diff --git a/deploy/cpp/include/paddlex/paddlex.h b/deploy/cpp/include/paddlex/paddlex.h
index e4d34ba47041b2ba74c21a95925198e2378f3bae..327058e4bd3251f41be82309f154b41eae11027c 100644
--- a/deploy/cpp/include/paddlex/paddlex.h
+++ b/deploy/cpp/include/paddlex/paddlex.h
@@ -233,6 +233,6 @@ class Model {
   // a predictor which run the model predicting
   std::unique_ptr<paddle::PaddlePredictor> predictor_;
   // input channel
-  int input_channel;
+  int input_channel_;
 };
 }  // namespace PaddleX
diff --git a/deploy/cpp/src/paddlex.cpp b/deploy/cpp/src/paddlex.cpp
index a018ffd0f94911b18dd4d59cad1fbe2f3ff391b5..5d33ae0e60285f41a2c24cca0ce96f51b54478bf 100644
--- a/deploy/cpp/src/paddlex.cpp
+++ b/deploy/cpp/src/paddlex.cpp
@@ -135,9 +135,9 @@ bool Model::load_config(const std::string& yaml_input) {
     labels[index] = item.as<std::string>();
   }
   if (config["_init_params"]["input_channel"].IsDefined()) {
-    input_channel = config["_init_params"]["input_channel"].as<int>();
+    input_channel_ = config["_init_params"]["input_channel"].as<int>();
   } else {
-    input_channel = 3;
+    input_channel_ = 3;
   }
   return true;
 }
@@ -184,7 +184,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
   auto in_tensor = predictor_->GetInputTensor("image");
   int h = inputs_.new_im_size_[0];
   int w = inputs_.new_im_size_[1];
-  in_tensor->Reshape({1, input_channel, h, w});
+  in_tensor->Reshape({1, input_channel_, h, w});
   in_tensor->copy_from_cpu(inputs_.im_data_.data());
   predictor_->ZeroCopyRun();
   // get result
@@ -231,12 +231,12 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
   auto in_tensor = predictor_->GetInputTensor("image");
   int h = inputs_batch_[0].new_im_size_[0];
   int w = inputs_batch_[0].new_im_size_[1];
-  in_tensor->Reshape({batch_size, input_channel, h, w});
-  std::vector<float> inputs_data(batch_size * input_channel * h * w);
+  in_tensor->Reshape({batch_size, input_channel_, h, w});
+  std::vector<float> inputs_data(batch_size * input_channel_ * h * w);
   for (int i = 0; i < batch_size; ++i) {
     std::copy(inputs_batch_[i].im_data_.begin(),
               inputs_batch_[i].im_data_.end(),
-              inputs_data.begin() + i * input_channel * h * w);
+              inputs_data.begin() + i * input_channel_ * h * w);
   }
   in_tensor->copy_from_cpu(inputs_data.data());
   // in_tensor->copy_from_cpu(inputs_.im_data_.data());
@@ -290,7 +290,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
   int h = inputs_.new_im_size_[0];
   int w = inputs_.new_im_size_[1];
   auto im_tensor = predictor_->GetInputTensor("image");
-  im_tensor->Reshape({1, input_channel, h, w});
+  im_tensor->Reshape({1, input_channel_, h, w});
   im_tensor->copy_from_cpu(inputs_.im_data_.data());
 
   if (name == "YOLOv3" || name == "PPYOLO") {
@@ -444,12 +444,12 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
   int h = inputs_batch_[0].new_im_size_[0];
   int w = inputs_batch_[0].new_im_size_[1];
   auto im_tensor = predictor_->GetInputTensor("image");
-  im_tensor->Reshape({batch_size, input_channel, h, w});
-  std::vector<float> inputs_data(batch_size * input_channel * h * w);
+  im_tensor->Reshape({batch_size, input_channel_, h, w});
+  std::vector<float> inputs_data(batch_size * input_channel_ * h * w);
   for (int i = 0; i < batch_size; ++i) {
     std::copy(inputs_batch_[i].im_data_.begin(),
               inputs_batch_[i].im_data_.end(),
-              inputs_data.begin() + i * input_channel * h * w);
+              inputs_data.begin() + i * input_channel_ * h * w);
   }
   im_tensor->copy_from_cpu(inputs_data.data());
   if (name == "YOLOv3" || name == "PPYOLO") {
@@ -589,7 +589,7 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
   int h = inputs_.new_im_size_[0];
   int w = inputs_.new_im_size_[1];
   auto im_tensor = predictor_->GetInputTensor("image");
-  im_tensor->Reshape({1, input_channel, h, w});
+  im_tensor->Reshape({1, input_channel_, h, w});
   im_tensor->copy_from_cpu(inputs_.im_data_.data());
 
   // predict
@@ -703,12 +703,12 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
   int h = inputs_batch_[0].new_im_size_[0];
   int w = inputs_batch_[0].new_im_size_[1];
   auto im_tensor = predictor_->GetInputTensor("image");
-  im_tensor->Reshape({batch_size, input_channel, h, w});
-  std::vector<float> inputs_data(batch_size * input_channel * h * w);
+  im_tensor->Reshape({batch_size, input_channel_, h, w});
+  std::vector<float> inputs_data(batch_size * input_channel_ * h * w);
   for (int i = 0; i < batch_size; ++i) {
     std::copy(inputs_batch_[i].im_data_.begin(),
               inputs_batch_[i].im_data_.end(),
-              inputs_data.begin() + i * input_channel * h * w);
+              inputs_data.begin() + i * input_channel_ * h * w);
   }
   im_tensor->copy_from_cpu(inputs_data.data());
   // im_tensor->copy_from_cpu(inputs_.im_data_.data());