def __init__(self):
super(get_model, self).__init__()
self.part_num = 3
self.resolution = 150
self.dimension = 3
self.reps = 2 #Conv block repetition factor
self.m = 32 #Unet number of features
self.nPlanes = [
self.m, 2 * self.m, 3 * self.m, 4 * self.m, 5 * self.m
] #UNet number of features per level
self.sparseModel = scn.Sequential().add(
scn.InputLayer(
self.dimension,
torch.LongTensor([self.resolution * 8 + 15] * 3),
mode=3)).add(
scn.SubmanifoldConvolution(
self.dimension, 1, self.m, 3, False)).add(
scn.FullyConvolutionalNet(
self.dimension,
self.reps,
self.nPlanes,
residual_blocks=False,
downsample=[3, 2])).add(
scn.BatchNormReLU(sum(self.nPlanes))).add(
scn.OutputLayer(self.dimension))
self.nc = 64
self.linear = nn.Linear(sum(self.nPlanes), self.nc)
self.convs1 = torch.nn.Conv1d(self.nc * 3, 128, 1)
self.convs2 = torch.nn.Conv1d(128, 64, 1)
self.convs3 = torch.nn.Conv1d(64, self.part_num, 1)
self.bns1 = nn.BatchNorm1d(128)
self.bns2 = nn.BatchNorm1d(64)
def __init__(self):
nn.Module.__init__(self)
self.sparseModel = scn.Sequential().add(
scn.InputLayer(dimension, data.spatialSize, mode=3)).add(
scn.SubmanifoldConvolution(dimension, 1, m, 3, False)).add(
scn.FullyConvolutionalNet(
dimension,
reps,
nPlanes,
residual_blocks=False,
downsample=[3, 2])).add(scn.BatchNormReLU(
sum(nPlanes))).add(scn.OutputLayer(dimension))
self.linear = nn.Linear(sum(nPlanes), data.nClassesTotal)
def __init__(self):
nn.Module.__init__(self)
self.sparseModel = scn.Sequential().add(
scn.InputLayer(dimension, full_scale, mode=4)).add(
scn.SubmanifoldConvolution(dimension, 3, m, 3, False)).add(
scn.FullyConvolutionalNet(
dimension, block_reps,
[m, 2 * m, 3 * m, 4 * m, 5 * m, 6 * m, 7 * m],
residual_blocks)).add(scn.BatchNormReLU(448)).add(
scn.OutputLayer(dimension))
self.linearx = nn.Linear(448, 448)
for _, para in enumerate(self.sparseModel.parameters()):
para.requires_grad = False
def __init__(self, nf_in, nf, nf_out, max_data_size):
nn.Module.__init__(self)
data_dim = 3
self.p0 = scn.InputLayer(data_dim, max_data_size, mode=0)
self.p1 = scn.SubmanifoldConvolution(data_dim,
nf_in,
nf,
filter_size=FSIZE0,
bias=False)
self.p2 = scn.FullyConvolutionalNet(
data_dim, reps=1, nPlanes=[nf, nf, nf], residual_blocks=True
) #nPlanes=[nf, nf*2, nf*2], residual_blocks=True)
self.p3 = scn.BatchNormReLU(nf * 3)
self.p4 = scn.OutputLayer(data_dim)
self.linear = nn.Linear(nf * 3, nf_out)
def __init__(self,
nf_in,
nf,
pass_occ,
pass_feats,
max_data_size,
truncation=3):
nn.Module.__init__(self)
data_dim = 3
self.pass_occ = pass_occ
self.pass_feats = pass_feats
self.nf_in = nf_in
self.nf = nf
self.truncation = truncation
self.p0 = scn.InputLayer(data_dim, max_data_size, mode=0)
self.p1 = scn.SubmanifoldConvolution(data_dim,
nf_in,
nf,
filter_size=FSIZE0,
bias=False)
self.p2 = scn.FullyConvolutionalNet(data_dim,
reps=1,
nPlanes=[nf, nf, nf],
residual_blocks=True)
self.p3 = scn.BatchNormReLU(nf * 3)
self.p4 = scn.OutputLayer(data_dim)
# upsampled
self.n0 = scn.InputLayer(data_dim, max_data_size, mode=0)
self.n1 = scn.SubmanifoldConvolution(data_dim,
nf * 3,
nf,
filter_size=FSIZE0,
bias=False)
self.n2 = scn.BatchNormReLU(nf)
self.n3 = scn.OutputLayer(data_dim)
self.linear = nn.Linear(nf, 1)
self.linearsdf = nn.Linear(nf, 1)
