def __init__(self, num_classes, input_channels=3):
super().__init__()
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModule(
npoint=512,
radius=0.2,
nsample=64,
mlp=[input_channels, 64, 64, 128]
)
)
self.SA_modules.append(
PointnetSAModule(
npoint=128, radius=0.4, nsample=64, mlp=[128, 128, 128, 256]
)
)
self.SA_modules.append(PointnetSAModule(mlp=[256, 256, 512, 1024]))
self.FC_layer = nn.Sequential(
pt_utils.FC(1024, 512, bn=True),
nn.Dropout(p=0.5),
pt_utils.FC(512, 256, bn=True),
nn.Dropout(p=0.5),
pt_utils.FC(256, num_classes, activation=None)
)
def __init__(self, num_classes, input_channels=0, use_xyz=True):
super().__init__()
self.xyz = None
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
RSCNNSAModuleMSG(
npoint=512,
radii=[0.23],
nsamples=[48],
mlps=[[input_channels, 128]],
first_layer=True,
use_xyz=use_xyz,
))
self.SA_modules.append(
RSCNNSAModuleMSG(
npoint=128,
radii=[0.32],
nsamples=[64],
mlps=[[128, 512]],
use_xyz=False,
last_layer=True,
))
self.SA_modules.append(
# global pooling
RSCNNSAModule(nsample=128, mlp=[512, 1024], use_xyz=False))
self.FC_layer = nn.Sequential(
pt_utils.FC(1024, 512, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5),
pt_utils.FC(512, 256, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5), pt_utils.FC(256, num_classes, activation=None))
def __init__(self, num_classes, input_channels=3):
super().__init__()
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModuleMSG(npoint=512,
radii=[0.1, 0.2, 0.4],
nsamples=[32, 64, 128],
mlps=[[input_channels, 64],
[input_channels, 128],
[input_channels, 128]]))
input_channels = 64 + 128 + 128
self.SA_modules.append(
PointnetSAModuleMSG(npoint=128,
radii=[0.2, 0.4, 0.8],
nsamples=[16, 32, 64],
mlps=[[input_channels, 128],
[input_channels, 256],
[input_channels, 256]]))
self.SA_modules.append(
PointnetSAModule(mlp=[128 + 256 + 256, 256, 512, 1024]))
self.FC_layer = nn.Sequential(
pt_utils.FC(1024, 512, bn=True), nn.Dropout(p=0.5),
pt_utils.FC(512, 256, bn=True), nn.Dropout(p=0.5),
pt_utils.FC(256, num_classes, activation=None))
def __init__(self, num_classes):
super().__init__()
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=512,
radii=[0.2],
nsamples=[32],
mlps=[[6, 64, 64, 128]],
use_xyz=True,
first_layer=True,
))
self.SA_modules.append(
PointnetSAModuleMSG(npoint=128,
radii=[0.4],
nsamples=[64],
mlps=[[128 + 9, 128, 128, 256]],
use_xyz=False,
last_layer=True))
self.SA_modules.append(
# global pooling
PointnetSAModule(nsample=128,
mlp=[256, 256, 512, 1024],
use_xyz=False))
self.FC_layer = nn.Sequential(
pt_utils.FC(1024, 512, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5),
pt_utils.FC(512, 256, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5), pt_utils.FC(256, num_classes, activation=None))
def __init__(self,
num_classes,
input_channels=0,
relation_prior=1,
use_xyz=True):
super().__init__()
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModuleMSG(npoint=512,
e1s=[0.25],
e2s=[0.15],
e3s=[0.15],
nsamples=[48],
mlps=[[input_channels, 128]],
first_layer=True,
use_xyz=use_xyz,
relation_prior=relation_prior))
self.SA_modules.append(
PointnetSAModuleMSG(npoint=128,
e1s=[0.50],
e2s=[0.30],
e3s=[0.30],
nsamples=[64],
mlps=[[128, 512]],
use_xyz=use_xyz,
relation_prior=relation_prior))
self.SA_modules.append(
# global convolutional pooling
PointnetSAModule(nsample=128, mlp=[512, 1024], use_xyz=use_xyz))
self.FC_layer = nn.Sequential(
pt_utils.FC(1024, 512, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5),
pt_utils.FC(512, 256, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5), pt_utils.FC(256, num_classes, activation=None))
def __init__(self, num_classes, input_channels=0, use_xyz=True):
super().__init__()
self.SA_modules = nn.ModuleList()
# stage 1 begin
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=512,
radii=[0.25],
nsamples=[64],
mlps=[[input_channels, 96]],
use_xyz=use_xyz,
pool=True
)
)
# stage 1 end
# stage 2 begin
input_channels = 96
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=128,
radii=[0.32],
nsamples=[64],
mlps=[[input_channels, 93]],
use_xyz=use_xyz,
pool=True
)
)
input_channels = 93
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=128,
radii=[0.39],
nsamples=[16],
mlps=[[input_channels, 96]],
group_number=2,
use_xyz=use_xyz,
after_pool=True
)
)
input_channels = 117
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=128,
radii=[0.39],
nsamples=[16],
mlps=[[input_channels, 96]],
group_number=2,
use_xyz=use_xyz
)
)
input_channels = 141
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=128,
radii=[0.39],
nsamples=[16],
mlps=[[input_channels, 96]],
group_number=2,
use_xyz=use_xyz,
before_pool=True
)
)
# stage 2 end
# global pooling
input_channels = 165
self.SA_modules.append(
PointnetSAModule(
mlp=[input_channels, 512], use_xyz=use_xyz
)
)
self.FC_layer = nn.Sequential(
pt_utils.FC(512, 512, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5),
pt_utils.FC(512, 256, activation=nn.ReLU(inplace=True), bn=True),
nn.Dropout(p=0.5),
pt_utils.FC(256, num_classes, activation=None)
)