def __init__(self,
input_feature_dim=0,
scale=1,
use_mlp=False,
mlp_dim=None):
super().__init__()
self.use_mlp = use_mlp
self.sa1 = PointnetSAModuleVotes(
npoint=2048,
radius=0.2,
nsample=64,
mlp=[input_feature_dim, 64 * scale, 64 * scale, 128 * scale],
use_xyz=True,
normalize_xyz=True)
self.sa2 = PointnetSAModuleVotes(
npoint=1024,
radius=0.4,
nsample=32,
mlp=[128 * scale, 128 * scale, 128 * scale, 256 * scale],
use_xyz=True,
normalize_xyz=True)
self.sa3 = PointnetSAModuleVotes(
npoint=512,
radius=0.8,
nsample=16,
mlp=[256 * scale, 128 * scale, 128 * scale, 256 * scale],
use_xyz=True,
normalize_xyz=True)
self.sa4 = PointnetSAModuleVotes(
npoint=256,
radius=1.2,
nsample=16,
mlp=[256 * scale, 128 * scale, 128 * scale, 256 * scale],
use_xyz=True,
normalize_xyz=True)
if scale == 1:
self.fp1 = PointnetFPModule(mlp=[256 + 256, 512, 512])
self.fp2 = PointnetFPModule(mlp=[512 + 256, 512, 512])
else:
self.fp1 = PointnetFPModule(
mlp=[256 * scale + 256 * scale, 256 * scale, 256 * scale])
self.fp2 = PointnetFPModule(
mlp=[256 * scale + 256 * scale, 256 * scale, 256 * scale])
if use_mlp:
self.head = MLP(mlp_dim)
self.all_feat_names = [
"sa1",
"sa2",
"sa3",
"sa4",
"fp1",
"fp2",
]
def __init__(self, input_feature_dim=0):
super().__init__()
self.sa1 = PointnetSAModuleVotes(npoint=2048,
radius=0.2,
nsample=64,
mlp=[input_feature_dim, 64, 64, 128],
use_xyz=True,
normalize_xyz=True)
self.sa2 = PointnetSAModuleVotes(npoint=1024,
radius=0.4,
nsample=32,
mlp=[128, 128, 128, 256],
use_xyz=True,
normalize_xyz=True)
self.sa3 = PointnetSAModuleVotes(npoint=512,
radius=0.8,
nsample=16,
mlp=[256, 128, 128, 256],
use_xyz=True,
normalize_xyz=True)
self.sa4 = PointnetSAModuleVotes(npoint=256,
radius=1.2,
nsample=16,
mlp=[256, 128, 128, 256],
use_xyz=True,
normalize_xyz=True)
self.fp1 = PointnetFPModule(mlp=[256 + 256, 256, 256])
self.fp2 = PointnetFPModule(mlp=[256 + 256, 256, 256])
def __init__(self, num_classes, input_channels=3):
super().__init__()
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModule(npoint=1024,
radius=0.1,
nsample=32,
mlp=[input_channels, 32, 32, 64]))
self.SA_modules.append(
PointnetSAModule(npoint=256,
radius=0.2,
nsample=32,
mlp=[64, 64, 64, 128]))
self.SA_modules.append(
PointnetSAModule(npoint=64,
radius=0.4,
nsample=32,
mlp=[128, 128, 128, 256]))
self.SA_modules.append(
PointnetSAModule(npoint=16,
radius=0.8,
nsample=32,
mlp=[256, 256, 256, 512]))
self.FP_modules = nn.ModuleList()
self.FP_modules.append(
PointnetFPModule(mlp=[128 + input_channels, 128, 128, 128]))
self.FP_modules.append(PointnetFPModule(mlp=[256 + 64, 256, 128]))
self.FP_modules.append(PointnetFPModule(mlp=[256 + 128, 256, 256]))
self.FP_modules.append(PointnetFPModule(mlp=[512 + 256, 256, 256]))
self.FC_layer = nn.Sequential(
pt_utils.Conv1d(128, 128, bn=True), nn.Dropout(),
