Super resolution and gaussian blur

cdim/diffusion/diffusion_pipeline.py

@@ -90,6 +90,7 @@ def run_diffusion(
                 else:
                     raise ValueError(f"Unsupported combination: loss {loss_type} noise {noise_function.name}")
 
-            image -= 5 / torch.linalg.norm(image.grad) * image.grad
+            image -= 10 / torch.linalg.norm(image.grad) * image.grad
+            image = image.detach().requires_grad_()
 
-    return image
+    return image

cdim/operators/__init__.py

@@ -23,3 +23,5 @@ def get_operator(name: str, **kwargs):
 from .random_box_masker import RandomBoxMasker
 from .random_pixel_masker import RandomPixelMasker
 from .identity_operator import IdentityOperator
+from .super_resolution_operator import SuperResolutionOperator
+from .gaussian_blur_operator import GaussianBlurOperator

cdim/operators/blur_kernel.py

@@ -0,0 +1,31 @@
+import numpy as np
+import torch
+import scipy
+import torch.nn.functional as F
+from torch import nn
+
+
+class BlurKernel(nn.Module):
+    def __init__(self, blur_type='gaussian', kernel_size=31, std=3.0, device=None):
+        super().__init__()
+        self.blur_type = blur_type
+        self.kernel_size = kernel_size
+        self.std = std
+        self.device = device
+        self.padding = self.kernel_size // 2
+
+        if self.blur_type == "gaussian":
+            n = np.zeros((self.kernel_size, self.kernel_size))
+            n[self.kernel_size // 2, self.kernel_size // 2] = 1
+            k = scipy.ndimage.gaussian_filter(n, sigma=self.std)
+            k = torch.from_numpy(k).float()
+            k = k.unsqueeze(0).unsqueeze(0)  # Shape (1, 1, kernel_size, kernel_size)
+            k = k.repeat(3, 1, 1, 1)  # Shape (3, 1, kernel_size, kernel_size)
+            self.register_buffer('kernel', k)
+        else:
+            raise ValueError(f"Unknown blur type {self.blur_type}")
+
+    def forward(self, x):
+        x = F.pad(x, [self.padding]*4, mode='reflect')
+        x = F.conv2d(x, self.kernel, groups=3)
+        return x

cdim/operators/gaussian_blur_operator.py

@@ -0,0 +1,16 @@
+import torch
+from cdim.operators import register_operator
+from cdim.operators.blur_kernel import BlurKernel
+
+@register_operator(name='gaussian_blur')
+class GaussianBlurOperator:
+    def __init__(self, kernel_size, intensity, device='cpu'):
+        self.device = device
+        self.kernel_size = kernel_size
+        self.conv = BlurKernel(blur_type='gaussian',
+                               kernel_size=kernel_size,
+                               std=intensity,
+                               device=device).to(device)
+
+    def __call__(self, data, **kwargs):
+        return self.conv(data)

