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Datasets:
idoco
/
PopVQA

Tasks:
Visual Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
Community
2
PopVQA
File size: 4,692 Bytes 
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from dotenv import load_dotenv
load_dotenv()
from dataset_utils import *
import argparse

'''
This is a script to build (or expand) the PopVQA dataset.
Before running this script, make sure your directory contains the following:
1. A CSV file with the base dataframe containing the columns 's_uri' (from wikidata) and 'type' (to match against the relation templates).
2. a dir named 'relation_templates' containing CSV files with the relation templates for each type. The templates are triplets 'uri' - 'relation' - 'template'.
See the existing files for reference.
Run the script with the following command:
python build_dataset.py --base-df <path_to_base_df> --start <start_step> --end <end_step>
'''

def main(args):
    dir_name, file_name = os.path.split(args.base_df)
    base_name, _ = os.path.splitext(file_name)
    base_df = pd.read_csv(args.base_df).drop_duplicates('s_uri')
    assert 'type' in base_df.columns, "The base dataframe must contain a 'type' column."

    types = base_df['type'].unique()
    
    for entity_type in base_df['type'].unique():
        template_path = os.path.join(dir_name, "relation_templates", f"relation_templates_{entity_type}.csv")
        assert os.path.isfile(template_path), f"Missing relation template for type '{entity_type}' at: {template_path}"

    all_question_dfs = []

    for entity_type in types:
        type_df = base_df[base_df['type'] == entity_type].copy()
        type_dir = os.path.join(dir_name, entity_type)
        os.makedirs(type_dir, exist_ok=True)

        template_path = os.path.join(dir_name, "relation_templates", f"relation_templates_{entity_type}.csv")
        templates = pd.read_csv(template_path)

        print(f"Processing type: {entity_type}")
        if args.start <= 0:
            subject_to_relation = get_all_properties(type_df)
            subject_to_relation = subject_to_relation[subject_to_relation['r_uri'].isin(templates['uri'])]
            subject_to_relation.to_csv(os.path.join(type_dir, f"{base_name}_subject_to_relation.csv"), index=False)

        if args.start <= 1 and args.end >= 1:
            if args.start == 1:
                subject_to_relation = pd.read_csv(os.path.join(type_dir, f"{base_name}_subject_to_relation.csv"))
            aliases = get_aliases(subject_to_relation)
            aliases.to_csv(os.path.join(type_dir, f"{base_name}_all_aliases.csv"), index=False)

        if args.start <= 2 and args.end >= 2:
            if args.start == 2:
                subject_to_relation = pd.read_csv(os.path.join(type_dir, f"{base_name}_subject_to_relation.csv"))
                aliases = pd.read_csv(os.path.join(type_dir, f"{base_name}_all_aliases.csv"))
            a_types = attribute_type(subject_to_relation)
            a_types.to_csv(os.path.join(type_dir, f"{base_name}_complete_attribute_types.csv"), index=False)

        if args.start <= 3 and args.end >= 3:
            if args.start == 3:
                subject_to_relation = pd.read_csv(os.path.join(type_dir, f"{base_name}_subject_to_relation.csv"))
                aliases = pd.read_csv(os.path.join(type_dir, f"{base_name}_all_aliases.csv"))
                a_types = pd.read_csv(os.path.join(type_dir, f"{base_name}_complete_attribute_types.csv"))
            triplets = aggregate_triplets(type_df, aliases, subject_to_relation, a_types, add_unesco=False)
            triplets.to_csv(os.path.join(type_dir, f"{base_name}_question_triplets.csv"), index=False)

        if args.start <= 4 and args.end >= 4:
            if args.start == 4:
                triplets = pd.read_csv(os.path.join(type_dir, f"{base_name}_question_triplets.csv"))
            triplets = build_prompts(type_df, triplets, templates)
            triplets['type'] = entity_type
            triplets.to_csv(os.path.join(type_dir, f"{base_name}_questions.csv"), index=False)
            all_question_dfs.append(triplets)

    # Combine all question files and write to the top-level directory
    if all_question_dfs:
        combined_df = pd.concat(all_question_dfs, ignore_index=True)
        combined_df.to_csv(os.path.join(dir_name, f"{base_name}_all_questions.csv"), index=False)
        print(f"Combined questions file saved to {os.path.join(dir_name, f'{base_name}_all_questions.csv')}")


def get_exp_parser():
    parser = argparse.ArgumentParser(add_help=False)
    parser.add_argument('--base-df', type=str)
    parser.add_argument('--start', type=int, default=0, help="Start step for building the dataset.")
    parser.add_argument('--end', type=int, default=4, help="End step for building the dataset.")
    return parser


if __name__ == "__main__":
    parser = get_exp_parser()
    args = parser.parse_args()
    main(args)