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73085ab cf932d8 ef8d70b 73085ab ef8d70b 73085ab | 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 | import os
import torch
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 robohusky.video_transformers import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor, get_index
)
from decord import VideoReader, cpu
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
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=True,
temperature=0.7,
max_new_tokens=1024
)
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
# Hugging Face 会调用这个函数，data 是原始输入
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 = request.get("image", None)
video = request.get("video", None)
if image:
pixel_values = self._load_image(image).unsqueeze(0).to(self.device)
prompt = prompt.replace("<image>", DEFAULT_IMG_START_TOKEN + DEFAULT_IMG_END_TOKEN)
elif video:
pixel_values = self._load_video(video).unsqueeze(0).to(self.device)
prompt = prompt.replace("<video>", DEFAULT_VIDEO_START_TOKEN + DEFAULT_VIDEO_END_TOKEN)
else:
pixel_values = None
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
)
return self.tokenizer.decode(output.sequences[0], skip_special_tokens=True)
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 open("/tmp/temp_video.mp4", "wb") as f:
f.write(video_bytes)
vr = VideoReader("/tmp/temp_video.mp4", ctx=cpu(0))
frame_indices = get_index(len(vr), num_segments)
frames = [Image.fromarray(vr[idx].asnumpy()) for idx in frame_indices]
transform = T.Compose([
GroupScale(224),
GroupCenterCrop(224),
Stack(),
ToTorchFormatTensor(),
GroupNormalize([0.48145466, 0.4578275, 0.40821073], [0.26862954, 0.26130258, 0.27577711])
])
return transform(frames)
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