423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2026-02-06 22:12:16 +08:00
Code Issues Packages Projects Releases Wiki Activity

Re-sync with internal repository

Browse Source
This commit is contained in:
Nikhila Ravi
2020-03-18 10:35:27 -07:00
parent 2480723adf
commit 3d3b2fdc46
7 changed files with 2805 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
pytorch3d/renderer/points/__init__.py Normal file
View File

@@ -0,0 +1 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.

227
pytorch3d/renderer/points/rasterize_points.py Normal file
View File

@@ -0,0 +1,227 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Optional
import torch
from pytorch3d import _C
from pytorch3d.renderer.mesh.rasterize_meshes import pix_to_ndc
# TODO(jcjohns): Support non-square images
def rasterize_points(
pointclouds,
image_size: int = 256,
radius: float = 0.01,
points_per_pixel: int = 8,
bin_size: Optional[int] = None,
max_points_per_bin: Optional[int] = None,
):
"""
Pointcloud rasterization
Args:
pointclouds: A Pointclouds object representing a batch of point clouds to be
rasterized. This is a batch of N pointclouds, where each point cloud
can have a different number of points; the coordinates of each point
are (x, y, z). The coordinates are expected to
be in normalized device coordinates (NDC): [-1, 1]^3 with the camera at
(0, 0, 0); the x-axis goes from left-to-right, the y-axis goes from
top-to-bottom, and the z-axis goes from back-to-front.
image_size: Integer giving the resolution of the rasterized image
radius (Optional): Float giving the radius (in NDC units) of the disk to
be rasterized for each point.
points_per_pixel (Optional): We will keep track of this many points per
pixel, returning the nearest points_per_pixel points along the z-axis
bin_size: Size of bins to use for coarse-to-fine rasterization. Setting
bin_size=0 uses naive rasterization; setting bin_size=None attempts to
set it heuristically based on the shape of the input. This should not
affect the output, but can affect the speed of the forward pass.
points_per_bin: Only applicable when using coarse-to-fine rasterization
(bin_size > 0); this is the maxiumum number of points allowed within each
bin. If more than this many points actually fall into a bin, an error
will be raised. This should not affect the output values, but can affect
the memory usage in the forward pass.
Returns:
3-element tuple containing
- **idx**: int32 Tensor of shape (N, image_size, image_size, points_per_pixel)
giving the indices of the nearest points at each pixel, in ascending
z-order. Concretely `idx[n, y, x, k] = p` means that `points[p]` is the kth
closest point (along the z-direction) to pixel (y, x) - note that points
represents the packed points of shape (P, 3).
Pixels that are hit by fewer than points_per_pixel are padded with -1.
- **zbuf**: Tensor of shape (N, image_size, image_size, points_per_pixel)
giving the z-coordinates of the nearest points at each pixel, sorted in
z-order. Concretely, if `idx[n, y, x, k] = p` then
`zbuf[n, y, x, k] = points[n, p, 2]`. Pixels hit by fewer than
points_per_pixel are padded with -1
- **dists2**: Tensor of shape (N, image_size, image_size, points_per_pixel)
giving the squared Euclidean distance (in NDC units) in the x/y plane
for each point closest to the pixel. Concretely if `idx[n, y, x, k] = p`
then `dists[n, y, x, k]` is the squared distance between the pixel (y, x)
and the point `(points[n, p, 0], points[n, p, 1])`. Pixels hit with fewer
than points_per_pixel are padded with -1.
"""
points_packed = pointclouds.points_packed()
cloud_to_packed_first_idx = pointclouds.cloud_to_packed_first_idx()
num_points_per_cloud = pointclouds.num_points_per_cloud()
if bin_size is None:
if not points_packed.is_cuda:
# Binned CPU rasterization not fully implemented
bin_size = 0
else:
# TODO: These heuristics are not well-thought out!
if image_size <= 64:
bin_size = 8
elif image_size <= 256:
bin_size = 16
elif image_size <= 512:
bin_size = 32
elif image_size <= 1024:
bin_size = 64
if max_points_per_bin is None:
max_points_per_bin = int(max(10000, points_packed.shape[0] / 5))
# Function.apply cannot take keyword args, so we handle defaults in this
# wrapper and call apply with positional args only
return _RasterizePoints.apply(
points_packed,
cloud_to_packed_first_idx,
num_points_per_cloud,
image_size,
radius,
points_per_pixel,
bin_size,
max_points_per_bin,
)
class _RasterizePoints(torch.autograd.Function):
@staticmethod
def forward(
ctx,
points, # (P, 3)
cloud_to_packed_first_idx,
num_points_per_cloud,
image_size: int = 256,
radius: float = 0.01,
points_per_pixel: int = 8,
bin_size: int = 0,
max_points_per_bin: int = 0,
):