pt_utils.Conv1d(128, num_classes, activation=None))
def __init__(self, num_classes, input_channels=9, use_xyz=True):
super().__init__()
self.SA_modules = nn.ModuleList()
c_in = input_channels
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=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]],
))
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]],
))
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]],
))
c_out_3 = 512 + 512
self.FP_modules = nn.ModuleList()
self.FP_modules.append(
PointnetFPModule(
mlp=[256 + (input_channels if use_xyz else 0), 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_layer = nn.Sequential(
pt_utils.Conv1d(128, 128, bn=True), nn.Dropout(),
pt_utils.Conv1d(128, num_classes, activation=None))
def __init__(self, input_feature_dim=0, SCALE=1):
super().__init__()
self.sa1 = PointnetSAModuleVotes(
npoint=2048,
radius=0.2,
nsample=64,
mlp=[0, 64*SCALE, 64*SCALE, 128*SCALE],
use_xyz=True,
normalize_xyz=True
)
self.sa2 = PointnetSAModuleVotes(
npoint=1024,
radius=0.4,
nsample=32,
mlp=[128*SCALE, 128*SCALE, 128*SCALE, 256*SCALE],
use_xyz=True,
normalize_xyz=True
)
self.sa3 = PointnetSAModuleVotes(
npoint=512,
radius=0.8,
nsample=16,
mlp=[256*SCALE, 128*SCALE, 128*SCALE, 256*SCALE],
use_xyz=True,
normalize_xyz=True
)
self.sa4 = PointnetSAModuleVotes(
npoint=256,
radius=1.2,
nsample=16,
mlp=[256*SCALE, 128*SCALE, 128*SCALE, 256*SCALE],
use_xyz=True,
normalize_xyz=True
)
if SCALE == 1:
self.fp1 = PointnetFPModule(mlp=[256+256,512,512])
self.fp2 = PointnetFPModule(mlp=[512+256,512,512])
else:
self.fp1 = PointnetFPModule(mlp=[256*SCALE+256*SCALE,256*SCALE,256*SCALE])
self.fp2 = PointnetFPModule(mlp=[256*SCALE+256*SCALE,256*SCALE,256*SCALE])
def __init__(self, input_feature_dim=0, width=1, depth=2):
super().__init__()
self.depth = depth
self.width = width
self.sa1 = PointnetSAModuleVotes(
npoint=2048,
radius=0.2,
nsample=64,
mlp=[input_feature_dim] + [64 * width for i in range(depth)] + [128 * width],
use_xyz=True,
normalize_xyz=True
)
self.sa2 = PointnetSAModuleVotes(
npoint=1024,
radius=0.4,
nsample=32,
mlp=[128 * width] + [128 * width for i in range(depth)] + [256 * width],
use_xyz=True,
normalize_xyz=True
)
self.sa3 = PointnetSAModuleVotes(
npoint=512,
radius=0.8,
nsample=16,
mlp=[256 * width] + [128 * width for i in range(depth)] + [256 * width],
use_xyz=True,
normalize_xyz=True
)
self.sa4 = PointnetSAModuleVotes(
npoint=256,
radius=1.2,
nsample=16,
mlp=[256 * width] + [128 * width for i in range(depth)] + [256 * width],
use_xyz=True,
normalize_xyz=True
)
self.fp1 = PointnetFPModule(mlp=[256 * width + 256 * width, 256 * width, 256 * width])
self.fp2 = PointnetFPModule(mlp=[256 * width + 256 * width, 256 * width, 288])
def __init__(self, num_classes):
super().__init__()
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=512,
radii=[0.2],
