kwcoco.category_tree module

The category_tree module defines the CategoryTree class, which is used for maintaining flat or hierarchical category information. The kwcoco version of this class only contains the datastructure and does not contain any torch operations. See the ndsampler version for the extension with torch operations.

class kwcoco.category_tree.CategoryTree(graph=None, checks=True)

Bases: NiceRepr

Wrapper that maintains flat or hierarchical category information.

Helps compute softmaxes and probabilities for tree-based categories where a directed edge (A, B) represents that A is a superclass of B.

Note

There are three basic properties that this object maintains:

node:
    Alphanumeric string names that should be generally descriptive.
    Using spaces and special characters in these names is
    discouraged, but can be done.  This is the COCO category "name"
    attribute.  For categories this may be denoted as (name, node,
    cname, catname).

id:
    The integer id of a category should ideally remain consistent.
    These are often given by a dataset (e.g. a COCO dataset).  This
    is the COCO category "id" attribute. For categories this is
    often denoted as (id, cid).

index:
    Contigous zero-based indices that indexes the list of
    categories.  These should be used for the fastest access in
    backend computation tasks. Typically corresponds to the
    ordering of the channels in the final linear layer in an
    associated model.  For categories this is often denoted as
    (index, cidx, idx, or cx).

Variables:

• idx_to_node (List[str]) – a list of class names. Implicitly maps from index to category name.

• id_to_node (Dict[int, str]) – maps integer ids to category names

• node_to_id (Dict[str, int]) – maps category names to ids

• node_to_idx (Dict[str, int]) – maps category names to indexes

• graph (networkx.Graph) – a Graph that stores any hierarchy information. For standard mutually exclusive classes, this graph is edgeless. Nodes in this graph can maintain category attributes / properties.

• idx_groups (List[List[int]]) – groups of category indices that share the same parent category.

Example

>>> from kwcoco.category_tree import *
>>> graph = nx.from_dict_of_lists({
>>>     'background': [],
>>>     'foreground': ['animal'],
>>>     'animal': ['mammal', 'fish', 'insect', 'reptile'],
>>>     'mammal': ['dog', 'cat', 'human', 'zebra'],
>>>     'zebra': ['grevys', 'plains'],
>>>     'grevys': ['fred'],
>>>     'dog': ['boxer', 'beagle', 'golden'],
>>>     'cat': ['maine coon', 'persian', 'sphynx'],
>>>     'reptile': ['bearded dragon', 't-rex'],
>>> }, nx.DiGraph)
>>> self = CategoryTree(graph)
>>> print(self)
<CategoryTree(nNodes=22, maxDepth=6, maxBreadth=4...)>

Example

>>> # The coerce classmethod is the easiest way to create an instance
>>> import kwcoco
>>> kwcoco.CategoryTree.coerce(['a', 'b', 'c'])
<CategoryTree...nNodes=3, nodes=...'a', 'b', 'c'...
>>> kwcoco.CategoryTree.coerce(4)
<CategoryTree...nNodes=4, nodes=...'class_1', 'class_2', 'class_3', ...
>>> kwcoco.CategoryTree.coerce(4)

Parameters:

• graph (nx.DiGraph) – either the graph representing a category hierarchy

• checks (bool, default=True) – if false, bypass input checks

copy()

classmethod from_mutex(nodes, bg_hack=True)

Parameters:

nodes (List[str]) – or a list of class names (in which case they will all be assumed to be mutually exclusive)

Example

>>> print(CategoryTree.from_mutex(['a', 'b', 'c']))
<CategoryTree(nNodes=3, ...)>

classmethod from_json(state)

Parameters:

state (Dict) – see __getstate__ / __json__ for details

classmethod from_coco(categories)

Create a CategoryTree object from coco categories

Parameters:

List[Dict] – list of coco-style categories

classmethod coerce(data, **kw)

Attempt to coerce data as a CategoryTree object.

This is primarily useful for when the software stack depends on categories being represent

This will work if the input data is a specially formatted json dict, a list of mutually exclusive classes, or if it is already a CategoryTree. Otherwise an error will be thrown.

Parameters:

• data (object) – a known representation of a category tree.

• **kwargs – input type specific arguments

Returns:

self

Return type:

CategoryTree

Raises:

• TypeError - if the input format is unknown –

• ValueError - if kwargs are not compatible with the input format –

Example

>>> import kwcoco
>>> classes1 = kwcoco.CategoryTree.coerce(3)  # integer
>>> classes2 = kwcoco.CategoryTree.coerce(classes1.__json__())  # graph dict
>>> classes3 = kwcoco.CategoryTree.coerce(['class_1', 'class_2', 'class_3'])  # mutex list
>>> classes4 = kwcoco.CategoryTree.coerce(classes1.graph)  # nx Graph
>>> classes5 = kwcoco.CategoryTree.coerce(classes1)  # cls
>>> # xdoctest: +REQUIRES(module:ndsampler)
>>> import ndsampler
>>> classes6 = ndsampler.CategoryTree.coerce(3)
>>> classes7 = ndsampler.CategoryTree.coerce(classes1)
>>> classes8 = kwcoco.CategoryTree.coerce(classes6)

classmethod demo(key='coco', **kwargs)

