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Remove point mesh edge kernels

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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
This commit is contained in:
Dave Schnizlein 2020-11-10 09:32:33 -08:00 committed by Facebook GitHub Bot
parent 8dcfe30f66
commit 804235b05a
6 changed files with 547 additions and 996 deletions
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3
pytorch3d/csrc/ext.cpp
View File

@ -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"

0
pytorch3d/csrc/point_mesh/point_mesh.cpp → pytorch3d/csrc/point_mesh/point_mesh_cpu.cpp
View File

232
pytorch3d/csrc/point_mesh/point_mesh_face.cu → pytorch3d/csrc/point_mesh/point_mesh_cuda.cu
View File

@ -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);
}

325
pytorch3d/csrc/point_mesh/point_mesh_face.h → pytorch3d/csrc/point_mesh/point_mesh_cuda.h
View File

@ -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);
}

651
pytorch3d/csrc/point_mesh/point_mesh_edge.cu
View File

@ -1,651 +0,0 @@
// 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);
}

332
pytorch3d/csrc/point_mesh/point_mesh_edge.h
View File

@ -1,332 +0,0 @@
// 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);
}