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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | #!/usr/bin/env python3
"""Evaluate MiniCPM-o 4.5 on Daily-Omni.
Daily-Omni videos include embedded audio; we extract it and feed both frames
and waveform to MiniCPM-o.
"""
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("daily_omni")
load_daily_omni = ch.load_daily_omni
extract_answer = ch.extract_answer
compute_metrics = ch.compute_metrics
print_summary = ch.print_summary
DEFAULT_DATA_DIR = ch.DEFAULT_DATA_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 Daily-Omni.")
p.add_argument("--model-id", type=str, default="openbmb/MiniCPM-o-4_5")
p.add_argument("--data-dir", type=Path, default=DEFAULT_DATA_DIR)
p.add_argument("--output-dir", type=Path,
default=Path("/home/ubuntu/eval_results/daily_omni_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_daily_omni")
p.add_argument("--max-frames", type=int, default=64)
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"])
p.add_argument("--no-audio", action="store_true",
help="Video-only mode (skip audio extraction).")
p.add_argument(
"--skip-audio-durations",
type=str,
default="",
help=(
"Comma-separated `video_duration` values from the dataset for which "
"audio is omitted (video-only for those clips). Useful when "
"MiniCPM-o forward fails on some lengths with audio+vision "
'(e.g. empty `raw_output` and log errors like "Expected size 122 '
'but got size 121"). Example: --skip-audio-durations 60s'
),
)
# 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
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 Daily-Omni dataset...")
test_data = load_daily_omni(args.data_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")
model, tokenizer = load_model(
args.model_id, attn_implementation=args.attn, init_audio=not args.no_audio,
)
skip_audio_durs = {
x.strip()
for x in args.skip_audio_durations.split(",")
if x.strip()
}
for item in tqdm(test_data, desc="Daily-Omni", unit="q"):
if item["question_id"] in processed:
continue
use_audio = not args.no_audio and (
item.get("video_duration", "") not in skip_audio_durs
)
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,
use_audio_from_video=use_audio,
)
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"],
"question_type": item.get("question_type", ""),
"content_parent_category": item.get("content_parent_category", ""),
"content_fine_category": item.get("content_fine_category", ""),
"video_category": item.get("video_category", ""),
"video_duration": item.get("video_duration", ""),
"question": item["question"],
"choices": item["choices"],
"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 daily_omni --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,
"data_dir": str(args.data_dir),
"max_new_tokens": args.max_new_tokens,
"temperature": args.temperature,
"max_frames": args.max_frames,
"fps": args.fps,
"attn": args.attn,
"no_audio": args.no_audio,
"skip_audio_durations": sorted(skip_audio_durs),
}
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()
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