# TODO: Add better error handling for when there are more than
# max_points_per_bin in any bin.
args = (
points,
cloud_to_packed_first_idx,
num_points_per_cloud,
image_size,
radius,
points_per_pixel,
bin_size,
max_points_per_bin,
)
idx, zbuf, dists = _C.rasterize_points(*args)
ctx.save_for_backward(points, idx)
return idx, zbuf, dists
@staticmethod
def backward(ctx, grad_idx, grad_zbuf, grad_dists):
grad_points = None
grad_cloud_to_packed_first_idx = None
grad_num_points_per_cloud = None
grad_image_size = None
grad_radius = None
grad_points_per_pixel = None
grad_bin_size = None
grad_max_points_per_bin = None
points, idx = ctx.saved_tensors
args = (points, idx, grad_zbuf, grad_dists)
grad_points = _C.rasterize_points_backward(*args)
grads = (
grad_points,
grad_cloud_to_packed_first_idx,
grad_num_points_per_cloud,
grad_image_size,
grad_radius,
grad_points_per_pixel,
grad_bin_size,
grad_max_points_per_bin,
)
return grads
def rasterize_points_python(
pointclouds,
image_size: int = 256,
radius: float = 0.01,
points_per_pixel: int = 8,
):
"""
Naive pure PyTorch implementation of pointcloud rasterization.
Inputs / Outputs: Same as above
"""
N = len(pointclouds)
S, K = image_size, points_per_pixel
device = pointclouds.device
points_packed = pointclouds.points_packed()
cloud_to_packed_first_idx = pointclouds.cloud_to_packed_first_idx()
num_points_per_cloud = pointclouds.num_points_per_cloud()
# Intialize output tensors.
point_idxs = torch.full(
(N, S, S, K), fill_value=-1, dtype=torch.int32, device=device
)
zbuf = torch.full(
(N, S, S, K), fill_value=-1, dtype=torch.float32, device=device
)
pix_dists = torch.full(
(N, S, S, K), fill_value=-1, dtype=torch.float32, device=device
)
# NDC is from [-1, 1]. Get pixel size using specified image size.
radius2 = radius * radius
# Iterate through the batch of point clouds.
for n in range(N):
point_start_idx = cloud_to_packed_first_idx[n]
point_stop_idx = point_start_idx + num_points_per_cloud[n]
# Iterate through the horizontal lines of the image from top to bottom.
for yi in range(S):
# Y coordinate of one end of the image. Reverse the ordering
# of yi so that +Y is pointing up in the image.
yfix = S - 1 - yi
yf = pix_to_ndc(yfix, S)
# Iterate through pixels on this horizontal line, left to right.
for xi in range(S):
# X coordinate of one end of the image. Reverse the ordering
# of xi so that +X is pointing to the left in the image.
xfix = S - 1 - xi
xf = pix_to_ndc(xfix, S)
top_k_points = []
# Check whether each point in the batch affects this pixel.
for p in range(point_start_idx, point_stop_idx):
px, py, pz = points_packed[p, :]
if pz < 0:
continue
dx = px - xf
dy = py - yf
dist2 = dx * dx + dy * dy
if dist2 < radius2:
top_k_points.append((pz, p, dist2))
top_k_points.sort()
if len(top_k_points) > K:
top_k_points = top_k_points[:K]
for k, (pz, p, dist2) in enumerate(top_k_points):
zbuf[n, yi, xi, k] = pz
point_idxs[n, yi, xi, k] = p
pix_dists[n, yi, xi, k] = dist2
return point_idxs, zbuf, pix_dists

992
pytorch3d/structures/pointclouds.py Normal file
View File

@@ -0,0 +1,992 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import torch
from . import utils as struct_utils
class Pointclouds(object):
"""
This class provides functions for working with batches of 3d point clouds,
and converting between representations.
Within Pointclouds, there are three different representations of the data.
List
- only used for input as a starting point to convert to other representations.
Padded
- has specific batch dimension.
Packed
- no batch dimension.
- has auxillary variables used to index into the padded representation.
Example
Input list of points = [[P_1], [P_2], ... , [P_N]]
where P_1, ... , P_N are the number of points in each cloud and N is the
number of clouds.
