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diff --git a/train.py b/train.py
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+++ b/train.py
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+import time
+from options.train_options import TrainOptions
+opt = TrainOptions().parse()  # set CUDA_VISIBLE_DEVICES before import torch
+
+from data.data_loader import CreateDataLoader
+from models.models import create_model
+from util.visualizer import Visualizer
+
+data_loader = CreateDataLoader(opt)
+dataset = data_loader.load_data()
+num_train = len(data_loader)
+print('#training images = %d' % num_train)
+
+model = create_model(opt)
+visualizer = Visualizer(opt)
+
+total_steps = 0
+
+for epoch in range(1, opt.niter + opt.niter_decay + 1):
+    epoch_start_time = time.time()
+    for i, data in enumerate(dataset):
+        iter_start_time = time.time()
+        total_steps += opt.batchSize
+        epoch_iter = total_steps % num_train
+        model.set_input(data)
+        model.optimize_parameters()
+
+        if total_steps % opt.display_freq == 0:
+            visualizer.display_current_results(model.get_current_visuals(), epoch)
+
+        if total_steps % opt.print_freq == 0:
+            errors = model.get_current_errors()
+            visualizer.print_current_errors(epoch, epoch_iter, errors, iter_start_time)
+            if opt.display_id > 0:
+                visualizer.plot_current_errors(epoch, epoch_iter, opt, errors)
+
+        if total_steps % opt.save_latest_freq == 0:
+            print('saving the latest model (epoch %d, total_steps %d)' %
+                  (epoch, total_steps))
+            model.save('latest')
+
+    if epoch % opt.save_epoch_freq == 0:
+        print('saving the model at the end of epoch %d, iters %d' %
+              (epoch, total_steps))
+        model.save('latest')
+        model.save(epoch)
+
+    print('End of epoch %d / %d \t Time Taken: %d sec' %
+          (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
+
+    if epoch > opt.niter:
+        model.update_learning_rate()