Merge branch 'feat_pointnet_ops' into 'master'

Feat pointnet ops

See merge request open-mmlab/mmdet.3d!34

mmdet3d/ops/interpolate/src/three_interpolate_cuda.cu

+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+
+__global__ void three_interpolate_kernel(int b, int c, int m, int n, const float *__restrict__ points,
+    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) {
+    // points: (B, C, M)
+    // idx: (B, N, 3)
+    // weight: (B, N, 3)
+    // output:
+    //      out: (B, C, N)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+
+    weight += bs_idx * n * 3 + pt_idx * 3;
+    points += bs_idx * c * m + c_idx * m;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+    out += bs_idx * c * n + c_idx * n;
+
+    out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]];
+}
+
+void three_interpolate_kernel_launcher(int b, int c, int m, int n,
+    const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream) {
+    // points: (B, C, M)
+    // idx: (B, N, 3)
+    // weight: (B, N, 3)
+    // output:
+    //      out: (B, C, N)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_kernel<<<blocks, threads, 0, stream>>>(b, c, m, n, points, idx, weight, out);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void three_interpolate_grad_kernel(int b, int c, int n, int m, const float *__restrict__ grad_out,
+    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) {
+    // grad_out: (B, C, N)
+    // weight: (B, N, 3)
+    // output:
+    //      grad_points: (B, C, M)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+
+    grad_out += bs_idx * c * n + c_idx * n + pt_idx;
+    weight += bs_idx * n * 3 + pt_idx * 3;
+    grad_points += bs_idx * c * m + c_idx * m;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+
+
+    atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]);
+    atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]);
+    atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]);
+}
+
+void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m, const float *grad_out,
+    const int *idx, const float *weight, float *grad_points, cudaStream_t stream) {
+    // grad_out: (B, C, N)
+    // weight: (B, N, 3)
+    // output:
+    //      grad_points: (B, C, M)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_grad_kernel<<<blocks, threads, 0, stream>>>(b, c, n, m, grad_out, idx, weight, grad_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}

mmdet3d/ops/interpolate/src/three_nn_cuda.cu

+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+
+__global__ void three_nn_kernel(int b, int n, int m, const float *__restrict__ unknown,
+    const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) {
+    // unknown: (B, N, 3)
+    // known: (B, M, 3)
+    // output:
+    //      dist2: (B, N, 3)
+    //      idx: (B, N, 3)
+
+    int bs_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || pt_idx >= n) return;
+
+    unknown += bs_idx * n * 3 + pt_idx * 3;
+    known += bs_idx * m * 3;
+    dist2 += bs_idx * n * 3 + pt_idx * 3;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+
+    float ux = unknown[0];
+    float uy = unknown[1];
+    float uz = unknown[2];
+
+    double best1 = 1e40, best2 = 1e40, best3 = 1e40;
+    int besti1 = 0, besti2 = 0, besti3 = 0;
+    for (int k = 0; k < m; ++k) {
+        float x = known[k * 3 + 0];
+        float y = known[k * 3 + 1];
+        float z = known[k * 3 + 2];
+        float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
+        if (d < best1) {
+            best3 = best2; besti3 = besti2;
+            best2 = best1; besti2 = besti1;
+            best1 = d; besti1 = k;
+        }
+        else if (d < best2) {
+            best3 = best2; besti3 = besti2;
+            best2 = d; besti2 = k;
+        }
+        else if (d < best3) {
+            best3 = d; besti3 = k;
+        }
+    }
+    dist2[0] = best1; dist2[1] = best2; dist2[2] = best3;
+    idx[0] = besti1; idx[1] = besti2; idx[2] = besti3;
+}
+
+
+void three_nn_kernel_launcher(int b, int n, int m, const float *unknown,
+    const float *known, float *dist2, int *idx, cudaStream_t stream) {
+    // unknown: (B, N, 3)
+    // known: (B, M, 3)
+    // output:
+    //      dist2: (B, N, 3)
+    //      idx: (B, N, 3)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    three_nn_kernel<<<blocks, threads, 0, stream>>>(b, n, m, unknown, known, dist2, idx);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}

