def _main_(args): config_path = args.conf weights_path = args.weights image_path = args.input keras.backend.tensorflow_backend.set_session(get_session()) with open(config_path) as config_buffer: config = json.load(config_buffer) if weights_path == '': weights_path = config['train']['saved_weights_name'] ############################### # Make the model ############################### yolo = YOLO(backend=config['model']['backend'], input_size=(config['model']['input_size_h'], config['model']['input_size_w']), labels=config['model']['labels'], max_box_per_image=config['model']['max_box_per_image'], anchors=config['model']['anchors'], gray_mode=config['model']['gray_mode']) ############################### # Load trained weights ############################### yolo.load_weights(weights_path) inference_model = yolo.get_inference_model() inference_model.save("inference.h5")
def _main_(args): config_path = args.conf weights_path = args.weights keras.backend.tensorflow_backend.set_session(get_session()) with open(config_path) as config_buffer: config = json.load(config_buffer) if weights_path == '': weights_path = config['train']['saved_weights_name'] yolo = YOLO(backend=config['model']['backend'], input_size=config['model']['input_size'], labels=config['model']['labels'], max_box_per_image=config['model']['max_box_per_image'], anchors=config['model']['anchors']) yolo.load_weights(weights_path) inference_model = yolo.get_inference_model() inference_model.save("{}_inference.h5".format( os.path.split(weights_path)[0])) print("done")
def _main_(args): config_path = args.conf weights_path = args.weights output_path = args.output with open(config_path) as config_buffer: config = json.load(config_buffer) ############################### # load the model ############################### # keras.backend.set_session(K.tf.Session(config=K.tf.ConfigProto(intra_op_parallelism_threads=8, inter_op_parallelism_threads=8))) yolo = YOLO(backend=config['model']['backend'], input_size=config['model']['input_size'], labels=config['model']['labels'], max_box_per_image=config['model']['max_box_per_image'], anchors=config['model']['anchors']) yolo.load_weights(weights_path) yolo_inf = yolo.get_inference_model() yolo_inf.load_weights(weights_path) frozen_graph = freeze_session( K.get_session(), output_names=[out.op.name for out in yolo_inf.outputs]) tf.train.write_graph(frozen_graph, output_path, "convertedModel.pb", as_text=False)