def build_minimal_wordnet_graph(wnids, multi_path=False):
G = nx.DiGraph()
for wnid in wnids:
G.add_node(wnid)
synset = wnid_to_synset(wnid)
set_node_label(G, synset)
if wnid == "n10129825": # hardcode 'girl' to not be child of 'woman'
if not multi_path:
G.add_edge(
"n09624168",
"n10129825") # child of 'male' (sibling to 'male_child')
else:
G.add_edge("n09619168", "n10129825") # child of 'female'
G.add_edge("n09619168", "n10129825") # child of 'female'
continue
hypernyms = [synset]
while hypernyms:
current = hypernyms.pop(0)
set_node_label(G, current)
for hypernym in current.hypernyms():
G.add_edge(synset_to_wnid(hypernym), synset_to_wnid(current))
hypernyms.append(hypernym)
if not multi_path:
break
children = [(key, wnid_to_synset(key).name()) for key in G.succ[wnid]]
assert (
len(children) == 0
), f"Node {wnid} ({synset.name()}) is not a leaf. Children: {children}"
return G
def pick_unseen_hypernym(G, common_hypernyms):
assert len(common_hypernyms) > 0
candidate = deepest_synset(common_hypernyms)
wnid = synset_to_wnid(candidate)
while common_hypernyms and wnid in G.nodes:
common_hypernyms -= {candidate}
if not common_hypernyms:
return None
candidate = deepest_synset(common_hypernyms)
wnid = synset_to_wnid(candidate)
return candidate
def add_node_to_graph(G, candidate, children):
root = get_root(G)
wnid = synset_to_wnid(candidate)
G.add_node(wnid)
set_node_label(G, candidate)
for child in children:
G.add_edge(wnid, child)
G.add_edge(root, wnid)
def set_node_label(G, synset):
nx.set_node_attributes(G, {synset_to_wnid(synset): synset_to_name(synset)},
"label")
def build_induced_graph(
wnids,
checkpoint,
model=None,
linkage="ward",
affinity="euclidean",
branching_factor=2,
dataset="CIFAR10",
state_dict=None,
):
num_classes = len(wnids)
assert (
checkpoint or model or state_dict
), "Need to specify either `checkpoint` or `method` or `state_dict`."
if state_dict:
centers = get_centers_from_state_dict(state_dict)
elif checkpoint:
centers = get_centers_from_checkpoint(checkpoint)
else:
centers = get_centers_from_model(model, num_classes, dataset)
assert num_classes == centers.size(0), (
f"The model FC supports {centers.size(0)} classes. However, the dataset"
f" {dataset} features {num_classes} classes. Try passing the "
"`--dataset` with the right number of classes.")
if centers.is_cuda:
centers = centers.cpu()
G = nx.DiGraph()
# add leaves
for wnid in wnids:
G.add_node(wnid)
set_node_label(G, wnid_to_synset(wnid))
# add rest of tree
clustering = AgglomerativeClustering(
linkage=linkage,
n_clusters=branching_factor,
affinity=affinity,
).fit(centers)
children = clustering.children_
index_to_wnid = {}
for index, pair in enumerate(map(tuple, children)):
child_wnids = []
child_synsets = []
for child in pair:
if child < num_classes:
child_wnid = wnids[child]
else:
child_wnid = index_to_wnid[child - num_classes]
child_wnids.append(child_wnid)
child_synsets.append(wnid_to_synset(child_wnid))
parent = get_wordnet_meaning(G, child_synsets)
parent_wnid = synset_to_wnid(parent)
G.add_node(parent_wnid)
set_node_label(G, parent)
index_to_wnid[index] = parent_wnid
for child_wnid in child_wnids:
G.add_edge(parent_wnid, child_wnid)
assert len(list(get_roots(G))) == 1, list(get_roots(G))
return G