Remove point mesh edge kernels

Summary:
Removes the now-unnecessary kernels from point mesh edge file

Migrates all point mesh functionality into one file.

Reviewed By: gkioxari

Differential Revision: D24550086

fbshipit-source-id: f924996cd38a7c2c1cf189d8a01611de4506cfa3

pytorch3d/csrc/ext.cpp

@@ -15,8 +15,7 @@
 #include "interp_face_attrs/interp_face_attrs.h"
 #include "knn/knn.h"
 #include "packed_to_padded_tensor/packed_to_padded_tensor.h"
-#include "point_mesh/point_mesh_edge.h"
-#include "point_mesh/point_mesh_face.h"
+#include "point_mesh/point_mesh_cuda.h"
 #include "rasterize_meshes/rasterize_meshes.h"
 #include "rasterize_points/rasterize_points.h"
 

pytorch3d/csrc/point_mesh/point_mesh_face.cu → pytorch3d/csrc/point_mesh/point_mesh_cuda.cu

@@ -12,7 +12,7 @@
 #include "utils/warp_reduce.cuh"
 
 // ****************************************************************************
-// *                          PointFaceDistance                               *
+// *                   Generic Forward/Backward Kernels                       *
 // ****************************************************************************
 
 __global__ void DistanceForwardKernel(
@@ -202,16 +202,6 @@ std::tuple<at::Tensor, at::Tensor> DistanceForwardCuda(
   return std::make_tuple(dists, idxs);
 }
 
-std::tuple<at::Tensor, at::Tensor> PointFaceDistanceForwardCuda(
-    const at::Tensor& points,
-    const at::Tensor& points_first_idx,
-    const at::Tensor& tris,
-    const at::Tensor& tris_first_idx,
-    const int64_t max_points) {
-  return DistanceForwardCuda(
-      points, 1, points_first_idx, tris, 3, tris_first_idx, max_points);
-}
-
 __global__ void DistanceBackwardKernel(
     const float* __restrict__ objects, // (O * oD * 3)
     const size_t objects_size, // O
@@ -365,6 +355,20 @@ std::tuple<at::Tensor, at::Tensor> DistanceBackwardCuda(
   return std::make_tuple(grad_points, grad_tris);
 }
 
+// ****************************************************************************
+// *                          PointFaceDistance                               *
+// ****************************************************************************
+
+std::tuple<at::Tensor, at::Tensor> PointFaceDistanceForwardCuda(
+    const at::Tensor& points,
+    const at::Tensor& points_first_idx,
+    const at::Tensor& tris,
+    const at::Tensor& tris_first_idx,
+    const int64_t max_points) {
+  return DistanceForwardCuda(
+      points, 1, points_first_idx, tris, 3, tris_first_idx, max_points);
+}
+
 std::tuple<at::Tensor, at::Tensor> PointFaceDistanceBackwardCuda(
     const at::Tensor& points,
     const at::Tensor& tris,
@@ -395,9 +399,54 @@ std::tuple<at::Tensor, at::Tensor> FacePointDistanceBackwardCuda(
   return DistanceBackwardCuda(tris, 3, points, 1, idx_tris, grad_dists);
 }
 
+// ****************************************************************************
+// *                          PointEdgeDistance                               *
+// ****************************************************************************
+
+std::tuple<at::Tensor, at::Tensor> PointEdgeDistanceForwardCuda(
+    const at::Tensor& points,
+    const at::Tensor& points_first_idx,
+    const at::Tensor& segms,
+    const at::Tensor& segms_first_idx,
+    const int64_t max_points) {
+  return DistanceForwardCuda(
+      points, 1, points_first_idx, segms, 2, segms_first_idx, max_points);
+}
+
+std::tuple<at::Tensor, at::Tensor> PointEdgeDistanceBackwardCuda(
+    const at::Tensor& points,
+    const at::Tensor& segms,
+    const at::Tensor& idx_points,
+    const at::Tensor& grad_dists) {
+  return DistanceBackwardCuda(points, 1, segms, 2, idx_points, grad_dists);
+}
+
+// ****************************************************************************
+// *                          EdgePointDistance                               *
+// ****************************************************************************
+
+std::tuple<at::Tensor, at::Tensor> EdgePointDistanceForwardCuda(
+    const at::Tensor& points,
+    const at::Tensor& points_first_idx,
+    const at::Tensor& segms,
+    const at::Tensor& segms_first_idx,
+    const int64_t max_segms) {
+  return DistanceForwardCuda(
+      segms, 2, segms_first_idx, points, 1, points_first_idx, max_segms);
+}
+
+std::tuple<at::Tensor, at::Tensor> EdgePointDistanceBackwardCuda(
+    const at::Tensor& points,
+    const at::Tensor& segms,
+    const at::Tensor& idx_segms,
+    const at::Tensor& grad_dists) {
+  return DistanceBackwardCuda(segms, 2, points, 1, idx_segms, grad_dists);
+}
+
 // ****************************************************************************
 // *                     PointFaceArrayDistance                               *
 // ****************************************************************************
+// TODO: Create wrapper function and merge kernel with other array kernel
 
