examples/hf-operator/bunny_demo.py

import sys
print("Python Version:", sys.version)
from dora import DoraStatus
import pyarrow as pa
from transformers import AutoProcessor, AutoModelForCausalLM,AutoTokenizer
from PIL import Image
import torch
import gc


CAMERA_WIDTH = 1280
CAMERA_HEIGHT = 720
#修改
# elements = 1555200
# # 一个可能的尺寸计算
# CAMERA_HEIGHT = 720
# CAMERA_WIDTH = elements // (3 * CAMERA_HEIGHT)
# print(CAMERA_WIDTH)
# PROCESSOR = AutoProcessor.from_pretrained("/home/peiji/Bunny-v1_0-2B-zh")
tokenizer = AutoTokenizer.from_pretrained(
    '/mnt/c/Bunny-v1_0-2B-zh/',
    trust_remote_code=True)
BAD_WORDS_IDS =tokenizer(
    ["<image>", "<fake_token_around_image>"], add_special_tokens=False
).input_ids
EOS_WORDS_IDS = tokenizer(
    "<end_of_utterance>", add_special_tokens=False
).input_ids + [tokenizer.eos_token_id]

# set device
device = 'cuda'  # or cpu
torch.set_default_device(device)

# create model
model = AutoModelForCausalLM.from_pretrained(
    '/mnt/c/Bunny-v1_0-2B-zh/',
    torch_dtype=torch.float16, # float32 for cpu
    device_map='auto',
    trust_remote_code=True
    )

print("load bunny model finish")


def ask_vlm(image, instruction):
    global model
    prompts = [
        "User:",
        image,
        f"{instruction}.<end_of_utterance>\n",
        "Assistant:",
    ]
    inputs = {k: torch.tensor(v).to("cuda") for k, v in PROCESSOR(prompts).items()}

    generated_ids = model.generate(
        **inputs,
        bad_words_ids=BAD_WORDS_IDS,
        max_new_tokens=25,
        repetition_penalty=1.2,
    )
    generated_texts = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True)

    gc.collect()
    torch.cuda.empty_cache()
    return generated_texts[0].split("\nAssistant: ")[1]


import time


class Operator:
    def __init__(self):
        self.image = None
        self.text = None

    def on_event(
        self,
        dora_event,
        send_output,
    ) -> DoraStatus:
        if dora_event["type"] == "INPUT":
            if dora_event["id"] == "image":
                self.image = (
                    dora_event["value"]
                    .to_numpy()
                    .reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
                )
            elif dora_event["id"] == "text":
                self.text = dora_event["value"][0].as_py()
                output = ask_vlm(self.image, self.text).lower()
                send_output(
                    "speak",
                    pa.array([output]),
                )
                if "yes" in output:
                    send_output(
                        "control",
                        pa.array([0.0, 0.0, 0.0, 0.0, 0.0, 50.0, 0.0]),
                    )
                    time.sleep(2)
                    send_output(
                        "control",
                        pa.array([0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]),
                    )
                elif "no" in output:
                    send_output(
                        "control",
                        pa.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 50.0]),
                    )
                    time.sleep(2)
                    send_output(
                        "control",
                        pa.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]),
                    )

        return DoraStatus.CONTINUE