reproduce docvqa results
This commit is contained in:
yichuan-w
@@ -223,17 +223,13 @@ def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
|
||||
model.eval()
|
||||
|
||||
# Match MTEB's exact query processing from ColPaliEngineWrapper.get_text_embeddings:
|
||||
# 1. MTEB receives batch["text"] which may already include instruction/prompt
|
||||
# 1. MTEB receives batch["text"] which already includes instruction/prompt (from _combine_queries_with_instruction_text)
|
||||
# 2. Manually adds: query_prefix + text + query_augmentation_token * 10
|
||||
# 3. Calls processor.process_queries(batch) where batch is now a list of strings
|
||||
# 4. process_queries adds: query_prefix + text + suffix (suffix = query_augmentation_token * 10)
|
||||
#
|
||||
# However, MTEB's approach results in duplicate addition (20 tokens total).
|
||||
# Since we're already adding the prompt in search_queries, let's try:
|
||||
# Option 1: Just call process_queries (let it handle all additions) - avoids duplicate
|
||||
# Option 2: Manual add + process_texts (to avoid duplicate)
|
||||
#
|
||||
# Testing shows Option 1 works better - just call process_queries without manual addition
|
||||
# This results in duplicate addition: query_prefix is added twice, query_augmentation_token * 20 total
|
||||
# We need to match this exactly to reproduce MTEB results
|
||||
|
||||
all_embeds = []
|
||||
batch_size = 32 # Match MTEB's default batch_size
|
||||
@@ -242,9 +238,15 @@ def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
|
||||
for i in tqdm(range(0, len(queries), batch_size), desc="Embedding queries"):
|
||||
batch_queries = queries[i:i + batch_size]
|
||||
|
||||
# Just call process_queries - it will add query_prefix + text + 10 tokens
|
||||
# This avoids duplicate addition that happens in MTEB's approach
|
||||
inputs = processor.process_queries(batch_queries)
|
||||
# Match MTEB: manually add query_prefix + text + query_augmentation_token * 10
|
||||
# Then process_queries will add them again (resulting in 20 augmentation tokens total)
|
||||
batch = [
|
||||
processor.query_prefix
|
||||
+ t
|
||||
+ processor.query_augmentation_token * 10
|
||||
for t in batch_queries
|
||||
]
|
||||
inputs = processor.process_queries(batch)
|
||||
inputs = {k: v.to(model.device) for k, v in inputs.items()}
|
||||
|
||||
if model.device.type == "cuda":
|
||||
@@ -1044,3 +1046,249 @@ class LeannMultiVector:
|
||||
"image_path": meta.get("image_path", ""),
|
||||
}
|
||||
return None
|
||||
|
||||
|
||||
class ViDoReBenchmarkEvaluator:
|
||||
"""
|
||||
A reusable class for evaluating ViDoRe benchmarks (v1 and v2).
|
||||
This class encapsulates common functionality for building indexes, searching, and evaluating.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str,
|
||||
use_fast_plaid: bool = False,
|
||||
top_k: int = 100,
|
||||
first_stage_k: int = 500,
|
||||
k_values: list[int] = None,
|
||||
):
|
||||
"""
|
||||
Initialize the evaluator.
|
||||
|
||||
Args:
|
||||
model_name: Model name ("colqwen2" or "colpali")
|
||||
use_fast_plaid: Whether to use Fast-Plaid instead of LEANN
|
||||
top_k: Top-k results to retrieve
|
||||
first_stage_k: First stage k for LEANN search
|
||||
k_values: List of k values for evaluation metrics
|
||||
"""
|
||||
self.model_name = model_name
|
||||
self.use_fast_plaid = use_fast_plaid
|
||||
self.top_k = top_k
|
||||
self.first_stage_k = first_stage_k
|
||||
self.k_values = k_values if k_values is not None else [1, 3, 5, 10, 100]
|
||||
|
||||
# Load model once (can be reused across tasks)
|
||||
self._model = None
|
||||
self._processor = None
|
||||
self._model_name_actual = None
|
||||
|
||||
def _load_model_if_needed(self):
|
||||
"""Lazy load the model."""
|
||||
if self._model is None:
|
||||
print(f"\nLoading model: {self.model_name}")
|
||||
self._model_name_actual, self._model, self._processor, _, _, _ = _load_colvision(self.model_name)
|
||||
print(f"Model loaded: {self._model_name_actual}")
|
||||
|
||||
def build_index_from_corpus(
|
||||
self,
|
||||
corpus: dict[str, Image.Image],
|
||||
index_path: str,
|
||||
rebuild: bool = False,
|
||||
) -> tuple[Any, list[str]]:
|
||||
"""
|
||||
Build index from corpus images.
