[Feature] Add spconv ops from mmdet3d (open-mmlab#1581)

* add ops (spconv) of mmdet3d * fix typo * refactor code * resolve comments in open-mmlab#1452 * fix compile error * fix bugs * fix bug * transform from 'types.h' to 'extension.h' * fix bug * transform from 'types.h' to 'extension.h' in parrots * add extension.h in pybind.cpp * add unittest * Recover code * (1) Remove prettyprint.h (2) Switch `T` to `scalar_t` (3) Remove useless lines (4) Refine example in docstring of sparse_modules.py * (1) rename from `cu.h` to `cuh` (2) remove useless files (3) move cpu files to `pytorch/cpu` * reorganize files * Add docstring for sparse_functional.py * use dispatcher * remove template * use dispatch in cuda ops * resolve Segmentation fault * remove useless files * fix lint * fix lint * fix lint * fix unittest in test_build_layers.py * add tensorview into include_dirs when compiling * recover all deleted files * fix lint and comments * recover setup.py * replace tv::GPU as tv::TorchGPU & support device guard * fix lint Co-authored-by: hdc <[email protected]> Co-authored-by: grimoire <[email protected]>
joemathai · Feb 18, 2022 · c1de4c9 · c1de4c9
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diff --git a/docs/en/understand_mmcv/ops.md b/docs/en/understand_mmcv/ops.md
@@ -35,6 +35,7 @@ We implement common CUDA ops used in detection, segmentation, etc.
 - SigmoidFocalLoss
 - SoftmaxFocalLoss
 - SoftNMS
+- Sparse Convolution
 - Synchronized BatchNorm
 - Voxelization
 - ThreeInterpolate

diff --git a/docs/zh_cn/understand_mmcv/ops.md b/docs/zh_cn/understand_mmcv/ops.md
@@ -34,6 +34,7 @@ MMCV 提供了检测、分割等任务中常用的 CUDA 算子
 - SigmoidFocalLoss
 - SoftmaxFocalLoss
 - SoftNMS
+- Sparse Convolution
 - Synchronized BatchNorm
 - Voxelization
 - ThreeInterpolate

diff --git a/mmcv/ops/__init__.py b/mmcv/ops/__init__.py
@@ -53,6 +53,12 @@
 from .rotated_feature_align import rotated_feature_align
 from .saconv import SAConv2d
 from .scatter_points import DynamicScatter, dynamic_scatter
+from .sparse_conv import (SparseConv2d, SparseConv3d, SparseConvTranspose2d,
+                          SparseConvTranspose3d, SparseInverseConv2d,
+                          SparseInverseConv3d, SubMConv2d, SubMConv3d)
+from .sparse_modules import SparseModule, SparseSequential
+from .sparse_pool import SparseMaxPool2d, SparseMaxPool3d
+from .sparse_structure import SparseConvTensor, scatter_nd
 from .sync_bn import SyncBatchNorm
 from .three_interpolate import three_interpolate
 from .three_nn import three_nn
@@ -84,6 +90,10 @@
     'furthest_point_sample_with_dist', 'PointsSampler', 'Correlation',
     'boxes_iou_bev', 'nms_bev', 'nms_normal_bev', 'Voxelization',
     'voxelization', 'dynamic_scatter', 'DynamicScatter', 'RoIAwarePool3d',
+    'SparseConv2d', 'SparseConv3d', 'SparseConvTranspose2d',
+    'SparseConvTranspose3d', 'SparseInverseConv2d', 'SparseInverseConv3d',
+    'SubMConv2d', 'SubMConv3d', 'SparseModule', 'SparseSequential',
+    'SparseMaxPool2d', 'SparseMaxPool3d', 'SparseConvTensor', 'scatter_nd',
     'points_in_boxes_part', 'points_in_boxes_cpu', 'points_in_boxes_all',
     'points_in_polygons', 'min_area_polygons', 'active_rotated_filter',
     'convex_iou', 'convex_giou'

