def __init__(self,
npoint=None,
radii=None,
nsample=None,
down_conv_nn=None,
bn=True,
activation="LeakyReLU",
use_xyz=True,
**kwargs):
assert len(radii) == len(nsample) == len(down_conv_nn)
super(PointNetMSGDown,
self).__init__(DenseFPSSampler(num_to_sample=npoint),
DenseRadiusNeighbourFinder(radii, nsample),
**kwargs)
self.use_xyz = use_xyz
self.npoint = npoint
self.mlps = nn.ModuleList()
for i in range(len(radii)):
mlp_spec = down_conv_nn[i]
if self.use_xyz:
mlp_spec[0] += 3
self.mlps.append(
pt_utils.SharedMLP(down_conv_nn[i],
bn=bn,
activation=get_activation(activation)))
def __init__(self,
up_conv_nn,
bn=True,
bias=False,
activation="LeakyReLU",
**kwargs):
super(DenseFPModule, self).__init__(None, **kwargs)
self.nn = pt_utils.SharedMLP(up_conv_nn,
bn=bn,
activation=get_activation(activation))
def __init__(self,
nn,
aggr="max",
bn=True,
activation="LeakyReLU",
**kwargs):
super(GlobalDenseBaseModule, self).__init__()
self.nn = pt_utils.SharedMLP(nn,
bn=bn,
activation=get_activation(activation))
if aggr.lower() not in ["mean", "max"]:
raise Exception(
"The aggregation provided is unrecognized {}".format(aggr))
self._aggr = aggr.lower()