 __global__ void PointFaceArrayForwardKernel(
     const float* __restrict__ points, // (P, 3)
@@ -565,3 +614,164 @@ std::tuple<at::Tensor, at::Tensor> PointFaceArrayDistanceBackwardCuda(
   AT_CUDA_CHECK(cudaGetLastError());
   return std::make_tuple(grad_points, grad_tris);
 }
+
+// ****************************************************************************
+// *                     PointEdgeArrayDistance                               *
+// ****************************************************************************
+// TODO: Create wrapper function and merge kernel with other array kernel
+
+__global__ void PointEdgeArrayForwardKernel(
+    const float* __restrict__ points, // (P, 3)
+    const float* __restrict__ segms, // (S, 2, 3)
+    float* __restrict__ dists, // (P, S)
+    const size_t P,
+    const size_t S) {
+  float3* points_f3 = (float3*)points;
+  float3* segms_f3 = (float3*)segms;
+
+  // Parallelize over P * S computations
+  const int num_threads = gridDim.x * blockDim.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+  for (int t_i = tid; t_i < P * S; t_i += num_threads) {
+    const int s = t_i / P; // segment index.
+    const int p = t_i % P; // point index
+    float3 a = segms_f3[s * 2 + 0];
+    float3 b = segms_f3[s * 2 + 1];
+
+    float3 point = points_f3[p];
+    float dist = PointLine3DistanceForward(point, a, b);
+    dists[p * S + s] = dist;
+  }
+}
+
+at::Tensor PointEdgeArrayDistanceForwardCuda(
+    const at::Tensor& points,
+    const at::Tensor& segms) {
+  // Check inputs are on the same device
+  at::TensorArg points_t{points, "points", 1}, segms_t{segms, "segms", 2};
+  at::CheckedFrom c = "PointEdgeArrayDistanceForwardCuda";
+  at::checkAllSameGPU(c, {points_t, segms_t});
+  at::checkAllSameType(c, {points_t, segms_t});
+
+  // Set the device for the kernel launch based on the device of the input
+  at::cuda::CUDAGuard device_guard(points.device());
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  const int64_t P = points.size(0);
+  const int64_t S = segms.size(0);
+
+  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
+  TORCH_CHECK(
+      (segms.size(1) == 2) && (segms.size(2) == 3),
+      "segms must be of shape Sx2x3");
+
+  at::Tensor dists = at::zeros({P, S}, points.options());
+
+  if (dists.numel() == 0) {
+    AT_CUDA_CHECK(cudaGetLastError());
+    return dists;
+  }
+
+  const size_t blocks = 1024;
+  const size_t threads = 64;
+
+  PointEdgeArrayForwardKernel<<<blocks, threads, 0, stream>>>(
+      points.contiguous().data_ptr<float>(),
+      segms.contiguous().data_ptr<float>(),
+      dists.data_ptr<float>(),
+      P,
+      S);
+
+  AT_CUDA_CHECK(cudaGetLastError());
+  return dists;
+}
+
+__global__ void PointEdgeArrayBackwardKernel(
+    const float* __restrict__ points, // (P, 3)
+    const float* __restrict__ segms, // (S, 2, 3)
+    const float* __restrict__ grad_dists, // (P, S)
+    float* __restrict__ grad_points, // (P, 3)
+    float* __restrict__ grad_segms, // (S, 2, 3)
+    const size_t P,
+    const size_t S) {
+  float3* points_f3 = (float3*)points;
+  float3* segms_f3 = (float3*)segms;
+
+  // Parallelize over P * S computations
+  const int num_threads = gridDim.x * blockDim.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+  for (int t_i = tid; t_i < P * S; t_i += num_threads) {
+    const int s = t_i / P; // segment index.
+    const int p = t_i % P; // point index
+    const float3 a = segms_f3[s * 2 + 0];
+    const float3 b = segms_f3[s * 2 + 1];
+
+    const float3 point = points_f3[p];
+    const float grad_dist = grad_dists[p * S + s];
+    const auto grads = PointLine3DistanceBackward(point, a, b, grad_dist);
+    const float3 grad_point = thrust::get<0>(grads);
+    const float3 grad_a = thrust::get<1>(grads);
+    const float3 grad_b = thrust::get<2>(grads);
+
+    atomicAdd(grad_points + p * 3 + 0, grad_point.x);
+    atomicAdd(grad_points + p * 3 + 1, grad_point.y);
+    atomicAdd(grad_points + p * 3 + 2, grad_point.z);
+
+    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 0, grad_a.x);
+    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 1, grad_a.y);
+    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 2, grad_a.z);
+
+    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 0, grad_b.x);
+    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 1, grad_b.y);
+    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 2, grad_b.z);
+  }
+}
+
+std::tuple<at::Tensor, at::Tensor> PointEdgeArrayDistanceBackwardCuda(
+    const at::Tensor& points,
+    const at::Tensor& segms,
+    const at::Tensor& grad_dists) {
+  // Check inputs are on the same device
+  at::TensorArg points_t{points, "points", 1}, segms_t{segms, "segms", 2},
+      grad_dists_t{grad_dists, "grad_dists", 3};
+  at::CheckedFrom c = "PointEdgeArrayDistanceBackwardCuda";
+  at::checkAllSameGPU(c, {points_t, segms_t, grad_dists_t});
+  at::checkAllSameType(c, {points_t, segms_t, grad_dists_t});
+
+  // Set the device for the kernel launch based on the device of the input
+  at::cuda::CUDAGuard device_guard(points.device());
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  const int64_t P = points.size(0);
+  const int64_t S = segms.size(0);
+
+  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
+  TORCH_CHECK(
+      (segms.size(1) == 2) && (segms.size(2) == 3),
+      "segms must be of shape Sx2x3");
+  TORCH_CHECK((grad_dists.size(0) == P) && (grad_dists.size(1) == S));
+
+  at::Tensor grad_points = at::zeros({P, 3}, points.options());
+  at::Tensor grad_segms = at::zeros({S, 2, 3}, segms.options());
+
+  if (grad_points.numel() == 0 || grad_segms.numel() == 0) {
+    AT_CUDA_CHECK(cudaGetLastError());
+    return std::make_tuple(grad_points, grad_segms);
+  }
+
+  const size_t blocks = 1024;
+  const size_t threads = 64;
+
+  PointEdgeArrayBackwardKernel<<<blocks, threads, 0, stream>>>(
+      points.contiguous().data_ptr<float>(),
+      segms.contiguous().data_ptr<float>(),
+      grad_dists.contiguous().data_ptr<float>(),
+      grad_points.data_ptr<float>(),
+      grad_segms.data_ptr<float>(),
+      P,
+      S);
+  AT_CUDA_CHECK(cudaGetLastError());
+  return std::make_tuple(grad_points, grad_segms);
+}

pytorch3d/csrc/point_mesh/point_mesh_face.h → pytorch3d/csrc/point_mesh/point_mesh_cuda.h

@@ -241,6 +241,242 @@ std::tuple<torch::Tensor, torch::Tensor> FacePointDistanceBackward(
   return FacePointDistanceBackwardCpu(points, tris, idx_tris, grad_dists);
 }
 
