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