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Merge pull request #68 from liaopeiyuan/cpu_radius
C++ CPU for radius and knn
2 parents 32fa325 + 29f9716 commit b75a19e

22 files changed

+2863
-201
lines changed

‎.travis.yml

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@@ -59,7 +59,8 @@ install:
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- conda install pytorch=${TORCH_VERSION} ${TOOLKIT} -c pytorch --yes
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- source script/torch.sh
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- pip install flake8 codecov
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- python setup.py install
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- pip install scipy==1.4.1
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- pip install .[test]
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script:
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- flake8 .
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- python setup.py test

‎CMakeLists.txt

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@@ -1,7 +1,7 @@
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cmake_minimum_required(VERSION 3.0)
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project(torchcluster)
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set(CMAKE_CXX_STANDARD 14)
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set(TORCHCLUSTER_VERSION 1.5.4)
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set(TORCHCLUSTER_VERSION 1.5.5)
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option(WITH_CUDA "Enable CUDA support" OFF)
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‎csrc/cpu/knn_cpu.cpp

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#include "knn_cpu.h"
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#include "utils.h"
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#include "utils/neighbors.cpp"
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torch::Tensor knn_cpu(torch::Tensor x, torch::Tensor y,
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torch::optional<torch::Tensor> ptr_x,
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torch::optional<torch::Tensor> ptr_y, int64_t k,
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int64_t num_workers) {
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CHECK_CPU(x);
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CHECK_INPUT(x.dim() == 2);
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CHECK_CPU(y);
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CHECK_INPUT(y.dim() == 2);
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if (ptr_x.has_value()) {
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CHECK_CPU(ptr_x.value());
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CHECK_INPUT(ptr_x.value().dim() == 1);
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}
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if (ptr_y.has_value()) {
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CHECK_CPU(ptr_y.value());
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CHECK_INPUT(ptr_y.value().dim() == 1);
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}
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std::vector<size_t> *out_vec = new std::vector<size_t>();
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AT_DISPATCH_ALL_TYPES(x.scalar_type(), "radius_cpu", [&] {
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auto x_data = x.data_ptr<scalar_t>();
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auto y_data = y.data_ptr<scalar_t>();
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auto x_vec = std::vector<scalar_t>(x_data, x_data + x.numel());
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auto y_vec = std::vector<scalar_t>(y_data, y_data + y.numel());
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if (!ptr_x.has_value()) {
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nanoflann_neighbors<scalar_t>(y_vec, x_vec, out_vec, 0, x.size(-1), 0,
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num_workers, k, 0);
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} else {
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auto sx = (ptr_x.value().narrow(0, 1, ptr_x.value().numel() - 1) -
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ptr_x.value().narrow(0, 0, ptr_x.value().numel() - 1));
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auto sy = (ptr_y.value().narrow(0, 1, ptr_y.value().numel() - 1) -
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ptr_y.value().narrow(0, 0, ptr_y.value().numel() - 1));
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auto sx_data = sx.data_ptr<int64_t>();
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auto sy_data = sy.data_ptr<int64_t>();
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auto sx_vec = std::vector<long>(sx_data, sx_data + sx.numel());
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auto sy_vec = std::vector<long>(sy_data, sy_data + sy.numel());
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batch_nanoflann_neighbors<scalar_t>(y_vec, x_vec, sy_vec, sx_vec, out_vec,
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k, x.size(-1), 0, k, 0);
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}
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});
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const int64_t size = out_vec->size() / 2;
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auto out = torch::from_blob(out_vec->data(), {size, 2},
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x.options().dtype(torch::kLong));
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return out.t().index_select(0, torch::tensor({1, 0}));
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}

‎csrc/cpu/knn_cpu.h

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#pragma once
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#include <torch/extension.h>
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torch::Tensor knn_cpu(torch::Tensor x, torch::Tensor y,
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torch::optional<torch::Tensor> ptr_x,
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torch::optional<torch::Tensor> ptr_y, int64_t k,
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int64_t num_workers);