+// ****************************************************************************
+// *                      PointEdgeDistance                                   *
+// ****************************************************************************
+
+// Computes the squared euclidean distance of each p in points to the closest
+// mesh edge belonging to the corresponding example in the batch of size N.
+//
+// Args:
+//    points: FloatTensor of shape (P, 3)
+//    points_first_idx: LongTensor of shape (N,) indicating the first point
+//         index for each example in the batch
+//    segms: FloatTensor of shape (S, 2, 3) of edge segments. The s-th edge
+//        segment is spanned by (segms[s, 0], segms[s, 1])
+//    segms_first_idx: LongTensor of shape (N,) indicating the first edge
+//        index for each example in the batch
+//    max_points: Scalar equal to max(P_i) for i in [0, N - 1] containing
+//        the maximum number of points in the batch and is used to set
+//        the grid dimensions in the CUDA implementation.
+//
+// Returns:
+//    dists: FloatTensor of shape (P,), where dists[p] is the squared euclidean
+//        distance of points[p] to the closest edge in the same example in the
+//        batch.
+//    idxs: LongTensor of shape (P,), where idxs[p] is the index of the closest
+//        edge in the batch.
+//        So, dists[p] = d(points[p], segms[idxs[p], 0], segms[idxs[p], 1]),
+//        where d(u, v0, v1) is the distance of u from the segment spanned by
+//        (v0, v1).
+//
+
+#ifdef WITH_CUDA
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForwardCuda(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& segms,
+    const torch::Tensor& segms_first_idx,
+    const int64_t max_points);
+#endif
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& segms,
+    const torch::Tensor& segms_first_idx,
+    const int64_t max_points);
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForward(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& segms,
+    const torch::Tensor& segms_first_idx,
+    const int64_t max_points) {
+  if (points.is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CUDA(points);
+    CHECK_CUDA(points_first_idx);
+    CHECK_CUDA(segms);
+    CHECK_CUDA(segms_first_idx);
+    return PointEdgeDistanceForwardCuda(
+        points, points_first_idx, segms, segms_first_idx, max_points);
+#else
+    AT_ERROR("Not compiled with GPU support.");
+#endif
+  }
+  return PointEdgeDistanceForwardCpu(
+      points, points_first_idx, segms, segms_first_idx, max_points);
+}
+
+// Backward pass for PointEdgeDistance.
+//
+// Args:
+//    points: FloatTensor of shape (P, 3)
+//    segms: FloatTensor of shape (S, 2, 3)
+//    idx_points: LongTensor of shape (P,) containing the indices
+//        of the closest edge in the example in the batch.
+//        This is computed by the forward pass.
+//    grad_dists: FloatTensor of shape (P,)
+//
+// Returns:
+//    grad_points: FloatTensor of shape (P, 3)
+//    grad_segms: FloatTensor of shape (S, 2, 3)
+//
+
+#ifdef WITH_CUDA
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackwardCuda(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& idx_points,
+    const torch::Tensor& grad_dists);
+#endif
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& idx_points,
+    const torch::Tensor& grad_dists);
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackward(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& idx_points,
+    const torch::Tensor& grad_dists) {
+  if (points.is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CUDA(points);
+    CHECK_CUDA(segms);
+    CHECK_CUDA(idx_points);
+    CHECK_CUDA(grad_dists);
+    return PointEdgeDistanceBackwardCuda(points, segms, idx_points, grad_dists);
+#else
+    AT_ERROR("Not compiled with GPU support.");
+#endif
+  }
+  return PointEdgeDistanceBackwardCpu(points, segms, idx_points, grad_dists);
+}
+
+// ****************************************************************************
+// *                      EdgePointDistance                                   *
+// ****************************************************************************
+
+// Computes the squared euclidean distance of each edge segment to the closest
+// point belonging to the corresponding example in the batch of size N.
+//
+// Args:
+//    points: FloatTensor of shape (P, 3)
+//    points_first_idx: LongTensor of shape (N,) indicating the first point
+//         index for each example in the batch
+//    segms: FloatTensor of shape (S, 2, 3) of edge segments. The s-th edge
+//        segment is spanned by (segms[s, 0], segms[s, 1])
+//    segms_first_idx: LongTensor of shape (N,) indicating the first edge
+//        index for each example in the batch
+//    max_segms: Scalar equal to max(S_i) for i in [0, N - 1] containing
+//        the maximum number of edges in the batch and is used to set
+//        the block dimensions in the CUDA implementation.
+//
+// Returns:
+//    dists: FloatTensor of shape (S,), where dists[s] is the squared
+//        euclidean distance of s-th edge to the closest point in the
+//        corresponding example in the batch.
+//    idxs: LongTensor of shape (S,), where idxs[s] is the index of the closest
+//        point in the example in the batch.
+//        So, dists[s] = d(points[idxs[s]], segms[s, 0], segms[s, 1]), where
+//        d(u, v0, v1) is the distance of u from the segment spanned by (v0, v1)
+//
+//
+
+#ifdef WITH_CUDA
+
+std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForwardCuda(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& segms,
+    const torch::Tensor& segms_first_idx,
+    const int64_t max_segms);
+#endif
+
+std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& segms,
+    const torch::Tensor& segms_first_idx,
+    const int64_t max_segms);
+
+std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForward(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& segms,
+    const torch::Tensor& segms_first_idx,
+    const int64_t max_segms) {
+  if (points.is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CUDA(points);
+    CHECK_CUDA(points_first_idx);
+    CHECK_CUDA(segms);
+    CHECK_CUDA(segms_first_idx);
+    return EdgePointDistanceForwardCuda(
+        points, points_first_idx, segms, segms_first_idx, max_segms);
+#else
+    AT_ERROR("Not compiled with GPU support.");
+#endif
+  }
+  return EdgePointDistanceForwardCpu(
+      points, points_first_idx, segms, segms_first_idx, max_segms);
+}
+
+// Backward pass for EdgePointDistance.
+//
+// Args:
+//    points: FloatTensor of shape (P, 3)
+//    segms: FloatTensor of shape (S, 2, 3)
+//    idx_segms: LongTensor of shape (S,) containing the indices
+//        of the closest point in the example in the batch.
+//        This is computed by the forward pass
+//    grad_dists: FloatTensor of shape (S,)
+//
+// Returns:
+//    grad_points: FloatTensor of shape (P, 3)
+//    grad_segms: FloatTensor of shape (S, 2, 3)
+//
+
+#ifdef WITH_CUDA
+
+std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackwardCuda(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& idx_segms,
+    const torch::Tensor& grad_dists);
+#endif
+
+std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& idx_segms,
+    const torch::Tensor& grad_dists);
+
+std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackward(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& idx_segms,
+    const torch::Tensor& grad_dists) {
+  if (points.is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CUDA(points);
+    CHECK_CUDA(segms);
+    CHECK_CUDA(idx_segms);
+    CHECK_CUDA(grad_dists);
+    return EdgePointDistanceBackwardCuda(points, segms, idx_segms, grad_dists);
+#else
+    AT_ERROR("Not compiled with GPU support.");
+#endif
+  }
+  return EdgePointDistanceBackwardCpu(points, segms, idx_segms, grad_dists);
+}
+
 // ****************************************************************************
 // *                       PointFaceArrayDistance                             *
 // ****************************************************************************
@@ -328,3 +564,92 @@ std::tuple<torch::Tensor, torch::Tensor> PointFaceArrayDistanceBackward(
   }
   return PointFaceArrayDistanceBackwardCpu(points, tris, grad_dists);
 }
+
+// ****************************************************************************
+// *                          PointEdgeArrayDistance                          *
+// ****************************************************************************
+
+// Computes the squared euclidean distance of each p in points to each edge
+// segment in segms.
+//
+// Args:
+//    points: FloatTensor of shape (P, 3)
+//    segms: FloatTensor of shape (S, 2, 3) of edge segments. The s-th
+//        edge segment is spanned by (segms[s, 0], segms[s, 1])
+//
+// Returns:
+//    dists: FloatTensor of shape (P, S), where dists[p, s] is the squared
+//        euclidean distance of points[p] to the segment spanned by
+//        (segms[s, 0], segms[s, 1])
+//
+// For pointcloud and meshes of batch size N, this function requires N
+// computations. The memory occupied is O(NPS) which can become quite large.
+// For example, a medium sized batch with N = 32 with P = 10000 and S = 5000
+// will require for the forward pass 5.8G of memory to store dists.
+
+#ifdef WITH_CUDA
+torch::Tensor PointEdgeArrayDistanceForwardCuda(
+    const torch::Tensor& points,
+    const torch::Tensor& segms);
+#endif
+
+torch::Tensor PointEdgeArrayDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& segms);
+
+torch::Tensor PointEdgeArrayDistanceForward(
+    const torch::Tensor& points,
+    const torch::Tensor& segms) {
+  if (points.is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CUDA(points);
+    CHECK_CUDA(segms);
+    return PointEdgeArrayDistanceForwardCuda(points, segms);
+#else
+    AT_ERROR("Not compiled with GPU support.");
+#endif
+  }
+  return PointEdgeArrayDistanceForwardCpu(points, segms);
+}
+
+// Backward pass for PointEdgeArrayDistance.
+//
+// Args:
+//    points: FloatTensor of shape (P, 3)
+//    segms: FloatTensor of shape (S, 2, 3)
+//    grad_dists: FloatTensor of shape (P, S)
+//
+// Returns:
+//   grad_points: FloatTensor of shape (P, 3)
+//   grad_segms: FloatTensor of shape (S, 2, 3)
+//
+
+#ifdef WITH_CUDA
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackwardCuda(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& grad_dists);
+#endif
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& grad_dists);
+
+std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackward(
+    const torch::Tensor& points,
+    const torch::Tensor& segms,
+    const torch::Tensor& grad_dists) {
+  if (points.is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CUDA(points);
+    CHECK_CUDA(segms);
+    CHECK_CUDA(grad_dists);
+    return PointEdgeArrayDistanceBackwardCuda(points, segms, grad_dists);
+#else
+    AT_ERROR("Not compiled with GPU support.");
+#endif
+  }
+  return PointEdgeArrayDistanceBackwardCpu(points, segms, grad_dists);
+}