|
||||
|
||||
Args:
|
||||
corpus: dict mapping corpus_id to PIL Image
|
||||
index_path: Path to save/load the index
|
||||
rebuild: Whether to rebuild even if index exists
|
||||
|
||||
Returns:
|
||||
tuple: (retriever or fast_plaid_index object, list of corpus_ids in order)
|
||||
"""
|
||||
self._load_model_if_needed()
|
||||
|
||||
# Ensure consistent ordering
|
||||
corpus_ids = sorted(corpus.keys())
|
||||
images = [corpus[cid] for cid in corpus_ids]
|
||||
|
||||
if self.use_fast_plaid:
|
||||
# Check if Fast-Plaid index exists
|
||||
if not rebuild and _load_fast_plaid_index_if_exists(index_path) is not None:
|
||||
print(f"Fast-Plaid index already exists at {index_path}")
|
||||
return _load_fast_plaid_index_if_exists(index_path), corpus_ids
|
||||
|
||||
print(f"Building Fast-Plaid index at {index_path}...")
|
||||
print("Embedding images...")
|
||||
doc_vecs = _embed_images(self._model, self._processor, images)
|
||||
|
||||
fast_plaid_index, build_time = _build_fast_plaid_index(
|
||||
index_path, doc_vecs, corpus_ids, images
|
||||
)
|
||||
print(f"Fast-Plaid index built in {build_time:.2f}s")
|
||||
return fast_plaid_index, corpus_ids
|
||||
else:
|
||||
# Check if LEANN index exists
|
||||
if not rebuild:
|
||||
retriever = _load_retriever_if_index_exists(index_path)
|
||||
if retriever is not None:
|
||||
print(f"LEANN index already exists at {index_path}")
|
||||
return retriever, corpus_ids
|
||||
|
||||
print(f"Building LEANN index at {index_path}...")
|
||||
print("Embedding images...")
|
||||
doc_vecs = _embed_images(self._model, self._processor, images)
|
||||
|
||||
retriever = _build_index(index_path, doc_vecs, corpus_ids, images)
|
||||
print(f"LEANN index built")
|
||||
return retriever, corpus_ids
|
||||
|
||||
def search_queries(
|
||||
self,
|
||||
queries: dict[str, str],
|
||||
corpus_ids: list[str],
|
||||
index_or_retriever: Any,
|
||||
fast_plaid_index_path: Optional[str] = None,
|
||||
task_prompt: Optional[dict[str, str]] = None,
|
||||
) -> dict[str, dict[str, float]]:
|
||||
"""
|
||||
Search queries against the index.
|
||||
|
||||
Args:
|
||||
queries: dict mapping query_id to query text
|
||||
corpus_ids: list of corpus_ids in the same order as the index
|
||||
index_or_retriever: index or retriever object
|
||||
fast_plaid_index_path: path to Fast-Plaid index (for metadata)
|
||||
task_prompt: Optional dict with prompt for query (e.g., {"query": "..."})
|
||||
|
||||
Returns:
|
||||
results: dict mapping query_id to dict of {corpus_id: score}
|
||||
"""
|
||||
self._load_model_if_needed()
|
||||
|
||||
print(f"Searching {len(queries)} queries (top_k={self.top_k})...")
|
||||
|
||||
query_ids = list(queries.keys())
|
||||
query_texts = [queries[qid] for qid in query_ids]
|
||||
|
||||
# Note: ColPaliEngineWrapper does NOT use task prompt from metadata
|
||||
# It uses query_prefix + text + query_augmentation_token (handled in _embed_queries)
|
||||
# So we don't append task_prompt here to match MTEB behavior
|
||||
|
||||
# Embed queries
|
||||
print("Embedding queries...")
|
||||
query_vecs = _embed_queries(self._model, self._processor, query_texts)
|
||||
|
||||
results = {}
|
||||
|
||||
for query_id, query_vec in zip(tqdm(query_ids, desc="Searching"), query_vecs):
|
||||
if self.use_fast_plaid:
|
||||
# Fast-Plaid search
|
||||
search_results, _ = _search_fast_plaid(index_or_retriever, query_vec, self.top_k)
|
||||
query_results = {}
|
||||
for score, doc_id in search_results:
|
||||
if doc_id < len(corpus_ids):
|
||||
corpus_id = corpus_ids[doc_id]
|
||||
query_results[corpus_id] = float(score)
|
||||
else:
|
||||
# LEANN search
|
||||
import torch
|
||||
query_np = query_vec.float().numpy() if isinstance(query_vec, torch.Tensor) else query_vec
|
||||
search_results = index_or_retriever.search_exact_all(query_np, topk=self.top_k)
|
||||
query_results = {}
|
||||
for score, doc_id in search_results:
|
||||
if doc_id < len(corpus_ids):
|
||||
corpus_id = corpus_ids[doc_id]
|
||||
query_results[corpus_id] = float(score)
|
||||
|
||||
results[query_id] = query_results
|
||||
|
||||
return results
|
||||
|
||||
@staticmethod
|
||||
def evaluate_results(
|
||||
results: dict[str, dict[str, float]],
|
||||
qrels: dict[str, dict[str, int]],
|
||||
k_values: list[int] = None,
|
||||
) -> dict[str, float]:
|
||||
"""
|
||||
Evaluate retrieval results using NDCG and other metrics.
