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43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
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from dataclasses import dataclass
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from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur # type: ignore
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import pytest
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import torch
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from refiners.fluxion.utils import gaussian_blur, manual_seed
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@dataclass
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class BlurInput:
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kernel_size: int | tuple[int, int]
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sigma: float | tuple[float, float] | None = None
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image_height: int = 512
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image_width: int = 512
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batch_size: int | None = 1
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BLUR_INPUTS: list[BlurInput] = [
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BlurInput(kernel_size=9),
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BlurInput(kernel_size=9, batch_size=None),
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BlurInput(kernel_size=9, sigma=1.0),
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BlurInput(kernel_size=9, sigma=1.0, image_height=768),
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BlurInput(kernel_size=(9, 5), sigma=(1.0, 0.8)),
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]
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@pytest.fixture(params=BLUR_INPUTS)
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def blur_input(request: pytest.FixtureRequest) -> BlurInput:
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return request.param
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def test_gaussian_blur(blur_input: BlurInput) -> None:
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manual_seed(2)
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tensor = torch.randn(3, blur_input.image_height, blur_input.image_width)
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if blur_input.batch_size is not None:
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tensor = tensor.expand(blur_input.batch_size, -1, -1, -1)
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ref_blur = torch_gaussian_blur(tensor, blur_input.kernel_size, blur_input.sigma) # type: ignore
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our_blur = gaussian_blur(tensor, blur_input.kernel_size, blur_input.sigma)
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assert torch.equal(our_blur, ref_blur)
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