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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 time
+from collections import OrderedDict
+from options.train_options import TrainOptions
+from data.data_loader import CreateDataLoader
+from models.models import create_model
+import util.util as util
+from util.visualizer import Visualizer
+import os
+import numpy as np
+import torch
+from torch.autograd import Variable
+
+opt = TrainOptions().parse()
+iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
+if opt.continue_train:
+    try:
+        start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
+    except:
+        start_epoch, epoch_iter = 1, 0
+    print('Resuming from epoch %d at iteration %d' % (start_epoch, epoch_iter))        
+else:    
+    start_epoch, epoch_iter = 1, 0
+
+if opt.debug:
+    opt.display_freq = 1
+    opt.print_freq = 1
+    opt.niter = 1
+    opt.niter_decay = 0
+    opt.max_dataset_size = 10
+
+data_loader = CreateDataLoader(opt)
+dataset = data_loader.load_data()
+dataset_size = len(data_loader)
+print('#training images = %d' % dataset_size)
+
+model = create_model(opt)
+visualizer = Visualizer(opt)
+
+total_steps = (start_epoch-1) * dataset_size + epoch_iter    
+for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1):
+    epoch_start_time = time.time()
+    if epoch != start_epoch:
+        epoch_iter = epoch_iter % dataset_size
+    for i, data in enumerate(dataset, start=epoch_iter):
+        iter_start_time = time.time()
+        total_steps += opt.batchSize
+        epoch_iter += opt.batchSize
+
+        # whether to collect output images
+        save_fake = total_steps % opt.display_freq == 0
+
+        ############## Forward Pass ######################
+        losses, generated = model(Variable(data['label']), Variable(data['inst']), 
+            Variable(data['image']), Variable(data['feat']), infer=save_fake)
+
+        # sum per device losses
+        losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ]
+        loss_dict = dict(zip(model.module.loss_names, losses))
+
+        # calculate final loss scalar
+        loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5
+        loss_G = loss_dict['G_GAN'] + loss_dict['G_GAN_Feat'] + loss_dict['G_VGG']
+
+        ############### Backward Pass ####################
+        # update generator weights
+        model.module.optimizer_G.zero_grad()
+        loss_G.backward()
+        model.module.optimizer_G.step()
+
+        # update discriminator weights
+        model.module.optimizer_D.zero_grad()
+        loss_D.backward()
+        model.module.optimizer_D.step()
+
+        #call(["nvidia-smi", "--format=csv", "--query-gpu=memory.used,memory.free"]) 
+
+        ############## Display results and errors ##########
+        ### print out errors
+        if total_steps % opt.print_freq == 0:
+            errors = {k: v.data[0] if not isinstance(v, (int,long,float)) else v for k, v in loss_dict.items()}
+            t = (time.time() - iter_start_time) / opt.batchSize
+            visualizer.print_current_errors(epoch, epoch_iter, errors, t)
+            visualizer.plot_current_errors(errors, total_steps)
+
+        ### display output images
+        if save_fake:
+            visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
+                                   ('synthesized_image', util.tensor2im(generated.data[0])),
+                                   ('real_image', util.tensor2im(data['image'][0]))])
+            visualizer.display_current_results(visuals, epoch, total_steps)
+
+        ### save latest model
+        if total_steps % opt.save_latest_freq == 0:
+            print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps))
+            model.module.save('latest')            
+            np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d')
+       
+    # end of epoch 
+	iter_end_time = time.time()
+    print('End of epoch %d / %d \t Time Taken: %d sec' %
+          (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
+
+    ### save model for this epoch
+    if epoch % opt.save_epoch_freq == 0:
+        print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))        
+        model.module.save('latest')
+        model.module.save(epoch)
+        np.savetxt(iter_path, (epoch+1, 0), delimiter=',', fmt='%d')
+
+    ### instead of only training the local enhancer, train the entire network after certain iterations
+    if (opt.niter_fix_global != 0) and (epoch == opt.niter_fix_global):
+        model.module.update_fixed_params()
+
+    ### linearly decay learning rate after certain iterations
+    if epoch > opt.niter:
+        model.module.update_learning_rate()