add model encrypt,test=develop

paddle_inference/paddle/include/paddle_engine.h

@@ -17,12 +17,13 @@
 #include <pthread.h>
 #include <fstream>
 #include <map>
+#include <memory>
 #include <string>
 #include <vector>
 #include "core/configure/include/configure_parser.h"
 #include "core/configure/inferencer_configure.pb.h"
-#include "core/predictor/framework/infer.h"
 #include "core/predictor/common/utils.h"
+#include "core/predictor/framework/infer.h"
 #include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
@@ -37,31 +38,31 @@ using paddle_infer::CreatePredictor;
 
 DECLARE_int32(gpuid);
 
-const static int max_batch = 32;
-const static int min_subgraph_size = 3;
+static const int max_batch = 32;
+static const int min_subgraph_size = 3;
 
 // Engine Base
 class PaddleEngineBase {
  public:
   virtual ~PaddleEngineBase() {}
   virtual std::vector<std::string> GetInputNames() {
-    return _predictor -> GetInputNames();
+    return _predictor->GetInputNames();
   }
 
   virtual std::unique_ptr<Tensor> GetInputHandle(const std::string& name) {
-    return _predictor -> GetInputHandle(name);
+    return _predictor->GetInputHandle(name);
   }
 
   virtual std::vector<std::string> GetOutputNames() {
-    return _predictor -> GetOutputNames();
+    return _predictor->GetOutputNames();
   }
 
   virtual std::unique_ptr<Tensor> GetOutputHandle(const std::string& name) {
-    return _predictor -> GetOutputHandle(name);
+    return _predictor->GetOutputHandle(name);
   }
 
   virtual bool Run() {
-    if (!_predictor -> Run()) {
+    if (!_predictor->Run()) {
       LOG(ERROR) << "Failed call Run with paddle predictor";
       return false;
     }
@@ -76,7 +77,7 @@ class PaddleEngineBase {
       return -1;
     }
     Predictor* prep = static_cast<Predictor*>(predictor);
-    _predictor = prep -> Clone();
+    _predictor = prep->Clone();
     if (_predictor.get() == NULL) {
       LOG(ERROR) << "fail to clone paddle predictor: " << predictor;
       return -1;
@@ -103,8 +104,8 @@ class PaddleInferenceEngine : public PaddleEngineBase {
 
     Config config;
     // todo, auto config(zhangjun)
-    if(engine_conf.has_combined_model()) {
-      if(!engine_conf.combined_model()) {
+    if (engine_conf.has_combined_model()) {
+      if (!engine_conf.combined_model()) {
         config.SetModel(model_path);
       } else {
         config.SetParamsFile(model_path + "/__params__");
@@ -140,19 +141,33 @@ class PaddleInferenceEngine : public PaddleEngineBase {
       // 2 MB l3 cache
       config.EnableXpu(2 * 1024 * 1024);
     }
-    if (engine_conf.has_enable_ir_optimization() && !engine_conf.enable_ir_optimization()) {
+    if (engine_conf.has_enable_ir_optimization() &&
+        !engine_conf.enable_ir_optimization()) {
       config.SwitchIrOptim(false);
     } else {
       config.SwitchIrOptim(true);
     }
 
-    if (engine_conf.has_enable_memory_optimization() && engine_conf.enable_memory_optimization()) {
+    if (engine_conf.has_enable_memory_optimization() &&
+        engine_conf.enable_memory_optimization()) {
       config.EnableMemoryOptim();
     }
 
-    if (false) {
-      // todo, encrypt model
-      //analysis_config.SetModelBuffer();
+    if (engine_conf.has_encrypted_model() && engine_conf.encrypted_mode()) {
+      // decrypt model
+      std::string model_buffer, params_buffer, key_buffer;
+      ReadBinaryFile(model_path + "encrypt_model", &model_buffer);
+      ReadBinaryFile(model_path + "encrypt_params", &params_buffer);
+      ReadBinaryFile(model_path + "key", &key_buffer);
+
+      auto cipher = paddle::MakeCipher("");
+      std::string real_model_buffer = cipher->Decrypt(model_buffer, key_buffer);
+      std::string real_params_buffer =
+          cipher->Decrypt(params_buffer, key_buffer);
+      config.SetModelBuffer(&real_model_buffer[0],
+                            real_model_buffer.size(),
+                            &real_params_buffer[0],
+                            real_params_buffer.size());
     }
 
     predictor::AutoLock lock(predictor::GlobalCreateMutex::instance());