mirror of
https://github.com/finegrain-ai/refiners.git
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97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
from dataclasses import dataclass
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from warnings import warn
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import pytest
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import torch
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from PIL import Image
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from torch import device as Device, dtype as DType
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from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur # type: ignore
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from refiners.fluxion.utils import (
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gaussian_blur,
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image_to_tensor,
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manual_seed,
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no_grad,
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summarize_tensor,
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tensor_to_image,
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)
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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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dtype: DType = torch.float32
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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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BlurInput(kernel_size=9, dtype=torch.float16),
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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(test_device: Device, blur_input: BlurInput) -> None:
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if test_device.type == "cpu" and blur_input.dtype == torch.float16:
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warn("half float is not supported on the CPU because of `torch.mm`, skipping")
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pytest.skip()
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manual_seed(2)
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tensor = torch.randn(3, blur_input.image_height, blur_input.image_width, device=test_device, dtype=blur_input.dtype)
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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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def test_image_to_tensor() -> None:
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image = Image.new("RGB", (512, 512))
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assert image_to_tensor(image).shape == (1, 3, 512, 512)
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assert image_to_tensor(image.convert("L")).shape == (1, 1, 512, 512)
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assert image_to_tensor(image.convert("RGBA")).shape == (1, 4, 512, 512)
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def test_tensor_to_image() -> None:
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assert tensor_to_image(torch.zeros(1, 3, 512, 512)).mode == "RGB"
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assert tensor_to_image(torch.zeros(1, 1, 512, 512)).mode == "L"
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assert tensor_to_image(torch.zeros(1, 4, 512, 512)).mode == "RGBA"
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assert tensor_to_image(torch.zeros(1, 3, 512, 512, dtype=torch.bfloat16)).mode == "RGB"
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def test_summarize_tensor() -> None:
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).int())
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).float())
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).double())
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assert summarize_tensor(torch.complex(torch.zeros(1, 3, 512, 512), torch.zeros(1, 3, 512, 512)))
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).bfloat16())
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).bool())
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def test_no_grad() -> None:
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x = torch.randn(1, 1, requires_grad=True)
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with torch.no_grad():
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y = x + 1
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assert not y.requires_grad
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with no_grad():
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z = x + 1
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assert not z.requires_grad
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w = x + 1
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assert w.requires_grad
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