mirror of
https://github.com/wassname/Pointnet2_PyTorch.git
synced 2026-07-09 00:20:12 +08:00
Initial commit
This commit is contained in:
erikwijmans
@@ -0,0 +1,243 @@
|
||||
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 PointnetSAModuleMSG(nn.Module):
|
||||
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):
|
||||
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))
|
||||
mlp_spec = mlps[i]
|
||||
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn))
|
||||
|
||||
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 = []
|
||||
new_xyz = pointnet2_utils.gather_points(
|
||||
xyz, pointnet2_utils.furthest_point_sample(xyz, self.npoint))
|
||||
for i in range(len(self.groupers)):
|
||||
new_points = self.groupers[i](xyz, new_xyz, points)
|
||||
|
||||
new_points = self.mlps[i](new_points.permute(
|
||||
0, 3, 1, 2)) # (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 = new_points.transpose(
|
||||
1, 2).contiguous() # (B, npoint, mlp[-1])
|
||||
|
||||
new_points_list.append(new_points)
|
||||
|
||||
return new_xyz, torch.cat(new_points_list, dim=-1)
|
||||
|
||||
|
||||
class PointnetSAModule(nn.Module):
|
||||
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):
|
||||
super().__init__()
|
||||
self.npoint = npoint
|
||||
|
||||
if self.npoint is not None:
|
||||
assert radius is not None
|
||||
assert nsample is not None
|
||||
self.grouper = pointnet2_utils.QueryAndGroup(radius, nsample)
|
||||
else:
|
||||
self.grouper = pointnet2_utils.GroupAll()
|
||||
|
||||
self.mlp = pt_utils.SharedMLP(mlp, bn=bn)
|
||||
|
||||
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, mlp[-1]) tensor of the new_points descriptors
|
||||
"""
|
||||
|
||||
if self.npoint is not None:
|
||||
new_xyz = pointnet2_utils.gather_points(
|
||||
xyz, pointnet2_utils.furthest_point_sample(xyz, self.npoint))
|
||||
else:
|
||||
new_xyz = xyz.new([[[0, 0, 0]]]).expand(xyz.size(0), 1, 3)
|
||||
|
||||
new_points = self.grouper(xyz, new_xyz,
|
||||
points) # (B, npoint, nsample, 3 + C)
|
||||
|
||||
new_points = self.mlp(new_points.permute(
|
||||
0, 3, 1, 2)) # (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 = new_points.transpose(
|
||||
1, 2).contiguous() # (B, npoint, mlp[-1])
|
||||
|
||||
return new_xyz, new_points
|
||||
|
||||
|
||||
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, n, C1) tensor of the features to be propigated to
|
||||
known_feats : torch.Tensor
|
||||
(B, m, C2) tensor of features to be propigated
|
||||
|
||||
Returns
|
||||
-------
|
||||
new_points : torch.Tensor
|
||||
(B, n, mlp[-1]) 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, n, C2 + C1)
|
||||
else:
|
||||
new_points = interpolated_feats
|
||||
|
||||
new_points = new_points.unsqueeze(-1).transpose(1,
|
||||
2) #(B, C2 + C1, n, 1)
|
||||
new_points = self.mlp(new_points)
|
||||
|
||||
return new_points.squeeze(-1).transpose(
|
||||
1, 2).contiguous() #(B, n, mlp[-1])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from torch.autograd import Variable
|
||||
torch.manual_seed(1)
|
||||
torch.cuda.manual_seed_all(1)
|
||||
xyz = Variable(torch.randn(2, 10, 3).cuda(), requires_grad=True)
|
||||
xyz_feats = Variable(torch.randn(2, 10, 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)
|
||||
Reference in New Issue
Block a user