utils_v2.py

import json
import os
import pandas as pd
from utils import create_hyperlinked_names, process_model_size

def sum_lol(lol):
    assert isinstance(lol, list) and all(isinstance(i, list) for i in lol), f"Input should be a list of lists, got {type(lol)}"
    total = []
    for sublist in lol:
        total.extend(sublist)
    return total

SCORE_BASE_DIR = "scores"
META_DATA = ["model_name", "model_size", "url"]
DATASETS = {
    "image": {
        "I-CLS": ['VOC2007', 'N24News', 'SUN397', 'ObjectNet', 'Country211', 'Place365', 'ImageNet-1K', 'HatefulMemes', 'ImageNet-A', 'ImageNet-R'], 
        "I-QA": ['OK-VQA', 'A-OKVQA', 'DocVQA', 'InfographicsVQA', 'ChartQA', 'Visual7W-Pointing', 'ScienceQA', 'GQA', 'TextVQA', 'VizWiz'], 
        "I-RET": ['VisDial', 'CIRR', 'VisualNews_t2i', 'VisualNews_i2t', 'MSCOCO_t2i', 'MSCOCO_i2t', 'NIGHTS', 'WebQA', 'FashionIQ', 'Wiki-SS-NQ', 'OVEN', 'EDIS'],
        "I-VG": ['MSCOCO', 'RefCOCO', 'RefCOCO-Matching', 'Visual7W']
        }, 
    "visdoc": {
        "ViDoRe-V1": ['ViDoRe_arxivqa', 'ViDoRe_docvqa', 'ViDoRe_infovqa', 'ViDoRe_tabfquad', 'ViDoRe_tatdqa', 'ViDoRe_shiftproject', 'ViDoRe_syntheticDocQA_artificial_intelligence', 'ViDoRe_syntheticDocQA_energy', 'ViDoRe_syntheticDocQA_government_reports', 'ViDoRe_syntheticDocQA_healthcare_industry'], 
        "ViDoRe-V2": ["ViDoRe_esg_reports_human_labeled_v2", "ViDoRe_biomedical_lectures_v2", "ViDoRe_economics_reports_v2", "ViDoRe_esg_reports_v2"],  # Following Abandoned: "ViDoRe_biomedical_lectures_v2_multilingual", "ViDoRe_economics_reports_v2_multilingual", "ViDoRe_esg_reports_v2_multilingual"
        "VisRAG": ['VisRAG_ArxivQA', 'VisRAG_ChartQA', 'VisRAG_MP-DocVQA', 'VisRAG_SlideVQA', 'VisRAG_InfoVQA', 'VisRAG_PlotQA'], 
        "VisDoc-OOD": ['ViDoSeek-page', 'ViDoSeek-doc', 'MMLongBench-page', 'MMLongBench-doc']
        }, 
    "video": {
        "V-CLS": ['K700', 'UCF101', 'HMDB51', 'SmthSmthV2', 'Breakfast'], 
        "V-QA": ['Video-MME', 'MVBench', 'NExTQA', 'EgoSchema'], 
        "V-RET": ['MSR-VTT', 'MSVD', 'DiDeMo', 'VATEX', 'YouCook2'], 
        "V-MRET": ['QVHighlight', 'Charades-STA', 'MomentSeeker', 'ActivityNetQA']
        }
}
ALL_DATASETS_SPLITS = {k: sum_lol(list(v.values())) for k, v in DATASETS.items()}
ALL_DATASETS = sum_lol(list(ALL_DATASETS_SPLITS.values()))
MODALITIES = list(DATASETS.keys())
SPECIAL_METRICS = {
    '__default__': 'hit@1',
}

BASE_COLS = ['Rank', 'Models', 'Model Size(B)']
BASE_DATA_TITLE_TYPE = ['number', 'markdown', 'str', 'markdown']

COLUMN_NAMES = BASE_COLS + ["Overall", 'Image-Overall', 'Video-Overall', 'Visdoc-Overall']
DATA_TITLE_TYPE = BASE_DATA_TITLE_TYPE + \
                    ['number'] * 3

SUB_TASKS_I = ["I-CLS", "I-QA", "I-RET", "I-VG"]
TASKS_I = ['Image-Overall'] + SUB_TASKS_I + ALL_DATASETS_SPLITS['image']
COLUMN_NAMES_I = BASE_COLS + TASKS_I
DATA_TITLE_TYPE_I = BASE_DATA_TITLE_TYPE + \
                    ['number'] * len(TASKS_I + SUB_TASKS_I)

SUB_TASKS_V = ["V-CLS", "V-QA", "V-RET", "V-MRET"]
TASKS_V = ['Video-Overall'] + SUB_TASKS_V + ALL_DATASETS_SPLITS['video']
COLUMN_NAMES_V = BASE_COLS + TASKS_V
DATA_TITLE_TYPE_V = BASE_DATA_TITLE_TYPE + \
                    ['number'] * len(TASKS_V + SUB_TASKS_V)

SUB_TASKS_D = ['ViDoRe-V1', 'ViDoRe-V2', 'VisRAG', 'VisDoc-OOD']
TASKS_D = ['Visdoc-Overall'] + SUB_TASKS_D + ALL_DATASETS_SPLITS['visdoc']
COLUMN_NAMES_D = BASE_COLS + TASKS_D
DATA_TITLE_TYPE_D = BASE_DATA_TITLE_TYPE + \
                    ['number'] * len(TASKS_D + SUB_TASKS_D)

