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, temb, 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) # return [B, C, M, U] C=feat dim, M=# centers, U=# neighbours if self.include_coordinates: neighbor_features = torch.cat([neighbor_coordinates, neighbor_features], dim=1) return neighbor_features, F.grouping(temb, neighbor_indices) def extra_repr(self): return 'radius={}, num_neighbors={}{}'.format( self.radius, self.num_neighbors, ', include coordinates' if self.include_coordinates else '') class BallQueryHO(nn.Module): "no point feature, but only relative and abs coordinate" def __init__(self, radius, num_neighbors, include_relative=False): super().__init__() self.radius = radius self.num_neighbors = num_neighbors self.include_relative = include_relative def forward(self, points_coords, centers_coords, points_features=None): """ if not enough points inside the given radius, the entries will be zero if too many points inside the radius, the order is random??? (not sure) :param points_coords: (B, 3, N) :param centers_coords: (B, 3, M) :param points_features: None :return: """ 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) # (B, 3, M, U) if self.include_relative: neighbor_coordinates_rela = neighbor_coordinates - centers_coords.unsqueeze(-1) neighbor_coordinates = torch.cat([neighbor_coordinates, neighbor_coordinates_rela], 1) # (B, 6, M, U) # flatten the coordinate neighbor_coordinates = neighbor_coordinates.permute(0, 1, 3, 2) # (B, 3/6, U, M) neighbor_coordinates = torch.flatten(neighbor_coordinates, 1, 2) # (B, 3*U, M) return neighbor_coordinates def extra_repr(self): return 'radius={}, num_neighbors={}{}'.format( self.radius, self.num_neighbors, ', include relative' if self.include_relative else '')