mmdet3d/ops/interpolate/three_interpolate.py

+from typing import Tuple
+
+import torch
+from torch.autograd import Function
+
+from . import interpolate_ext
+
+
+class ThreeInterpolate(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, indices: torch.Tensor,
+                weight: torch.Tensor) -> torch.Tensor:
+        """Performs weighted linear interpolation on 3 features
+
+        Args:
+            features (Tensor): (B, C, M) Features descriptors to be
+                interpolated from
+            indices (Tensor): (B, n, 3) index three nearest neighbors
+                of the target features in features
+            weight (Tensor): (B, n, 3) weights of interpolation
+
+        Returns:
+            Tensor: (B, C, N) tensor of the interpolated features
+        """
+        assert features.is_contiguous()
+        assert indices.is_contiguous()
+        assert weight.is_contiguous()
+
+        B, c, m = features.size()
+        n = indices.size(1)
+        ctx.three_interpolate_for_backward = (indices, weight, m)
+        output = torch.cuda.FloatTensor(B, c, n)
+
+        interpolate_ext.three_interpolate_wrapper(B, c, m, n, features,
+                                                  indices, weight, output)
+        return output
+
+    @staticmethod
+    def backward(
+        ctx, grad_out: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Backward of three interpolate
+
+        Args:
+            grad_out (Tensor): (B, C, N) tensor with gradients of outputs
+
+        Returns:
+            Tensor: (B, C, M) tensor with gradients of features
+        """
+        idx, weight, m = ctx.three_interpolate_for_backward
+        B, c, n = grad_out.size()
+
+        grad_features = torch.cuda.FloatTensor(B, c, m).zero_()
+        grad_out_data = grad_out.data.contiguous()
+
+        interpolate_ext.three_interpolate_grad_wrapper(B, c, n, m,
+                                                       grad_out_data, idx,
+                                                       weight,
+                                                       grad_features.data)
+        return grad_features, None, None
+
+
+three_interpolate = ThreeInterpolate.apply

mmdet3d/ops/interpolate/three_nn.py

+from typing import Tuple
+
+import torch
+from torch.autograd import Function
+
+from . import interpolate_ext
+
+
+class ThreeNN(Function):
+
+    @staticmethod
+    def forward(ctx, target: torch.Tensor,
+                source: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Find the top-3 nearest neighbors of the target set from the source set.
+
+        Args:
+            target (Tensor): shape (B, N, 3), points set that needs to
+                find the nearest neighbors.
+            source (Tensor): shape (B, M, 3), points set that is used
+                to find the nearest neighbors of points in target set.
+
+        Returns:
+            Tensor: shape (B, N, 3), L2 distance of each point in target
+                set to their corresponding nearest neighbors.
+        """
+        assert target.is_contiguous()
+        assert source.is_contiguous()
+
+        B, N, _ = target.size()
+        m = source.size(1)
+        dist2 = torch.cuda.FloatTensor(B, N, 3)
+        idx = torch.cuda.IntTensor(B, N, 3)
+
+        interpolate_ext.three_nn_wrapper(B, N, m, target, source, dist2, idx)
+        return torch.sqrt(dist2), idx
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None
+
+
+three_nn = ThreeNN.apply

setup.py

@@ -269,6 +269,33 @@ if __name__ == '__main__':
                     'src/roiaware_pool3d_kernel.cu',
                     'src/points_in_boxes_cuda.cu',
                 ]),
+            make_cuda_ext(
+                name='ball_query_ext',
+                module='mmdet3d.ops.ball_query',
+                sources=['src/ball_query.cpp'],
+                sources_cuda=['src/ball_query_cuda.cu']),
+            make_cuda_ext(
+                name='group_points_ext',
+                module='mmdet3d.ops.group_points',
+                sources=['src/group_points.cpp'],
+                sources_cuda=['src/group_points_cuda.cu']),
+            make_cuda_ext(
+                name='interpolate_ext',
+                module='mmdet3d.ops.interpolate',
+                sources=['src/interpolate.cpp'],
+                sources_cuda=[
+                    'src/three_interpolate_cuda.cu', 'src/three_nn_cuda.cu'
+                ]),
+            make_cuda_ext(
+                name='furthest_point_sample_ext',
+                module='mmdet3d.ops.furthest_point_sample',
+                sources=['src/furthest_point_sample.cpp'],
+                sources_cuda=['src/furthest_point_sample_cuda.cu']),
+            make_cuda_ext(
+                name='gather_points_ext',
+                module='mmdet3d.ops.gather_points',
+                sources=['src/gather_points.cpp'],
+                sources_cuda=['src/gather_points_cuda.cu'])
         ],
         cmdclass={'build_ext': BuildExtension},
         zip_safe=False)

