reproduce docvqa results
This commit is contained in:
yichuan-w
@@ -25,38 +25,14 @@ Usage:
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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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from typing import 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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ViDoReBenchmarkEvaluator,
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)
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_ensure_repo_paths_importable(__file__)
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@@ -181,194 +157,14 @@ def load_vidore_v2_data(
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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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# Filter out queries without any relevant documents (matching MTEB behavior)
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# This is important for correct NDCG calculation
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qrels_filtered = {qid: rel_docs for qid, rel_docs in qrels.items() if len(rel_docs) > 0}
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queries_filtered = {qid: query_text for qid, query_text in queries.items() if qid in qrels_filtered}
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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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print(f"After filtering queries without positives: {len(queries_filtered)} queries, {len(qrels_filtered)} query-relevance mappings")
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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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return corpus, queries_filtered, qrels_filtered
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def evaluate_task(
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@@ -432,10 +228,14 @@ def evaluate_task(
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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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# Initialize evaluator
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evaluator = ViDoReBenchmarkEvaluator(
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model_name=model_name,
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use_fast_plaid=use_fast_plaid,
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top_k=top_k,
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first_stage_k=first_stage_k,
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k_values=k_values,
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)
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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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@@ -444,32 +244,24 @@ def evaluate_task(
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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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index_or_retriever, corpus_ids_ordered = evaluator.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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results = evaluator.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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scores = evaluator.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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