minigo/main.py at v199 · WayneFd/minigo

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

# 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 argparse

import argh

import os.path

import collections

import random

import re

import socket

import sys

import time

import cloud_logging

from tqdm import tqdm

import gzip

import numpy as np

import tensorflow as tf

from tensorflow import gfile

import go

import dual_net

from gtp_wrapper import make_gtp_instance, MCTSPlayer

import preprocessing

import selfplay_mcts

from utils import logged_timer as timer

import evaluation

import sgf_wrapper

import utils

def _ensure_dir_exists(directory):

if directory.startswith('gs://'):

return

os.makedirs(directory, exist_ok=True)

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

readouts: 'How many simulations to run per move'=100,

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

verbose=1):

engine = make_gtp_instance(load_file,

readouts_per_move=readouts,

verbosity=verbose,

cgos_mode=cgos_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(save_file):

dual_net.DualNetworkTrainer(save_file).bootstrap()

def train(chunk_dir, save_file, load_file=None, generation_num=0,

logdir=None, num_steps=None):

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

tf_records = [f for f in tf_records

if (generation_num - 50) < int(os.path.basename(f)[:6]) <= generation_num]

n = dual_net.DualNetworkTrainer(save_file)

with timer("Training"):

n.train(tf_records, init_from=load_file, logdir=logdir, num_steps=num_steps)

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'='data/evaluate/sgf',

readouts: 'How many readouts to make per move.'=400,

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

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

black_model = os.path.abspath(black_model)

white_model = os.path.abspath(white_model)

with timer("Loading weights"):

black_net = dual_net.DualNetwork(black_model)

white_net = dual_net.DualNetwork(white_model)

with timer("%d games" % games):

players = evaluation.play_match(black_net, white_net, games, readouts, verbose)

for idx,p in enumerate(players):

fname ="{:s}-vs-{:s}-{:d}".format(black_net.name, white_net.name, idx)

with open(os.path.join(output_dir, fname + '.sgf'), 'w') as f:

f.write(sgf_wrapper.make_sgf(p[0].position.recent,

p[0].make_result_string(p[0].position),

black_name=os.path.basename(black_model),

white_name=os.path.basename(white_model)))

def selfplay(

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

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

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

readouts: 'How many simulations to run per move'=100,

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

resign_threshold : 'absolute value of threshold to resign at' = 0.95):

_ensure_dir_exists(output_sgf)

_ensure_dir_exists(output_dir)

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

network = dual_net.DualNetwork(load_file)

network.name = os.path.basename(load_file)

with timer("Playing game"):

player = selfplay_mcts.play(network, readouts, resign_threshold, verbose)

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

game_data = player.extract_data()

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

f.write(player.to_sgf())

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

preprocessing.make_dataset_from_selfplay(game_data, fname)

def gather(

input_directory: 'where to look for games'='data/selfplay/',

output_directory: 'where to put collected games'='data/training_chunks/',

examples_per_record: 'how many tf.examples to gather in each chunk'=20000):

_ensure_dir_exists(output_directory)

models = [model_dir.strip('/') for model_dir in gfile.ListDirectory(input_directory)]

with timer("Finding existing tfrecords..."):

model_gamedata = {

model: gfile.Glob(

os.path.join(input_directory, model, '**', '*.tfrecord.zz'))

for model in models

}

print("Found %d models" % len(models))

for model_name, record_files in sorted(model_gamedata.items()):

print(" %s: %s files" % (model_name, len(record_files)))

meta_file = os.path.join(output_directory, 'meta.txt')

try:

with gfile.GFile(meta_file, 'r') as f:

already_processed = set(f.read().split())

except tf.errors.NotFoundError:

already_processed = set()

num_already_processed = len(already_processed)

for model_name, record_files in sorted(model_gamedata.items()):

with timer("Processing %s" % model_name):

if set(record_files) <= already_processed:

print("%s is already fully processed" % model_name)

continue

for i, example_batch in enumerate(

tqdm(preprocessing.shuffle_tf_examples(examples_per_record, record_files))):

output_record = os.path.join(output_directory,

'{}-{}.tfrecord.zz'.format(model_name, str(i)))

preprocessing.write_tf_examples(output_record, example_batch, serialize=False)

already_processed.update(record_files)

print("Processed %s new files" % (len(already_processed) - num_already_processed))

with gfile.GFile(meta_file, 'w') as f:

f.write('\n'.join(sorted(already_processed)))

parser = argparse.ArgumentParser()

argh.add_commands(parser, [gtp, bootstrap, train, selfplay, gather, evaluate])

if __name__ == '__main__':

cloud_logging.configure()

argh.dispatch(parser)

FilesExpand file tree

main.py

Latest commit

History

main.py

File metadata and controls