torch_concepts.data.CUBDataset

class CUBDataset(root: str | None = None, image_size: int = 224, concept_subset: list | None = None, label_descriptions: Mapping | None = None)

Dataset class for CUB-200-2011 (Caltech-UCSD Birds).

CUB-200-2011 contains 11,788 bird images across 200 species classes, annotated with 112 binary semantic attributes selected by Koh et al. [CBM Paper] from the full set of 312 CUB attributes.

Official train / val / test splits from the pre-processed pickle files are preserved; use NativeSplitter in the corresponding datamodule.

The concept vector per sample contains:

• columns 0-111: 112 binary semantic attributes (cardinality 1 each)

• column 112: bird species index 0-199 (cardinality 200)

Parameters:

• root (str, optional) – Root directory that contains class_attr_data_10/ and CUB_200_2011/. Defaults to ./data/CUB200.

• image_size (int, optional) – Side length (px) images are resized to. Defaults to 224.

• concept_subset (list of str, optional) – Subset of concept names to retain. None keeps all 113.

• label_descriptions (dict, optional) – Mapping from concept name to human-readable description.

__init__(root: str | None = None, image_size: int = 224, concept_subset: list | None = None, label_descriptions: Mapping | None = None)

Methods

__init__([root, image_size, concept_subset, ...])
add_exogenous(name, value[, convert_precision])
add_scaler(key, scaler)	Add a scaler for preprocessing a specific tensor.
build()	Process raw CUB pickle files and save cached dataset artefacts.
collate(samples)	Collate samples into a batch, re-annotating the ground-truth concepts.
download()	Downloads the CUB dataset if it is not already present.
load()	Loads raw dataset and preprocess data.
load_raw()	Load processed artefacts from disk.
maybe_build()
maybe_download()
remove_exogenous(name)
set_concepts(concepts)	Set concept annotations for the dataset.
set_graph(graph)	Set the adjacency matrix of the causal graph between concepts as a pandas DataFrame.

Attributes

annotations	Annotations for the concepts in the dataset.
concept_names	List of concept names in the dataset.
exogenous	Mapping of dataset's exogenous variables.
graph	Adjacency matrix of the causal graph between concepts.
has_concepts	Whether the dataset has concept annotations.
has_exogenous	Whether the dataset has exogenous information.
n_concepts	Number of concepts in the dataset.
n_exogenous	Number of exogenous variables in the dataset.
n_features	Shape of features in dataset's input (excluding number of samples).
n_samples	Number of samples in the dataset.
processed_filenames	The list of processed filenames in the self.root_dir folder that must be present in order to skip build().
processed_paths	The absolute paths of the processed files that must be present in order to skip building.
raw_filenames	The list of raw filenames in the self.root_dir folder that must be present in order to skip download().
raw_paths	The absolute paths of the raw files that must be present in order to skip downloading.
root_dir
shape	Shape of the input tensor.