pytorch3d/csrc/point_mesh/point_mesh_edge.cu

-// Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <algorithm>
-#include <list>
-#include <queue>
-#include <tuple>
-#include "utils/float_math.cuh"
-#include "utils/geometry_utils.cuh"
-#include "utils/warp_reduce.cuh"
-
-// ****************************************************************************
-// *                          PointEdgeDistance                               *
-// ****************************************************************************
-
-__global__ void PointEdgeForwardKernel(
-    const float* __restrict__ points, // (P, 3)
-    const int64_t* __restrict__ points_first_idx, // (B,)
-    const float* __restrict__ segms, // (S, 2, 3)
-    const int64_t* __restrict__ segms_first_idx, // (B,)
-    float* __restrict__ dist_points, // (P,)
-    int64_t* __restrict__ idx_points, // (P,)
-    const size_t B,
-    const size_t P,
-    const size_t S) {
-  float3* points_f3 = (float3*)points;
-  float3* segms_f3 = (float3*)segms;
-
-  // Single shared memory buffer which is split and cast to different types.
-  extern __shared__ char shared_buf[];
-  float* min_dists = (float*)shared_buf; // float[NUM_THREADS]
-  int64_t* min_idxs = (int64_t*)&min_dists[blockDim.x]; // int64_t[NUM_THREADS]
-
-  const size_t batch_idx = blockIdx.y; // index of batch element.
-
-  // start and end for points in batch
-  const int64_t startp = points_first_idx[batch_idx];
-  const int64_t endp = batch_idx + 1 < B ? points_first_idx[batch_idx + 1] : P;
-
-  // start and end for segments in batch_idx
-  const int64_t starts = segms_first_idx[batch_idx];
-  const int64_t ends = batch_idx + 1 < B ? segms_first_idx[batch_idx + 1] : S;
-
-  const size_t i = blockIdx.x; // index of point within batch element.
-  const size_t tid = threadIdx.x; // thread idx
-
-  // Each block will compute one element of the output idx_points[startp + i],
-  // dist_points[startp + i]. Within the block we will use threads to compute
-  // the distances between points[startp + i] and segms[j] for all j belonging
-  // in the same batch as i, i.e. j in [starts, ends]. Then use a block
-  // reduction to take an argmin of the distances.
-
-  // If i exceeds the number of points in batch_idx, then do nothing
-  if (i < (endp - startp)) {
-    // Retrieve (startp + i) point
-    const float3 p_f3 = points_f3[startp + i];
-
-    // Compute the distances between points[startp + i] and segms[j] for
-    // all j belonging in the same batch as i, i.e. j in [starts, ends].
-    // Here each thread will reduce over (ends-starts) / blockDim.x in serial,
-    // and store its result to shared memory
-    float min_dist = FLT_MAX;
-    size_t min_idx = 0;
-    for (size_t j = tid; j < (ends - starts); j += blockDim.x) {
-      const float3 v0 = segms_f3[(starts + j) * 2 + 0];
-      const float3 v1 = segms_f3[(starts + j) * 2 + 1];
-      float dist = PointLine3DistanceForward(p_f3, v0, v1);
-      min_dist = (j == tid) ? dist : min_dist;
-      min_idx = (dist <= min_dist) ? (starts + j) : min_idx;
-      min_dist = (dist <= min_dist) ? dist : min_dist;
-    }
-    min_dists[tid] = min_dist;
-    min_idxs[tid] = min_idx;
-    __syncthreads();
-
-    // Perform reduction in shared memory.
-    for (int s = blockDim.x / 2; s > 32; s >>= 1) {
-      if (tid < s) {
-        if (min_dists[tid] > min_dists[tid + s]) {
-          min_dists[tid] = min_dists[tid + s];
-          min_idxs[tid] = min_idxs[tid + s];
-        }
-      }
-      __syncthreads();
-    }
-
-    // Unroll the last 6 iterations of the loop since they will happen
-    // synchronized within a single warp.
-    if (tid < 32)
-      WarpReduce<float>(min_dists, min_idxs, tid);
-
-    // Finally thread 0 writes the result to the output buffer.
-    if (tid == 0) {
-      idx_points[startp + i] = min_idxs[0];
-      dist_points[startp + i] = min_dists[0];
-    }
-  }
-}
-
-std::tuple<at::Tensor, at::Tensor> PointEdgeDistanceForwardCuda(
-    const at::Tensor& points,
-    const at::Tensor& points_first_idx,
-    const at::Tensor& segms,
-    const at::Tensor& segms_first_idx,
-    const int64_t max_points) {
-  // Check inputs are on the same device
-  at::TensorArg points_t{points, "points", 1},
-      points_first_idx_t{points_first_idx, "points_first_idx", 2},
-      segms_t{segms, "segms", 3},
-      segms_first_idx_t{segms_first_idx, "segms_first_idx", 4};
-  at::CheckedFrom c = "PointEdgeDistanceForwardCuda";
-  at::checkAllSameGPU(
-      c, {points_t, points_first_idx_t, segms_t, segms_first_idx_t});
-  at::checkAllSameType(c, {points_t, segms_t});
-
-  // Set the device for the kernel launch based on the device of the input
-  at::cuda::CUDAGuard device_guard(points.device());
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  const int64_t P = points.size(0);
-  const int64_t S = segms.size(0);
-  const int64_t B = points_first_idx.size(0);
-
-  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
-  TORCH_CHECK(
-      (segms.size(1) == 2) && (segms.size(2) == 3),
-      "segms must be of shape Sx2x3");
-  TORCH_CHECK(segms_first_idx.size(0) == B);
-
-  // clang-format off
-  at::Tensor dists = at::zeros({P,}, points.options());
-  at::Tensor idxs = at::zeros({P,}, points_first_idx.options());
-  // clang-format on
-
-  if (dists.numel() == 0) {
-    AT_CUDA_CHECK(cudaGetLastError());
-    return std::make_tuple(dists, idxs);
-  }
-
-  const int threads = 128;
-  const dim3 blocks(max_points, B);
-  size_t shared_size = threads * sizeof(size_t) + threads * sizeof(int64_t);
-
-  PointEdgeForwardKernel<<<blocks, threads, shared_size, stream>>>(
-      points.contiguous().data_ptr<float>(),
-      points_first_idx.contiguous().data_ptr<int64_t>(),
-      segms.contiguous().data_ptr<float>(),
-      segms_first_idx.contiguous().data_ptr<int64_t>(),
-      dists.data_ptr<float>(),
-      idxs.data_ptr<int64_t>(),
-      B,
-      P,
-      S);
-
-  AT_CUDA_CHECK(cudaGetLastError());
-  return std::make_tuple(dists, idxs);
-}
-
-__global__ void PointEdgeBackwardKernel(
-    const float* __restrict__ points, // (P, 3)
-    const float* __restrict__ segms, // (S, 2, 3)
