def get_synsets_from_class_ids(class_ids):
"""Returns the Synsets of the appropriate subgraph corresponding to class_ids.
For each class id in class_ids, the corresponding Synset is found among the
Synsets of the subgraph corresponding to the split that is chosen for the
fine-grainedness analysis.
Args:
class_ids: A np.array of ints in the range between 1 and the total number of
classes that contains the two class id's chosen for an episode.
Returns:
A list of Synsets.
Raises:
ValueError: The dataset specification is not found in the expected location.
"""
# First load the DatasetSpecification of ImageNet.
dataset_records_path = os.path.join(FLAGS.records_root_dir, 'ilsvrc_2012')
imagenet_data_spec = dataset_spec.load_dataset_spec(dataset_records_path)
# A set of Synsets of the split's subgraph.
split_enum = get_finegrainedness_split_enum()
split_subgraph = imagenet_data_spec.split_subgraphs[split_enum]
# Go from class_ids (integers in the range from 1 to the total number of
# classes in the Split) to WordNet id's, e.g n02075296.
wn_ids = []
for class_id in class_ids:
wn_ids.append(imagenet_data_spec.class_names[class_id])
# Find the Synsets in split_subgraph whose WordNet id's are wn_ids.
synsets = imagenet_spec.get_synsets_from_ids(wn_ids, split_subgraph)
return [synsets[wn_id] for wn_id in wn_ids]
def test_on_toy_graph(self):
specification = create_toy_graph()
toy_graph, _, _ = specification
wn_ids = [5, 0, 6]
id_to_synset = imagenet_spec.get_synsets_from_ids(wn_ids, toy_graph)
self.assertEqual(set(id_to_synset.keys()), set(wn_ids))
for wn_id, synset in id_to_synset.items():
self.assertEqual(wn_id, synset.wn_id)