114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
from functools import lru_cache
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from typing import Dict, Type, Union
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import torch
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from PIL import Image
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from vllm.config import ModelConfig
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from vllm.inputs.registry import InputContext
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from vllm.logger import init_logger
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from vllm.transformers_utils.image_processor import get_image_processor
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from .base import MultiModalData, MultiModalPlugin
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logger = init_logger(__name__)
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cached_get_image_processor = lru_cache(get_image_processor)
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class ImagePixelData(MultiModalData):
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"""
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The pixel data of an image. Can be one of:
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- :class:`PIL.Image.Image`: An image object. Requires that a HuggingFace
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processor is available to the model.
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- :class:`torch.Tensor`: The raw pixel data which is passed to the model
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without additional pre-processing.
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"""
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def __init__(self, image: Union[Image.Image, torch.Tensor]) -> None:
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if isinstance(image, Image.Image):
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# So that this class can be created inside the Image context manager
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image.load()
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self.image = image
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def __repr__(self) -> str:
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image = self.image
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if isinstance(image, Image.Image):
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return f"{type(self).__name__}(image={image})"
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return (f"{type(self).__name__}(image=torch.Tensor(shape="
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f"{image.shape}, dtype={image.dtype}))")
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class ImagePixelPlugin(MultiModalPlugin[ImagePixelData]):
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def get_data_type(self) -> Type[ImagePixelData]:
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return ImagePixelData
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def _get_hf_image_processor(self, model_config: ModelConfig):
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vlm_config = model_config.multimodal_config
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if vlm_config is None or vlm_config.image_processor is None:
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return None
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return cached_get_image_processor(
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vlm_config.image_processor,
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trust_remote_code=model_config.trust_remote_code,
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revision=vlm_config.image_processor_revision,
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)
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def _default_input_mapper(self, ctx: InputContext,
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data: ImagePixelData) -> Dict[str, torch.Tensor]:
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model_config = ctx.model_config
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image = data.image
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if isinstance(image, Image.Image):
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image_processor = self._get_hf_image_processor(model_config)
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if image_processor is None:
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raise RuntimeError("No HuggingFace processor is available"
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"to process the image object")
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try:
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return image_processor.preprocess(image, return_tensors="pt") \
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.to(model_config.dtype).data
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except Exception:
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logger.error("Failed to process image (%s)", image)
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raise
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elif isinstance(image, torch.Tensor):
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pixel_values = image.to(model_config.dtype)
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return {"pixel_values": pixel_values}
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raise TypeError(f"Invalid image type: {type(image)}")
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class ImageFeatureData(MultiModalData):
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"""
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The feature vector of an image, passed directly to the model.
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This should be the output of the vision tower.
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"""
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def __init__(self, image_features: torch.Tensor) -> None:
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self.image_features = image_features
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def __repr__(self) -> str:
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image_features = self.image_features
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return (f"{type(self).__name__}(image_features=torch.Tensor(shape="
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f"{image_features.shape}, dtype={image_features.dtype}))")
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class ImageFeaturePlugin(MultiModalPlugin[ImageFeatureData]):
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def get_data_type(self) -> Type[ImageFeatureData]:
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return ImageFeatureData
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def _default_input_mapper(
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self, ctx: InputContext,
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data: ImageFeatureData) -> Dict[str, torch.Tensor]:
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model_config = ctx.model_config
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image_features = data.image_features.to(model_config.dtype)
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return {"image_features": image_features}
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