-    const int64_t* __restrict__ idx_points, // (P,)
-    const float* __restrict__ grad_dists, // (P,)
-    float* __restrict__ grad_points, // (P, 3)
-    float* __restrict__ grad_segms, // (S, 2, 3)
-    const size_t P) {
-  float3* points_f3 = (float3*)points;
-  float3* segms_f3 = (float3*)segms;
-
-  const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
-  const size_t stride = gridDim.x * blockDim.x;
-
-  for (size_t p = tid; p < P; p += stride) {
-    const float3 p_f3 = points_f3[p];
-
-    const int64_t sidx = idx_points[p];
-    const float3 v0 = segms_f3[sidx * 2 + 0];
-    const float3 v1 = segms_f3[sidx * 2 + 1];
-
-    const float grad_dist = grad_dists[p];
-
-    const auto grads = PointLine3DistanceBackward(p_f3, v0, v1, grad_dist);
-    const float3 grad_point = thrust::get<0>(grads);
-    const float3 grad_v0 = thrust::get<1>(grads);
-    const float3 grad_v1 = thrust::get<2>(grads);
-
-    atomicAdd(grad_points + p * 3 + 0, grad_point.x);
-    atomicAdd(grad_points + p * 3 + 1, grad_point.y);
-    atomicAdd(grad_points + p * 3 + 2, grad_point.z);
-
-    atomicAdd(grad_segms + sidx * 2 * 3 + 0 * 3 + 0, grad_v0.x);
-    atomicAdd(grad_segms + sidx * 2 * 3 + 0 * 3 + 1, grad_v0.y);
-    atomicAdd(grad_segms + sidx * 2 * 3 + 0 * 3 + 2, grad_v0.z);
-
-    atomicAdd(grad_segms + sidx * 2 * 3 + 1 * 3 + 0, grad_v1.x);
-    atomicAdd(grad_segms + sidx * 2 * 3 + 1 * 3 + 1, grad_v1.y);
-    atomicAdd(grad_segms + sidx * 2 * 3 + 1 * 3 + 2, grad_v1.z);
-  }
-}
-
-std::tuple<at::Tensor, at::Tensor> PointEdgeDistanceBackwardCuda(
-    const at::Tensor& points,
-    const at::Tensor& segms,
-    const at::Tensor& idx_points,
-    const at::Tensor& grad_dists) {
-  // Check inputs are on the same device
-  at::TensorArg points_t{points, "points", 1},
-      idx_points_t{idx_points, "idx_points", 2}, segms_t{segms, "segms", 3},
-      grad_dists_t{grad_dists, "grad_dists", 4};
-  at::CheckedFrom c = "PointEdgeDistanceBackwardCuda";
-  at::checkAllSameGPU(c, {points_t, idx_points_t, segms_t, grad_dists_t});
-  at::checkAllSameType(c, {points_t, segms_t, grad_dists_t});
-
-  // Set the device for the kernel launch based on the device of the input
-  at::cuda::CUDAGuard device_guard(points.device());
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  const int64_t P = points.size(0);
-  const int64_t S = segms.size(0);
-
-  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
-  TORCH_CHECK(
-      (segms.size(1) == 2) && (segms.size(2) == 3),
-      "segms must be of shape Sx2x3");
-  TORCH_CHECK(idx_points.size(0) == P);
-  TORCH_CHECK(grad_dists.size(0) == P);
-
-  // clang-format off
-  at::Tensor grad_points = at::zeros({P, 3}, points.options());
-  at::Tensor grad_segms = at::zeros({S, 2, 3}, segms.options());
-  // clang-format on
-
-  if (grad_points.numel() == 0 || grad_segms.numel() == 0) {
-    AT_CUDA_CHECK(cudaGetLastError());
-    return std::make_tuple(grad_points, grad_segms);
-  }
-
-  const int blocks = 64;
-  const int threads = 512;
-
-  PointEdgeBackwardKernel<<<blocks, threads, 0, stream>>>(
-      points.contiguous().data_ptr<float>(),
-      segms.contiguous().data_ptr<float>(),
-      idx_points.contiguous().data_ptr<int64_t>(),
-      grad_dists.contiguous().data_ptr<float>(),
-      grad_points.data_ptr<float>(),
-      grad_segms.data_ptr<float>(),
-      P);
-
-  AT_CUDA_CHECK(cudaGetLastError());
-  return std::make_tuple(grad_points, grad_segms);
-}
-
-// ****************************************************************************
-// *                          EdgePointDistance                               *
-// ****************************************************************************
-
-__global__ void EdgePointForwardKernel(
-    const float* __restrict__ points, // (P, 3)
-    const int64_t* __restrict__ points_first_idx, // (B,)
-    const float* __restrict__ segms, // (S, 2, 3)
-    const int64_t* __restrict__ segms_first_idx, // (B,)
-    float* __restrict__ dist_segms, // (S,)
-    int64_t* __restrict__ idx_segms, // (S,)
-    const size_t B,
-    const size_t P,
-    const size_t S) {
-  float3* points_f3 = (float3*)points;
-  float3* segms_f3 = (float3*)segms;
-
-  // Single shared memory buffer which is split and cast to different types.
-  extern __shared__ char shared_buf[];
-  float* min_dists = (float*)shared_buf; // float[NUM_THREADS]
-  int64_t* min_idxs = (int64_t*)&min_dists[blockDim.x]; // int64_t[NUM_THREADS]
-
-  const size_t batch_idx = blockIdx.y; // index of batch element.
-
-  // start and end for points in batch_idx
-  const int64_t startp = points_first_idx[batch_idx];
-  const int64_t endp = batch_idx + 1 < B ? points_first_idx[batch_idx + 1] : P;
-
-  // start and end for segms in batch_idx
-  const int64_t starts = segms_first_idx[batch_idx];
-  const int64_t ends = batch_idx + 1 < B ? segms_first_idx[batch_idx + 1] : S;
-
-  const size_t i = blockIdx.x; // index of point within batch element.
-  const size_t tid = threadIdx.x; // thread index
-
-  // Each block will compute one element of the output idx_segms[starts + i],
-  // dist_segms[starts + i]. Within the block we will use threads to compute
-  // the distances between segms[starts + i] and points[j] for all j belonging
-  // in the same batch as i, i.e. j in [startp, endp]. Then use a block
-  // reduction to take an argmin of the distances.
-
-  // If i exceeds the number of segms in batch_idx, then do nothing
-  if (i < (ends - starts)) {
-    const float3 v0 = segms_f3[(starts + i) * 2 + 0];
-    const float3 v1 = segms_f3[(starts + i) * 2 + 1];
-
-    // Compute the distances between segms[starts + i] and points[j] for
-    // all j belonging in the same batch as i, i.e. j in [startp, endp].
-    // Here each thread will reduce over (endp-startp) / blockDim.x in serial,
-    // and store its result to shared memory
-    float min_dist = FLT_MAX;
-    size_t min_idx = 0;
-    for (size_t j = tid; j < (endp - startp); j += blockDim.x) {
-      // Retrieve (startp + i) point
-      const float3 p_f3 = points_f3[startp + j];
-
-      float dist = PointLine3DistanceForward(p_f3, v0, v1);
-      min_dist = (j == tid) ? dist : min_dist;