diff --git a/mmcv/ops/csrc/common/cuda/spconv/indice.cuh b/mmcv/ops/csrc/common/cuda/spconv/indice.cuh
@@ -0,0 +1,236 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef INDICE_CU_H_
+#define INDICE_CU_H_
+#include <utils/spconv/spconv/geometry.h>
+#include <utils/spconv/tensorview/tensorview.h>
+
+#include <utils/spconv/tensorview/helper_kernel.cuh>
+
+template <typename Index, typename IndexGrid, unsigned NDim,
+          int KernelMaxVolume = 256>
+__global__ void prepareIndicePairsKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<Index> indicesOut,
+    tv::TensorView<IndexGrid> gridsOut, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indiceNum, tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> kernelSize,
+    const tv::SimpleVector<Index, NDim> stride,
+    const tv::SimpleVector<Index, NDim> padding,
+    const tv::SimpleVector<Index, NDim> dilation,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index kernelVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[KernelMaxVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  auto indicePairsDim2 = indicePairs.dim(2);
+  Index index;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    numValidPoints = getValidOutPos<Index, NDim>(
+        indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
+        stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
+        validPoints);
+    for (Index i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      auto oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
+      indicePairs(offset, 0, oldNum) = ix;
+      index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape.data()) +
+              spatialVolume * indicesIn(ix, 0);
+      indicePairs(offset, 1, oldNum) = index;
+      indicePairUnique[offset * indicePairsDim2 + oldNum] = index;
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim,
+          int KernelMaxVolume = 256>
+__global__ void prepareDeConvIndicePairsKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<Index> indicesOut,
+    tv::TensorView<IndexGrid> gridsOut, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indiceNum, tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> kernelSize,
+    const tv::SimpleVector<Index, NDim> stride,
+    const tv::SimpleVector<Index, NDim> padding,
+    const tv::SimpleVector<Index, NDim> dilation,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index kernelVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[KernelMaxVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  auto indicePairsDim2 = indicePairs.dim(2);
+  Index index;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    numValidPoints = getValidOutPosTranspose<Index, NDim>(
+        indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
+        stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
+        validPoints);
+    for (Index i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      auto oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
+      indicePairs(offset, 0, oldNum) = ix;
+      index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape.data()) +
+              spatialVolume * indicesIn(ix, 0);
+      indicePairs(offset, 1, oldNum) = index;
+      indicePairUnique[offset * indicePairsDim2 + oldNum] = index;
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void assignGridAndIndiceOutKernel(
+    tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
+    int numAct, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> outSpatialShape, int batchSize) {
+  Index index;
+  auto indicesOutPtr = indicesOut.data();
+  for (int ix : tv::KernelLoopX<int>(numAct)) {
+    index = indicePairUnique[ix];
+    gridsOut[index] = ix;
+    index = tv::rowArrayIdxInv<Index, NDim>(
+        index, indicesOutPtr + ix * (NDim + 1) + 1, outSpatialShape.data());
+    indicesOut[ix * (NDim + 1)] = index % batchSize;
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void assignIndicePairsKernel(
+    tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
+    int numActIn, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  Index index;
+  int kernelVolume = indicePairs.dim(0);
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    for (int i = 0; i < kernelVolume; ++i) {
+      index = indicePairs(i, 1, ix);
+      if (index > -1) {
+        indicePairs(i, 1, ix) = gridsOut[index];
+      }
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void prepareSubMGridKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index index = 0;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    index = tv::rowArrayIdx<Index, NDim>(indicesIn.data() + ix * (NDim + 1) + 1,
+                                         outSpatialShape.data()) +
+            spatialVolume * indicesIn(ix, 0);
+    gridsOut[index] = ix;
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim,
+          int KernelMaxVolume = 256>
+__global__ void getSubMIndicePairsKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
+    tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
+    const tv::SimpleVector<Index, NDim> kernelSize,
+    const tv::SimpleVector<Index, NDim> stride,
+    const tv::SimpleVector<Index, NDim> padding,
+    const tv::SimpleVector<Index, NDim> dilation,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[KernelMaxVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  Index index = 0;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    numValidPoints = getValidOutPos<Index, NDim>(
+        indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
+        stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
+        validPoints);
+    for (int i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape.data()) +
+              spatialVolume * indicesIn(ix, 0);
+      if (gridsOut[index] > -1) {
+        auto oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
+        indicePairs(offset, 1, oldNum) = gridsOut[index];
+        indicePairs(offset, 0, oldNum) = ix;
+      }
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void resetGridKernel(const Index *indicePairUnique,
+                                tv::TensorView<IndexGrid> gridsOut,
+                                int numAct) {
+  for (int ix : tv::KernelLoopX<int>(numAct)) {
+    gridsOut[indicePairUnique[ix]] = -1;
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void resetGridSubMKernel(
+    const Index *indices, tv::TensorView<IndexGrid> gridsOut,
+    const tv::SimpleVector<Index, NDim> outSpatialShape, int numAct) {
+  int outSpatialShapeReg[NDim];
+  for (int i = 0; i < NDim; ++i) {
+    outSpatialShapeReg[i] = outSpatialShape[i];
+  }
+  Index spatialVolume = 1;
+  auto indsPtr = indices;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index index;
+  for (int ix : tv::KernelLoopX<int>(numAct)) {
+    indsPtr = indices + ix * (NDim + 1);
+    index = tv::rowArrayIdx<Index, NDim>(indsPtr + 1, outSpatialShapeReg);
+    gridsOut[index + spatialVolume * indsPtr[0]] = -1;
+  }
+}
+
+#endif