# 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.
# pyre-unsafe
from typing import Optional, Tuple, TYPE_CHECKING, 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:
# pyre-fixme[6]: For 1st param expected `Optional[dtype]` but got
# `Union[Tuple[int], int]`.
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")
# pyre-fixme[6]: For 1st param expected `Optional[dtype]` but got
# `Union[Tuple[int], int]`.
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
```