fix: spellings

Signed-off-by: Barış Zeren <baris@leodrive.ai>

Author: Barış Zeren

perception/autoware_tensorrt_rtmdet/README.md

@@ -3,7 +3,7 @@
 ## Purpose
 
 RTMDet is a real-time instance segmentation model which can be used for detecting objects like cars, pedestrians,
-bicycles, etc. in a scene. This package provides a ROS2 interface for RTMDet using TensorRT.
+bicycles, etc. in a scene. This package provides a ROS 2 interface for RTMDet using TensorRT.
 
 ## Inner-workings / Algorithms
 

perception/autoware_tensorrt_rtmdet/include/tensorrt_rtmdet/tensorrt_rtmdet.hpp

@@ -171,12 +171,12 @@ class TrtRTMDet
   CudaUniquePtr<float[]> input_d_;
 
   // Device pointer for outputs
-  CudaUniquePtr<float[]> out_dets_d_;
+  CudaUniquePtr<float[]> out_detections_d_;
   CudaUniquePtr<int32_t[]> out_labels_d_;
   CudaUniquePtr<float[]> out_masks_d_;
 
   // Host pointer for outputs
-  std::unique_ptr<float[]> out_dets_h_;
+  std::unique_ptr<float[]> out_detections_h_;
   std::unique_ptr<int32_t[]> out_labels_h_;
   std::unique_ptr<float[]> out_masks_h_;
 

perception/autoware_tensorrt_rtmdet/src/tensorrt_rtmdet.cpp

@@ -120,19 +120,19 @@ TrtRTMDet::TrtRTMDet(
 
   input_d_ = autoware::cuda_utils::make_unique<float[]>(
     batch_size_ * input_dims.d[1] * input_dims.d[2] * input_dims.d[3]);
-  out_dets_d_ = autoware::cuda_utils::make_unique<float[]>(batch_size_ * max_detections_ * 5);
+  out_detections_d_ = autoware::cuda_utils::make_unique<float[]>(batch_size_ * max_detections_ * 5);
   out_labels_d_ = autoware::cuda_utils::make_unique<int32_t[]>(batch_size_ * max_detections_);
   out_masks_d_ = autoware::cuda_utils::make_unique<float[]>(
     batch_size_ * max_detections_ * model_input_width_ * model_input_height_);
 
-  out_dets_h_ = std::make_unique<float[]>(batch_size_ * max_detections_ * 5);
+  out_detections_h_ = std::make_unique<float[]>(batch_size_ * max_detections_ * 5);
   out_labels_h_ = std::make_unique<int32_t[]>(batch_size_ * max_detections_);
   out_masks_h_ = std::make_unique<float[]>(
     batch_size_ * max_detections_ * model_input_width_ * model_input_height_);
 
 #if (NV_TENSORRT_MAJOR * 1000) + (NV_TENSORRT_MINOR * 100) + NV_TENSOR_PATCH >= 8050
   std::vector<void *> buffers = {
-    input_d_.get(), out_dets_d_.get(), out_labels_d_.get(), out_masks_d_.get()};
+    input_d_.get(), out_detections_d_.get(), out_labels_d_.get(), out_masks_d_.get()};
   trt_common_->setupBindings(buffers);
 #endif
 }
@@ -238,12 +238,12 @@ bool TrtRTMDet::feedforward(
 #endif
 
   const auto batch_size = images.size();
-  out_dets_h_.reset(new float[batch_size_ * max_detections_ * 5]);
+  out_detections_h_.reset(new float[batch_size_ * max_detections_ * 5]);
   out_labels_h_.reset(new int32_t[batch_size_ * max_detections_]);
   out_masks_h_.reset(new float[batch_size_ * 20 * model_input_width_ * model_input_height_]);
 
   CHECK_CUDA_ERROR(cudaMemcpyAsync(
-    out_dets_h_.get(), out_dets_d_.get(), sizeof(float) * batch_size_ * max_detections_ * 5,
+    out_detections_h_.get(), out_detections_d_.get(), sizeof(float) * batch_size_ * max_detections_ * 5,
     cudaMemcpyDeviceToHost, *stream_));
   CHECK_CUDA_ERROR(cudaMemcpyAsync(
     out_labels_h_.get(), out_labels_d_.get(), sizeof(int32_t) * batch_size_ * max_detections_,
@@ -260,22 +260,22 @@ bool TrtRTMDet::feedforward(
   for (size_t batch = 0; batch < batch_size; ++batch) {
     ObjectArray object_array;
     for (uint32_t index = 0; index < max_detections_; ++index) {
-      if (out_dets_h_[(batch * max_detections_ * 5) + ((5 * index) + 4)] < score_threshold_) {
+      if (out_detections_h_[(batch * max_detections_ * 5) + ((5 * index) + 4)] < score_threshold_) {
         break;
       }
 
       Object object{};
       object.mask_index = index;
       object.class_id = out_labels_h_[(batch * max_detections_) + index];
       object.x1 = static_cast<uint32_t>(
-        out_dets_h_[(batch * max_detections_ * 5) + ((5 * index) + 0)] / scale_width_);
+        out_detections_h_[(batch * max_detections_ * 5) + ((5 * index) + 0)] / scale_width_);
       object.y1 = static_cast<uint32_t>(
-        out_dets_h_[(batch * max_detections_ * 5) + ((5 * index) + 1)] / scale_height_);
+        out_detections_h_[(batch * max_detections_ * 5) + ((5 * index) + 1)] / scale_height_);
       object.x2 = static_cast<uint32_t>(
-        out_dets_h_[(batch * max_detections_ * 5) + ((5 * index) + 2)] / scale_width_);
+        out_detections_h_[(batch * max_detections_ * 5) + ((5 * index) + 2)] / scale_width_);
       object.y2 = static_cast<uint32_t>(
-        out_dets_h_[(batch * max_detections_ * 5) + ((5 * index) + 3)] / scale_height_);
-      object.score = out_dets_h_[(batch * max_detections_ * 5) + ((5 * index) + 4)];
+        out_detections_h_[(batch * max_detections_ * 5) + ((5 * index) + 3)] / scale_height_);
+      object.score = out_detections_h_[(batch * max_detections_ * 5) + ((5 * index) + 4)];
       object_array.push_back(object);
     }
     ObjectArray nms_objects;