Merge branch 'develop' into develop

core/general-server/op/general_rec_op.cpp

@@ -14,11 +14,11 @@
 
 #include "core/general-server/op/general_rec_op.h"
 #include <algorithm>
+#include <functional>
 #include <iostream>
 #include <memory>
 #include <sstream>
 #include <vector>
-#include <functional>
 #include "core/predictor/framework/infer.h"
 #include "core/predictor/framework/memory.h"
 #include "core/predictor/framework/resource.h"

examples/C++/PaddleOCR/ocr/ocr_cpp_client.py

@@ -21,6 +21,7 @@ import os
 import cv2
 from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import OCRReader
 
 client = Client()
 # TODO:load_client need to load more than one client model.
@@ -44,5 +45,13 @@ for img_file in os.listdir(test_img_dir):
         feed={"image": image},
         fetch=["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"],
         batch=True)
-    #print("{} {}".format(fetch_map["price"][0], data[0][1][0]))
-    print(fetch_map)
+    result = {}
+    result["score"] = fetch_map["softmax_0.tmp_0"]
+    del fetch_map["softmax_0.tmp_0"]
+    rec_res = OCRReader().postprocess(fetch_map, with_score=False)
+    res_lst = []
+    for res in rec_res:
+        res_lst.append(res[0])
+    result["res"] = res_lst
+
+    print(result)

examples/C++/xpu/bert/README.md

@@ -7,7 +7,7 @@ tar zxvf bert_base_chinese.tar.gz
 ```
 ### convert model
 ```
-python3 -m paddle_serving_client.convert --dirname bert_base_chinese --model_filename bert_base_chinese/model.pdmodel --params_filename bert_base_chinese/model.pdiparams
+python3 -m paddle_serving_client.convert --dirname bert_base_chinese --model_filename model.pdmodel --params_filename model.pdiparams
 ```
 ### or, you can get the serving saved model directly
 ```

python/paddle_serving_app/reader/batching.py

@@ -108,7 +108,7 @@ def pad_batch_data(insts,
         input_mask_data = np.array(
             [[1] * len(inst) + [0] * (max_len - len(inst)) for inst in insts])
         input_mask_data = np.expand_dims(input_mask_data, axis=-1)
-        return_list += [input_mask_data.astype("float32")]
+        return_list += [input_mask_data]
 
     if return_max_len:
         return_list += [max_len]