cdim/operators/resizer.py

@@ -0,0 +1,198 @@
+# This code was taken from: https://github.com/assafshocher/resizer by Assaf Shocher
+import numpy as np
+import torch
+from math import pi
+from torch import nn
+
+
+class Resizer(nn.Module):
+    def __init__(self, in_shape, scale_factor=None, output_shape=None, kernel=None, antialiasing=True):
+        super(Resizer, self).__init__()
+
+        # First standardize values and fill missing arguments (if needed) by deriving scale from output shape or vice versa
+        scale_factor, output_shape = self.fix_scale_and_size(in_shape, output_shape, scale_factor)
+
+        # Choose interpolation method, each method has the matching kernel size
+        method, kernel_width = {
+            "cubic": (cubic, 4.0),
+            "lanczos2": (lanczos2, 4.0),
+            "lanczos3": (lanczos3, 6.0),
+            "box": (box, 1.0),
+            "linear": (linear, 2.0),
+            None: (cubic, 4.0)  # set default interpolation method as cubic
+        }.get(kernel)
+
+        # Antialiasing is only used when downscaling
+        antialiasing *= (np.any(np.array(scale_factor) < 1))
+
+        # Sort indices of dimensions according to scale of each dimension. since we are going dim by dim this is efficient
+        sorted_dims = np.argsort(np.array(scale_factor))
+        self.sorted_dims = [int(dim) for dim in sorted_dims if scale_factor[dim] != 1]
+
+        # Iterate over dimensions to calculate local weights for resizing and resize each time in one direction
+        field_of_view_list = []
+        weights_list = []
+        for dim in self.sorted_dims:
+            # for each coordinate (along 1 dim), calculate which coordinates in the input image affect its result and the
+            # weights that multiply the values there to get its result.
+            weights, field_of_view = self.contributions(in_shape[dim], output_shape[dim], scale_factor[dim], method,
+                                                        kernel_width, antialiasing)
+
+            # convert to torch tensor
+            weights = torch.tensor(weights.T, dtype=torch.float32)
+
+            # We add singleton dimensions to the weight matrix so we can multiply it with the big tensor we get for
+            # tmp_im[field_of_view.T], (bsxfun style)
+            weights_list.append(
+                nn.Parameter(torch.reshape(weights, list(weights.shape) + (len(scale_factor) - 1) * [1]),
+                             requires_grad=False))
+            field_of_view_list.append(
+                nn.Parameter(torch.tensor(field_of_view.T.astype(np.int32), dtype=torch.long), requires_grad=False))
+
+        self.field_of_view = nn.ParameterList(field_of_view_list)
+        self.weights = nn.ParameterList(weights_list)
+
+    def forward(self, in_tensor):
+        x = in_tensor
+
+        # Use the affecting position values and the set of weights to calculate the result of resizing along this 1 dim
+        for dim, fov, w in zip(self.sorted_dims, self.field_of_view, self.weights):
+            # To be able to act on each dim, we swap so that dim 0 is the wanted dim to resize
+            x = torch.transpose(x, dim, 0)
+
+            # This is a bit of a complicated multiplication: x[field_of_view.T] is a tensor of order image_dims+1.
+            # for each pixel in the output-image it matches the positions the influence it from the input image (along 1 dim
+            # only, this is why it only adds 1 dim to 5the shape). We then multiply, for each pixel, its set of positions with
+            # the matching set of weights. we do this by this big tensor element-wise multiplication (MATLAB bsxfun style:
+            # matching dims are multiplied element-wise while singletons mean that the matching dim is all multiplied by the
+            # same number
+            x = torch.sum(x[fov] * w, dim=0)
+
+            # Finally we swap back the axes to the original order
+            x = torch.transpose(x, dim, 0)
+
+        return x
+
+    def fix_scale_and_size(self, input_shape, output_shape, scale_factor):
+        # First fixing the scale-factor (if given) to be standardized the function expects (a list of scale factors in the
+        # same size as the number of input dimensions)
+        if scale_factor is not None:
+            # By default, if scale-factor is a scalar we assume 2d resizing and duplicate it.
+            if np.isscalar(scale_factor) and len(input_shape) > 1:
+                scale_factor = [scale_factor, scale_factor]
+
+            # We extend the size of scale-factor list to the size of the input by assigning 1 to all the unspecified scales
+            scale_factor = list(scale_factor)
+            scale_factor = [1] * (len(input_shape) - len(scale_factor)) + scale_factor
+
+        # Fixing output-shape (if given): extending it to the size of the input-shape, by assigning the original input-size
+        # to all the unspecified dimensions
+        if output_shape is not None:
+            output_shape = list(input_shape[len(output_shape):]) + list(np.uint(np.array(output_shape)))
+
+        # Dealing with the case of non-give scale-factor, calculating according to output-shape. note that this is
+        # sub-optimal, because there can be different scales to the same output-shape.
+        if scale_factor is None:
+            scale_factor = 1.0 * np.array(output_shape) / np.array(input_shape)
+
+        # Dealing with missing output-shape. calculating according to scale-factor
+        if output_shape is None:
+            output_shape = np.uint(np.ceil(np.array(input_shape) * np.array(scale_factor)))
+
+        return scale_factor, output_shape
+
+    def contributions(self, in_length, out_length, scale, kernel, kernel_width, antialiasing):
+        # This function calculates a set of 'filters' and a set of field_of_view that will later on be applied
+        # such that each position from the field_of_view will be multiplied with a matching filter from the