# SPHINX IGNORE
List | Padded | Packed
---------------------------|-------------------------|------------------------
[[P_1], ... , [P_N]] | size = (N, max(P_n), 3) | size = (sum(P_n), 3)
| |
Example for locations | |
or colors: | |
| |
P_1 = 3, P_2 = 4, P_3 = 5 | size = (3, 5, 3) | size = (12, 3)
| |
List([ | tensor([ | tensor([
[ | [ | [0.1, 0.3, 0.5],
[0.1, 0.3, 0.5], | [0.1, 0.3, 0.5], | [0.5, 0.2, 0.1],
[0.5, 0.2, 0.1], | [0.5, 0.2, 0.1], | [0.6, 0.8, 0.7],
[0.6, 0.8, 0.7] | [0.6, 0.8, 0.7], | [0.1, 0.3, 0.3],
], | [0, 0, 0], | [0.6, 0.7, 0.8],
[ | [0, 0, 0] | [0.2, 0.3, 0.4],
[0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3],
[0.6, 0.7, 0.8], | [ | [0.7, 0.3, 0.6],
[0.2, 0.3, 0.4], | [0.1, 0.3, 0.3], | [0.2, 0.4, 0.8],
[0.1, 0.5, 0.3] | [0.6, 0.7, 0.8], | [0.9, 0.5, 0.2],
], | [0.2, 0.3, 0.4], | [0.2, 0.3, 0.4],
[ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8],
[0.7, 0.3, 0.6], | [0, 0, 0] | ])
[0.2, 0.4, 0.8], | ], |
[0.9, 0.5, 0.2], | [ |
[0.2, 0.3, 0.4], | [0.7, 0.3, 0.6], |
[0.9, 0.3, 0.8], | [0.2, 0.4, 0.8], |
] | [0.9, 0.5, 0.2], |
]) | [0.2, 0.3, 0.4], |
| [0.9, 0.3, 0.8] |
| ] |
| ]) |
-----------------------------------------------------------------------------
Auxillary variables for packed representation
Name | Size | Example from above
-------------------------------|---------------------|-----------------------
| |
packed_to_cloud_idx | size = (sum(P_n)) | tensor([
| | 0, 0, 0, 1, 1, 1,
| | 1, 2, 2, 2, 2, 2
| | )]
| | size = (12)
| |
cloud_to_packed_first_idx | size = (N) | tensor([0, 3, 7])
| | size = (3)
| |
num_points_per_cloud | size = (N) | tensor([3, 4, 5])
| | size = (3)
| |
padded_to_packed_idx | size = (sum(P_n)) | tensor([
| | 0, 1, 2, 5, 6, 7,
| | 8, 10, 11, 12, 13,
| | 14
| | )]
| | size = (12)
-----------------------------------------------------------------------------
# SPHINX IGNORE
"""
_INTERNAL_TENSORS = [
"_points_packed",
"_points_padded",
"_normals_packed",
"_normals_padded",
"_features_packed",
"_features_padded",
"_packed_to_cloud_idx",
"_cloud_to_packed_first_idx",
"_num_points_per_cloud",
"_padded_to_packed_idx",
"valid",
"equisized",
]
def __init__(self, points, normals=None, features=None):
"""
Args:
points:
Can be either
- List where each element is a tensor of shape (num_points, 3)
containing the (x, y, z) coordinates of each point.
- Padded float tensor with shape (num_clouds, num_points, 3).
normals:
Can be either
- List where each element is a tensor of shape (num_points, 3)
containing the normal vector for each point.
- Padded float tensor of shape (num_clouds, num_points, 3).
features:
Can be either
- List where each element is a tensor of shape (num_points, C)
containing the features for the points in the cloud.
- Padded float tensor of shape (num_clouds, num_points, C).
where C is the number of channels in the features.
For example 3 for RGB color.
Refer to comments above for descriptions of List and Padded
representations.
