def main(args=None):
# parse arguments
args = parse_args()
args = load_setting_cfg(args)
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
set_gpu()
# make save path if it doesn't exist
if args.save_path is not None and not os.path.exists(args.save_path):
os.makedirs(args.save_path)
# create the generator
generator = create_generator(args)
# load the model
print('Loading model, this may take a second...')
model = create_models(num_classes=generator.num_classes())
# print(predict_model.summary())
# model.load_weights(filepath=args.weight_path, by_name=True)
if os.path.exists(args.weight_path):
model.load_weights(filepath=args.weight_path)
print(args.weight_path)
# predict_model.load_weights(filepath=args.weight_path, by_name=True)
print('loading weight : {} , this may take a second...'.format(
args.weight_path))
# start evaluation
print('start evaluate')
average_precisions = evaluate(generator,
model,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold,
max_detections=args.max_detections,
save_path=args.save_path)
# print evaluation
for label, average_precision in average_precisions.items():
print(generator.label_to_name(label),
'{:.4f}'.format(average_precision))
print('mAP: {:.4f}'.format(
sum(average_precisions.values()) / len(average_precisions)))
def check_args(parsed_args):
# TODO check the args
# reload parse arguments
args = load_setting_cfg(parsed_args)
return args