refactor code to avoid memory leak

AlphaGo/.gitignore

@@ -1,5 +1,5 @@
 data
-checkpoints
+checkpoint*
 random
 *.log
 *.txt

AlphaGo/game.py

@@ -58,8 +58,9 @@ class Game:
 
     def clear(self):
         if self.name == "go":
+            del self.board[:]
             self.board = [utils.EMPTY] * (self.size ** 2)
-            self.history = []
+            del self.history[:]
         if self.name == "reversi":
             self.board = self.game_engine.get_board()
         for _ in range(self.history_length):

AlphaGo/model.py

@@ -9,6 +9,7 @@ import tensorflow as tf
 import tensorflow.contrib.layers as layers
 
 import multi_gpu
+from utils import Data
 
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
@@ -71,14 +72,6 @@ def value_head(input, is_training):
     h = layers.fully_connected(h, 1, activation_fn=tf.nn.tanh, weights_regularizer=layers.l2_regularizer(1e-4))
     return h
 
-
-class Data(object):
-    def __init__(self):
-        self.boards = []
-        self.probs = []
-        self.winner = 0
-
-
 class ResNet(object):
     def __init__(self, board_size, action_num, history_length=1, residual_block_num=10, black_checkpoint_path=None,
                  white_checkpoint_path=None):
@@ -238,7 +231,6 @@ class ResNet(object):
             os.mkdir(self.save_path + 'black')
             os.mkdir(self.save_path + 'white')
 
-        new_file_list = []
         all_file_list = []
         training_data = {'states': [], 'probs': [], 'winner': []}
 
@@ -257,6 +249,7 @@ class ResNet(object):
                     self.training_data['probs'].append(probs)
                     self.training_data['winner'].append(winner)
                     self.training_data['length'].append(states.shape[0])
+                del new_file_list[:]
                 new_file_list = list(set(os.listdir(data_path)).difference(all_file_list))
 
             if len(self.training_data['states']) != self.window_length:
@@ -300,7 +293,8 @@ class ResNet(object):
                     self.white_saver.save(self.sess, self.save_path + 'white/' + ckpt_file)
 
                 for key in training_data.keys():
-                    training_data[key] = []
+                    del training_data[key][:]
+                    #training_data[key] = []
                 iters += 1
 
     def _file_to_training_data(self, file_name):

AlphaGo/play.py

@@ -5,6 +5,7 @@ import time
 import os
 from game import Game
 from engine import GTPEngine
+from utils import Data
 import utils
 from time import gmtime, strftime
 
@@ -15,15 +16,6 @@ if python_version < (3, 0):
 else:
     import _pickle as cPickle
 
-class Data(object):
-    def __init__(self):
-        self.boards = []
-        self.probs = []
-        self.winner = 0
-
-    def reset(self):
-        self.__init__()
-
 
 if __name__ == '__main__':
     """

AlphaGo/utils.py

@@ -5,6 +5,15 @@
 # $Author: renyong15 © <mails.tsinghua.edu.cn>
 #
 
+class Data(object):
+    def __init__(self):
+        self.boards = []
+        self.probs = []
+        self.winner = 0
+
+    def reset(self):
+        self.__init__()
+
 WHITE = -1
 EMPTY = 0
 BLACK = +1