519 lines
17 KiB
Python
Executable File
519 lines
17 KiB
Python
Executable File
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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''' Pointnet2 layers.
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Modified based on: https://github.com/erikwijmans/Pointnet2_PyTorch
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Extended with the following:
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1. Uniform sampling in each local region (sample_uniformly)
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2. Return sampled points indices to support votenet.
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'''
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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 os
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import sys
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(BASE_DIR)
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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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features: 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 features
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features : torch.Tensor
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(B, N, C) tensor of the descriptors of the the features
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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 features' xyz
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new_features : torch.Tensor
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(B, npoint, \sum_k(mlps[k][-1])) tensor of the new_features descriptors
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"""
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new_features_list = []
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xyz_flipped = xyz.transpose(1, 2).contiguous()
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new_xyz = pointnet2_utils.gather_operation(
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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_features = self.groupers[i](
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xyz, new_xyz, features
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) # (B, C, npoint, nsample)
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new_features = self.mlps[i](
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new_features
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) # (B, mlp[-1], npoint, nsample)
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new_features = F.max_pool2d(
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new_features, kernel_size=[1, new_features.size(3)]
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) # (B, mlp[-1], npoint, 1)
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new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
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new_features_list.append(new_features)
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return new_xyz, torch.cat(new_features_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 features
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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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sample_uniformly: bool = False
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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, sample_uniformly=sample_uniformly)
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if npoint is not None else pointnet2_utils.GroupAll(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(PointnetSAModuleMSG):
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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 features
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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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mlps=[mlp],
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npoint=npoint,
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radii=[radius],
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nsamples=[nsample],
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bn=bn,
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use_xyz=use_xyz
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)
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class PointnetSAModuleVotes(nn.Module):
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''' Modified based on _PointnetSAModuleBase and PointnetSAModuleMSG
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with extra support for returning point indices for getting their GT votes '''
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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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pooling: str = 'max',
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sigma: float = None, # for RBF pooling
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normalize_xyz: bool = False, # noramlize local XYZ with radius
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sample_uniformly: bool = False,
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ret_unique_cnt: bool = False
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):
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super().__init__()
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self.npoint = npoint
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self.radius = radius
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self.nsample = nsample
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self.pooling = pooling
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self.mlp_module = None
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self.use_xyz = use_xyz
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self.sigma = sigma
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if self.sigma is None:
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self.sigma = self.radius/2
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self.normalize_xyz = normalize_xyz
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self.ret_unique_cnt = ret_unique_cnt
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if npoint is not None:
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self.grouper = pointnet2_utils.QueryAndGroup(radius, nsample,
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use_xyz=use_xyz, ret_grouped_xyz=True, normalize_xyz=normalize_xyz,
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sample_uniformly=sample_uniformly, ret_unique_cnt=ret_unique_cnt)
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else:
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self.grouper = pointnet2_utils.GroupAll(use_xyz, ret_grouped_xyz=True)
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mlp_spec = mlp
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if use_xyz and len(mlp_spec)>0:
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mlp_spec[0] += 3
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self.mlp_module = pt_utils.SharedMLP(mlp_spec, bn=bn)
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def forward(self, xyz: torch.Tensor,
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features: torch.Tensor = None,
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inds: 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 features
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features : torch.Tensor
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(B, C, N) tensor of the descriptors of the the features
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inds : torch.Tensor
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(B, npoint) tensor that stores index to the xyz points (values in 0-N-1)
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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 features' xyz
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new_features : torch.Tensor
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(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
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inds: torch.Tensor
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(B, npoint) tensor of the inds
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"""
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xyz_flipped = xyz.transpose(1, 2).contiguous()
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if inds is None:
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inds = pointnet2_utils.furthest_point_sample(xyz, self.npoint)
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else:
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assert(inds.shape[1] == self.npoint)
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new_xyz = pointnet2_utils.gather_operation(
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xyz_flipped, inds
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).transpose(1, 2).contiguous() if self.npoint is not None else None
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if not self.ret_unique_cnt:
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grouped_features, grouped_xyz = self.grouper(
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xyz, new_xyz, features
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) # (B, C, npoint, nsample)
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else:
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grouped_features, grouped_xyz, unique_cnt = self.grouper(
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xyz, new_xyz, features
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) # (B, C, npoint, nsample), (B,3,npoint,nsample), (B,npoint)
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new_features = self.mlp_module(
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grouped_features
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) # (B, mlp[-1], npoint, nsample)
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if self.pooling == 'max':
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new_features = F.max_pool2d(
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new_features, kernel_size=[1, new_features.size(3)]
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) # (B, mlp[-1], npoint, 1)
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elif self.pooling == 'avg':
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new_features = F.avg_pool2d(
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new_features, kernel_size=[1, new_features.size(3)]
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) # (B, mlp[-1], npoint, 1)
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elif self.pooling == 'rbf':
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# Use radial basis function kernel for weighted sum of features (normalized by nsample and sigma)
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# Ref: https://en.wikipedia.org/wiki/Radial_basis_function_kernel
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rbf = torch.exp(-1 * grouped_xyz.pow(2).sum(1,keepdim=False) / (self.sigma**2) / 2) # (B, npoint, nsample)
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new_features = torch.sum(new_features * rbf.unsqueeze(1), -1, keepdim=True) / float(self.nsample) # (B, mlp[-1], npoint, 1)
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new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
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if not self.ret_unique_cnt:
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return new_xyz, new_features, inds