TABLE_INTRODUCTION = """**MMEB**: Massive MultiModal Embedding Benchmark \n
                        Models are ranked based on **Overall**"""
TABLE_INTRODUCTION_I = """**I-CLS**: Image Classification, **I-QA**: (Image) Visual Question Answering, **I-RET**: Image Retrieval, **I-VG**: (Image) Visual Grounding \n
                        Models are ranked based on **Image-Overall**"""
TABLE_INTRODUCTION_V = """**V-CLS**: Video Classification, **V-QA**: (Video) Visual Question Answering, **V-RET**: Video Retrieval, **V-MRET**: Video Moment Retrieval \n
                        Models are ranked based on **Video-Overall**"""
TABLE_INTRODUCTION_D = """**VisDoc**: Visual Document Understanding \n
                        Models are ranked based on **VisDoc**"""

LEADERBOARD_INFO = """
## Dataset Summary
"""

CITATION_BUTTON_TEXT = r"""TBA"""

def load_single_json(file_path):
    with open(file_path, 'r') as file:
        data = json.load(file)
    return data

def load_data(base_dir=SCORE_BASE_DIR):
    all_data = []
    for file_name in os.listdir(base_dir):
        if file_name.endswith('.json'):
            file_path = os.path.join(base_dir, file_name)
            data = load_single_json(file_path)
            all_data.append(data)
    return all_data

def load_scores(raw_scores=None):
    """This function loads the raw scores from the user provided scores summary and flattens them into a single dictionary."""
    all_scores = {}
    for modality, datasets_list in DATASETS.items(): # Ex.: ('image', {'I-CLS': [...], 'I-QA': [...]})
        for sub_task, datasets in datasets_list.items(): # Ex.: ('I-CLS', ['VOC2007', 'N24News', ...])
            for dataset in datasets: # Ex.: 'VOC2007'
                score = raw_scores.get(modality, {}).get(dataset, 0.0)
                score = 0.0 if score == "FILE_N/A" else score
                metric = SPECIAL_METRICS.get(dataset, 'hit@1')
                if isinstance(score, dict):
                    if modality == 'visdoc':
                        metric = "ndcg_linear@5" if "ndcg_linear@5" in score else "ndcg@5"
                    score = score.get(metric, 0.0)
                all_scores[dataset] = round(score * 100.0, 2)
    return all_scores

def calculate_score(raw_scores=None):
    """This function calculates the overall average scores for all datasets as well as avg scores for each modality and sub-task based on the raw scores.
    """
    def get_avg(sum_score, leng):
        avg = sum_score / leng if leng > 0 else 0.0
        avg = round(avg, 2)  # Round to 2 decimal places
        return avg
    
    all_scores = load_scores(raw_scores)
    avg_scores = {}

    # Calculate overall score for all datasets
    avg_scores['Overall'] = get_avg(sum(all_scores.values()), len(ALL_DATASETS))

    # Calculate scores for each modality
    for modality in MODALITIES:
        datasets_for_each_modality = ALL_DATASETS_SPLITS[modality]
        avg_scores[f"{modality.capitalize()}-Overall"] = get_avg(
            sum(all_scores.get(dataset, 0.0) for dataset in datasets_for_each_modality),
            len(datasets_for_each_modality)
        )
    
    # Calculate scores for each sub-task
    for modality, datasets_list in DATASETS.items():
        for sub_task, datasets in datasets_list.items():
            sub_task_score = sum(all_scores.get(dataset, 0.0) for dataset in datasets)
            avg_scores[sub_task] = get_avg(sub_task_score, len(datasets))

    all_scores.update(avg_scores)
    return all_scores

def generate_model_row(data):
    metadata = data['metadata']
    row = {
        'Models': metadata.get('model_name', None), 
        'Model Size(B)': metadata.get('model_size', None),
        'URL': metadata.get('url', None), 
        'Data Source': metadata.get('data_source', 'Self-Reported'),
    }
    scores = calculate_score(data['metrics'])
    row.update(scores)
    return row

def rank_models(df, column='Overall', rank_name='Rank'):
    """Ranks the models based on the specific score."""
    df = df.sort_values(by=column, ascending=False).reset_index(drop=True)
    df[rank_name] = range(1, len(df) + 1)
    return df

def get_df():
    """Generates a DataFrame from the loaded data."""
    all_data = load_data()
    rows = [generate_model_row(data) for data in all_data]
    df = pd.DataFrame(rows)
    df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size)
    df = create_hyperlinked_names(df)
    df = rank_models(df, column='Overall')
    return df

def refresh_data():
    df = get_df()
    return df[COLUMN_NAMES]

def search_and_filter_models(df, query, min_size, max_size):
    filtered_df = df.copy()
    
    if query:
        filtered_df = filtered_df[filtered_df['Models'].str.contains(query, case=False, na=False)]

    size_mask = filtered_df['Model Size(B)'].apply(lambda x: 
        (min_size <= 1000.0 <= max_size) if x == 'unknown' 
        else (min_size <= x <= max_size))
    
    filtered_df = filtered_df[size_mask]
    
    return filtered_df[COLUMN_NAMES]