Merge branch 'develop' into low-precision

paddle_inference/paddle/include/paddle_engine.h

@@ -108,9 +108,9 @@ class PaddleInferenceEngine : public PaddleEngineBase {
     if (engine_conf.has_encrypted_model() && engine_conf.encrypted_model()) {
       // decrypt model
       std::string model_buffer, params_buffer, key_buffer;
-      predictor::ReadBinaryFile(model_path + "encrypt_model", &model_buffer);
-      predictor::ReadBinaryFile(model_path + "encrypt_params", &params_buffer);
-      predictor::ReadBinaryFile(model_path + "key", &key_buffer);
+      predictor::ReadBinaryFile(model_path + "/encrypt_model", &model_buffer);
+      predictor::ReadBinaryFile(model_path + "/encrypt_params", &params_buffer);
+      predictor::ReadBinaryFile(model_path + "/key", &key_buffer);
 
       auto cipher = paddle::MakeCipher("");
       std::string real_model_buffer = cipher->Decrypt(model_buffer, key_buffer);

python/examples/encryption/README.md

@@ -31,6 +31,7 @@ dirname is the folder path where the model is located. If the parameter is discr
 The key is stored in the `key` file, and the encrypted model file and server-side configuration file are stored in the `encrypt_server` directory.
 client-side configuration file are stored in the `encrypt_client` directory.
 
+**Notice：** When encryption prediction is used, the model configuration and parameter folder loaded by server and client should be encrypt_server/ and encrypt_client/
 ## Start Encryption Service
 CPU Service
 ```
@@ -43,5 +44,5 @@ python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_
 
 ## Prediction
 ```
-python test_client.py uci_housing_client/serving_client_conf.prototxt
+python test_client.py encrypt_client/serving_client_conf.prototxt
 ```

python/examples/encryption/README_CN.md

@@ -31,6 +31,8 @@ def serving_encryption():
 
 密钥保存在`key`文件中，加密模型文件以及server端配置文件保存在`encrypt_server`目录下，client端配置文件保存在`encrypt_client`目录下。
 
+**注意：** 当使用加密预测时，服务端和客户端启动加载的模型配置和参数文件夹是encrypt_server/和encrypt_client/
+
 ## 启动加密预测服务
 CPU预测服务
 ```
@@ -43,5 +45,5 @@ python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_
 
 ## 预测
 ```
-python test_client.py uci_housing_client/serving_client_conf.prototxt
+python test_client.py encrypt_client/
 ```

python/paddle_serving_server/server.py

@@ -42,23 +42,29 @@ from concurrent import futures
 
 class Server(object):
     def __init__(self):
+        """
+        self.model_toolkit_conf:'list'=[] # The quantity of self.model_toolkit_conf is equal to the InferOp quantity/Engine--OP
+        self.model_conf:'collections.OrderedDict()' # Save the serving_server_conf.prototxt content (feed and fetch information) this is a map for multi-model in a workflow
+        self.workflow_fn:'str'="workflow.prototxt" # Only one for one Service/Workflow
+        self.resource_fn:'str'="resource.prototxt" # Only one for one Service,model_toolkit_fn and general_model_config_fn is recorded in this file
+        self.infer_service_fn:'str'="infer_service.prototxt" # Only one for one Service,Service--Workflow
+        self.model_toolkit_fn:'list'=[] # ["general_infer_0/model_toolkit.prototxt"]The quantity is equal to the InferOp quantity,Engine--OP
+        self.general_model_config_fn:'list'=[] # ["general_infer_0/general_model.prototxt"]The quantity is equal to the InferOp quantity,Feed and Fetch --OP
+        self.subdirectory:'list'=[] # The quantity is equal to the InferOp quantity, and name = node.name = engine.name
+        self.model_config_paths:'collections.OrderedDict()' # Save the serving_server_conf.prototxt path (feed and fetch information) this is a map for multi-model in a workflow
+        """
         self.server_handle_ = None
         self.infer_service_conf = None
-        self.model_toolkit_conf = [
-        ]  #The quantity is equal to the InferOp quantity,Engine--OP
+        self.model_toolkit_conf = []
         self.resource_conf = None
         self.memory_optimization = False
         self.ir_optimization = False
-        # save the serving_server_conf.prototxt content (feed and fetch information) this is a map for multi-model in a workflow
         self.model_conf = collections.OrderedDict()
-        self.workflow_fn = "workflow.prototxt"  #only one for one Service,Workflow--Op 
-        self.resource_fn = "resource.prototxt"  #only one for one Service,model_toolkit_fn and  general_model_config_fn is recorded in this file
-        self.infer_service_fn = "infer_service.prototxt"  #only one for one Service,Service--Workflow
-        #["general_infer_0/model_toolkit.prototxt"]The quantity is equal to the InferOp quantity,Engine--OP
+        self.workflow_fn = "workflow.prototxt"
+        self.resource_fn = "resource.prototxt"
+        self.infer_service_fn = "infer_service.prototxt"
         self.model_toolkit_fn = []
-        #["general_infer_0/general_model.prototxt"]The quantity is equal to the InferOp quantity,Feed and Fetch --OP
         self.general_model_config_fn = []
-        #The quantity is equal to the InferOp quantity, and name = node.name = engine.name
         self.subdirectory = []
         self.cube_config_fn = "cube.conf"
         self.workdir = ""
@@ -78,7 +84,6 @@ class Server(object):
         self.use_trt = False
         self.use_lite = False
         self.use_xpu = False
-        # save the serving_server_conf.prototxt path (feed and fetch information) this is a map for multi-model in a workflow
         self.model_config_paths = collections.OrderedDict()
         self.product_name = None
         self.container_id = None