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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch | |
| from torch import nn | |
| from torch.autograd import Function | |
| from ..utils import ext_loader | |
| ext_module = ext_loader.load_ext( | |
| '_ext', | |
| ['dynamic_point_to_voxel_forward', 'dynamic_point_to_voxel_backward']) | |
| class _DynamicScatter(Function): | |
| def forward(ctx, feats, coors, reduce_type='max'): | |
| """convert kitti points(N, >=3) to voxels. | |
| Args: | |
| feats (torch.Tensor): [N, C]. Points features to be reduced | |
| into voxels. | |
| coors (torch.Tensor): [N, ndim]. Corresponding voxel coordinates | |
| (specifically multi-dim voxel index) of each points. | |
| reduce_type (str, optional): Reduce op. support 'max', 'sum' and | |
| 'mean'. Default: 'max'. | |
| Returns: | |
| voxel_feats (torch.Tensor): [M, C]. Reduced features, input | |
| features that shares the same voxel coordinates are reduced to | |
| one row. | |
| voxel_coors (torch.Tensor): [M, ndim]. Voxel coordinates. | |
| """ | |
| results = ext_module.dynamic_point_to_voxel_forward( | |
| feats, coors, reduce_type) | |
| (voxel_feats, voxel_coors, point2voxel_map, | |
| voxel_points_count) = results | |
| ctx.reduce_type = reduce_type | |
| ctx.save_for_backward(feats, voxel_feats, point2voxel_map, | |
| voxel_points_count) | |
| ctx.mark_non_differentiable(voxel_coors) | |
| return voxel_feats, voxel_coors | |
| def backward(ctx, grad_voxel_feats, grad_voxel_coors=None): | |
| (feats, voxel_feats, point2voxel_map, | |
| voxel_points_count) = ctx.saved_tensors | |
| grad_feats = torch.zeros_like(feats) | |
| # TODO: whether to use index put or use cuda_backward | |
| # To use index put, need point to voxel index | |
| ext_module.dynamic_point_to_voxel_backward( | |
| grad_feats, grad_voxel_feats.contiguous(), feats, voxel_feats, | |
| point2voxel_map, voxel_points_count, ctx.reduce_type) | |
| return grad_feats, None, None | |
| dynamic_scatter = _DynamicScatter.apply | |
| class DynamicScatter(nn.Module): | |
| """Scatters points into voxels, used in the voxel encoder with dynamic | |
| voxelization. | |
| Note: | |
| The CPU and GPU implementation get the same output, but have numerical | |
| difference after summation and division (e.g., 5e-7). | |
| Args: | |
| voxel_size (list): list [x, y, z] size of three dimension. | |
| point_cloud_range (list): The coordinate range of points, [x_min, | |
| y_min, z_min, x_max, y_max, z_max]. | |
| average_points (bool): whether to use avg pooling to scatter points | |
| into voxel. | |
| """ | |
| def __init__(self, voxel_size, point_cloud_range, average_points: bool): | |
| super().__init__() | |
| self.voxel_size = voxel_size | |
| self.point_cloud_range = point_cloud_range | |
| self.average_points = average_points | |
| def forward_single(self, points, coors): | |
| """Scatters points into voxels. | |
| Args: | |
| points (torch.Tensor): Points to be reduced into voxels. | |
| coors (torch.Tensor): Corresponding voxel coordinates (specifically | |
| multi-dim voxel index) of each points. | |
| Returns: | |
| voxel_feats (torch.Tensor): Reduced features, input features that | |
| shares the same voxel coordinates are reduced to one row. | |
| voxel_coors (torch.Tensor): Voxel coordinates. | |
| """ | |
| reduce = 'mean' if self.average_points else 'max' | |
| return dynamic_scatter(points.contiguous(), coors.contiguous(), reduce) | |
| def forward(self, points, coors): | |
| """Scatters points/features into voxels. | |
| Args: | |
| points (torch.Tensor): Points to be reduced into voxels. | |
| coors (torch.Tensor): Corresponding voxel coordinates (specifically | |
| multi-dim voxel index) of each points. | |
| Returns: | |
| voxel_feats (torch.Tensor): Reduced features, input features that | |
| shares the same voxel coordinates are reduced to one row. | |
| voxel_coors (torch.Tensor): Voxel coordinates. | |
| """ | |
| if coors.size(-1) == 3: | |
| return self.forward_single(points, coors) | |
| else: | |
| batch_size = coors[-1, 0] + 1 | |
| voxels, voxel_coors = [], [] | |
| for i in range(batch_size): | |
| inds = torch.where(coors[:, 0] == i) | |
| voxel, voxel_coor = self.forward_single( | |
| points[inds], coors[inds][:, 1:]) | |
| coor_pad = nn.functional.pad( | |
| voxel_coor, (1, 0), mode='constant', value=i) | |
| voxel_coors.append(coor_pad) | |
| voxels.append(voxel) | |
| features = torch.cat(voxels, dim=0) | |
| feature_coors = torch.cat(voxel_coors, dim=0) | |
| return features, feature_coors | |
| def __repr__(self): | |
| s = self.__class__.__name__ + '(' | |
| s += 'voxel_size=' + str(self.voxel_size) | |
| s += ', point_cloud_range=' + str(self.point_cloud_range) | |
| s += ', average_points=' + str(self.average_points) | |
| s += ')' | |
| return s | |