Farthest point sampling C++

Summary: C++ implementation of iterative farthest point sampling.

Reviewed By: jcjohnson

Differential Revision: D30349887

fbshipit-source-id: d25990f857752633859fe00283e182858a870269

pytorch3d/csrc/ext.cpp

@@ -27,6 +27,7 @@
 #include "rasterize_meshes/rasterize_meshes.h"
 #include "rasterize_points/rasterize_points.h"
 #include "sample_pdf/sample_pdf.h"
+#include "sample_farthest_points/sample_farthest_points.h"
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("face_areas_normals_forward", &FaceAreasNormalsForward);
@@ -40,9 +41,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 #endif
   m.def("knn_points_idx", &KNearestNeighborIdx);
   m.def("knn_points_backward", &KNearestNeighborBackward);
-
-  // Ball Query
   m.def("ball_query", &BallQuery);
+  m.def("sample_farthest_points", &FarthestPointSampling);
   m.def(
       "mesh_normal_consistency_find_verts", &MeshNormalConsistencyFindVertices);
   m.def("gather_scatter", &GatherScatter);

pytorch3d/csrc/sample_farthest_points/sample_farthest_points.cpp

@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <torch/extension.h>
+#include <iterator>
+#include <random>
+#include <vector>
+
+at::Tensor FarthestPointSamplingCpu(
+    const at::Tensor& points,
+    const at::Tensor& lengths,
+    const at::Tensor& K,
+    const bool random_start_point) {
+  // Get constants
+  const int64_t N = points.size(0);
+  const int64_t P = points.size(1);
+  const int64_t D = points.size(2);
+  const int64_t max_K = torch::max(K).item<int64_t>();
+
+  // Initialize an output array for the sampled indices
+  // of shape (N, max_K)
+  auto long_opts = lengths.options();
+  torch::Tensor sampled_indices = torch::full({N, max_K}, -1, long_opts);
+
+  // Create accessors for all tensors
+  auto points_a = points.accessor<float, 3>();
+  auto lengths_a = lengths.accessor<int64_t, 1>();
+  auto k_a = K.accessor<int64_t, 1>();
+  auto sampled_indices_a = sampled_indices.accessor<int64_t, 2>();
+
+  // Initialize a mask to prevent duplicates
+  // If true, the point has already been selected.
+  std::vector<unsigned char> selected_points_mask(P, false);
+
+  // Initialize to infinity a vector of
+  // distances from each point to any of the previously selected points
+  std::vector<float> dists(P, std::numeric_limits<float>::max());
+
+  // Initialize random number generation for random starting points
+  std::random_device rd;
+  std::default_random_engine eng(rd());
+
+  for (int64_t n = 0; n < N; ++n) {
+    // Resize and reset points mask and distances for each batch
+    selected_points_mask.resize(lengths_a[n]);
+    dists.resize(lengths_a[n]);
+    std::fill(selected_points_mask.begin(), selected_points_mask.end(), false);
+    std::fill(dists.begin(), dists.end(), std::numeric_limits<float>::max());
+
+    // Select a starting point index and save it
+    std::uniform_int_distribution<int> distr(0, lengths_a[n] - 1);
+    int64_t last_idx = random_start_point ? distr(eng) : 0;
+    sampled_indices_a[n][0] = last_idx;
+
+    // Set the value of the mask at this point to false
+    selected_points_mask[last_idx] = true;
+
+    // For heterogeneous pointclouds, use the minimum of the
+    // length for that cloud compared to K as the number of
+    // points to sample
+    const int64_t batch_k = std::min(lengths_a[n], k_a[n]);
+
+    // Iteratively select batch_k points per batch
+    for (int64_t k = 1; k < batch_k; ++k) {
+      // Iterate through all the points
+      for (int64_t p = 0; p < lengths_a[n]; ++p) {
+        if (selected_points_mask[p]) {
+          // For already selected points set the distance to 0.0
+          dists[p] = 0.0;
+          continue;
+        }
+
+        // Calculate the distance to the last selected point
+        float dist2 = 0.0;
+        for (int64_t d = 0; d < D; ++d) {
+          float diff = points_a[n][last_idx][d] - points_a[n][p][d];
+          dist2 += diff * diff;
+        }
+
+        // If the distance of this point to the last selected point is closer
+        // than the distance to any of the previously selected points, then
+        // update this distance
+        if (dist2 < dists[p]) {
+          dists[p] = dist2;
+        }
+      }
+
+      // The aim is to pick the point that has the largest
+      // nearest neighbour distance to any of the already selected points
+      auto itr = std::max_element(dists.begin(), dists.end());
+      last_idx = std::distance(dists.begin(), itr);
+
+      // Save selected point
+      sampled_indices_a[n][k] = last_idx;
+
+      // Set the mask value to true to prevent duplicates.
+      selected_points_mask[last_idx] = true;
+    }
+  }
+
+  return sampled_indices;
+}

