[Feature] Add interpolate ops from mmdet3d (#1355)

* add ops (interpolate) in mmdet3d

* refactor code

* fix typo

* fix typo

* fix typo

* refactor code

docs/understand_mmcv/ops.md

@@ -25,5 +25,7 @@ We implement common CUDA ops used in detection, segmentation, etc.
 - SoftmaxFocalLoss
 - SoftNMS
 - Synchronized BatchNorm
+- ThreeInterpolate
+- ThreeNN
 - Weight standardization
 - Correlation

docs_zh_CN/understand_mmcv/ops.md

@@ -25,5 +25,7 @@ MMCV 提供了检测、分割等任务中常用的 CUDA 算子
 - SoftmaxFocalLoss
 - SoftNMS
 - Synchronized BatchNorm
+- ThreeInterpolate
+- ThreeNN
 - Weight standardization
 - Correlation

mmcv/ops/__init__.py

@@ -40,6 +40,8 @@ from .roi_align_rotated import RoIAlignRotated, roi_align_rotated
 from .roi_pool import RoIPool, roi_pool
 from .saconv import SAConv2d
 from .sync_bn import SyncBatchNorm
+from .three_interpolate import three_interpolate
+from .three_nn import three_nn
 from .tin_shift import TINShift, tin_shift
 from .upfirdn2d import upfirdn2d
 
@@ -59,7 +61,8 @@ __all__ = [
     'SAConv2d', 'TINShift', 'tin_shift', 'box_iou_rotated', 'nms_rotated',
     'knn', 'ball_query', 'upfirdn2d', 'FusedBiasLeakyReLU',
     'fused_bias_leakyrelu', 'RoIAlignRotated', 'roi_align_rotated',
-    'pixel_group', 'contour_expand', 'MultiScaleDeformableAttention',
-    'BorderAlign', 'border_align', 'gather_points', 'furthest_point_sample',
+    'pixel_group', 'contour_expand', 'three_nn', 'three_interpolate',
+    'MultiScaleDeformableAttention', 'BorderAlign', 'border_align',
+    'gather_points', 'furthest_point_sample',
     'furthest_point_sample_with_dist', 'PointsSampler', 'Correlation'
 ]

mmcv/ops/csrc/common/cuda/three_interpolate_cuda_kernel.cuh

+// Copyright (c) OpenMMLab. All rights reserved
+#ifndef THREE_INTERPOLATE_CUDA_KERNEL_CUH
+#define THREE_INTERPOLATE_CUDA_KERNEL_CUH
+
+#ifdef MMCV_USE_PARROTS
+#include "parrots_cuda_helper.hpp"
+#else
+#include "pytorch_cuda_helper.hpp"
+#endif
+
+template <typename T>
+__global__ void three_interpolate_forward_cuda_kernel(
+    int b, int c, int m, int n, const T *points, const int *__restrict__ idx,
+    const T *weight, T *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]];
+}
+
+template <typename T>
+__global__ void three_interpolate_backward_cuda_kernel(
+    int b, int c, int n, int m, const T *grad_out, const int *__restrict__ idx,
+    const T *weight, T *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]);
+}
+
+#endif  // THREE_INTERPOLATE_CUDA_KERNEL_CUH

mmcv/ops/csrc/common/cuda/three_nn_cuda_kernel.cuh

+// Copyright (c) OpenMMLab. All rights reserved
+#ifndef THREE_NN_CUDA_KERNEL_CUH
+#define THREE_NN_CUDA_KERNEL_CUH
+
+#ifdef MMCV_USE_PARROTS
+#include "parrots_cuda_helper.hpp"
+#else
+#include "pytorch_cuda_helper.hpp"
+#endif
+
+template <typename T>
+__global__ void three_nn_forward_cuda_kernel(int b, int n, int m,
+                                             const T *unknown, const T *known,
+                                             T *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;
+
+  T ux = unknown[0];
+  T uy = unknown[1];
+  T uz = unknown[2];
+
+  double best1 = 1e40, best2 = 1e40, best3 = 1e40;
+  int besti1 = 0, besti2 = 0, besti3 = 0;
+  for (int k = 0; k < m; ++k) {
+    T x = known[k * 3 + 0];
+    T y = known[k * 3 + 1];
+    T z = known[k * 3 + 2];
+    T 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;
+}
+
+#endif  // THREE_NN_CUDA_KERNEL_CUH

