Update CCPi-Regularisation plugin for version 24.0.0

CHANGELOG.md

@@ -1,4 +1,5 @@
 * x.x.x
+  - Update to new CCPi-Regularisation toolkit v24.0.0. This is a backward incompatible release of the toolkit.
   - Set CMake Policy CMP0148 to OLD to avoid warnings in CMake 3.27
   - AcquisitionGeometry prints the first and last 10 angles, or all if there are 30 or less, rather than the first 20
   - Added a weight argument to the L1Norm function

README.md

@@ -36,7 +36,7 @@ where:
 
 - `astra-toolbox` (requires an NVIDIA GPU) enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) projectors (GPLv3 license)
 - `tigre` (requires an NVIDIA GPU) enables support for [TIGRE](https://github.com/CERN/TIGRE) toolbox projectors (BSD license)
-- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/vais-ral/CCPi-Regularisation-Toolkit)
+- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit)
 - `tomophantom` can generate phantoms to use as test data [Tomophantom](https://github.com/dkazanc/TomoPhantom)
 
 ### Dependencies

Wrappers/Python/cil/plugins/ccpi_regularisation/functions/regularisers.py

@@ -18,9 +18,9 @@
 
 try:
     from ccpi.filters import regularisers
-    from ccpi.filters.cpu_regularisers import TV_ENERGY
+    from ccpi.filters.TV import TV_ENERGY
 except ImportError as exc:
-    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=20.04"`') from exc
+    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=24"`') from exc
 
 
 from cil.framework import DataOrder
@@ -229,14 +229,17 @@ def _fista_on_dual_rof(self, in_arr, tau):
 
         """
 
-        res , info = regularisers.FGP_TV(\
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_TV(\
               in_arr,\
               self.alpha * tau,\
               self.max_iteration,\
               self.tolerance,\
               self.methodTV,\
               self.nonnegativity,\
-              self.device)
+              infovector = info,
+              device = self.device)
 
         return res, info
 
@@ -315,14 +318,18 @@ def alpha1(self):
         return 1.
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.TGV(in_arr,
+
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.TGV(in_arr,
               self.alpha * tau,
               self.alpha1,
               self.alpha2,
               self.max_iteration,
               self.LipshitzConstant,
               self.tolerance,
-              self.device)
+              infovector = info,
+              device = self.device)
 
         # info: return number of iteration and reached tolerance
         # https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
@@ -400,7 +407,10 @@ def __call__(self,x):
         return np.nan
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.FGP_dTV(\
+
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_dTV(\
                 in_arr,\
                 self.reference,\
                 self.alpha * tau,\
@@ -409,7 +419,8 @@ def proximal_numpy(self, in_arr, tau):
                 self.eta,\
                 self.methodTV,\
                 self.nonnegativity,\
-                self.device)
+                infovector = info,
+                device = self.device)
         return res, info
 
     def convex_conjugate(self, x):
@@ -454,7 +465,7 @@ def __call__(self,x):
     def proximal_numpy(self, in_arr, tau):
         # remove any dimension of size 1
         in_arr = np.squeeze(in_arr)
-
+    
         res = regularisers.TNV(in_arr,
               self.alpha * tau,
               self.max_iteration,

Wrappers/Python/test/test_PluginsRegularisation.py

@@ -105,7 +105,7 @@ def test_functionality_FGP_TV(self):
         fcil = FGP_TV()
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.FGP_TV(datarr, fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0, 1, 'cpu' )
+        outrgl = regularisers.FGP_TV(datarr, fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0, 1, device='cpu' )
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
 
@@ -118,7 +118,7 @@ def test_functionality_TGV(self):
         fcil = TGV()
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.TGV(datarr, fcil.alpha*tau, 1,1, fcil.max_iteration, 12, fcil.tolerance, 'cpu' )
+        outrgl = regularisers.TGV(datarr, fcil.alpha*tau, 1,1, fcil.max_iteration, 12, fcil.tolerance, device='cpu' )
 
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
@@ -133,7 +133,7 @@ def test_functionality_FGP_dTV(self):
         fcil = FGP_dTV(ref)
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.FGP_dTV(datarr, ref.as_array(), fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0.01, 0, 1, 'cpu' )
+        outrgl = regularisers.FGP_dTV(datarr, ref.as_array(), fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0.01, 0, 1, device='cpu' )
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
 

Wrappers/Python/test/utils.py

@@ -100,12 +100,13 @@ def initialise_tests():
 #ccpi-regularisation toolkit
 module_info = importlib.util.find_spec("ccpi")
 if module_info != None:
-    module_info = importlib.util.find_spec("ccpi.filters.cpu_regularisers")
+    module_info = importlib.util.find_spec("ccpi.filters.regularisers")
 
 if module_info is None:
     has_ccpi_regularisation = False
 else:
     has_ccpi_regularisation = True
+
 system_state['has_ccpi_regularisation']= has_ccpi_regularisation
 
 

recipe/meta.yaml

@@ -38,7 +38,7 @@ test:
     - tomophantom=2.0.0       # [ linux ]
     - tigre=2.4               # [ not osx ]
     - packaging
-    - ccpi-regulariser=22.0.0 # [ not osx ]
+    - ccpi-regulariser=24.0.0 # [ not osx ]
     - astra-toolbox>=1.9.9.dev5,<2.1
 
   source_files:
@@ -93,7 +93,7 @@ requirements:
     - tomophantom=2.0.0
     - astra-toolbox>=1.9.9.dev5,<2.1
     - tigre=2.4
-    - ccpi-regulariser=22.0.0
+    - ccpi-regulariser=24.0.0
     - ipywidgets <8
 
 about:

scripts/create_local_env_for_cil_development.sh

@@ -75,7 +75,7 @@ if test $test_deps = 0; then
 else
   conda_args+=(
     astra-toolbox'>=1.9.9.dev5,<2.1'
-    ccpi-regulariser=22.0.0
+    ccpi-regulariser=24.0.0
     cil-data
     cvxpy
     ipywidgets

scripts/requirements-test.yml

@@ -23,7 +23,7 @@ dependencies:
   - numpy=1.24
   - cil-data
   - tigre=2.4
-  - ccpi-regulariser=22.0.0
+  - ccpi-regulariser=24.0.0
   - tomophantom=2.0.0
   - astra-toolbox >=1.9.9.dev5,<2.1
   - cvxpy