annotator/mmpkg/mmcv/ops/voxelize.py

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.nn.modules.utils import _pair

from ..utils import ext_loader

ext_module = ext_loader.load_ext(
    '_ext', ['dynamic_voxelize_forward', 'hard_voxelize_forward'])


class _Voxelization(Function):

    @staticmethod
    def forward(ctx,
                points,
                voxel_size,
                coors_range,
                max_points=35,
                max_voxels=20000):
        """Convert kitti points(N, >=3) to voxels.

        Args:
            points (torch.Tensor): [N, ndim]. Points[:, :3] contain xyz points
                and points[:, 3:] contain other information like reflectivity.
            voxel_size (tuple or float): The size of voxel with the shape of
                [3].
            coors_range (tuple or float): The coordinate range of voxel with
                the shape of [6].
            max_points (int, optional): maximum points contained in a voxel. if
                max_points=-1, it means using dynamic_voxelize. Default: 35.
            max_voxels (int, optional): maximum voxels this function create.
                for second, 20000 is a good choice. Users should shuffle points
                before call this function because max_voxels may drop points.
                Default: 20000.

        Returns:
            voxels_out (torch.Tensor): Output voxels with the shape of [M,
                max_points, ndim]. Only contain points and returned when
                max_points != -1.
            coors_out (torch.Tensor): Output coordinates with the shape of
                [M, 3].
            num_points_per_voxel_out (torch.Tensor): Num points per voxel with
                the shape of [M]. Only returned when max_points != -1.
        """
        if max_points == -1 or max_voxels == -1:
            coors = points.new_zeros(size=(points.size(0), 3), dtype=torch.int)
            ext_module.dynamic_voxelize_forward(points, coors, voxel_size,
                                                coors_range, 3)
            return coors
        else:
            voxels = points.new_zeros(
                size=(max_voxels, max_points, points.size(1)))
            coors = points.new_zeros(size=(max_voxels, 3), dtype=torch.int)
            num_points_per_voxel = points.new_zeros(
                size=(max_voxels, ), dtype=torch.int)
            voxel_num = ext_module.hard_voxelize_forward(
                points, voxels, coors, num_points_per_voxel, voxel_size,
                coors_range, max_points, max_voxels, 3)
            # select the valid voxels
            voxels_out = voxels[:voxel_num]
            coors_out = coors[:voxel_num]
            num_points_per_voxel_out = num_points_per_voxel[:voxel_num]
            return voxels_out, coors_out, num_points_per_voxel_out


voxelization = _Voxelization.apply


class Voxelization(nn.Module):
    """Convert kitti points(N, >=3) to voxels.

    Please refer to `PVCNN <https://arxiv.org/abs/1907.03739>`_ for more
    details.

    Args:
        voxel_size (tuple or float): The size of voxel with the shape of [3].
        point_cloud_range (tuple or float): The coordinate range of voxel with
            the shape of [6].
        max_num_points (int): maximum points contained in a voxel. if
            max_points=-1, it means using dynamic_voxelize.
        max_voxels (int, optional): maximum voxels this function create.
            for second, 20000 is a good choice. Users should shuffle points
            before call this function because max_voxels may drop points.
            Default: 20000.
    """

    def __init__(self,
                 voxel_size,
                 point_cloud_range,
                 max_num_points,
                 max_voxels=20000):
        super().__init__()

        self.voxel_size = voxel_size
        self.point_cloud_range = point_cloud_range
        self.max_num_points = max_num_points
        if isinstance(max_voxels, tuple):
            self.max_voxels = max_voxels
        else:
            self.max_voxels = _pair(max_voxels)

        point_cloud_range = torch.tensor(
            point_cloud_range, dtype=torch.float32)
        voxel_size = torch.tensor(voxel_size, dtype=torch.float32)
        grid_size = (point_cloud_range[3:] -
                     point_cloud_range[:3]) / voxel_size
        grid_size = torch.round(grid_size).long()
        input_feat_shape = grid_size[:2]
        self.grid_size = grid_size
        # the origin shape is as [x-len, y-len, z-len]
        # [w, h, d] -> [d, h, w]
        self.pcd_shape = [*input_feat_shape, 1][::-1]

    def forward(self, input):
        if self.training:
            max_voxels = self.max_voxels[0]
        else:
            max_voxels = self.max_voxels[1]

        return voxelization(input, self.voxel_size, self.point_cloud_range,
                            self.max_num_points, max_voxels)

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += 'voxel_size=' + str(self.voxel_size)
        s += ', point_cloud_range=' + str(self.point_cloud_range)
        s += ', max_num_points=' + str(self.max_num_points)
        s += ', max_voxels=' + str(self.max_voxels)
        s += ')'
        return s