CPU implementation for point_mesh functions

Summary:
point_mesh functions were missing CPU implementations.
The indices returned are not always matching, possibly due to numerical instability.

Reviewed By: gkioxari

Differential Revision: D21594264

fbshipit-source-id: 3016930e2a9a0f3cd8b3ac4c94a92c9411c0989d

pytorch3d/csrc/point_mesh/point_mesh.cpp

@@ -0,0 +1,398 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+#include <torch/extension.h>
+#include <array>
+#include <limits>
+#include "utils/geometry_utils.h"
+#include "utils/vec3.h"
+
+// - We start with implementations of simple operations on points, edges and
+// faces. The hull of H points is a point if H=1, an edge if H=2, a face if H=3.
+
+template <typename T>
+vec3<T> ExtractPoint(const at::TensorAccessor<T, 1>& t) {
+  return vec3(t[0], t[1], t[2]);
+}
+
+template <class Accessor>
+struct ExtractHullHelper {
+  template <int H>
+  static std::array<vec3<std::remove_pointer_t<typename Accessor::PtrType>>, H>
+  get(const Accessor& t);
+
+  template <>
+  static std::array<vec3<std::remove_pointer_t<typename Accessor::PtrType>>, 1>
+  get<1>(const Accessor& t) {
+    return {ExtractPoint(t)};
+  }
+
+  template <>
+  static std::array<vec3<std::remove_pointer_t<typename Accessor::PtrType>>, 2>
+  get<2>(const Accessor& t) {
+    return {ExtractPoint(t[0]), ExtractPoint(t[1])};
+  }
+
+  template <>
+  static std::array<vec3<std::remove_pointer_t<typename Accessor::PtrType>>, 3>
+  get<3>(const Accessor& t) {
+    return {ExtractPoint(t[0]), ExtractPoint(t[1]), ExtractPoint(t[2])};
+  }
+};
+
+template <int H, typename Accessor>
+std::array<vec3<std::remove_pointer_t<typename Accessor::PtrType>>, H>
+ExtractHull(const Accessor& t) {
+  return ExtractHullHelper<Accessor>::template get<H>(t);
+}
+
+template <typename T>
+void IncrementPoint(at::TensorAccessor<T, 1>&& t, const vec3<T>& point) {
+  t[0] += point.x;
+  t[1] += point.y;
+  t[2] += point.z;
+}
+
+// distance between the convex hull of A points and B points
+// this could be done in c++17 with tuple_cat and invoke
+template <typename T>
+T HullDistance(
+    const std::array<vec3<T>, 1>& a,
+    const std::array<vec3<T>, 2>& b) {
+  using std::get;
+  return PointLine3DistanceForward(get<0>(a), get<0>(b), get<1>(b));
+}
+template <typename T>
+T HullDistance(
+    const std::array<vec3<T>, 1>& a,
+    const std::array<vec3<T>, 3>& b) {
+  using std::get;
+  return PointTriangle3DistanceForward(
+      get<0>(a), get<0>(b), get<1>(b), get<2>(b));
+}
+template <typename T>
+T HullDistance(
+    const std::array<vec3<T>, 2>& a,
+    const std::array<vec3<T>, 1>& b) {
+  return HullDistance(b, a);
+}
+template <typename T>
+T HullDistance(
+    const std::array<vec3<T>, 3>& a,
+    const std::array<vec3<T>, 1>& b) {
+  return HullDistance(b, a);
+}
+
+template <typename T>
+void HullHullDistanceBackward(
+    const std::array<vec3<T>, 1>& a,
+    const std::array<vec3<T>, 2>& b,
+    T grad_dist,
+    at::TensorAccessor<T, 1>&& grad_a,
+    at::TensorAccessor<T, 2>&& grad_b) {
+  using std::get;
+  auto res =
+      PointLine3DistanceBackward(get<0>(a), get<0>(b), get<1>(b), grad_dist);
