style: Autoformat

cpp/include/tensor.hpp

@@ -25,6 +25,7 @@ using dimensions = struct {
  * @returns a new tensor with 0 padding.
  */
 [[nodiscard]] torch::Tensor
-change_sparse_representation(const torch::Tensor &input, tensor_size_t batch_idx);
+change_sparse_representation(const torch::Tensor &input,
+                             tensor_size_t batch_idx);
 
 #endif // !TENSOR_HPP

cpp/include/transformations.hpp

@@ -42,11 +42,12 @@ void scale_random(at::Tensor points, at::Tensor labels, float sigma,
  *
  * @param point_cloud is the point cloud that contains the points that will be
  *                    scaled.
- * @param labels      are the labels belonging to the aforementioned point cloud.
+ * @param labels      are the labels belonging to the aforementioned point
+ *                    cloud.
  * @param sigma       is the the standard deviation of the truncated normal
  *                    distribution.
- * @param max_scale   is the upper limit of the truncated normal distribution. The
- *                    lower limit is the inverse.
+ * @param max_scale   is the upper limit of the truncated normal distribution.
+ *                    The lower limit is the inverse.
  */
 void scale_local(at::Tensor point_cloud, at::Tensor labels, float sigma,
                  float max_scale);
@@ -122,8 +123,7 @@ void rotate_random(at::Tensor points, at::Tensor labels, float sigma);
  */
 [[nodiscard]] std::pair<torch::Tensor, torch::Tensor>
 delete_labels_by_min_points(const at::Tensor &points, const at::Tensor &labels,
-                            const at::Tensor &names,
-                            tensor_size_t min_points);
+                            const at::Tensor &names, tensor_size_t min_points);
 
 /**
  * Checks the amount of points for each bounding box.
@@ -144,7 +144,8 @@ delete_labels_by_min_points(const at::Tensor &points, const at::Tensor &labels,
  */
 [[nodiscard]] inline std::pair<torch::Tensor, torch::Tensor>
 _delete_labels_by_min_points(const at::Tensor &points, const at::Tensor &labels,
-                             const at::Tensor &names, const tensor_size_t min_points,
+                             const at::Tensor &names,
+                             const tensor_size_t min_points,
                              const tensor_size_t batch_idx) {
 
   const tensor_size_t num_labels = labels.size(0);
@@ -153,7 +154,8 @@ _delete_labels_by_min_points(const at::Tensor &points, const at::Tensor &labels,
   const tensor_size_t label_features = labels.size(1);
   const tensor_size_t name_features = names.size(1);
 
-  const auto point_indices = torch::zeros({num_labels, num_points}, torch::kI32);
+  const auto point_indices =
+      torch::zeros({num_labels, num_points}, torch::kI32);
   points_in_boxes_cpu(
       labels.contiguous(),
       points.index({torch::indexing::Slice(), torch::indexing::Slice(0, 3)})

cpp/src/tensor.cpp

@@ -48,7 +48,8 @@ change_sparse_representation(const torch::Tensor &input,
       };
 
   auto gather_tensors = [num_tensors, num_features](
-                            const torch::Tensor &in, const tensor_size_t batch_idx_,
+                            const torch::Tensor &in,
+                            const tensor_size_t batch_idx_,
                             const size_t num_batches, tensor_size_t max_count) {
     std::vector<torch::Tensor> batch;
     batch.reserve(num_batches);

cpp/src/transformations.cpp

@@ -484,7 +484,8 @@ local_to_local_transform(const torch::Tensor &from_pose,
                          const torch::Tensor &to_pose) {
 
   const auto local_to_world = local_to_world_transform(from_pose);
-  const auto world_to_local = torch::linalg_inv(local_to_world_transform(to_pose));
+  const auto world_to_local =
+      torch::linalg_inv(local_to_world_transform(to_pose));
 
   return world_to_local.mm(local_to_world);
 }

cpp/src/utils.cpp

@@ -10,17 +10,17 @@ namespace torch_utils {
 
 [[nodiscard]] torch::Tensor boxes_to_corners(const torch::Tensor &boxes) {
 
-    const auto t = torch::tensor({
-                       {1, -1, -1},
-                       {1, 1, -1},
-                       {-1, 1, -1},
-                       {-1, -1, -1},
-                       {1, -1, 1},
-                       {1, 1, 1},
-                       {-1, 1, 1},
-                       {-1, -1, 1},
-                   }) /
-                   2;
+  const auto t = torch::tensor({
+                     {1, -1, -1},
+                     {1, 1, -1},
+                     {-1, 1, -1},
+                     {-1, -1, -1},
+                     {1, -1, 1},
+                     {1, 1, 1},
+                     {-1, 1, 1},
+                     {-1, -1, 1},
+                 }) /
+                 2;
 
   auto corners = boxes.index({Slice(), None, Slice(3, 6)}).repeat({1, 8, 1}) *
                  t.index({None, Slice(), Slice()});

cpp/src/weather.cpp

@@ -43,8 +43,7 @@ calculate_factors(const fog_parameter metric, const float viewing_dist) {
 
 [[nodiscard]] std::optional<std::vector<torch::Tensor>>
 fog(const torch::Tensor &point_cloud, const float prob,
-    const fog_parameter metric,
-    const float sigma, const int mean) {
+    const fog_parameter metric, const float sigma, const int mean) {
 
   auto rng = get_rng();
   std::uniform_real_distribution<float> distrib(0, HUNDRED_PERCENT - 1);
@@ -74,8 +73,7 @@ fog(const torch::Tensor &point_cloud, const float prob,
 
 [[nodiscard]] torch::Tensor
 fog(torch::Tensor point_cloud, const fog_parameter metric,
-    const float viewing_dist,
-    point_cloud_data::intensity_range max_intensity) {
+    const float viewing_dist, point_cloud_data::intensity_range max_intensity) {
 
   const auto [extinction_factor, beta, delete_probability] =
       calculate_factors(metric, viewing_dist);
@@ -247,8 +245,8 @@ void universal_weather(torch::Tensor point_cloud, const float prob,
                        const float sigma, const int mean, const float ext_a,
                        const float ext_b, const float beta_a,
                        const float beta_b, const float del_a, const float del_b,
-                       const int int_a,
-                       const int int_b, const int mean_int, const int int_range) {
+                       const int int_a, const int int_b, const int mean_int,
+                       const int int_range) {
 
   auto rng = get_rng();
   std::uniform_real_distribution<float> distrib(0, HUNDRED_PERCENT - 1);