refiners/tests/fluxion/test_utils.py
2023-10-05 16:30:27 +02:00

43 lines
1.3 KiB
Python

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