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
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
