minigo/main.py at docs · artasparks/minigo

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# 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.

import argh

import argparse

import os.path

import random

import socket

import sys

import tempfile

import time

import dual_net

import evaluation

import preprocessing

import selfplay_mcts

from gtp_wrapper import make_gtp_instance

import utils

import cloud_logging

import tensorflow as tf

from absl import flags

from tqdm import tqdm

from tensorflow import gfile

# How many positions we should aggregate per 'chunk'.

EXAMPLES_PER_RECORD = 10000

# How many positions to draw from for our training window.

# AGZ used the most recent 500k games, which, assuming 250 moves/game = 125M

WINDOW_SIZE = 125000000

def gtp(load_file: "The path to the network model files"=None,

cgos_mode: 'Whether to use CGOS time constraints'=False,

kgs_mode: 'Whether to use KGS courtesy-pass'=False,

verbose=1):

engine = make_gtp_instance(load_file,

verbosity=verbose,

cgos_mode=cgos_mode,

kgs_mode=kgs_mode)

sys.stderr.write("GTP engine ready\n")

sys.stderr.flush()

while not engine.disconnect:

inpt = input()

# handle either single lines at a time

# or multiple commands separated by '\n'

try:

cmd_list = inpt.split("\n")

except:

cmd_list = [inpt]

for cmd in cmd_list:

engine_reply = engine.send(cmd)

sys.stdout.write(engine_reply)

sys.stdout.flush()

def bootstrap(

working_dir: 'tf.estimator working directory. If not set, defaults to a random tmp dir'=None,

model_save_path: 'Where to export the first bootstrapped generation'=None):

if working_dir is None:

with tempfile.TemporaryDirectory() as working_dir:

utils.ensure_dir_exists(working_dir)

utils.ensure_dir_exists(os.path.dirname(model_save_path))

dual_net.bootstrap(working_dir)

dual_net.export_model(working_dir, model_save_path)

else:

utils.ensure_dir_exists(working_dir)

utils.ensure_dir_exists(os.path.dirname(model_save_path))

dual_net.bootstrap(working_dir)

dual_net.export_model(working_dir, model_save_path)

freeze_graph(model_save_path)

def train_dir(

working_dir: 'tf.estimator working directory.',

chunk_dir: 'Directory where training chunks are.',

model_save_path: 'Where to export the completed generation.',

generation_num: 'Which generation you are training.'=0):

tf_records = sorted(gfile.Glob(os.path.join(chunk_dir, '*.tfrecord.zz')))

tf_records = tf_records[-1 * (WINDOW_SIZE // EXAMPLES_PER_RECORD):]

train(working_dir, tf_records, model_save_path, generation_num)

def train(

working_dir: 'tf.estimator working directory.',

tf_records: 'list of files of tf_records to train on',

model_save_path: 'Where to export the completed generation.',

generation_num: 'Which generation you are training.'=0):

print("Training on:", tf_records[0], "to", tf_records[-1])

with utils.logged_timer("Training"):

dual_net.train(working_dir, tf_records, generation_num)

dual_net.export_model(working_dir, model_save_path)

freeze_graph(model_save_path)

def validate(

working_dir: 'tf.estimator working directory',

*tf_record_dirs: 'Directories where holdout data are',

checkpoint_name: 'Which checkpoint to evaluate (None=latest)'=None,

validate_name: 'Name for validation set (i.e., selfplay or human)'=None):

tf_records = []

with utils.logged_timer("Building lists of holdout files"):

for record_dir in tf_record_dirs:

tf_records.extend(gfile.Glob(os.path.join(record_dir, '*.zz')))

first_record = os.path.basename(tf_records[0])

last_record = os.path.basename(tf_records[-1])

with utils.logged_timer("Validating from {} to {}".format(first_record, last_record)):

dual_net.validate(

working_dir, tf_records, checkpoint_name=checkpoint_name,

name=validate_name)

def evaluate(

black_model: 'The path to the model to play black',

white_model: 'The path to the model to play white',

output_dir: 'Where to write the evaluation results'='sgf/evaluate',

games: 'the number of games to play'=16,

verbose: 'How verbose the players should be (see selfplay)' = 1):

