def _build_graph(self, features, cluster_features, labels, density, knns):
adj = fast_knns2spmat(knns, self.k)
adj, adj_row_sum = row_normalize(adj)
indices, values, shape = sparse_mx_to_indices_values(adj)
g = dgl.graph((indices[1], indices[0]))
g.ndata['features'] = torch.FloatTensor(features)
g.ndata['cluster_features'] = torch.FloatTensor(cluster_features)
g.ndata['labels'] = torch.LongTensor(labels)
g.ndata['density'] = torch.FloatTensor(density)
g.edata['affine'] = torch.FloatTensor(values)
# A Bipartite from DGL sampler will not store global eid, so we explicitly save it here
g.edata['global_eid'] = g.edges(form='eid')
g.ndata['norm'] = torch.FloatTensor(adj_row_sum)
g.apply_edges(lambda edges: {'raw_affine': edges.data['affine'] / edges.dst['norm']})
g.apply_edges(lambda edges: {'labels_conn': (edges.src['labels'] == edges.dst['labels']).long()})
g.apply_edges(lambda edges: {'mask_conn': (edges.src['density'] > edges.dst['density']).bool()})
return g
def __init__(self, cfg):
feat_path = cfg['feat_path']
label_path = cfg.get('label_path', None)
knn_graph_path = cfg.get('knn_graph_path', None)
self.k = cfg['k']
self.feature_dim = cfg['feature_dim']
self.is_norm_feat = cfg.get('is_norm_feat', True)
self.save_decomposed_adj = cfg.get('save_decomposed_adj', False)
self.th_sim = cfg.get('th_sim', 0.)
self.max_conn = cfg.get('max_conn', 1)
self.conf_metric = cfg.get('conf_metric')
with Timer('read meta and feature'):
if label_path is not None:
self.lb2idxs, self.idx2lb = read_meta(label_path)
self.inst_num = len(self.idx2lb)
self.gt_labels = intdict2ndarray(self.idx2lb)
self.ignore_label = False
else:
self.inst_num = -1
self.ignore_label = True
self.features = read_probs(feat_path, self.inst_num,
self.feature_dim)
if self.is_norm_feat:
self.features = l2norm(self.features)
if self.inst_num == -1:
self.inst_num = self.features.shape[0]
self.size = 1 # take the entire graph as input
with Timer('read knn graph'):
if os.path.isfile(knn_graph_path):
knns = np.load(knn_graph_path)['data']
else:
if knn_graph_path is not None:
print('knn_graph_path does not exist: {}'.format(
knn_graph_path))
prefix = osp.dirname(feat_path)
name = rm_suffix(osp.basename(feat_path))
# find root folder of `features`
prefix = osp.dirname(prefix)
knn_prefix = osp.join(prefix, 'knns', name)
knns = build_knns(knn_prefix, self.features, cfg.knn_method,
cfg.knn)
adj = fast_knns2spmat(knns, self.k, self.th_sim, use_sim=True)
# build symmetric adjacency matrix
adj = build_symmetric_adj(adj, self_loop=True)
adj = row_normalize(adj)
if self.save_decomposed_adj:
adj = sparse_mx_to_indices_values(adj)
self.adj_indices, self.adj_values, self.adj_shape = adj
else:
self.adj = adj
# convert knns to (dists, nbrs)
self.dists, self.nbrs = knns2ordered_nbrs(knns)
print('feature shape: {}, k: {}, norm_feat: {}'.format(
self.features.shape, self.k, self.is_norm_feat))
if not self.ignore_label:
with Timer('Prepare ground-truth label'):
self.labels = confidence(feats=self.features,
dists=self.dists,
nbrs=self.nbrs,
metric=self.conf_metric,
idx2lb=self.idx2lb,
lb2idxs=self.lb2idxs)
if cfg.eval_interim:
_, self.peaks = confidence_to_peaks(
self.dists, self.nbrs, self.labels, self.max_conn)
def __init__(self, cfg):
feat_path = cfg['feat_path']
label_path = cfg.get('label_path', None)
knn_graph_path = cfg.get('knn_graph_path', None)
self.k = cfg['k']
self.feature_dim = cfg['feature_dim']
self.is_norm_feat = cfg.get('is_norm_feat', True)
self.save_decomposed_adj = cfg.get('save_decomposed_adj', False)
self.th_sim = cfg.get('th_sim', 0.)
