630 lines
21 KiB
Python
630 lines
21 KiB
Python
#!/usr/bin/env python3
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"""
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Modular script to reproduce NDCG results for ViDoRe v2 benchmark.
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This script uses the interface from leann_multi_vector.py to:
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1. Download ViDoRe v2 datasets
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2. Build indexes (LEANN or Fast-Plaid)
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3. Perform retrieval
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4. Evaluate using NDCG metrics
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Usage:
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# Evaluate all ViDoRe v2 tasks
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python vidore_v2_benchmark.py --model colqwen2 --tasks all
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# Evaluate specific task
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python vidore_v2_benchmark.py --model colqwen2 --task Vidore2ESGReportsRetrieval
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# Use Fast-Plaid index
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python vidore_v2_benchmark.py --model colqwen2 --use-fast-plaid --fast-plaid-index-path ./indexes/vidore_fastplaid
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# Rebuild index
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python vidore_v2_benchmark.py --model colqwen2 --rebuild-index
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"""
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import argparse
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import json
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import os
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import time
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from pathlib import Path
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from typing import Any, Optional
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import numpy as np
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from datasets import load_dataset
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from PIL import Image
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from tqdm import tqdm
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# Import MTEB for evaluation metrics
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try:
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import pytrec_eval
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from mteb._evaluators.retrieval_metrics import (
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calculate_retrieval_scores,
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make_score_dict,
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)
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except ImportError:
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print("Warning: MTEB not available. Install with: pip install mteb")
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pytrec_eval = None
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from leann_multi_vector import (
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_ensure_repo_paths_importable,
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_load_colvision,
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_embed_images,
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_embed_queries,
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_build_index,
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_load_retriever_if_index_exists,
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_build_fast_plaid_index,
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_load_fast_plaid_index_if_exists,
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_search_fast_plaid,
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_get_fast_plaid_image,
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_get_fast_plaid_metadata,
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)
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_ensure_repo_paths_importable(__file__)
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# Language name to dataset language field value mapping
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# Dataset uses ISO 639-3 + ISO 15924 format (e.g., "eng-Latn")
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LANGUAGE_MAPPING = {
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"english": "eng-Latn",
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"french": "fra-Latn",
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"spanish": "spa-Latn",
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"german": "deu-Latn",
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}
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# ViDoRe v2 task configurations
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# Prompts match MTEB task metadata prompts
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VIDORE_V2_TASKS = {
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"Vidore2ESGReportsRetrieval": {
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"dataset_path": "vidore/esg_reports_v2",
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"revision": "0542c0d03da0ec1c8cbc517c8d78e7e95c75d3d3",
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"languages": ["french", "spanish", "english", "german"],
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"Vidore2EconomicsReportsRetrieval": {
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"dataset_path": "vidore/economics_reports_v2",
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"revision": "b3e3a04b07fbbaffe79be49dabf92f691fbca252",
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"languages": ["french", "spanish", "english", "german"],
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"Vidore2BioMedicalLecturesRetrieval": {
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"dataset_path": "vidore/biomedical_lectures_v2",
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"revision": "a29202f0da409034d651614d87cd8938d254e2ea",
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"languages": ["french", "spanish", "english", "german"],
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"Vidore2ESGReportsHLRetrieval": {
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"dataset_path": "vidore/esg_reports_human_labeled_v2",
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"revision": "6d467dedb09a75144ede1421747e47cf036857dd",
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# Note: This dataset doesn't have language filtering - all queries are English
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"languages": None, # No language filtering needed
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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}
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def load_vidore_v2_data(
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dataset_path: str,
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revision: Optional[str] = None,
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split: str = "test",
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language: Optional[str] = None,
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):
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"""
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Load ViDoRe v2 dataset.
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Returns:
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corpus: dict mapping corpus_id to PIL Image
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queries: dict mapping query_id to query text
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qrels: dict mapping query_id to dict of {corpus_id: relevance_score}
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"""
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print(f"Loading dataset: {dataset_path} (split={split}, language={language})")
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# Load queries
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query_ds = load_dataset(dataset_path, "queries", split=split, revision=revision)
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# Check if dataset has language field before filtering
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has_language_field = len(query_ds) > 0 and "language" in query_ds.column_names
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if language and has_language_field:
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# Map language name to dataset language field value (e.g., "english" -> "eng-Latn")
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dataset_language = LANGUAGE_MAPPING.get(language, language)
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query_ds_filtered = query_ds.filter(lambda x: x.get("language") == dataset_language)
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# Check if filtering resulted in empty dataset
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if len(query_ds_filtered) == 0:
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print(f"Warning: No queries found after filtering by language '{language}' (mapped to '{dataset_language}').")
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# Try with original language value (dataset might use simple names like 'english')
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print(f"Trying with original language value '{language}'...")
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query_ds_filtered = query_ds.filter(lambda x: x.get("language") == language)
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if len(query_ds_filtered) == 0:
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# Try to get a sample to see actual language values
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try:
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sample_ds = load_dataset(dataset_path, "queries", split=split, revision=revision)
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if len(sample_ds) > 0 and "language" in sample_ds.column_names:
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sample_langs = set(sample_ds["language"])
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print(f"Available language values in dataset: {sample_langs}")
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except Exception:
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pass
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else:
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print(f"Found {len(query_ds_filtered)} queries using original language value '{language}'")
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query_ds = query_ds_filtered
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queries = {}
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for row in query_ds:
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query_id = f"query-{split}-{row['query-id']}"
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queries[query_id] = row["query"]
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# Load corpus (images)
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corpus_ds = load_dataset(dataset_path, "corpus", split=split, revision=revision)
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corpus = {}
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for row in corpus_ds:
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corpus_id = f"corpus-{split}-{row['corpus-id']}"
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# Extract image from the dataset row
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if "image" in row:
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corpus[corpus_id] = row["image"]
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elif "page_image" in row:
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corpus[corpus_id] = row["page_image"]
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else:
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raise ValueError(f"No image field found in corpus. Available fields: {list(row.keys())}")
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# Load qrels (relevance judgments)
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qrels_ds = load_dataset(dataset_path, "qrels", split=split, revision=revision)
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qrels = {}
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for row in qrels_ds:
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query_id = f"query-{split}-{row['query-id']}"
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corpus_id = f"corpus-{split}-{row['corpus-id']}"
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if query_id not in qrels:
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qrels[query_id] = {}
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qrels[query_id][corpus_id] = int(row["score"])
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print(f"Loaded {len(queries)} queries, {len(corpus)} corpus items, {len(qrels)} query-relevance mappings")
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# Filter qrels to only include queries that exist
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qrels = {qid: rel_docs for qid, rel_docs in qrels.items() if qid in queries}
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return corpus, queries, qrels
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def build_index_from_corpus(
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corpus: dict[str, Image.Image],
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model,
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processor,
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index_path: str,
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use_fast_plaid: bool = False,
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rebuild: bool = False,
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):
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"""
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Build index from corpus images.
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Returns:
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tuple: (retriever or fast_plaid_index object, list of corpus_ids in order)
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"""
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# Ensure consistent ordering
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corpus_ids = sorted(corpus.keys()) # Sort for consistency
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images = [corpus[cid] for cid in corpus_ids]
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if use_fast_plaid:
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# Check if Fast-Plaid index exists
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if not rebuild and _load_fast_plaid_index_if_exists(index_path) is not None:
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print(f"Fast-Plaid index already exists at {index_path}")
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return _load_fast_plaid_index_if_exists(index_path), corpus_ids
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print(f"Building Fast-Plaid index at {index_path}...")
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# Embed images
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print("Embedding images...")
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doc_vecs = _embed_images(model, processor, images)
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# Build index
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fast_plaid_index, build_time = _build_fast_plaid_index(
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index_path, doc_vecs, corpus_ids, images
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)
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print(f"Fast-Plaid index built in {build_time:.2f}s")
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return fast_plaid_index, corpus_ids
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else:
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# Check if LEANN index exists
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if not rebuild:
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retriever = _load_retriever_if_index_exists(index_path)
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if retriever is not None:
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print(f"LEANN index already exists at {index_path}")
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return retriever, corpus_ids
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print(f"Building LEANN index at {index_path}...")
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# Embed images
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print("Embedding images...")
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doc_vecs = _embed_images(model, processor, images)
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# Build index
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retriever = _build_index(index_path, doc_vecs, corpus_ids, images)
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print(f"LEANN index built")
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return retriever, corpus_ids
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def search_queries(
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queries: dict[str, str],
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corpus_ids: list[str],
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model,
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processor,
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index_or_retriever: Any,
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use_fast_plaid: bool = False,
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fast_plaid_index_path: Optional[str] = None,
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top_k: int = 100,
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first_stage_k: int = 500,
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task_prompt: Optional[dict[str, str]] = None,
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) -> dict[str, dict[str, float]]:
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"""
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Search queries against the index.
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Args:
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queries: dict mapping query_id to query text
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corpus_ids: list of corpus_ids in the same order as the index
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model: model object
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processor: processor object
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index_or_retriever: index or retriever object
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use_fast_plaid: whether using Fast-Plaid
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fast_plaid_index_path: path to Fast-Plaid index (for metadata)
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top_k: top-k results to retrieve
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first_stage_k: first stage k for LEANN search
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task_prompt: Optional dict with prompt for query (e.g., {"query": "..."})
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Returns:
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results: dict mapping query_id to dict of {corpus_id: score}
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"""
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print(f"Searching {len(queries)} queries (top_k={top_k})...")
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query_ids = list(queries.keys())
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query_texts = [queries[qid] for qid in query_ids]
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# Match MTEB: combine queries with instruction/prompt if provided
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# MTEB's _combine_queries_with_instruction_text does: query + " " + instruction
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if task_prompt and "query" in task_prompt:
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instruction = task_prompt["query"]
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query_texts = [q + " " + instruction for q in query_texts]
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print(f"Added task prompt to queries: {instruction}")
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# Embed queries
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print("Embedding queries...")
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query_vecs = _embed_queries(model, processor, query_texts)
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results = {}
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for query_id, query_vec in zip(tqdm(query_ids, desc="Searching"), query_vecs):
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if use_fast_plaid:
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# Fast-Plaid search
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search_results, _ = _search_fast_plaid(index_or_retriever, query_vec, top_k)
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# Convert doc_id back to corpus_id
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query_results = {}
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for score, doc_id in search_results:
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if doc_id < len(corpus_ids):
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corpus_id = corpus_ids[doc_id]
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query_results[corpus_id] = float(score)
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else:
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# LEANN search
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query_np = query_vec.float().numpy()
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search_results = index_or_retriever.search_exact_all(query_np, topk=top_k)
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# Convert doc_id back to corpus_id
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query_results = {}
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for score, doc_id in search_results:
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if doc_id < len(corpus_ids):
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corpus_id = corpus_ids[doc_id]
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query_results[corpus_id] = float(score)
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results[query_id] = query_results
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return results
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def evaluate_results(
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results: dict[str, dict[str, float]],
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qrels: dict[str, dict[str, int]],
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k_values: list[int] = [1, 3, 5, 10, 100],
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) -> dict[str, float]:
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"""
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Evaluate retrieval results using NDCG and other metrics.
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Returns:
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Dictionary of metric scores
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"""
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if pytrec_eval is None:
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raise ImportError("pytrec_eval is required for evaluation. Install with: pip install pytrec-eval")
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# Check if we have any queries to evaluate
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if len(results) == 0:
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print("Warning: No queries to evaluate. Returning zero scores.")
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# Return zero scores for all metrics
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scores = {}
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for k in k_values:
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scores[f"ndcg_at_{k}"] = 0.0
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scores[f"map_at_{k}"] = 0.0
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scores[f"recall_at_{k}"] = 0.0
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scores[f"precision_at_{k}"] = 0.0
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scores[f"mrr_at_{k}"] = 0.0
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return scores
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print(f"Evaluating results with k_values={k_values}...")
