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'''This module handles task-dependent operations (A) and noises (n) to simulate a measurement y=Ax+n.'''
from abc import ABC, abstractmethod
from functools import partial
from torch.nn import functional as F
from torchvision import torch
from src.flair.utils.blur_util import Blurkernel
from src.flair.utils.img_util import fft2d
import numpy as np
from src.flair.utils.resizer import Resizer
from src.flair.utils.utils_sisr import pre_calculate_FK, pre_calculate_nonuniform
from torch.fft import fft2, ifft2
from src.flair.motionblur.motionblur import Kernel
# =================
# Operation classes
# =================
__OPERATOR__ = {}
_GAMMA_FACTOR = 2.2
def register_operator(name: str):
def wrapper(cls):
if __OPERATOR__.get(name, None):
raise NameError(f"Name {name} is already registered!")
__OPERATOR__[name] = cls
return cls
return wrapper
def get_operator(name: str, **kwargs):
if __OPERATOR__.get(name, None) is None:
raise NameError(f"Name {name} is not defined.")
return __OPERATOR__[name](**kwargs)
class LinearOperator(ABC):
@abstractmethod
def forward(self, data, **kwargs):
# calculate A * X
pass
@abstractmethod
def noisy_forward(self, data, **kwargs):
# calculate A * X + n
pass
@abstractmethod
def transpose(self, data, **kwargs):
# calculate A^T * X
pass
def ortho_project(self, data, **kwargs):
# calculate (I - A^T * A)X
return data - self.transpose(self.forward(data, **kwargs), **kwargs)
def project(self, data, measurement, **kwargs):
# calculate (I - A^T * A)Y - AX
return self.ortho_project(measurement, **kwargs) - self.forward(data, **kwargs)
@register_operator(name='noise')
class DenoiseOperator(LinearOperator):
def __init__(self, device):
self.device = device
def forward(self, data):
return data
def noisy_forward(self, data):
return data
def transpose(self, data):
return data
def ortho_project(self, data):
return data
def project(self, data):
return data
@register_operator(name='sr_bicubic')
class SuperResolutionOperator(LinearOperator):
def __init__(self,
in_shape,
scale_factor,
noise,
noise_scale,
device):
self.device = device
self.up_sample = partial(F.interpolate, scale_factor=scale_factor)
self.down_sample = Resizer(in_shape, 1/scale_factor).to(device)
self.noise = get_noise(name=noise, scale=noise_scale)
def A(self, data, **kwargs):
return self.forward(data, **kwargs)
def forward(self, data, **kwargs):
return self.down_sample(data)
def noisy_forward(self, data, **kwargs):
return self.noise.forward(self.down_sample(data))
def transpose(self, data, **kwargs):
return self.up_sample(data)
def project(self, data, measurement, **kwargs):
return data - self.transpose(self.forward(data)) + self.transpose(measurement)
@register_operator(name='deblur_motion')
class MotionBlurOperator(LinearOperator):
def __init__(self,
kernel_size,
intensity,
device):
self.device = device
self.kernel_size = kernel_size
self.conv = Blurkernel(blur_type='motion',
kernel_size=kernel_size,
std=intensity,
device=device).to(device) # should we keep this device term?