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else:
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return new_xyz, new_features, inds, unique_cnt
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class PointnetSAModuleMSGVotes(nn.Module):
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''' Modified based on _PointnetSAModuleBase and PointnetSAModuleMSG
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with extra support for returning point indices for getting their GT votes '''
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def __init__(
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self,
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*,
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mlps: List[List[int]],
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npoint: int,
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radii: List[float],
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nsamples: List[int],
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bn: bool = True,
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use_xyz: bool = True,
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sample_uniformly: bool = False
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):
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super().__init__()
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assert(len(mlps) == len(nsamples) == len(radii))
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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, sample_uniformly=sample_uniformly)
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if npoint is not None else pointnet2_utils.GroupAll(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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features: torch.Tensor = None, inds: 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 features
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features : torch.Tensor
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(B, C, C) tensor of the descriptors of the the features
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inds : torch.Tensor
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(B, npoint) tensor that stores index to the xyz points (values in 0-N-1)
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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 features' xyz
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new_features : torch.Tensor
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(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
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inds: torch.Tensor
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(B, npoint) tensor of the inds
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"""
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new_features_list = []
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xyz_flipped = xyz.transpose(1, 2).contiguous()
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if inds is None:
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inds = pointnet2_utils.furthest_point_sample(xyz, self.npoint)
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new_xyz = pointnet2_utils.gather_operation(
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xyz_flipped, inds
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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_features = self.groupers[i](
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xyz, new_xyz, features
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) # (B, C, npoint, nsample)
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new_features = self.mlps[i](
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new_features
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) # (B, mlp[-1], npoint, nsample)
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new_features = F.max_pool2d(
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new_features, kernel_size=[1, new_features.size(3)]
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) # (B, mlp[-1], npoint, 1)
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new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
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new_features_list.append(new_features)
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return new_xyz, torch.cat(new_features_list, dim=1), inds
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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 features
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known : torch.Tensor
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(B, m, 3) tensor of the xyz positions of the known features
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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_features : torch.Tensor
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(B, mlp[-1], n) tensor of the features of the unknown features
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"""
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if known is not None:
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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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else:
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interpolated_feats = known_feats.expand(
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*known_feats.size()[0:2], unknown.size(1)
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)
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if unknow_feats is not None:
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new_features = 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_features = interpolated_feats
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new_features = new_features.unsqueeze(-1)
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new_features = self.mlp(new_features)
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return new_features.squeeze(-1)
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class PointnetLFPModuleMSG(nn.Module):
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''' Modified based on _PointnetSAModuleBase and PointnetSAModuleMSG
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learnable feature propagation layer.'''
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def __init__(
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self,
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*,
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mlps: List[List[int]],
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radii: List[float],
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nsamples: List[int],
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post_mlp: List[int],
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bn: bool = True,
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use_xyz: bool = True,
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sample_uniformly: bool = False
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):
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super().__init__()
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assert(len(mlps) == len(nsamples) == len(radii))
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self.post_mlp = pt_utils.SharedMLP(post_mlp, bn=bn)
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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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sample_uniformly=sample_uniformly)
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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, xyz2: torch.Tensor, xyz1: torch.Tensor,
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features2: torch.Tensor, features1: torch.Tensor) -> torch.Tensor:
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r""" Propagate features from xyz1 to xyz2.
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Parameters
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----------
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xyz2 : torch.Tensor
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(B, N2, 3) tensor of the xyz coordinates of the features
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xyz1 : torch.Tensor
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(B, N1, 3) tensor of the xyz coordinates of the features
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features2 : torch.Tensor
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(B, C2, N2) tensor of the descriptors of the the features
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features1 : torch.Tensor
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(B, C1, N1) tensor of the descriptors of the the features
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Returns
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-------
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new_features1 : torch.Tensor
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(B, \sum_k(mlps[k][-1]), N1) tensor of the new_features descriptors
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"""
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new_features_list = []
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for i in range(len(self.groupers)):
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new_features = self.groupers[i](
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xyz1, xyz2, features1
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) # (B, C1, N2, nsample)
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new_features = self.mlps[i](
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new_features
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) # (B, mlp[-1], N2, nsample)
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new_features = F.max_pool2d(
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new_features, kernel_size=[1, new_features.size(3)]
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) # (B, mlp[-1], N2, 1)
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new_features = new_features.squeeze(-1) # (B, mlp[-1], N2)
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if features2 is not None:
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new_features = torch.cat([new_features, features2],
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dim=1) #(B, mlp[-1] + C2, N2)
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new_features = new_features.unsqueeze(-1)
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new_features = self.post_mlp(new_features)
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new_features_list.append(new_features)
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return torch.cat(new_features_list, dim=1).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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|
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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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|
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for _ in range(1):
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_, new_features = test_module(xyz, xyz_feats)
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new_features.backward(
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|
torch.cuda.FloatTensor(*new_features.size()).fill_(1)
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|
)
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|
print(new_features)
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|
print(xyz.grad)
|