Harry MellorandClaude Opus 5
8c1557a79c
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Signed-off-by: Harry Mellor <hej.mellor@gmail.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Literal
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import torch
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from PIL import Image
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from .base import get_vllm_public_assets
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VLM_IMAGES_DIR = "vision_model_images"
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ImageAssetName = Literal[
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"stop_sign",
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"cherry_blossom",
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"hato",
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"2560px-Gfp-wisconsin-madison-the-nature-boardwalk",
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"Grayscale_8bits_palette_sample_image",
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"1280px-Venn_diagram_rgb",
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"RGBA_comp",
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"237-400x300",
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"231-200x300",
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"27-500x500",
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"17-150x600",
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"handelsblatt-preview",
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"paper-11",
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]
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@dataclass(frozen=True)
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class ImageAsset:
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name: ImageAssetName
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def get_path(self, ext: str) -> Path:
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"""Return s3 path for given image."""
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return get_vllm_public_assets(
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filename=f"{self.name}.{ext}", s3_prefix=VLM_IMAGES_DIR
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)
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@property
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def pil_image(self) -> Image.Image:
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return self.pil_image_ext(ext="jpg")
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def pil_image_ext(self, ext: str) -> Image.Image:
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image_path = self.get_path(ext=ext)
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return Image.open(image_path)
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@property
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def image_embeds(self) -> torch.Tensor:
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"""Image embeddings, only used for testing purposes with llava 1.5."""
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image_path = self.get_path("pt")
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return torch.load(image_path, map_location="cpu", weights_only=True)
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def read_bytes(self, ext: str) -> bytes:
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p = Path(self.get_path(ext))
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return p.read_bytes()
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