mmcv/ops/csrc/pytorch/cuda/three_interpolate_cuda.cu

+// Modified from
+// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/interpolate_gpu.cu
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "pytorch_cuda_helper.hpp"
+#include "three_interpolate_cuda_kernel.cuh"
+
+void ThreeInterpolateForwardCUDAKernelLauncher(int b, int c, int m, int n,
+                                               const Tensor points,
+                                               const Tensor idx,
+                                               const Tensor weight,
+                                               Tensor out) {
+  // points: (B, C, M)
+  // idx: (B, N, 3)
+  // weight: (B, N, 3)
+  // output:
+  //      out: (B, C, N)
+
+  at::cuda::CUDAGuard device_guard(points.device());
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  // blockIdx.x(col), blockIdx.y(row)
+  dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);
+  dim3 threads(THREADS_PER_BLOCK);
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      points.scalar_type(), "three_interpolate_forward_cuda_kernel", [&] {
+        three_interpolate_forward_cuda_kernel<scalar_t>
+            <<<blocks, threads, 0, stream>>>(
+                b, c, m, n, points.data_ptr<scalar_t>(), idx.data_ptr<int>(),
+                weight.data_ptr<scalar_t>(), out.data_ptr<scalar_t>());
+      });
+
+  AT_CUDA_CHECK(cudaGetLastError());
+}
+
+void ThreeInterpolateBackwardCUDAKernelLauncher(int b, int c, int n, int m,
+                                                const Tensor grad_out,
+                                                const Tensor idx,
+                                                const Tensor weight,
+                                                Tensor grad_points) {
+  // grad_out: (B, C, N)
+  // weight: (B, N, 3)
+  // output:
+  //      grad_points: (B, C, M)
+
+  at::cuda::CUDAGuard device_guard(grad_out.device());
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  // blockIdx.x(col), blockIdx.y(row)
+  dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);
+  dim3 threads(THREADS_PER_BLOCK);
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      grad_out.scalar_type(), "three_interpolate_backward_cuda_kernel", [&] {
+        three_interpolate_backward_cuda_kernel<scalar_t>
+            <<<blocks, threads, 0, stream>>>(
+                b, c, n, m, grad_out.data_ptr<scalar_t>(), idx.data_ptr<int>(),
+                weight.data_ptr<scalar_t>(), grad_points.data_ptr<scalar_t>());
+      });
+
+  AT_CUDA_CHECK(cudaGetLastError());
+}

mmcv/ops/csrc/pytorch/cuda/three_nn_cuda.cu

+// Modified from
+// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/interpolate_gpu.cu
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "pytorch_cuda_helper.hpp"
+#include "three_nn_cuda_kernel.cuh"
+
+void ThreeNNForwardCUDAKernelLauncher(int b, int n, int m, const Tensor unknown,
+                                      const Tensor known, Tensor dist2,
+                                      Tensor idx) {
+  // unknown: (B, N, 3)
+  // known: (B, M, 3)
+  // output:
+  //      dist2: (B, N, 3)
+  //      idx: (B, N, 3)
+
+  at::cuda::CUDAGuard device_guard(unknown.device());
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  // blockIdx.x(col), blockIdx.y(row)
+  dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b);
+  dim3 threads(THREADS_PER_BLOCK);
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      unknown.scalar_type(), "three_nn_forward_cuda_kernel", [&] {
+        three_nn_forward_cuda_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
+            b, n, m, unknown.data_ptr<scalar_t>(), known.data_ptr<scalar_t>(),
+            dist2.data_ptr<scalar_t>(), idx.data_ptr<int>());
+      });
+
+  AT_CUDA_CHECK(cudaGetLastError());
+}

mmcv/ops/csrc/pytorch/pybind.cpp

@@ -74,6 +74,19 @@ void softmax_focal_loss_backward(Tensor input, Tensor target, Tensor weight,
                                  Tensor buff, Tensor grad_input, float gamma,
                                  float alpha);
 
