refine code

python/paddle/v2/fluid/io.py

@@ -192,6 +192,33 @@ def get_inference_program(target_vars, main_program=None):
     return inference_program
 
 
+def prepend_feed_ops(inference_program, feeded_var_names):
+    global_block = inference_program.global_block()
+    feed_var = global_block.create_var(
+        name='feed', type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True)
+
+    for i, name in enumerate(feeded_var_names):
+        out = global_block.var(name)
+        global_block.prepend_op(
+            type='feed',
+            inputs={'X': [feed_var]},
+            outputs={'Out': [out]},
+            attrs={'col': i})
+
+
+def append_fetch_ops(inference_program, fetch_var_names):
+    global_block = inference_program.global_block()
+    fetch_var = global_block.create_var(
+        name='fetch', type=core.VarDesc.VarType.FETCH_LIST, persistable=True)
+
+    for i, name in enumerate(fetch_var_names):
+        global_block.append_op(
+            type='fetch',
+            inputs={'X': [name]},
+            outputs={'Out': [fetch_var]},
+            attrs={'col': i})
+
+
 def save_inference_model(dirname,
                          feeded_var_names,
                          target_vars,
@@ -244,27 +271,8 @@ def save_inference_model(dirname,
 
     # Save only programDesc of inference_program in binary format
     # in another file: __model__.dat
-    global_block = inference_program.global_block()
-    feed_var = global_block.create_var(
-        name='feed', type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True)
-
-    for i, name in enumerate(feeded_var_names):
-        out = global_block.var(name)
-        global_block.prepend_op(
-            type='feed',
-            inputs={'X': [feed_var]},
-            outputs={'Out': [out]},
-            attrs={'col': i})
-
-    fetch_var = global_block.create_var(
-        name='fetch', type=core.VarDesc.VarType.FETCH_LIST, persistable=True)
-
-    for i, name in enumerate(fetch_var_names):
-        global_block.append_op(
-            type='fetch',
-            inputs={'X': [name]},
-            outputs={'Out': [fetch_var]},
-            attrs={'col': i})
+    prepend_feed_ops(inference_program, feeded_var_names)
+    append_fetch_ops(inference_program, fetch_var_names)
 
     with open(model_file_name + ".dat", "wb") as fp:
         fp.write(inference_program.desc.serialize_to_string())