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# Convert qrels to pytrec_eval format
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qrels_pytrec = {}
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for qid, rel_docs in qrels.items():
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qrels_pytrec[qid] = {did: score for did, score in rel_docs.items()}
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# Evaluate
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eval_result = calculate_retrieval_scores(
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results=results,
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qrels=qrels_pytrec,
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k_values=k_values,
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)
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# Format scores
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scores = make_score_dict(
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ndcg=eval_result.ndcg,
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_map=eval_result.map,
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recall=eval_result.recall,
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precision=eval_result.precision,
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mrr=eval_result.mrr,
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naucs=eval_result.naucs,
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naucs_mrr=eval_result.naucs_mrr,
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cv_recall=eval_result.cv_recall,
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task_scores={},
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)
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return scores
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def evaluate_task(
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task_name: str,
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model_name: str,
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index_path: str,
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use_fast_plaid: bool = False,
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fast_plaid_index_path: Optional[str] = None,
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language: Optional[str] = None,
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rebuild_index: bool = False,
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top_k: int = 100,
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first_stage_k: int = 500,
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k_values: list[int] = [1, 3, 5, 10, 100],
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output_dir: Optional[str] = None,
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):
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"""
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Evaluate a single ViDoRe v2 task.
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"""
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print(f"\n{'='*80}")
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print(f"Evaluating task: {task_name}")
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print(f"{'='*80}")
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# Get task config
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if task_name not in VIDORE_V2_TASKS:
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raise ValueError(f"Unknown task: {task_name}. Available: {list(VIDORE_V2_TASKS.keys())}")
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task_config = VIDORE_V2_TASKS[task_name]
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dataset_path = task_config["dataset_path"]
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revision = task_config["revision"]
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# Determine language
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if language is None:
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# Use first language if multiple available
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languages = task_config.get("languages")
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if languages is None:
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# Task doesn't support language filtering (e.g., Vidore2ESGReportsHLRetrieval)
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language = None
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elif len(languages) == 1:
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language = languages[0]
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else:
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language = None
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# Load data
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corpus, queries, qrels = load_vidore_v2_data(
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dataset_path=dataset_path,
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revision=revision,
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split="test",
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language=language,
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)
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# Check if we have any queries
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if len(queries) == 0:
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print(f"\nWarning: No queries found for task {task_name} with language {language}. Skipping evaluation.")
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# Return zero scores
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scores = {}
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for k in k_values:
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scores[f"ndcg_at_{k}"] = 0.0
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scores[f"map_at_{k}"] = 0.0
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scores[f"recall_at_{k}"] = 0.0
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scores[f"precision_at_{k}"] = 0.0
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scores[f"mrr_at_{k}"] = 0.0
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return scores
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# Load model
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print(f"\nLoading model: {model_name}")
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model_name_actual, model, processor, device_str, device, dtype = _load_colvision(model_name)
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print(f"Model loaded: {model_name_actual}")
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# Build or load index
