fix unit test cases

test=develop

python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py

@@ -231,14 +231,17 @@ def infer(use_cuda, inference_program, params_dirname):
     # Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one 
     # level of detail info, indicating that `data` consists of two sequences 
     # of length 3 and 2, respectively. 
-    user_id = fluid.create_lod_tensor([[1]], [[1]], place)
-    gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
-    age_id = fluid.create_lod_tensor([[0]], [[1]], place)
-    job_id = fluid.create_lod_tensor([[10]], [[1]], place)
-    movie_id = fluid.create_lod_tensor([[783]], [[1]], place)
-    category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place)
-    movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]], [[5]],
-                                          place)
+    user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
+    gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
+    age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
+    job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
+    movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
+    category_id = fluid.create_lod_tensor(
+        [np.array(
+            [10, 8, 9], dtype='int64')], [[3]], place)
+    movie_title = fluid.create_lod_tensor(
+        [np.array(
+            [1069, 4140, 2923, 710, 988], dtype='int64')], [[5]], place)
 
     results = inferencer.infer(
         {

python/paddle/fluid/tests/book/test_recommender_system.py

@@ -271,26 +271,30 @@ def infer(use_cuda, save_dirname=None):
         # Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
         # level of detail info, indicating that `data` consists of two sequences
         # of length 3 and 2, respectively.
-        user_id = fluid.create_lod_tensor([[1]], [[1]], place)
+        user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
 
         assert feed_target_names[1] == "gender_id"
-        gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
+        gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
 
         assert feed_target_names[2] == "age_id"
-        age_id = fluid.create_lod_tensor([[0]], [[1]], place)
+        age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
 
         assert feed_target_names[3] == "job_id"
-        job_id = fluid.create_lod_tensor([[10]], [[1]], place)
+        job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
 
         assert feed_target_names[4] == "movie_id"
-        movie_id = fluid.create_lod_tensor([[783]], [[1]], place)
+        movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
 
         assert feed_target_names[5] == "category_id"
-        category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place)
+        category_id = fluid.create_lod_tensor(
+            [np.array(
+                [10, 8, 9], dtype='int64')], [[3]], place)
 
         assert feed_target_names[6] == "movie_title"
-        movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]],
-                                              [[5]], place)
+        movie_title = fluid.create_lod_tensor(
+            [np.array(
+                [1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
+            place)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.

python/paddle/fluid/tests/unittests/test_auc_op.py

@@ -24,7 +24,7 @@ class TestAucOp(OpTest):
     def setUp(self):
         self.op_type = "auc"
         pred = np.random.random((128, 2)).astype("float32")
-        labels = np.random.randint(0, 2, (128, 1))
+        labels = np.random.randint(0, 2, (128, 1)).astype("int64")
         num_thresholds = 200
 
         stat_pos = np.zeros((num_thresholds + 1, )).astype("int64")

python/paddle/fluid/tests/unittests/test_nce.py

@@ -68,7 +68,8 @@ class TestNCE(OpTest):
         weight = np.random.randn(num_classes, dim).astype(np.float32)
         bias = np.random.randn(num_classes).astype(np.float32)
         sample_weight = np.random.randn(batch_size).astype(np.float32)
-        labels = np.random.randint(0, num_classes, (batch_size, num_true_class))
+        labels = np.random.randint(0, num_classes,
+                                   (batch_size, num_true_class)).astype("int64")
         self.attrs = {
             'num_total_classes': num_classes,
             'num_neg_samples': num_neg_samples,