clang-format | Format fbsource with clang-format 21.

Reviewed By: ChristianK275

Differential Revision: D85317706

fbshipit-source-id: b399c5c4b75252999442b7d7d2778e7a241b0025

pytorch3d/csrc/ext.cpp

@@ -105,7 +105,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   py::class_<
       pulsar::pytorch::Renderer,
       std::shared_ptr<pulsar::pytorch::Renderer>>(m, "PulsarRenderer")
-      .def(py::init<
+      .def(
+          py::init<
               const uint&,
               const uint&,
               const uint&,

pytorch3d/csrc/point_mesh/point_mesh_cpu.cpp

@@ -174,8 +174,8 @@ std::tuple<at::Tensor, at::Tensor> HullHullDistanceForwardCpu(
   at::Tensor idxs = at::zeros({A_N,}, as_first_idx.options());
   // clang-format on
 
-  auto as_a = as.accessor < float, H1 == 1 ? 2 : 3 > ();
+  auto as_a = as.accessor<float, H1 == 1 ? 2 : 3>();
-  auto bs_a = bs.accessor < float, H2 == 1 ? 2 : 3 > ();
+  auto bs_a = bs.accessor<float, H2 == 1 ? 2 : 3>();
   auto as_first_idx_a = as_first_idx.accessor<int64_t, 1>();
   auto bs_first_idx_a = bs_first_idx.accessor<int64_t, 1>();
   auto dists_a = dists.accessor<float, 1>();
@@ -230,10 +230,10 @@ std::tuple<at::Tensor, at::Tensor> HullHullDistanceBackwardCpu(
   at::Tensor grad_as = at::zeros_like(as);
   at::Tensor grad_bs = at::zeros_like(bs);
 
-  auto as_a = as.accessor < float, H1 == 1 ? 2 : 3 > ();
+  auto as_a = as.accessor<float, H1 == 1 ? 2 : 3>();
-  auto bs_a = bs.accessor < float, H2 == 1 ? 2 : 3 > ();
+  auto bs_a = bs.accessor<float, H2 == 1 ? 2 : 3>();
-  auto grad_as_a = grad_as.accessor < float, H1 == 1 ? 2 : 3 > ();
+  auto grad_as_a = grad_as.accessor<float, H1 == 1 ? 2 : 3>();
-  auto grad_bs_a = grad_bs.accessor < float, H2 == 1 ? 2 : 3 > ();
+  auto grad_bs_a = grad_bs.accessor<float, H2 == 1 ? 2 : 3>();
   auto idx_bs_a = idx_bs.accessor<int64_t, 1>();
   auto grad_dists_a = grad_dists.accessor<float, 1>();
 

pytorch3d/csrc/pulsar/host/commands.h

@@ -357,11 +357,11 @@ void MAX_WS(
 //
 //
 #define END_PARALLEL() \
-  end_parallel :;      \
+  end_parallel:;       \
   }
 #define END_PARALLEL_NORET() }
 #define END_PARALLEL_2D() \
-  end_parallel :;         \
+  end_parallel:;          \
   }                       \
   }
 #define END_PARALLEL_2D_NORET() \

pytorch3d/csrc/pulsar/include/renderer.construct.device.h

@@ -52,7 +52,7 @@ HOST void construct(
   self->cam.film_width = width;
   self->cam.film_height = height;
   self->max_num_balls = max_num_balls;
-  MALLOC(self->result_d, float, width* height* n_channels);
+  MALLOC(self->result_d, float, width * height * n_channels);
   self->cam.orthogonal_projection = orthogonal_projection;
   self->cam.right_handed = right_handed_system;
   self->cam.background_normalization_depth = background_normalization_depth;
@@ -93,7 +93,7 @@ HOST void construct(
   MALLOC(self->di_sorted_d, DrawInfo, max_num_balls);
   MALLOC(self->region_flags_d, char, max_num_balls);
   MALLOC(self->num_selected_d, size_t, 1);
-  MALLOC(self->forw_info_d, float, width* height * (3 + 2 * n_track));
+  MALLOC(self->forw_info_d, float, width * height * (3 + 2 * n_track));
   MALLOC(self->min_max_pixels_d, IntersectInfo, 1);
   MALLOC(self->grad_pos_d, float3, max_num_balls);
   MALLOC(self->grad_col_d, float, max_num_balls* n_channels);

pytorch3d/csrc/pulsar/include/renderer.h

@@ -255,7 +255,7 @@ GLOBAL void calc_signature(
  * for every iteration through the loading loop every thread could add a
  * 'hit' to the buffer.
  */
-#define RENDER_BUFFER_SIZE RENDER_BLOCK_SIZE* RENDER_BLOCK_SIZE * 2
+#define RENDER_BUFFER_SIZE RENDER_BLOCK_SIZE * RENDER_BLOCK_SIZE * 2
 /**
  * The threshold after which the spheres that are in the render buffer
  * are rendered and the buffer is flushed.

pytorch3d/csrc/utils/dispatch.cuh

@@ -99,8 +99,7 @@ namespace {
 // and increment it via template recursion until it is equal to the run-time
 // argument N.
 template <
-    template <typename, int64_t>
+    template <typename, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -124,8 +123,7 @@ struct DispatchKernelHelper1D {
 // 1D dispatch: Specialization when curN == maxN
 // We need this base case to avoid infinite template recursion.
 template <
-    template <typename, int64_t>
+    template <typename, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -145,8 +143,7 @@ struct DispatchKernelHelper1D<Kernel, T, minN, maxN, maxN, Args...> {
 // the run-time values of N and M, at which point we dispatch to the run
 // method of the kernel.
 template <
-    template <typename, int64_t, int64_t>
+    template <typename, int64_t, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -203,8 +200,7 @@ struct DispatchKernelHelper2D {
 
 // 2D dispatch, specialization for curN == maxN
 template <
-    template <typename, int64_t, int64_t>
+    template <typename, int64_t, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -243,8 +239,7 @@ struct DispatchKernelHelper2D<
 
 // 2D dispatch, specialization for curM == maxM
 template <
-    template <typename, int64_t, int64_t>
+    template <typename, int64_t, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -283,8 +278,7 @@ struct DispatchKernelHelper2D<
 
 // 2D dispatch, specialization for curN == maxN, curM == maxM
 template <
-    template <typename, int64_t, int64_t>
+    template <typename, int64_t, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -313,8 +307,7 @@ struct DispatchKernelHelper2D<
 
 // This is the function we expect users to call to dispatch to 1D functions
 template <
-    template <typename, int64_t>
+    template <typename, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -330,8 +323,7 @@ void DispatchKernel1D(const int64_t N, Args... args) {
 
 // This is the function we expect users to call to dispatch to 2D functions
 template <
-    template <typename, int64_t, int64_t>
+    template <typename, int64_t, int64_t> class Kernel,
-    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,