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Add K & KT as customizable input for recon #2

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8 changes: 7 additions & 1 deletion medutils/optimization/base_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,13 @@ def solve(self, f, max_iter):
raise NotImplementedError

class BaseReconOptimizer(BaseOptimizer):
def __init__(self, A, AH, mode, lambd, beta=None, tau=None):
def __init__(self, A, AH, mode, lambd, K=None, KT=None, beta=None, tau=None):
'''
:param K: expected to be a lamdba function, e.g. K = lambda x, beta, mode: Nabla(mode=mode, beta=beta).forward(x)
:param KT: expected to be a lamdba function, e.g. KT = lambda x, beta, mode: NablaT(mode=mode, beta=beta).forward(x)
'''
self.A = A
self.AH = AH
self.K = K
self.KT = KT
super().__init__(mode, lambd, beta, tau)
21 changes: 11 additions & 10 deletions medutils/optimization_th/recon_optimizer_th.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
import tqdm
import torch
import numpy as np
from functools import partial

class TVReconOptimizer(BaseReconOptimizer):
""" Total Variation
Expand All @@ -24,14 +25,14 @@ class TVReconOptimizer(BaseReconOptimizer):
"""
def solve(self, y, max_iter):
# setup operators
K = Nabla(self.mode, self.beta)
KT = NablaT(self.mode, self.beta)
K = Nabla(self.mode, self.beta) if self.K is None else partial(self.K, mode=self.mode, beta=self.beta)
KT = NablaT(self.mode, self.beta) if self.KT is None else partial(self.KT, mode=self.mode, beta=self.beta)

A = self.A
AH = self.AH

# setup constants
L = K.L
L = Nabla(self.mode, self.beta).L # ToDo: Adjust to input K
if self.tau != None:
tau = self.tau
else:
Expand Down Expand Up @@ -86,7 +87,7 @@ def __init__(self, A, AH, mode, lambd, alpha0, alpha1, beta=None):
self.alpha1 = alpha1

def solve(self, y, max_iter):
# setup operators
# setup operator # ToDo: Adapt to custom operators
K = Nabla(self.mode, self.beta)
KT = NablaT(self.mode, self.beta)
E = NablaSym(self.mode, self.beta)
Expand Down Expand Up @@ -168,11 +169,11 @@ def __init__(self, A, AH, mode, lambd, alpha1, s, beta1=None, beta2=None):
self.beta2 = beta2

def solve(self, y, max_iter):
# setup operators
K_beta1 = Nabla(self.mode, self.beta1)
KT_beta1 = NablaT(self.mode, self.beta1)
K_beta2 = Nabla(self.mode, self.beta2)
KT_beta2 = NablaT(self.mode, self.beta2)
# setup operators # ToDo: Adapt to custom operators
K_beta1 = Nabla(self.mode, self.beta1) if self.K is None else partial(self.K, mode=self.mode, beta=self.beta1)
KT_beta1 = NablaT(self.mode, self.beta1) if self.KT is None else partial(self.KT, mode=self.mode, beta=self.beta1)
K_beta2 = Nabla(self.mode, self.beta2) if self.K is None else partial(self.KT, mode=self.mode, beta=self.beta2)
KT_beta2 = NablaT(self.mode, self.beta2) if self.KT is None else partial(self.KT, mode=self.mode, beta=self.beta2)

A = self.A
AH = self.AH
Expand Down Expand Up @@ -266,7 +267,7 @@ def __init__(self, A, AH, mode, lambd, alpha0, alpha1, s, beta1=None, beta2=None
self.beta2 = beta2

def solve(self, y, max_iter):
# setup operators
# setup operators # ToDo: Adapt to custom operators
K_beta1 = Nabla(self.mode, self.beta1)
KT_beta1 = NablaT(self.mode, self.beta1)
E_beta1 = NablaSym(self.mode, self.beta1)
Expand Down