nsamples=[64],
mlps=[[6, 64, 64, 128]],
first_layer=True,
use_xyz=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,
)
)
# global pooling
self.SA_modules.append(
PointnetSAModule(
nsample=128,
mlp=[256, 256, 512, 1024],
use_xyz=False
)
)
self.FP_modules = nn.ModuleList()
self.FP_modules.append(PointnetFPModule(mlp=[128, 128, 128, 128]))
self.FP_modules.append(PointnetFPModule(mlp=[384, 256, 128]))
self.FP_modules.append(PointnetFPModule(mlp=[1280, 256, 256]))
self.FC_layer = nn.Sequential(
pt_utils.Conv1d(128, 128, bn=True), nn.Dropout(),
pt_utils.Conv1d(128, num_classes, activation=None)
)
def __init__(self, num_classes, input_channels=3, use_xyz=True, bn=True):
super().__init__()
NPOINTS = [1024, 256, 64, 16]
RADIUS = [[0.05, 0.1], [0.1, 0.2], [0.2, 0.4], [0.4, 0.8]]
NSAMPLE = [[16, 32], [16, 32], [16, 32], [16, 32]]
MLPS = [[[16, 16, 32], [32, 32, 64]], [[64, 64, 128], [64, 96, 128]],
[[128, 196, 256], [128, 196, 256]],
[[256, 256, 512], [256, 384, 512]]]
FP_MLPS = [[128, 128], [256, 256], [512, 512], [512, 512]]
CLS_FC = [128]
DP_RATIO = 0.5
self.SA_modules = nn.ModuleList()
channel_in = input_channels
skip_channel_list = [input_channels]
for k in range(NPOINTS.__len__()):
mlps = MLPS[k].copy()
channel_out = 0
for idx in range(mlps.__len__()):
mlps[idx] = [channel_in] + mlps[idx]
channel_out += mlps[idx][-1]
self.SA_modules.append(
PointnetSAModuleMSG(npoint=NPOINTS[k],
radii=RADIUS[k],
nsamples=NSAMPLE[k],
mlps=mlps,
use_xyz=use_xyz,
bn=bn))
skip_channel_list.append(channel_out)
channel_in = channel_out
self.FP_modules = nn.ModuleList()
for k in range(FP_MLPS.__len__()):
pre_channel = FP_MLPS[
k + 1][-1] if k + 1 < len(FP_MLPS) else channel_out
self.FP_modules.append(
PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] +
FP_MLPS[k],
bn=bn))
cls_layers = []
pre_channel = FP_MLPS[0][-1]
for k in range(0, CLS_FC.__len__()):
cls_layers.append(pt_utils.Conv1d(pre_channel, CLS_FC[k], bn=bn))
pre_channel = CLS_FC[k]
cls_layers.append(
pt_utils.Conv1d(pre_channel, num_classes, activation=None, bn=bn))
cls_layers.insert(1, nn.Dropout(DP_RATIO))
self.cls_layer = nn.Sequential(*cls_layers)
def __init__(self, num_classes, input_channels=3, use_xyz=True, bn=True):
super().__init__()
self.SA_modules = nn.ModuleList()
channel_in = input_channels
skip_channel_list = [input_channels]
for k in range(NPOINTS.__len__()):
mlps = MLPS[k].copy()
channel_out = 0
for idx in range(mlps.__len__()):
mlps[idx] = [channel_in] + mlps[idx]
channel_out += mlps[idx][-1]
self.SA_modules.append(
PointnetSAModuleMSG(npoint=NPOINTS[k],
radii=RADIUS[k],
nsamples=NSAMPLE[k],
mlps=mlps,
use_xyz=use_xyz,
bn=bn))
skip_channel_list.append(channel_out)
channel_in = channel_out
self.FP_modules = nn.ModuleList()
for k in range(FP_MLPS.__len__()):
pre_channel = FP_MLPS[
k + 1][-1] if k + 1 < len(FP_MLPS) else channel_out