+  IncrementPoint(std::move(grad_a), get<0>(res));
+  IncrementPoint(grad_b[0], get<1>(res));
+  IncrementPoint(grad_b[1], get<2>(res));
+}
+template <typename T>
+void HullHullDistanceBackward(
+    const std::array<vec3<T>, 1>& a,
+    const std::array<vec3<T>, 3>& b,
+    T grad_dist,
+    at::TensorAccessor<T, 1>&& grad_a,
+    at::TensorAccessor<T, 2>&& grad_b) {
+  using std::get;
+  auto res = PointTriangle3DistanceBackward(
+      get<0>(a), get<0>(b), get<1>(b), get<2>(b), grad_dist);
+  IncrementPoint(std::move(grad_a), get<0>(res));
+  IncrementPoint(grad_b[0], get<1>(res));
+  IncrementPoint(grad_b[1], get<2>(res));
+  IncrementPoint(grad_b[2], get<3>(res));
+}
+template <typename T>
+void HullHullDistanceBackward(
+    const std::array<vec3<T>, 3>& a,
+    const std::array<vec3<T>, 1>& b,
+    T grad_dist,
+    at::TensorAccessor<T, 2>&& grad_a,
+    at::TensorAccessor<T, 1>&& grad_b) {
+  return HullHullDistanceBackward(
+      b, a, grad_dist, std::move(grad_b), std::move(grad_a));
+}
+template <typename T>
+void HullHullDistanceBackward(
+    const std::array<vec3<T>, 2>& a,
+    const std::array<vec3<T>, 1>& b,
+    T grad_dist,
+    at::TensorAccessor<T, 2>&& grad_a,
+    at::TensorAccessor<T, 1>&& grad_b) {
+  return HullHullDistanceBackward(
+      b, a, grad_dist, std::move(grad_b), std::move(grad_a));
+}
+
+template <int H>
+void ValidateShape(const at::Tensor& as) {
+  if (H == 1) {
+    TORCH_CHECK(as.size(1) == 3);
+  } else {
+    TORCH_CHECK(as.size(2) == 3 && as.size(1) == H);
+  }
+}
+
+// ----------- Here begins the implementation of each top-level
+//             function using non-type template parameters to
+//             implement all the cases in one go. ----------- //
+
+template <int H1, int H2>
+std::tuple<at::Tensor, at::Tensor> HullHullDistanceForwardCpu(
+    const at::Tensor& as,
+    const at::Tensor& as_first_idx,
+    const at::Tensor& bs,
+    const at::Tensor& bs_first_idx) {
+  const int64_t A_N = as.size(0);
+  const int64_t B_N = bs.size(0);
+  const int64_t BATCHES = as_first_idx.size(0);
+
+  ValidateShape<H1>(as);
+  ValidateShape<H2>(bs);
+
+  TORCH_CHECK(bs_first_idx.size(0) == BATCHES);
+
+  // clang-format off
+  at::Tensor dists = at::zeros({A_N,}, as.options());
+  at::Tensor idxs = at::zeros({A_N,}, as_first_idx.options());
+  // clang-format on
+
+  auto as_a = as.accessor < float, H1 == 1 ? 2 : 3 > ();
+  auto bs_a = bs.accessor < float, H2 == 1 ? 2 : 3 > ();
+  auto as_first_idx_a = as_first_idx.accessor<int64_t, 1>();
+  auto bs_first_idx_a = bs_first_idx.accessor<int64_t, 1>();
+  auto dists_a = dists.accessor<float, 1>();
+  auto idxs_a = idxs.accessor<int64_t, 1>();
+  int64_t a_batch_end = 0;
+  int64_t b_batch_start = 0, b_batch_end = 0;
+  int64_t batch_idx = 0;
+  for (int64_t a_n = 0; a_n < A_N; ++a_n) {
+    if (a_n == a_batch_end) {
+      ++batch_idx;
+      b_batch_start = b_batch_end;
+      if (batch_idx == BATCHES) {
+        a_batch_end = std::numeric_limits<int64_t>::max();
+        b_batch_end = B_N;
+      } else {
+        a_batch_end = as_first_idx_a[batch_idx];
+        b_batch_end = bs_first_idx_a[batch_idx];
+      }
+    }
+    float min_dist = std::numeric_limits<float>::max();
