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Allow TV proximal to be calculated in place #1941

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Allow TV proximal to be calculated in place #1941

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2eca759
Stupid solution
MargaretDuff Mar 7, 2024
b53eb3b
Merge
MargaretDuff Oct 1, 2024
6b2bef9
A non-satisfactory fix
MargaretDuff Oct 1, 2024
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13 changes: 7 additions & 6 deletions Wrappers/Python/cil/optimisation/functions/TotalVariation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,10 +254,6 @@ def __call__(self, x):
def proximal(self, x, tau, out=None):
r""" Returns the proximal operator of the TotalVariation function at :code:`x` ."""

if id(x)==id(out):
raise InPlaceError(message="TotalVariation.proximal cannot be used in place")


if self.strong_convexity_constant > 0:

strongly_convex_factor = (1 + tau * self.strong_convexity_constant)
Expand All @@ -266,7 +262,8 @@ def proximal(self, x, tau, out=None):
solution = self._fista_on_dual_rof(x, tau, out=out)

if self.strong_convexity_constant > 0:
x *= strongly_convex_factor
if id(x) != id(solution):
x *= strongly_convex_factor
tau *= strongly_convex_factor

return solution
Expand Down Expand Up @@ -306,12 +303,16 @@ def _fista_on_dual_rof(self, x, tau, out=None):

if out is None:
out = self.gradient_operator.domain_geometry().allocate(0)
if id(x) == id(out):
x_eval= x.copy()
else:
x_eval = x

for k in range(self.iterations):

t0 = t
self.gradient_operator.adjoint(tmp_q, out=out)
out.sapyb(tau_reg_neg, x, 1.0, out=out)
out.sapyb(tau_reg_neg, x_eval, 1.0, out=out)
self.projection_C(out, tau=None, out=out)

self.gradient_operator.direct(out, out=p1)
Expand Down
3 changes: 2 additions & 1 deletion Wrappers/Python/cil/optimisation/operators/Operator.py
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Expand Up @@ -151,7 +151,8 @@ def domain(self):
@property
def range(self):
return self.range_geometry()



def __rmul__(self, scalar):
'''Defines the multiplication by a scalar on the left

Expand Down
1 change: 1 addition & 0 deletions Wrappers/Python/test/test_out_in_place.py
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Expand Up @@ -111,6 +111,7 @@ def setUp(self):
(WeightedL2NormSquared(weight=b_ig), ig),
(TotalVariation(backend='c', warm_start=False, max_iteration=100), ig),
(TotalVariation(backend='numpy', warm_start=False, max_iteration=100), ig),
(TotalVariation(backend='numpy', warm_start=False, max_iteration=100, strong_convexity_constant=0.5), ig),
(OperatorCompositionFunction(L2NormSquared(), A), ig),
(MixedL21Norm(), bg),
(SmoothMixedL21Norm(epsilon=0.3), bg),
Expand Down
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