Merge pull request tobegit3hub#26 from justinsu/master

add sparse_predict_client.cc

Author: tobegit3hub

cpp_predict_client/sparse_predict_client.cc

@@ -0,0 +1,173 @@
+/* A c++ version of sparse_predict_client
+ * Build it like inception_client.cc
+ =======================================================*/
+#include <iostream>
+#include <fstream>
+
+#include <grpc++/create_channel.h>
+#include "tensorflow_serving/apis/prediction_service.grpc.pb.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/util/command_line_flags.h"
+
+using grpc::Channel;
+using grpc::ClientContext;
+using grpc::ClientReader;
+using grpc::ClientReaderWriter;
+using grpc::ClientWriter;
+using grpc::Status;
+
+
+using tensorflow::serving::PredictRequest;
+using tensorflow::serving::PredictResponse;
+using tensorflow::serving::PredictionService;
+
+typedef google::protobuf::Map< std::string, tensorflow::TensorProto > OutMap;
+
+
+class ServingClient {
+ public:
+  ServingClient(std::shared_ptr<Channel> channel)
+      : stub_(PredictionService::NewStub(channel)) {
+  }
+  
+  std::string callPredict(std::string model_name) {
+    PredictRequest predictRequest;
+    PredictResponse response;
+    ClientContext context;
+
+    predictRequest.mutable_model_spec()->set_name(model_name);
+
+    google::protobuf::Map< std::string, tensorflow::TensorProto >& inputs = 
+        *predictRequest.mutable_inputs();
+
+    // Example libSVM data:
+    // 0 5:1 6:1 17:1 21:1 35:1 40:1 53:1 63:1 71:1 73:1 74:1 76:1 80:1 83:1
+    // 1 5:1 7:1 17:1 22:1 36:1 40:1 51:1 63:1 67:1 73:1 74:1 76:1 81:1 83:1
+    
+    // Generate keys proto
+    tensorflow::TensorProto keys_tensor_proto;
+    keys_tensor_proto.set_dtype(tensorflow::DataType::DT_INT32);
+    keys_tensor_proto.add_int_val(1);
+    keys_tensor_proto.add_int_val(2);
+    keys_tensor_proto.mutable_tensor_shape()->add_dim()->set_size(2);
+    
+    inputs["keys"] = keys_tensor_proto;
+
+    
+    // Generate indexs TensorProto
+    tensorflow::TensorProto indexs_tensor_proto;
+    indexs_tensor_proto.set_dtype(tensorflow::DataType::DT_INT64);
+    long indexs[28][2] = { {0, 0}, {0, 1}, {0, 2}, {0, 3}, {0, 4}, {0, 5},
+                          {0, 6}, {0, 7}, {0, 8}, {0, 9}, {0, 10}, {0, 11},
+                          {0, 12}, {0, 13}, {1, 0}, {1, 1}, {1, 2}, {1, 3},
+                          {1, 4}, {1, 5}, {1, 6}, {1, 7}, {1, 8}, {1, 9},
+                          {1, 10}, {1, 11}, {1, 12}, {1, 13} };
+    for (int i = 0; i < 28; i++) {
+        for (int j = 0; j < 2; j++) {
+            indexs_tensor_proto.add_int64_val(indexs[i][j]);  
+        }  
+    }
+    indexs_tensor_proto.mutable_tensor_shape()->add_dim()->set_size(28);
+    indexs_tensor_proto.mutable_tensor_shape()->add_dim()->set_size(2);
+
+    inputs["indexs"] = indexs_tensor_proto;
+    std::cout << "Generate indexs tensorproto ok." << std::endl;
+  
+    // Generate ids TensorProto
+    tensorflow::TensorProto ids_tensor_proto;
+    ids_tensor_proto.set_dtype(tensorflow::DataType::DT_INT64);
+    int ids[28] = {5, 6, 17, 21, 35, 40, 53, 63, 71, 73, 74, 76, 80, 83, 5,
+                       7, 17, 22, 36, 40, 51, 63, 67, 73, 74, 76, 81, 83};
+    for (int i = 0; i < 28; i++) {
+        ids_tensor_proto.add_int64_val(ids[i]); 
+    }
+    ids_tensor_proto.mutable_tensor_shape()->add_dim()->set_size(28);
+    
+    inputs["ids"] = ids_tensor_proto;
+    std::cout << "Generate ids tensorproto ok." << std::endl;
+
+    // Generate values TensorProto
+    tensorflow::TensorProto values_tensor_proto;
+    values_tensor_proto.set_dtype(tensorflow::DataType::DT_FLOAT);
+    float values[] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+                          1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+                          1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
+    for (int i = 0; i < 28; i++) {
+        values_tensor_proto.add_float_val(values[i]); 
+    }
+    values_tensor_proto.mutable_tensor_shape()->add_dim()->set_size(28);
+
+    inputs["values"] = values_tensor_proto;
+    std::cout << "Generate values tensorproto ok." << std::endl;
+
+    // Generate shape TensorProto
+    tensorflow::TensorProto shape_tensor_proto;
+    shape_tensor_proto.set_dtype(tensorflow::DataType::DT_INT64);
+    shape_tensor_proto.add_int64_val(2); // ins num
+    shape_tensor_proto.add_int64_val(124); // feature num
+    shape_tensor_proto.mutable_tensor_shape()->add_dim()->set_size(2);
+    
+    inputs["shape"] = shape_tensor_proto;
+    std::cout << "Generate shape tensorproto ok." << std::endl;
+
+    
+    Status status = stub_->Predict(&context, predictRequest, &response);
+    
+    std::cout << "check status.." << std::endl;
+
+    if (status.ok()) {
+      std::cout << "call predict ok" << std::endl;
+      std::cout << "outputs size is "<< response.outputs_size() << std::endl;
+      OutMap& map_outputs =  *response.mutable_outputs();
+      OutMap::iterator iter;
+      int output_index = 0;
+      
+      for(iter = map_outputs.begin();iter != map_outputs.end(); ++iter){
+        tensorflow::TensorProto& result_tensor_proto= iter->second;
+        tensorflow::Tensor tensor;
+        bool converted = tensor.FromProto(result_tensor_proto);
+        if (converted) {
+          std::cout << "the " <<iter->first <<" result tensor[" << output_index << "] is:" <<
+               std::endl << tensor.SummarizeValue(13) << std::endl;
+        }else {
+          std::cout << "the " <<iter->first <<" result tensor[" << output_index << 
+               "] convert failed." << std::endl;
+        }
+        ++output_index;
+      }
+      return "Done.";
+    } else {
+      std::cout << "gRPC call return code: " 
+          <<status.error_code() << ": " << status.error_message()
+          << std::endl;
+      return "gRPC failed.";
+    }
+  }
+
+ private:
+  std::unique_ptr<PredictionService::Stub> stub_;
+};
+
+int main(int argc, char** argv) {
+  std::string server_port = "localhost:9000";
+  std::string model_name = "sparse";
+  std::vector<tensorflow::Flag> flag_list = {
+      tensorflow::Flag("server_port", &server_port, 
+          "the IP and port of the server"),
+      tensorflow::Flag("model_name", &model_name, "name of model")
+  };
+  std::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    std::cout << usage;
+    return -1;
+  }
+
+  ServingClient guide(
+      grpc::CreateChannel( server_port,
+                          grpc::InsecureChannelCredentials()));
+  std::cout << "Calling sparse predictor..." << std::endl;
+  std::cout << guide.callPredict(model_name) << std::endl;
+
+  return 0;
+}