+void three_interpolate_forward(int b, int c, int m, int n, Tensor points_tensor,
+                               Tensor idx_tensor, Tensor weight_tensor,
+                               Tensor out_tensor);
+
+void three_interpolate_backward(int b, int c, int n, int m,
+                                Tensor grad_out_tensor, Tensor idx_tensor,
+                                Tensor weight_tensor,
+                                Tensor grad_points_tensor);
+
+void three_nn_forward(int b, int n, int m, Tensor unknown_tensor,
+                      Tensor known_tensor, Tensor dist2_tensor,
+                      Tensor idx_tensor);
+
 void bbox_overlaps(const Tensor bboxes1, const Tensor bboxes2, Tensor ious,
                    const int mode, const bool aligned, const int offset);
 
@@ -343,6 +356,18 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         "softmax_focal_loss_backward", py::arg("input"), py::arg("target"),
         py::arg("weight"), py::arg("buff"), py::arg("grad_input"),
         py::arg("gamma"), py::arg("alpha"));
+  m.def("three_interpolate_forward", &three_interpolate_forward,
+        "three_interpolate_forward", py::arg("b"), py::arg("c"), py::arg("m"),
+        py::arg("n"), py::arg("points_tensor"), py::arg("idx_tensor"),
+        py::arg("weight_tensor"), py::arg("out_tensor"));
+  m.def("three_interpolate_backward", &three_interpolate_backward,
+        "three_interpolate_backward", py::arg("b"), py::arg("c"), py::arg("n"),
+        py::arg("m"), py::arg("grad_out_tensor"), py::arg("idx_tensor"),
+        py::arg("weight_tensor"), py::arg("grad_points_tensor"));
+  m.def("three_nn_forward", &three_nn_forward, "three_nn_forward", py::arg("b"),
+        py::arg("n"), py::arg("m"), py::arg("unknown_tensor"),
+        py::arg("known_tensor"), py::arg("dist2_tensor"),
+        py::arg("idx_tensor"));
   m.def("bbox_overlaps", &bbox_overlaps, "bbox_overlaps", py::arg("bboxes1"),
         py::arg("bboxes2"), py::arg("ious"), py::arg("mode"),
         py::arg("aligned"), py::arg("offset"));

mmcv/ops/csrc/pytorch/three_interpolate.cpp

+// Modified from
+// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/interpolate.cpp
+
+#include "pytorch_cpp_helper.hpp"
+
+#ifdef MMCV_WITH_CUDA
+void ThreeInterpolateForwardCUDAKernelLauncher(int b, int c, int m, int n,
+                                               const Tensor points,
+                                               const Tensor idx,
+                                               const Tensor weight, Tensor out);
+
+void three_interpolate_forward_cuda(int b, int c, int m, int n,
+                                    const Tensor points, const Tensor idx,
+                                    const Tensor weight, Tensor out) {
+  ThreeInterpolateForwardCUDAKernelLauncher(b, c, m, n, points, idx, weight,
+                                            out);
+};
+
+void ThreeInterpolateBackwardCUDAKernelLauncher(int b, int c, int n, int m,
+                                                const Tensor grad_out,
+                                                const Tensor idx,
+                                                const Tensor weight,
+                                                Tensor grad_points);
+
+void three_interpolate_backward_cuda(int b, int c, int n, int m,
+                                     const Tensor grad_out, const Tensor idx,
+                                     const Tensor weight, Tensor grad_points) {
+  ThreeInterpolateBackwardCUDAKernelLauncher(b, c, n, m, grad_out, idx, weight,
+                                             grad_points);
+};
+#endif
+
+void three_interpolate_forward(int b, int c, int m, int n, Tensor points_tensor,
+                               Tensor idx_tensor, Tensor weight_tensor,
+                               Tensor out_tensor) {
+  if (points_tensor.device().is_cuda()) {
+#ifdef MMCV_WITH_CUDA
+    three_interpolate_forward_cuda(b, c, m, n, points_tensor, idx_tensor,
+                                   weight_tensor, out_tensor);
+#else
+    AT_ERROR("three_interpolate is not compiled with GPU support");
+#endif
+  } else {
+    AT_ERROR("three_interpolate is not implemented on CPU");
+  }
+}
+
+void three_interpolate_backward(int b, int c, int n, int m,
+                                Tensor grad_out_tensor, Tensor idx_tensor,
+                                Tensor weight_tensor,
+                                Tensor grad_points_tensor) {
+  if (grad_out_tensor.device().is_cuda()) {
+#ifdef MMCV_WITH_CUDA
+    three_interpolate_backward_cuda(b, c, n, m, grad_out_tensor, idx_tensor,
+                                    weight_tensor, grad_points_tensor);
+#else
+    AT_ERROR("three_interpolate is not compiled with GPU support");
+#endif
+  } else {
+    AT_ERROR("three_interpolate is not implemented on CPU");
+  }
+}

