def test_new_tree_called_once_self_play(self):
from engine import Tree
fn = Tree.new_tree
self.count = 0
def monkey_patch_new_tree(*args, **kwargs):
self.count += 1
return fn(*args, **kwargs)
Tree.new_tree = monkey_patch_new_tree
model = DummyModel()
mcts_simulations = 32 # We want some mcts exploration
play_game(model, model, mcts_simulations, conf['STOP_EXPLORATION'], self_play=True, num_moves=10)
self.assertEqual(self.count, 1) # Only one tree was created
def test_new_tree_called_twice_evaluation(self):
from engine import Tree
fn = Tree.new_tree
self.count = 0
def monkey_patch_new_tree(*args, **kwargs):
self.count += 1
return fn(*args, **kwargs)
Tree.new_tree = monkey_patch_new_tree
model = DummyModel()
mcts_simulations = 160 # We want some mcts exploration so both tree overlap
play_game(model, model, mcts_simulations, stop_exploration=0, self_play=False, num_moves=100)
# This works because we deactivate exploration and dirichlet noise in order to have
# deterministic play
self.assertEqual(self.count, 2) # Only 2 trees were created
def evaluate(best_model, tested_model):
total = 0
wins = 0
desc = "Evaluation %s vs %s" % (tested_model.name, best_model.name)
tq = tqdm(range(EVALUATE_N_GAMES), desc=desc)
for game in tq:
start = datetime.datetime.now()
if random() < .5:
model1, model2 = best_model, tested_model
else:
model2, model1 = best_model, tested_model
game_data = play_game(model1, model2, MCTS_SIMULATIONS, stop_exploration=0)
stop = datetime.datetime.now()
winner_model = game_data['winner_model']
if winner_model == tested_model.name:
wins += 1
total += 1
moves = len(game_data['moves'])
new_desc = desc + " (winrate:%s%% %.2fs/move)" % (int(wins/total*100), (stop - start).seconds / moves)
tq.set_description(new_desc)
save_game_data(best_model.name, game_data)
if wins/total > EVALUATE_MARGIN:
print("We found a new best model : %s!" % tested_model.name)
elect_model_as_best_model(tested_model)
return True
return False
def test_play(self):
model = DummyModel()
mcts_simulations = 8 # mcts batch size is 8 and we need at least one batch
game_data = play_game(model,
model,
mcts_simulations,
conf['STOP_EXPLORATION'],
self_play=True,
num_moves=2)
winner = game_data['winner']
test_board1, player = game_init()
board = game_data['moves'][0]['board']
self.assertTrue(np.array_equal(board,
test_board1)) # First board is empty
self.assertEqual(winner,
0) # White should win with 5.5 komi after 2 moves
for move, move_data in enumerate(
game_data['moves'][::2]): # Black player lost
value_target = 1 if winner == move_data['player'] else -1
self.assertEqual(move_data['player'], 1)
self.assertEqual(value_target, -1)
for move, move_data in enumerate(
game_data['moves'][1::2]): # White player won
value_target = 1 if winner == move_data['player'] else -1
self.assertEqual(move_data['player'], 0)
self.assertEqual(value_target, 1)
def test_save_sgf(self):
model = DummyModel()
mcts_simulations = 8 # mcts batch size is 8 and we need at least one batch
game_data = play_game(model, model, mcts_simulations, conf['STOP_EXPLORATION'], self_play=True, num_moves=100)
os.makedirs("games/test_model")
save_game_sgf("test_model", 1, game_data)
os.remove("games/test_model/game_001.sgf")
os.removedirs("games/test_model")
def evaluate(best_model, tested_model):
total = 0
wins = 0
desc = "Evaluation %s vs %s" % (tested_model.name, best_model.name)
tq = tqdm(range(EVALUATE_N_GAMES), desc=desc)
for i in tq:
_, _, winner_model = play_game(best_model,
tested_model,
MCTS_SIMULATIONS,
stop_exploration=0)
if winner_model == tested_model:
wins += 1
total += 1
new_desc = desc + " (winrate:%s%%)" % int(wins / total * 100)
tq.set_description(new_desc)
if wins / total > EVALUATE_MARGIN:
print("We found a new best model : %s!" % tested_model.name)
elect_model_as_best_model(tested_model)
return True
return False
def run(self):
# set environment
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(self._gpuid)
logger.info('cuda_visible_device %s',
os.environ["CUDA_VISIBLE_DEVICES"])
if conf['THREAD_SIMULATION']:
init_simulation_workers()
# load models
latest_model, best_model = self.load_model()
while True:
if latest_model.name != best_model.name:
total = 0
wins = 0
desc = "Evaluation %s vs %s" % (latest_model.name,
best_model.name)
tq = tqdm(range(EVALUATE_N_GAMES), desc=desc)
for game in tq:
if self._one_game_only >= 0 and tq != game:
continue
directory = os.path.join(EVAL_DIR, latest_model.name,
"game_%03d" % game)
if os.path.isdir(directory):
continue
try:
os.makedirs(directory)
except Exception:
continue
start = datetime.datetime.now()
game_data = play_game(best_model,
latest_model,
MCTS_SIMULATIONS,
stop_exploration=0)
stop = datetime.datetime.now()
# Some statistics
winner_model = game_data['winner_model']
if winner_model == latest_model.name:
wins += 1
total += 1
moves = len(game_data['moves'])
new_desc = desc + " (winrate:%s%% %.2fs/move)" % (int(
wins / total * 100), (stop - start).seconds / moves)
tq.set_description(new_desc)
# save_game_data(best_model.name, game, game_data)
self.save_eval_game(latest_model.name, game, winner_model)
if self._one_game_only >= 0:
break
else:
logger.info("No new trained model")
if self._forever:
logger.info("Sleep for %s seconds", conf['SLEEP_SECONDS'])
time.sleep(conf['SLEEP_SECONDS'])
if not self._forever:
break
latest_model, best_model = self.load_model()
destroy_simulation_workers()
if len(sys.argv) < 3:
print(
'Missing parameters. 3 parameters required to start game play: num_of_game, model1_name, model2_name'
)
else:
model1_win = 0
model2_win = 0
num_games = sys.argv[1]
model1_name = sys.argv[2]
model2_name = sys.argv[3]
print("Loading models...")
model1 = load_model(os.path.join('..', conf['MODEL_DIR'], model1_name),
custom_objects={'loss': loss})
print("Loaded model", model1_name)
model2 = load_model(os.path.join('..', conf['MODEL_DIR'], model2_name),
custom_objects={'loss': loss})
print("Loaded model", model2_name)
desc = "Duel %s vs %s" % (model1.name, model2.name)
tq = tqdm(range(int(num_games)), desc=desc)
for game in tq:
boards_and_policies, winner, winner_model = play_game(
model1, model2, MCTS_SIMULATIONS, stop_exploration=0)
if winner_model == model1:
model1_win += 1
else:
model2_win += 1
new_desc = desc + " (%s %d - %d %s)" % (model1_name, model1_win,
model2_win, model2_name)
tq.set_description(new_desc)