-      min_idx = (dist <= min_dist) ? (startp + j) : min_idx;
-      min_dist = (dist <= min_dist) ? dist : min_dist;
-    }
-    min_dists[tid] = min_dist;
-    min_idxs[tid] = min_idx;
-    __syncthreads();
-
-    // Perform reduction in shared memory.
-    for (int s = blockDim.x / 2; s > 32; s >>= 1) {
-      if (tid < s) {
-        if (min_dists[tid] > min_dists[tid + s]) {
-          min_dists[tid] = min_dists[tid + s];
-          min_idxs[tid] = min_idxs[tid + s];
-        }
-      }
-      __syncthreads();
-    }
-
-    // Unroll the last 6 iterations of the loop since they will happen
-    // synchronized within a single warp.
-    if (tid < 32)
-      WarpReduce<float>(min_dists, min_idxs, tid);
-
-    // Finally thread 0 writes the result to the output buffer.
-    if (tid == 0) {
-      idx_segms[starts + i] = min_idxs[0];
-      dist_segms[starts + i] = min_dists[0];
-    }
-  }
-}
-
-std::tuple<at::Tensor, at::Tensor> EdgePointDistanceForwardCuda(
-    const at::Tensor& points,
-    const at::Tensor& points_first_idx,
-    const at::Tensor& segms,
-    const at::Tensor& segms_first_idx,
-    const int64_t max_segms) {
-  // Check inputs are on the same device
-  at::TensorArg points_t{points, "points", 1},
-      points_first_idx_t{points_first_idx, "points_first_idx", 2},
-      segms_t{segms, "segms", 3},
-      segms_first_idx_t{segms_first_idx, "segms_first_idx", 4};
-  at::CheckedFrom c = "EdgePointDistanceForwardCuda";
-  at::checkAllSameGPU(
-      c, {points_t, points_first_idx_t, segms_t, segms_first_idx_t});
-  at::checkAllSameType(c, {points_t, segms_t});
-
-  // Set the device for the kernel launch based on the device of the input
-  at::cuda::CUDAGuard device_guard(points.device());
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  const int64_t P = points.size(0);
-  const int64_t S = segms.size(0);
-  const int64_t B = points_first_idx.size(0);
-
-  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
-  TORCH_CHECK(
-      (segms.size(1) == 2) && (segms.size(2) == 3),
-      "segms must be of shape Sx2x3");
-  TORCH_CHECK(segms_first_idx.size(0) == B);
-
-  // clang-format off
-  at::Tensor dists = at::zeros({S,}, segms.options());
-  at::Tensor idxs = at::zeros({S,}, segms_first_idx.options());
-  // clang-format on
-
-  if (dists.numel() == 0) {
-    AT_CUDA_CHECK(cudaGetLastError());
-    return std::make_tuple(dists, idxs);
-  }
-
-  const int threads = 128;
-  const dim3 blocks(max_segms, B);
-  size_t shared_size = threads * sizeof(size_t) + threads * sizeof(int64_t);
-
-  EdgePointForwardKernel<<<blocks, threads, shared_size, stream>>>(
-      points.contiguous().data_ptr<float>(),
-      points_first_idx.contiguous().data_ptr<int64_t>(),
-      segms.contiguous().data_ptr<float>(),
-      segms_first_idx.contiguous().data_ptr<int64_t>(),
-      dists.data_ptr<float>(),
-      idxs.data_ptr<int64_t>(),
-      B,
-      P,
-      S);
-  AT_CUDA_CHECK(cudaGetLastError());
-  return std::make_tuple(dists, idxs);
-}
-
-__global__ void EdgePointBackwardKernel(
-    const float* __restrict__ points, // (P, 3)
-    const float* __restrict__ segms, // (S, 2, 3)
-    const int64_t* __restrict__ idx_segms, // (S,)
-    const float* __restrict__ grad_dists, // (S,)
-    float* __restrict__ grad_points, // (P, 3)
-    float* __restrict__ grad_segms, // (S, 2, 3)
-    const size_t S) {
-  float3* points_f3 = (float3*)points;
-  float3* segms_f3 = (float3*)segms;
-
-  const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
-  const size_t stride = gridDim.x * blockDim.x;
-
-  for (size_t s = tid; s < S; s += stride) {
-    const float3 v0 = segms_f3[s * 2 + 0];
-    const float3 v1 = segms_f3[s * 2 + 1];
-
-    const int64_t pidx = idx_segms[s];
-
-    const float3 p_f3 = points_f3[pidx];
-
-    const float grad_dist = grad_dists[s];
-
-    const auto grads = PointLine3DistanceBackward(p_f3, v0, v1, grad_dist);
-    const float3 grad_point = thrust::get<0>(grads);
-    const float3 grad_v0 = thrust::get<1>(grads);
-    const float3 grad_v1 = thrust::get<2>(grads);
-
-    atomicAdd(grad_points + pidx * 3 + 0, grad_point.x);
-    atomicAdd(grad_points + pidx * 3 + 1, grad_point.y);
-    atomicAdd(grad_points + pidx * 3 + 2, grad_point.z);
-
-    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 0, grad_v0.x);
-    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 1, grad_v0.y);
-    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 2, grad_v0.z);
-
-    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 0, grad_v1.x);
-    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 1, grad_v1.y);
-    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 2, grad_v1.z);
-  }
-}
-
-std::tuple<at::Tensor, at::Tensor> EdgePointDistanceBackwardCuda(
-    const at::Tensor& points,
-    const at::Tensor& segms,
-    const at::Tensor& idx_segms,
-    const at::Tensor& grad_dists) {
-  // Check inputs are on the same device
-  at::TensorArg points_t{points, "points", 1},
-      idx_segms_t{idx_segms, "idx_segms", 2}, segms_t{segms, "segms", 3},
-      grad_dists_t{grad_dists, "grad_dists", 4};
-  at::CheckedFrom c = "PointEdgeDistanceBackwardCuda";
-  at::checkAllSameGPU(c, {points_t, idx_segms_t, segms_t, grad_dists_t});
-  at::checkAllSameType(c, {points_t, segms_t, grad_dists_t});
-
-  // Set the device for the kernel launch based on the device of the input
-  at::cuda::CUDAGuard device_guard(points.device());
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  const int64_t P = points.size(0);
-  const int64_t S = segms.size(0);
-
-  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
-  TORCH_CHECK(
-      (segms.size(1) == 2) && (segms.size(2) == 3),
-      "segms must be of shape Sx2x3");
-  TORCH_CHECK(idx_segms.size(0) == S);
-  TORCH_CHECK(grad_dists.size(0) == S);
-
-  // clang-format off
-  at::Tensor grad_points = at::zeros({P, 3}, points.options());
-  at::Tensor grad_segms = at::zeros({S, 2, 3}, segms.options());
-  // clang-format on
-
-  const int blocks = 64;
-  const int threads = 512;
-
-  EdgePointBackwardKernel<<<blocks, threads, 0, stream>>>(
-      points.contiguous().data_ptr<float>(),
