def _get_values(self, data_blob, dtype_enum, shape_string):
"""Obtains values for histogram data given blob and dtype enum.
Args:
data_blob: The blob obtained from the database.
dtype_enum: The enum representing the dtype.
shape_string: A comma-separated string of numbers denoting shape.
Returns:
The histogram values as a list served to the frontend.
"""
buf = np.frombuffer(data_blob,
dtype=tf.DType(dtype_enum).as_numpy_dtype)
return buf.reshape([int(i) for i in shape_string.split(',')]).tolist()
def _get_value(self, scalar_data_blob, dtype_enum):
"""Obtains value for scalar event given blob and dtype enum.
Args:
scalar_data_blob: The blob obtained from the database.
dtype_enum: The enum representing the dtype.
Returns:
The scalar value.
"""
tensorflow_dtype = tf.DType(dtype_enum)
buf = np.frombuffer(scalar_data_blob, dtype=tensorflow_dtype.as_numpy_dtype)
return np.asscalar(buf)
def pr_curves_impl(self, runs, tag):
"""Creates the JSON object for the PR curves response for a run-tag combo.
Arguments:
runs: A list of runs to fetch the curves for.
tag: The tag to fetch the curves for.
Raises:
ValueError: If no PR curves could be fetched for a run and tag.
Returns:
The JSON object for the PR curves route response.
"""
if self._db_connection_provider:
# Serve data from the database.
db = self._db_connection_provider()
# We select for steps greater than -1 because the writer inserts
# placeholder rows en masse. The check for step filters out those rows.
cursor = db.execute(
'''
SELECT
Runs.run_name,
Tensors.step,
Tensors.computed_time,
Tensors.data,
Tensors.dtype,
Tensors.shape,
Tags.plugin_data
FROM Tensors
JOIN Tags
ON Tensors.series = Tags.tag_id
JOIN Runs
ON Tags.run_id = Runs.run_id
WHERE
Runs.run_name IN (%s)
AND Tags.tag_name = ?
AND Tags.plugin_name = ?
AND Tensors.step > -1
ORDER BY Tensors.step
''' % ','.join(['?'] * len(runs)), runs + [tag, metadata.PLUGIN_NAME])
response_mapping = {}
for (run, step, wall_time, data, dtype, shape,
plugin_data) in cursor:
if run not in response_mapping:
response_mapping[run] = []
buf = np.frombuffer(data, dtype=tf.DType(dtype).as_numpy_dtype)
data_array = buf.reshape([int(i) for i in shape.split(',')])
plugin_data_proto = plugin_data_pb2.PrCurvePluginData()
string_buffer = np.frombuffer(plugin_data, dtype=np.dtype('b'))
plugin_data_proto.ParseFromString(
tf.compat.as_bytes(string_buffer.tostring()))
thresholds = self._compute_thresholds(
plugin_data_proto.num_thresholds)
entry = self._make_pr_entry(step, wall_time, data_array,
thresholds)
response_mapping[run].append(entry)
else:
# Serve data from events files.
response_mapping = {}
for run in runs:
try:
tensor_events = self._multiplexer.Tensors(run, tag)
except KeyError:
raise ValueError(
'No PR curves could be found for run %r and tag %r' %
(run, tag))
content = self._multiplexer.SummaryMetadata(
run, tag).plugin_data.content
pr_curve_data = metadata.parse_plugin_metadata(content)
thresholds = self._compute_thresholds(
pr_curve_data.num_thresholds)
response_mapping[run] = [
self._process_tensor_event(e, thresholds)
for e in tensor_events
]
return response_mapping
