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| 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 '') | |