handler.py

import os
import torch
import base64
from PIL import Image
from io import BytesIO
from typing import Dict, Any
from transformers import LlamaTokenizer, GenerationConfig
from robohusky.model.modeling_husky_embody2 import HuskyForConditionalGeneration
from decord import VideoReader, cpu
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
import tempfile

DEFAULT_IMG_START_TOKEN = "<img>"
DEFAULT_IMG_END_TOKEN = "</img>"
DEFAULT_VIDEO_START_TOKEN = "<vid>"
DEFAULT_VIDEO_END_TOKEN = "</vid>"

class EndpointHandler:
    def __init__(self, model_path: str = "."):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.tokenizer = LlamaTokenizer.from_pretrained(model_path, use_fast=False)
        self.model = HuskyForConditionalGeneration.from_pretrained(
            model_path, torch_dtype=torch.float16 if self.device == "cuda" else torch.float32
        ).to(self.device).eval()

        self.gen_config = GenerationConfig(
            bos_token_id=1,
            do_sample=False,
            temperature=0.7,
            max_new_tokens=10240
        )

    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
        inputs = self.preprocess(data)
        prediction = self.inference(inputs)
        return self.postprocess(prediction)

    def preprocess(self, request: Dict[str, Any]) -> Dict[str, Any]:
        prompt = request["inputs"]
        image_b64 = request.get("image", None)
        video_b64 = request.get("video", None)

        pixel_values = None

        if image_b64:
            image_bytes = base64.b64decode(image_b64)
            pixel_values = self._load_image(image_bytes).unsqueeze(0)  # [1, 3, 224, 224]
            if self.device == "cuda":
                pixel_values = pixel_values.half()
            pixel_values = pixel_values.to(self.device)
            prompt = prompt.replace("<image>", DEFAULT_IMG_START_TOKEN + DEFAULT_IMG_END_TOKEN)

        elif video_b64:
            video_bytes = base64.b64decode(video_b64)
            pixel_values = self._load_video(video_bytes)
            if self.device == "cuda":
                pixel_values = pixel_values.half()
            pixel_values = pixel_values.to(self.device)
            prompt = prompt.replace("<video>", DEFAULT_VIDEO_START_TOKEN + DEFAULT_VIDEO_END_TOKEN)

        return {
            "prompt": prompt,
            "pixel_values": pixel_values
        }

    def inference(self, inputs: Dict[str, Any]) -> str:
        prompt = inputs["prompt"]
        pixel_values = inputs["pixel_values"]

        model_inputs = self.tokenizer([prompt], return_tensors="pt")
        model_inputs.pop("token_type_ids", None)
        model_inputs = {k: v.to(self.device) for k, v in model_inputs.items()}

        if pixel_values is not None:
            output = self.model.generate(
                **model_inputs,
                pixel_values=pixel_values,
                generation_config=self.gen_config,
                return_dict_in_generate=True,
                output_scores=True
            )
        else:
            output = self.model.language_model.generate(
                **model_inputs,
                generation_config=self.gen_config,
                return_dict_in_generate=True,
                output_scores=True
            )
        # 🧠 打印 debug 信息
        generated_ids = output.sequences[0]
        print("📍生成的 token ids：", generated_ids.tolist())
        raw_text = self.tokenizer.decode(generated_ids, skip_special_tokens=False)
        clean_text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
        print("🧾 带特殊符号的输出：", raw_text)
        print("✅ 去掉特殊符号的输出：", clean_text)

        return clean_text  # 返回干净版本
    def postprocess(self, output: str) -> Dict[str, str]:
        return {"output": output.strip()}

    def _load_image(self, image_bytes: bytes) -> torch.Tensor:
        image = Image.open(BytesIO(image_bytes)).convert('RGB')
        crop_pct = 224 / 256
        size = int(224 / crop_pct)
        transform = T.Compose([
            T.Resize(size, interpolation=InterpolationMode.BICUBIC),
            T.CenterCrop(224),
            T.ToTensor(),
            T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
        ])
        return transform(image)

    def _load_video(self, video_bytes: bytes, num_segments=8) -> torch.Tensor:
        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
            tmpfile.write(video_bytes)
            video_path = tmpfile.name

        vr = VideoReader(video_path, ctx=cpu(0))
        total_frames = len(vr)
        indices = self.get_index(total_frames, num_segments)
        frames = [Image.fromarray(vr[i].asnumpy()) for i in indices]

        transform = T.Compose([
            T.Resize(224, interpolation=InterpolationMode.BICUBIC),
            T.CenterCrop(224),
            T.ToTensor(),
            T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
        ])
        processed = [transform(frame) for frame in frames]  # each: [3, 224, 224]
        video_tensor = torch.stack(processed, dim=0)  # [T, 3, 224, 224]
        video_tensor = video_tensor.permute(1, 0, 2, 3)  # [3, T, 224, 224]
        video_tensor = video_tensor.unsqueeze(0)  # [1, 3, T, 224, 224] ✅
        return video_tensor

    def get_index(self, num_frames: int, num_segments: int):
        if num_frames < num_segments:
            return list(range(num_frames)) + [num_frames - 1] * (num_segments - num_frames)
        interval = num_frames / num_segments
        return [int(interval * i) for i in range(num_segments)]