-      segms.contiguous().data_ptr<float>(),
-      idx_segms.contiguous().data_ptr<int64_t>(),
-      grad_dists.contiguous().data_ptr<float>(),
-      grad_points.data_ptr<float>(),
-      grad_segms.data_ptr<float>(),
-      S);
-
-  return std::make_tuple(grad_points, grad_segms);
-}
-
-// ****************************************************************************
-// *                     PointEdgeArrayDistance                               *
-// ****************************************************************************
-
-__global__ void PointEdgeArrayForwardKernel(
-    const float* __restrict__ points, // (P, 3)
-    const float* __restrict__ segms, // (S, 2, 3)
-    float* __restrict__ dists, // (P, S)
-    const size_t P,
-    const size_t S) {
-  float3* points_f3 = (float3*)points;
-  float3* segms_f3 = (float3*)segms;
-
-  // Parallelize over P * S computations
-  const int num_threads = gridDim.x * blockDim.x;
-  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
-
-  for (int t_i = tid; t_i < P * S; t_i += num_threads) {
-    const int s = t_i / P; // segment index.
-    const int p = t_i % P; // point index
-    float3 a = segms_f3[s * 2 + 0];
-    float3 b = segms_f3[s * 2 + 1];
-
-    float3 point = points_f3[p];
-    float dist = PointLine3DistanceForward(point, a, b);
-    dists[p * S + s] = dist;
-  }
-}
-
-at::Tensor PointEdgeArrayDistanceForwardCuda(
-    const at::Tensor& points,
-    const at::Tensor& segms) {
-  // Check inputs are on the same device
-  at::TensorArg points_t{points, "points", 1}, segms_t{segms, "segms", 2};
-  at::CheckedFrom c = "PointEdgeArrayDistanceForwardCuda";
-  at::checkAllSameGPU(c, {points_t, segms_t});
-  at::checkAllSameType(c, {points_t, segms_t});
-
-  // Set the device for the kernel launch based on the device of the input
-  at::cuda::CUDAGuard device_guard(points.device());
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  const int64_t P = points.size(0);
-  const int64_t S = segms.size(0);
-
-  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
-  TORCH_CHECK(
-      (segms.size(1) == 2) && (segms.size(2) == 3),
-      "segms must be of shape Sx2x3");
-
-  at::Tensor dists = at::zeros({P, S}, points.options());
-
-  if (dists.numel() == 0) {
-    AT_CUDA_CHECK(cudaGetLastError());
-    return dists;
-  }
-
-  const size_t blocks = 1024;
-  const size_t threads = 64;
-
-  PointEdgeArrayForwardKernel<<<blocks, threads, 0, stream>>>(
-      points.contiguous().data_ptr<float>(),
-      segms.contiguous().data_ptr<float>(),
-      dists.data_ptr<float>(),
-      P,
-      S);
-
-  AT_CUDA_CHECK(cudaGetLastError());
-  return dists;
-}
-
-__global__ void PointEdgeArrayBackwardKernel(
-    const float* __restrict__ points, // (P, 3)
-    const float* __restrict__ segms, // (S, 2, 3)
-    const float* __restrict__ grad_dists, // (P, S)
-    float* __restrict__ grad_points, // (P, 3)
-    float* __restrict__ grad_segms, // (S, 2, 3)
-    const size_t P,
-    const size_t S) {
-  float3* points_f3 = (float3*)points;
-  float3* segms_f3 = (float3*)segms;
-
-  // Parallelize over P * S computations
-  const int num_threads = gridDim.x * blockDim.x;
-  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
-
-  for (int t_i = tid; t_i < P * S; t_i += num_threads) {
-    const int s = t_i / P; // segment index.
-    const int p = t_i % P; // point index
-    const float3 a = segms_f3[s * 2 + 0];
-    const float3 b = segms_f3[s * 2 + 1];
-
-    const float3 point = points_f3[p];
-    const float grad_dist = grad_dists[p * S + s];
-    const auto grads = PointLine3DistanceBackward(point, a, b, grad_dist);
-    const float3 grad_point = thrust::get<0>(grads);
-    const float3 grad_a = thrust::get<1>(grads);
-    const float3 grad_b = thrust::get<2>(grads);
-
-    atomicAdd(grad_points + p * 3 + 0, grad_point.x);
-    atomicAdd(grad_points + p * 3 + 1, grad_point.y);
-    atomicAdd(grad_points + p * 3 + 2, grad_point.z);
-
-    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 0, grad_a.x);
-    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 1, grad_a.y);
-    atomicAdd(grad_segms + s * 2 * 3 + 0 * 3 + 2, grad_a.z);
-
-    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 0, grad_b.x);
-    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 1, grad_b.y);
-    atomicAdd(grad_segms + s * 2 * 3 + 1 * 3 + 2, grad_b.z);
-  }
-}
-
-std::tuple<at::Tensor, at::Tensor> PointEdgeArrayDistanceBackwardCuda(
-    const at::Tensor& points,
-    const at::Tensor& segms,
-    const at::Tensor& grad_dists) {
-  // Check inputs are on the same device
-  at::TensorArg points_t{points, "points", 1}, segms_t{segms, "segms", 2},
-      grad_dists_t{grad_dists, "grad_dists", 3};
-  at::CheckedFrom c = "PointEdgeArrayDistanceBackwardCuda";
-  at::checkAllSameGPU(c, {points_t, segms_t, grad_dists_t});
-  at::checkAllSameType(c, {points_t, segms_t, grad_dists_t});
-
-  // Set the device for the kernel launch based on the device of the input
-  at::cuda::CUDAGuard device_guard(points.device());
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  const int64_t P = points.size(0);
-  const int64_t S = segms.size(0);
-
-  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
-  TORCH_CHECK(
-      (segms.size(1) == 2) && (segms.size(2) == 3),
-      "segms must be of shape Sx2x3");
-  TORCH_CHECK((grad_dists.size(0) == P) && (grad_dists.size(1) == S));
-
-  at::Tensor grad_points = at::zeros({P, 3}, points.options());
-  at::Tensor grad_segms = at::zeros({S, 2, 3}, segms.options());
-
-  if (grad_points.numel() == 0 || grad_segms.numel() == 0) {
-    AT_CUDA_CHECK(cudaGetLastError());
-    return std::make_tuple(grad_points, grad_segms);
-  }
-
-  const size_t blocks = 1024;
-  const size_t threads = 64;
-
-  PointEdgeArrayBackwardKernel<<<blocks, threads, 0, stream>>>(
-      points.contiguous().data_ptr<float>(),
-      segms.contiguous().data_ptr<float>(),
-      grad_dists.contiguous().data_ptr<float>(),
-      grad_points.data_ptr<float>(),
-      grad_segms.data_ptr<float>(),
-      P,
-      S);
-  AT_CUDA_CHECK(cudaGetLastError());
-  return std::make_tuple(grad_points, grad_segms);
-}

