# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Logger component."""
import collections
import os.path
from typing import Any, Optional
from absl import logging
from fedjax.core import util
tf = util.import_tf()
[docs]class Logger:
"""Class to encapsulate tf.summary.SummaryWriter logging logic."""
[docs] def __init__(self, root_dir: Optional[str] = None):
"""Initializes summary writers and log directory."""
self._root_dir = root_dir
self._summary_writers = collections.OrderedDict()
[docs] def log(self, writer_name: str, metric_name: str, metric_value: Any,
round_num: int):
"""Records metric using specified summary writer.
Logs at INFO verbosity. Also, if root_dir is set and metric_value is:
- a scalar value, convertible to a float32 Tensor, writes scalar summary
- a vector, convertible to a float32 Tensor, writes histogram summary
Args:
writer_name: Name of summary writer.
metric_name: Name of metric to log.
metric_value: Value of metric to log.
round_num: Round number to log.
"""
logging.info('round %d %s: %s = %s', round_num, writer_name, metric_name,
metric_value)
if self._root_dir is None:
return
if writer_name not in self._summary_writers:
self._summary_writers[writer_name] = tf.summary.create_file_writer(
os.path.join(self._root_dir, writer_name))
with self._summary_writers[writer_name].as_default():
try:
if getattr(metric_value, 'ndim', 0) > 0:
tf.summary.histogram(metric_name, metric_value, step=round_num)
else:
tf.summary.scalar(metric_name, metric_value, step=round_num)
except (ValueError, tf.errors.UnimplementedError) as e:
logging.info('Failed to log summary with exception %s', e)
pass