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