 7, "key2"), 2)
self.assertEqual(len(r.Items("key2")), 8)
self.assertEqual(len(r.Items("key1")), 10)
self.assertEqual(r.FilterItems(lambda x: x <= 3, "key1"), 6)
self.assertEqual(len(r.Items("key1")), 4)
self.assertEqual(len(r.Items("key2")), 8)
def __init__(
self,
path,
size_guidance=None,
compression_bps=NORMAL_HISTOGRAM_BPS,
purge_orphaned_data=True,
):
"""Construct the `EventAccumulator`.
Args:
path: A file path to a directory containing tf events files, or a single
tf events file. The accumulator will load events from this path.
size_guidance: Information on how much data the EventAccumulator should
store in memory. The DEFAULT_SIZE_GUIDANCE tries not to store too much
so as to avoid OOMing the client. The size_guidance should be a map
from a `tagType` string to an integer representing the number of
items to keep per tag for items of that `tagType`. If the size is 0,
all events are stored.
compression_bps: Information on how the `EventAccumulator` should compress
histogram data for the `CompressedHistograms` tag (for details see
`ProcessCompressedHistogram`).
purge_orphaned_data: Whether to discard any events that were "orphaned" by
a TensorFlow restart.
"""
size_guidance = size_guidance or DEFAULT_SIZE_GUIDANCE
sizes = {}
for key in DEFAULT_SIZE_GUIDANCE:
if key in size_guidance:
sizes[key] = size_guidance[key]
else:
sizes[key] = DEFAULT_SIZE_GUIDANCE[key]
self._first_event_timestamp = None
self.scalars = reservoir.Reservoir(size=sizes[SCALARS])
self._graph = None
self._graph_from_metagraph = False
self._meta_graph = None
self._tagged_metadata = {}
self.summary_metadata = {}
self.histograms = reservoir.Reservoir(size=sizes[HISTOGRAMS])
self.compressed_histograms = reservoir.Reservoir(
size=sizes[COMPRESSED_HISTOGRAMS], always_keep_last=False
)
self.images = reservoir.Reservoir(size=sizes[IMAGES])
self.audios = reservoir.Reservoir(size=sizes[AUDIO])
self.tensors = reservoir.Reservoir(size=sizes[TENSORS])
# Keep a mapping from plugin name to a dict mapping from tag to plugin data
# content obtained from the SummaryMetadata (metadata field of Value) for
# that plugin (This is not the entire SummaryMetadata proto - only the
# content for that plugin). The SummaryWriter only keeps the content on the
# first event encountered per tag, so we must store that first instance of
# content for each tag.
self._plugin_to_tag_to_content = collections.defaultdict(dict)
self._generator_mutex = threading.Lock()
self.path = path
self._generator = _GeneratorFromPath(path)
self._compression_bps = compression_bps
self.purge_orphaned_data = purge_orphaned_data
self.most_recent_step = -1
self.most_recent_wall_time = -1
self.file_version = None
# The attributes that get built up by the accumulator
self.accumulated_attrs = (
"scalars",
"histograms",
"compressed_histograms",
"images",
"audios",
)
self._tensor_summaries = {}
def testRespectsSize(self):
r = reservoir.Reservoir(42)
self.assertEqual(r._buckets["meaning of life"]._max_size, 42)
def testEmptyReservoir(self):
r = reservoir.Reservoir(1)
self.assertFalse(r.Keys())
def __init__(self,
path,
size_guidance=DEFAULT_SIZE_GUIDANCE,
compression_bps=NORMAL_HISTOGRAM_BPS,
purge_orphaned_data=True):
"""Construct the `EventAccumulator`.
Args:
path: A file path to a directory containing tf events files, or a single
tf events file. The accumulator will load events from this path.
size_guidance: Information on how much data the EventAccumulator should
store in memory. The DEFAULT_SIZE_GUIDANCE tries not to store too much
so as to avoid OOMing the client. The size_guidance should be a map
from a `tagType` string to an integer representing the number of
items to keep per tag for items of that `tagType`. If the size is 0,
all events are stored.
compression_bps: Information on how the `EventAccumulator` should compress
histogram data for the `CompressedHistograms` tag (for details see
`ProcessCompressedHistogram`).
purge_orphaned_data: Whether to discard any events that were "orphaned" by
a TensorFlow restart.
