mirror of
https://github.com/wassname/Pointnet2_PyTorch.git
synced 2026-07-23 12:40:28 +08:00
Some faster/better kernels. Tensors with points are now kept in (b, c, ...) format as this is easier for pytorch
This commit is contained in:
erikwijmans
+71
-93
@@ -7,7 +7,59 @@ import pytorch_utils as pt_utils
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from typing import List
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class PointnetSAModuleMSG(nn.Module):
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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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point : 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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@@ -48,49 +100,13 @@ class PointnetSAModuleMSG(nn.Module):
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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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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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point : 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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new_xyz = pointnet2_utils.gather_points(
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xyz, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
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)
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for i in range(len(self.groupers)):
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new_points = self.groupers[i](xyz, new_xyz, points)
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new_points = self.mlps[i](new_points.permute(0, 3, 1, 2)
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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 = new_points.transpose(
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1, 2
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).contiguous() # (B, npoint, mlp[-1])
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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 PointnetSAModule(nn.Module):
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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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@@ -119,57 +135,22 @@ class PointnetSAModule(nn.Module):
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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.grouper = pointnet2_utils.QueryAndGroup(
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radius, nsample, use_xyz=use_xyz
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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.grouper = pointnet2_utils.GroupAll(use_xyz=use_xyz)
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self.groupers.append(pointnet2_utils.GroupAll(use_xyz=use_xyz))
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self.mlp = pt_utils.SharedMLP(mlp, bn=bn)
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if use_xyz:
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mlp[0] += 3
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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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point : 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, mlp[-1]) tensor of the new_points descriptors
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"""
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if self.npoint is not None:
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new_xyz = pointnet2_utils.gather_points(
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xyz, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
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)
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else:
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new_xyz = xyz.data.new([[[0, 0, 0]]]).expand(xyz.size(0), 1, 3)
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new_points = self.grouper(
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xyz, new_xyz, points
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) # (B, npoint, nsample, 3 + C)
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new_points = self.mlp(new_points.permute(0, 3, 1, 2)
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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 = new_points.transpose(1, 2
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).contiguous() # (B, npoint, mlp[-1])
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return new_xyz, new_points
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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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@@ -199,14 +180,14 @@ class PointnetFPModule(nn.Module):
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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, n, C1) tensor of the features to be propigated to
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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, m, C2) tensor of features to be propigated
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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, n, mlp[-1]) tensor of the features of the unknown points
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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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@@ -219,17 +200,14 @@ class PointnetFPModule(nn.Module):
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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, n, C2 + C1)
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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).transpose(
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1, 2
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) #(B, C2 + C1, n, 1)
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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).transpose(1, 2
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).contiguous() #(B, n, mlp[-1])
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return new_points.squeeze(-1)
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if __name__ == "__main__":
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