def __init__(self, dataset, path_graph=None, path_wnids=None, classes=()):
if not path_graph:
path_graph = dataset_to_default_path_graph(dataset)
if not path_wnids:
path_wnids = dataset_to_default_path_wnids(dataset)
if not classes:
classes = dataset_to_dummy_classes(dataset)
super().__init__()
assert all([dataset, path_graph, path_wnids, classes])
self.classes = classes
self.nodes = Node.get_nodes(path_graph, path_wnids, classes)
self.G = self.nodes[0].G
self.wnid_to_node = {node.wnid: node for node in self.nodes}
self.wnids = get_wnids(path_wnids)
self.wnid_to_class = {
wnid: cls
for wnid, cls in zip(self.wnids, self.classes)
}
self.correct = 0
self.total = 0
self.I = torch.eye(len(classes))
def __init__(self,
dataset,
model,
arch=None,
path_graph=None,
path_wnids=None,
classes=None,
hierarchy=None,
pretrained=None,
**kwargs):
super().__init__()
if dataset and not hierarchy and not path_graph:
assert arch, 'Must specify `arch` if no `hierarchy` or `path_graph`'
hierarchy = f'induced-{arch}'
if dataset and hierarchy and not path_graph:
path_graph = hierarchy_to_path_graph(dataset, hierarchy)
if dataset and not path_graph:
path_graph = dataset_to_default_path_graph(dataset)
if dataset and not path_wnids:
path_wnids = dataset_to_default_path_wnids(dataset)
if dataset and not classes:
classes = dataset_to_dummy_classes(dataset)
if pretrained and not arch:
raise UserWarning(
'To load a pretrained NBDT, you need to specify the `arch`. '
'`arch` is the name of the architecture. e.g., ResNet18')
if isinstance(model, str):
raise NotImplementedError('Model must be nn.Module')
self.init(dataset, model, path_graph, path_wnids, classes,
arch=arch, pretrained=pretrained, hierarchy=hierarchy, **kwargs)
def __init__(self,
dataset,
criterion,
path_graph=None,
path_wnids=None,
classes=None,
hierarchy=None,
Rules=HardEmbeddedDecisionRules,
**kwargs):
super().__init__()
if dataset and hierarchy and not path_graph:
path_graph = hierarchy_to_path_graph(dataset, hierarchy)
if dataset and not path_graph:
path_graph = dataset_to_default_path_graph(dataset)
if dataset and not path_wnids:
path_wnids = dataset_to_default_path_wnids(dataset)
if dataset and not classes:
classes = dataset_to_dummy_classes(dataset)
self.init(dataset,
criterion,
path_graph,
path_wnids,
classes,
Rules=Rules,
**kwargs)
def set_default_values(args):
assert not (
args.hierarchy and args.path_graph
), "Only one, between --hierarchy and --path-graph can be provided."
if args.hierarchy and not args.path_graph:
args.path_graph = hierarchy_to_path_graph(args.dataset, args.hierarchy)
if not args.path_graph:
args.path_graph = dataset_to_default_path_graph(args.dataset)
if not args.path_wnids:
args.path_wnids = dataset_to_default_path_wnids(args.dataset)
def set_default_values(args):
assert not (args.hierarchy and args.path_graph), \
'Only one, between --hierarchy and --path-graph can be provided.'
if 'TreeSupLoss' not in args.loss:
return
if args.hierarchy and not args.path_graph:
args.path_graph = hierarchy_to_path_graph(args.dataset, args.hierarchy)
if not args.path_graph:
args.path_graph = dataset_to_default_path_graph(args.dataset)
if not args.path_wnids:
args.path_wnids = dataset_to_default_path_wnids(args.dataset)
def __init__(self,
dataset,
path_graph=None,
path_wnids=None,
classes=None,
hierarchy=None):
if dataset and hierarchy and not path_graph:
path_graph = hierarchy_to_path_graph(dataset, hierarchy)
if dataset and not path_graph:
path_graph = dataset_to_default_path_graph(dataset)
if dataset and not path_wnids:
path_wnids = dataset_to_default_path_wnids(dataset)
if dataset and not classes:
classes = dataset_to_dummy_classes(dataset)
self.load_hierarchy(dataset, path_graph, path_wnids, classes)