"""
self.device = None
# Indicates whether the clouds in the list/batch have the same number
# of points.
self.equisized = False
# Boolean indicator for each cloud in the batch.
# True if cloud has non zero number of points, False otherwise.
self.valid = None
self._N = 0 # batch size (number of clouds)
self._P = 0 # (max) number of points per cloud
self._C = None # number of channels in the features
# List of Tensors of points and features.
self._points_list = None
self._normals_list = None
self._features_list = None
# Number of points per cloud.
self._num_points_per_cloud = None # N
# Packed representation.
self._points_packed = None # (sum(P_n), 3)
self._normals_packed = None # (sum(P_n), 3)
self._features_packed = None # (sum(P_n), C)
self._packed_to_cloud_idx = None # sum(P_n)
# Index of each cloud's first point in the packed points.
# Assumes packing is sequential.
self._cloud_to_packed_first_idx = None # N
# Padded representation.
self._points_padded = None # (N, max(P_n), 3)
self._normals_padded = None # (N, max(P_n), 3)
self._features_padded = None # (N, max(P_n), C)
# Index to convert points from flattened padded to packed.
self._padded_to_packed_idx = None # N * max_P
# Identify type of points.
if isinstance(points, list):
self._points_list = points
self._N = len(self._points_list)
self.device = torch.device("cpu")
self.valid = torch.zeros(
(self._N,), dtype=torch.bool, device=self.device
)
self._num_points_per_cloud = []
if self._N > 0:
for p in self._points_list:
if len(p) > 0 and (p.dim() != 2 or p.shape[1] != 3):
raise ValueError(
"Clouds in list must be of shape Px3 or empty"
)
self.device = self._points_list[0].device
num_points_per_cloud = torch.tensor(
[len(p) for p in self._points_list], device=self.device
)
self._P = num_points_per_cloud.max()
self.valid = torch.tensor(
[len(p) > 0 for p in self._points_list],
dtype=torch.bool,
device=self.device,
)
if len(num_points_per_cloud.unique()) == 1:
self.equisized = True
self._num_points_per_cloud = num_points_per_cloud
elif torch.is_tensor(points):
if points.dim() != 3 or points.shape[2] != 3:
raise ValueError("Points tensor has incorrect dimensions.")
self._points_padded = points
self._N = self._points_padded.shape[0]
self._P = self._points_padded.shape[1]
self.device = self._points_padded.device
self.valid = torch.ones(
(self._N,), dtype=torch.bool, device=self.device
)
self._num_points_per_cloud = torch.tensor(
[self._P] * self._N, device=self.device
)
self.equisized = True
else:
raise ValueError(
"Points must be either a list or a tensor with \
shape (batch_size, P, 3) where P is the maximum number of \
points in a cloud."
)
# parse normals
normals_parsed = self._parse_auxiliary_input(normals)
self._normals_list, self._normals_padded, normals_C = normals_parsed
if normals_C is not None and normals_C != 3:
raise ValueError("Normals are expected to be 3-dimensional")
# parse features
features_parsed = self._parse_auxiliary_input(features)
self._features_list, self._features_padded, features_C = features_parsed
if features_C is not None:
self._C = features_C
def _parse_auxiliary_input(self, aux_input):
"""
Interpret the auxiliary inputs (normals, features) given to __init__.
Args:
aux_input:
Can be either
- List where each element is a tensor of shape (num_points, C)
containing the features for the points in the cloud.
- Padded float tensor of shape (num_clouds, num_points, C).
For normals, C = 3
Returns:
3-element tuple of list, padded, num_channels.
If aux_input is list, then padded is None. If aux_input is a tensor, then list is None.
"""
if aux_input is None or self._N == 0:
return None, None, None
aux_input_C = None
if isinstance(aux_input, list):
if len(aux_input) != self._N:
raise ValueError(
"Points and auxiliary input must be the same length."
)
for p, d in zip(self._num_points_per_cloud, aux_input):
if p != d.shape[0]:
raise ValueError(
"A cloud has mismatched numbers of points and inputs"
)
if p > 0:
if d.dim() != 2:
raise ValueError(
"A cloud auxiliary input must be of shape PxC or empty"
)
if aux_input_C is None:
aux_input_C = d.shape[1]
if aux_input_C != d.shape[1]:
raise ValueError(
"The clouds must have the same number of channels"
)
return aux_input, None, aux_input_C
elif torch.is_tensor(aux_input):
if aux_input.dim() != 3:
raise ValueError(
"Auxiliary input tensor has incorrect dimensions."
)
if self._N != aux_input.shape[0]:
raise ValueError("Points and inputs must be the same length.")
if self._P != aux_input.shape[1]:
raise ValueError(
"Inputs tensor must have the right maximum \
number of points in each cloud."
)
aux_input_C = aux_input.shape[2]
return None, aux_input, aux_input_C
else:
raise ValueError(
"Auxiliary input must be either a list or a tensor with \
shape (batch_size, P, C) where P is the maximum number of \
points in a cloud."
