Source code for pytorch3d.ops.utils

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

from typing import TYPE_CHECKING, Optional, Tuple, Union

import torch

from .knn import knn_points


if TYPE_CHECKING:
    from pytorch3d.structures import Pointclouds


def masked_gather(points: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
    """
    Helper function for torch.gather to collect the points at
    the given indices in idx where some of the indices might be -1 to
    indicate padding. These indices are first replaced with 0.
    Then the points are gathered after which the padded values
    are set to 0.0.

    Args:
        points: (N, P, D) float32 tensor of points
        idx: (N, K) or (N, P, K) long tensor of indices into points, where
            some indices are -1 to indicate padding

    Returns:
        selected_points: (N, K, D) float32 tensor of points
            at the given indices
    """

    if len(idx) != len(points):
        raise ValueError("points and idx must have the same batch dimension")

    N, P, D = points.shape

    if idx.ndim == 3:
        # Case: KNN, Ball Query where idx is of shape (N, P', K)
        # where P' is not necessarily the same as P as the
        # points may be gathered from a different pointcloud.
        K = idx.shape[2]
        # Match dimensions for points and indices
        idx_expanded = idx[..., None].expand(-1, -1, -1, D)
        points = points[:, :, None, :].expand(-1, -1, K, -1)
    elif idx.ndim == 2:
        # Farthest point sampling where idx is of shape (N, K)
        idx_expanded = idx[..., None].expand(-1, -1, D)
    else:
        raise ValueError("idx format is not supported %s" % repr(idx.shape))

    idx_expanded_mask = idx_expanded.eq(-1)
    idx_expanded = idx_expanded.clone()
    # Replace -1 values with 0 for gather
    idx_expanded[idx_expanded_mask] = 0
    # Gather points
    selected_points = points.gather(dim=1, index=idx_expanded)
    # Replace padded values
    selected_points[idx_expanded_mask] = 0.0
    return selected_points


[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 # pyre-fixme[16]: Item `Pointclouds` of `Union[Pointclouds, Tensor]` has # no attribute `shape`. 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"]) -> bool: """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