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# Copyright 2018 Google, Inc. All Rights Reserved.
#
# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import sonnet as snt
import tensorflow as tf
from learning_unsupervised_learning import optimizers
from learning_unsupervised_learning import utils
from learning_unsupervised_learning import summary_utils
from learning_unsupervised_learning import variable_replace
class MultiTrialMetaObjective(snt.AbstractModule):
def __init__(self, samples_per_class, averages, **kwargs):
self.samples_per_class = samples_per_class
self.averages = averages
self.dataset_map = {}
super(MultiTrialMetaObjective,
self).__init__(**kwargs)
def _build(self, dataset, feature_transformer):
if self.samples_per_class is not None:
if dataset not in self.dataset_map:
# datasets are outside of frames from while loops
with tf.control_dependencies(None):
self.dataset_map[dataset] = utils.sample_n_per_class(
dataset, self.samples_per_class)
dataset = self.dataset_map[dataset]
stats = collections.defaultdict(list)
losses = []
# TODO(lmetz) move this to ingraph control flow?
for _ in xrange(self.averages):
loss, stat = self._build_once(dataset, feature_transformer)
losses.append(loss)
for k, v in stat.items():
stats[k].append(v)
stats = {k: tf.add_n(v) / float(len(v)) for k, v in stats.items()}
for k, v in stats.items():
tf.summary.scalar(k, v)
return tf.add_n(losses) / float(len(losses))
def local_variables(self):
"""List of variables that need to be updated for each evaluation.
These variables should not be stored on a parameter server and
should be reset every computation of a meta_objective loss.
Returns:
vars: list of tf.Variable
"""
return list(
snt.get_variables_in_module(self, tf.GraphKeys.TRAINABLE_VARIABLES))
def remote_variables(self):
return []
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