fix proto change

doc/HTTP_SERVICE_CN.md

@@ -42,7 +42,7 @@ python3.6 -m paddle_serving_server.serve --model uci_housing_model --thread 10 -
 
 为了方便用户快速的使用Http方式请求Server端预测服务，我们已经将常用的Http请求的数据体封装、压缩、请求加密等功能封装为一个HttpClient类提供给用户，方便用户使用。
 
-使用HttpClient最简单只需要三步，1、创建一个HttpClient对象。2、加载Client端的prototxt配置文件（本例中为python/examples/fit_a_line/目录下的uci_housing_client/serving_client_conf.prototxt)，3、调用Predict函数，通过Http方式请求预测服务。
+使用HttpClient最简单只需要四步，1、创建一个HttpClient对象。2、加载Client端的prototxt配置文件（本例中为python/examples/fit_a_line/目录下的uci_housing_client/serving_client_conf.prototxt)。3、调用coonect函数。4、调用Predict函数，通过Http方式请求预测服务。
 
 此外，您可以根据自己的需要配置Server端IP、Port、服务名称（此服务名称需要与[`core/general-server/proto/general_model_service.proto`](../core/general-server/proto/general_model_service.proto)文件中的Service服务名和rpc方法名对应，即`GeneralModelService`字段和`inference`字段），设置Request数据体压缩，设置Response支持压缩传输，模型加密预测（需要配置Server端使用模型加密）、设置响应超时时间等功能。
 
