implement data collection and part of training
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rtz19970824
parent
1e2567c174
commit
2dad8e4020
@ -183,11 +183,15 @@ class GTPEngine():
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return 'unknown player', False
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def cmd_get_score(self, args, **kwargs):
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return self._game.game_engine.executor_get_score(True), None
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return self._game.game_engine.executor_get_score(True), True
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def cmd_show_board(self, args, **kwargs):
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return self._game.board, True
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def cmd_get_prob(self, args, **kwargs):
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return self._game.prob, True
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if __name__ == "main":
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game = Game()
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engine = GTPEngine(game_obj=Game)
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@ -58,24 +58,9 @@ class Game:
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def set_komi(self, k):
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self.komi = k
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def generate_nn_input(self, latest_boards, color):
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state = np.zeros([1, self.size, self.size, 17])
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for i in range(8):
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state[0, :, :, i] = np.array(np.array(latest_boards[i]) == np.ones(self.size ** 2)).reshape(self.size, self.size)
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state[0, :, :, i + 8] = np.array(np.array(latest_boards[i]) == -np.ones(self.size ** 2)).reshape(self.size, self.size)
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if color == utils.BLACK:
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state[0, :, :, 16] = np.ones([self.size, self.size])
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if color == utils.WHITE:
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state[0, :, :, 16] = np.zeros([self.size, self.size])
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return state
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def think(self, latest_boards, color):
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# TODO : using copy is right, or should we change to deepcopy?
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self.game_engine.simulate_latest_boards = copy.copy(latest_boards)
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self.game_engine.simulate_board = copy.copy(latest_boards[-1])
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nn_input = self.generate_nn_input(self.game_engine.simulate_latest_boards, color)
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mcts = MCTS(self.game_engine, self.evaluator, [self.game_engine.simulate_latest_boards, color], self.size ** 2 + 1, inverse=True)
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mcts.search(max_step=5)
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mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], self.size ** 2 + 1, inverse=True)
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mcts.search(max_step=1)
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temp = 1
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prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
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choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]
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@ -1,6 +1,7 @@
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import os
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import time
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import sys
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import cPickle
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import numpy as np
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import tensorflow as tf
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@ -167,4 +168,19 @@ class ResNet(object):
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#TODO: design the interface between the environment and training
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def train(self, mode='memory', *args, **kwargs):
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if mode == 'memory':
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pass
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if mode == 'file':
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self.train_with_file(data_path=kwargs['data_path'], checkpoint_path=kwargs['checkpoint_path'])
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def train_with_file(self, data_path, checkpoint_path):
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if not os.path.exists(data_path):
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raise ValueError("{} doesn't exist".format(data_path))
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file_list = os.listdir(data_path)
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if file_list <= 50:
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time.sleep(1)
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else:
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file_list.sort(key=lambda file: os.path.getmtime(data_path + file) if not os.path.isdir(
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data_path + file) else 0)
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@ -5,6 +5,18 @@ import re
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import Pyro4
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import time
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import os
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import cPickle
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class Data(object):
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def __init__(self):
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self.boards = []
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self.probs = []
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self.winner = 0
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def reset(self):
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self.__init__()
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if __name__ == '__main__':
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"""
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@ -13,10 +25,13 @@ if __name__ == '__main__':
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"""
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# TODO : we should set the network path in a more configurable way.
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parser = argparse.ArgumentParser()
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parser.add_argument("--result_path", type=str, default="./data/")
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parser.add_argument("--black_weight_path", type=str, default=None)
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parser.add_argument("--white_weight_path", type=str, default=None)
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args = parser.parse_args()
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if not os.path.exists(args.result_path):
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os.mkdir(args.result_path)
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# black_weight_path = "./checkpoints"
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# white_weight_path = "./checkpoints_origin"
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if args.black_weight_path is not None and (not os.path.exists(args.black_weight_path)):
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@ -35,10 +50,12 @@ if __name__ == '__main__':
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time.sleep(1)
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# start two different player with different network weights.
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agent_v0 = subprocess.Popen(['python', '-u', 'player.py', '--role=black', '--checkpoint_path=' + str(args.black_weight_path)],
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agent_v0 = subprocess.Popen(
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['python', '-u', 'player.py', '--role=black', '--checkpoint_path=' + str(args.black_weight_path)],
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stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
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agent_v1 = subprocess.Popen(['python', '-u', 'player.py', '--role=white', '--checkpoint_path=' + str(args.white_weight_path)],
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agent_v1 = subprocess.Popen(
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['python', '-u', 'player.py', '--role=white', '--checkpoint_path=' + str(args.white_weight_path)],
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stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
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server_list = ""
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@ -50,6 +67,7 @@ if __name__ == '__main__':
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print "Start black player at : " + str(agent_v0.pid)
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print "Start white player at : " + str(agent_v1.pid)
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data = Data()
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player = [None] * 2
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player[0] = Pyro4.Proxy("PYRONAME:black")
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player[1] = Pyro4.Proxy("PYRONAME:white")
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@ -63,7 +81,8 @@ if __name__ == '__main__':
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evaluate_rounds = 1
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game_num = 0
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while game_num < evaluate_rounds:
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try:
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while True:
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num = 0
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pass_flag = [False, False]
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print("Start game {}".format(game_num))
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@ -89,13 +108,31 @@ if __name__ == '__main__':
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for j in range(size):
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print show[board[i * size + j]] + " ",
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print "\n",
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data.boards.append(board)
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prob = player[turn].run_cmd(str(num) + ' get_prob')
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data.probs.append(prob)
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score = player[turn].run_cmd(str(num) + ' get_score')
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print "Finished : ", score.split(" ")[1]
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# TODO: generalize the player
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if score > 0:
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data.winner = 1
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if score < 0:
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data.winner = -1
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player[0].run_cmd(str(num) + ' clear_board')
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player[1].run_cmd(str(num) + ' clear_board')
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file_list = os.listdir(args.result_path)
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if not file_list:
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data_num = 0
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else:
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file_list.sort(key=lambda file: os.path.getmtime(args.result_path + file) if not os.path.isdir(
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args.result_path + file) else 0)
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data_num = eval(file_list[-1][:-4]) + 1
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print(file_list)
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with open("./data/" + str(data_num) + ".pkl", "w") as file:
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picklestring = cPickle.dump(data, file)
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data.reset()
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game_num += 1
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except KeyboardInterrupt:
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subprocess.call(["kill", "-9", str(agent_v0.pid)])
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subprocess.call(["kill", "-9", str(agent_v1.pid)])
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print "Kill all player, finish all game."
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@ -20,6 +20,7 @@ class Player(object):
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#return "inside the Player of player.py"
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return self.engine.run_cmd(command)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--checkpoint_path", type=str, default=None)
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