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diff --git a/recursive.py b/recursive.py
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+### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
+### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+import os
+from collections import OrderedDict
+from options.test_options import TestOptions
+from data.data_loader import CreateDataLoader
+from models.models import create_model
+import util.util as util
+from util.visualizer import Visualizer
+from util import html
+import torch
+from run_engine import run_trt_engine, run_onnx
+
+opt = TestOptions().parse(save=False)
+opt.nThreads = 1   # test code only supports nThreads = 1
+opt.batchSize = 1  # test code only supports batchSize = 1
+opt.serial_batches = True  # no shuffle
+opt.no_flip = True  # no flip
+
+data_loader = CreateDataLoader(opt)
+dataset = data_loader.load_data()
+visualizer = Visualizer(opt)
+# create website
+web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
+webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
+
+for i, data in enumerate(dataset):
+    if i >= opt.how_many:
+        break
+    if opt.data_type == 16:
+        data['label'] = data['label'].half()
+        data['inst']  = data['inst'].half()
+    elif opt.data_type == 8:
+        data['label'] = data['label'].uint8()
+        data['inst']  = data['inst'].uint8()
+    minibatch = 1 
+    generated = model.inference(data['label'], data['inst'])
+        
+    visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
+                           ('synthesized_image', util.tensor2im(generated.data[0]))])
+    img_path = data['path']
+    print('process image... %s' % img_path)
+    visualizer.save_images(webpage, visuals, img_path)
+
+webpage.save()