+    size_t min_idx = 0;
+    auto a = ExtractHull<H1>(as_a[a_n]);
+    for (int64_t b_n = b_batch_start; b_n < b_batch_end; ++b_n) {
+      float dist = HullDistance(a, ExtractHull<H2>(bs_a[b_n]));
+      if (dist <= min_dist) {
+        min_dist = dist;
+        min_idx = b_n;
+      }
+    }
+    dists_a[a_n] = min_dist;
+    idxs_a[a_n] = min_idx;
+  }
+
+  return std::make_tuple(dists, idxs);
+}
+
+template <int H1, int H2>
+std::tuple<at::Tensor, at::Tensor> HullHullDistanceBackwardCpu(
+    const at::Tensor& as,
+    const at::Tensor& bs,
+    const at::Tensor& idx_bs,
+    const at::Tensor& grad_dists) {
+  const int64_t A_N = as.size(0);
+
+  TORCH_CHECK(idx_bs.size(0) == A_N);
+  TORCH_CHECK(grad_dists.size(0) == A_N);
+  ValidateShape<H1>(as);
+  ValidateShape<H2>(bs);
+
+  at::Tensor grad_as = at::zeros_like(as);
+  at::Tensor grad_bs = at::zeros_like(bs);
+
+  auto as_a = as.accessor < float, H1 == 1 ? 2 : 3 > ();
+  auto bs_a = bs.accessor < float, H2 == 1 ? 2 : 3 > ();
+  auto grad_as_a = grad_as.accessor < float, H1 == 1 ? 2 : 3 > ();
+  auto grad_bs_a = grad_bs.accessor < float, H2 == 1 ? 2 : 3 > ();
+  auto idx_bs_a = idx_bs.accessor<int64_t, 1>();
+  auto grad_dists_a = grad_dists.accessor<float, 1>();
+
+  for (int64_t a_n = 0; a_n < A_N; ++a_n) {
+    auto a = ExtractHull<H1>(as_a[a_n]);
+    auto b = ExtractHull<H2>(bs_a[idx_bs_a[a_n]]);
+    HullHullDistanceBackward(
+        a, b, grad_dists_a[a_n], grad_as_a[a_n], grad_bs_a[idx_bs_a[a_n]]);
+  }
+  return std::make_tuple(grad_as, grad_bs);
+}
+
+template <int H>
+torch::Tensor PointHullArrayDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& bs) {
+  const int64_t P = points.size(0);
+  const int64_t B_N = bs.size(0);
+
+  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
+  ValidateShape<H>(bs);
+
+  at::Tensor dists = at::zeros({P, B_N}, points.options());
+  auto points_a = points.accessor<float, 2>();
+  auto bs_a = bs.accessor<float, 3>();
+  auto dists_a = dists.accessor<float, 2>();
+  for (int64_t p = 0; p < P; ++p) {
+    auto point = ExtractHull<1>(points_a[p]);
+    auto dest = dists_a[p];
+    for (int64_t b_n = 0; b_n < B_N; ++b_n) {
+      auto b = ExtractHull<H>(bs_a[b_n]);
+      dest[b_n] = HullDistance(point, b);
+    }
+  }
+  return dists;
+}
+
+template <int H>
+std::tuple<at::Tensor, at::Tensor> PointHullArrayDistanceBackwardCpu(
+    const at::Tensor& points,
+    const at::Tensor& bs,
+    const at::Tensor& grad_dists) {
+  const int64_t P = points.size(0);
+  const int64_t B_N = bs.size(0);
+
+  TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
+  ValidateShape<H>(bs);
+  TORCH_CHECK((grad_dists.size(0) == P) && (grad_dists.size(1) == B_N));
+
+  at::Tensor grad_points = at::zeros({P, 3}, points.options());
+  at::Tensor grad_bs = at::zeros({B_N, H, 3}, bs.options());
+
+  auto points_a = points.accessor<float, 2>();
+  auto bs_a = bs.accessor<float, 3>();
+  auto grad_dists_a = grad_dists.accessor<float, 2>();
+  auto grad_points_a = grad_points.accessor<float, 2>();
+  auto grad_bs_a = grad_bs.accessor<float, 3>();
+  for (int64_t p = 0; p < P; ++p) {
+    auto point = ExtractHull<1>(points_a[p]);