pytorch3d/csrc/sample_farthest_points/sample_farthest_points.h

@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+#include <torch/extension.h>
+#include <tuple>
+#include "utils/pytorch3d_cutils.h"
+
+// Iterative farthest point sampling algorithm [1] to subsample a set of
+// K points from a given pointcloud. At each iteration, a point is selected
+// which has the largest nearest neighbor distance to any of the
+// already selected points.
+
+// Farthest point sampling provides more uniform coverage of the input
+// point cloud compared to uniform random sampling.
+
+// [1] Charles R. Qi et al, "PointNet++: Deep Hierarchical Feature Learning
+//     on Point Sets in a Metric Space", NeurIPS 2017.
+
+// Args:
+//     points: (N, P, D) float32 Tensor containing the batch of pointclouds.
+//     lengths: (N,) long Tensor giving the number of points in each pointcloud
+//        (to support heterogeneous batches of pointclouds).
+//     K: a tensor of length (N,) giving the number of
+//        samples to select for each element in the batch.
+//        The number of samples is typically << P.
+//     random_start_point: bool, if True, a random point is selected as the
+//        starting point for iterative sampling.
+// Returns:
+//     selected_indices: (N, K) array of selected indices. If the values in
+//        K are not all the same, then the shape will be (N, max(K), D), and
+//        padded with -1 for batch elements where k_i < max(K). The selected
+//        points are gathered in the pytorch autograd wrapper.
+
+at::Tensor FarthestPointSamplingCpu(
+    const at::Tensor& points,
+    const at::Tensor& lengths,
+    const at::Tensor& K,
+    const bool random_start_point);
+
+// Exposed implementation.
+at::Tensor FarthestPointSampling(
+    const at::Tensor& points,
+    const at::Tensor& lengths,
+    const at::Tensor& K,
+    const bool random_start_point) {
+  if (points.is_cuda() || lengths.is_cuda() || K.is_cuda()) {
+    AT_ERROR("CUDA implementation not yet supported");
+  }
+  return FarthestPointSamplingCpu(points, lengths, K, random_start_point);
+}

pytorch3d/ops/sample_farthest_points.py

@@ -8,11 +8,12 @@ from random import randint
 from typing import Optional, Tuple, Union, List
 
 import torch
+from pytorch3d import _C
 
 from .utils import masked_gather
 
 
-def sample_farthest_points_naive(
+def sample_farthest_points(
     points: torch.Tensor,
     lengths: Optional[torch.Tensor] = None,
     K: Union[int, List, torch.Tensor] = 50,
@@ -34,7 +35,7 @@ def sample_farthest_points_naive(
         points: (N, P, D) array containing the batch of pointclouds
         lengths: (N,) number of points in each pointcloud (to support heterogeneous
             batches of pointclouds)
-        K: samples you want in each sampled point cloud (this is typically << P). If
+        K: samples required in each sampled point cloud (this is typically << P). If
             K is an int then the same number of samples are selected for each
             pointcloud in the batch. If K is a tensor is should be length (N,)
             giving the number of samples to select for each element in the batch
@@ -52,6 +53,50 @@ def sample_farthest_points_naive(
     N, P, D = points.shape
     device = points.device
 
+    # Validate inputs
+    if lengths is None:
+        lengths = torch.full((N,), P, dtype=torch.int64, device=device)
+
+    if lengths.shape[0] != N:
+        raise ValueError("points and lengths must have same batch dimension.")
+
+    # TODO: support providing K as a ratio of the total number of points instead of as an int
+    if isinstance(K, int):
+        K = torch.full((N,), K, dtype=torch.int64, device=device)
+    elif isinstance(K, list):
+        K = torch.tensor(K, dtype=torch.int64, device=device)
+
+    if K.shape[0] != N:
+        raise ValueError("K and points must have the same batch dimension")
+
+    # Check dtypes are correct and convert if necessary
+    if not (points.dtype == torch.float32):
+        points = points.to(torch.float32)
+    if not (lengths.dtype == torch.int64):
+        lengths = lengths.to(torch.int64)
+    if not (K.dtype == torch.int64):
+        K = K.to(torch.int64)
+
+    with torch.no_grad():
+        # pyre-fixme[16]: `pytorch3d_._C` has no attribute `sample_farthest_points`.
+        idx = _C.sample_farthest_points(points, lengths, K, random_start_point)
+    sampled_points = masked_gather(points, idx)
+
+    return sampled_points, idx
+
+
+def sample_farthest_points_naive(
+    points: torch.Tensor,
+    lengths: Optional[torch.Tensor] = None,
+    K: Union[int, List, torch.Tensor] = 50,
+    random_start_point: bool = False,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Same Args/Returns as sample_farthest_points
+    """
+    N, P, D = points.shape
+    device = points.device
+
     # Validate inputs
     if lengths is None:
         lengths = torch.full((N,), P, dtype=torch.int64, device=device)