self.max_conn = cfg.get('max_conn', 1)
self.conf_metric = cfg.get('conf_metric')
self.num_process = cfg.get('num_process',16)
with Timer('read meta and feature'):
if label_path is not None:
self.lb2idxs, self.idx2lb = read_meta(label_path)
self.inst_num = len(self.idx2lb)
self.gt_labels = intdict2ndarray(self.idx2lb)
self.ignore_label = False
else:
self.inst_num = -1
self.ignore_label = True
self.features = read_probs(feat_path, self.inst_num,
self.feature_dim)
if self.is_norm_feat:
self.features = l2norm(self.features)
if self.inst_num == -1:
self.inst_num = self.features.shape[0]
self.size = 1 # take the entire graph as input
with Timer('read knn graph'):
if os.path.isfile(knn_graph_path):
knns = np.load(knn_graph_path)['data'] # num_imgs*2*k
else:
if knn_graph_path is not None:
print('knn_graph_path does not exist: {}'.format(
knn_graph_path))
knn_prefix = os.path.join(cfg.prefix, 'knns', cfg.name)
# 通过faiss实现k近邻搜索，此处作者faiss_gpu版本实现可能有问题，但faiss大规模在cpu上跑还是慢
# 当然faiss有针内存和计算速度方面的优化，PQ,IVF等，可参考faiss
knns = build_knns(knn_prefix, self.features, cfg.knn_method,
cfg.knn,self.num_process)
# 依据k近邻搜索结果构建邻接矩阵
adj = fast_knns2spmat(knns, self.k, self.th_sim, use_sim=True)
# build symmetric adjacency matrix
adj = build_symmetric_adj(adj, self_loop=True)
adj = row_normalize(adj)
if self.save_decomposed_adj:
adj = sparse_mx_to_indices_values(adj)
self.adj_indices, self.adj_values, self.adj_shape = adj
else:
self.adj = adj
# convert knns to (dists, nbrs)
self.dists, self.nbrs = knns2ordered_nbrs(knns) # num_imgs*k
print('feature shape: {}, k: {}, norm_feat: {}'.format(
self.features.shape, self.k, self.is_norm_feat))
if not self.ignore_label:
with Timer('Prepare ground-truth label'):
self.labels = confidence(feats=self.features,
dists=self.dists,
nbrs=self.nbrs,
metric=self.conf_metric,
idx2lb=self.idx2lb,
lb2idxs=self.lb2idxs)
if cfg.eval_interim:
_, self.peaks = confidence_to_peaks(
self.dists, self.nbrs, self.labels, self.max_conn)
def __init__(self, cfg):
feat_path = cfg['feat_path']
label_path = cfg.get('label_path', None)
knn_graph_path = cfg.get('knn_graph_path', None)
self.k = cfg['k']
self.feature_dim = cfg['feature_dim']
self.is_norm_feat = cfg.get('is_norm_feat', True)
self.save_decomposed_adj = cfg.get('save_decomposed_adj', False)
self.th_sim = cfg.get('th_sim', 0.)
self.conf_metric = cfg.get('conf_metric')
with Timer('read meta and feature'):
if label_path is not None:
self.lb2idxs, self.idx2lb = read_meta(label_path)
self.inst_num = len(self.idx2lb)
self.cls_num = len(self.lb2idxs)
self.gt_labels = intdict2ndarray(self.idx2lb)
self.ignore_label = False
else:
self.inst_num = -1
self.ignore_label = True
self.features = read_probs(feat_path, self.inst_num,
self.feature_dim)
if self.is_norm_feat:
self.features = l2norm(self.features)
if self.inst_num == -1:
self.inst_num = self.features.shape[0]
self.size = 1 # take the entire graph as input
with Timer('Compute center feature'):
self.center_fea = np.zeros((self.cls_num, self.features.shape[1]))
for i in range(self.cls_num):
self.center_fea[i] = np.mean(self.features[self.lb2idxs[i]], 0)
self.center_fea = l2norm(self.center_fea)
with Timer('read knn graph'):
if os.path.isfile(knn_graph_path):
print("load knns from the knn_path")
self.knns = np.load(knn_graph_path)['data']
else:
if knn_graph_path is not None:
print('knn_graph_path does not exist: {}'.format(
knn_graph_path))
knn_prefix = os.path.join(cfg.prefix, 'knns', cfg.name)
self.knns = build_knns(knn_prefix, self.features,
cfg.knn_method, cfg.knn)
adj = fast_knns2spmat(self.knns, self.k, self.th_sim, use_sim=True)
# build symmetric adjacency matrix
adj = build_symmetric_adj(adj, self_loop=True)
#print('adj before norm')
#print(adj)
adj = row_normalize(adj)
if self.save_decomposed_adj:
adj = sparse_mx_to_indices_values(adj)
self.adj_indices, self.adj_values, self.adj_shape = adj
else:
self.adj = adj
# convert knns to (dists, nbrs)
self.dists, self.nbrs = knns2ordered_nbrs(self.knns)
print('feature shape: {}, k: {}, norm_feat: {}'.format(
self.features.shape, self.k, self.is_norm_feat))