fix cube and cascade

python/examples/cascade_rcnn/get_data.sh

-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/cascade_rcnn_r50_fpx_1x_serving.tar.gz
-tar xf cascade_rcnn_r50_fpx_1x_serving.tar.gz
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco_serving.tar.gz
+tar xf cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco_serving.tar.gz
+

python/examples/criteo_ctr_with_cube/test_client.py

@@ -16,7 +16,7 @@
 from paddle_serving_client import Client
 import sys
 import os
-import criteo as criteo
+import criteo_reader as criteo
 import time
 from paddle_serving_client.metric import auc
 import numpy as np
@@ -35,21 +35,26 @@ reader = dataset.infer_reader(test_filelists, batch, buf_size)
 label_list = []
 prob_list = []
 start = time.time()
-for ei in range(10000):
+for ei in range(1):
     if py_version == 2:
         data = reader().next()
     else:
         data = reader().__next__()
     feed_dict = {}
-    feed_dict['dense_input'] = data[0][0]
+    feed_dict['dense_input'] = np.array(data[0][0]).reshape(1, len(data[0][0]))
+
     for i in range(1, 27):
-        feed_dict["embedding_{}.tmp_0".format(i - 1)] = np.array(data[0][i]).reshape(-1)
+        feed_dict["embedding_{}.tmp_0".format(i - 1)] = np.array(data[0][i]).reshape(len(data[0][i]))
         feed_dict["embedding_{}.tmp_0.lod".format(i - 1)] = [0, len(data[0][i])]
-    fetch_map = client.predict(feed=feed_dict, fetch=["prob"])
+    for key in feed_dict.keys():
+        if "lod" not in key:
+            print("key: {}, shape: {}".format(key, feed_dict[key].shape))
+    fetch_map = client.predict(feed=feed_dict, fetch=["prob"],batch=True)
     print(fetch_map)
     prob_list.append(fetch_map['prob'][0][1])
     label_list.append(data[0][-1][0])
 
+
 print(auc(label_list, prob_list))
 end = time.time()
 print(end - start)