178 lines
7.1 KiB
C++
178 lines
7.1 KiB
C++
/*
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RoI-aware point cloud feature pooling
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Reference paper: https://arxiv.org/abs/1907.03670
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Written by Shaoshuai Shi
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All Rights Reserved 2019-2020.
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*/
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#include <torch/serialize/tensor.h>
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#include <torch/extension.h>
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#include <assert.h>
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//#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ")
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//#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x, " must be contiguous ")
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//#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
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void roiaware_pool3d_launcher(int boxes_num, int pts_num, int channels, int max_pts_each_voxel,
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int out_x, int out_y, int out_z, const float *rois, const float *pts, const float *pts_feature,
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int *argmax, int *pts_idx_of_voxels, float *pooled_features, int pool_method);
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void roiaware_pool3d_backward_launcher(int boxes_num, int out_x, int out_y, int out_z, int channels, int max_pts_each_voxel,
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const int *pts_idx_of_voxels, const int *argmax, const float *grad_out, float *grad_in, int pool_method);
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void points_in_boxes_launcher(int batch_size, int boxes_num, int pts_num, const float *boxes,
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const float *pts, int *box_idx_of_points);
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int roiaware_pool3d_gpu(at::Tensor rois, at::Tensor pts, at::Tensor pts_feature, at::Tensor argmax,
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at::Tensor pts_idx_of_voxels, at::Tensor pooled_features, int pool_method){
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// params rois: (N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
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// params pts: (npoints, 3) [x, y, z]
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// params pts_feature: (npoints, C)
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// params argmax: (N, out_x, out_y, out_z, C)
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// params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
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// params pooled_features: (N, out_x, out_y, out_z, C)
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// params pool_method: 0: max_pool 1: avg_pool
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// CHECK_INPUT(rois);
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// CHECK_INPUT(pts);
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// CHECK_INPUT(pts_feature);
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// CHECK_INPUT(argmax);
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// CHECK_INPUT(pts_idx_of_voxels);
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// CHECK_INPUT(pooled_features);
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int boxes_num = rois.size(0);
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int pts_num = pts.size(0);
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int channels = pts_feature.size(1);
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int max_pts_each_voxel = pts_idx_of_voxels.size(4); // index 0 is the counter
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int out_x = pts_idx_of_voxels.size(1);
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int out_y = pts_idx_of_voxels.size(2);
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int out_z = pts_idx_of_voxels.size(3);
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assert ((out_x < 256) && (out_y < 256) && (out_z < 256)); // we encode index with 8bit
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const float *rois_data = rois.data<float>();
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const float *pts_data = pts.data<float>();
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const float *pts_feature_data = pts_feature.data<float>();
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int *argmax_data = argmax.data<int>();
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int *pts_idx_of_voxels_data = pts_idx_of_voxels.data<int>();
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float *pooled_features_data = pooled_features.data<float>();
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roiaware_pool3d_launcher(boxes_num, pts_num, channels, max_pts_each_voxel, out_x, out_y, out_z,
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rois_data, pts_data, pts_feature_data, argmax_data, pts_idx_of_voxels_data, pooled_features_data, pool_method);
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return 1;
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}
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int roiaware_pool3d_gpu_backward(at::Tensor pts_idx_of_voxels, at::Tensor argmax, at::Tensor grad_out, at::Tensor grad_in, int pool_method){
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// params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
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// params argmax: (N, out_x, out_y, out_z, C)
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// params grad_out: (N, out_x, out_y, out_z, C)
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// params grad_in: (npoints, C), return value
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// params pool_method: 0: max_pool 1: avg_pool
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// CHECK_INPUT(pts_idx_of_voxels);
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// CHECK_INPUT(argmax);
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// CHECK_INPUT(grad_out);
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// CHECK_INPUT(grad_in);
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int boxes_num = pts_idx_of_voxels.size(0);
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int out_x = pts_idx_of_voxels.size(1);
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int out_y = pts_idx_of_voxels.size(2);
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int out_z = pts_idx_of_voxels.size(3);
