fix(tensorrt): update tensorrt code of lidar_centerpoint

Author: M. Fatih Cırıt

perception/lidar_centerpoint/include/lidar_centerpoint/network/tensorrt_wrapper.hpp

@@ -64,6 +64,7 @@ class TensorRTWrapper
     const std::string & onnx_path, const std::string & engine_path, const std::string & precision);
 
   unique_ptr<nvinfer1::IExecutionContext> context_ = nullptr;
+  unique_ptr<nvinfer1::ICudaEngine> engine_ = nullptr;
 
 protected:
   virtual bool setProfile(
@@ -86,7 +87,6 @@ class TensorRTWrapper
 
   unique_ptr<nvinfer1::IRuntime> runtime_ = nullptr;
   unique_ptr<nvinfer1::IHostMemory> plan_ = nullptr;
-  unique_ptr<nvinfer1::ICudaEngine> engine_ = nullptr;
 };
 
 }  // namespace centerpoint

perception/lidar_centerpoint/lib/centerpoint_trt.cpp

@@ -38,8 +38,9 @@ CenterPointTRT::CenterPointTRT(
   encoder_trt_ptr_ = std::make_unique<VoxelEncoderTRT>(config_, verbose_);
   encoder_trt_ptr_->init(
     encoder_param.onnx_path(), encoder_param.engine_path(), encoder_param.trt_precision());
-  encoder_trt_ptr_->context_->setBindingDimensions(
-    0,
+  std::string name_tensor_encoder_in = encoder_trt_ptr_->engine_->getIOTensorName(0);
+  encoder_trt_ptr_->context_->setInputShape(
+    name_tensor_encoder_in.c_str(),
     nvinfer1::Dims3(
       config_.max_voxel_size_, config_.max_point_in_voxel_size_, config_.encoder_in_feature_size_));
 
@@ -49,10 +50,11 @@ CenterPointTRT::CenterPointTRT(
     config_.head_out_dim_size_, config_.head_out_rot_size_,    config_.head_out_vel_size_};
   head_trt_ptr_ = std::make_unique<HeadTRT>(out_channel_sizes, config_, verbose_);
   head_trt_ptr_->init(head_param.onnx_path(), head_param.engine_path(), head_param.trt_precision());
-  head_trt_ptr_->context_->setBindingDimensions(
-    0, nvinfer1::Dims4(
-         config_.batch_size_, config_.encoder_out_feature_size_, config_.grid_size_y_,
-         config_.grid_size_x_));
+  std::string name_tensor_head_in = head_trt_ptr_->engine_->getIOTensorName(0);
+  head_trt_ptr_->context_->setInputShape(
+    name_tensor_head_in.c_str(), nvinfer1::Dims4(
+                                   config_.batch_size_, config_.encoder_out_feature_size_,
+                                   config_.grid_size_y_, config_.grid_size_x_));
 
   initPtr();
 
@@ -166,8 +168,7 @@ void CenterPointTRT::inference()
   }
 
   // pillar encoder network
-  std::vector<void *> encoder_buffers{encoder_in_features_d_.get(), pillar_features_d_.get()};
-  encoder_trt_ptr_->context_->enqueueV2(encoder_buffers.data(), stream_, nullptr);
+  encoder_trt_ptr_->context_->enqueueV3(stream_);
 
   // scatter
   CHECK_CUDA_ERROR(scatterFeatures_launch(
@@ -176,11 +177,7 @@ void CenterPointTRT::inference()
     spatial_features_d_.get(), stream_));
 
   // head network
-  std::vector<void *> head_buffers = {spatial_features_d_.get(), head_out_heatmap_d_.get(),
-                                      head_out_offset_d_.get(),  head_out_z_d_.get(),
-                                      head_out_dim_d_.get(),     head_out_rot_d_.get(),
-                                      head_out_vel_d_.get()};
-  head_trt_ptr_->context_->enqueueV2(head_buffers.data(), stream_, nullptr);
+  head_trt_ptr_->context_->enqueueV3(stream_);
 }
 
 void CenterPointTRT::postProcess(std::vector<Box3D> & det_boxes3d)