update ms bug

PaddleRec/multiview_simnet/nets.py

@@ -125,34 +125,34 @@ class MultiviewSimnet(object):
     def train_net(self):
         # input fields for query, pos_title, neg_title
         q_slots = [
-            io.data(
-                name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="q%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.query_encoders))
         ]
         pt_slots = [
-            io.data(
-                name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="pt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
         nt_slots = [
-            io.data(
-                name="nt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="nt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
 
         # lookup embedding for each slot
         q_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=query, size=self.emb_shape, param_attr="emb")
             for query in q_slots
         ]
         pt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in pt_slots
         ]
         nt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in nt_slots
         ]
@@ -205,23 +205,23 @@ class MultiviewSimnet(object):
 
     def pred_net(self, query_fields, pos_title_fields, neg_title_fields):
         q_slots = [
-            io.data(
-                name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="q%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.query_encoders))
         ]
         pt_slots = [
-            io.data(
-                name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="pt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
         # lookup embedding for each slot
         q_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=query, size=self.emb_shape, param_attr="emb")
             for query in q_slots
         ]
         pt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in pt_slots
         ]