def load_annotations()

in lib/datasets/epic.py [0:0]

• 38 lines of code
• 8 McCabe index (conditional complexity)

def load_annotations(is_train):
    """Load EPIC-Kitchens annotations."""
    annotations = []

    verb_set = set()
    noun_set = set()

    filename = os.path.join(cfg.EPIC.ANNOTATION_DIR, cfg.EPIC.ANNOTATIONS)
    with open(filename, 'rb') as f:
        f.readline()
        reader = csv.reader(f)
        for row in reader:
            person = row[1]

            if is_train:
                if int(person[1:]) not in TRAIN_PERSON_INDICES:
                    continue
            else:
                if int(person[1:]) in TRAIN_PERSON_INDICES:
                    continue

            #uid,participant_id,video_id,narration,start_timestamp,stop_timestamp,start_frame,stop_frame,verb,verb_class,noun,noun_class,all_nouns,all_noun_classes
            video_name = row[2]
            start_frame = sec_to_frame(time_to_sec(row[4]))
            stop_frame = sec_to_frame(time_to_sec(row[5]))
            verb = int(row[-5])
            noun = int(row[-3])

            assert verb < NUM_CLASSES_VERB, verb
            assert verb >= 0, verb
            assert noun < NUM_CLASSES_NOUN, noun
            assert noun >= 0, noun

            annotations.append(
                (person, video_name, start_frame, stop_frame, verb, noun))
            verb_set.add(verb)
            noun_set.add(noun)

    logger.info('See %d verbs and %d nouns in the dataset loaded.' % (
        len(verb_set), len(noun_set)))

    cur_label_set = verb_set if cfg.EPIC.CLASS_TYPE == 'verb' else noun_set
    if len(cur_label_set) != cfg.MODEL.NUM_CLASSES:
        logger.warn(
            '# classes seen (%d) != MODEL.NUM_CLASSES' % len(cur_label_set))
    assert len(annotations) == (cfg.TRAIN.DATASET_SIZE if is_train
                                else cfg.TEST.DATASET_SIZE)
    return annotations