Fix: fix test.py syntax error, fix global_step not change issue, fix …

…infinite loop

test.py

@@ -18,35 +18,39 @@
 
 
 if __name__ == '__main__':
-    # Init model.
-    is_training, global_step, _, loss, predict_rgb, color_image_rgb, gray_image, file_paths = init_model(train=False)
+    # Init model
+    is_training, _, _, loss, predict_rgb, color_image_rgb, gray_image, file_paths = init_model(train=False)
 
-    # Init scaffold, hooks and config.
+    # Init scaffold, hooks and config
     scaffold = tf.train.Scaffold()
     summary_hook = tf.train.SummarySaverHook(output_dir=testing_summary, save_steps=display_step, scaffold=scaffold)
     checkpoint_hook = tf.train.CheckpointSaverHook(checkpoint_dir=summary_path, save_steps=saving_step, scaffold=scaffold)
-    num_step_hook = tf.train.StopAtStepHook(num_steps=len(file_paths))
-    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True, gpu_options=(tf.GPUOptions(allow_growth=True))
+    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True, gpu_options=(tf.GPUOptions(allow_growth=True)))
     session_creator = tf.train.ChiefSessionCreator(scaffold=scaffold, config=config, checkpoint_dir=summary_path)
 
-    # Create a session for running operations in the Graph.
+    # Create a session for running operations in the Graph
     with tf.train.MonitoredSession(session_creator=session_creator, hooks=[checkpoint_hook, summary_hook]) as sess:
         print("🤖 Start testing...")
+        step = 0
         avg_loss = 0
 
         while not sess.should_stop():
-            # Get global_step.
-            step, l, pred, color, gray = sess.run([global_step, loss, predict_rgb, color_image_rgb, gray_image], feed_dict={is_training: False})
+            step += 1
+
+            l, pred, color, gray = sess.run([loss, predict_rgb, color_image_rgb, gray_image], feed_dict={is_training: False})
 
             if step % display_step == 0:
                 # Print batch loss.
-                print("📖 Iter %d, Minibatch Loss = %f" % (step, l))
+                print("📖 Testing iter %d, Minibatch Loss = %f" % (step, l))
                 avg_loss += float(l)
 
                 # Save testing image.
                 summary_image = concat_images(gray[0], pred[0])
                 summary_image = concat_images(summary_image, color[0])
                 plt.imsave("%s/images/%d.png" % (testing_summary, step), summary_image)
 
+            if step >= len(file_paths) / batch_size:
+                break
+
         print("🎉 Testing finished!")
         print("👀 Total average loss: %f" % (avg_loss / len(file_paths)))