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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, Tuple, Union
import torch
from torch import nn as nn
from torch.autograd import Function
from ..utils import ext_loader
from .ball_query import ball_query
from .knn import knn
ext_module = ext_loader.load_ext('_ext', [
'group_points_forward', 'group_points_backward',
'stack_group_points_forward', 'stack_group_points_backward'
])
class QueryAndGroup(nn.Module):
"""Groups points with a ball query of radius.
Args:
max_radius (float): The maximum radius of the balls.
If None is given, we will use kNN sampling instead of ball query.
sample_num (int): Maximum number of features to gather in the ball.
min_radius (float, optional): The minimum radius of the balls.
Default: 0.
use_xyz (bool, optional): Whether to use xyz.
Default: True.
return_grouped_xyz (bool, optional): Whether to return grouped xyz.
Default: False.
normalize_xyz (bool, optional): Whether to normalize xyz.
Default: False.
uniform_sample (bool, optional): Whether to sample uniformly.
Default: False
return_unique_cnt (bool, optional): Whether to return the count of
unique samples. Default: False.
return_grouped_idx (bool, optional): Whether to return grouped idx.
Default: False.
"""
def __init__(self,
max_radius: float,
sample_num: int,
min_radius: float = 0.,
use_xyz: bool = True,
return_grouped_xyz: bool = False,
normalize_xyz: bool = False,
uniform_sample: bool = False,
return_unique_cnt: bool = False,
return_grouped_idx: bool = False):
super().__init__()
self.max_radius = max_radius
self.min_radius = min_radius
self.sample_num = sample_num
self.use_xyz = use_xyz
self.return_grouped_xyz = return_grouped_xyz
self.normalize_xyz = normalize_xyz
self.uniform_sample = uniform_sample
self.return_unique_cnt = return_unique_cnt
self.return_grouped_idx = return_grouped_idx
if self.return_unique_cnt:
assert self.uniform_sample, \
'uniform_sample should be True when ' \
'returning the count of unique samples'
if self.max_radius is None:
assert not self.normalize_xyz, \
'can not normalize grouped xyz when max_radius is None'
def forward(
self,
points_xyz: torch.Tensor,
center_xyz: torch.Tensor,
features: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple]:
"""
Args:
points_xyz (torch.Tensor): (B, N, 3) xyz coordinates of the
points.
center_xyz (torch.Tensor): (B, npoint, 3) coordinates of the
centriods.
features (torch.Tensor): (B, C, N) The features of grouped
points.
Returns:
Tuple | torch.Tensor: (B, 3 + C, npoint, sample_num) Grouped
concatenated coordinates and features of points.
"""
# if self.max_radius is None, we will perform kNN instead of ball query
# idx is of shape [B, npoint, sample_num]
if self.max_radius is None:
idx = knn(self.sample_num, points_xyz, center_xyz, False)
idx = idx.transpose(1, 2).contiguous()
else:
idx = ball_query(self.min_radius, self.max_radius, self.sample_num,
points_xyz, center_xyz)
if self.uniform_sample:
unique_cnt = torch.zeros((idx.shape[0], idx.shape[1]))
for i_batch in range(idx.shape[0]):
for i_region in range(idx.shape[1]):
unique_ind = torch.unique(idx[i_batch, i_region, :])
num_unique = unique_ind.shape[0]
unique_cnt[i_batch, i_region] = num_unique
sample_ind = torch.randint(
0,
num_unique, (self.sample_num - num_unique, ),
dtype=torch.long)
all_ind = torch.cat((unique_ind, unique_ind[sample_ind]))
idx[i_batch, i_region, :] = all_ind
xyz_trans = points_xyz.transpose(1, 2).contiguous()
# (B, 3, npoint, sample_num)
grouped_xyz = grouping_operation(xyz_trans, idx)
grouped_xyz_diff = grouped_xyz - \
center_xyz.transpose(1, 2).unsqueeze(-1) # relative offsets
if self.normalize_xyz:
grouped_xyz_diff /= self.max_radius
if features is not None:
grouped_features = grouping_operation(features, idx)
if self.use_xyz:
# (B, C + 3, npoint, sample_num)
new_features = torch.cat([grouped_xyz_diff, grouped_features],
dim=1)
else:
new_features = grouped_features
else:
assert (self.use_xyz
), 'Cannot have not features and not use xyz as a feature!'
new_features = grouped_xyz_diff
ret = [new_features]
if self.return_grouped_xyz:
ret.append(grouped_xyz)
if self.return_unique_cnt:
ret.append(unique_cnt)
if self.return_grouped_idx:
ret.append(idx)
if len(ret) == 1:
return ret[0]
else:
return tuple(ret)
class GroupAll(nn.Module):
"""Group xyz with feature.
