def run_MCTS(args, start_idx=0, iteration=0):
net_to_play="%s_iter%d.pth.tar" % (args.neural_net_name, iteration)
net = ConnectNet()
cuda = torch.cuda.is_available()
if cuda:
net.cuda()
if args.MCTS_num_processes > 1:
logger.info("Preparing model for multi-process MCTS...")
mp.set_start_method("spawn",force=True)
net.share_memory()
net.eval()
current_net_filename = os.path.join("./model_data/",\
net_to_play)
if os.path.isfile(current_net_filename):
checkpoint = torch.load(current_net_filename)
net.load_state_dict(checkpoint['state_dict'])
logger.info("Loaded %s model." % current_net_filename)
else:
torch.save({'state_dict': net.state_dict()}, os.path.join("./model_data/",\
net_to_play))
logger.info("Initialized model.")
processes = []
if args.MCTS_num_processes > mp.cpu_count():
num_processes = mp.cpu_count()
logger.info("Required number of processes exceed number of CPUs! Setting MCTS_num_processes to %d" % num_processes)
else:
num_processes = args.MCTS_num_processes
logger.info("Spawning %d processes..." % num_processes)
with torch.no_grad():
for i in range(num_processes):
p = mp.Process(target=MCTS_self_play, args=(net, args.num_games_per_MCTS_process,
start_idx, i, args, iteration))
p.start()
processes.append(p)
for p in processes:
p.join()
logger.info("Finished multi-process MCTS!")
elif args.MCTS_num_processes == 1:
logger.info("Preparing model for MCTS...")
net.eval()
current_net_filename = os.path.join("./model_data/",\
net_to_play)
if os.path.isfile(current_net_filename):
checkpoint = torch.load(current_net_filename)
net.load_state_dict(checkpoint['state_dict'])
logger.info("Loaded %s model." % current_net_filename)
else:
torch.save({'state_dict': net.state_dict()}, os.path.join("./model_data/",\
net_to_play))
logger.info("Initialized model.")
with torch.no_grad():
MCTS_self_play(net, args.num_games_per_MCTS_process, start_idx, 0, args, iteration)
logger.info("Finished MCTS!")
def evaluate_nets(args, iteration_1, iteration_2):
logger.info("Loading nets...")
current_net = "%s_iter%d.pth.tar" % (args.neural_net_name, iteration_2)
best_net = "%s_iter%d.pth.tar" % (args.neural_net_name, iteration_1)
current_net_filename = os.path.join("./model_data/",\
current_net)
best_net_filename = os.path.join("./model_data/",\
best_net)
logger.info("Current net: %s" % current_net)
logger.info("Previous (Best) net: %s" % best_net)
current_cnet = ConnectNet()
best_cnet = ConnectNet()
cuda = torch.cuda.is_available()
if cuda:
current_cnet.cuda()
best_cnet.cuda()
if not os.path.isdir("./evaluator_data/"):
os.mkdir("evaluator_data")
if args.MCTS_num_processes > 1:
mp.set_start_method("spawn", force=True)
current_cnet.share_memory()
best_cnet.share_memory()
current_cnet.eval()
best_cnet.eval()
checkpoint = torch.load(current_net_filename)
current_cnet.load_state_dict(checkpoint['state_dict'])
checkpoint = torch.load(best_net_filename)
best_cnet.load_state_dict(checkpoint['state_dict'])
processes = []
if args.MCTS_num_processes > mp.cpu_count():
num_processes = mp.cpu_count()
logger.info(
"Required number of processes exceed number of CPUs! Setting MCTS_num_processes to %d"
% num_processes)
else:
num_processes = args.MCTS_num_processes
logger.info("Spawning %d processes..." % num_processes)
with torch.no_grad():
for i in range(num_processes):
p = mp.Process(target=fork_process,
args=(arena(current_cnet, best_cnet),
args.num_evaluator_games, i))
p.start()
processes.append(p)
for p in processes:
p.join()
wins_ratio = 0.0
for i in range(num_processes):
stats = load_pickle("wins_cpu_%i" % (i))
wins_ratio += stats['best_win_ratio']
wins_ratio = wins_ratio / num_processes
if wins_ratio >= 0.55:
return iteration_2
else:
return iteration_1
elif args.MCTS_num_processes == 1:
current_cnet.eval()
best_cnet.eval()
checkpoint = torch.load(current_net_filename)
current_cnet.load_state_dict(checkpoint['state_dict'])
checkpoint = torch.load(best_net_filename)
best_cnet.load_state_dict(checkpoint['state_dict'])
arena1 = arena(current_cnet=current_cnet, best_cnet=best_cnet)
arena1.evaluate(num_games=args.num_evaluator_games, cpu=0)
stats = load_pickle("wins_cpu_%i" % (0))
if stats.best_win_ratio >= 0.55:
return iteration_2
else:
return iteration_1
best_net = "current_net_trained1_iter2.pth.tar"
current_net_filename = os.path.join("./model_data/",\
current_net)
best_net_filename = os.path.join("./model_data/",\
best_net)
current_cnet = ConnectNet()
best_cnet = ConnectNet()
cuda = torch.cuda.is_available()
if cuda:
current_cnet.cuda()
best_cnet.cuda()
if multiprocessing == 1:
mp.set_start_method("spawn", force=True)
current_cnet.share_memory()
best_cnet.share_memory()
current_cnet.eval()
best_cnet.eval()
checkpoint = torch.load(current_net_filename)
current_cnet.load_state_dict(checkpoint['state_dict'])
checkpoint = torch.load(best_net_filename)
best_cnet.load_state_dict(checkpoint['state_dict'])
processes = []
for i in range(6):
p = mp.Process(target=fork_process,
args=(arena(current_cnet, best_cnet), 10, i))
p.start()
processes.append(p)
save_as_pickle(
"dataset_cpu%i_%i_%s" %
(cpu, idxx, datetime.datetime.today().strftime("%Y-%m-%d")),
dataset_p)
if __name__ == "__main__":
multiprocessing = 1
if multiprocessing == 1:
net_to_play = "c4_current_net_trained2_iter5.pth.tar"
mp.set_start_method("spawn", force=True)
net = ConnectNet()
cuda = torch.cuda.is_available()
if cuda:
net.cuda()
net.share_memory()
net.eval()
print("hi")
# torch.save({'state_dict': net.state_dict()}, os.path.join("./model_data/",\
# "c4_current_net.pth.tar"))
current_net_filename = os.path.join("./model_data/",\
net_to_play)
checkpoint = torch.load(current_net_filename)
net.load_state_dict(checkpoint['state_dict'])
processes = []
for i in range(6):
p = mp.Process(target=MCTS_self_play, args=(net, 25, i))
p.start()
processes.append(p)
for p in processes: