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84c8a9d 5e31798 84c8a9d 5e31798 84c8a9d 5e31798 84c8a9d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 | """DW-KhotTaeVL-2B-QueryFrames — query-aware frame selection for video MCQ.
Single-file inference module. Wraps stock Qwen3-VL-2B-Instruct with a
CLIP-ViT-L/14 query-aware frame selector and an optional task-type-aware
uniform-fallback policy.
Usage::
from dw_queryframes import QueryFrames
fv = QueryFrames(device="mps")
answer = fv.answer_mcq(
video_path="cooking.mp4",
question="What does the chef do after pouring the oil?",
options=["Stirs the oil", "Adds salt", "Pours broth", "Chops herbs"],
task_type=None, # or "Action Recognition" etc. for hybrid mode
)
License: Apache 2.0 (this code)
Copyright 2026 Deaw (HF: @commandeaw)
Base model: Qwen3-VL-2B-Instruct (Apache 2.0)
Frame scorer: openai/clip-vit-large-patch14 (MIT)
Always credit Qwen3-VL-Instruct as the base when using this work.
"""
from __future__ import annotations
import re
import os
from pathlib import Path
from typing import Optional
import torch
import torch.nn.functional as F
from PIL import Image
# Tasks where stock-64f does NOT outperform stock-8f on Video-MME mini
# (measured: Object Reasoning Δ -0.083, Temporal Reasoning Δ +0.000).
# For these tasks, frame-coverage is not the bottleneck; uniform sampling
# is at least as good as query-aware. The hybrid policy uses uniform
# selection for these task types when a label is provided.
NO_FRAME_GAIN_TASKS = frozenset({"Object Reasoning", "Temporal Reasoning"})
PROMPT_TEMPLATE = (
"Select the best answer based on the video.\n\n"
"Question: {question}\n"
"Options:\n{options}\n"
"Answer with only the letter."
)
LETTER_RE = re.compile(r"\b([ABCD])\b", re.IGNORECASE)
ANSWER_LINE_RE = re.compile(r"Answer:\s*([ABCD])\b", re.IGNORECASE)
class QueryFrames:
"""Query-aware frame selection over stock Qwen3-VL-2B-Instruct."""
def __init__(
self,
base_model: str = "Qwen/Qwen3-VL-2B-Instruct",
clip_model: str = "openai/clip-vit-large-patch14",
device: str = "auto",
max_pixels: int = 262_144,
max_new_tokens: int = 8,
n_frames: int = 8,
n_candidates: int = 32,
):
os.environ.setdefault("PYTORCH_ENABLE_MPS_FALLBACK", "1")
self.device = self._resolve_device(device)
self.n_frames = n_frames
self.n_candidates = n_candidates
self.max_new_tokens = max_new_tokens
from transformers import (
AutoProcessor, Qwen3VLForConditionalGeneration,
CLIPModel, CLIPProcessor,
)
self.qwen_processor = AutoProcessor.from_pretrained(base_model, max_pixels=max_pixels)
self.qwen_model = Qwen3VLForConditionalGeneration.from_pretrained(
base_model, dtype=torch.bfloat16,
).to(self.device).eval()
self.clip_model = CLIPModel.from_pretrained(
clip_model, torch_dtype=torch.float32,
).to(self.device).eval()
self.clip_processor = CLIPProcessor.from_pretrained(clip_model)
@staticmethod
def _resolve_device(device: str) -> str:
if device == "auto":
if torch.backends.mps.is_available():
return "mps"
if torch.cuda.is_available():
return "cuda"
return "cpu"
return device
def sample_uniform_candidates(self, video_path: str | Path) -> list[Image.Image]:
"""Sample ``n_candidates`` uniformly-spaced frames as PIL images."""
import decord
vid = decord.VideoReader(str(video_path))
total = len(vid)
step = total / (self.n_candidates + 1)
indices = [int((i + 1) * step) for i in range(self.n_candidates)]
return [Image.fromarray(vid[i].asnumpy()) for i in indices]
def select_frames(
self,
candidates: list[Image.Image],
question: str,
) -> list[Image.Image]:
"""Return ``n_frames`` images: top-K by CLIP similarity to question,
sorted by original temporal index (preserving sequence)."""