+    auto grad_point = grad_points_a[p];
+    auto grad_dist = grad_dists_a[p];
+    for (int64_t b_n = 0; b_n < B_N; ++b_n) {
+      auto b = ExtractHull<H>(bs_a[b_n]);
+      HullHullDistanceBackward(
+          point, b, grad_dist[b_n], std::move(grad_point), grad_bs_a[b_n]);
+    }
+  }
+  return std::make_tuple(grad_points, grad_bs);
+}
+
+// ---------- Here begin the exported functions ------------ //
+
+std::tuple<torch::Tensor, torch::Tensor> PointFaceDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& tris,
+    const torch::Tensor& tris_first_idx) {
+  return HullHullDistanceForwardCpu<1, 3>(
+      points, points_first_idx, tris, tris_first_idx);
+}
+
+std::tuple<torch::Tensor, torch::Tensor> PointFaceDistanceBackwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& tris,
+    const torch::Tensor& idx_points,
+    const torch::Tensor& grad_dists) {
+  return HullHullDistanceBackwardCpu<1, 3>(
+      points, tris, idx_points, grad_dists);
+}
+
+std::tuple<torch::Tensor, torch::Tensor> FacePointDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& points_first_idx,
+    const torch::Tensor& tris,
+    const torch::Tensor& tris_first_idx) {
+  return HullHullDistanceForwardCpu<3, 1>(
+      tris, tris_first_idx, points, points_first_idx);
+}
+
+std::tuple<torch::Tensor, torch::Tensor> FacePointDistanceBackwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& tris,
+    const torch::Tensor& idx_tris,
+    const torch::Tensor& grad_dists) {
+  auto res =
+      HullHullDistanceBackwardCpu<3, 1>(tris, points, idx_tris, grad_dists);
+  return std::make_tuple(std::get<1>(res), std::get<0>(res));
+}
+
+torch::Tensor PointEdgeArrayDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& segms) {
+  return PointHullArrayDistanceForwardCpu<2>(points, segms);
+}
+
+std::tuple<at::Tensor, at::Tensor> PointFaceArrayDistanceBackwardCpu(
+    const at::Tensor& points,
+    const at::Tensor& tris,
+    const at::Tensor& grad_dists) {
+  return PointHullArrayDistanceBackwardCpu<3>(points, tris, grad_dists);
+}
+
+torch::Tensor PointFaceArrayDistanceForwardCpu(
+    const torch::Tensor& points,
+    const torch::Tensor& tris) {
+  return PointHullArrayDistanceForwardCpu<3>(points, tris);
+}
+
+std::tuple<at::Tensor, at::Tensor> PointEdgeArrayDistanceBackwardCpu(
+    const at::Tensor& points,
+    const at::Tensor& segms,
+    const at::Tensor& grad_dists) {
+  return PointHullArrayDistanceBackwardCpu<2>(points, segms, grad_dists);
+}
+
+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*/) {
+  return HullHullDistanceForwardCpu<1, 2>(
+      points, points_first_idx, segms, segms_first_idx);
+}
+
+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) {
+  return HullHullDistanceBackwardCpu<1, 2>(
+      points, segms, idx_points, grad_dists);
+}
+
+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*/) {
+  return HullHullDistanceForwardCpu<2, 1>(
+      segms, segms_first_idx, points, points_first_idx);
+}
+
+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) {
+  auto res =
+      HullHullDistanceBackwardCpu<2, 1>(segms, points, idx_segms, grad_dists);
+  return std::make_tuple(std::get<1>(res), std::get<0>(res));
+}