def __init__(self, **kwargs):
if os.path.exists(CNN_FEATURE_EXTRACTOR):
app_logger.info("loading... {}".format(CNN_FEATURE_EXTRACTOR))
self.feature_extractor = pickle.load(open(CNN_FEATURE_EXTRACTOR))
app_logger.info("done")
else:
self.feature_extractor = CnnFeatureExtractor(use_gpu, CAFFE_MODEL, MODEL_TYPE, image_feature_dim)
pickle.dump(self.feature_extractor, open(CNN_FEATURE_EXTRACTOR, 'w'))
app_logger.info("pickle.dump finished")
self.agent_service = AgentService(BRICA_CONFIG_FILE, self.feature_extractor)
self.result_logger = ResultLogger()
def __init__(self, sess, logdir, num_workers, visualize):
self.latest_stage = -1
self.sess = sess
with sess.as_default():
model = make_network()
dnds = []
for i in range(3):
dnds.append(DND())
global_agent = Agent(model, dnds, 3, name='global')
self.agents = []
self.popped_agents = {}
self.popped_locks = {}
# CREATE NEW AGENT(S)
for i in range(num_workers):
# CREATE PLOTTER PER AGENT
plotter = (AnimatedLineGraph(0, 0, max_val=50)
if visualize else None)
self.agents.append(
Agent(model,
dnds,
3,
name='worker{}'.format(i),
plotter=plotter))
summary_writer = tf.summary.FileWriter(logdir, sess.graph)
for agent in self.agents:
agent.set_summary_writer(summary_writer)
initialize()
# load feature extractor (alex net)
if os.path.exists(TF_CNN_FEATURE_EXTRACTOR):
config = tf.ConfigProto(gpu_options=tf.GPUOptions(
visible_device_list='0', allow_growth=True))
gpu_config = config # TODO: remove this
app_logger.info(
"loading... {}".format(TF_CNN_FEATURE_EXTRACTOR))
self.feature_extractor = FeatureExtractor(
sess_name='AlexNet', sess_config=gpu_config)
app_logger.info("done")
else:
raise Exception
self.agent_service = AgentService(BRICA_CONFIG_FILE,
self.feature_extractor, sess)
self.result_logger = ResultLogger()
class Root(object):
def __init__(self, **kwargs):
if os.path.exists(CNN_FEATURE_EXTRACTOR):
app_logger.info("loading... {}".format(CNN_FEATURE_EXTRACTOR))
self.feature_extractor = pickle.load(open(CNN_FEATURE_EXTRACTOR, 'rb'))
app_logger.info("done")
else:
self.feature_extractor = CnnFeatureExtractor(use_gpu, CAFFE_MODEL, MODEL_TYPE, image_feature_dim)
pickle.dump(self.feature_extractor, open(CNN_FEATURE_EXTRACTOR, 'wb'))
app_logger.info("pickle.dump finished")
self.agent_service = AgentService(BRICA_CONFIG_FILE, self.feature_extractor)
self.result_logger = ResultLogger()
@cherrypy.expose()
def flush(self, identifier):
self.agent_service.initialize(identifier)
@cherrypy.expose
def create(self, identifier):
body = cherrypy.request.body.read()
reward, observation, rotation, movement = unpack(body)
inbound_logger.info('reward: {}, depth: {}'.format(reward, observation['depth']))
feature = self.feature_extractor.feature(observation)
self.result_logger.initialize()
result = self.agent_service.create(reward, feature, identifier)
self.result_logger.add_agent(self.agent_service.agents[identifier])
outbound_logger.info('action: {}'.format(result))
return str(result)
@cherrypy.expose
def step(self, identifier):
body = cherrypy.request.body.read()
reward, observation, rotation, movement = unpack(body)
inbound_logger.info('reward: {}, depth: {}'.format(reward, observation['depth']))
result = self.agent_service.step(reward, observation, identifier)
self.result_logger.step()
outbound_logger.info('result: {}'.format(result))
return str(result)
@cherrypy.expose
def reset(self, identifier):
body = cherrypy.request.body.read()
reward, success, failure, elapsed, finished = unpack_reset(body)
inbound_logger.info('reward: {}, success: {}, failure: {}, elapsed: {}'.format(
reward, success, failure, elapsed))
result = self.agent_service.reset(reward, identifier)
self.result_logger.report(success, failure, finished)
outbound_logger.info('result: {}'.format(result))
return str(result)
class Root(object):
def __init__(self, sess, logdir, num_workers, visualize):
self.latest_stage = -1
self.sess = sess