tests/bm_sample_farthest_points.py

@@ -0,0 +1,37 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from itertools import product
+
+from fvcore.common.benchmark import benchmark
+from test_sample_farthest_points import TestFPS
+
+
+def bm_fps() -> None:
+    kwargs_list = []
+    backends = ["cpu", "cuda:0"]
+    Ns = [8, 32]
+    Ps = [64, 256]
+    Ds = [3]
+    Ks = [24]
+    test_cases = product(Ns, Ps, Ds, Ks, backends)
+    for case in test_cases:
+        N, P, D, K, d = case
+        kwargs_list.append({"N": N, "P": P, "D": D, "K": K, "device": d})
+
+    benchmark(
+        TestFPS.sample_farthest_points_naive,
+        "FPS_NAIVE_PYTHON",
+        kwargs_list,
+        warmup_iters=1,
+    )
+
+    kwargs_list = [k for k in kwargs_list if k["device"] == "cpu"]
+    benchmark(TestFPS.sample_farthest_points, "FPS_CPU", kwargs_list, warmup_iters=1)
+
+
+if __name__ == "__main__":
+    bm_fps()

tests/test_sample_farthest_points.py

@@ -8,13 +8,15 @@ import unittest
 
 import torch
 from common_testing import TestCaseMixin, get_random_cuda_device
-from pytorch3d.ops.sample_farthest_points import sample_farthest_points_naive
+from pytorch3d.ops.sample_farthest_points import (
+    sample_farthest_points_naive,
+    sample_farthest_points,
+)
 from pytorch3d.ops.utils import masked_gather
 
 
 class TestFPS(TestCaseMixin, unittest.TestCase):
-    def test_simple(self):
+    def _test_simple(self, fps_func, device="cpu"):
-        device = get_random_cuda_device()
         # fmt: off
         points = torch.tensor(
             [
@@ -44,7 +46,7 @@ class TestFPS(TestCaseMixin, unittest.TestCase):
         )
         # fmt: on
         expected_inds = torch.tensor([[0, 4], [0, 7]], dtype=torch.int64, device=device)
-        out_points, out_inds = sample_farthest_points_naive(points, K=2)
+        out_points, out_inds = fps_func(points, K=2)
         self.assertClose(out_inds, expected_inds)
 
         # Gather the points
@@ -55,24 +57,37 @@ class TestFPS(TestCaseMixin, unittest.TestCase):
         expected_inds = torch.tensor(
             [[0, 4, 1], [0, 7, -1]], dtype=torch.int64, device=device
         )
-        out_points, out_inds = sample_farthest_points_naive(points, K=[3, 2])
+        out_points, out_inds = fps_func(points, K=[3, 2])
         self.assertClose(out_inds, expected_inds)
 
         # Gather the points
         expected_points = masked_gather(points, expected_inds)
         self.assertClose(out_points, expected_points)
 
-    def test_random_heterogeneous(self):
+    def _test_compare_random_heterogeneous(self, device="cpu"):
-        device = get_random_cuda_device()
+        N, P, D, K = 5, 20, 5, 8
-        N, P, D, K = 5, 40, 5, 8
+        points = torch.randn((N, P, D), device=device, dtype=torch.float32)
-        points = torch.randn((N, P, D), device=device)
+        out_points_naive, out_idxs_naive = sample_farthest_points_naive(points, K=K)
-        out_points, out_idxs = sample_farthest_points_naive(points, K=K)
+        out_points, out_idxs = sample_farthest_points(points, K=K)
         self.assertTrue(out_idxs.min() >= 0)
+        self.assertClose(out_idxs, out_idxs_naive)
+        self.assertClose(out_points, out_points_naive)
         for n in range(N):
             self.assertEqual(out_idxs[n].ne(-1).sum(), K)
 
