def build_pointnet(self, cfg):
"""Copy channel list because PointNet modifies it in-place."""
pnet = PointnetSAModuleMSG(
npoint=-1, radii=cfg.GRIDPOOL.RADII_PN,
nsamples=cfg.SAMPLES_PN,
mlps=deepcopy(cfg.GRIDPOOL.MLPS_PN), use_xyz=True,
)
return pnet
def build_pointnet(self, cfg):
"""Copy channel list because PointNet modifies it in-place."""
pnet = PointnetSAModuleMSG(
npoint=-1, radii=cfg.gridpool_radii,
nsamples=cfg.gridpool_samples,
mlps=cfg.gridpool_mlps.copy(), use_xyz=True,
)
return pnet
def build_pointnets(self, cfg):
"""Copy list because PointNet modifies it in-place."""
pnets = []
for i, mlps in enumerate(cfg.PSA.MLPS):
pnets += [PointnetSAModuleMSG(
npoint=-1, radii=cfg.PSA.RADII[i],
nsamples=cfg.SAMPLES_PN,
mlps=deepcopy(mlps), use_xyz=True,
)]
return nn.Sequential(*pnets)
def __init__(self, input_channels=6, do_interpolation=False):
super().__init__()
print('PNv2_ASAP-1')
self.do_interpolation = do_interpolation
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=True,
bn=True))
skip_channel_list.append(channel_out)
channel_in = channel_out
self.tem_sa1 = PointnetSAModule(mlp=MLP1,
npoint=NPOINT1,
nsample=NSAMPLE1,
radius=RADIUS1)
self.fuse_layer1_atten = nn.Conv1d(MLP1[-1] * 2,
2,
kernel_size=1,
stride=1)
self.fuse_layer1 = nn.Conv1d(MLP1[-1], 384, kernel_size=1, stride=1)
self.tem_fp1 = PointnetFPModule(mlp=FP_MLP1)
self.tem_dropout = nn.Dropout(DP_RATIO)
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]))
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=True))
pre_channel = CLS_FC[k]
cls_layers.append(pt_utils.Conv1d(pre_channel, 20, activation=None))
cls_layers.insert(1, nn.Dropout(0.5))
self.cls_layer = nn.Sequential(*cls_layers)
def build_pointnets(self, cfg):
pnets = []
for i in range(len(cfg.strides)):
pnets += [
PointnetSAModuleMSG(
npoint=-1,
radii=cfg.radii[i],
nsamples=cfg.nsamples[i],
mlps=cfg.mlps[i],
use_xyz=True,
)
]
return nn.Sequential(*pnets)
def build_pointnets(self, cfg):
"""Copy channel list because PointNet modifies it in-place."""
pnets = []
for i in range(len(cfg.mlps)):
pnets += [
PointnetSAModuleMSG(
npoint=-1,
radii=cfg.radii[i],
nsamples=cfg.nsamples[i],
mlps=cfg.mlps[i].copy(),
use_xyz=True,
)
]
return nn.Sequential(*pnets)
def __init__(self, num_classes, input_channels=3, use_xyz=True):
super(Pointnet2MSG, self).__init__()
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=512,
radii=[0.1, 0.2, 0.4],
nsamples=[16, 32, 128],
mlps=[
[input_channels, 32, 32, 64],
[input_channels, 64, 64, 128],
[input_channels, 64, 96, 128],
],
use_xyz=use_xyz,
))
input_channels = 64 + 128 + 128
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=128,
radii=[0.2, 0.4, 0.8],
nsamples=[32, 64, 128],
mlps=[
[input_channels, 64, 64, 128],
[input_channels, 128, 128, 256],
[input_channels, 128, 128, 256],
],
use_xyz=use_xyz,
))
self.SA_modules.append(
PointnetSAModule(mlp=[128 + 256 + 256, 256, 512, 1024],
use_xyz=use_xyz))
self.FC_layer = (pt_utils.Seq(1024).fc(512, bn=True).dropout(0.5).fc(
256, bn=True).dropout(0.5).fc(num_classes, activation=None))
def __init__(self, cfg):
"""
TODO: Read Pointnet params from config object.
"""
super(PV_RCNN, self).__init__()
self.pnet = PointnetSAModuleMSG(
npoint=-1, radii=[0.1, 0.5], nsamples=[16, 32],
mlps=[[16, 32, 64], [16, 32, 128]], use_xyz=True,
)
self.voxel_generator = spconv.utils.VoxelGenerator(
voxel_size=cfg.voxel_size,
point_cloud_range=cfg.grid_bounds,
max_voxels=cfg.max_voxels,
max_num_points=cfg.max_num_points,
)
grid_shape = np.r_[self.voxel_generator.grid_size[::-1]] + [1, 0, 0]
self.cnn = CNN_3D(C_in=cfg.C_in, grid_shape=grid_shape, cfg=cfg)
self.cfg = cfg
def __init__(self, input_channels=6):
super().__init__()
self.SA_modules = nn.ModuleList()
channel_in = input_channels
skip_channel_list = [input_channels]
for k in range(NPOINTS.__len__()): # k = 0,1,2,3
mlps = MLPS[k].copy() # mlps=MLPS[0], MLPS[1], MLPS[2], MLPS[3]
channel_out = 0
for idx in range(mlps.__len__()): # idx = 0,1
mlps[idx] = [channel_in] + mlps[
idx] #mlps=[0] = [0, 16, 16, 32] mlps[1] = [0, 32, 32, 64]
channel_out += mlps[idx][-1] # channel_out = 32+64 = 96
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=NPOINTS[k],
radii=RADIUS[k],
nsamples=NSAMPLE[k],
mlps=mlps, #[[0, 16, 16, 32], [0, 32, 32, 64]]
use_xyz=True,
bn=True))
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]))
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=True))
pre_channel = CLS_FC[k]
cls_layers.append(pt_utils.Conv1d(pre_channel, 1, activation=None))
cls_layers.insert(1, nn.Dropout(0.5))
self.cls_layer = nn.Sequential(*cls_layers)
def __init__(self,
input_channels=6,
out_dim=32,
npoints=[4096, 1024, 256, 64],
radius=[[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]]):
super().__init__()
NPOINTS = npoints
RADIUS = radius
FP_MLPS[0] = [out_dim, out_dim]
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=True,
bn=True))
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]))
'''