+        # 'weights' based on the interpolation method and the distance of the sub-pixel location from the pixel centers
+        # around it. This is only done for one dimension of the image.
+
+        # When anti-aliasing is activated (default and only for downscaling) the receptive field is stretched to size of
+        # 1/sf. this means filtering is more 'low-pass filter'.
+        fixed_kernel = (lambda arg: scale * kernel(scale * arg)) if antialiasing else kernel
+        kernel_width *= 1.0 / scale if antialiasing else 1.0
+
+        # These are the coordinates of the output image
+        out_coordinates = np.arange(1, out_length + 1)
+
+        # since both scale-factor and output size can be provided simulatneously, perserving the center of the image requires shifting
+        # the output coordinates. the deviation is because out_length doesn't necesary equal in_length*scale.
+        # to keep the center we need to subtract half of this deivation so that we get equal margins for boths sides and center is preserved.
+        shifted_out_coordinates = out_coordinates - (out_length - in_length * scale) / 2
+
+        # These are the matching positions of the output-coordinates on the input image coordinates.
+        # Best explained by example: say we have 4 horizontal pixels for HR and we downscale by SF=2 and get 2 pixels:
+        # [1,2,3,4] -> [1,2]. Remember each pixel number is the middle of the pixel.
+        # The scaling is done between the distances and not pixel numbers (the right boundary of pixel 4 is transformed to
+        # the right boundary of pixel 2. pixel 1 in the small image matches the boundary between pixels 1 and 2 in the big
+        # one and not to pixel 2. This means the position is not just multiplication of the old pos by scale-factor).
+        # So if we measure distance from the left border, middle of pixel 1 is at distance d=0.5, border between 1 and 2 is
+        # at d=1, and so on (d = p - 0.5).  we calculate (d_new = d_old / sf) which means:
+        # (p_new-0.5 = (p_old-0.5) / sf)     ->          p_new = p_old/sf + 0.5 * (1-1/sf)
+        match_coordinates = shifted_out_coordinates / scale + 0.5 * (1 - 1 / scale)
+
+        # This is the left boundary to start multiplying the filter from, it depends on the size of the filter
+        left_boundary = np.floor(match_coordinates - kernel_width / 2)
+
+        # Kernel width needs to be enlarged because when covering has sub-pixel borders, it must 'see' the pixel centers
+        # of the pixels it only covered a part from. So we add one pixel at each side to consider (weights can zeroize them)
+        expanded_kernel_width = np.ceil(kernel_width) + 2
+
+        # Determine a set of field_of_view for each each output position, these are the pixels in the input image
+        # that the pixel in the output image 'sees'. We get a matrix whos horizontal dim is the output pixels (big) and the
+        # vertical dim is the pixels it 'sees' (kernel_size + 2)
+        field_of_view = np.squeeze(
+            np.int16(np.expand_dims(left_boundary, axis=1) + np.arange(expanded_kernel_width) - 1))
+
+        # Assign weight to each pixel in the field of view. A matrix whos horizontal dim is the output pixels and the
+        # vertical dim is a list of weights matching to the pixel in the field of view (that are specified in
+        # 'field_of_view')
+        weights = fixed_kernel(1.0 * np.expand_dims(match_coordinates, axis=1) - field_of_view - 1)
+
+        # Normalize weights to sum up to 1. be careful from dividing by 0
+        sum_weights = np.sum(weights, axis=1)
+        sum_weights[sum_weights == 0] = 1.0
+        weights = 1.0 * weights / np.expand_dims(sum_weights, axis=1)
+
+        # We use this mirror structure as a trick for reflection padding at the boundaries
+        mirror = np.uint(np.concatenate((np.arange(in_length), np.arange(in_length - 1, -1, step=-1))))
+        field_of_view = mirror[np.mod(field_of_view, mirror.shape[0])]
+
+        # Get rid of  weights and pixel positions that are of zero weight
+        non_zero_out_pixels = np.nonzero(np.any(weights, axis=0))
+        weights = np.squeeze(weights[:, non_zero_out_pixels])
+        field_of_view = np.squeeze(field_of_view[:, non_zero_out_pixels])
+
+        # Final products are the relative positions and the matching weights, both are output_size X fixed_kernel_size
+        return weights, field_of_view
+
+
+# These next functions are all interpolation methods. x is the distance from the left pixel center
+
+
+def cubic(x):
+    absx = np.abs(x)
+    absx2 = absx ** 2
+    absx3 = absx ** 3
+    return ((1.5 * absx3 - 2.5 * absx2 + 1) * (absx <= 1) +
+            (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * ((1 < absx) & (absx <= 2)))
+
+
+def lanczos2(x):
+    return (((np.sin(pi * x) * np.sin(pi * x / 2) + np.finfo(np.float32).eps) /
+             ((pi ** 2 * x ** 2 / 2) + np.finfo(np.float32).eps))
+            * (abs(x) < 2))
+
+
+def box(x):
+    return ((-0.5 <= x) & (x < 0.5)) * 1.0
+
+
+def lanczos3(x):
+    return (((np.sin(pi * x) * np.sin(pi * x / 3) + np.finfo(np.float32).eps) /
+             ((pi ** 2 * x ** 2 / 3) + np.finfo(np.float32).eps))
+            * (abs(x) < 3))
+
+
+def linear(x):
+    return (x + 1) * ((-1 <= x) & (x < 0)) + (1 - x) * ((0 <= x) & (x <= 1))

cdim/operators/super_resolution_operator.py

@@ -0,0 +1,15 @@
+import torch
+
+from cdim.operators import register_operator
+from cdim.operators.resizer import Resizer
+
+
+@register_operator(name='super_resolution')
+class SuperResolutionOperator:
+    def __init__(self, scale=4, in_shape=(1, 3, 256, 256), device='cpu'):
+        self.device = device
+        self.down_sample = Resizer(in_shape, 1/scale).to(device)
+
+    def __call__(self, data, **kwargs):
+        return self.down_sample(data)
+

operator_configs/gaussian_blur_config.yaml

@@ -0,0 +1,3 @@
+name: gaussian_blur
+kernel_size: 61
+intensity: 3.0

operator_configs/super_resolution_config.yaml

@@ -0,0 +1,3 @@
+name: super_resolution
+scale: 8
+in_shape: !!python/tuple [1, 3, 256, 256]