)
def __len__(self):
return self._N
def __getitem__(self, index):
"""
Args:
index: Specifying the index of the cloud to retrieve.
Can be an int, slice, list of ints or a boolean tensor.
Returns:
Pointclouds object with selected clouds. The tensors are not cloned.
"""
normals, features = None, None
if isinstance(index, int):
points = [self.points_list()[index]]
if self.normals_list() is not None:
normals = [self.normals_list()[index]]
if self.features_list() is not None:
features = [self.features_list()[index]]
elif isinstance(index, slice):
points = self.points_list()[index]
if self.normals_list() is not None:
normals = self.normals_list()[index]
if self.features_list() is not None:
features = self.features_list()[index]
elif isinstance(index, list):
points = [self.points_list()[i] for i in index]
if self.normals_list() is not None:
normals = [self.normals_list()[i] for i in index]
if self.features_list() is not None:
features = [self.features_list()[i] for i in index]
elif isinstance(index, torch.Tensor):
if index.dim() != 1 or index.dtype.is_floating_point:
raise IndexError(index)
# NOTE consider converting index to cpu for efficiency
if index.dtype == torch.bool:
# advanced indexing on a single dimension
index = index.nonzero()
index = index.squeeze(1) if index.numel() > 0 else index
index = index.tolist()
points = [self.points_list()[i] for i in index]
if self.normals_list() is not None:
normals = [self.normals_list()[i] for i in index]
if self.features_list() is not None:
features = [self.features_list()[i] for i in index]
else:
raise IndexError(index)
return Pointclouds(points=points, normals=normals, features=features)
def isempty(self) -> bool:
"""
Checks whether any cloud is valid.
Returns:
bool indicating whether there is any data.
"""
return self._N == 0 or self.valid.eq(False).all()
def points_list(self):
"""
Get the list representation of the points.
Returns:
list of tensors of points of shape (P_n, 3).
"""
if self._points_list is None:
assert (
self._points_padded is not None
), "points_padded is required to compute points_list."
points_list = []
for i in range(self._N):
points_list.append(
self._points_padded[i, : self.num_points_per_cloud()[i]]
)
self._points_list = points_list
return self._points_list
def normals_list(self):
"""
Get the list representation of the normals.
Returns:
list of tensors of normals of shape (P_n, 3).
"""
if self._normals_list is None:
if self._normals_padded is None:
# No normals provided so return None
return None
self._normals_list = []
for i in range(self._N):
self._normals_list.append(
self._normals_padded[i, : self.num_points_per_cloud()[i]]
)
return self._normals_list
def features_list(self):
"""
Get the list representation of the features.
Returns:
list of tensors of features of shape (P_n, C).
"""
if self._features_list is None:
if self._features_padded is None:
# No features provided so return None
return None
self._features_list = []
for i in range(self._N):
self._features_list.append(
self._features_padded[i, : self.num_points_per_cloud()[i]]
)
return self._features_list
def points_packed(self):
"""
Get the packed representation of the points.
Returns:
tensor of points of shape (sum(P_n), 3).
"""
self._compute_packed()
return self._points_packed
def normals_packed(self):
"""
Get the packed representation of the normals.
Returns:
tensor of normals of shape (sum(P_n), 3).
"""
self._compute_packed()
return self._normals_packed
def features_packed(self):
"""
Get the packed representation of the features.
Returns:
tensor of features of shape (sum(P_n), C).
"""
self._compute_packed()
return self._features_packed
def packed_to_cloud_idx(self):
"""
Return a 1D tensor x with length equal to the total number of points.
packed_to_cloud_idx()[i] gives the index of the cloud which contains
points_packed()[i].
Returns:
1D tensor of indices.
"""
self._compute_packed()
return self._packed_to_cloud_idx
def cloud_to_packed_first_idx(self):
"""
Return a 1D tensor x with length equal to the number of clouds such that
the first point of the ith cloud is points_packed[x[i]].
Returns:
1D tensor of indices of first items.
"""
self._compute_packed()
return self._cloud_to_packed_first_idx
def num_points_per_cloud(self):
"""
Return a 1D tensor x with length equal to the number of clouds giving
the number of points in each cloud.
Returns:
1D tensor of sizes.
"""
return self._num_points_per_cloud
def points_padded(self):
"""
Get the padded representation of the points.
Returns:
tensor of points of shape (N, max(P_n), 3).
"""
self._compute_padded()
return self._points_padded
def normals_padded(self):
"""
Get the padded representation of the normals.
Returns:
tensor of normals of shape (N, max(P_n), 3).
