init

vllm/assets/image.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Literal
+
+import torch
+from PIL import Image
+
+from .base import get_vllm_public_assets
+
+VLM_IMAGES_DIR = "vision_model_images"
+
+ImageAssetName = Literal["stop_sign", "cherry_blossom", "hato",
+                         "2560px-Gfp-wisconsin-madison-the-nature-boardwalk",
+                         "Grayscale_8bits_palette_sample_image",
+                         "1280px-Venn_diagram_rgb", "RGBA_comp", "237-400x300",
+                         "231-200x300", "27-500x500", "17-150x600",
+                         "handelsblatt-preview", "paper-11"]
+
+
+@dataclass(frozen=True)
+class ImageAsset:
+    name: ImageAssetName
+
+    def get_path(self, ext: str) -> Path:
+        """
+        Return s3 path for given image.
+        """
+        return get_vllm_public_assets(filename=f"{self.name}.{ext}",
+                                      s3_prefix=VLM_IMAGES_DIR)
+
+    @property
+    def pil_image(self, ext="jpg") -> Image.Image:
+
+        image_path = self.get_path(ext)
+        return Image.open(image_path)
+
+    @property
+    def image_embeds(self) -> torch.Tensor:
+        """
+        Image embeddings, only used for testing purposes with llava 1.5.
+        """
+        image_path = self.get_path('pt')
+        return torch.load(image_path, map_location="cpu", weights_only=True)
+
+    def read_bytes(self, ext: str) -> bytes:
+        p = Path(self.get_path(ext))
+        return p.read_bytes()