83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
import asyncio
|
|
from typing import List, Union
|
|
|
|
from sglang.srt.managers.image_processors.base_image_processor import (
|
|
BaseImageProcessor as SGLangBaseImageProcessor,
|
|
)
|
|
from sglang.srt.managers.image_processors.base_image_processor import (
|
|
get_global_processor,
|
|
)
|
|
from sglang.srt.models.deepseek_janus_pro import MultiModalityCausalLM
|
|
|
|
|
|
class JanusProProcessor(SGLangBaseImageProcessor):
|
|
def __init__(self, hf_config, server_args, _processor):
|
|
super().__init__(hf_config, server_args, _processor)
|
|
|
|
@staticmethod
|
|
def _process_images_task(images, input_text):
|
|
processor = get_global_processor()
|
|
result = processor.__call__(
|
|
prompt=input_text, images=images, return_tensors="pt"
|
|
)
|
|
return {
|
|
"input_ids": result["input_ids"],
|
|
"pixel_values": result["pixel_values"],
|
|
"images_emb_mask": result["images_emb_mask"],
|
|
"im_start_id": processor.image_start_id,
|
|
"im_end_id": processor.image_end_id,
|
|
"im_token_id": processor.image_id,
|
|
}
|
|
|
|
async def _process_images(self, images, input_text):
|
|
if self.executor is not None:
|
|
loop = asyncio.get_event_loop()
|
|
image_inputs = await loop.run_in_executor(
|
|
self.executor,
|
|
JanusProProcessor._process_images_task,
|
|
images,
|
|
input_text,
|
|
)
|
|
else:
|
|
image_inputs = self._processor(
|
|
images=images, text=input_text, return_tensors="pt"
|
|
)
|
|
|
|
return image_inputs
|
|
|
|
async def process_images_async(
|
|
self,
|
|
image_data: List[Union[str, bytes]],
|
|
input_ids,
|
|
request_obj,
|
|
max_req_input_len,
|
|
**kwargs,
|
|
):
|
|
if not image_data:
|
|
return None
|
|
|
|
if not isinstance(image_data, list):
|
|
image_data = [image_data]
|
|
|
|
base_out = self.load_images(
|
|
input_ids=input_ids,
|
|
image_data=image_data,
|
|
image_token="<image_placeholder>",
|
|
max_req_input_len=max_req_input_len,
|
|
)
|
|
images = base_out.all_frames
|
|
res = await self._process_images(images=images, input_text=base_out.input_text)
|
|
|
|
return {
|
|
"input_ids": res["input_ids"].flatten().tolist(),
|
|
"pixel_values": res["pixel_values"],
|
|
"images_emb_mask": res["images_emb_mask"],
|
|
"image_hashes": base_out.image_hashes,
|
|
"im_start_id": res["im_start_id"],
|
|
"im_end_id": res["im_end_id"],
|
|
"im_token_id": res["im_token_id"],
|
|
}
|
|
|
|
|
|
ImageProcessorMapping = {MultiModalityCausalLM: JanusProProcessor}
|