with tf.Session(graph=imported_graph) as sess:
with tf.device("/gpu:0"):
softmax_tensor = sess.graph.get_tensor_by_name("final_result:0")
predictions = sess.run(softmax_tensor, {"DecodeJpeg:0": image_array})
predictions = np.squeeze(predictions)
answer = {}
for node_id in range(len(predictions)):
answer[self.labels[node_id]] = predictions[node_id]
After Change
return graph_def
def detect(self, images):
np_images = [self._pil_to_np(image) for image in images]
pool = ThreadPool()
with tf.Graph().as_default() as imported_graph:
tf.import_graph_def(self.graph_def, name="")
with tf.Session(graph=imported_graph) as sess:
with tf.device("/gpu:0"):
softmax_tensor = sess.graph.get_tensor_by_name("final_result:0")
threads = [pool.apply_async(self.operation,
args=(sess, softmax_tensor, np_images[image_number], image_number,)) for
image_number in range(len(np_images))]
answers = []
for thread in threads:
prediction, image_number = thread.get()