self.FP_modules.append(
PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] +
FP_MLPS[k],
bn=bn))
cls_layers = []
pre_channel = FP_MLPS[0][-1]
for k in range(0, CLS_FC.__len__()):
cls_layers.append(pt_utils.Conv1d(pre_channel, CLS_FC[k], bn=bn))
pre_channel = CLS_FC[k]
cls_layers.append(
pt_utils.Conv1d(pre_channel, num_classes, activation=None, bn=bn))
cls_layers.insert(1, nn.Dropout(0.5))
self.cls_layer = nn.Sequential(*cls_layers)
def _build_model(self):
self.SA_modules = nn.ModuleList()
for i in range(len(self.npoints)):
self.SA_modules.append(
PointnetSAModule(
npoint=self.npoints[i],
radius=self.radii[i], # note: radius-radii
nsample=self.nsamples[i],
mlp=self.mlps[i],
use_xyz=self.use_xyz))
self.FP_modules = nn.ModuleList()
for i in range(len(self.mlps_fp)):
self.FP_modules.append(PointnetFPModule(mlp=self.mlps_fp[i]))
self.fc_layer = nn.Sequential(
nn.Conv1d(128, 128, kernel_size=1, bias=False),
nn.BatchNorm1d(128),
nn.ReLU(True),
nn.Dropout(0.5),
nn.Conv1d(128, self.num_classes, kernel_size=1),
)
def __init__(self, num_classes, input_channels=0, relation_prior=1, use_xyz=True):
super().__init__()
self.SA_modules = nn.ModuleList()
c_in = input_channels
self.SA_modules.append( # 0
PointnetSAModuleMSG(
npoint=1024,
radii=[0.075, 0.1, 0.125],
nsamples=[16, 32, 48],
mlps=[[c_in, 64], [c_in, 64], [c_in, 64]],
first_layer=True,
use_xyz=use_xyz,
relation_prior=relation_prior
)
)
c_out_0 = 64*3
c_in = c_out_0
self.SA_modules.append( # 1
PointnetSAModuleMSG(
npoint=256,
radii=[0.1, 0.15, 0.2],
nsamples=[16, 48, 64],
mlps=[[c_in, 128], [c_in, 128], [c_in, 128]],
use_xyz=use_xyz,
relation_prior=relation_prior
)
)
c_out_1 = 128*3
c_in = c_out_1
self.SA_modules.append( # 2
PointnetSAModuleMSG(
npoint=64,
radii=[0.2, 0.3, 0.4],
nsamples=[16, 32, 48],
mlps=[[c_in, 256], [c_in, 256], [c_in, 256]],
use_xyz=use_xyz,
relation_prior=relation_prior
)
)
c_out_2 = 256*3
c_in = c_out_2
self.SA_modules.append( # 3
PointnetSAModuleMSG(
npoint=16,
radii=[0.4, 0.6, 0.8],
nsamples=[16, 24, 32],
mlps=[[c_in, 512], [c_in, 512], [c_in, 512]],
use_xyz=use_xyz,
relation_prior=relation_prior
)
)
c_out_3 = 512*3
self.SA_modules.append( # 4 global pooling
PointnetSAModule(
nsample = 16,
mlp=[c_out_3, 128], use_xyz=use_xyz
)
)
global_out = 128
self.SA_modules.append( # 5 global pooling
PointnetSAModule(
nsample = 64,
mlp=[c_out_2, 128], use_xyz=use_xyz
)
)
global_out2 = 128
self.FP_modules = nn.ModuleList()
self.FP_modules.append(
PointnetFPModule(mlp=[256 + input_channels, 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.context_prior=cp.Context(128+global_out+global_out2+16,128+global_out+global_out2+16)
self.FC_layer = nn.Sequential(
pt_utils.Conv1d((128+global_out+global_out2+16)*3, 128, bn=True), nn.Dropout(),
pt_utils.Conv1d(128, num_classes, activation=None)
)