"""
self._compute_padded()
return self._normals_padded
def features_padded(self):
"""
Get the padded representation of the features.
Returns:
tensor of features of shape (N, max(P_n), 3).
"""
self._compute_padded()
return self._features_padded
def padded_to_packed_idx(self):
"""
Return a 1D tensor x with length equal to the total number of points
such that points_packed()[i] is element x[i] of the flattened padded
representation.
The packed representation can be calculated as follows.
.. code-block:: python
p = points_padded().reshape(-1, 3)
points_packed = p[x]
Returns:
1D tensor of indices.
"""
self._compute_packed()
if self._padded_to_packed_idx is not None:
return self._padded_to_packed_idx
if self._N == 0:
self._padded_to_packed_idx = []
else:
self._padded_to_packed_idx = torch.cat(
[
torch.arange(v, dtype=torch.int64, device=self.device)
+ i * self._P
for (i, v) in enumerate(self._num_points_per_cloud)
],
dim=0,
)
return self._padded_to_packed_idx
def _compute_padded(self, refresh: bool = False):
"""
Computes the padded version from points_list, normals_list and features_list.
Args:
refresh: whether to force the recalculation.
"""
if not (refresh or self._points_padded is None):
return
self._normals_padded, self._features_padded = None, None
if self.isempty():
self._points_padded = torch.zeros(
(self._N, 0, 3), device=self.device
)
else:
self._points_padded = struct_utils.list_to_padded(
self.points_list(),
(self._P, 3),
pad_value=0.0,
equisized=self.equisized,
)
if self.normals_list() is not None:
self._normals_padded = struct_utils.list_to_padded(
self.normals_list(),
(self._P, 3),
pad_value=0.0,
equisized=self.equisized,
)
if self.features_list() is not None:
self._features_padded = struct_utils.list_to_padded(
self.features_list(),
(self._P, self._C),
pad_value=0.0,
equisized=self.equisized,
)
# TODO(nikhilar) Improve performance of _compute_packed.
def _compute_packed(self, refresh: bool = False):
"""
Computes the packed version from points_list, normals_list and
features_list and sets the values of auxillary tensors.
Args:
refresh: Set to True to force recomputation of packed
representations. Default: False.
"""
if not (
refresh
or any(
v is None
for v in [
self._points_packed,
self._packed_to_cloud_idx,
self._cloud_to_packed_first_idx,
]
)
):
return
# Packed can be calculated from padded or list, so can call the
# accessor function for the lists.
points_list = self.points_list()
normals_list = self.normals_list()
features_list = self.features_list()
if self.isempty():
self._points_packed = torch.zeros(
(0, 3), dtype=torch.float32, device=self.device
)
self._packed_to_cloud_idx = torch.zeros(
(0,), dtype=torch.int64, device=self.device
)
self._cloud_to_packed_first_idx = torch.zeros(
(0,), dtype=torch.int64, device=self.device
)
self._normals_packed = None
self._features_packed = None
return
points_list_to_packed = struct_utils.list_to_packed(points_list)
self._points_packed = points_list_to_packed[0]
if not torch.allclose(
self._num_points_per_cloud, points_list_to_packed[1]
):
raise ValueError("Inconsistent list to packed conversion")
self._cloud_to_packed_first_idx = points_list_to_packed[2]
self._packed_to_cloud_idx = points_list_to_packed[3]
self._normals_packed, self._features_packed = None, None
if normals_list is not None:
normals_list_to_packed = struct_utils.list_to_packed(normals_list)
self._normals_packed = normals_list_to_packed[0]
if features_list is not None:
features_list_to_packed = struct_utils.list_to_packed(features_list)
self._features_packed = features_list_to_packed[0]
def clone(self):
"""
Deep copy of Pointclouds object. All internal tensors are cloned
individually.
Returns:
new Pointclouds object.
"""
# instantiate new pointcloud with the representation which is not None
# (either list or tensor) to save compute.
new_points, new_normals, new_features = None, None, None
if self._points_list is not None:
new_points = [v.clone() for v in self.points_list()]
normals_list = self.normals_list()
features_list = self.features_list()
if normals_list is not None:
new_normals = [n.clone() for n in normals_list]
if features_list is not None:
new_features = [f.clone() for f in features_list]
elif self._points_padded is not None:
new_points = self.points_padded().clone()
normals_padded = self.normals_padded()
features_padded = self.features_padded()
if normals_padded is not None:
new_normals = self.normals_padded().clone()
if features_padded is not None:
new_features = self.features_padded().clone()
other = Pointclouds(
points=new_points, normals=new_normals, features=new_features
)
for k in self._INTERNAL_TENSORS:
v = getattr(self, k)
if torch.is_tensor(v):
setattr(other, k, v.clone())
return other
def to(self, device, copy: bool = False):
"""
Match functionality of torch.Tensor.to()
If copy = True or the self Tensor is on a different device, the
returned tensor is a copy of self with the desired torch.device.