Parameters:

key (str) – specify which demo dataset to use. Can be ‘coco’ (which uses the default coco demo data). Can be ‘btree’ which creates a binary tree and accepts kwargs ‘r’ and ‘h’ for branching-factor and height. Can be ‘btree2’, which is the same as btree but returns strings

CommandLine

xdoctest -m ~/code/kwcoco/kwcoco/category_tree.py CategoryTree.demo

Example

>>> from kwcoco.category_tree import *
>>> self = CategoryTree.demo()
>>> print('self = {}'.format(self))
self = <CategoryTree(nNodes=10, maxDepth=2, maxBreadth=4...)>

to_coco()

Converts to a coco-style data structure

Yields:

Dict – coco category dictionaries

property id_to_idx

Example:

>>> import kwcoco
>>> self = kwcoco.CategoryTree.demo()
>>> self.id_to_idx[1]

property idx_to_id

Example:

>>> import kwcoco
>>> self = kwcoco.CategoryTree.demo()
>>> self.idx_to_id[0]

idx_to_ancestor_idxs(include_self=True)

Mapping from a class index to its ancestors

Parameters:

include_self (bool, default=True) – if True includes each node as its own ancestor.

idx_to_descendants_idxs(include_self=False)

Mapping from a class index to its descendants (including itself)

Parameters:

include_self (bool, default=False) – if True includes each node as its own descendant.

idx_pairwise_distance()

Get a matrix encoding the distance from one class to another.

Distances

• from parents to children are positive (descendants),

• from children to parents are negative (ancestors),

• between unreachable nodes (wrt to forward and reverse graph) are nan.

is_mutex()

Returns True if all categories are mutually exclusive (i.e. flat)

If true, then the classes may be represented as a simple list of class names without any loss of information, otherwise the underlying category graph is necessary to preserve all knowledge.

Todo

• [ ] what happens when we have a dummy root?

property num_classes

property class_names

property category_names

property cats

Returns a mapping from category names to category attributes.

If this category tree was constructed from a coco-dataset, then this will contain the coco category attributes.

Returns:

Dict[str, Dict[str, object]]

Example

>>> from kwcoco.category_tree import *
>>> self = CategoryTree.demo()
>>> print('self.cats = {!r}'.format(self.cats))

index(node)

Return the index that corresponds to the category name

Parameters:

node (str) – the name of the category

Returns:

int

take(indexes)

Create a subgraph based on the selected class indexes

subgraph(subnodes, closure=True)

Create a subgraph based on the selected class nodes (i.e. names)

Example

>>> self = CategoryTree.from_coco([
>>>     {'id': 130, 'name': 'n3', 'supercategory': 'n1'},
>>>     {'id': 410, 'name': 'n1', 'supercategory': None},
>>>     {'id': 640, 'name': 'n4', 'supercategory': 'n3'},
>>>     {'id': 220, 'name': 'n2', 'supercategory': 'n1'},
>>>     {'id': 560, 'name': 'n6', 'supercategory': 'n2'},
>>>     {'id': 350, 'name': 'n5', 'supercategory': 'n2'},
>>> ])
>>> self.print_graph()
>>> subnodes = ['n3', 'n6', 'n4', 'n1']
>>> new1 = self.subgraph(subnodes, closure=1)
>>> new1.print_graph()
...
>>> print('new1.idx_to_id = {}'.format(ub.urepr(new1.idx_to_id, nl=0)))
>>> print('new1.idx_to_node = {}'.format(ub.urepr(new1.idx_to_node, nl=0)))
new1.idx_to_id = [130, 560, 640, 410]
new1.idx_to_node = ['n3', 'n6', 'n4', 'n1']

>>> indexes = [2, 1, 0, 5]
>>> new2 = self.take(indexes)
>>> new2.print_graph()
...
>>> print('new2.idx_to_id = {}'.format(ub.urepr(new2.idx_to_id, nl=0)))
>>> print('new2.idx_to_node = {}'.format(ub.urepr(new2.idx_to_node, nl=0)))
new2.idx_to_id = [640, 410, 130, 350]
new2.idx_to_node = ['n4', 'n1', 'n3', 'n5']

>>> subnodes = ['n3', 'n6', 'n4', 'n1']
>>> new3 = self.subgraph(subnodes, closure=0)
>>> new3.print_graph()

_build_index()

construct lookup tables

show()

forest_str()

print_graph()

normalize()

Applies a normalization scheme to the categories.

Note: this may break other tasks that depend on exact category names.

Returns:

CategoryTree

Example

>>> from kwcoco.category_tree import *  # NOQA
>>> import kwcoco
>>> orig = kwcoco.CategoryTree.demo('animals_v1')
>>> self = kwcoco.CategoryTree(nx.relabel_nodes(orig.graph, str.upper))
>>> norm = self.normalize()