+        # Test case where K > P
+        K = 30
+        points1 = torch.randn((N, P, D), dtype=torch.float32, device=device)
+        points2 = points1.clone()
+        points1.requires_grad = True
+        points2.requires_grad = True
         lengths = torch.randint(low=1, high=P, size=(N,), device=device)
-        out_points, out_idxs = sample_farthest_points_naive(points, lengths, K=50)
+        out_points_naive, out_idxs_naive = sample_farthest_points_naive(
+            points1, lengths, K=K
+        )
+        out_points, out_idxs = sample_farthest_points(points2, lengths, K=K)
+        self.assertClose(out_idxs, out_idxs_naive)
+        self.assertClose(out_points, out_points_naive)
 
         for n in range(N):
             # Check that for heterogeneous batches, the max number of
@@ -85,8 +100,15 @@ class TestFPS(TestCaseMixin, unittest.TestCase):
             vals, counts = torch.unique(out_idxs[n][val_mask], return_counts=True)
             self.assertTrue(counts.le(1).all())
 
-    def test_errors(self):
+        # Check gradients
-        device = get_random_cuda_device()
+        grad_sampled_points = torch.ones((N, K, D), dtype=torch.float32, device=device)
+        loss1 = (out_points_naive * grad_sampled_points).sum()
+        loss1.backward()
+        loss2 = (out_points * grad_sampled_points).sum()
+        loss2.backward()
+        self.assertClose(points1.grad, points2.grad, atol=5e-6)
+
+    def _test_errors(self, fps_func, device="cpu"):
         N, P, D, K = 5, 40, 5, 8
         points = torch.randn((N, P, D), device=device)
         wrong_batch_dim = torch.randint(low=1, high=K, size=(K,), device=device)
@@ -99,8 +121,7 @@ class TestFPS(TestCaseMixin, unittest.TestCase):
         with self.assertRaisesRegex(ValueError, "points and lengths must have"):
             sample_farthest_points_naive(points, lengths=wrong_batch_dim, K=K)
 
-    def test_random_start(self):
+    def _test_random_start(self, fps_func, device="cpu"):
-        device = get_random_cuda_device()
         N, P, D, K = 5, 40, 5, 8
         points = torch.randn((N, P, D), device=device)
         out_points, out_idxs = sample_farthest_points_naive(
@@ -109,3 +130,64 @@ class TestFPS(TestCaseMixin, unittest.TestCase):
         # Check the first index is not 0 for all batch elements
         # when random_start_point = True
         self.assertTrue(out_idxs[:, 0].sum() > 0)
+
+    def _test_gradcheck(self, fps_func, device="cpu"):
+        N, P, D, K = 2, 5, 3, 2
+        points = torch.randn(
+            (N, P, D), dtype=torch.float32, device=device, requires_grad=True
+        )
+        torch.autograd.gradcheck(
+            fps_func,
+            (points, None, K),
+            check_undefined_grad=False,
+            eps=2e-3,
+            atol=0.001,
+        )
+
+    def test_sample_farthest_points_naive(self):
+        device = get_random_cuda_device()
+        self._test_simple(sample_farthest_points_naive, device)
+        self._test_errors(sample_farthest_points_naive, device)
+        self._test_random_start(sample_farthest_points_naive, device)
+        self._test_gradcheck(sample_farthest_points_naive, device)
+
+    def test_sample_farthest_points_cpu(self):
+        self._test_simple(sample_farthest_points, "cpu")
+        self._test_errors(sample_farthest_points, "cpu")
+        self._test_compare_random_heterogeneous("cpu")
+        self._test_random_start(sample_farthest_points, "cpu")
+        self._test_gradcheck(sample_farthest_points, "cpu")
+
+    @staticmethod
+    def sample_farthest_points_naive(N: int, P: int, D: int, K: int, device: str):
+        device = torch.device(device)
+        pts = torch.randn(
+            N, P, D, dtype=torch.float32, device=device, requires_grad=True
+        )
+        grad_pts = torch.randn(N, K, D, dtype=torch.float32, device=device)
+        torch.cuda.synchronize()
+
+        def output():
+            out_points, _ = sample_farthest_points_naive(pts, K=K)
+            loss = (out_points * grad_pts).sum()
+            loss.backward()
+            torch.cuda.synchronize()
+
+        return output
+
+    @staticmethod
+    def sample_farthest_points(N: int, P: int, D: int, K: int, device: str):
+        device = torch.device(device)
+        pts = torch.randn(
+            N, P, D, dtype=torch.float32, device=device, requires_grad=True
+        )
+        grad_pts = torch.randn(N, K, D, dtype=torch.float32, device=device)
+        torch.cuda.synchronize()
+
+        def output():
+            out_points, _ = sample_farthest_points(pts, K=K)
+            loss = (out_points * grad_pts).sum()
+            loss.backward()
+            torch.cuda.synchronize()
+
+        return output