def __init__(self,
in_channels,
out_channels,
mesh_file,
stride=1,
bias=True):
assert stride in [1, 2]
super(_MeshConv, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.ncoeff = 4
self.coeffs = Parameter(
torch.Tensor(out_channels, in_channels, self.ncoeff))
self.set_coeffs()
# load mesh file
pkl = pickle.load(open(mesh_file, "rb"))
self.pkl = pkl
self.nv = self.pkl['V'].shape[0]
G = sparse2tensor(pkl['G']) # gradient matrix V->F, 3#F x #V
NS = torch.tensor(
pkl['NS'], dtype=torch.float32) # north-south vector field, #F x 3
EW = torch.tensor(
pkl['EW'], dtype=torch.float32) # east-west vector field, #F x 3
self.register_buffer("G", G)
self.register_buffer("NS", NS)
self.register_buffer("EW", EW)
def __init__(self,
in_channels,
out_channels,
mesh_file,
stride=2,
bias=True):
assert (stride == 2)
super(MeshConv_transpose, self).__init__(in_channels, out_channels,
mesh_file, stride, bias)
pkl = self.pkl
self.nv_prev = self.pkl['nv_prev']
self.nv_pad = self.nv - self.nv_prev
L = sparse2tensor(pkl['L'].tocoo()) # laplacian matrix V->V
F2V = sparse2tensor(pkl['F2V'].tocoo()) # F->V, #V x #F
self.register_buffer("L", L)
self.register_buffer("F2V", F2V)
def __init__(self,
in_channels,
out_channels,
mesh_file,
stride=1,
bias=True):
super(MeshConv, self).__init__(in_channels, out_channels, mesh_file,
stride, bias)
pkl = self.pkl
if stride == 2:
self.nv_prev = pkl['nv_prev']
L = sparse2tensor(pkl['L'].tocsr()
[:self.nv_prev].tocoo()) # laplacian matrix V->V
F2V = sparse2tensor(
pkl['F2V'].tocsr()[:self.nv_prev].tocoo()) # F->V, #V x #F
else: # stride == 1
self.nv_prev = pkl['V'].shape[0]
L = sparse2tensor(pkl['L'].tocoo())
F2V = sparse2tensor(pkl['F2V'].tocoo())
self.register_buffer("L", L)
self.register_buffer("F2V", F2V)