self.kernel_size =kernel_size
self.intensity = intensity
self.kernel = Kernel(size=(kernel_size, kernel_size), intensity=intensity)
kernel = torch.tensor(self.kernel.kernelMatrix, dtype=torch.float32)
self.conv.update_weights(kernel)
def forward(self, data, **kwargs):
# A^T * A
return self.conv(data)
def noisy_forward(self, data, **kwargs):
pass
def transpose(self, data, **kwargs):
return data
def change_kernel(self):
self.kernel = Kernel(size=(self.kernel_size, self.kernel_size), intensity=self.intensity)
kernel = torch.tensor(self.kernel.kernelMatrix, dtype=torch.float32)
self.conv.update_weights(kernel)
def get_kernel(self):
kernel = self.kernel.kernelMatrix.type(torch.float32).to(self.device)
return kernel.view(1, 1, self.kernel_size, self.kernel_size)
def A(self, data):
return self.forward(data)
def At(self, data):
return self.transpose(data)
@register_operator(name='deblur_gauss')
class GaussialBlurOperator(LinearOperator):
def __init__(self,
kernel_size,
intensity,
device):
self.device = device
self.kernel_size = kernel_size
self.conv = Blurkernel(blur_type='gaussian',
kernel_size=kernel_size,
std=intensity,
device=device).to(device)
self.kernel = self.conv.get_kernel()
self.conv.update_weights(self.kernel.type(torch.float32))
def forward(self, data, **kwargs):
return self.conv(data)
def noisy_forward(self, data, **kwargs):
pass
def transpose(self, data, **kwargs):
return data
def get_kernel(self):
return self.kernel.view(1, 1, self.kernel_size, self.kernel_size)
def apply_kernel(self, data, kernel):
self.conv.update_weights(kernel.type(torch.float32))
return self.conv(data)
def A(self, data):
return self.forward(data)
def At(self, data):
return self.transpose(data)
@register_operator(name='inpainting')
class InpaintingOperator(LinearOperator):
'''This operator get pre-defined mask and return masked image.'''
def __init__(self,
noise,
noise_scale,
device):
self.device = device
self.noise = get_noise(name=noise, scale=noise_scale)
def forward(self, data, **kwargs):
try:
return data * kwargs.get('mask', None).to(self.device)
except:
raise ValueError("Require mask")
def noisy_forward(self, data, **kwargs):
return self.noise.forward(self.forward(data, **kwargs))
def transpose(self, data, **kwargs):
return data
def ortho_project(self, data, **kwargs):
return data - self.forward(data, **kwargs)
# Operator for BlindDPS.
@register_operator(name='blind_blur')
class BlindBlurOperator(LinearOperator):
def __init__(self, device, **kwargs) -> None:
self.device = device
def forward(self, data, kernel, **kwargs):
return self.apply_kernel(data, kernel)
def transpose(self, data, **kwargs):
return data
def apply_kernel(self, data, kernel):
#TODO: faster way to apply conv?:W
b_img = torch.zeros_like(data).to(self.device)
for i in range(3):
b_img[:, i, :, :] = F.conv2d(data[:, i:i+1, :, :], kernel, padding='same')
return b_img
class NonLinearOperator(ABC):
@abstractmethod
def forward(self, data, **kwargs):
pass
@abstractmethod
def noisy_forward(self, data, **kwargs):
pass
def project(self, data, measurement, **kwargs):
return data + measurement - self.forward(data)
@register_operator(name='phase_retrieval')
class PhaseRetrievalOperator(NonLinearOperator):
def __init__(self,
oversample,
noise,
noise_scale,
device):
self.pad = int((oversample / 8.0) * 256)
self.device = device
self.noise = get_noise(name=noise, scale=noise_scale)
def forward(self, data, **kwargs):
padded = F.pad(data, (self.pad, self.pad, self.pad, self.pad))
amplitude = fft2d(padded).abs()
return amplitude
def noisy_forard(self, data, **kwargs):
return self.noise.forward(self.forward(data, **kwargs))
@register_operator(name='nonuniform_blur')
class NonuniformBlurOperator(LinearOperator):
def __init__(self, in_shape, kernel_size, device,
kernels=None, masks=None):
self.device = device
self.kernel_size = kernel_size
self.in_shape = in_shape
# TODO: generalize
if kernels is None and masks is None:
self.kernels = np.load('./functions/nonuniform/kernels/000001.npy')
self.masks = np.load('./functions/nonuniform/masks/000001.npy')
self.kernels = torch.tensor(self.kernels).to(device)
self.masks = torch.tensor(self.masks).to(device)
# approximate in image space
def forward_img(self, data):
K = self.kernel_size
data = F.pad(data, (K//2, K//2, K//2, K//2), mode="reflect")
kernels = self.kernels.transpose(0, 1)
data_rgb_batch = data.transpose(0, 1)
conv_rgb_batch = F.conv2d(data_rgb_batch, kernels)
y_rgb_batch = conv_rgb_batch * self.masks
y_rgb_batch = torch.sum(y_rgb_batch, dim=1, keepdim=True)
y = y_rgb_batch.transpose(0, 1)
return y
# NOTE: Only using this operator will make the problem nonlinear (gamma-correction)
def forward_nonlinear(self, data, flatten=False, noiseless=False):
# 1. Usual nonuniform blurring degradataion pipeline
kernels = self.kernels.transpose(0, 1)
FK, FKC = pre_calculate_FK(kernels)
y = ifft2(FK * fft2(data)).real
y = y.transpose(0, 1)
y_rgb_batch = self.masks * y
y_rgb_batch = torch.sum(y_rgb_batch, dim=1, keepdim=True)
y = y_rgb_batch.transpose(0, 1)
F2KM, FKFMy = pre_calculate_nonuniform(data, y, FK, FKC, self.masks)
self.pre_calculated = (FK, FKC, F2KM, FKFMy)
# 2. Gamma-correction
y = (y + 1) / 2
y = y ** (1 / _GAMMA_FACTOR)
y = (y - 0.5) / 0.5
return y
def noisy_forward(self, data, **kwargs):
return self.noise.forward(self.forward(data))
# exact in Fourier
def forward(self, data, flatten=False, noiseless=False):
# [1, 25, 33, 33] -> [25, 1, 33, 33]
kernels = self.kernels.transpose(0, 1)
# [25, 1, 512, 512]
FK, FKC = pre_calculate_FK(kernels)
# [25, 3, 512, 512]
y = ifft2(FK * fft2(data)).real
# [3, 25, 512, 512]
y = y.transpose(0, 1)
y_rgb_batch = self.masks * y
# [3, 1, 512, 512]
y_rgb_batch = torch.sum(y_rgb_batch, dim=1, keepdim=True)
# [1, 3, 512, 512]
y = y_rgb_batch.transpose(0, 1)
F2KM, FKFMy = pre_calculate_nonuniform(data, y, FK, FKC, self.masks)
self.pre_calculated = (FK, FKC, F2KM, FKFMy)
return y
def transpose(self, y, flatten=False):
kernels = self.kernels.transpose(0, 1)
FK, FKC = pre_calculate_FK(kernels)
# 1. braodcast and multiply
# [3, 1, 512, 512]
y_rgb_batch = y.transpose(0, 1)
# [3, 25, 512, 512]
y_rgb_batch = y_rgb_batch.repeat(1, 25, 1, 1)
y = self.masks * y_rgb_batch
# 2. transpose of convolution in Fourier
# [25, 3, 512, 512]
y = y.transpose(0, 1)
ATy_broadcast = ifft2(FKC * fft2(y)).real
# [1, 3, 512, 512]
ATy = torch.sum(ATy_broadcast, dim=0, keepdim=True)
return ATy
def A(self, data):
return self.forward(data)
def At(self, data):
return self.transpose(data)
# =============
# Noise classes
# =============
__NOISE__ = {}
def register_noise(name: str):
def wrapper(cls):
if __NOISE__.get(name, None):
raise NameError(f"Name {name} is already defined!")
__NOISE__[name] = cls
return cls
return wrapper
def get_noise(name: str, **kwargs):
if __NOISE__.get(name, None) is None:
raise NameError(f"Name {name} is not defined.")
noiser = __NOISE__[name](**kwargs)
noiser.__name__ = name
return noiser
class Noise(ABC):
def __call__(self, data):
return self.forward(data)
@abstractmethod
def forward(self, data):
pass
@register_noise(name='clean')
class Clean(Noise):
def __init__(self, **kwargs):
pass
def forward(self, data):
return data
@register_noise(name='gaussian')
class GaussianNoise(Noise):
def __init__(self, scale):
self.scale = scale
def forward(self, data):
return data + torch.randn_like(data, device=data.device) * self.scale
@register_noise(name='poisson')
class PoissonNoise(Noise):
def __init__(self, scale):
self.scale = scale
def forward(self, data):
'''
Follow skimage.util.random_noise.
'''
# version 3 (stack-overflow)
import numpy as np
data = (data + 1.0) / 2.0
data = data.clamp(0, 1)
device = data.device
data = data.detach().cpu()
data = torch.from_numpy(np.random.poisson(data * 255.0 * self.scale) / 255.0 / self.scale)
data = data * 2.0 - 1.0
data = data.clamp(-1, 1)
return data.to(device)
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