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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.
'''Utilities to create, read, write tf.Examples.'''
import functools
import random
import coords
import features as features_lib
import go
import sgf_wrapper
import symmetries
import numpy as np
import tensorflow as tf
TF_RECORD_CONFIG = tf.python_io.TFRecordOptions(
tf.python_io.TFRecordCompressionType.ZLIB)
# The shuffle buffer size determines how far an example could end up from
# where it started; this and the interleave parameters in preprocessing can give
# us an approximation of a uniform sampling. The default of 4M is used in
# training, but smaller numbers can be used for aggregation or validation.
SHUFFLE_BUFFER_SIZE = 2000000
def _one_hot(index):
* go.N + 1], dtype=np.float32)
onehot[index] = 1
return onehot
def make_tf_example(features, pi, value):
'''
Args:
features: [N, N, FEATURE_DIM] nparray of uint8
pi: [N * N + 1] nparray of float32
value: float
'''
return tf.train.Example(features=tf.train.Features(feature={
'x': tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[features.tostring()])),
'pi': tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[pi.tostring()])),
'outcome': tf.train.Feature(
float_list=tf.train.FloatList(
value=[value]))}))
def write_tf_examples(filename, tf_examples, serialize=True):
'''
Args:
filename: Where to write tf.records
tf_examples: An iterable of tf.Example
serialize: whether to serialize the examples.
'''
with tf.python_io.TFRecordWriter(
filename, options=TF_RECORD_CONFIG) as writer:
for ex in tf_examples:
if serialize:
writer.write(ex.SerializeToString())
else:
writer.write(ex)
def batch_parse_tf_example(batch_size, example_batch):
'''
Args:
example_batch: a batch of tf.Example
Returns:
A tuple (feature_tensor, dict of output tensors)
'''
features = {
'x': tf.FixedLenFeature([], tf.string),
'pi': tf.FixedLenFeature([], tf.string),
'outcome': tf.FixedLenFeature([], tf.float32),
}
parsed = tf.parse_example(example_batch, features)
x = tf.decode_raw(parsed['x'], tf.uint8)
x = tf.cast(x, tf.float32)
x = tf.reshape(x, [batch_size, go.N, go.N,
features_lib.NEW_FEATURES_PLANES])
pi = tf.decode_raw(parsed['pi'], tf.float32)
pi = tf.reshape(pi, [batch_size, go.N * go.N + 1])
outcome = parsed['outcome']
outcome.set_shape([batch_size])
return x, {'pi_tensor': pi, 'value_tensor': outcome}
def read_tf_records(batch_size, tf_records, num_repeats=None,
shuffle_records=True, shuffle_examples=True,
shuffle_buffer_size=None,
filter_amount=1.0):
'''
Args:
batch_size: batch size to return
tf_records: a list of tf_record filenames
num_repeats: how many times the data should be read (default: infinite)
shuffle_records: whether to shuffle the order of files read
shuffle_examples: whether to shuffle the tf.Examples
shuffle_buffer_size: how big of a buffer to fill before shuffling.
filter_amount: what fraction of records to keep
Returns:
a tf dataset of batched tensors
'''
if shuffle_buffer_size is None:
shuffle_buffer_size = SHUFFLE_BUFFER_SIZE
if shuffle_records:
random.shuffle(tf_records)
record_list = tf.data.Dataset.from_tensor_slices(tf_records)
# compression_type here must agree with write_tf_examples
# cycle_length = how many tfrecord files are read in parallel
# block_length = how many tf.Examples are read from each file before
# moving to the next file
# The idea is to shuffle both the order of the files being read,
# and the examples being read from the files.
dataset = record_list.interleave(lambda x:
tf.data.TFRecordDataset(
x, compression_type='ZLIB'),
cycle_length=64, block_length=16)
dataset = dataset.filter(lambda x: tf.less(
tf.random_uniform([1]), filter_amount)[0])
if num_repeats is not None:
dataset = dataset.repeat(num_repeats)
else:
dataset = dataset.repeat()
if shuffle_examples:
dataset = dataset.shuffle(buffer_size=shuffle_buffer_size)
dataset = dataset.batch(batch_size)
return dataset
def _random_rotation(x_tensor, outcome_tensor):
def rotate_py_func(x, pi):
syms, x_rot = symmetries.randomize_symmetries_feat(x)
pi_rot = [symmetries.apply_symmetry_pi(s, p) for s, p in zip(syms, pi)]
return x_rot, pi_rot
pi_tensor = outcome_tensor['pi_tensor']
x_rot_tensor, pi_rot_tensor = tuple(tf.py_func(
rotate_py_func,
[x_tensor, pi_tensor],
[tf.float32, tf.float32],
stateful=False))
x_rot_tensor.set_shape(x_tensor.get_shape())
pi_rot_tensor.set_shape(pi_tensor.get_shape())
outcome_tensor['pi_tensor'] = pi_rot_tensor
return x_rot_tensor, outcome_tensor
def get_input_tensors(batch_size, tf_records, num_repeats=None,
shuffle_records=True, shuffle_examples=True,
shuffle_buffer_size=None,
filter_amount=0.05, random_rotation=False):
'''Read tf.Records and prepare them for ingestion by dual_net. See
`read_tf_records` for parameter documentation.
Returns a dict of tensors (see return value of batch_parse_tf_example)
'''
if shuffle_buffer_size is None:
shuffle_buffer_size = SHUFFLE_BUFFER_SIZE
dataset = read_tf_records(batch_size, tf_records, num_repeats=num_repeats,
shuffle_records=shuffle_records,
shuffle_examples=shuffle_examples,
shuffle_buffer_size=shuffle_buffer_size,
filter_amount=filter_amount)
dataset = dataset.filter(lambda t: tf.equal(tf.shape(t)[0], batch_size))
dataset = dataset.map(functools.partial(
batch_parse_tf_example, batch_size))
if random_rotation:
dataset = dataset.map(_random_rotation)
return dataset.make_one_shot_iterator().get_next()
def make_dataset_from_selfplay(data_extracts):
'''
Returns an iterable of tf.Examples.
Args:
data_extracts: An iterable of (position, pi, result) tuples
'''
tf_examples = (make_tf_example(features_lib.extract_features(pos), pi, result)
for pos, pi, result in data_extracts)
return tf_examples
def make_dataset_from_sgf(sgf_filename, tf_record):
pwcs = sgf_wrapper.replay_sgf_file(sgf_filename)
tf_examples = map(_make_tf_example_from_pwc, pwcs)
write_tf_examples(tf_record, tf_examples)
def _make_tf_example_from_pwc(position_w_context):
features = features_lib.extract_features(position_w_context.position)
pi = _one_hot(coords.to_flat(position_w_context.next_move))
value = position_w_context.result
return make_tf_example(features, pi, value)
def shuffle_tf_examples(gather_size, records_to_shuffle):
'''Read through tf.Record and yield shuffled, but unparsed tf.Examples
Args:
gather_size: The number of tf.Examples to be gathered together
records_to_shuffle: A list of filenames
Returns:
An iterator yielding lists of bytes, which are serialized tf.Examples.
'''
dataset = read_tf_records(gather_size, records_to_shuffle, num_repeats=1)
batch = dataset.make_one_shot_iterator().get_next()
sess = tf.Session()
while True:
try:
result = sess.run(batch)
yield list(result)
except tf.errors.OutOfRangeError:
break