mirror of https://github.com/vllm-project/vllm.git
63 lines
2.0 KiB
Python
63 lines
2.0 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 pathlib import Path
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import numpy as np
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import pytest
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import torch
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from PIL import Image, ImageDraw
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from vllm.multimodal.hasher import MultiModalHasher
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ASSETS_DIR = Path(__file__).parent / "assets"
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assert ASSETS_DIR.exists()
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# NOTE: Images that are the same visually are allowed to have the same hash
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@pytest.mark.parametrize("mode_pair", [("1", "L"), ("RGBA", "CMYK")])
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def test_hash_collision_image_mode(mode_pair):
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mode1, mode2 = mode_pair
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image1 = Image.new(mode1, size=(10, 10), color=1)
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image2 = Image.new(mode2, size=(10, 10), color=1)
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
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def test_hash_collision_image_palette():
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# These images differ only in Image.palette._palette
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image1 = Image.open(ASSETS_DIR / "image1.png")
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image2 = Image.open(ASSETS_DIR / "image2.png")
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
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def test_hash_collision_image_transpose():
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image1 = Image.new("1", size=(10, 20))
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ImageDraw.Draw(image1).line([(0, 0), (10, 0)])
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image2 = Image.new("1", size=(20, 10))
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ImageDraw.Draw(image2).line([(0, 0), (0, 10)])
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
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def test_hash_collision_tensor_shape():
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# The hash should be different though the data is the same when flattened
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arr1 = torch.zeros((5, 10, 20, 3))
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arr2 = torch.zeros((10, 20, 5, 3))
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(data=arr1) != hasher.hash_kwargs(data=arr2)
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def test_hash_collision_array_shape():
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# The hash should be different though the data is the same when flattened
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arr1 = np.zeros((5, 10, 20, 3))
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arr2 = np.zeros((10, 20, 5, 3))
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(data=arr1) != hasher.hash_kwargs(data=arr2)
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