"""
sizes = {}
for key in DEFAULT_SIZE_GUIDANCE:
if key in size_guidance:
sizes[key] = size_guidance[key]
else:
sizes[key] = DEFAULT_SIZE_GUIDANCE[key]
self._first_event_timestamp = None
self._scalars = reservoir.Reservoir(size=sizes[SCALARS])
# Unlike the other reservoir, the reservoir for health pills is keyed by the
# name of the op instead of the tag. This lets us efficiently obtain the
# health pills per node.
self._health_pills = reservoir.Reservoir(size=sizes[HEALTH_PILLS])
self._graph = None
self._graph_from_metagraph = False
self._meta_graph = None
self._tagged_metadata = {}
self._histograms = reservoir.Reservoir(size=sizes[HISTOGRAMS])
self._compressed_histograms = reservoir.Reservoir(
size=sizes[COMPRESSED_HISTOGRAMS], always_keep_last=False)
self._images = reservoir.Reservoir(size=sizes[IMAGES])
self._audio = reservoir.Reservoir(size=sizes[AUDIO])
self._tensors = reservoir.Reservoir(size=sizes[TENSORS])
self._generator_mutex = threading.Lock()
self.path = path
self._generator = _GeneratorFromPath(path)
self._compression_bps = compression_bps
self.purge_orphaned_data = purge_orphaned_data
self.most_recent_step = -1
self.most_recent_wall_time = -1
self.file_version = None
# The attributes that get built up by the accumulator
self.accumulated_attrs = ('_scalars', '_histograms',
'_compressed_histograms', '_images', '_audio')
self._tensor_summaries = {}
def __init__(self,
path,
size_guidance=None,
tensor_size_guidance=None,
purge_orphaned_data=True):
"""Construct the `EventAccumulator`.
Args:
path: A file path to a directory containing tf events files, or a single
tf events file. The accumulator will load events from this path.
size_guidance: Information on how much data the EventAccumulator should
store in memory. The DEFAULT_SIZE_GUIDANCE tries not to store too much
so as to avoid OOMing the client. The size_guidance should be a map
from a `tagType` string to an integer representing the number of
items to keep per tag for items of that `tagType`. If the size is 0,
all events are stored.
tensor_size_guidance: Like `size_guidance`, but allowing finer
granularity for tensor summaries. Should be a map from the
`plugin_name` field on the `PluginData` proto to an integer
representing the number of items to keep per tag. Plugins for
which there is no entry in this map will default to the value of
`size_guidance[event_accumulator.TENSORS]`. Defaults to `{}`.
purge_orphaned_data: Whether to discard any events that were "orphaned" by
a TensorFlow restart.
"""
size_guidance = dict(size_guidance or DEFAULT_SIZE_GUIDANCE)
sizes = {}
for key in DEFAULT_SIZE_GUIDANCE:
if key in size_guidance:
sizes[key] = size_guidance[key]
else:
sizes[key] = DEFAULT_SIZE_GUIDANCE[key]
self._size_guidance = size_guidance
self._tensor_size_guidance = dict(tensor_size_guidance or {})
self._first_event_timestamp = None
self.scalars = reservoir.Reservoir(size=sizes[SCALARS])
self._graph = None
self._graph_from_metagraph = False
self._meta_graph = None
self._tagged_metadata = {}
self.summary_metadata = {}
self.audios = reservoir.Reservoir(size=sizes[AUDIO])
self.tensors_by_tag = {}
self._tensors_by_tag_lock = threading.Lock()
# Keep a mapping from plugin name to a dict mapping from tag to plugin data
# content obtained from the SummaryMetadata (metadata field of Value) for
# that plugin (This is not the entire SummaryMetadata proto - only the
# content for that plugin). The SummaryWriter only keeps the content on the
# first event encountered per tag, so we must store that first instance of
# content for each tag.
self._plugin_to_tag_to_content = collections.defaultdict(dict)
self._generator_mutex = threading.Lock()
self.path = path
self._generator = _GeneratorFromPath(path)
self.purge_orphaned_data = purge_orphaned_data
self.most_recent_step = -1
self.most_recent_wall_time = -1
self.file_version = None
# The attributes that get built up by the accumulator
self.accumulated_attrs = ('scalars', 'audios')
self._tensor_summaries = {}
