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api.py
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api.py
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import numpy as np
from multiprocessing import Pipe, connection
from threading import Thread
from time import time
from logging import getLogger
from reversi_zero.agent.model import ReversiModel
from reversi_zero.config import Config
from reversi_zero.lib.model_helpler import reload_newest_next_generation_model_if_changed, load_best_model_weight, \
save_as_best_model, reload_best_model_weight_if_changed
import tensorflow as tf
logger = getLogger(__name__)
class ReversiModelAPI:
def __init__(self, config: Config, agent_model):
"""
:param config:
:param reversi_zero.agent.model.ReversiModel agent_model:
"""
self.config = config
self.agent_model = agent_model
def predict(self, x):
assert x.ndim in (3, 4)
assert x.shape == (2, 8, 8) or x.shape[1:] == (2, 8, 8)
orig_x = x
if x.ndim == 3:
x = x.reshape(1, 2, 8, 8)
policy, value = self._do_predict(x)
if orig_x.ndim == 3:
return policy[0], value[0]
else:
return policy, value
def _do_predict(self, x):
return self.agent_model.model.predict_on_batch(x)
class MultiProcessReversiModelAPIServer:
# https://github.com/Akababa/Chess-Zero/blob/nohistory/src/chess_zero/agent/api_chess.py
def __init__(self, config: Config):
"""
:param config:
"""
self.config = config
self.model = None # type: ReversiModel
self.connections = []
def get_api_client(self):
me, you = Pipe()
self.connections.append(me)
return MultiProcessReversiModelAPIClient(self.config, None, you)
def start_serve(self):
self.model = self.load_model()
# threading workaround: https://github.com/keras-team/keras/issues/5640
self.model.model._make_predict_function()
self.graph = tf.get_default_graph()
prediction_worker = Thread(target=self.prediction_worker, name="prediction_worker")
prediction_worker.daemon = True
prediction_worker.start()
def prediction_worker(self):
logger.debug("prediction_worker started")
average_prediction_size = []
last_model_check_time = time()
while True:
if last_model_check_time+60 < time():
self.try_reload_model()
last_model_check_time = time()
logger.debug(f"average_prediction_size={np.average(average_prediction_size)}")
average_prediction_size = []
ready_conns = connection.wait(self.connections, timeout=0.001) # type: list[Connection]
if not ready_conns:
continue
data = []
size_list = []
for conn in ready_conns:
x = conn.recv()
data.append(x) # shape: (k, 2, 8, 8)
size_list.append(x.shape[0]) # save k
average_prediction_size.append(np.sum(size_list))
array = np.concatenate(data, axis=0)
policy_ary, value_ary = self.model.model.predict_on_batch(array)
idx = 0
for conn, s in zip(ready_conns, size_list):
conn.send((policy_ary[idx:idx+s], value_ary[idx:idx+s]))
idx += s
def load_model(self):
from reversi_zero.agent.model import ReversiModel
model = ReversiModel(self.config)
loaded = False
if not self.config.opts.new:
if self.config.play.use_newest_next_generation_model:
loaded = reload_newest_next_generation_model_if_changed(model) or load_best_model_weight(model)
else:
loaded = load_best_model_weight(model) or reload_newest_next_generation_model_if_changed(model)
if not loaded:
model.build()
save_as_best_model(model)
return model
def try_reload_model(self):
try:
logger.debug("check model")
if self.config.play.use_newest_next_generation_model:
reload_newest_next_generation_model_if_changed(self.model, clear_session=True)
else:
reload_best_model_weight_if_changed(self.model, clear_session=True)
except Exception as e:
logger.error(e)
class MultiProcessReversiModelAPIClient(ReversiModelAPI):
def __init__(self, config: Config, agent_model, conn):
"""
:param config:
:param reversi_zero.agent.model.ReversiModel agent_model:
:param Connection conn:
"""
super().__init__(config, agent_model)
self.connection = conn
def _do_predict(self, x):
self.connection.send(x)
return self.connection.recv()