|
||||
|
||||
Args:
|
||||
results: dict mapping query_id to dict of {corpus_id: score}
|
||||
qrels: dict mapping query_id to dict of {corpus_id: relevance_score}
|
||||
k_values: List of k values for evaluation metrics
|
||||
|
||||
Returns:
|
||||
Dictionary of metric scores
|
||||
"""
|
||||
try:
|
||||
import pytrec_eval
|
||||
from mteb._evaluators.retrieval_metrics import (
|
||||
calculate_retrieval_scores,
|
||||
make_score_dict,
|
||||
)
|
||||
except ImportError:
|
||||
raise ImportError("pytrec_eval is required for evaluation. Install with: pip install pytrec-eval")
|
||||
|
||||
if k_values is None:
|
||||
k_values = [1, 3, 5, 10, 100]
|
||||
|
||||
# Check if we have any queries to evaluate
|
||||
if len(results) == 0:
|
||||
print("Warning: No queries to evaluate. Returning zero scores.")
|
||||
scores = {}
|
||||
for k in k_values:
|
||||
scores[f"ndcg_at_{k}"] = 0.0
|
||||
scores[f"map_at_{k}"] = 0.0
|
||||
scores[f"recall_at_{k}"] = 0.0
|
||||
scores[f"precision_at_{k}"] = 0.0
|
||||
scores[f"mrr_at_{k}"] = 0.0
|
||||
return scores
|
||||
|
||||
print(f"Evaluating results with k_values={k_values}...")
|
||||
print(f"Before filtering: {len(results)} results, {len(qrels)} qrels")
|
||||
|
||||
# Filter to ensure qrels and results have the same query set
|
||||
# This matches MTEB behavior: only evaluate queries that exist in both
|
||||
# pytrec_eval only evaluates queries in qrels, so we need to ensure
|
||||
# results contains all queries in qrels, and filter out queries not in qrels
|
||||
results_filtered = {qid: res for qid, res in results.items() if qid in qrels}
|
||||
qrels_filtered = {qid: rel_docs for qid, rel_docs in qrels.items() if qid in results_filtered}
|
||||
|
||||
print(f"After filtering: {len(results_filtered)} results, {len(qrels_filtered)} qrels")
|
||||
|
||||
if len(results_filtered) != len(qrels_filtered):
|
||||
print(f"Warning: Mismatch between results ({len(results_filtered)}) and qrels ({len(qrels_filtered)}) queries")
|
||||
missing_in_results = set(qrels.keys()) - set(results.keys())
|
||||
if missing_in_results:
|
||||
print(f"Queries in qrels but not in results: {len(missing_in_results)} queries")
|
||||
print(f"First 5 missing queries: {list(missing_in_results)[:5]}")
|
||||
|
||||
# Convert qrels to pytrec_eval format
|
||||
qrels_pytrec = {}
|
||||
for qid, rel_docs in qrels_filtered.items():
|
||||
qrels_pytrec[qid] = {did: score for did, score in rel_docs.items()}
|
||||
|
||||
# Evaluate
|
||||
eval_result = calculate_retrieval_scores(
|
||||
results=results_filtered,
|
||||
qrels=qrels_pytrec,
|
||||
k_values=k_values,
|
||||
)
|
||||
|
||||
# Format scores
|
||||
scores = make_score_dict(
|
||||
ndcg=eval_result.ndcg,
|
||||
_map=eval_result.map,
|
||||
recall=eval_result.recall,
|
||||
precision=eval_result.precision,
|
||||
mrr=eval_result.mrr,
|
||||
naucs=eval_result.naucs,
|
||||
naucs_mrr=eval_result.naucs_mrr,
|
||||
cv_recall=eval_result.cv_recall,
|
||||
task_scores={},
|
||||
)
|
||||
|
||||
return scores
|
||||
|
||||
Reference in New Issue
Block a user