fix(tensorrt): update tensorrt code of tensorrt_yolo

Signed-off-by: M. Fatih Cırıt <mfc@leodrive.ai>

Author: M. Fatih Cırıt

Diff for: perception/tensorrt_yolo/lib/include/trt_yolo.hpp

@@ -138,13 +138,15 @@ class Net
   cuda::unique_ptr<float[]> out_scores_d_ = nullptr;
   cuda::unique_ptr<float[]> out_boxes_d_ = nullptr;
   cuda::unique_ptr<float[]> out_classes_d_ = nullptr;
+  std::string name_tensor_in_;
+  std::string name_tensor_out_;
 
   void load(const std::string & path);
   bool prepare();
   std::vector<float> preprocess(
     const cv::Mat & in_img, const int c, const int h, const int w) const;
   // Infer using pre-allocated GPU buffers {data, scores, boxes}
-  void infer(std::vector<void *> & buffers, const int batch_size);
+  void infer(const int batch_size);
 };
 
 bool set_cuda_device(int gpu_id)

Diff for: perception/tensorrt_yolo/lib/src/trt_yolo.cpp

@@ -122,6 +122,8 @@ Net::Net(const std::string & path, bool verbose)
     std::cout << "Fail to prepare engine" << std::endl;
     return;
   }
+  name_tensor_in_ = engine_->getIOTensorName(0);
+  name_tensor_out_ = engine_->getIOTensorName(engine_->getNbIOTensors() - 1);
 }
 
 Net::~Net()
@@ -267,6 +269,8 @@ Net::Net(
     std::cout << "Fail to create engine" << std::endl;
     return;
   }
+  name_tensor_in_ = engine_->getIOTensorName(0);
+  name_tensor_out_ = engine_->getIOTensorName(engine_->getNbIOTensors() - 1);
 }
 
 void Net::save(const std::string & path) const
@@ -276,15 +280,16 @@ void Net::save(const std::string & path) const
   file.write(reinterpret_cast<const char *>(plan_->data()), plan_->size());
 }
 
-void Net::infer(std::vector<void *> & buffers, const int batch_size)
+void Net::infer(const int batch_size)
 {
   if (!context_) {
     throw std::runtime_error("Fail to create context");
   }
-  auto input_dims = engine_->getBindingDimensions(0);
-  context_->setBindingDimensions(
-    0, nvinfer1::Dims4(batch_size, input_dims.d[1], input_dims.d[2], input_dims.d[3]));
-  context_->enqueueV2(buffers.data(), stream_, nullptr);
+  const auto input_dims = engine_->getTensorShape(name_tensor_in_.c_str());
+  context_->setInputShape(
+    name_tensor_in_.c_str(),
+    nvinfer1::Dims4(batch_size, input_dims.d[1], input_dims.d[2], input_dims.d[3]));
+  context_->enqueueV3(stream_);
   cudaStreamSynchronize(stream_);
 }
 
@@ -294,10 +299,8 @@ bool Net::detect(const cv::Mat & in_img, float * out_scores, float * out_boxes,
   const auto input = preprocess(in_img, input_dims.at(0), input_dims.at(2), input_dims.at(1));
   CHECK_CUDA_ERROR(
     cudaMemcpy(input_d_.get(), input.data(), input.size() * sizeof(float), cudaMemcpyHostToDevice));
-  std::vector<void *> buffers = {
-    input_d_.get(), out_scores_d_.get(), out_boxes_d_.get(), out_classes_d_.get()};
   try {
-    infer(buffers, 1);
+    infer(1);
   } catch (const std::runtime_error & e) {
     return false;
   }
@@ -316,13 +319,14 @@ bool Net::detect(const cv::Mat & in_img, float * out_scores, float * out_boxes,
 
 std::vector<int> Net::getInputDims() const
 {
-  auto dims = engine_->getBindingDimensions(0);
+  const auto dims = engine_->getTensorShape(name_tensor_in_.c_str());
   return {dims.d[1], dims.d[2], dims.d[3]};
 }
 
 int Net::getMaxBatchSize() const
 {
-  return engine_->getProfileDimensions(0, 0, nvinfer1::OptProfileSelector::kMAX).d[0];
+  return engine_->getProfileShape(name_tensor_in_.c_str(), 0, nvinfer1::OptProfileSelector::kMAX)
+    .d[0];
 }
 
 int Net::getInputSize() const
@@ -333,6 +337,6 @@ int Net::getInputSize() const
   return input_size;
 }
 
-int Net::getMaxDetections() const { return engine_->getBindingDimensions(1).d[1]; }
+int Net::getMaxDetections() const { return engine_->getTensorShape(name_tensor_out_.c_str()).d[1]; }
 
 }  // namespace yolo