def __init__(self):
super(Net_D, self).__init__()
self.sa1_module = SAModule(0.5, 0.1, MLP([3 + 3, 64, 64, 128]))
self.sa2_module = SAModule(0.5, 0.5, MLP([128 + 3, 128, 128, 256]))
self.lin1 = torch.nn.Linear(256, 128)
self.lin3 = torch.nn.Linear(128, 1)
def __init__(self, num_classes):
super(Net, self).__init__()
self.sa1_module = SAModule(0.2, 0.2, MLP([3, 64, 64, 128]))
self.sa2_module = SAModule(0.25, 0.4, MLP([128 + 3, 128, 128, 256]))
self.sa3_module = GlobalSAModule(MLP([256 + 3, 256, 512, 1024]))
self.fp3_module = FPModule(1, MLP([1024 + 256, 256, 256]))
self.fp2_module = FPModule(3, MLP([256 + 128, 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, num_classes)
def __init__(self, num_classes):
super(Net, self).__init__()
# Input channels account for both `pos` and node features.
self.sa1_module = SAModule(0.2, 0.2, MLP([3 + 3, 64, 64, 128]))
self.sa2_module = SAModule(0.25, 0.4, MLP([128 + 3, 128, 128, 256]))
self.sa3_module = GlobalSAModule(MLP([256 + 3, 256, 512, 1024]))
self.fp3_module = FPModule(1, MLP([1024 + 256, 256, 256]))
self.fp2_module = FPModule(3, MLP([256 + 128, 256, 128]))
self.fp1_module = FPModule(3, MLP([128 + 3, 128, 128, 128]))
self.lin1 = torch.nn.Linear(128, 128)
self.lin2 = torch.nn.Linear(128, 128)
self.lin3 = torch.nn.Linear(128, num_classes)