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from collections import namedtuple
import pytorch_lightning as pl
import torch
import torch.nn as nn
from pointnet2_ops.pointnet2_modules import PointnetFPModule, PointnetSAModuleMSG
from pointnet2.models.pointnet2_ssg_sem import PointNet2SemSegSSG
class PointNet2SemSegMSG(PointNet2SemSegSSG):
def _build_model(self):
self.SA_modules = nn.ModuleList()
c_in = 6
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=1024,
radii=[0.05, 0.1],
nsamples=[16, 32],
mlps=[[c_in, 16, 16, 32], [c_in, 32, 32, 64]],
use_xyz=self.hparams["model.use_xyz"],
)
)
c_out_0 = 32 + 64
c_in = c_out_0
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=256,
radii=[0.1, 0.2],
nsamples=[16, 32],
mlps=[[c_in, 64, 64, 128], [c_in, 64, 96, 128]],
use_xyz=self.hparams["model.use_xyz"],
)
)
c_out_1 = 128 + 128
c_in = c_out_1
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=64,
radii=[0.2, 0.4],
nsamples=[16, 32],
mlps=[[c_in, 128, 196, 256], [c_in, 128, 196, 256]],
use_xyz=self.hparams["model.use_xyz"],
)
)
c_out_2 = 256 + 256
c_in = c_out_2
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=16,
radii=[0.4, 0.8],
nsamples=[16, 32],
mlps=[[c_in, 256, 256, 512], [c_in, 256, 384, 512]],
use_xyz=self.hparams["model.use_xyz"],
)
)
c_out_3 = 512 + 512
self.FP_modules = nn.ModuleList()
self.FP_modules.append(PointnetFPModule(mlp=[256 + 6, 128, 128]))
self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_0, 256, 256]))
self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_1, 512, 512]))
self.FP_modules.append(PointnetFPModule(mlp=[c_out_3 + c_out_2, 512, 512]))
self.fc_lyaer = nn.Sequential(
nn.Conv1d(128, 128, kernel_size=1, bias=False),
nn.BatchNorm1d(128),
nn.ReLU(True),
nn.Dropout(0.5),
nn.Conv1d(128, 13, kernel_size=1),
)
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