tests/test_pointnet_ops.py

+import pytest
+import torch
+
+from mmdet3d.ops import (ball_query, furthest_point_sample, gather_points,
+                         grouping_operation, three_interpolate, three_nn)
+
+
+def test_fps():
+    if not torch.cuda.is_available():
+        pytest.skip()
+    xyz = torch.tensor([[[-0.2748, 1.0020, -1.1674], [0.1015, 1.3952, -1.2681],
+                         [-0.8070, 2.4137,
+                          -0.5845], [-1.0001, 2.1982, -0.5859],
+                         [0.3841, 1.8983, -0.7431]],
+                        [[-1.0696, 3.0758,
+                          -0.1899], [-0.2559, 3.5521, -0.1402],
+                         [0.8164, 4.0081, -0.1839], [-1.1000, 3.0213, -0.8205],
+                         [-0.0518, 3.7251, -0.3950]]]).cuda()
+
+    idx = furthest_point_sample(xyz, 3)
+    expected_idx = torch.tensor([[0, 2, 4], [0, 2, 1]]).cuda()
+    assert torch.all(idx == expected_idx)
+
+
+def test_ball_query():
+    if not torch.cuda.is_available():
+        pytest.skip()
+    new_xyz = torch.tensor([[[-0.0740, 1.3147, -1.3625],
+                             [-2.2769, 2.7817, -0.2334],
+                             [-0.4003, 2.4666, -0.5116],
+                             [-0.0740, 1.3147, -1.3625],
+                             [-0.0740, 1.3147, -1.3625]],
+                            [[-2.0289, 2.4952, -0.1708],
+                             [-2.0668, 6.0278, -0.4875],
+                             [0.4066, 1.4211, -0.2947],
+                             [-2.0289, 2.4952, -0.1708],
+                             [-2.0289, 2.4952, -0.1708]]]).cuda()
+
+    xyz = torch.tensor([[[-0.0740, 1.3147, -1.3625], [0.5555, 1.0399, -1.3634],
+                         [-0.4003, 2.4666,
+                          -0.5116], [-0.5251, 2.4379, -0.8466],
+                         [-0.9691, 1.1418,
+                          -1.3733], [-0.2232, 0.9561, -1.3626],
+                         [-2.2769, 2.7817, -0.2334],
+                         [-0.2822, 1.3192, -1.3645], [0.1533, 1.5024, -1.0432],
+                         [0.4917, 1.1529, -1.3496]],
+                        [[-2.0289, 2.4952,
+                          -0.1708], [-0.7188, 0.9956, -0.5096],
+                         [-2.0668, 6.0278, -0.4875], [-1.9304, 3.3092, 0.6610],
+                         [0.0949, 1.4332, 0.3140], [-1.2879, 2.0008, -0.7791],
+                         [-0.7252, 0.9611, -0.6371], [0.4066, 1.4211, -0.2947],
+                         [0.3220, 1.4447, 0.3548], [-0.9744, 2.3856,
+                                                    -1.2000]]]).cuda()
+
+    idx = ball_query(0.2, 5, xyz, new_xyz)
+    expected_idx = torch.tensor([[[0, 0, 0, 0, 0], [6, 6, 6, 6, 6],
+                                  [2, 2, 2, 2, 2], [0, 0, 0, 0, 0],
+                                  [0, 0, 0, 0, 0]],
+                                 [[0, 0, 0, 0, 0], [2, 2, 2, 2, 2],
+                                  [7, 7, 7, 7, 7], [0, 0, 0, 0, 0],
+                                  [0, 0, 0, 0, 0]]]).cuda()
+
+    assert torch.all(idx == expected_idx)
+
+
+def test_grouping_points():
+    if not torch.cuda.is_available():
+        pytest.skip()
+    idx = torch.tensor([[[0, 0, 0], [3, 3, 3], [8, 8, 8], [0, 0, 0], [0, 0, 0],
+                         [0, 0, 0]],
+                        [[0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0], [0, 0, 0],
+                         [0, 0, 0]]]).int().cuda()
+    festures = torch.tensor([[[
+        0.5798, -0.7981, -0.9280, -1.3311, 1.3687, 0.9277, -0.4164, -1.8274,
+        0.9268, 0.8414
+    ],
+                              [
+                                  5.4247, 1.5113, 2.3944, 1.4740, 5.0300,
+                                  5.1030, 1.9360, 2.1939, 2.1581, 3.4666
+                              ],
+                              [
+                                  -1.6266, -1.0281, -1.0393, -1.6931, -1.3982,
+                                  -0.5732, -1.0830, -1.7561, -1.6786, -1.6967
+                              ]],
+                             [[
+                                 -0.0380, -0.1880, -1.5724, 0.6905, -0.3190,
