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fa82a9d | 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 | #!/usr/bin/env python3
"""Caption one real video frame with the official VBench Tag2Text pipeline (B5).
Runs **on the scoring host**, where the pinned VBench checkout and the Tag2Text
weights live; it uses ``vbench.utils.load_video`` / ``tag2text_transform`` and
``tag2text_caption`` exactly as ``vbench.scene`` does, so the captions match the
production distribution instead of an approximation.
The prompt split is enforced here: only prompts whose frozen split matches
``--split-filter`` are captioned, so test prompts can never leak into training.
Usage::
python scripts/caption_frames.py --manifest <scene.csv> --video-root <dir> \
--frozen-split splits/e0_prompt_split.csv --split-filter dev_only \
--output captions-dev.jsonl --limit 200 --device cuda:1
"""
from __future__ import annotations
import argparse
import csv
import json
import os
from pathlib import Path
import sys
def load_frozen_split(path: Path) -> dict[str, set[str]]:
frozen: dict[str, set[str]] = {}
with path.open(newline="", encoding="utf-8") as handle:
for row in csv.DictReader(handle):
frozen.setdefault(row["prompt_id"], set()).add(row["split"])
return frozen
def split_of(frozen: dict[str, set[str]], prompt: str) -> str:
splits = frozen.get(prompt)
if not splits:
return "outside"
if splits == {"dev"}:
return "dev_only"
if splits == {"test"}:
return "test_only"
return "dev_and_test"
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("--manifest", required=True, help="human-preference scene.csv")
parser.add_argument("--video-root", required=True, help="directory that video paths in the manifest are relative to")
parser.add_argument("--frozen-split", required=True)
parser.add_argument("--split-filter", default="dev_only", choices=["dev_only", "test_only", "dev_and_test", "outside", "all"])
parser.add_argument("--vbench-root", default="/root/wenbiao_zhao/VBench")
parser.add_argument("--weights", default="/root/.cache/vbench/caption_model/tag2text_swin_14m.pth")
parser.add_argument("--output", required=True)
parser.add_argument("--num-frames", type=int, default=16)
parser.add_argument("--frame-index", default="middle", help="middle | first | last | integer")
parser.add_argument("--frame-fractions", default=None, help="comma list of positions in [0,1); overrides --frame-index and emits one row per frame")
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--device", default="cuda:0")
parser.add_argument("--caption-retries", type=int, default=3)
parser.add_argument("--save-frames", default=None, help="directory to write the captioned frame as PNG (for vision labelling)")
args = parser.parse_args(argv)
frozen = load_frozen_split(Path(args.frozen_split))
rows: list[dict[str, str]] = []
seen: set[tuple[str, str]] = set()
with Path(args.manifest).open(newline="", encoding="utf-8") as handle:
for row in csv.DictReader(handle):
prompt = row.get("prompt_en", "")
if args.split_filter != "all" and split_of(frozen, prompt) != args.split_filter:
continue
for key in ("video_a_path", "video_b_path"):
relative = row.get(key)
if not relative:
continue
if (prompt, relative) in seen:
continue
seen.add((prompt, relative))
rows.append({"prompt": prompt, "relative_path": relative, "generator": relative.split("/")[1] if "/" in relative else "unknown", "instance_id": row.get("instance_id", "")})
if args.limit is not None:
rows = rows[: args.limit]
print(json.dumps({"selected_videos": len(rows), "unique_prompts": len({row['prompt'] for row in rows}), "split_filter": args.split_filter}), flush=True)
sys.path.insert(0, str(Path(args.vbench_root).resolve()))
import torch # noqa: E402
from vbench.third_party.tag2Text.tag2text import tag2text_caption # noqa: E402
from vbench.utils import load_video, tag2text_transform # noqa: E402
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
model = tag2text_caption(pretrained=args.weights, image_size=384, vit="swin_b").to(device).eval()
transform = tag2text_transform(384)
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
frame_dir = Path(args.save_frames) if args.save_frames else None
if frame_dir is not None:
frame_dir.mkdir(parents=True, exist_ok=True)
written = failed = 0
with output.open("w", encoding="utf-8") as handle:
for row in rows:
video_path = Path(args.video_root) / row["relative_path"]
if not video_path.exists():
failed += 1
handle.write(json.dumps({**row, "status": "missing_video", "video_path": str(video_path)}, ensure_ascii=False) + "\n")
continue
try:
frames = load_video(str(video_path), num_frames=args.num_frames, return_tensor=False, width=384, height=384)
except Exception as error: # noqa: BLE001 - report, never crash the batch
failed += 1
handle.write(json.dumps({**row, "status": f"load_error:{type(error).__name__}", "video_path": str(video_path)}, ensure_ascii=False) + "\n")
continue
if args.frame_fractions:
indices = [min(len(frames) - 1, max(0, int(float(frac) * len(frames)))) for frac in args.frame_fractions.split(",")]
elif args.frame_index == "middle":
indices = [len(frames) // 2]
elif args.frame_index == "first":
indices = [0]
elif args.frame_index == "last":
indices = [len(frames) - 1]
else:
indices = [min(int(args.frame_index), len(frames) - 1)]
for position, index in enumerate(indices):
frame = frames[index]
caption = None
for _ in range(max(1, args.caption_retries)):
try:
tensor = transform(frame).to(device).unsqueeze(0)
with torch.no_grad():
captions, _tags = model.generate(tensor, tag_input=None, return_tag_predict=True)
caption = captions[0] if isinstance(captions, (list, tuple)) else str(captions)
break
except Exception: # noqa: BLE001
continue
if caption is None:
failed += 1
handle.write(json.dumps({**row, "status": "caption_error", "video_path": str(video_path)}, ensure_ascii=False) + "\n")
continue
frame_path = None
if frame_dir is not None:
safe = f"{written:05d}_{abs(hash((row['prompt'], row['relative_path']))) % 10**10}.png"
frame_path = frame_dir / safe
try:
from PIL import Image # noqa: PLC0415 - available on the scoring host
if hasattr(frame, "convert"):
image = frame.convert("RGB")
else:
import numpy as np # noqa: PLC0415
array = np.asarray(frame)
image = Image.fromarray(array.astype("uint8")).convert("RGB")
image.save(frame_path)
except Exception as error: # noqa: BLE001 - frame saving is best effort but must be visible
print(json.dumps({"frame_save_error": f"{type(error).__name__}: {error}"}), flush=True)
frame_path = None
written += 1
handle.write(
json.dumps(
{
**row,
"status": "ok",
"video_path": str(video_path),
"frame_index": index,
"frames_sampled": len(frames),
"caption": caption,
"frame_path": str(frame_path) if frame_path else None,
},
ensure_ascii=False,
)
+ "\n"
)
if written % 25 == 0:
print(json.dumps({"written": written, "failed": failed}), flush=True)
print(json.dumps({"status": "done", "written": written, "failed": failed, "output": str(output)}), flush=True)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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