Implementation of GaussianMixtureMasksTransform

lightly/transforms/__init__.py

@@ -18,6 +18,7 @@
 from lightly.transforms.dino_transform import DINOTransform, DINOViewTransform
 from lightly.transforms.fast_siam_transform import FastSiamTransform
 from lightly.transforms.gaussian_blur import GaussianBlur
+from lightly.transforms.gaussian_mixture_masks_transform import GaussianMixtureMask
 from lightly.transforms.irfft2d_transform import IRFFT2DTransform
 from lightly.transforms.jigsaw import Jigsaw
 from lightly.transforms.mae_transform import MAETransform

lightly/transforms/gaussian_mixture_masks_transform.py

@@ -0,0 +1,107 @@
+from typing import Tuple
+
+import torch
+import torch.fft
+from torch import Tensor
+
+from lightly.transforms.irfft2d_transform import IRFFT2DTransform
+
+
+class GaussianMixtureMask:
+    """Applies a Gaussian Mixture Mask in the Fourier domain to a single-channel image.
+
+    The mask is created using random Gaussian kernels, which are applied in
+    the frequency domain via RFFT2D, and then the IRFFT2D is used to return
+    to the spatial domain. The transformation is applied to each image channel separately.
+
+    Attributes:
+        num_gaussians: Number of Gaussian kernels to generate in the mixture mask.
+        std_range: Tuple containing the minimum and maximum standard deviation for the Gaussians.
+    """
+
+    def __init__(
+        self, num_gaussians: int = 20, std_range: Tuple[float, float] = (10, 15)
+    ):
+        """Initializes GaussianMixtureMasks with the given parameters.
+
+        Args:
+            num_gaussians: Number of Gaussian kernels to generate in the mixture mask.
+            std_range: Tuple containing the minimum and maximum standard deviation for the Gaussians.
+        """
+        self.num_gaussians = num_gaussians
+        self.std_range = std_range
+
+    def gaussian_kernel(
+        self, size: Tuple[int, int], sigma: Tensor, center: Tensor
+    ) -> Tensor:
+        """Generates a 2D Gaussian kernel.
+
+        Args:
+            size: Tuple specifying the dimensions of the Gaussian kernel (H, W).
+            sigma: Tensor specifying the standard deviation of the Gaussian.
+            center: Tensor specifying the center of the Gaussian kernel.
+
+        Returns:
+            Tensor: A 2D Gaussian kernel.
+        """
+        u, v = torch.meshgrid(torch.arange(0, size[0]), torch.arange(0, size[1]))
+        u = u.to(sigma.device)
+        v = v.to(sigma.device)
+        u0, v0 = center
+        gaussian = torch.exp(
+            -((u - u0) ** 2 / (2 * sigma[0] ** 2) + (v - v0) ** 2 / (2 * sigma[1] ** 2))
+        )
+
+        return gaussian
+
+    def apply_gaussian_mixture_mask(
+        self, freq_image: Tensor, num_gaussians: int, std: Tuple[float, float]
+    ) -> Tensor:
+        """Applies the Gaussian mixture mask to a frequency-domain image.
+
+        Args:
+            freq_image: Tensor representing the frequency-domain image of shape (C, H, W//2+1).
+            num_gaussians: Number of Gaussian kernels to generate in the mask.
+            std: Tuple specifying the standard deviation range for the Gaussians.
+
+        Returns:
+            Tensor: Image after applying the Gaussian mixture mask.
+        """
+        image_size = freq_image.shape[1:]
+        original_height = image_size[0]
+        original_width = 2 * (image_size[1] - 1)
+
+        original_shape = (original_height, original_width)
+
+        self.irfft2d_transform = IRFFT2DTransform(original_shape)
+
+        size = freq_image[0].shape
+
+        mask = freq_image.new_ones(freq_image.shape)
+
+        for _ in range(num_gaussians):
+            u0 = torch.randint(0, size[0], (1,), device=freq_image.device)
+            v0 = torch.randint(0, size[1], (1,), device=freq_image.device)
+            center = torch.tensor((u0, v0), device=freq_image.device)
+            sigma = torch.rand(2, device=freq_image.device) * (std[1] - std[0]) + std[0]
+
+            g_kernel = self.gaussian_kernel((size[0], size[1]), sigma, center)
+            mask -= g_kernel
+
+        filtered_freq_image = freq_image * mask
+        filtered_image = self.irfft2d_transform(filtered_freq_image).abs()
+        return filtered_image
+
+    def __call__(self, freq_image: Tensor) -> Tensor:
+        """Applies the Gaussian mixture mask transformation to the input frequency-domain image.
+
+        Args:
+            freq_image: Tensor representing a frequency-domain image of shape (C, H, W//2+1).
+
+        Returns:
+            Tensor: The transformed image after applying the Gaussian mixture mask.
+        """
+        transformed_channel: Tensor = self.apply_gaussian_mixture_mask(
+            freq_image, self.num_gaussians, self.std_range
+        )
+        return transformed_channel

tests/transforms/test_gaussian_mixture_masks.py

@@ -0,0 +1,10 @@
+import torch
+
+from lightly.transforms import GaussianMixtureMask
+
+
+def test() -> None:
+    transform = GaussianMixtureMask(20, (10, 15))
+    image = torch.rand(3, 32, 17)
+    output = transform(image)
+    assert output.shape == (3, 32, 32)