import numpy as np import torch from torch.autograd import Function from modules.functional.backend import _backend __all__ = ["gather", "furthest_point_sample", "logits_mask"] class Gather(Function): @staticmethod def forward(ctx, features, indices): """ Gather :param ctx: :param features: features of points, FloatTensor[B, C, N] :param indices: centers' indices in points, IntTensor[b, m] :return: centers_coords: coordinates of sampled centers, FloatTensor[B, C, M] """ features = features.contiguous() indices = indices.int().contiguous() ctx.save_for_backward(indices) ctx.num_points = features.size(-1) return _backend.gather_features_forward(features, indices) @staticmethod def backward(ctx, grad_output): (indices,) = ctx.saved_tensors grad_features = _backend.gather_features_backward(grad_output.contiguous(), indices, ctx.num_points) return grad_features, None gather = Gather.apply def furthest_point_sample(coords, num_samples): """ Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance to the sampled point set :param coords: coordinates of points, FloatTensor[B, 3, N] :param num_samples: int, M :return: centers_coords: coordinates of sampled centers, FloatTensor[B, 3, M] """ coords = coords.contiguous() indices = _backend.furthest_point_sampling(coords, num_samples) return gather(coords, indices) def logits_mask(coords, logits, num_points_per_object): """ Use logits to sample points :param coords: coords of points, FloatTensor[B, 3, N] :param logits: binary classification logits, FloatTensor[B, 2, N] :param num_points_per_object: M, #points per object after masking, int :return: selected_coords: FloatTensor[B, 3, M] masked_coords_mean: mean coords of selected points, FloatTensor[B, 3] mask: mask to select points, BoolTensor[B, N] """ batch_size, _, num_points = coords.shape mask = torch.lt(logits[:, 0, :], logits[:, 1, :]) # [B, N] num_candidates = torch.sum(mask, dim=-1, keepdim=True) # [B, 1] masked_coords = coords * mask.view(batch_size, 1, num_points) # [B, C, N] masked_coords_mean = ( torch.sum(masked_coords, dim=-1) / torch.max(num_candidates, torch.ones_like(num_candidates)).float() ) # [B, C] selected_indices = torch.zeros((batch_size, num_points_per_object), device=coords.device, dtype=torch.int32) for i in range(batch_size): current_mask = mask[i] # [N] current_candidates = current_mask.nonzero().view(-1) current_num_candidates = current_candidates.numel() if current_num_candidates >= num_points_per_object: choices = np.random.choice(current_num_candidates, num_points_per_object, replace=False) selected_indices[i] = current_candidates[choices] elif current_num_candidates > 0: choices = np.concatenate( [ np.arange(current_num_candidates).repeat(num_points_per_object // current_num_candidates), np.random.choice( current_num_candidates, num_points_per_object % current_num_candidates, replace=False ), ] ) np.random.shuffle(choices) selected_indices[i] = current_candidates[choices] selected_coords = gather(masked_coords - masked_coords_mean.view(batch_size, -1, 1), selected_indices) return selected_coords, masked_coords_mean, mask