def predict_frnn(image):
cfg.TEST.HAS_RPN = True # Use RPN for proposals
# load network
global saver, net
if saver is None or net is None:
saver, net = prepare_model.load_model(sess, args.demo_net, tf_model,
len(CLASSES))
return predict_proc(sess, net, image)
def main():
cfg.TEST.HAS_RPN = True # Use RPN for proposals
tf_model = prepare_model.get_tf_model(args.model_dir, args.model_data)
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
saver, net = prepare_model.load_model(sess, args.demo_net, tf_model,
len(CLASSES))
predict_test(sess, net, args)
# def predict_frnn(image):
# cfg.TEST.HAS_RPN = True # Use RPN for proposals
#
# tf_model = prepare_model.get_tf_model(args.model_dir, args.model_data)
# # set config
# tfconfig = tf.ConfigProto(allow_soft_placement=True)
# tfconfig.gpu_options.allow_growth = True
#
# # init session
# sess = tf.Session(config=tfconfig)
# # load network
# saver, net = prepare_model.load_model(sess, args.demo_net, tf_model, len(CLASSES))
#
# return predict_proc(sess, net, image)
#
# def main():
# cfg.TEST.HAS_RPN = True # Use RPN for proposals
#
# tf_model = prepare_model.get_tf_model(args.model_dir, args.model_data)
# # set config
# tfconfig = tf.ConfigProto(allow_soft_placement=True)
# tfconfig.gpu_options.allow_growth = True
#
# # init session
# sess = tf.Session(config=tfconfig)
# # load network
# saver, net = prepare_model.load_model(sess, args.demo_net, tf_model, len(CLASSES))
#
# predict_test(sess, net, args)
#
# if __name__ == '__main__':
# main()
def main():
cfg.TEST.HAS_RPN = True # Use RPN for proposals
tf_model = prepare_model.get_tf_model(args.model_dir, args.model_data)
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
saver, net = prepare_model.load_model(sess, args.demo_net, tf_model,
len(CLASSES))
predict_test(sess, net, args)
def get_model(batch_model, iter, demo_net):
cfg.TEST.HAS_RPN = True # Use RPN for proposals
model_dir = batch_model
model_data = "{}_faster_rcnn_iter_{}.ckpt".format(demo_net, iter)
tf_model = prepare_model.get_tf_model(model_dir, model_data)
print(tf_model)
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess_test = tf.Session(config=tfconfig)
# load network
CLASSES = pascal_voc.read_classes(
osp.join(cfg.ROOT_DIR, 'experiments', 'classes_cfgs',
'com_classes_169.txt'))
print(len(CLASSES))
saver, net_test = prepare_model.load_model(sess_test, demo_net, tf_model,
len(CLASSES))
return sess_test, net_test, model_dir, model_data, CLASSES
# classes_path = os.path.join(cfg.ROOT_DIR,'experiments', 'classes_cfgs',"{}".format("com_classes_169.txt"))
CLASSES = pascal_voc.read_classes(classes_path)
print(classes_path)
print(CLASSES)
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
tf_model = prepare_model.get_tf_model(args.model_dir, args.model_data)
print(tf_model)
# init session
sess = tf.Session(config=tfconfig)
saver, net = prepare_model.load_model(sess, args.demo_net, tf_model,
len(CLASSES))
def predict_frnn(image):
cfg.TEST.HAS_RPN = True # Use RPN for proposals
# load network
global saver, net
if saver is None or net is None:
saver, net = prepare_model.load_model(sess, args.demo_net, tf_model,
len(CLASSES))
return predict_proc(sess, net, image)
def main():