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)
