mirror of
https://github.com/wassname/Pointnet2_PyTorch.git
synced 2026-06-27 16:00:07 +08:00
wassname
238 lines
6.9 KiB
Python
238 lines
6.9 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
import pointnet2_utils
|
|
import pytorch_utils as pt_utils
|
|
from typing import List
|
|
|
|
|
|
class _PointnetSAModuleBase(nn.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.npoint = None
|
|
self.groupers = None
|
|
self.mlps = None
|
|
|
|
def forward(self, xyz: torch.Tensor,
|
|
points: torch.Tensor = None) -> (torch.Tensor, torch.Tensor):
|
|
r"""
|
|
Parameters
|
|
----------
|
|
xyz : torch.Tensor
|
|
(B, N, 3) tensor of the xyz coordinates of the points
|
|
point : torch.Tensor
|
|
(B, N, C) tensor of the descriptors of the the points
|
|
|
|
Returns
|
|
-------
|
|
new_xyz : torch.Tensor
|
|
(B, npoint, 3) tensor of the new points' xyz
|
|
new_points : torch.Tensor
|
|
(B, npoint, \sum_k(mlps[k][-1])) tensor of the new_points descriptors
|
|
"""
|
|
|
|
new_points_list = []
|
|
|
|
xyz_flipped = xyz.transpose(1, 2).contiguous()
|
|
new_xyz = pointnet2_utils.gather_points(
|
|
xyz_flipped,
|
|
pointnet2_utils.furthest_point_sample(xyz, self.npoint)
|
|
).transpose(1, 2).contiguous() if self.npoint is not None else None
|
|
|
|
for i in range(len(self.groupers)):
|
|
new_points = self.groupers[i](
|
|
xyz, new_xyz, points
|
|
) # (B, C, npoint, nsample)
|
|
|
|
new_points = self.mlps[i](
|
|
new_points
|
|
) # (B, mlp[-1], npoint, nsample)
|
|
new_points = F.max_pool2d(
|
|
new_points, kernel_size=[1, new_points.size(3)]
|
|
) # (B, mlp[-1], npoint, 1)
|
|
new_points = new_points.squeeze(-1) # (B, mlp[-1], npoint)
|
|
|
|
new_points_list.append(new_points)
|
|
|
|
return new_xyz, torch.cat(new_points_list, dim=1)
|
|
|
|
|
|
class PointnetSAModuleMSG(_PointnetSAModuleBase):
|
|
r"""Pointnet set abstrction layer with multiscale grouping
|
|
|
|
Parameters
|
|
----------
|
|
npoint : int
|
|
Number of points
|
|
radii : list of float32
|
|
list of radii to group with
|
|
nsamples : list of int32
|
|
Number of samples in each ball query
|
|
mlps : list of list of int32
|
|
Spec of the pointnet before the global max_pool for each scale
|
|
bn : bool
|
|
Use batchnorm
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
npoint: int,
|
|
radii: List[float],
|
|
nsamples: List[int],
|
|
mlps: List[List[int]],
|
|
bn: bool = True,
|
|
use_xyz: bool = True
|
|
):
|
|
super().__init__()
|
|
|
|
assert len(radii) == len(nsamples) == len(mlps)
|
|
|
|
self.npoint = npoint
|
|
self.groupers = nn.ModuleList()
|
|
self.mlps = nn.ModuleList()
|
|
for i in range(len(radii)):
|
|
radius = radii[i]
|
|
nsample = nsamples[i]
|
|
self.groupers.append(
|
|
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
|
|
)
|
|
mlp_spec = mlps[i]
|
|
if use_xyz:
|
|
mlp_spec[0] += 3
|
|
|
|
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn))
|
|
|
|
|
|
class PointnetSAModule(_PointnetSAModuleBase):
|
|
r"""Pointnet set abstrction layer
|
|
|
|
Parameters
|
|
----------
|
|
npoint : int
|
|
Number of points
|
|
radius : float
|
|
Radius of ball
|
|
nsample : int
|
|
Number of samples in the ball query
|
|
mlp : list
|
|
Spec of the pointnet before the global max_pool
|
|