If copy = False and the self Tensor already has the correct torch.device,
then self is returned.
Args:
device: Device id for the new tensor.
copy: Boolean indicator whether or not to clone self. Default False.
Returns:
Pointclouds object.
"""
if not copy and self.device == device:
return self
other = self.clone()
if self.device != device:
other.device = device
if other._N > 0:
other._points_list = [v.to(device) for v in other.points_list()]
if other._normals_list is not None:
other._normals_list = [
n.to(device) for n in other.normals_list()
]
if other._features_list is not None:
other._features_list = [
f.to(device) for f in other.features_list()
]
for k in self._INTERNAL_TENSORS:
v = getattr(self, k)
if torch.is_tensor(v):
setattr(other, k, v.to(device))
return other
def cpu(self):
return self.to(torch.device("cpu"))
def cuda(self):
return self.to(torch.device("cuda"))
def get_cloud(self, index: int):
"""
Get tensors for a single cloud from the list representation.
Args:
index: Integer in the range [0, N).
Returns:
points: Tensor of shape (P, 3).
normals: Tensor of shape (P, 3)
features: LongTensor of shape (P, C).
"""
if not isinstance(index, int):
raise ValueError("Cloud index must be an integer.")
if index < 0 or index > self._N:
raise ValueError(
"Cloud index must be in the range [0, N) where \
N is the number of clouds in the batch."
)
points = self.points_list()[index]
normals, features = None, None
if self.normals_list() is not None:
normals = self.normals_list()[index]
if self.features_list() is not None:
features = self.features_list()[index]
return points, normals, features
# TODO(nikhilar) Move function to a utils file.
def split(self, split_sizes: list):
"""
Splits Pointclouds object of size N into a list of Pointclouds objects
of size len(split_sizes), where the i-th Pointclouds object is of size
split_sizes[i]. Similar to torch.split().
Args:
split_sizes: List of integer sizes of Pointclouds objects to be
returned.
Returns:
list[PointClouds].
"""
if not all(isinstance(x, int) for x in split_sizes):
raise ValueError("Value of split_sizes must be a list of integers.")
cloudlist = []
curi = 0
for i in split_sizes:
cloudlist.append(self[curi : curi + i])
curi += i
return cloudlist
def offset_(self, offsets_packed):
"""
Translate the point clouds by an offset. In place operation.
Args:
offsets_packed: A Tensor of the same shape as self.points_packed
giving offsets to be added to all points.
Returns:
self.
"""
points_packed = self.points_packed()
if offsets_packed.shape != points_packed.shape:
raise ValueError("Offsets must have dimension (all_p, 3).")
self._points_packed = points_packed + offsets_packed
new_points_list = list(
self._points_packed.split(self.num_points_per_cloud().tolist(), 0)
)
# Note that since _compute_packed() has been executed, points_list
# cannot be None even if not provided during construction.
self._points_list = new_points_list
if self._points_padded is not None:
for i, points in enumerate(new_points_list):
if len(points) > 0:
self._points_padded[i, : points.shape[0], :] = points
return self
# TODO(nikhilar) Move out of place operator to a utils file.
def offset(self, offsets_packed):
"""
Out of place offset.
Args:
offsets_packed: A Tensor of the same shape as self.points_packed
giving offsets to be added to all points.
Returns:
new Pointclouds object.
"""
new_clouds = self.clone()
return new_clouds.offset_(offsets_packed)
def scale_(self, scale):
"""
Multiply the coordinates of this object by a scalar value.
- i.e. enlarge/dilate
In place operation.
Args:
scale: A scalar, or a Tensor of shape (N,).
Returns:
self.
"""
if not torch.is_tensor(scale):
scale = torch.full(len(self), scale)
new_points_list = []
points_list = self.points_list()
for i, old_points in enumerate(points_list):
new_points_list.append(scale[i] * old_points)
self._points_list = new_points_list
if self._points_packed is not None:
self._points_packed = torch.cat(new_points_list, dim=0)
if self._points_padded is not None:
for i, points in enumerate(new_points_list):
if len(points) > 0:
self._points_padded[i, : points.shape[0], :] = points
return self
def scale(self, scale):
"""
Out of place scale_.
