File size: 6,580 Bytes
b2c2640 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | #!/usr/bin/env python3
"""Evaluate MiniCPM-o 4.5 on Video-MME.
Reuses the data loader and metrics from CleverHans-Evaluation's Qwen3-Omni
eval_videomme.py and swaps out the inference with MiniCPM-o. Video-MME is
video-only (no audio), so we do NOT pass audio in.
"""
from __future__ import annotations
import _common
import argparse
import gc
import io
import contextlib
import json
from pathlib import Path
import torch
from tqdm import tqdm
ch = _common.ch("videomme")
load_videomme = ch.load_videomme
extract_answer = ch.extract_answer
compute_metrics = ch.compute_metrics
print_summary = ch.print_summary
DEFAULT_VIDEO_DIR = ch.DEFAULT_VIDEO_DIR
DEFAULT_OUTPUT_DIR = ch.DEFAULT_OUTPUT_DIR
from minicpmo_inference import load_model, run_inference
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="Evaluate MiniCPM-o on Video-MME.")
p.add_argument("--model-id", type=str, default="openbmb/MiniCPM-o-4_5")
p.add_argument("--video-dir", type=Path, default=DEFAULT_VIDEO_DIR)
p.add_argument("--output-dir", type=Path,
default=Path("/home/ubuntu/eval_results/videomme_minicpmo"))
p.add_argument("--max-samples", type=int, default=-1)
p.add_argument("--max-new-tokens", type=int, default=32)
p.add_argument("--temperature", type=float, default=0.0)
p.add_argument("--label", type=str, default="minicpmo_videomme")
p.add_argument("--max-frames", type=int, default=64,
help="Max frames sampled from each video (MiniCPM-o uses "
"PIL images).")
p.add_argument("--fps", type=float, default=1.0)
p.add_argument("--attn", type=str, default="flash_attention_2",
choices=["sdpa", "flash_attention_2", "eager"])
# vLLM flags: parity-only (MiniCPM-o 4.5 multimodal vLLM not yet supported).
p.add_argument("--vllm", action="store_true", default=False,
help="(no-op for MiniCPM-o 4.5; auto-falls back to transformers).")
p.add_argument("--tp", type=int, default=None)
p.add_argument("--gpu-memory-utilization", type=float, default=0.90)
p.add_argument("--max-model-len", type=int, default=65536)
p.add_argument("--batch-size", type=int, default=32)
# Data-parallel sharding: split test set into K slices, process slice N
p.add_argument("--shard", type=int, default=0)
p.add_argument("--num-shards", type=int, default=1)
return p.parse_args()
def main() -> None:
args = parse_args()
out_dir = args.output_dir / args.label
out_dir.mkdir(parents=True, exist_ok=True)
shard_suffix = (f".shard{args.shard}of{args.num_shards}"
if args.num_shards > 1 else "")
results_jsonl = out_dir / f"eval_results{shard_suffix}.jsonl"
metrics_json = out_dir / "metrics.json"
summary_txt = out_dir / "summary.txt"
if args.vllm:
print("[warn] --vllm requested but MiniCPM-o 4.5 multimodal vLLM is not "
"supported upstream yet; falling back to transformers.")
print("[data] Loading Video-MME dataset...")
test_data = load_videomme(args.video_dir, args.max_samples)
if args.num_shards > 1:
test_data = [x for i, x in enumerate(test_data) if i % args.num_shards == args.shard]
print(f"[shard] shard {args.shard}/{args.num_shards}: {len(test_data)} questions")
else:
print(f"[data] {len(test_data)} questions ready")
processed: set = set()
if results_jsonl.exists():
with open(results_jsonl) as f:
for line in f:
obj = json.loads(line)
processed.add(obj["question_id"])
print(f"[resume] {len(processed)} already processed, skipping")
model, tokenizer = load_model(args.model_id, attn_implementation=args.attn,
init_audio=False)
for item in tqdm(test_data, desc="Video-MME", unit="q"):
if item["question_id"] in processed:
continue
try:
raw_output = run_inference(
model, tokenizer,
video_path=item["video_path"],
audio_path=None,
prompt=item["prompt"],
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
max_frames=args.max_frames,
fps=args.fps,
)
except Exception as exc:
import traceback
print(f" [error] {item['question_id']}: {exc}")
traceback.print_exc()
raw_output = ""
pred = extract_answer(raw_output)
result = {
"question_id": item["question_id"],
"video_id": item["video_id"],
"duration": item["duration"],
"domain": item["domain"],
"sub_category": item["sub_category"],
"task_type": item["task_type"],
"question": item["question"],
"options": item["options"],
"gt_answer": item["gt_answer"],
"pred_answer": pred,
"raw_output": raw_output,
}
with open(results_jsonl, "a", encoding="utf-8") as f:
f.write(json.dumps(result, ensure_ascii=False) + "\n")
processed.add(item["question_id"])
gc.collect()
torch.cuda.empty_cache()
if args.num_shards > 1:
print(f"\n[shard {args.shard}/{args.num_shards}] Done. Results: {results_jsonl}")
print(f"[shard] Run merge_shards.py --bench videomme --label-dir {out_dir}")
return
all_results = []
if results_jsonl.exists():
with open(results_jsonl) as f:
for line in f:
all_results.append(json.loads(line))
metrics = compute_metrics(all_results)
metrics["eval_config"] = {
"model_id": args.model_id,
"video_dir": str(args.video_dir),
"max_new_tokens": args.max_new_tokens,
"temperature": args.temperature,
"max_frames": args.max_frames,
"fps": args.fps,
"attn": args.attn,
}
with open(metrics_json, "w", encoding="utf-8") as f:
json.dump(metrics, f, indent=2, ensure_ascii=False)
print_summary(metrics, args.label)
with open(summary_txt, "w", encoding="utf-8") as f:
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
print_summary(metrics, args.label)
f.write(buf.getvalue())
print(f"\n[output] Results: {results_jsonl}")
print(f"[output] Metrics: {metrics_json}")
print(f"[output] Summary: {summary_txt}")
if __name__ == "__main__":
main()
|