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model_v2_linsat.py
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model_v2_linsat.py
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import itertools
from torch_sparse import spmm, SparseTensor
from models.BBGM.affinity_layer import InnerProductWithWeightsAffinity
from models.BBGM.sconv_archs import SiameseSConvOnNodes, SiameseNodeFeaturesToEdgeFeatures
from src.feature_align import feature_align
from src.factorize_graph_matching import construct_aff_mat, construct_sparse_aff_mat
from src.utils.pad_tensor import pad_tensor
from models.NGM.gnn import GNNLayer, SPGNNLayer, PYGNNLayer
from LinSATNet import linsat_layer
from models.AFAT.sinkhorn_topk import greedy_perm
from src.lap_solvers.hungarian import hungarian
from src.utils.config import cfg
from src.backbone import *
CNN = eval(cfg.BACKBONE)
def lexico_iter(lex):
return itertools.combinations(lex, 2)
def normalize_over_channels(x):
channel_norms = torch.norm(x, dim=1, keepdim=True)
return x / channel_norms
def concat_features(embeddings, num_vertices):
res = torch.cat([embedding[:, :num_v] for embedding, num_v in zip(embeddings, num_vertices)], dim=-1)
return res.transpose(0, 1)
class Net(CNN):
def __init__(self):
super(Net, self).__init__()
self.message_pass_node_features = SiameseSConvOnNodes(input_node_dim=cfg.NGM.FEATURE_CHANNEL * 2)
self.build_edge_features_from_node_features = SiameseNodeFeaturesToEdgeFeatures(
total_num_nodes=self.message_pass_node_features.num_node_features
)
self.global_state_dim = cfg.NGM.FEATURE_CHANNEL * 2
self.vertex_affinity = InnerProductWithWeightsAffinity(
self.global_state_dim, self.message_pass_node_features.num_node_features)
self.edge_affinity = InnerProductWithWeightsAffinity(
self.global_state_dim,
self.build_edge_features_from_node_features.num_edge_features)
self.rescale = cfg.PROBLEM.RESCALE
self.tau = cfg.NGM.SK_TAU
self.mgm_tau = cfg.NGM.MGM_SK_TAU
self.sparse = cfg.NGM.SPARSE_MODEL
self.gnn_layer = cfg.NGM.GNN_LAYER
if not self.sparse:
for i in range(self.gnn_layer):
tau = cfg.NGM.SK_TAU
if i == 0:
gnn_layer = GNNLayer(1, 1,
cfg.NGM.GNN_FEAT[i] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i],
sk_channel=cfg.NGM.SK_EMB, sk_tau=tau, edge_emb=cfg.NGM.EDGE_EMB)
else:
gnn_layer = GNNLayer(cfg.NGM.GNN_FEAT[i - 1] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i - 1],
cfg.NGM.GNN_FEAT[i] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i],
sk_channel=cfg.NGM.SK_EMB, sk_tau=tau, edge_emb=cfg.NGM.EDGE_EMB)
self.add_module('gnn_layer_{}'.format(i), gnn_layer)
else:
self.geometric = True
if self.geometric:
for i in range(self.gnn_layer):
tau = cfg.NGM.SK_TAU
if i == 0:
gnn_layer = PYGNNLayer(1, 1,
cfg.NGM.GNN_FEAT[i] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i],
sk_channel=cfg.NGM.SK_EMB, sk_tau=tau, edge_emb=cfg.NGM.EDGE_EMB)
else:
gnn_layer = PYGNNLayer(cfg.NGM.GNN_FEAT[i - 1] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i - 1],
cfg.NGM.GNN_FEAT[i] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i],
sk_channel=cfg.NGM.SK_EMB, sk_tau=tau, edge_emb=cfg.NGM.EDGE_EMB)
self.add_module('gnn_layer_{}'.format(i), gnn_layer)
else:
for i in range(self.gnn_layer):
tau = cfg.NGM.SK_TAU
if i == 0:
gnn_layer = SPGNNLayer(1, 1,