pytorch3d/csrc/point_mesh/point_mesh_edge.h

-// Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
-
-#pragma once
-#include <torch/extension.h>
-#include <cstdio>
-#include <tuple>
-#include "utils/pytorch3d_cutils.h"
-
-// ****************************************************************************
-// *                      PointEdgeDistance                                   *
-// ****************************************************************************
-
-// Computes the squared euclidean distance of each p in points to the closest
-// mesh edge belonging to the corresponding example in the batch of size N.
-//
-// Args:
-//    points: FloatTensor of shape (P, 3)
-//    points_first_idx: LongTensor of shape (N,) indicating the first point
-//         index for each example in the batch
-//    segms: FloatTensor of shape (S, 2, 3) of edge segments. The s-th edge
-//        segment is spanned by (segms[s, 0], segms[s, 1])
-//    segms_first_idx: LongTensor of shape (N,) indicating the first edge
-//        index for each example in the batch
-//    max_points: Scalar equal to max(P_i) for i in [0, N - 1] containing
-//        the maximum number of points in the batch and is used to set
-//        the grid dimensions in the CUDA implementation.
-//
-// Returns:
-//    dists: FloatTensor of shape (P,), where dists[p] is the squared euclidean
-//        distance of points[p] to the closest edge in the same example in the
-//        batch.
-//    idxs: LongTensor of shape (P,), where idxs[p] is the index of the closest
-//        edge in the batch.
-//        So, dists[p] = d(points[p], segms[idxs[p], 0], segms[idxs[p], 1]),
-//        where d(u, v0, v1) is the distance of u from the segment spanned by
-//        (v0, v1).
-//
-
-#ifdef WITH_CUDA
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForwardCuda(
-    const torch::Tensor& points,
-    const torch::Tensor& points_first_idx,
-    const torch::Tensor& segms,
-    const torch::Tensor& segms_first_idx,
-    const int64_t max_points);
-#endif
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForwardCpu(
-    const torch::Tensor& points,
-    const torch::Tensor& points_first_idx,
-    const torch::Tensor& segms,
-    const torch::Tensor& segms_first_idx,
-    const int64_t max_points);
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForward(
-    const torch::Tensor& points,
-    const torch::Tensor& points_first_idx,
-    const torch::Tensor& segms,
-    const torch::Tensor& segms_first_idx,
-    const int64_t max_points) {
-  if (points.is_cuda()) {
-#ifdef WITH_CUDA
-    CHECK_CUDA(points);
-    CHECK_CUDA(points_first_idx);
-    CHECK_CUDA(segms);
-    CHECK_CUDA(segms_first_idx);
-    return PointEdgeDistanceForwardCuda(
-        points, points_first_idx, segms, segms_first_idx, max_points);
-#else
-    AT_ERROR("Not compiled with GPU support.");
-#endif
-  }
-  return PointEdgeDistanceForwardCpu(
-      points, points_first_idx, segms, segms_first_idx, max_points);
-}
-
-// Backward pass for PointEdgeDistance.
-//
-// Args:
-//    points: FloatTensor of shape (P, 3)
-//    segms: FloatTensor of shape (S, 2, 3)
-//    idx_points: LongTensor of shape (P,) containing the indices
-//        of the closest edge in the example in the batch.
-//        This is computed by the forward pass.
-//    grad_dists: FloatTensor of shape (P,)
-//
-// Returns:
-//    grad_points: FloatTensor of shape (P, 3)
-//    grad_segms: FloatTensor of shape (S, 2, 3)
-//
-
-#ifdef WITH_CUDA
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackwardCuda(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& idx_points,
-    const torch::Tensor& grad_dists);
-#endif
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackwardCpu(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& idx_points,
-    const torch::Tensor& grad_dists);
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackward(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& idx_points,
-    const torch::Tensor& grad_dists) {
-  if (points.is_cuda()) {
-#ifdef WITH_CUDA
-    CHECK_CUDA(points);
-    CHECK_CUDA(segms);
-    CHECK_CUDA(idx_points);
-    CHECK_CUDA(grad_dists);
-    return PointEdgeDistanceBackwardCuda(points, segms, idx_points, grad_dists);
-#else
-    AT_ERROR("Not compiled with GPU support.");
-#endif
-  }
-  return PointEdgeDistanceBackwardCpu(points, segms, idx_points, grad_dists);
-}
-
-// ****************************************************************************
-// *                      EdgePointDistance                                   *
-// ****************************************************************************
-
-// Computes the squared euclidean distance of each edge segment to the closest
-// point belonging to the corresponding example in the batch of size N.
-//
-// Args:
-//    points: FloatTensor of shape (P, 3)
-//    points_first_idx: LongTensor of shape (N,) indicating the first point
-//         index for each example in the batch
-//    segms: FloatTensor of shape (S, 2, 3) of edge segments. The s-th edge
-//        segment is spanned by (segms[s, 0], segms[s, 1])
-//    segms_first_idx: LongTensor of shape (N,) indicating the first edge
-//        index for each example in the batch
-//    max_segms: Scalar equal to max(S_i) for i in [0, N - 1] containing
-//        the maximum number of edges in the batch and is used to set
-//        the block dimensions in the CUDA implementation.
-//
-// Returns:
-//    dists: FloatTensor of shape (S,), where dists[s] is the squared
-//        euclidean distance of s-th edge to the closest point in the
-//        corresponding example in the batch.
-//    idxs: LongTensor of shape (S,), where idxs[s] is the index of the closest
-//        point in the example in the batch.
-//        So, dists[s] = d(points[idxs[s]], segms[s, 0], segms[s, 1]), where
-//        d(u, v0, v1) is the distance of u from the segment spanned by (v0, v1)
-//
-//
-
-#ifdef WITH_CUDA
-
-std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForwardCuda(
-    const torch::Tensor& points,
-    const torch::Tensor& points_first_idx,
-    const torch::Tensor& segms,
-    const torch::Tensor& segms_first_idx,
-    const int64_t max_segms);
-#endif
-
-std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForwardCpu(
-    const torch::Tensor& points,
-    const torch::Tensor& points_first_idx,
-    const torch::Tensor& segms,
-    const torch::Tensor& segms_first_idx,
-    const int64_t max_segms);
-
-std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForward(
-    const torch::Tensor& points,
-    const torch::Tensor& points_first_idx,
-    const torch::Tensor& segms,
-    const torch::Tensor& segms_first_idx,
-    const int64_t max_segms) {
-  if (points.is_cuda()) {
-#ifdef WITH_CUDA
-    CHECK_CUDA(points);
-    CHECK_CUDA(points_first_idx);
-    CHECK_CUDA(segms);
-    CHECK_CUDA(segms_first_idx);
-    return EdgePointDistanceForwardCuda(
-        points, points_first_idx, segms, segms_first_idx, max_segms);
-#else
-    AT_ERROR("Not compiled with GPU support.");
-#endif
-  }
-  return EdgePointDistanceForwardCpu(
-      points, points_first_idx, segms, segms_first_idx, max_segms);
-}
-
-// Backward pass for EdgePointDistance.
-//
-// Args:
-//    points: FloatTensor of shape (P, 3)
-//    segms: FloatTensor of shape (S, 2, 3)
-//    idx_segms: LongTensor of shape (S,) containing the indices
-//        of the closest point in the example in the batch.
-//        This is computed by the forward pass
-//    grad_dists: FloatTensor of shape (S,)
-//
-// Returns:
-//    grad_points: FloatTensor of shape (P, 3)
-//    grad_segms: FloatTensor of shape (S, 2, 3)
-//
-
-#ifdef WITH_CUDA
-
-std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackwardCuda(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& idx_segms,
-    const torch::Tensor& grad_dists);
-#endif
-
-std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackwardCpu(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& idx_segms,
-    const torch::Tensor& grad_dists);
-
-std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackward(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& idx_segms,
-    const torch::Tensor& grad_dists) {
-  if (points.is_cuda()) {
-#ifdef WITH_CUDA
-    CHECK_CUDA(points);
-    CHECK_CUDA(segms);
-    CHECK_CUDA(idx_segms);
-    CHECK_CUDA(grad_dists);
-    return EdgePointDistanceBackwardCuda(points, segms, idx_segms, grad_dists);
-#else
-    AT_ERROR("Not compiled with GPU support.");
-#endif
-  }
-  return EdgePointDistanceBackwardCpu(points, segms, idx_segms, grad_dists);
-}
-
-// ****************************************************************************
-// *                          PointEdgeArrayDistance                          *
-// ****************************************************************************
-
-// Computes the squared euclidean distance of each p in points to each edge
-// segment in segms.
-//
-// Args:
-//    points: FloatTensor of shape (P, 3)
-//    segms: FloatTensor of shape (S, 2, 3) of edge segments. The s-th
-//        edge segment is spanned by (segms[s, 0], segms[s, 1])
-//
-// Returns:
-//    dists: FloatTensor of shape (P, S), where dists[p, s] is the squared
-//        euclidean distance of points[p] to the segment spanned by
-//        (segms[s, 0], segms[s, 1])
-//
-// For pointcloud and meshes of batch size N, this function requires N
-// computations. The memory occupied is O(NPS) which can become quite large.
-// For example, a medium sized batch with N = 32 with P = 10000 and S = 5000
-// will require for the forward pass 5.8G of memory to store dists.
-
-#ifdef WITH_CUDA
-torch::Tensor PointEdgeArrayDistanceForwardCuda(
-    const torch::Tensor& points,
-    const torch::Tensor& segms);
-#endif
-
-torch::Tensor PointEdgeArrayDistanceForwardCpu(
-    const torch::Tensor& points,
-    const torch::Tensor& segms);
-
-torch::Tensor PointEdgeArrayDistanceForward(
-    const torch::Tensor& points,
-    const torch::Tensor& segms) {
-  if (points.is_cuda()) {
-#ifdef WITH_CUDA
-    CHECK_CUDA(points);
-    CHECK_CUDA(segms);
-    return PointEdgeArrayDistanceForwardCuda(points, segms);
-#else
-    AT_ERROR("Not compiled with GPU support.");
-#endif
-  }
-  return PointEdgeArrayDistanceForwardCpu(points, segms);
-}
-
-// Backward pass for PointEdgeArrayDistance.
-//
-// Args:
-//    points: FloatTensor of shape (P, 3)
-//    segms: FloatTensor of shape (S, 2, 3)
-//    grad_dists: FloatTensor of shape (P, S)
-//
-// Returns:
-//   grad_points: FloatTensor of shape (P, 3)
-//   grad_segms: FloatTensor of shape (S, 2, 3)
-//
-
-#ifdef WITH_CUDA
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackwardCuda(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& grad_dists);
-#endif
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackwardCpu(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& grad_dists);
-
-std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackward(
-    const torch::Tensor& points,
-    const torch::Tensor& segms,
-    const torch::Tensor& grad_dists) {
-  if (points.is_cuda()) {
-#ifdef WITH_CUDA
-    CHECK_CUDA(points);
-    CHECK_CUDA(segms);
-    CHECK_CUDA(grad_dists);
-    return PointEdgeArrayDistanceBackwardCuda(points, segms, grad_dists);
-#else
-    AT_ERROR("Not compiled with GPU support.");
-#endif
-  }
-  return PointEdgeArrayDistanceBackwardCpu(points, segms, grad_dists);
-}