with sess.as_default():
model = make_network()
dnds = []
for i in range(3):
dnds.append(DND())
global_agent = Agent(model, dnds, 3, name='global')
self.agents = []
self.popped_agents = {}
self.popped_locks = {}
# CREATE NEW AGENT(S)
for i in range(num_workers):
# CREATE PLOTTER PER AGENT
plotter = (AnimatedLineGraph(0, 0, max_val=50)
if visualize else None)
self.agents.append(
Agent(model,
dnds,
3,
name='worker{}'.format(i),
plotter=plotter))
summary_writer = tf.summary.FileWriter(logdir, sess.graph)
for agent in self.agents:
agent.set_summary_writer(summary_writer)
initialize()
# load feature extractor (alex net)
if os.path.exists(TF_CNN_FEATURE_EXTRACTOR):
config = tf.ConfigProto(gpu_options=tf.GPUOptions(
visible_device_list='0', allow_growth=True))
gpu_config = config # TODO: remove this
app_logger.info(
"loading... {}".format(TF_CNN_FEATURE_EXTRACTOR))
self.feature_extractor = FeatureExtractor(
sess_name='AlexNet', sess_config=gpu_config)
app_logger.info("done")
else:
raise Exception
self.agent_service = AgentService(BRICA_CONFIG_FILE,
self.feature_extractor, sess)
self.result_logger = ResultLogger()
@cherrypy.expose()
def flush(self, identifier):
if identifier not in self.popped_agents:
if len(self.agents) > 0:
self.popped_locks[identifier] = Lock()
agent = self.agents.pop(0)
self.popped_agents[identifier] = agent
self.agent_service.initialize(identifier, agent)
else:
return
else:
agent = self.popped_agents[identifier]
self.popped_locks[identifier].acquire()
with self.sess.as_default():
self.agent_service.initialize(identifier, agent)
self.popped_locks[identifier].release()
@cherrypy.expose
def create(self, identifier):
if identifier not in self.popped_agents:
if __debug__:
os.system('spd-say "Agent Created"')
if len(self.agents) > 0:
self.popped_locks[identifier] = Lock()
agent = self.agents.pop(0)
self.popped_agents[identifier] = agent
else:
return
else:
agent = self.popped_agents[identifier]
self.popped_locks[identifier].acquire()
with self.sess.as_default():
body = cherrypy.request.body.read()
reward, observation, rotation, movement, scene_num = unpack(body)
self.latest_stage = max(scene_num, self.latest_stage)
inbound_logger.info('id: {}, reward: {}, depth: {}'.format(
identifier, reward, observation['depth']))
feature = self.feature_extractor.feature(observation)
self.result_logger.initialize()
result = self.agent_service.create(reward, feature, identifier,
agent)
outbound_logger.info('id:{}, action: {}'.format(
identifier, result))
self.popped_locks[identifier].release()
return str(result)
@cherrypy.expose
def step(self, identifier):
if identifier in self.popped_locks:
self.popped_locks[identifier].acquire()
with self.sess.as_default():
body = cherrypy.request.body.read()
reward, observation, rotation, movement, scene_num = unpack(body)
self.latest_stage = max(scene_num, self.latest_stage)
inbound_logger.info('id: {}, reward: {}, depth: {}'.format(
identifier, reward, observation['depth']))
result = self.agent_service.step(reward, rotation, movement,
observation, identifier)
self.result_logger.step()
outbound_logger.info('id: {}, result: {}'.format(
identifier, result))
if identifier in self.popped_locks:
self.popped_locks[identifier].release()
return str(result) + "/" + str(self.latest_stage)
@cherrypy.expose
def reset(self, identifier):
if identifier in self.popped_locks:
self.popped_locks[identifier].acquire()
with self.sess.as_default():
body = cherrypy.request.body.read()
reward, success, failure, elapsed, finished = unpack_reset(body)
inbound_logger.info(
'reward: {}, success: {}, failure: {}, elapsed: {}'.format(
reward, success, failure, elapsed))
result = self.agent_service.reset(reward, identifier)
self.result_logger.report(success, failure, finished)
outbound_logger.info('result: {}'.format(result))
if identifier in self.popped_locks:
self.popped_locks[identifier].release()
return str(result)