mmcv/ops/csrc/pytorch/three_nn.cpp

+// Modified from
+// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/interpolate.cpp
+
+#include "pytorch_cpp_helper.hpp"
+
+#ifdef MMCV_WITH_CUDA
+void ThreeNNForwardCUDAKernelLauncher(int b, int n, int m, const Tensor unknown,
+                                      const Tensor known, Tensor dist2,
+                                      Tensor idx);
+
+void three_nn_forward_cuda(int b, int n, int m, const Tensor unknown,
+                           const Tensor known, Tensor dist2, Tensor idx) {
+  ThreeNNForwardCUDAKernelLauncher(b, n, m, unknown, known, dist2, idx);
+};
+#endif
+
+void three_nn_forward(int b, int n, int m, Tensor unknown_tensor,
+                      Tensor known_tensor, Tensor dist2_tensor,
+                      Tensor idx_tensor) {
+  if (unknown_tensor.device().is_cuda()) {
+#ifdef MMCV_WITH_CUDA
+    three_nn_forward_cuda(b, n, m, unknown_tensor, known_tensor, dist2_tensor,
+                          idx_tensor);
+#else
+    AT_ERROR("three_nn is not compiled with GPU support");
+#endif
+  } else {
+    AT_ERROR("three_nn is not implemented on CPU");
+  }
+}

mmcv/ops/three_interpolate.py

+from typing import Tuple
+
+import torch
+from torch.autograd import Function
+
+from ..utils import ext_loader
+
+ext_module = ext_loader.load_ext(
+    '_ext', ['three_interpolate_forward', 'three_interpolate_backward'])
+
+
+class ThreeInterpolate(Function):
+    """Performs weighted linear interpolation on 3 features.
+
+    Please refer to `Paper of PointNet++ <https://arxiv.org/abs/1706.02413>`_
+    for more details.
+    """
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, indices: torch.Tensor,
+                weight: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            features (Tensor): (B, C, M) Features descriptors to be
+                interpolated
+            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)
+
+        ext_module.three_interpolate_forward(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]:
+        """
+        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()
+
+        ext_module.three_interpolate_backward(B, c, n, m, grad_out_data, idx,
+                                              weight, grad_features.data)
+        return grad_features, None, None
+
+
+three_interpolate = ThreeInterpolate.apply

mmcv/ops/three_nn.py

+from typing import Tuple
+
+import torch
+from torch.autograd import Function
+
+from ..utils import ext_loader
+
+ext_module = ext_loader.load_ext('_ext', ['three_nn_forward'])
+
+
+class ThreeNN(Function):
+    """Find the top-3 nearest neighbors of the target set from the source set.
+
+    Please refer to `Paper of PointNet++ <https://arxiv.org/abs/1706.02413>`_
+    for more details.
+    """
+
+    @staticmethod
+    def forward(ctx, target: torch.Tensor,
+                source: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        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.
+        """
+        target = target.contiguous()
+        source = source.contiguous()
+
+        B, N, _ = target.size()
+        m = source.size(1)
+        dist2 = torch.cuda.FloatTensor(B, N, 3)
+        idx = torch.cuda.IntTensor(B, N, 3)
+
+        ext_module.three_nn_forward(B, N, m, target, source, dist2, idx)
+
+        ctx.mark_non_differentiable(idx)
+
+        return torch.sqrt(dist2), idx
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None
+
+
+three_nn = ThreeNN.apply

tests/test_ops/test_three_interpolate.py

+import pytest
+import torch
+
+from mmcv.ops import three_interpolate
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available(), reason='requires CUDA support')
+def test_three_interpolate():
+    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)

tests/test_ops/test_three_nn.py

+import pytest
+import torch
+
+from mmcv.ops import three_nn
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available(), reason='requires CUDA support')
+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)