cfg.NGM.GNN_FEAT[i] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i],
sk_channel=cfg.NGM.SK_EMB, sk_tau=tau, edge_emb=cfg.NGM.EDGE_EMB)
else:
gnn_layer = SPGNNLayer(cfg.NGM.GNN_FEAT[i - 1] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i - 1],
cfg.NGM.GNN_FEAT[i] + cfg.NGM.SK_EMB, cfg.NGM.GNN_FEAT[i],
sk_channel=cfg.NGM.SK_EMB, sk_tau=tau, edge_emb=cfg.NGM.EDGE_EMB)
self.add_module('gnn_layer_{}'.format(i), gnn_layer)
self.classifier = nn.Linear(cfg.NGM.GNN_FEAT[-1] + cfg.NGM.SK_EMB, 1)
def forward(
self,
data_dict,
):
images = data_dict['images']
points = data_dict['Ps']
n_points = data_dict['ns']
A_src, A_tgt = data_dict['As']
graphs = data_dict['pyg_graphs']
batch_size = data_dict['gt_perm_mat'].shape[0]
num_graphs = len(images)
global_list = []
orig_graph_list = []
node_feature_list = []
for image, p, n_p, graph in zip(images, points, n_points, graphs):
# extract feature
nodes = self.node_layers(image)
edges = self.edge_layers(nodes)
global_list.append(self.final_layers(edges).reshape((nodes.shape[0], -1)))
nodes = normalize_over_channels(nodes)
edges = normalize_over_channels(edges)
# arrange features
U = concat_features(feature_align(nodes, p, n_p, self.rescale), n_p)
F = concat_features(feature_align(edges, p, n_p, self.rescale), n_p)
node_features = torch.cat((U, F), dim=1)
node_feature_list.append(node_features.detach())
graph.x = node_features
graph = self.message_pass_node_features(graph)
orig_graph = self.build_edge_features_from_node_features(graph)
orig_graph_list.append(orig_graph)
global_weights_list = [
torch.cat([global_src, global_tgt], axis=-1) for global_src, global_tgt in lexico_iter(global_list)
]
global_weights_list = [normalize_over_channels(g) for g in global_weights_list]
unary_affs_list = [
self.vertex_affinity([item.x for item in g_1], [item.x for item in g_2], global_weights)
for (g_1, g_2), global_weights in zip(lexico_iter(orig_graph_list), global_weights_list)
]
quadratic_affs_list = [
self.edge_affinity([item.edge_attr for item in g_1], [item.edge_attr for item in g_2], global_weights)
for (g_1, g_2), global_weights in zip(lexico_iter(orig_graph_list), global_weights_list)
]
quadratic_affs_list = [[0.5 * x for x in quadratic_affs] for quadratic_affs in quadratic_affs_list]
s_list, mgm_s_list, x_list, mgm_x_list, indices = [], [], [], [], []
for unary_affs, quadratic_affs, (idx1, idx2) in zip(unary_affs_list, quadratic_affs_list, lexico_iter(range(num_graphs))):
if not self.sparse:
kro_G, kro_H = data_dict['KGHs'] if num_graphs == 2 else data_dict['KGHs']['{},{}'.format(idx1, idx2)]
Kp = torch.stack(pad_tensor(unary_affs), dim=0)
Ke = torch.stack(pad_tensor(quadratic_affs), dim=0)
K = construct_aff_mat(Ke, Kp, kro_G, kro_H)
if num_graphs == 2: data_dict['aff_mat'] = K
if cfg.NGM.FIRST_ORDER:
emb = Kp.transpose(1, 2).contiguous().view(Kp.shape[0], -1, 1)
else:
emb = torch.ones(K.shape[0], K.shape[1], 1, device=K.device)
if cfg.NGM.POSITIVE_EDGES:
A = (K > 0).to(K.dtype)
else:
A = (K != 0).to(K.dtype)
emb_K = K.unsqueeze(-1)
# NGM qap solver
for i in range(self.gnn_layer):
gnn_layer = getattr(self, 'gnn_layer_{}'.format(i))
emb_K, emb = gnn_layer(A, emb_K, emb, n_points[idx1], n_points[idx2])
else:
kro_G, kro_H = data_dict['KGHs_sparse'] if num_graphs == 2 else data_dict['KGHs_sparse']['{},{}'.format(idx1, idx2)]
Kp = torch.stack(pad_tensor(unary_affs), dim=0)
Ke = torch.stack(pad_tensor(quadratic_affs), dim=0)
K_value, row_idx, col_idx = construct_sparse_aff_mat(Ke, Kp, kro_G, kro_H)
if cfg.NGM.FIRST_ORDER:
emb = Kp.transpose(1, 2).contiguous().view(Kp.shape[0], -1, 1)
else:
emb = torch.ones(cfg.BATCH_SIZE, Kp.shape[1] * Kp.shape[2], 1, device=K_value.device)
# NGM qap solver
if self.geometric:
adj = SparseTensor(row=row_idx.long(), col=col_idx.long(), value=K_value,
sparse_sizes=(Kp.shape[1] * Kp.shape[2], Kp.shape[1] * Kp.shape[2]))
for i in range(self.gnn_layer):
gnn_layer = getattr(self, 'gnn_layer_{}'.format(i))
emb = gnn_layer(adj, emb, n_points[idx1], n_points[idx2])
else:
K_index = torch.cat((row_idx.unsqueeze(0), col_idx.unsqueeze(0)), dim=0).long()
A_value = torch.ones(K_value.shape, device=K_value.device)
tmp = torch.ones([Kp.shape[1] * Kp.shape[2]], device=K_value.device).unsqueeze(-1)
normed_A_value = 1 / torch.flatten(
spmm(K_index, A_value, Kp.shape[1] * Kp.shape[2], Kp.shape[1] * Kp.shape[2], tmp))
A_index = torch.linspace(0, Kp.shape[1] * Kp.shape[2] - 1, Kp.shape[1] * Kp.shape[2]).unsqueeze(0)
A_index = torch.repeat_interleave(A_index, 2, dim=0).long().to(K_value.device)
for i in range(self.gnn_layer):
gnn_layer = getattr(self, 'gnn_layer_{}'.format(i))
emb = gnn_layer(K_value, K_index, normed_A_value, A_index, emb, n_points[idx1], n_points[idx2])
v = self.classifier(emb)
s = v.view(v.shape[0], points[idx2].shape[1], -1).transpose(1, 2)
gt_ks = torch.tensor(
[torch.sum(data_dict['gt_perm_mat'][i]) for i in range(data_dict['gt_perm_mat'].shape[0])],
dtype=torch.float32, device=s.device)
ss_out = torch.zeros(s.shape, dtype=torch.float32, device=s.device)
for ii in range(s.shape[0]):
p0 = n_points[idx1][ii]
p1 = n_points[idx2][ii]
constraint = torch.zeros(p0 + p1, p0 * p1, ### p0 + p1 + 1
dtype=torch.float32, device=s.device)
b = torch.zeros(p0 + p1, dtype=torch.float32, device=s.device) ### p0 + p1 + 1
for cons_id in range(p0 + p1):
tmp = torch.zeros(p0, p1, dtype=torch.float32, device=s.device)
if cons_id < p0:
tmp[cons_id, 0:p1] = 1
else:
tmp[0:p0, cons_id - p0] = 1
constraint[cons_id, :] = tmp.reshape(-1)
b[cons_id] = 1
E = torch.ones(1, p0 * p1, dtype=torch.float32, device=s.device)
f = torch.zeros(1, dtype=torch.float32, device=s.device)
f[0] = gt_ks[ii]
### tmp = torch.ones(p0, p1, dtype=torch.float32, device=s.device)
### constraint[-1, :] = tmp.reshape(-1)
### b[-1] = gt_ks[ii]
input = s[ii, 0:p0, 0:p1].reshape(-1)
ss_out[ii, 0:p0, 0:p1] = linsat_layer(input, A=constraint, b=b, E=E, f=f, max_iter=2 * cfg.NGM.SK_ITER_NUM,
tau=self.tau).reshape(p0, p1)
x = hungarian(ss_out, n_points[idx1], n_points[idx2])
top_indices = torch.argsort(x.mul(ss_out).reshape(x.shape[0], -1), descending=True, dim=-1)
x = torch.zeros(ss_out.shape, device=ss_out.device)
x = greedy_perm(x, top_indices, gt_ks)
s_list.append(ss_out)
x_list.append(x)
indices.append((idx1, idx2))
if cfg.PROBLEM.TYPE == '2GM' or cfg.PROBLEM.TYPE == 'IMT':
data_dict.update({
'ds_mat': s_list[0],
'perm_mat': x_list[0],
})
elif cfg.PROBLEM.TYPE == 'MGM':
data_dict.update({
'ds_mat_list': mgm_s_list,
'perm_mat_list': mgm_x_list,
'graph_indices': indices,
})
return data_dict