From 4d9b5e57b5710f339bc829a19064389dff9adb5a Mon Sep 17 00:00:00 2001
From: xujiaqi01 <173596896@qq.com>
Date: Wed, 1 Apr 2020 23:20:28 +0800
Subject: [PATCH] fix save error of xdeepfm (#4507)

* fix save error of xdeepfm
* test=develop
---
 PaddleRec/ctr/dcn/cluster_train.py     | 5 ++---
 PaddleRec/ctr/dcn/infer.py             | 2 +-
 PaddleRec/ctr/xdeepfm/cluster_train.py | 5 ++---
 PaddleRec/ctr/xdeepfm/infer.py         | 2 +-
 PaddleRec/ctr/xdeepfm/local_train.py   | 2 +-
 5 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/PaddleRec/ctr/dcn/cluster_train.py b/PaddleRec/ctr/dcn/cluster_train.py
index 5f8cc95f..e791727b 100644
--- a/PaddleRec/ctr/dcn/cluster_train.py
+++ b/PaddleRec/ctr/dcn/cluster_train.py
@@ -169,13 +169,12 @@ def train():
                 debug=False,
                 print_period=args.print_steps)
             model_dir = os.path.join(args.model_output_dir,
-                                     'epoch_' + str(epoch_id + 1))
+                                     'epoch_' + str(epoch_id + 1), "checkpoint")
             sys.stderr.write('epoch%d is finished and takes %f s\n' % (
                 (epoch_id + 1), time.time() - start))
             if args.trainer_id == 0:  # only trainer 0 save model
                 print("save model in {}".format(model_dir))
-                fluid.io.save_persistables(
-                    executor=exe, dirname=model_dir, main_program=main_program)
+                fluid.save(main_program, model_dir)
 
         print("train time cost {:.4f}".format(time.time() - start_time))
         print("finish training")
diff --git a/PaddleRec/ctr/dcn/infer.py b/PaddleRec/ctr/dcn/infer.py
index 83e49e0b..ab019f97 100644
--- a/PaddleRec/ctr/dcn/infer.py
+++ b/PaddleRec/ctr/dcn/infer.py
@@ -64,7 +64,7 @@ def infer():
                 feed_list=dcn_model.data_list, place=place)
 
             exe.run(startup_program)
-            fluid.io.load(fluid.default_main_program(), cur_model_path)
+            fluid.load(fluid.default_main_program(), cur_model_path)
 
             for var in dcn_model.auc_states:  # reset auc states
                 set_zero(var.name, scope=inference_scope, place=place)
diff --git a/PaddleRec/ctr/xdeepfm/cluster_train.py b/PaddleRec/ctr/xdeepfm/cluster_train.py
index e1d318b5..77e1e152 100644
--- a/PaddleRec/ctr/xdeepfm/cluster_train.py
+++ b/PaddleRec/ctr/xdeepfm/cluster_train.py
@@ -162,13 +162,12 @@ def train():
                 debug=False,
                 print_period=args.print_steps)
             model_dir = os.path.join(args.model_output_dir,
-                                     'epoch_' + str(epoch_id + 1))
+                                     'epoch_' + str(epoch_id + 1), "checkpoint")
             sys.stderr.write('epoch%d is finished and takes %f s\n' % (
                 (epoch_id + 1), time.time() - start))
             if args.trainer_id == 0:  # only trainer 0 save model
                 print("save model in {}".format(model_dir))
-                fluid.io.save_persistables(
-                    executor=exe, dirname=model_dir, main_program=main_program)
+                fluid.save(main_program, model_dir)
 
         print("train time cost {:.4f}".format(time.time() - start_time))
         print("finish training")
diff --git a/PaddleRec/ctr/xdeepfm/infer.py b/PaddleRec/ctr/xdeepfm/infer.py
index 1c6277ad..2b0ac131 100644
--- a/PaddleRec/ctr/xdeepfm/infer.py
+++ b/PaddleRec/ctr/xdeepfm/infer.py
@@ -50,7 +50,7 @@ def infer():
             feeder = fluid.DataFeeder(feed_list=data_list, place=place)
 
             exe.run(startup_program)
-            fluid.io.load(fluid.default_main_program(), cur_model_path)
+            fluid.load(fluid.default_main_program(), cur_model_path)
 
             for var in auc_states:  # reset auc states
                 set_zero(var.name, scope=inference_scope, place=place)
diff --git a/PaddleRec/ctr/xdeepfm/local_train.py b/PaddleRec/ctr/xdeepfm/local_train.py
index 2bb7e1de..c3c0e31f 100644
--- a/PaddleRec/ctr/xdeepfm/local_train.py
+++ b/PaddleRec/ctr/xdeepfm/local_train.py
@@ -58,7 +58,7 @@ def train():
                                  'epoch_' + str(epoch_id + 1), "checkpoint")
         sys.stderr.write('epoch%d is finished and takes %f s\n' % (
             (epoch_id + 1), time.time() - start))
-        fluid.io.save_persistables(fluid.default_main_program(), model_dir)
+        fluid.save(fluid.default_main_program(), model_dir)
 
 
 if __name__ == '__main__':
-- 
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