def test_voc(): # load net num_classes = len(CUSTOM_CLASSES) + 1 # +1 background net = build_ssd('test', 300, num_classes) # initialize SSD net.load_state_dict(torch.load(args.trained_model)) net.eval() print('Finished loading model!') # load data testset = customDetection(args.voc_root, [('shenhe', 'test')], None, customAnnotationTransform()) if args.cuda: net = net.cuda() cudnn.benchmark = True # evaluation test_net(args.save_folder, net, args.cuda, testset, BaseTransform(net.size, (104, 117, 123)), thresh=args.visual_threshold)
def evaluate_detections(box_list, output_dir, dataset): write_voc_results_file(box_list, dataset) do_python_eval(output_dir) if __name__ == '__main__': # load net num_classes = len(labelmap) + 1 # +1 for background net = build_ssd('test', 300, num_classes) # initialize SSD net.load_state_dict(torch.load(args.trained_model)) net.eval() print('Finished loading model!') # load data dataset = customDetection(args.custom_root, [('shenhe', set_type)], BaseTransform(300, dataset_mean), customAnnotationTransform()) if args.cuda: net = net.cuda() cudnn.benchmark = True # evaluation test_net(args.save_folder, net, args.cuda, dataset, BaseTransform(net.size, dataset_mean), args.top_k, 300, thresh=args.confidence_threshold)