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diff --git a/Codes/models.py b/Codes/models.py
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+import tensorflow as tf
+
+import unet
+import pix2pix
+
+from flownet2.src.flowlib import flow_to_image
+from flownet2.src.flownet_sd.flownet_sd import FlowNetSD  # Ok
+from flownet2.src.training_schedules import LONG_SCHEDULE
+from flownet2.src.net import Mode
+
+
+slim = tf.contrib.slim
+
+
+def generator(inputs, layers, features_root=64, filter_size=3, pool_size=2, output_channel=3):
+    return unet.unet(inputs, layers, features_root, filter_size, pool_size, output_channel)
+
+
+def discriminator(inputs, num_filers=(128, 256, 512, 512)):
+    logits, end_points = pix2pix.pix2pix_discriminator(inputs, num_filers)
+    return logits, end_points['predictions']
+
+
+def flownet(input_a, input_b, height, width, reuse=None):
+    net = FlowNetSD(mode=Mode.TEST)
+    # train preds flow
+    input_a = (input_a + 1.0) / 2.0     # flownet receives image with color space in [0, 1]
+    input_b = (input_b + 1.0) / 2.0     # flownet receives image with color space in [0, 1]
+    # input size is 384 x 512
+    input_a = tf.image.resize_images(input_a, [height, width])
+    input_b = tf.image.resize_images(input_b, [height, width])
+    flows = net.model(
+        inputs={'input_a': input_a, 'input_b': input_b},
+        training_schedule=LONG_SCHEDULE,
+        trainable=False, reuse=reuse
+    )
+    return flows['flow']
+
+
+def initialize_flownet(sess, checkpoint):
+    flownet_vars = slim.get_variables_to_restore(include=['FlowNetSD'])
+    flownet_saver = tf.train.Saver(flownet_vars)
+    print('FlownetSD restore from {}!'.format(checkpoint))
+    flownet_saver.restore(sess, checkpoint)