+                                 0.7798, -0.3693, -0.9457, -0.2942, -1.8527
+                             ],
+                              [
+                                  1.1773, 1.5009, 2.6399, 5.9242, 1.0962,
+                                  2.7346, 6.0865, 1.5555, 4.3303, 2.8229
+                              ],
+                              [
+                                  -0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
+                                  -1.4049, 0.4990, -0.7037, -0.9924, 0.0386
+                              ]]]).cuda()
+
+    output = grouping_operation(festures, idx)
+    expected_output = torch.tensor([[[[0.5798, 0.5798, 0.5798],
+                                      [-1.3311, -1.3311, -1.3311],
+                                      [0.9268, 0.9268, 0.9268],
+                                      [0.5798, 0.5798, 0.5798],
+                                      [0.5798, 0.5798, 0.5798],
+                                      [0.5798, 0.5798, 0.5798]],
+                                     [[5.4247, 5.4247, 5.4247],
+                                      [1.4740, 1.4740, 1.4740],
+                                      [2.1581, 2.1581, 2.1581],
+                                      [5.4247, 5.4247, 5.4247],
+                                      [5.4247, 5.4247, 5.4247],
+                                      [5.4247, 5.4247, 5.4247]],
+                                     [[-1.6266, -1.6266, -1.6266],
+                                      [-1.6931, -1.6931, -1.6931],
+                                      [-1.6786, -1.6786, -1.6786],
+                                      [-1.6266, -1.6266, -1.6266],
+                                      [-1.6266, -1.6266, -1.6266],
+                                      [-1.6266, -1.6266, -1.6266]]],
+                                    [[[-0.0380, -0.0380, -0.0380],
+                                      [-0.3693, -0.3693, -0.3693],
+                                      [-1.8527, -1.8527, -1.8527],
+                                      [-0.0380, -0.0380, -0.0380],
+                                      [-0.0380, -0.0380, -0.0380],
+                                      [-0.0380, -0.0380, -0.0380]],
+                                     [[1.1773, 1.1773, 1.1773],
+                                      [6.0865, 6.0865, 6.0865],
+                                      [2.8229, 2.8229, 2.8229],
+                                      [1.1773, 1.1773, 1.1773],
+                                      [1.1773, 1.1773, 1.1773],
+                                      [1.1773, 1.1773, 1.1773]],
+                                     [[-0.6646, -0.6646, -0.6646],
+                                      [0.4990, 0.4990, 0.4990],
+                                      [0.0386, 0.0386, 0.0386],
+                                      [-0.6646, -0.6646, -0.6646],
+                                      [-0.6646, -0.6646, -0.6646],
+                                      [-0.6646, -0.6646, -0.6646]]]]).cuda()
+    assert torch.allclose(output, expected_output)
+
+
+def test_gather_points():
+    if not torch.cuda.is_available():
+        pytest.skip()
+    features = torch.tensor([[[
+        -1.6095, -0.1029, -0.8876, -1.2447, -2.4031, 0.3708, -1.1586, -1.4967,
+        -0.4800, 0.2252
+    ],
+                              [
+                                  1.9138, 3.4979, 1.6854, 1.5631, 3.6776,
+                                  3.1154, 2.1705, 2.5221, 2.0411, 3.1446
+                              ],
+                              [
+                                  -1.4173, 0.3073, -1.4339, -1.4340, -1.2770,
+                                  -0.2867, -1.4162, -1.4044, -1.4245, -1.4074
+                              ]],
+                             [[
+                                 0.2160, 0.0842, 0.3661, -0.2749, -0.4909,
+                                 -0.6066, -0.8773, -0.0745, -0.9496, 0.1434
+                             ],
+                              [
+                                  1.3644, 1.8087, 1.6855, 1.9563, 1.2746,
+                                  1.9662, 0.9566, 1.8778, 1.1437, 1.3639