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index_path_full = index_path if not use_fast_plaid else fast_plaid_index_path
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if index_path_full is None:
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index_path_full = f"./indexes/{task_name}_{model_name}"
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if use_fast_plaid:
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index_path_full = f"./indexes/{task_name}_{model_name}_fastplaid"
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index_or_retriever, corpus_ids_ordered = build_index_from_corpus(
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corpus=corpus,
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model=model,
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processor=processor,
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index_path=index_path_full,
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use_fast_plaid=use_fast_plaid,
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rebuild=rebuild_index,
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)
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# Search queries
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task_prompt = task_config.get("prompt")
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results = search_queries(
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queries=queries,
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corpus_ids=corpus_ids_ordered,
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model=model,
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processor=processor,
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index_or_retriever=index_or_retriever,
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use_fast_plaid=use_fast_plaid,
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fast_plaid_index_path=fast_plaid_index_path,
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top_k=top_k,
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first_stage_k=first_stage_k,
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task_prompt=task_prompt,
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)
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# Evaluate
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scores = evaluate_results(results, qrels, k_values=k_values)
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# Print results
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print(f"\n{'='*80}")
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print(f"Results for {task_name}:")
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print(f"{'='*80}")
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for metric, value in scores.items():
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if isinstance(value, (int, float)):
|
|
print(f" {metric}: {value:.5f}")
|
|
|
|
# Save results
|
|
if output_dir:
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
results_file = os.path.join(output_dir, f"{task_name}_results.json")
|
|
scores_file = os.path.join(output_dir, f"{task_name}_scores.json")
|
|
|
|
with open(results_file, "w") as f:
|
|
json.dump(results, f, indent=2)
|
|
print(f"\nSaved results to: {results_file}")
|
|
|
|
with open(scores_file, "w") as f:
|
|
json.dump(scores, f, indent=2)
|
|
print(f"Saved scores to: {scores_file}")
|
|
|
|
return scores
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(
|
|
description="Evaluate ViDoRe v2 benchmark using LEANN/Fast-Plaid indexing"
|
|
)
|
|
parser.add_argument(
|
|
"--model",
|
|
type=str,
|
|
default="colqwen2",
|
|
choices=["colqwen2", "colpali"],
|
|
help="Model to use",
|
|
)
|
|
parser.add_argument(
|
|
"--task",
|
|
type=str,
|
|
default=None,
|
|
help="Specific task to evaluate (or 'all' for all tasks)",
|
|
)
|
|
parser.add_argument(
|
|
"--tasks",
|
|
type=str,
|
|
default="all",
|
|
help="Tasks to evaluate: 'all' or comma-separated list",
|
|
)
|
|
parser.add_argument(
|
|
"--index-path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to LEANN index (auto-generated if not provided)",
|
|
)
|
|
parser.add_argument(
|
|
"--use-fast-plaid",
|
|
action="store_true",
|
|
help="Use Fast-Plaid instead of LEANN",
|
|
)
|
|
parser.add_argument(
|
|
"--fast-plaid-index-path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to Fast-Plaid index (auto-generated if not provided)",
|
|
)
|
|
parser.add_argument(
|
|
"--rebuild-index",
|
|
action="store_true",
|
|
help="Rebuild index even if it exists",
|
|
)
|
|
parser.add_argument(
|
|
"--language",
|
|
type=str,
|
|
default=None,
|
|
help="Language to evaluate (default: first available)",
|
|
)
|
|
parser.add_argument(
|
|
"--top-k",
|
|
type=int,
|
|
default=100,
|
|
help="Top-k results to retrieve",
|
|
)
|
|
parser.add_argument(
|
|
"--first-stage-k",
|
|
type=int,
|
|
default=500,
|
|
help="First stage k for LEANN search",
|
|
)
|
|
parser.add_argument(
|
|
"--k-values",
|
|
type=str,
|
|
default="1,3,5,10,100",
|
|
help="Comma-separated k values for evaluation (e.g., '1,3,5,10,100')",
|
|
)
|
|
parser.add_argument(
|
|
"--output-dir",
|
|
type=str,
|
|
default="./vidore_v2_results",
|
|
help="Output directory for results",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Parse k_values
|
|
k_values = [int(k.strip()) for k in args.k_values.split(",")]
|
|
|
|
# Determine tasks to evaluate
|
|
if args.task:
|
|
tasks_to_eval = [args.task]
|
|
elif args.tasks.lower() == "all":
|
|
tasks_to_eval = list(VIDORE_V2_TASKS.keys())
|
|
else:
|
|
tasks_to_eval = [t.strip() for t in args.tasks.split(",")]
|
|
|
|
print(f"Tasks to evaluate: {tasks_to_eval}")
|
|
|
|
# Evaluate each task
|
|
all_scores = {}
|
|
for task_name in tasks_to_eval:
|
|
try:
|
|
scores = evaluate_task(
|
|
task_name=task_name,
|
|
model_name=args.model,
|
|
index_path=args.index_path,
|
|
use_fast_plaid=args.use_fast_plaid,
|
|
fast_plaid_index_path=args.fast_plaid_index_path,
|
|
language=args.language,
|
|
rebuild_index=args.rebuild_index,
|
|
top_k=args.top_k,
|
|
first_stage_k=args.first_stage_k,
|
|
k_values=k_values,
|
|
output_dir=args.output_dir,
|
|
)
|
|
all_scores[task_name] = scores
|
|
except Exception as e:
|
|
print(f"\nError evaluating {task_name}: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
continue
|
|
|
|
# Print summary
|
|
if all_scores:
|
|
print(f"\n{'='*80}")
|
|
print("SUMMARY")
|
|
print(f"{'='*80}")
|
|
for task_name, scores in all_scores.items():
|
|
print(f"\n{task_name}:")
|
|
# Print main metrics
|
|
for metric in ["ndcg_at_5", "ndcg_at_10", "ndcg_at_100", "map_at_10", "recall_at_10"]:
|
|
if metric in scores:
|
|
print(f" {metric}: {scores[metric]:.5f}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|