def __init__(self, model_url=USE_URL): if "TFHUB_CACHE_DIR" in os.environ: tfdata = os.environ['TFHUB_CACHE_DIR'] model_url= tfdata + "/" + cachedir # model_url = tfdata print(tfdata) else: print("TFHUB_CACHE_DIR=None") graph = Graph() with graph.as_default(): embed = hub.Module(model_url) self.sentences = tf.placeholder(dtype=tf.string, shape=[None]) self.encoded_text = tf.cast(embed(self.sentences), tf.float32) init_op = tf.group([tf.global_variables_initializer(), tf.tables_initializer()]) graph.finalize() self.session = tf.Session(graph=graph) self.session.run(init_op)
def create_detector(self, verbose, mtcnn_kwargs): """ Create the mtcnn detector """ self.verbose = verbose if self.verbose: print("Adding MTCNN detector") self.kwargs = mtcnn_kwargs mtcnn_graph = Graph() with mtcnn_graph.as_default(): mtcnn_session = Session() with mtcnn_session.as_default(): pnet, rnet, onet = create_mtcnn(mtcnn_session, self.data_path) mtcnn_graph.finalize() self.kwargs["pnet"] = pnet self.kwargs["rnet"] = rnet self.kwargs["onet"] = onet self.initialized = True
def load_model(self, verbose, dummy, ratio): """ Load the Keras Model """ self.verbose = verbose if self.verbose: print("Initializing keras model...") keras_graph = Graph() with keras_graph.as_default(): config = ConfigProto() if ratio: config.gpu_options.per_process_gpu_memory_fraction = ratio self.session = Session(config=config) with self.session.as_default(): self.model = keras.models.load_model( self.model_path, custom_objects={'TorchBatchNorm2D': TorchBatchNorm2D}) self.model.predict(dummy) keras_graph.finalize() self.initialized = True