From e00b88c681f0c906576c4cff0ac7db872ce5ff59 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Sat, 19 Oct 2019 21:18:47 +0100
Subject: Revert "added FGP_dTV (#32)" (#34)
This reverts commit 8839dffdee7ef1fff72eb305bf09fe30917ec238.
---
Wrappers/Python/ccpi/plugins/regularisers.py | 45 ----------------------------
1 file changed, 45 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py
index ef79231..6ed9fb2 100644
--- a/Wrappers/Python/ccpi/plugins/regularisers.py
+++ b/Wrappers/Python/ccpi/plugins/regularisers.py
@@ -91,51 +91,6 @@ class FGP_TV(Function):
out = x.copy()
out.fill(res)
return out
-
-class FGP_dTV(Function):
- def __init__(self, refdata, regularisation_parameter, iterations,
- tolerance, eta_const, methodTV, nonneg, device='cpu'):
- # set parameters
- self.lambdaReg = regularisation_parameter
- self.iterationsTV = iterations
- self.tolerance = tolerance
- self.methodTV = methodTV
- self.nonnegativity = nonneg
- self.device = device # string for 'cpu' or 'gpu'
- self.refData = np.asarray(refdata.as_array(), dtype=np.float32)
- self.eta = eta_const
-
- def __call__(self,x):
- # evaluate objective function of TV gradient
- EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2)
- return 0.5*EnergyValTV[0]
- def proximal(self,x,tau, out=None):
- pars = {'algorithm' : FGP_dTV, \
- 'input' : np.asarray(x.as_array(), dtype=np.float32),\
- 'regularization_parameter':self.lambdaReg*tau, \
- 'number_of_iterations' :self.iterationsTV ,\
- 'tolerance_constant':self.tolerance,\
- 'methodTV': self.methodTV ,\
- 'nonneg': self.nonnegativity ,\
- 'eta_const' : self.eta,\
- 'refdata':self.refData}
- #inputData, refdata, regularisation_parameter, iterations,
- # tolerance_param, eta_const, methodTV, nonneg, device='cpu'
- res , info = regularisers.FGP_dTV(pars['input'],
- pars['refdata'],
- pars['regularization_parameter'],
- pars['number_of_iterations'],
- pars['tolerance_constant'],
- pars['eta_const'],
- pars['methodTV'],
- pars['nonneg'],
- self.device)
- if out is not None:
- out.fill(res)
- else:
- out = x.copy()
- out.fill(res)
- return out
class SB_TV(Function):
def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,printing,device):
--
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