+                              ],
+                              [
+                                  -0.7172, 0.1692, 0.2241, 0.0721, -0.7540,
+                                  0.0462, -0.6227, 0.3223, -0.6944, -0.5294
+                              ]]]).cuda()
+
+    idx = torch.tensor([[0, 1, 4, 0, 0, 0], [0, 5, 6, 0, 0, 0]]).int().cuda()
+
+    output = gather_points(features, idx)
+    expected_output = torch.tensor(
+        [[[-1.6095, -0.1029, -2.4031, -1.6095, -1.6095, -1.6095],
+          [1.9138, 3.4979, 3.6776, 1.9138, 1.9138, 1.9138],
+          [-1.4173, 0.3073, -1.2770, -1.4173, -1.4173, -1.4173]],
+         [[0.2160, -0.6066, -0.8773, 0.2160, 0.2160, 0.2160],
+          [1.3644, 1.9662, 0.9566, 1.3644, 1.3644, 1.3644],
+          [-0.7172, 0.0462, -0.6227, -0.7172, -0.7172, -0.7172]]]).cuda()
+
+    assert torch.allclose(output, expected_output)
+
+
+def test_three_interpolate():
+    if not torch.cuda.is_available():
+        pytest.skip()
+    features = torch.tensor([[[2.4350, 4.7516, 4.4995, 2.4350, 2.4350, 2.4350],
+                              [3.1236, 2.6278, 3.0447, 3.1236, 3.1236, 3.1236],
+                              [2.6732, 2.8677, 2.6436, 2.6732, 2.6732, 2.6732],
+                              [0.0124, 7.0150, 7.0199, 0.0124, 0.0124, 0.0124],
+                              [0.3207, 0.0000, 0.3411, 0.3207, 0.3207,
+                               0.3207]],
+                             [[0.0000, 0.9544, 2.4532, 0.0000, 0.0000, 0.0000],
+                              [0.5346, 1.9176, 1.4715, 0.5346, 0.5346, 0.5346],
+                              [0.0000, 0.2744, 2.0842, 0.0000, 0.0000, 0.0000],
+                              [0.3414, 1.5063, 1.6209, 0.3414, 0.3414, 0.3414],
+                              [0.5814, 0.0103, 0.0000, 0.5814, 0.5814,
+                               0.5814]]]).cuda()
+
+    idx = torch.tensor([[[0, 1, 2], [2, 3, 4], [2, 3, 4], [0, 1, 2], [0, 1, 2],
+                         [0, 1, 3]],
+                        [[0, 2, 3], [1, 3, 4], [2, 1, 4], [0, 2, 4], [0, 2, 4],
+                         [0, 1, 2]]]).int().cuda()
+
+    weight = torch.tensor([[[3.3333e-01, 3.3333e-01, 3.3333e-01],
+                            [1.0000e+00, 5.8155e-08, 2.2373e-08],
+                            [1.0000e+00, 1.7737e-08, 1.7356e-08],
+                            [3.3333e-01, 3.3333e-01, 3.3333e-01],
+                            [3.3333e-01, 3.3333e-01, 3.3333e-01],
+                            [3.3333e-01, 3.3333e-01, 3.3333e-01]],
+                           [[3.3333e-01, 3.3333e-01, 3.3333e-01],
+                            [1.0000e+00, 1.3651e-08, 7.7312e-09],
+                            [1.0000e+00, 1.7148e-08, 1.4070e-08],
+                            [3.3333e-01, 3.3333e-01, 3.3333e-01],
+                            [3.3333e-01, 3.3333e-01, 3.3333e-01],
+                            [3.3333e-01, 3.3333e-01, 3.3333e-01]]]).cuda()
+
+    output = three_interpolate(features, idx, weight)
+    expected_output = torch.tensor([[[
+        3.8953e+00, 4.4995e+00, 4.4995e+00, 3.8953e+00, 3.8953e+00, 3.2072e+00
+    ], [
+        2.9320e+00, 3.0447e+00, 3.0447e+00, 2.9320e+00, 2.9320e+00, 2.9583e+00
+    ], [
+        2.7281e+00, 2.6436e+00, 2.6436e+00, 2.7281e+00, 2.7281e+00, 2.7380e+00
+    ], [
+        4.6824e+00, 7.0199e+00, 7.0199e+00, 4.6824e+00, 4.6824e+00, 2.3466e+00
+    ], [
+        2.2060e-01, 3.4110e-01, 3.4110e-01, 2.2060e-01, 2.2060e-01, 2.1380e-01
+    ]],
+                                    [[
+                                        8.1773e-01, 9.5440e-01, 2.4532e+00,
+                                        8.1773e-01, 8.1773e-01, 1.1359e+00
+                                    ],
+                                     [
+                                         8.4689e-01, 1.9176e+00, 1.4715e+00,
+                                         8.4689e-01, 8.4689e-01, 1.3079e+00
+                                     ],
+                                     [
+                                         6.9473e-01, 2.7440e-01, 2.0842e+00,
+                                         6.9473e-01, 6.9473e-01, 7.8619e-01
+                                     ],
