def graph_conv_cheby(self, x, cl, L, lmax, Fout, K):
# parameters
# B = batch size
# V = nb vertices
# Fin = nb input features
# Fout = nb output features
# K = Chebyshev order & support size
B, V, Fin = x.size()
B, V, Fin = int(B), int(V), int(Fin)
# rescale Laplacian
# lmax = lmax_L(L)
L = rescale_L(L, lmax)
# convert scipy sparse matric L to pytorch
L = L.tocoo()
indices = np.column_stack((L.row, L.col)).T
indices = indices.astype(np.int64)
indices = torch.from_numpy(indices)
indices = indices.type(torch.LongTensor)
L_data = L.data.astype(np.float32)
L_data = torch.from_numpy(L_data)
L_data = L_data.type(torch.FloatTensor)
L = torch.sparse.FloatTensor(indices, L_data, torch.Size(L.shape))
L = Variable(L, requires_grad=False)
if torch.cuda.is_available():
L = L.cuda()
# transform to Chebyshev basis
x0 = x.permute(1, 2, 0).contiguous() # V x Fin x B ### 928*1*100
x0 = x0.view([V, Fin * B]) # V x Fin*B ### 928*100
x = x0.unsqueeze(0) # 1 x V x Fin*B ### 1*928*100
def concat(x, x_):
x_ = x_.unsqueeze(0) # 1 x V x Fin*B
return torch.cat((x, x_), 0) # K x V x Fin*B
if K > 1:
x1 = my_sparse_mm()(L, x0) # V x Fin*B ### 928*100
x = torch.cat((x, x1.unsqueeze(0)),
0) # 2 x V x Fin*B ### 2*928*100
for k in range(2, K):
x2 = 2 * my_sparse_mm()(L, x1) - x0 ### 928*100
x = torch.cat((x, x2.unsqueeze(0)), 0) # M x Fin*B ### 3*928*100
x0, x1 = x1, x2
x = x.view([K, V, Fin, B]) # K x V x Fin x B ### 25*928*1*100
x = x.permute(3, 1, 2,
0).contiguous() # B x V x Fin x K ### 100*928*1*25
x = x.view([B * V, Fin * K]) # B*V x Fin*K ### 92800*25
# Compose linearly Fin features to get Fout features
x = cl(x) # B*V x Fout ### 92800*32
x = x.view([B, V, Fout]) # B x V x Fout ### 100*928*32
return x
def graph_conv_cheby(self, x, cl, L, lmax, Fout, K):
# parameters
# B = batch size
# V = number of vertices
# Fin = number of input features
# Fout = number of output features
# K = Chebyshev order & support size
B, V, Fin = x.size()
B, V, Fin = int(B), int(V), int(Fin)
# rescale Laplacian
lmax = lmax_L(L)
L = rescale_L(L, lmax)
# convert scipy sparse matrix L to pytorch
L = L.tocoo()
indices = np.column_stack((L.row, L.col)).T
indices = indices.astype(np.int64)
indices = torch.from_numpy(indices)
indices = indices.type(torch.LongTensor)
L_data = L.data.astype(np.float32)
L_data = torch.from_numpy(L_data)
L_data = L_data.type(torch.FloatTensor)
L = torch.sparse.FloatTensor(indices, L_data, torch.Size(L.shape))
L = L.requires_grad_(False).to(args.device)
# transform to Chebyshev basis
x0 = x.permute(1, 2, 0).contiguous() # V x Fin x B
x0 = x0.view([V, Fin * B]) # V x Fin*B
x = x0.unsqueeze(0) # 1 x V x Fin*B
if K > 1:
x1 = my_sparse_mm()(L, x0) # V x Fin*B
x = torch.cat((x, x1.unsqueeze(0)), 0) # 2 x V x Fin*B
for k in range(2, K):
x2 = 2 * my_sparse_mm()(L, x1) - x0
x = torch.cat((x, x2.unsqueeze(0)), 0) # M x Fin*B
x0, x1 = x1, x2
x = x.view([K, V, Fin, B]) # K x V x Fin x B
x = x.permute(3, 1, 2, 0).contiguous() # B x V x Fin x K
x = x.view([B * V, Fin * K]) # B*V x Fin*K
# Compose linearly Fin features to get Fout features
x = cl(x) # B*V x Fout
x = x.view([B, V, Fout]) # B x V x Fout
return x
def graph_conv_cheby(self, x, cl, L, Fout, K):
# parameters
# B = batch size
# V = nb vertices
# Fin = nb input features
# Fout = nb output features
# K = Chebyshev order & support size
B, V, Fin = x.size()
B, V, Fin = int(B), int(V), int(Fin)
# rescale Laplacian
lmax = lmax_L(L)
L = rescale_L(L, lmax)
# convert scipy sparse matric L to pytorch
L = sparse_mx_to_torch_sparse_tensor(L)
if torch.cuda.is_available():
L = L.cuda()
# transform to Chebyshev basis
x0 = x.permute(1, 2, 0).contiguous() # V x Fin x B
x0 = x0.view([V, Fin * B]) # V x Fin*B
x = x0.unsqueeze(0) # 1 x V x Fin*B
def concat(x, x_):
x_ = x_.unsqueeze(0) # 1 x V x Fin*B
return torch.cat((x, x_), 0) # K x V x Fin*B
if K > 1:
x1 = my_sparse_mm()(L, x0) # V x Fin*B
x = torch.cat((x, x1.unsqueeze(0)), 0) # 2 x V x Fin*B
for k in range(2, K):
x2 = 2 * my_sparse_mm()(L, x1) - x0
x = torch.cat((x, x2.unsqueeze(0)),
0) # M x Fin*B --> K x V x Fin*B
x0, x1 = x1, x2
x = x.view([K, V, Fin, B]) # K x V x Fin x B
x = x.permute(3, 1, 2, 0).contiguous() # B x V x Fin x K
x = x.view([B * V, Fin * K]) # B*V x Fin*K
# Compose linearly Fin features to get Fout features
x = cl(x) # B*V x Fout
x = x.view([B, V, Fout]) # B x V x Fout
return x
def build_coarse_graphs(mesh_face, joint_num, skeleton, flip_pairs, levels=9):
joint_adj = build_adj(joint_num, skeleton, flip_pairs)
# Build graph
mesh_adj = build_graph(mesh_face, mesh_face.max() + 1)
graph_Adj, graph_L, graph_perm = coarsen(mesh_adj, levels=levels)
input_Adj = sp.csr_matrix(joint_adj)
input_Adj.eliminate_zeros()
input_L = laplacian(input_Adj, normalized=True)
graph_L[-1] = input_L
graph_Adj[-1] = input_Adj
# Compute max eigenvalue of graph Laplacians, rescale Laplacian
graph_lmax = []
renewed_lmax = []
for i in range(levels):
graph_lmax.append(lmax_L(graph_L[i]))
graph_L[i] = rescale_L(graph_L[i], graph_lmax[i])
# renewed_lmax.append(lmax_L(graph_L[i]))
return graph_Adj, graph_L, graph_perm, perm_index_reverse(graph_perm[0])