‎csrc/cpu/radius_cpu.cpp

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#include "radius_cpu.h"
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#include "utils.h"
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#include "utils/neighbors.cpp"
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torch::Tensor radius_cpu(torch::Tensor x, torch::Tensor y,
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torch::optional<torch::Tensor> ptr_x,
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torch::optional<torch::Tensor> ptr_y, double r,
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int64_t max_num_neighbors, int64_t num_workers) {
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CHECK_CPU(x);
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CHECK_INPUT(x.dim() == 2);
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CHECK_CPU(y);
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CHECK_INPUT(y.dim() == 2);
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if (ptr_x.has_value()) {
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CHECK_CPU(ptr_x.value());
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CHECK_INPUT(ptr_x.value().dim() == 1);
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}
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if (ptr_y.has_value()) {
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CHECK_CPU(ptr_y.value());
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CHECK_INPUT(ptr_y.value().dim() == 1);
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}
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std::vector<size_t> *out_vec = new std::vector<size_t>();
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AT_DISPATCH_ALL_TYPES(x.scalar_type(), "radius_cpu", [&] {
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auto x_data = x.data_ptr<scalar_t>();
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auto y_data = y.data_ptr<scalar_t>();
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auto x_vec = std::vector<scalar_t>(x_data, x_data + x.numel());
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auto y_vec = std::vector<scalar_t>(y_data, y_data + y.numel());
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if (!ptr_x.has_value()) {
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nanoflann_neighbors<scalar_t>(y_vec, x_vec, out_vec, r, x.size(-1),
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max_num_neighbors, num_workers, 0, 1);
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} else {
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auto sx = (ptr_x.value().narrow(0, 1, ptr_x.value().numel() - 1) -
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ptr_x.value().narrow(0, 0, ptr_x.value().numel() - 1));
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auto sy = (ptr_y.value().narrow(0, 1, ptr_y.value().numel() - 1) -
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ptr_y.value().narrow(0, 0, ptr_y.value().numel() - 1));
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auto sx_data = sx.data_ptr<int64_t>();
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auto sy_data = sy.data_ptr<int64_t>();
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auto sx_vec = std::vector<long>(sx_data, sx_data + sx.numel());
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auto sy_vec = std::vector<long>(sy_data, sy_data + sy.numel());
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batch_nanoflann_neighbors<scalar_t>(y_vec, x_vec, sy_vec, sx_vec, out_vec,
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r, x.size(-1), max_num_neighbors, 0,
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1);
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}
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});
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const int64_t size = out_vec->size() / 2;
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auto out = torch::from_blob(out_vec->data(), {size, 2},
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x.options().dtype(torch::kLong));
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return out.t().index_select(0, torch::tensor({1, 0}));
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}

‎csrc/cpu/radius_cpu.h

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#pragma once
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#include <torch/extension.h>
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torch::Tensor radius_cpu(torch::Tensor x, torch::Tensor y,
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torch::optional<torch::Tensor> ptr_x,
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torch::optional<torch::Tensor> ptr_y, double r,
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int64_t max_num_neighbors, int64_t num_workers);

‎csrc/cpu/utils/cloud.h

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#pragma once
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#include <ATen/ATen.h>
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#include <algorithm>
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#include <cmath>
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#include <iomanip>
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#include <iostream>
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#include <map>
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#include <numeric>
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#include <unordered_map>
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#include <vector>
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#include <time.h>
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template <typename scalar_t> struct PointCloud {
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std::vector<std::vector<scalar_t> *> pts;
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void set(std::vector<scalar_t> new_pts, int dim) {
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std::vector<std::vector<scalar_t> *> temp(new_pts.size() / dim);
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for (size_t i = 0; i < new_pts.size(); i++) {
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if (i % dim == 0) {
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std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
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for (size_t j = 0; j < (size_t)dim; j++) {
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(*point)[j] = new_pts[i + j];
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}
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temp[i / dim] = point;
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}
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}
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pts = temp;
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}
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void set_batch(std::vector<scalar_t> new_pts, size_t begin, long size,
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int dim) {
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std::vector<std::vector<scalar_t> *> temp(size);
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for (size_t i = 0; i < (size_t)size; i++) {
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std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
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for (size_t j = 0; j < (size_t)dim; j++) {
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(*point)[j] = new_pts[dim * (begin + i) + j];
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}
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temp[i] = point;
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}
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pts = temp;
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}
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// Must return the number of data points.
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inline size_t kdtree_get_point_count() const { return pts.size(); }
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// Returns the dim'th component of the idx'th point in the class:
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inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const {
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return (*pts[idx])[dim];
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}
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// Optional bounding-box computation: return false to default to a standard
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// bbox computation loop.
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// Return true if the BBOX was already computed by the class and returned in
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// "bb" so it can be avoided to redo it again. Look at bb.size() to find out
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// the expected dimensionality (e.g. 2 or 3 for point clouds)
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template <class BBOX> bool kdtree_get_bbox(BBOX & /* bb */) const {
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return false;
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}
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};

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