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12acbba | 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 | #!/usr/bin/env python
"""Run Mage-VL-Base image or video inference offline or through SGLang."""
import argparse
import base64
import mimetypes
from pathlib import Path
def image_url(image: str) -> str:
if image.startswith(("http://", "https://", "data:")):
return image
path = Path(image)
mime = mimetypes.guess_type(path.name)[0] or "image/jpeg"
encoded = base64.b64encode(path.read_bytes()).decode("ascii")
return f"data:{mime};base64,{encoded}"
def sample_video(video: str, num_frames: int):
import cv2
import numpy as np
from PIL import Image
capture = cv2.VideoCapture(video)
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
if frame_count <= 0:
capture.release()
raise ValueError(f"Could not read video: {video}")
indices = np.linspace(0, frame_count - 1, min(num_frames, frame_count), dtype=int)
frames = []
for index in indices:
capture.set(cv2.CAP_PROP_POS_FRAMES, int(index))
ok, frame = capture.read()
if not ok:
capture.release()
raise ValueError(f"Could not decode frame {index} from: {video}")
frames.append(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))
capture.release()
return frames
def run_offline(args):
import os
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
model_path = args.model
if args.video and args.video_backend == "codec" and args.codec_engine == "neural":
if not os.path.isdir(model_path):
from huggingface_hub import snapshot_download
model_path = snapshot_download(args.model)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_path, trust_remote_code=True, torch_dtype="auto", device_map="auto"
).eval()
media_type = "image" if args.image else "video"
messages = [{"role": "user", "content": [
{"type": media_type}, {"type": "text", "text": args.question},
]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
if args.image:
inputs = processor(
text=[text], images=[Image.open(args.image).convert("RGB")], return_tensors="pt"
)
elif args.video_backend == "codec":
codec_config = {
"engine": "hevc" if args.codec_engine == "traditional" else "dcvc-rt",
"target_canvas": args.num_frames,
"patch": 16,
}
if args.codec_engine == "neural":
codec_config["dcvc"] = {
"pkg_dir": os.path.join(model_path, "neural_codec"),
"device": str(model.device),
}
inputs = processor(
text=[text],
videos=[args.video],
video_backend="codec",
max_pixels=args.max_pixels,
codec_config=codec_config,
return_tensors="pt",
padding=True,
)
else:
inputs = processor(
text=[text],
videos=[sample_video(args.video, args.num_frames)],
return_tensors="pt",
padding=True,
)
inputs = {k: (v.to(model.device) if hasattr(v, "to") else v) for k, v in inputs.items()}
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
with torch.inference_mode():
output = model.generate(**inputs, max_new_tokens=args.max_new_tokens, do_sample=False)
answer = processor.tokenizer.decode(
output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True
)
print(answer.strip())
def run_online(args):
from openai import OpenAI
if args.image:
content = [{"type": "image_url", "image_url": {"url": image_url(args.image)}}]
else:
content = [
{"type": "image_url", "image_url": {"url": frame_url(frame)}}
for frame in sample_video(args.video, args.num_frames)
]
content.append({"type": "text", "text": args.question})
client = OpenAI(base_url=args.base_url, api_key=args.api_key)
response = client.chat.completions.create(
model=args.model,
messages=[{"role": "user", "content": content}],
max_tokens=args.max_new_tokens,
)
print(response.choices[0].message.content)
def frame_url(frame) -> str:
import io
buffer = io.BytesIO()
frame.save(buffer, format="JPEG")
encoded = base64.b64encode(buffer.getvalue()).decode("ascii")
return f"data:image/jpeg;base64,{encoded}"
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--mode", choices=("offline", "online"), required=True)
media = parser.add_mutually_exclusive_group(required=True)
media.add_argument("--image", help="Local image path")
media.add_argument("--video", help="Local video path")
parser.add_argument("--video-backend", choices=("frames", "codec"), default="frames")
parser.add_argument(
"--codec-engine", choices=("traditional", "neural"), default="traditional"
)
parser.add_argument("--num-frames", type=int, default=32)
parser.add_argument("--max-pixels", type=int, default=150000)
parser.add_argument("--question", default="Describe this media.")
parser.add_argument("--model", default="microsoft/Mage-VL")
parser.add_argument("--max-new-tokens", type=int, default=256)
parser.add_argument("--base-url", default="http://localhost:30000/v1")
parser.add_argument("--api-key", default="EMPTY")
args = parser.parse_args()
if args.mode == "online" and args.video_backend == "codec":
parser.error("online video inference supports only --video-backend frames")
if args.num_frames <= 0:
parser.error("--num-frames must be positive")
(run_offline if args.mode == "offline" else run_online)(args)
if __name__ == "__main__":
main()
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