def __init__(self):
self.model = None
self.optimizer = "adam"
self.graph = tf.get_default_graph()
config = ConfigProto()
config.gpu_options.allow_growth = True
self.session = Session(config=config)
keras.backend.set_session(self.session)
def define_video_config():
"""
Определяются параметры использования видеокарты
"""
# Определяются параметры использования видеокарты
config_proto = ConfigProto()
# config_proto.gpu_options.per_process_gpu_memory_fraction = 0.8
config_proto.gpu_options.allow_growth = True
session = InteractiveSession(config=config_proto)
def prepare_environment():
np.random.seed(1)
random.seed(1)
from tensorflow import ConfigProto
from tensorflow import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
def __init__(self, model_dir, img_list):
GPUsetting = GPUOptions(per_process_gpu_memory_fraction = 1, allow_growth = True)
sess = Session(config = ConfigProto(gpu_options = GPUsetting))
self.model_dir = os.path.join(C.check_point_path, model_dir)
self.batch_size = 64
self.img_list = img_list
self.list_size = len(self.img_list)
self.model = load_model(self.model_dir, custom_objects = {'resize_and_normalize': resize_and_normalize})
def _dojob(ready, e, queue):
prctl.set_name('AI detector - do job')
global session1, session2, ip_model, mac_model
ip_graph = Graph()
config = ConfigProto()
config.gpu_options.allow_growth = True
with ip_graph.as_default():
session1 = Session(config=config)
with session1.as_default():
ip_model = K.models.load_model(
'gru_ip_4tuple.hdf5', custom_objects={'attention': attention})
ip_model._make_predict_function()
mac_graph = Graph()
with mac_graph.as_default():
session2 = Session(config=config)
with session2.as_default():
mac_model = K.models.load_model(
'gru_mac_4tuple.hdf5', custom_objects={'attention': attention})
mac_model._make_predict_function()
ready.set()
print 'set ready'
last = time.time()
global ignore_packet
while e.is_set() == False:
if queue.empty() == False:
obj = queue.get()
if (obj[0], obj[1]) in ignore_packet:
if obj[3] <= ignore_packet[(obj[0], obj[1])]:
continue
feature_extract((obj[2], obj[3]))
if time.time() - last >= polling_interval:
print queue.qsize()
global flow_statics, src_addr_list, memory_data
# calculate features in last 5 seconds
result = calculate_feature(flow_statics)
memory_data.pop(0)
memory_data.append(result)
t_run_exp = threading.Thread(target=_run_exp,
args=(
result,
src_addr_list,
memory_data,
))
t_run_exp.start()
t_run_exp.join()
flow_statics = {}
src_addr_list = {}
last = time.time()
K.backend.clear_session()
del ip_model
del mac_model
def config_session(self):
config = ConfigProto(
#device_count = {'CPU': 1},
inter_op_parallelism_threads=6,
intra_op_parallelism_threads=6,
allow_soft_placement=True,
)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.9
with tf.device('/job:localhost/replica:0/task:0/device:GPU:0'):
#graph = tf.get_default_graph()
sess = Session(config=config)
return sess
def load(self, checkpoint_path, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
with tf.variable_scope('model') as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(inputs, input_lengths)
self.wav_output = audio.inv_spectrogram_tensorflow(
self.model.linear_outputs[0])
print('Loading checkpoint: %s' % checkpoint_path)
config = ConfigProto()
config.gpu_options.allow_growth = True
self.session = tf.Session(config=config)
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def load_model(architecture_file, mtype='base'):
import models
from tensorflow import GPUOptions, ConfigProto, Session
checkdir = '/'.join(architecture_file.split('/')[:-1]) + '/'
print('\n' * 2, '-' * _repeat_, '\n:: Open Session\n', '-' * _repeat_,
'\n')
gpu_options = GPUOptions(per_process_gpu_memory_fraction=0.5)
config = ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
sess = Session(config=config)
print('\n', '-' * _repeat_)
model = models.__dict__[mtype].__MODEL__()
pkg = {'model': model, 'architecture': architecture_file, 'dir': checkdir}
models.base.__MODEL__.load_architecture(pkg)
model.set_session(sess)
model.build(training=False)
model.load(pkg)
return model