Args:
scale: A scalar, or a Tensor of shape (N,).
Returns:
new Pointclouds object.
"""
new_clouds = self.clone()
return new_clouds.scale_(scale)
# TODO(nikhilar) Move function to utils file.
def get_bounding_boxes(self):
"""
Compute an axis-aligned bounding box for each cloud.
Returns:
bboxes: Tensor of shape (N, 3, 2) where bbox[i, j] gives the
min and max values of cloud i along the jth coordinate axis.
"""
all_mins, all_maxes = [], []
for points in self.points_list():
cur_mins = points.min(dim=0)[0] # (3,)
cur_maxes = points.max(dim=0)[0] # (3,)
all_mins.append(cur_mins)
all_maxes.append(cur_maxes)
all_mins = torch.stack(all_mins, dim=0) # (N, 3)
all_maxes = torch.stack(all_maxes, dim=0) # (N, 3)
bboxes = torch.stack([all_mins, all_maxes], dim=2)
return bboxes
def extend(self, N: int):
"""
Create new Pointclouds which contains each cloud N times.
Args:
N: number of new copies of each cloud.
Returns:
new Pointclouds object.
"""
if not isinstance(N, int):
raise ValueError("N must be an integer.")
if N <= 0:
raise ValueError("N must be > 0.")
new_points_list, new_normals_list, new_features_list = [], None, None
for points in self.points_list():
new_points_list.extend(points.clone() for _ in range(N))
if self.normals_list() is not None:
new_normals_list = []
for normals in self.normals_list():
new_normals_list.extend(normals.clone() for _ in range(N))
if self.features_list() is not None:
new_features_list = []
for features in self.features_list():
new_features_list.extend(features.clone() for _ in range(N))
return Pointclouds(
points=new_points_list,
normals=new_normals_list,
features=new_features_list,
)
def update_padded(
self,
new_points_padded,
new_normals_padded=None,
new_features_padded=None,
):
"""
Returns a Pointcloud structure with updated padded tensors and copies of
the auxiliary tensors. This function allows for an update of
points_padded (and normals and features) without having to explicitly
convert it to the list representation for heterogeneous batches.
Args:
new_points_padded: FloatTensor of shape (N, P, 3)
new_normals_padded: (optional) FloatTensor of shape (N, P, 3)
new_features_padded: (optional) FloatTensors of shape (N, P, C)
Returns:
Pointcloud with updated padded representations
"""
def check_shapes(x, size):
if x.shape[0] != size[0]:
raise ValueError(
"new values must have the same batch dimension."
)
if x.shape[1] != size[1]:
raise ValueError(
"new values must have the same number of points."
)
if size[2] is not None:
if x.shape[2] != size[2]:
raise ValueError(
"new values must have the same number of channels."
)
check_shapes(new_points_padded, [self._N, self._P, 3])
if new_normals_padded is not None:
check_shapes(new_normals_padded, [self._N, self._P, 3])
if new_features_padded is not None:
check_shapes(new_features_padded, [self._N, self._P, self._C])
new = Pointclouds(
points=new_points_padded,
normals=new_normals_padded,
features=new_features_padded,
)
# overwrite the equisized flag
new.equisized = self.equisized
# copy normals
if new_normals_padded is None:
# If no normals are provided, keep old ones (shallow copy)
new._normals_list = self._normals_list
new._normals_padded = self._normals_padded
new._normals_packed = self._normals_packed
# copy features
if new_features_padded is None:
# If no features are provided, keep old ones (shallow copy)
new._features_list = self._features_list
new._features_padded = self._features_padded
new._features_packed = self._features_packed
# copy auxiliary tensors
copy_tensors = [
"_packed_to_cloud_idx",
"_cloud_to_packed_first_idx",
"_num_points_per_cloud",
"_padded_to_packed_idx",
"valid",
]
for k in copy_tensors:
v = getattr(self, k)
if torch.is_tensor(v):
setattr(new, k, v) # shallow copy
# update points
new._points_padded = new_points_padded
assert new._points_list is None
assert new._points_packed is None
# update normals and features if provided
if new_normals_padded is not None:
new._normals_padded = new_normals_padded
new._normals_list = None
new._normals_packed = None
if new_features_padded is not None:
new._features_padded = new_features_padded
new._features_list = None
new._features_packed = None
return new