Args:
use_xyz (bool): Whether to use xyz.
"""
def __init__(self, use_xyz: bool = True):
super().__init__()
self.use_xyz = use_xyz
def forward(self,
xyz: torch.Tensor,
new_xyz: torch.Tensor,
features: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Args:
xyz (Tensor): (B, N, 3) xyz coordinates of the features.
new_xyz (Tensor): new xyz coordinates of the features.
features (Tensor): (B, C, N) features to group.
Returns:
Tensor: (B, C + 3, 1, N) Grouped feature.
"""
grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
if features is not None:
grouped_features = features.unsqueeze(2)
if self.use_xyz:
# (B, 3 + C, 1, N)
new_features = torch.cat([grouped_xyz, grouped_features],
dim=1)
else:
new_features = grouped_features
else:
new_features = grouped_xyz
return new_features
class GroupingOperation(Function):
"""Group feature with given index."""
@staticmethod
def forward(
ctx,
features: torch.Tensor,
indices: torch.Tensor,
features_batch_cnt: Optional[torch.Tensor] = None,
indices_batch_cnt: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Args:
features (Tensor): Tensor of features to group, input shape is
(B, C, N) or stacked inputs (N1 + N2 ..., C).
indices (Tensor): The indices of features to group with, input
shape is (B, npoint, nsample) or stacked inputs
(M1 + M2 ..., nsample).
features_batch_cnt (Tensor, optional): Input features nums in
each batch, just like (N1, N2, ...). Defaults to None.
New in version 1.7.0.
indices_batch_cnt (Tensor, optional): Input indices nums in
each batch, just like (M1, M2, ...). Defaults to None.
New in version 1.7.0.
Returns:
Tensor: Grouped features, the shape is (B, C, npoint, nsample)
or (M1 + M2 ..., C, nsample).
"""
features = features.contiguous()
indices = indices.contiguous()
if features_batch_cnt is not None and indices_batch_cnt is not None:
assert features_batch_cnt.dtype == torch.int
assert indices_batch_cnt.dtype == torch.int
M, nsample = indices.size()
N, C = features.size()
B = indices_batch_cnt.shape[0]
output = features.new_zeros((M, C, nsample))
ext_module.stack_group_points_forward(
features,
features_batch_cnt,
indices,
indices_batch_cnt,
output,
b=B,
m=M,
c=C,
nsample=nsample)
ctx.for_backwards = (B, N, indices, features_batch_cnt,
indices_batch_cnt)
else:
B, nfeatures, nsample = indices.size()
_, C, N = features.size()
output = features.new_zeros(B, C, nfeatures, nsample)
ext_module.group_points_forward(
features,
indices,
output,
b=B,
c=C,
n=N,
npoints=nfeatures,
nsample=nsample)
ctx.for_backwards = (indices, N)
return output
@staticmethod
def backward(ctx, grad_out: torch.Tensor) -> Tuple:
"""
Args:
grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients
of the output from forward.
Returns:
Tensor: (B, C, N) gradient of the features.
"""
if len(ctx.for_backwards) != 5:
idx, N = ctx.for_backwards
B, C, npoint, nsample = grad_out.size()
grad_features = grad_out.new_zeros(B, C, N)
grad_out_data = grad_out.data.contiguous()
ext_module.group_points_backward(
grad_out_data,
idx,
grad_features.data,
b=B,
c=C,
n=N,
npoints=npoint,
nsample=nsample)
return grad_features, None
else:
B, N, idx, features_batch_cnt, idx_batch_cnt = ctx.for_backwards
M, C, nsample = grad_out.size()
grad_features = grad_out.new_zeros(N, C)
grad_out_data = grad_out.data.contiguous()
ext_module.stack_group_points_backward(
grad_out_data,
idx,
idx_batch_cnt,
features_batch_cnt,
grad_features.data,
b=B,
c=C,
m=M,
n=N,
nsample=nsample)
return grad_features, None, None, None
grouping_operation = GroupingOperation.apply
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