utils.ensure_dir_exists(output_dir)

with utils.logged_timer("Loading weights"):

black_net = dual_net.DualNetwork(black_model)

white_net = dual_net.DualNetwork(white_model)

with utils.logged_timer("%d games" % games):

evaluation.play_match(

black_net, white_net, games, output_dir, verbose)

def selfplay(

load_file: "The path to the network model files",

output_dir: "Where to write the games"="data/selfplay",

holdout_dir: "Where to write the games"="data/holdout",

output_sgf: "Where to write the sgfs"="sgf/",

verbose: '>=2 will print debug info, >=3 will print boards' = 1,

holdout_pct: 'how many games to hold out for validation' = 0.05):

clean_sgf = os.path.join(output_sgf, 'clean')

full_sgf = os.path.join(output_sgf, 'full')

utils.ensure_dir_exists(clean_sgf)

utils.ensure_dir_exists(full_sgf)

utils.ensure_dir_exists(output_dir)

utils.ensure_dir_exists(holdout_dir)

with utils.logged_timer("Loading weights from %s ... " % load_file):

network = dual_net.DualNetwork(load_file)

with utils.logged_timer("Playing game"):

player = selfplay_mcts.play(network, verbose)

output_name = '{}-{}'.format(int(time.time()), socket.gethostname())

game_data = player.extract_data()

with gfile.GFile(os.path.join(clean_sgf, '{}.sgf'.format(output_name)), 'w') as f:

f.write(player.to_sgf(use_comments=False))

with gfile.GFile(os.path.join(full_sgf, '{}.sgf'.format(output_name)), 'w') as f:

f.write(player.to_sgf())

tf_examples = preprocessing.make_dataset_from_selfplay(game_data)

# Hold out 5% of games for evaluation.

if random.random() < holdout_pct:

fname = os.path.join(holdout_dir, "{}.tfrecord.zz".format(output_name))

else:

fname = os.path.join(output_dir, "{}.tfrecord.zz".format(output_name))

preprocessing.write_tf_examples(fname, tf_examples)

def convert(load_file, dest_file):

from tensorflow.python.framework import meta_graph

features, labels = dual_net.get_inference_input()

dual_net.model_fn(features, labels, tf.estimator.ModeKeys.PREDICT,

dual_net.get_default_hyperparams())

sess = tf.Session()

# retrieve the global step as a python value

ckpt = tf.train.load_checkpoint(load_file)

global_step_value = ckpt.get_tensor('global_step')

# restore all saved weights, except global_step

meta_graph_def = meta_graph.read_meta_graph_file(

load_file + '.meta')

stored_var_names = set([n.name

for n in meta_graph_def.graph_def.node

if n.op == 'VariableV2'])

stored_var_names.remove('global_step')

var_list = [v for v in tf.global_variables()

if v.op.name in stored_var_names]

tf.train.Saver(var_list=var_list).restore(sess, load_file)

# manually set the global step

global_step_tensor = tf.train.get_or_create_global_step()

assign_op = tf.assign(global_step_tensor, global_step_value)

sess.run(assign_op)

# export a new savedmodel that has the right global step type

tf.train.Saver().save(sess, dest_file)

sess.close()

tf.reset_default_graph()

def freeze_graph(load_file):

""" Loads a network and serializes just the inference parts for use by e.g. the C++ binary """

n = dual_net.DualNetwork(load_file)

out_graph = tf.graph_util.convert_variables_to_constants(

n.sess, n.sess.graph.as_graph_def(), ["policy_output", "value_output"])

with open(os.path.join(load_file + '.pb'), 'wb') as f:

f.write(out_graph.SerializeToString())

parser = argparse.ArgumentParser()

argh.add_commands(parser, [gtp, bootstrap, train, freeze_graph,

selfplay, evaluate, validate, convert])

if __name__ == '__main__':

cloud_logging.configure()

# Let absl.flags parse known flags from argv, then pass the remaining flags

# into argh for dispatching.

remaining_argv = flags.FLAGS(sys.argv, known_only=True)

argh.dispatch(parser, argv=remaining_argv[1:])

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