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0a4deb9 | 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 | #!/usr/bin/env python3
"""
Generate chosen responses for ActivityNet DPO data using GPT-5.4.
Usage:
export OPENAI_API_KEY="sk-..."
python generate_activitynet_chosen.py
"""
import json
import os
import sys
import base64
import time
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor, as_completed
import av
import numpy as np
from openai import OpenAI
# === Config ===
VIDEO_DIR = "/opt/dlami/nvme/sharegpt_activitynet_raw/activitynet/videos"
DPO_SOURCE = "/opt/dlami/nvme/sharegpt_instructions/video_instruction/train/dpo/sft_dpo_17k.jsonl"
OUTPUT_FILE = "/opt/dlami/nvme/activitynet_gpt_chosen.jsonl"
NUM_FRAMES = 10
MAX_WORKERS = 8 # parallel API calls
MODEL = "gpt-5.4-2026-03-05"
client = OpenAI()
def extract_frames(video_path: str, num_frames: int = 10) -> list[bytes]:
"""Extract evenly-spaced frames from video, return as JPEG bytes."""
container = av.open(video_path)
stream = next(s for s in container.streams if s.type == "video")
total_frames = stream.frames
if total_frames == 0:
total_frames = 300 # fallback
indices = np.linspace(0, total_frames - 1, num_frames).astype(int)
frames = []
for i, frame in enumerate(container.decode(stream)):
if i in indices:
img = frame.to_image()
import io
buf = io.BytesIO()
img.save(buf, format="JPEG", quality=85)
frames.append(buf.getvalue())
if len(frames) >= num_frames:
break
container.close()
return frames
def generate_chosen(video_id: str, prompt: str) -> dict:
"""Call GPT-5.4 with video frames + question, return chosen response."""
video_path = os.path.join(VIDEO_DIR, f"{video_id}.mp4")
if not os.path.exists(video_path):
return {"video": video_id, "error": f"video not found: {video_path}"}
try:
frames = extract_frames(video_path, NUM_FRAMES)
except Exception as e:
return {"video": video_id, "error": f"frame extraction failed: {e}"}
image_messages = []
for i, frame_bytes in enumerate(frames):
b64 = base64.b64encode(frame_bytes).decode("utf-8")
image_messages.append({
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{b64}", "detail": "low"}
})
system_prompt = (
"You are watching a video. The following images are evenly-spaced frames extracted from the video. "
"Answer the question based on what you observe in these frames. "
"Be specific, accurate, and detailed. Describe visual details you actually see. "
"Do not hallucinate or guess about things not visible in the frames."
)
user_content = [
{"type": "text", "text": f"These are {len(frames)} frames from a video:"},
*image_messages,
{"type": "text", "text": f"\nQuestion: {prompt}\n\nPlease provide a detailed and accurate answer."},
]
for attempt in range(3):
try:
response = client.chat.completions.create(
model=MODEL,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_content},
],
max_completion_tokens=512,
temperature=0.3,
)
answer = response.choices[0].message.content.strip()
return {
"video": video_id,
"prompt": prompt,
"chosen_gpt": answer,
"tokens_used": response.usage.total_tokens if response.usage else 0,
}
except Exception as e:
if attempt < 2:
time.sleep(2 ** (attempt + 1))
else:
return {"video": video_id, "error": f"API failed after 3 retries: {e}"}
def main():
# Load ActivityNet DPO entries
activitynet = []
with open(DPO_SOURCE) as f:
for line in f:
d = json.loads(line)
if d.get("video", "").startswith("v_"):
activitynet.append(d)
print(f"ActivityNet DPO entries: {len(activitynet)}")
# Resume from existing output
done_ids = set()
if os.path.exists(OUTPUT_FILE):
with open(OUTPUT_FILE) as f:
for line in f:
d = json.loads(line)
done_ids.add(f"{d['video']}_{d.get('prompt', '')[:50]}")
print(f"Resuming: {len(done_ids)} already done")
todo = []
for d in activitynet:
key = f"{d['video']}_{d['prompt'][:50]}"
if key not in done_ids:
todo.append(d)
print(f"To process: {len(todo)}")
if not todo:
print("All done!")
return
# Process with thread pool
total_tokens = 0
success = 0
errors = 0
with open(OUTPUT_FILE, "a") as out_f:
with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
futures = {
executor.submit(generate_chosen, d["video"], d["prompt"]): d
for d in todo
}
for i, future in enumerate(as_completed(futures), 1):
result = future.result()
if "error" in result:
errors += 1
print(f" [{i}/{len(todo)}] ERROR {result['video']}: {result['error']}")
else:
success += 1
total_tokens += result.get("tokens_used", 0)
out_f.write(json.dumps(result, ensure_ascii=False) + "\n")
out_f.flush()
if i % 100 == 0:
print(f" [{i}/{len(todo)}] success={success}, errors={errors}, tokens={total_tokens:,}")
print(f"\nDone! success={success}, errors={errors}, total_tokens={total_tokens:,}")
print(f"Output: {OUTPUT_FILE}")
if __name__ == "__main__":
main()
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