Add option to configure the name of the input layer of remote model.

This binary is useful to test other models based on resnet that may have slightly different names.

PiperOrigin-RevId: 602779121

Author: tensorflower-gardener

tensorflow_serving/example/resnet_client.cc

@@ -119,6 +119,7 @@ class ServingClient {
 
   tensorflow::string callPredict(const tensorflow::string& model_name,
                                  const tensorflow::string& model_signature_name,
+                                 const tensorflow::string& input_name,
                                  const tensorflow::string& file_path) {
     PredictRequest predictRequest;
     PredictResponse response;
@@ -140,7 +141,7 @@ class ServingClient {
       return "execution failed";
     }
 
-    inputs["input_1"] = proto;
+    inputs[input_name] = proto;
 
     Status status = stub_->Predict(&context, predictRequest, &response);
 
@@ -182,13 +183,15 @@ int main(int argc, char** argv) {
   tensorflow::string image_file = "";
   tensorflow::string model_name = "resnet";
   tensorflow::string model_signature_name = "serving_default";
+  tensorflow::string input_name = "input_1";
   std::vector<tensorflow::Flag> flag_list = {
       tensorflow::Flag("server_port", &server_port,
                        "the IP and port of the server"),
       tensorflow::Flag("image_file", &image_file, "the path to the image"),
       tensorflow::Flag("model_name", &model_name, "name of model"),
       tensorflow::Flag("model_signature_name", &model_signature_name,
-                       "name of model signature")};
+                       "name of model signature"),
+      tensorflow::Flag("input_name", &input_name, "name of input tensor")};
 
   tensorflow::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
@@ -201,7 +204,8 @@ int main(int argc, char** argv) {
       grpc::CreateChannel(server_port, grpc::InsecureChannelCredentials()));
   std::cout << "calling predict using file: " << image_file << "  ..."
             << std::endl;
-  std::cout << guide.callPredict(model_name, model_signature_name, image_file)
+  std::cout << guide.callPredict(model_name, model_signature_name, input_name,
+                                 image_file)
             << std::endl;
   return 0;
 }