Fix multiprocessing for Windows by using the __name__ == '__main__' i…

…diom

.gitignore

@@ -40,3 +40,4 @@ test/.coverage
 */**/*.dylib*
 test/data/legacy_serialized.pt
 *~
+.idea

train.py

@@ -4,54 +4,55 @@
 from models.models import create_model
 from util.visualizer import Visualizer
 
-opt = TrainOptions().parse()
-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 = 0
-
-for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
-    epoch_start_time = time.time()
-    iter_data_time = time.time()
-    epoch_iter = 0
-
-    for i, data in enumerate(dataset):
-        iter_start_time = time.time()
-        if total_steps % opt.print_freq == 0:
-            t_data = iter_start_time - iter_data_time
-        visualizer.reset()
-        total_steps += opt.batchSize
-        epoch_iter += opt.batchSize
-        model.set_input(data)
-        model.optimize_parameters()
-
-        if total_steps % opt.display_freq == 0:
-            save_result = total_steps % opt.update_html_freq == 0
-            visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
-
-        if total_steps % opt.print_freq == 0:
-            errors = model.get_current_errors()
-            t = (time.time() - iter_start_time) / opt.batchSize
-            visualizer.print_current_errors(epoch, epoch_iter, errors, t, t_data)
-            if opt.display_id > 0:
-                visualizer.plot_current_errors(epoch, float(epoch_iter) / dataset_size, opt, errors)
-
-        if total_steps % opt.save_latest_freq == 0:
-            print('saving the latest model (epoch %d, total_steps %d)' %
+if __name__ == '__main__':
+    opt = TrainOptions().parse()
+    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 = 0
+
+    for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
+        epoch_start_time = time.time()
+        iter_data_time = time.time()
+        epoch_iter = 0
+
+        for i, data in enumerate(dataset):
+            iter_start_time = time.time()
+            if total_steps % opt.print_freq == 0:
+                t_data = iter_start_time - iter_data_time
+            visualizer.reset()
+            total_steps += opt.batchSize
+            epoch_iter += opt.batchSize
+            model.set_input(data)
+            model.optimize_parameters()
+
+            if total_steps % opt.display_freq == 0:
+                save_result = total_steps % opt.update_html_freq == 0
+                visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
+
+            if total_steps % opt.print_freq == 0:
+                errors = model.get_current_errors()
+                t = (time.time() - iter_start_time) / opt.batchSize
+                visualizer.print_current_errors(epoch, epoch_iter, errors, t, t_data)
+                if opt.display_id > 0:
+                    visualizer.plot_current_errors(epoch, float(epoch_iter) / dataset_size, 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')
+
+            iter_data_time = time.time()
+        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)
 
-        iter_data_time = time.time()
-    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))
-    model.update_learning_rate()
+        print('End of epoch %d / %d \t Time Taken: %d sec' %
+              (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
+        model.update_learning_rate()