inputs = self.clip_processor(
text=[question], images=candidates,
return_tensors="pt", padding=True, truncation=True,
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.inference_mode():
# transformers ≤ 4.x returns a tensor directly; ≥ 5.x returns
# a BaseModelOutputWithPooling whose .pooler_output is the
# projected embedding. Handle both.
text_out = self.clip_model.get_text_features(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
)
text_emb = (text_out.pooler_output
if hasattr(text_out, "pooler_output") else text_out)
image_out = self.clip_model.get_image_features(
pixel_values=inputs["pixel_values"]
)
image_embs = (image_out.pooler_output
if hasattr(image_out, "pooler_output") else image_out)
text_emb = F.normalize(text_emb, dim=-1)
image_embs = F.normalize(image_embs, dim=-1)
sims = (text_emb @ image_embs.T).squeeze(0).float().cpu()
topk = sims.topk(self.n_frames).indices.tolist()
topk_sorted = sorted(topk)
return [candidates[i] for i in topk_sorted]
def select_uniform(self, candidates: list[Image.Image]) -> list[Image.Image]:
"""Return ``n_frames`` images sampled uniformly from candidates."""
step = len(candidates) / self.n_frames
idx = [int((k + 0.5) * step) for k in range(self.n_frames)]
idx = [min(i, len(candidates) - 1) for i in idx]
return [candidates[i] for i in idx]
def answer_mcq(
self,
video_path: str | Path,
question: str,
options: list[str],
task_type: Optional[str] = None,
) -> dict:
"""Answer one MCQ question on a video.
Args:
video_path: path to .mp4 (or any decord-readable video)
question: string question (no options)
options: list of 4 option strings (will be lettered A-D)
task_type: optional task category. If provided and matches
a known no-frame-gain task, falls back to
uniform sampling for collision-safe behavior.
Returns:
dict with keys: pred (letter), raw (model output),
frames_used ("query_aware" | "uniform_fallback"),
n_candidates, latency_clip_s, latency_gen_s.
"""
import time
candidates = self.sample_uniform_candidates(video_path)
# Decide policy.
use_uniform = task_type in NO_FRAME_GAIN_TASKS
t1 = time.time()
if use_uniform:
frames = self.select_uniform(candidates)
else:
frames = self.select_frames(candidates, question)
clip_dt = time.time() - t1
# Build Qwen prompt and run inference.
opts_text = "\n".join(f"{chr(65+i)}. {str(o).strip()}"
for i, o in enumerate(options))
prompt = PROMPT_TEMPLATE.format(question=question, options=opts_text)
messages = [{"role": "user", "content":
[{"type": "image"} for _ in frames]
+ [{"type": "text", "text": prompt}]}]
text_in = self.qwen_processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
)
inputs = self.qwen_processor(
text=[text_in], images=frames,
return_tensors="pt", padding=True,
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
t2 = time.time()
with torch.inference_mode():
out_ids = self.qwen_model.generate(
**inputs,
max_new_tokens=self.max_new_tokens,
do_sample=False,
temperature=1.0,
)
gen_dt = time.time() - t2
new_tokens = out_ids[0, inputs["input_ids"].shape[1]:]
raw = self.qwen_processor.tokenizer.decode(
new_tokens, skip_special_tokens=True,
)
pred = self._extract_letter(raw)
return {
"pred": pred,
"raw": raw,
"frames_used": "uniform_fallback" if use_uniform else "query_aware",
"n_candidates": self.n_candidates,
"latency_clip_s": round(clip_dt, 3),
"latency_gen_s": round(gen_dt, 3),
}
@staticmethod
def _extract_letter(text: str) -> Optional[str]:
s = text or ""
m = ANSWER_LINE_RE.search(s)
if m:
return m.group(1).upper()
m = LETTER_RE.search(s)
return m.group(1).upper() if m else None
__all__ = ["QueryFrames", "NO_FRAME_GAIN_TASKS"]
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