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from typing import Dict, List, Any
from PIL import Image
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
from transformers.image_utils import to_numpy_array, PILImageResampling, ChannelDimension
from transformers.image_transforms import resize, to_channel_dimension_format
class EndpointHandler:
def __init__(self, model_path: str):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.processor = AutoProcessor.from_pretrained(
model_path,
# token=api_token
)
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
# token=api_token,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
).to(self.device)
self.image_seq_len = self.model.config.perceiver_config.resampler_n_latents
self.bos_token = self.processor.tokenizer.bos_token
self.bad_words_ids = self.processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
def convert_to_rgb(self, image: Image.Image) -> Image.Image:
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
def custom_transform(self, image: Image.Image) -> torch.Tensor:
image = self.convert_to_rgb(image)
image = to_numpy_array(image)
image = resize(image, (960, 960), resample=PILImageResampling.BILINEAR)
image = self.processor.image_processor.rescale(image, scale=1 / 255)
image = self.processor.image_processor.normalize(
image,
mean=self.processor.image_processor.image_mean,
std=self.processor.image_processor.image_std
)
image = to_channel_dimension_format(image, ChannelDimension.FIRST)
return torch.tensor(image)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
image = data.get("inputs")
if isinstance(image, str):
image = Image.open(image)
inputs = self.processor.tokenizer(
f"{self.bos_token}<fake_token_around_image>{'<image>' * self.image_seq_len}<fake_token_around_image>",
return_tensors="pt",
add_special_tokens=False,
)
inputs["pixel_values"] = self.processor.image_processor([image], transform=self.custom_transform)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
generated_ids = self.model.generate(**inputs, bad_words_ids=self.bad_words_ids, max_length=4096)
generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# print(generated_text)
# return {"text": generated_text}
# Format the output as an array of dictionaries with 'label' and 'score'
output = [{"label": text, "score": 1.0} for text in generated_texts]
return output |