remove the graph

bob/learn/tensorflow/script/predict_bio.py

@@ -160,42 +160,40 @@ def main(argv=None):
     output_dir = get_from_config_or_commandline(
         config, 'output_dir', args, defaults, False)
 
-    with tf.Graph().as_default():
-
-        generator, output_types, output_shapes = bio_generator(
-            database, preprocessor, groups, number_of_parallel_jobs,
-            biofile_to_label, multiple_samples)
-
-        predict_input_fn = bio_predict_input_fn(generator,
-                                                output_types, output_shapes)
-
-        predictions = estimator.predict(
-            predict_input_fn,
-            predict_keys=predict_keys,
-            hooks=hooks,
-            checkpoint_path=checkpoint_path,
-        )
-
-        pool = Pool()
-        try:
-            pred_buffer = defaultdict(list)
-            for i, pred in enumerate(predictions):
-                key = pred['keys']
-                prob = pred.get('probabilities', pred.get('embeddings'))
-                pred_buffer[key].append(prob)
-                if i == 0:
-                    last_key = key
-                if last_key == key:
-                    continue
-                else:
-                    save_predictions(pool, output_dir, last_key, pred_buffer)
-                    last_key = key
-            # else below is for the for loop
+    generator, output_types, output_shapes = bio_generator(
+        database, preprocessor, groups, number_of_parallel_jobs,
+        biofile_to_label, multiple_samples)
+
+    predict_input_fn = bio_predict_input_fn(generator,
+                                            output_types, output_shapes)
+
+    predictions = estimator.predict(
+        predict_input_fn,
+        predict_keys=predict_keys,
+        hooks=hooks,
+        checkpoint_path=checkpoint_path,
+    )
+
+    pool = Pool()
+    try:
+        pred_buffer = defaultdict(list)
+        for i, pred in enumerate(predictions):
+            key = pred['keys']
+            prob = pred.get('probabilities', pred.get('embeddings'))
+            pred_buffer[key].append(prob)
+            if i == 0:
+                last_key = key
+            if last_key == key:
+                continue
             else:
-                save_predictions(pool, output_dir, key, pred_buffer)
-        finally:
-            pool.close()
-            pool.join()
+                save_predictions(pool, output_dir, last_key, pred_buffer)
+                last_key = key
+        # else below is for the for loop
+        else:
+            save_predictions(pool, output_dir, key, pred_buffer)
+    finally:
+        pool.close()
+        pool.join()
 
 
 if __name__ == '__main__':