def __init__(self, class_count, nfeatures=3):
super(
PointNet2MSGSeg,
self,
).__init__()
self.sa1_module = SAModuleMSG(512, [0.1, 0.2, 0.4], [16, 32, 128], [
MLP([nfeatures, 32, 32, 64]),
MLP([nfeatures, 64, 64, 128]),
MLP([nfeatures, 64, 96, 128])
])
#Because we concat the out of each layer as a feature of each point
nFeaturesL2 = 3 + 64 + 128 + 128
self.sa2_module = SAModuleMSG(128, [0.2, 0.4, 0.8], [32, 64, 128], [
MLP([nFeaturesL2, 64, 64, 128]),
MLP([nFeaturesL2, 128, 128, 256]),
MLP([nFeaturesL2, 128, 128, 256])
])
nFeaturesL3 = 3 + 128 + 256 + 256
self.sa3_module = GlobalSAModule(MLP([nFeaturesL3, 256, 512, 1024]))
self.fp3_module = FPModule(1, MLP([1664, 256, 256]))
self.fp2_module = FPModule(3, MLP([576, 256, 128]))
self.fp1_module = FPModule(3, MLP([128, 128, 128, 128]))
self.lin1 = torch.nn.Linear(128, 128)
self.lin2 = torch.nn.Linear(128, 128)
self.lin3 = torch.nn.Linear(128, class_count)
def __init__(self,
class_count,
n_features=3,
num_points=1024,
sort_pool_k=32):
super(PointNet2MRGSortPoolClass, self).__init__()
nFeaturesL2 = 3 + 128
shared_mpls = [
SAModuleFullPoint(0.4, 16, MLP([n_features, 64, 64, 128])),
SAModuleFullPoint(0.9, 32, MLP([nFeaturesL2, 128, 128, 256]))
]
# The mpls are shared to lower the model memory footprint
self.high_resolution_module = SAModuleMRG(num_points, 512, shared_mpls)
self.mid_resolution_module = SAModuleMRG(num_points, 256, shared_mpls)
self.low_resolution_module = SAModuleMRG(num_points, 128, shared_mpls)
self.readout = GlobalSortPool(MLP([789, 1024, 1024, 1024]),
k=sort_pool_k)
# Classification Layers
sort_pool_out = 1024 * sort_pool_k
self.lin1 = Lin(sort_pool_out, 512)
self.bn1 = nn.BatchNorm1d(512)
self.lin2 = Lin(512, 256)
self.bn2 = nn.BatchNorm1d(256)
self.lin3 = Lin(256, class_count)
def __init__(self, class_count, n_feature=3, sort_pool_k=32):
super(PointNet2MSGSortPoolSeg, self).__init__()
self.sa1_module = SAModuleMSG(512, [0.1,0.2,0.4], [16,32,128], [
MLP([n_feature, 32,32,64]),
MLP([n_feature, 64,64,128]),
MLP([n_feature, 64,96,128])
])
#Because we concat the outout of each layer as a feature of each point
n_features_l2 = 3 + 64 + 128 + 128
self.sa2_module = SAModuleMSG(128, [0.2,0.4,0.8], [32,64,128], [
MLP([n_features_l2, 64, 64, 128]),
MLP([n_features_l2, 128, 128, 256]),
MLP([n_features_l2, 128, 128, 256])
])
n_features_l3 = 3 + 128 + 256 + 256
self.sa3_module = GlobalSortPool(MLP([n_features_l3, 256, 512, 1024]), sort_pool_k)
#Segmentation Layers
classification_point_feature = 1024*sort_pool_k
self.fp3_module = FPLayer(1, MLP([classification_point_feature + 256, 256, 256]))
self.fp2_module = FPLayer(3, MLP([256 + 128, 256, 128]))
self.fp1_module = FPLayer(3, MLP([128, 128, 128, 128]))
self.lin1 = Lin(128, 128)
self.bn1 = nn.BatchNorm1d(128)
self.lin2 = Lin(128, 128)
self.bn2 = nn.BatchNorm1d(128)
self.lin3 = Lin(128, class_count)
def __init__(self, class_count, bn_momentum=0.1):
super(PointNet2Class, self).__init__()
self.sa1_module = SAModule(512, 0.2, 32, MLP([3, 64, 64, 128]))
self.sa2_module = SAModule(128, 0.4, 64, MLP([128 + 3, 128, 128, 256]))
self.sa3_module = GlobalSAModule(MLP([256 + 3, 256, 512, 1024]))
#Classification Layers
self.lin1 = Lin(1024, 512)
self.bn1 = nn.BatchNorm1d(512, momentum=bn_momentum)
self.lin2 = Lin(512, 256)
self.bn2 = nn.BatchNorm1d(256, momentum=bn_momentum)
self.lin3 = Lin(256, class_count)
def __init__(self, class_count, nfeatures=3):
super(PointNet2MSGClass, self).__init__()
self.sa1_module = SAModuleMSG(512, [0.1, 0.2, 0.4], [16, 32, 128], [
MLP([nfeatures, 32, 32, 64]),
MLP([nfeatures, 64, 64, 128]),
MLP([nfeatures, 64, 96, 128])
])
#Because we concat the out of each layer as a feature of each point
nFeaturesL2 = 3 + 64 + 128 + 128
self.sa2_module = SAModuleMSG(128, [0.2, 0.4, 0.8], [32, 64, 128], [
MLP([nFeaturesL2, 64, 64, 128]),
MLP([nFeaturesL2, 128, 128, 256]),
MLP([nFeaturesL2, 128, 128, 256])
])
nFeaturesL3 = 3 + 128 + 256 + 256
self.sa3_module = GlobalSAModule(MLP([nFeaturesL3, 256, 512, 1024]))
#Classification Layers
self.lin1 = Lin(1024, 512)
self.bn1 = nn.BatchNorm1d(512)
self.lin2 = Lin(512, 256)
self.bn2 = nn.BatchNorm1d(256)
self.lin3 = Lin(256, class_count)