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85 lines (67 loc) · 2.68 KB
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# Copyright 2018 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.
"""Validate a network.
Usage:
BOARD_SIZE=19 python validate.py tfrecord_dir/ tfrecord_dir2/
"""
import os
from absl import app, flags
from tensorflow import gfile
import dual_net
import preprocessing
import utils
flags.DEFINE_integer('examples_to_validate', 50 * 2048,
'Number of examples to run validation on.')
flags.DEFINE_string('validate_name', 'selfplay',
'Name of validation set (i.e. selfplay or human).')
flags.DEFINE_bool('expand_validation_dirs', True,
'Whether to expand the input paths by globbing. If false, '
'directly read and validate on the given files.')
# From dual_net.py
flags.declare_key_flag('work_dir')
flags.declare_key_flag('use_tpu')
flags.declare_key_flag('num_tpu_cores')
FLAGS = flags.FLAGS
def validate(*tf_records):
"""Validate a model's performance on a set of holdout data."""
if FLAGS.use_tpu:
def _input_fn(params):
return preprocessing.get_tpu_input_tensors(
params['batch_size'], tf_records, filter_amount=0.05)
else:
def _input_fn():
return preprocessing.get_input_tensors(
FLAGS.train_batch_size, tf_records, filter_amount=0.05,
shuffle_examples=False)
steps = FLAGS.examples_to_validate // FLAGS.train_batch_size
if FLAGS.use_tpu:
steps //= FLAGS.num_tpu_cores
estimator = dual_net.get_estimator()
with utils.logged_timer("Validating"):
estimator.evaluate(_input_fn, steps=steps, name=FLAGS.validate_name)
def main(argv):
"""Validate a model's performance on a set of holdout data."""
_, *validation_paths = argv
if FLAGS.expand_validation_dirs:
tf_records = []
with utils.logged_timer("Building lists of holdout files"):
for record_dir in validation_paths:
tf_records.extend(gfile.Glob(os.path.join(record_dir, '*.zz')))
else:
tf_records = validation_paths
if not tf_records:
raise RuntimeError("Did not find any holdout files for validating!")
validate(*tf_records)
if __name__ == "__main__":
app.run(main)