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int max_pts_each_voxel = pts_idx_of_voxels.size(4); // index 0 is the counter
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int channels = grad_out.size(4);
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const int *pts_idx_of_voxels_data = pts_idx_of_voxels.data<int>();
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const int *argmax_data = argmax.data<int>();
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const float *grad_out_data = grad_out.data<float>();
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float *grad_in_data = grad_in.data<float>();
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roiaware_pool3d_backward_launcher(boxes_num, out_x, out_y, out_z, channels, max_pts_each_voxel,
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pts_idx_of_voxels_data, argmax_data, grad_out_data, grad_in_data, pool_method);
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return 1;
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}
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int points_in_boxes_gpu(at::Tensor boxes_tensor, at::Tensor pts_tensor, at::Tensor box_idx_of_points_tensor){
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// params boxes: (B, N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
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// params pts: (B, npoints, 3) [x, y, z]
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// params boxes_idx_of_points: (B, npoints), default -1
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// CHECK_INPUT(boxes_tensor);
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// CHECK_INPUT(pts_tensor);
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// CHECK_INPUT(box_idx_of_points_tensor);
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int batch_size = boxes_tensor.size(0);
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int boxes_num = boxes_tensor.size(1);
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int pts_num = pts_tensor.size(1);
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const float *boxes = boxes_tensor.data<float>();
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const float *pts = pts_tensor.data<float>();
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int *box_idx_of_points = box_idx_of_points_tensor.data<int>();
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points_in_boxes_launcher(batch_size, boxes_num, pts_num, boxes, pts, box_idx_of_points);
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return 1;
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}
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inline void lidar_to_local_coords_cpu(float shift_x, float shift_y, float rot_angle, float &local_x, float &local_y){
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float cosa = cos(-rot_angle), sina = sin(-rot_angle);
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local_x = shift_x * cosa + shift_y * (-sina);
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local_y = shift_x * sina + shift_y * cosa;
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}
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inline int check_pt_in_box3d_cpu(const float *pt, const float *box3d, float &local_x, float &local_y){
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// param pt: (x, y, z)
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// param box3d: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
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const float MARGIN = 1e-2;
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float x = pt[0], y = pt[1], z = pt[2];
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float cx = box3d[0], cy = box3d[1], cz = box3d[2];
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float dx = box3d[3], dy = box3d[4], dz = box3d[5], rz = box3d[6];
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if (fabsf(z - cz) > dz / 2.0) return 0;
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lidar_to_local_coords_cpu(x - cx, y - cy, rz, local_x, local_y);
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float in_flag = (fabs(local_x) < dx / 2.0 + MARGIN) & (fabs(local_y) < dy / 2.0 + MARGIN);
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return in_flag;
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}
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int points_in_boxes_cpu(at::Tensor boxes_tensor, at::Tensor pts_tensor, at::Tensor pts_indices_tensor){
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// params boxes: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO NOT overlaps
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// params pts: (num_points, 3) [x, y, z]
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// params pts_indices: (N, num_points)
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// CHECK_CONTIGUOUS(boxes_tensor);
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// CHECK_CONTIGUOUS(pts_tensor);
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// CHECK_CONTIGUOUS(pts_indices_tensor);
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int boxes_num = boxes_tensor.size(0);
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int pts_num = pts_tensor.size(0);
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const float *boxes = boxes_tensor.data<float>();
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const float *pts = pts_tensor.data<float>();
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int *pts_indices = pts_indices_tensor.data<int>();
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float local_x = 0, local_y = 0;
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for (int i = 0; i < boxes_num; i++){
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for (int j = 0; j < pts_num; j++){
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int cur_in_flag = check_pt_in_box3d_cpu(pts + j * 3, boxes + i * 7, local_x, local_y);
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pts_indices[i * pts_num + j] = cur_in_flag;
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}
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}
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return 1;
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("forward", &roiaware_pool3d_gpu, "roiaware pool3d forward (CUDA)");
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m.def("backward", &roiaware_pool3d_gpu_backward, "roiaware pool3d backward (CUDA)");
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m.def("points_in_boxes_gpu", &points_in_boxes_gpu, "points_in_boxes_gpu forward (CUDA)");
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m.def("points_in_boxes_cpu", &points_in_boxes_cpu, "points_in_boxes_cpu forward (CUDA)");
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}
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