deprecate grad and prox

Norm2sq fixes for memopt

1 files changed, 29 insertions, 16 deletions

diff --git a/Wrappers/Python/ccpi/optimisation/funcs.py b/Wrappers/Python/ccpi/optimisation/funcs.py
index 99af275..4f84889 100755
--- a/Wrappers/Python/ccpi/optimisation/funcs.py
+++ b/Wrappers/Python/ccpi/optimisation/funcs.py
@@ -20,6 +20,7 @@
 from ccpi.optimisation.ops import Identity, FiniteDiff2D
 import numpy
 from ccpi.framework import DataContainer
+import warnings
 
 
 def isSizeCorrect(data1 ,data2):
@@ -40,8 +41,12 @@ class Function(object):
     def __init__(self):
         self.L = None
     def __call__(self,x, out=None):       raise NotImplementedError 
-    def grad(self, x):                    raise NotImplementedError
-    def prox(self, x, tau):               raise NotImplementedError
+    def grad(self, x):
+        warnings.warn("grad method is deprecated. use gradient instead", DeprecationWarning)
+        return self.gradient(x, out=None)
+    def prox(self, x, tau):
+        warnings.warn("prox method is deprecated. use proximal instead", DeprecationWarning)
+        return self.proximal(x,tau,out=None)
     def gradient(self, x, out=None):      raise NotImplementedError
     def proximal(self, x, tau, out=None): raise NotImplementedError
 
@@ -141,12 +146,20 @@ class Norm2sq(Function):
         self.A = A  # Should be an operator, default identity
         self.b = b  # Default zero DataSet?
         self.c = c  # Default 1.
-        self.memopt = memopt
         if memopt:
-            #self.direct_placehold = A.adjoint(b)
-            self.direct_placehold = A.allocate_direct()
-            self.adjoint_placehold = A.allocate_adjoint()
-            
+            try:
+                self.adjoint_placehold = A.range_geometry().allocate()
+                self.direct_placehold = A.domain_geometry().allocate()
+                self.memopt = True
+            except NameError as ne:
+                warnings.warn(str(ne))
+                self.memopt = False
+            except NotImplementedError as nie:
+                print (nie)
+                warnings.warn(str(nie))
+                self.memopt = False
+        else:
+            self.memopt = False
         
         # Compute the Lipschitz parameter from the operator if possible
         # Leave it initialised to None otherwise
@@ -157,10 +170,9 @@ class Norm2sq(Function):
         except NotImplementedError as noe:
             pass
         
-    def grad(self,x):
-        #return 2*self.c*self.A.adjoint( self.A.direct(x) - self.b )
-        return (2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b )
-    
+    #def grad(self,x):
+    #    return self.gradient(x, out=None)
+
     def __call__(self,x):
         #return self.c* np.sum(np.square((self.A.direct(x) - self.b).ravel()))
         #if out is None:
@@ -178,12 +190,13 @@ class Norm2sq(Function):
             self.A.direct(x, out=self.adjoint_placehold)
             self.adjoint_placehold.__isub__( self.b )
             self.A.adjoint(self.adjoint_placehold, out=self.direct_placehold)
-            self.direct_placehold.__imul__(2.0 * self.c)
-            # can this be avoided?
-            out.fill(self.direct_placehold)
+            #self.direct_placehold.__imul__(2.0 * self.c)
+            ## can this be avoided?
+            #out.fill(self.direct_placehold)
+            self.direct_placehold.multiply(2.0*self.c, out=out)
         else:
-            return self.grad(x)
-            
+            return (2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b )
+
 
 
 class ZeroFun(Function):