@@ -103,7 +103,7 @@ repeated int32 numbers = 1;
 ```
 #### elem_type
 
-表示数据类型，0 means int64, 1 means float32, 2 means int32, 3 means bytes(string)
+表示数据类型，0 means int64, 1 means float32, 2 means int32, 20 means bytes(string)
 
 #### fetch_var_names
 

java/src/main/java/io/paddle/serving/client/Client.java

@@ -59,9 +59,20 @@ import java.util.zip.GZIPInputStream;
 import java.util.zip.GZIPOutputStream;
  
 
-enum ElementType
-{
-    Int64_type, Float32_type, Int32_type, Bytes_type;
+class ElementType {
+    public static final int Int64_type = 0;
+    public static final int Float32_type = 1;
+    public static final int Int32_type = 2;
+    public static final int String_type = 20;
+    public static final Map<Integer, String> feedTypeToDataKey_;
+    static
+    {
+        feedTypeToDataKey_ = new HashMap<Integer, String>();
+        feedTypeToDataKey_.put(ElementType.Int64_type, "int64_data");
+        feedTypeToDataKey_.put(ElementType.Float32_type, "float_data");
+        feedTypeToDataKey_.put(ElementType.Int32_type, "int_data");
+        feedTypeToDataKey_.put(ElementType.String_type, "data");
+    }
 }
 
 class Profiler {
@@ -104,7 +115,6 @@ public class Client {
     private Map<String, Integer> feedTypes_;
     private Map<String, List<Integer>> feedShapes_;
     private Map<String, Integer> feedNameToIndex_;
-    private Map<Integer, String> feedTypeToDataKey_;
     private List<String> fetchNames_;
     private Map<String, Integer> fetchTypes_;
     private Set<String> lodTensorSet_;
@@ -147,12 +157,6 @@ public class Client {
         channel_ = null;
         blockingStub_ = null;
 
-        feedTypeToDataKey_ = new HashMap<Integer, String>();
-        feedTypeToDataKey_.put(0, "int64_data");
-        feedTypeToDataKey_.put(1, "float_data");
-        feedTypeToDataKey_.put(2, "int_data");
-        feedTypeToDataKey_.put(3, "data");
-
         profiler_ = new Profiler();
         boolean is_profile = false;
         String FLAGS_profile_client = System.getenv("FLAGS_profile_client");
@@ -525,7 +529,7 @@ public class Client {
                     jsonTensor.put("elem_type", element_type);
 
                     // 处理数据与shape
-                    String protoDataKey = feedTypeToDataKey_.get(element_type);
+                    String protoDataKey = ElementType.feedTypeToDataKey_.get(element_type);
                     // 如果是INDArray类型，先转为一维.
                     // 此时shape为INDArray的shape
                     if(objectValue instanceof INDArray){
@@ -535,11 +539,11 @@ public class Client {
                         for(long dim:indarrayShape){
                             shape.add((int)dim);
                         }
-                        if(element_type == ElementType.Int64_type.ordinal()){
+                        if(element_type == ElementType.Int64_type){
                             objectValue = tempIndArray.data().asLong();
-                        }else if(element_type == ElementType.Int32_type.ordinal()){
+                        }else if(element_type == ElementType.Int32_type){
                             objectValue = tempIndArray.data().asInt();
-                        }else if(element_type == ElementType.Float32_type.ordinal()){
+                        }else if(element_type == ElementType.Float32_type){
                             objectValue = tempIndArray.data().asFloat();
                         }else{
                             throw new Exception("INDArray 类型不支持");
@@ -564,11 +568,11 @@ public class Client {
                         // 此时无法获取batch信息，故对shape不处理
                         // 由于Proto中为Repeated,需要把数据包装成list
                         if(objectValue instanceof String){
-                            if(feedTypes_.get(protoDataKey)!= ElementType.Bytes_type.ordinal()){
+                            if(feedTypes_.get(protoDataKey)!= ElementType.String_type){
                                 throw new Exception("feedvar is not string-type,feed can`t be a single string.");
                             }
                         }else{
-                            if(feedTypes_.get(protoDataKey)== ElementType.Bytes_type.ordinal()){
+                            if(feedTypes_.get(protoDataKey)== ElementType.String_type){
                                 throw new Exception("feedvar is string-type,feed, feed can`t be a single int or others.");
                             }
                         }
@@ -662,17 +666,17 @@ public class Client {
                         for(long dim:indarrayShape){
                             shape.add((int)dim);
                         }   
-                        if(element_type == ElementType.Int64_type.ordinal()){
+                        if(element_type == ElementType.Int64_type){
                             
                             List<Long> iter = Arrays.stream(tempIndArray.data().asLong()).boxed().collect(Collectors.toList());
                             tensor_builder.addAllInt64Data(iter);
                             
-                        }else if(element_type == ElementType.Int32_type.ordinal()){
+                        }else if(element_type == ElementType.Int32_type){
                             
                             List<Integer> iter = Arrays.stream(tempIndArray.data().asInt()).boxed().collect(Collectors.toList());
                             tensor_builder.addAllIntData(iter);
                             
-                        }else if(element_type == ElementType.Float32_type.ordinal()){
+                        }else if(element_type == ElementType.Float32_type){
                             List<Float> iter = Arrays.asList(ArrayUtils.toObject(tempIndArray.data().asFloat()));
                             tensor_builder.addAllFloatData(iter);
                             
@@ -684,13 +688,13 @@ public class Client {
                         // 如果是数组类型，则无须处理，直接使用即可。
                         // 且数组无法嵌套，此时batch无法从数据中获取
                         // 默认batch维度为1，或者feedVar的shape信息中已包含batch
-                        if(element_type == ElementType.Int64_type.ordinal()){
+                        if(element_type == ElementType.Int64_type){
                             List<Long> iter = Arrays.stream((long[])objectValue).boxed().collect(Collectors.toList());
                             tensor_builder.addAllInt64Data(iter);
-                        }else if(element_type == ElementType.Int32_type.ordinal()){
+                        }else if(element_type == ElementType.Int32_type){
                             List<Integer> iter = Arrays.stream((int[])objectValue).boxed().collect(Collectors.toList());
                             tensor_builder.addAllIntData(iter);
-                        }else if(element_type == ElementType.Float32_type.ordinal()){
+                        }else if(element_type == ElementType.Float32_type){
                             List<Float> iter = Arrays.asList(ArrayUtils.toObject((float[])objectValue));
                             tensor_builder.addAllFloatData(iter);
                         }else{
@@ -707,11 +711,11 @@ public class Client {
                             // 在index=0处，加上batch
                             shape.add(0, list.size());
                         }
-                        if(element_type == ElementType.Int64_type.ordinal()){
+                        if(element_type == ElementType.Int64_type){
                             tensor_builder.addAllInt64Data((List<Long>)(List)recursiveExtract(objectValue));
-                        }else if(element_type == ElementType.Int32_type.ordinal()){
+                        }else if(element_type == ElementType.Int32_type){
                             tensor_builder.addAllIntData((List<Integer>)(List)recursiveExtract(objectValue));
-                        }else if(element_type == ElementType.Float32_type.ordinal()){
+                        }else if(element_type == ElementType.Float32_type){
                             tensor_builder.addAllFloatData((List<Float>)(List)recursiveExtract(objectValue));
                         }else{
                             // 看接口是String还是Bytes
@@ -723,11 +727,11 @@ public class Client {
                         // 由于Proto中为Repeated,需要把数据包装成list
                         List<Object> tempList = new ArrayList<>();
                         tempList.add(objectValue);
-                        if(element_type == ElementType.Int64_type.ordinal()){
+                        if(element_type == ElementType.Int64_type){
                             tensor_builder.addAllInt64Data((List<Long>)(List)tempList);
-                        }else if(element_type == ElementType.Int32_type.ordinal()){
+                        }else if(element_type == ElementType.Int32_type){
                             tensor_builder.addAllIntData((List<Integer>)(List)tempList);
-                        }else if(element_type == ElementType.Float32_type.ordinal()){
+                        }else if(element_type == ElementType.Float32_type){
                             tensor_builder.addAllFloatData((List<Float>)(List)tempList);
                         }else{
                             // 看接口是String还是Bytes

python/examples/ocr/README.md

@@ -119,7 +119,7 @@ The pre-processing and post-processing is in the C + + server part, the image's
 
 so the value of parameter `feed_var` which is in the file `ocr_det_client/serving_client_conf.prototxt` should be changed.
 
-for this case, `feed_type` should be 3(which means the data type is string),`shape` should be 1.
+for this case, `feed_type` should be 20(which means the data type is string),`shape` should be 1.
 
 By passing in multiple client folder paths, the client can be started for multi model prediction.
 ```

python/examples/ocr/README_CN.md

@@ -118,7 +118,7 @@ python3 -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --por
 
 即`ocr_det_client/serving_client_conf.prototxt`中`feed_var`字段
 
-对于本示例而言，`feed_type`应修改为3(数据类型为string),`shape`为1.
+对于本示例而言，`feed_type`应修改为20(数据类型为string),`shape`为1.
 
 通过在客户端启动后加入多个client模型的client配置文件夹路径，启动client进行预测。
 ```

python/paddle_serving_client/httpclient.py

@@ -38,7 +38,12 @@ float32_type = 1
 int32_type = 2
 bytes_type = 20
 # this is corresponding to the proto
-proto_data_key_list = ["int64_data", "float_data", "int_data", "data"]
+proto_data_key_list = {
+    0: "int64_data",
+    1: "float_data",
+    2: "int_data",
+    20: "data"
+}
 
 
 def list_flatten(items, ignore_types=(str, bytes)):