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,
nfeatures,
batch_size,
bn_momentum=0.1,
nPoints=1024):
super(PointNetInputEnhanced, self).__init__()
self.nPoints = nPoints
self.nFeatures = nfeatures + 4 # 4 is number of radii used to count the points
self.batch_size = batch_size
self.de_layer = AddNeightboursCount(
max_points=[1024, 1024, 1024, 1024], radii=[0.1, 0.2, 0.4, 0.8])
self.net = Sequential(Conv1d(self.nFeatures, 64, 1, stride=1), ReLU(),
Conv1d(64, 64, 1, stride=1), ReLU(),
Conv1d(64, 128, 1, stride=1), ReLU(),
Conv1d(128, 1024, 1, stride=1), ReLU())
self.sa3_module = GlobalSAModule() # Maxpool
# Classification Layers
self.lin1 = Lin(1024, 512)
self.bn1 = BatchNorm1d(512, momentum=bn_momentum)
self.lin2 = Lin(512, 256)
self.bn2 = 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)
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, bn_momentum=0.1, nPoints=1024):
super(PointNetVanillaClass, self).__init__()
self.nPoints = nPoints
self.nFeatures = nfeatures
self.net = Sequential(Conv1d(nfeatures, 64, 1, stride=1), ReLU(),
Conv1d(64, 64, 1, stride=1), ReLU(),
Conv1d(64, 128, 1, stride=1), ReLU(),
Conv1d(128, 1024, 1, stride=1), ReLU())
self.sa3_module = GlobalSAModule() #Maxpool
#Classification Layers
self.lin1 = Lin(1024, 512)
self.bn1 = BatchNorm1d(512, momentum=bn_momentum)
self.lin2 = Lin(512, 256)
self.bn2 = BatchNorm1d(256, momentum=bn_momentum)
self.lin3 = Lin(256, class_count)
def __init__(self, class_count, n_features=3, num_points=1024):
super(PointNet2MRGLightClass, 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.low_resolution_module = SAModuleMRG(num_points, 256, shared_mpls)
self.readout = GlobalSAModule(MLP([527, 1024, 1024, 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)