+                                     [
+                                         7.6789e-01, 1.5063e+00, 1.6209e+00,
+                                         7.6789e-01, 7.6789e-01, 1.1562e+00
+                                     ],
+                                     [
+                                         3.8760e-01, 1.0300e-02, 8.3569e-09,
+                                         3.8760e-01, 3.8760e-01, 1.9723e-01
+                                     ]]]).cuda()
+
+    assert torch.allclose(output, expected_output, 1e-4)
+
+
+def test_three_nn():
+    known = torch.tensor([[[-1.8373, 3.5605,
+                            -0.7867], [0.7615, 2.9420, 0.2314],
+                           [-0.6503, 3.6637, -1.0622],
+                           [-1.8373, 3.5605, -0.7867],
+                           [-1.8373, 3.5605, -0.7867]],
+                          [[-1.3399, 1.9991, -0.3698],
+                           [-0.0799, 0.9698,
+                            -0.8457], [0.0858, 2.4721, -0.1928],
+                           [-1.3399, 1.9991, -0.3698],
+                           [-1.3399, 1.9991, -0.3698]]]).cuda()
+
+    unknown = torch.tensor([[[-1.8373, 3.5605, -0.7867],
+                             [0.7615, 2.9420, 0.2314],
+                             [-0.6503, 3.6637, -1.0622],
+                             [-1.5237, 2.3976, -0.8097],
+                             [-0.0722, 3.4017, -0.2880],
+                             [0.5198, 3.0661, -0.4605],
+                             [-2.0185, 3.5019, -0.3236],
+                             [0.5098, 3.1020, 0.5799],
+                             [-1.6137, 3.8443, -0.5269],
+                             [0.7341, 2.9626, -0.3189]],
+                            [[-1.3399, 1.9991, -0.3698],
+                             [-0.0799, 0.9698, -0.8457],
+                             [0.0858, 2.4721, -0.1928],
+                             [-0.9022, 1.6560, -1.3090],
+                             [0.1156, 1.6901, -0.4366],
+                             [-0.6477, 2.3576, -0.1563],
+                             [-0.8482, 1.1466, -1.2704],
+                             [-0.8753, 2.0845, -0.3460],
+                             [-0.5621, 1.4233, -1.2858],
+                             [-0.5883, 1.3114, -1.2899]]]).cuda()
+
+    dist, idx = three_nn(unknown, known)
+    expected_dist = torch.tensor([[[0.0000, 0.0000, 0.0000],
+                                   [0.0000, 2.0463, 2.8588],
+                                   [0.0000, 1.2229, 1.2229],
+                                   [1.2047, 1.2047, 1.2047],
+                                   [1.0011, 1.0845, 1.8411],
+                                   [0.7433, 1.4451, 2.4304],
+                                   [0.5007, 0.5007, 0.5007],
+                                   [0.4587, 2.0875, 2.7544],
+                                   [0.4450, 0.4450, 0.4450],
+                                   [0.5514, 1.7206, 2.6811]],
+                                  [[0.0000, 0.0000, 0.0000],
+                                   [0.0000, 1.6464, 1.6952],
+                                   [0.0000, 1.5125, 1.5125],
+                                   [1.0915, 1.0915, 1.0915],
+                                   [0.8197, 0.8511, 1.4894],
+                                   [0.7433, 0.8082, 0.8082],
+                                   [0.8955, 1.3340, 1.3340],
+                                   [0.4730, 0.4730, 0.4730],
+                                   [0.7949, 1.3325, 1.3325],
+                                   [0.7566, 1.3727, 1.3727]]]).cuda()
+    expected_idx = torch.tensor([[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4],
+                                  [2, 1, 0], [1, 2, 0], [0, 3, 4], [1, 2, 0],
+                                  [0, 3, 4], [1, 2, 0]],
+                                 [[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4],
+                                  [2, 1, 0], [2, 0, 3], [1, 0, 3], [0, 3, 4],
+                                  [1, 0, 3], [1, 0, 3]]]).cuda()
+
+    assert torch.allclose(dist, expected_dist, 1e-4)
+    assert torch.all(idx == expected_idx)