dmlc--dgl
Tianqi Zhang (张天启)
6999f88f3f
* add random_start option for FPS * change doc * change from random_start to start_idx * change error condition * change error msg Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
58 行
2.1 KiB
Python
58 行
2.1 KiB
Python
"""Farthest Point Sampler for pytorch Geometry package"""
|
|
#pylint: disable=no-member, invalid-name
|
|
|
|
import torch as th
|
|
from torch import nn
|
|
|
|
from ...base import DGLError
|
|
from ..capi import farthest_point_sampler
|
|
|
|
class FarthestPointSampler(nn.Module):
|
|
"""Farthest Point Sampler without the need to compute all pairs of distance.
|
|
|
|
In each batch, the algorithm starts with the sample index specified by ``start_idx``.
|
|
Then for each point, we maintain the minimum to-sample distance.
|
|
Finally, we pick the point with the maximum such distance.
|
|
This process will be repeated for ``sample_points`` - 1 times.
|
|
|
|
Parameters
|
|
----------
|
|
npoints : int
|
|
The number of points to sample in each batch.
|
|
"""
|
|
def __init__(self, npoints):
|
|
super(FarthestPointSampler, self).__init__()
|
|
self.npoints = npoints
|
|
|
|
def forward(self, pos, start_idx=None):
|
|
r"""Memory allocation and sampling
|
|
|
|
Parameters
|
|
----------
|
|
pos : tensor
|
|
The positional tensor of shape (B, N, C)
|
|
start_idx : int, optional
|
|
If given, appoint the index of the starting point,
|
|
otherwise randomly select a point as the start point.
|
|
(default: None)
|
|
|
|
Returns
|
|
-------
|
|
tensor of shape (B, self.npoints)
|
|
The sampled indices in each batch.
|
|
"""
|
|
device = pos.device
|
|
B, N, C = pos.shape
|
|
pos = pos.reshape(-1, C)
|
|
dist = th.zeros((B * N), dtype=pos.dtype, device=device)
|
|
if start_idx is None:
|
|
start_idx = th.randint(0, N - 1, (B, ), dtype=th.long, device=device)
|
|
else:
|
|
if start_idx >= N or start_idx < 0:
|
|
raise DGLError("Invalid start_idx, expected 0 <= start_idx < {}, got {}".format(
|
|
N, start_idx))
|
|
start_idx = th.full((B, ), start_idx, dtype=th.long, device=device)
|
|
result = th.zeros((self.npoints * B), dtype=th.long, device=device)
|
|
farthest_point_sampler(pos, B, self.npoints, dist, start_idx, result)
|
|
return result.reshape(B, self.npoints)
|