Source code for pytorch3d.ops.utils

# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import TYPE_CHECKING, Optional, Tuple, Union

import torch

from .knn import knn_points


if TYPE_CHECKING:
    from pytorch3d.structures import Pointclouds


[docs]def wmean( x: torch.Tensor, weight: Optional[torch.Tensor] = None, dim: Union[int, Tuple[int]] = -2, keepdim: bool = True, eps: float = 1e-9, ) -> torch.Tensor: """ Finds the mean of the input tensor across the specified dimension. If the `weight` argument is provided, computes weighted mean. Args: x: tensor of shape `(*, D)`, where D is assumed to be spatial; weights: if given, non-negative tensor of shape `(*,)`. It must be broadcastable to `x.shape[:-1]`. Note that the weights for the last (spatial) dimension are assumed same; dim: dimension(s) in `x` to average over; keepdim: tells whether to keep the resulting singleton dimension. eps: minimum clamping value in the denominator. Returns: the mean tensor: * if `weights` is None => `mean(x, dim)`, * otherwise => `sum(x*w, dim) / max{sum(w, dim), eps}`. """ args = {"dim": dim, "keepdim": keepdim} if weight is None: return x.mean(**args) if any( xd != wd and xd != 1 and wd != 1 for xd, wd in zip(x.shape[-2::-1], weight.shape[::-1]) ): raise ValueError("wmean: weights are not compatible with the tensor") return (x * weight[..., None]).sum(**args) / weight[..., None].sum(**args).clamp( eps )
[docs]def eyes( dim: int, N: int, device: Optional[torch.device] = None, dtype: torch.dtype = torch.float32, ) -> torch.Tensor: """ Generates a batch of `N` identity matrices of shape `(N, dim, dim)`. Args: **dim**: The dimensionality of the identity matrices. **N**: The number of identity matrices. **device**: The device to be used for allocating the matrices. **dtype**: The datatype of the matrices. Returns: **identities**: A batch of identity matrices of shape `(N, dim, dim)`. """ identities = torch.eye(dim, device=device, dtype=dtype) return identities[None].repeat(N, 1, 1)
[docs]def convert_pointclouds_to_tensor(pcl: Union[torch.Tensor, "Pointclouds"]): """ If `type(pcl)==Pointclouds`, converts a `pcl` object to a padded representation and returns it together with the number of points per batch. Otherwise, returns the input itself with the number of points set to the size of the second dimension of `pcl`. """ if is_pointclouds(pcl): X = pcl.points_padded() # type: ignore num_points = pcl.num_points_per_cloud() # type: ignore elif torch.is_tensor(pcl): X = pcl num_points = X.shape[1] * torch.ones( # type: ignore X.shape[0], device=X.device, dtype=torch.int64 ) else: raise ValueError( "The inputs X, Y should be either Pointclouds objects or tensors." ) return X, num_points
[docs]def is_pointclouds(pcl: Union[torch.Tensor, "Pointclouds"]): """Checks whether the input `pcl` is an instance of `Pointclouds` by checking the existence of `points_padded` and `num_points_per_cloud` functions. """ return hasattr(pcl, "points_padded") and hasattr(pcl, "num_points_per_cloud")
[docs]def get_point_covariances( points_padded: torch.Tensor, num_points_per_cloud: torch.Tensor, neighborhood_size: int, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Computes the per-point covariance matrices by of the 3D locations of K-nearest neighbors of each point. Args: **points_padded**: Input point clouds as a padded tensor of shape `(minibatch, num_points, dim)`. **num_points_per_cloud**: Number of points per cloud of shape `(minibatch,)`. **neighborhood_size**: Number of nearest neighbors for each point used to estimate the covariance matrices. Returns: **covariances**: A batch of per-point covariance matrices of shape `(minibatch, dim, dim)`. **k_nearest_neighbors**: A batch of `neighborhood_size` nearest neighbors for each of the point cloud points of shape `(minibatch, num_points, neighborhood_size, dim)`. """ # get K nearest neighbor idx for each point in the point cloud k_nearest_neighbors = knn_points( points_padded, points_padded, lengths1=num_points_per_cloud, lengths2=num_points_per_cloud, K=neighborhood_size, return_nn=True, ).knn # obtain the mean of the neighborhood pt_mean = k_nearest_neighbors.mean(2, keepdim=True) # compute the diff of the neighborhood and the mean of the neighborhood central_diff = k_nearest_neighbors - pt_mean # per-nn-point covariances per_pt_cov = central_diff.unsqueeze(4) * central_diff.unsqueeze(3) # per-point covariances covariances = per_pt_cov.mean(2) return covariances, k_nearest_neighbors