| 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 '') |
|
|
