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diff --git a/precompute_feature_maps.py b/precompute_feature_maps.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).

+from options.train_options import TrainOptions

+from data.data_loader import CreateDataLoader

+from models.models import create_model

+import numpy as np

+import os, time

+import util.util as util

+from torch.autograd import Variable

+import torch.nn as nn

+

+opt = TrainOptions().parse()

+opt.nThreads = 1

+opt.batchSize = 1 

+opt.serial_batches = True 

+opt.no_flip = True

+opt.instance_feat = True

+

+name = 'features'

+save_path = os.path.join(opt.checkpoints_dir, opt.name)

+

+############ Initialize #########

+data_loader = CreateDataLoader(opt)

+dataset = data_loader.load_data()

+dataset_size = len(data_loader)

+model = create_model(opt)

+util.mkdirs(os.path.join(opt.dataroot, opt.phase + '_feat'))

+

+######## Save precomputed feature maps for 1024p training #######

+for i, data in enumerate(dataset):

+	print('%d / %d images' % (i+1, dataset_size)) 

+	feat_map = model.module.netE.forward(Variable(data['image'].cuda(), volatile=True), data['inst'].cuda())

+	feat_map = nn.Upsample(scale_factor=2, mode='nearest')(feat_map)

+	image_numpy = util.tensor2im(feat_map.data[0])

+	save_path = data['path'][0].replace('/train_label/', '/train_feat/')

+	util.save_image(image_numpy, save_path)
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