bn : bool
|
|
Use batchnorm
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
mlp: List[int],
|
|
npoint: int = None,
|
|
radius: float = None,
|
|
nsample: int = None,
|
|
bn: bool = True,
|
|
use_xyz: bool = True
|
|
):
|
|
super().__init__()
|
|
self.npoint = npoint
|
|
self.groupers = nn.ModuleList()
|
|
self.mlps = nn.ModuleList()
|
|
|
|
if self.npoint is not None:
|
|
assert radius is not None
|
|
assert nsample is not None
|
|
self.groupers.append(
|
|
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
|
|
)
|
|
else:
|
|
self.groupers.append(pointnet2_utils.GroupAll(use_xyz=use_xyz))
|
|
|
|
if use_xyz:
|
|
mlp[0] += 3
|
|
|
|
self.mlps.append(pt_utils.SharedMLP(mlp, bn=bn))
|
|
|
|
|
|
class PointnetFPModule(nn.Module):
|
|
r"""Propigates the features of one set to another
|
|
|
|
Parameters
|
|
----------
|
|
mlp : list
|
|
Pointnet module parameters
|
|
bn : bool
|
|
Use batchnorm
|
|
"""
|
|
|
|
def __init__(self, *, mlp: List[int], bn: bool = True):
|
|
super().__init__()
|
|
self.mlp = pt_utils.SharedMLP(mlp, bn=bn)
|
|
|
|
def forward(
|
|
self, unknown: torch.Tensor, known: torch.Tensor,
|
|
unknow_feats: torch.Tensor, known_feats: torch.Tensor
|
|
) -> torch.Tensor:
|
|
r"""
|
|
Parameters
|
|
----------
|
|
unknown : torch.Tensor
|
|
(B, n, 3) tensor of the xyz positions of the unknown points
|
|
known : torch.Tensor
|
|
(B, m, 3) tensor of the xyz positions of the known points
|
|
unknow_feats : torch.Tensor
|
|
(B, C1, n) tensor of the features to be propigated to
|
|
known_feats : torch.Tensor
|
|
(B, C2, m) tensor of features to be propigated
|
|
|
|
Returns
|
|
-------
|
|
new_points : torch.Tensor
|
|
(B, mlp[-1], n) tensor of the features of the unknown points
|
|
"""
|
|
|
|
dist, idx = pointnet2_utils.three_nn(unknown, known)
|
|
dist_recip = 1.0 / (dist + 1e-8)
|
|
norm = torch.sum(dist_recip, dim=2, keepdim=True)
|
|
weight = dist_recip / norm
|
|
|
|
interpolated_feats = pointnet2_utils.three_interpolate(
|
|
known_feats, idx, weight
|
|
)
|
|
if unknow_feats is not None:
|
|
new_points = torch.cat([interpolated_feats, unknow_feats],
|
|
dim=1) #(B, C2 + C1, n)
|
|
else:
|
|
new_points = interpolated_feats
|
|
|
|
new_points = new_points.unsqueeze(-1)
|
|
new_points = self.mlp(new_points)
|
|
|
|
return new_points.squeeze(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from torch.autograd import Variable
|
|
torch.manual_seed(1)
|
|
torch.cuda.manual_seed_all(1)
|
|
xyz = Variable(torch.randn(2, 9, 3).cuda(), requires_grad=True)
|
|
xyz_feats = Variable(torch.randn(2, 9, 6).cuda(), requires_grad=True)
|
|
|
|
test_module = PointnetSAModuleMSG(
|
|
npoint=2, radii=[5.0, 10.0], nsamples=[6, 3], mlps=[[9, 3], [9, 6]]
|
|
)
|
|
test_module.cuda()
|
|
print(test_module(xyz, xyz_feats))
|
|
|
|
# test_module = PointnetFPModule(mlp=[6, 6])
|
|
# test_module.cuda()
|
|
# from torch.autograd import gradcheck
|
|
# inputs = (xyz, xyz, None, xyz_feats)
|
|
# test = gradcheck(test_module, inputs, eps=1e-6, atol=1e-4)
|
|
# print(test)
|
|
|
|
for _ in range(1):
|
|
_, new_points = test_module(xyz, xyz_feats)
|
|
new_points.backward(torch.cuda.FloatTensor(*new_points.size()).fill_(1))
|
|
print(new_points)
|
|
print(xyz.grad)
|