mmcv.ops.group_points 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
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'])
[文档]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,
sample_num,
min_radius=0,
use_xyz=True,
return_grouped_xyz=False,
normalize_xyz=False,
uniform_sample=False,
return_unique_cnt=False,
return_grouped_idx=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'
[文档] def forward(self, points_xyz, center_xyz, features=None):
"""
Args:
points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
center_xyz (Tensor): (B, npoint, 3) coordinates of the centriods.
features (Tensor): (B, C, N) Descriptors of the features.
Returns:
Tensor: (B, 3 + C, npoint, sample_num) Grouped feature.
"""
# 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)
[文档]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
[文档] def forward(self,
xyz: torch.Tensor,
new_xyz: torch.Tensor,
features: torch.Tensor = None):
"""
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) -> torch.Tensor:
"""
Args:
features (Tensor): (B, C, N) tensor of features to group.
indices (Tensor): (B, npoint, nsample) the indices of
features to group with.
Returns:
Tensor: (B, C, npoint, nsample) Grouped features.
"""
features = features.contiguous()
indices = indices.contiguous()
B, nfeatures, nsample = indices.size()
_, C, N = features.size()
output = torch.cuda.FloatTensor(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[torch.Tensor, torch.Tensor]:
"""
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.
"""
idx, N = ctx.for_backwards
B, C, npoint, nsample = grad_out.size()
grad_features = torch.cuda.FloatTensor(B, C, N).zero_()
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
grouping_operation = GroupingOperation.apply