| import torch | |
| import torch.nn as nn | |
| from . import functional as F | |
| __all__ = ['BallQuery'] | |
| class BallQuery(nn.Module): | |
| def __init__(self, radius, num_neighbors, include_coordinates=True): | |
| super().__init__() | |
| self.radius = radius | |
| self.num_neighbors = num_neighbors | |
| self.include_coordinates = include_coordinates | |
| def forward(self, points_coords, centers_coords, points_features=None): | |
| points_coords = points_coords.contiguous() | |
| centers_coords = centers_coords.contiguous() | |
| neighbor_indices = F.ball_query(centers_coords, points_coords, self.radius, self.num_neighbors) | |
| neighbor_coordinates = F.grouping(points_coords, neighbor_indices) | |
| neighbor_coordinates = neighbor_coordinates - centers_coords.unsqueeze(-1) | |
| if points_features is None: | |
| assert self.include_coordinates, 'No Features For Grouping' | |
| neighbor_features = neighbor_coordinates | |
| else: | |
| neighbor_features = F.grouping(points_features, neighbor_indices) | |
| if self.include_coordinates: | |
| neighbor_features = torch.cat([neighbor_coordinates, neighbor_features], dim=1) | |
| return neighbor_features | |
| def extra_repr(self): | |
| return 'radius={}, num_neighbors={}{}'.format( | |
| self.radius, self.num_neighbors, ', include coordinates' if self.include_coordinates else '') | |