resolve merge

.gitignore

@@ -1,5 +1,7 @@
 data/
 output/
+output_ews/
+
 tmp*/
 err*.txt
 info.txt

adam.py

@@ -60,16 +60,9 @@ def __init__(self, data, initial, initial_step_size, relaxation_eta,
         self.eps_adam = 1e-8 
 
         self.m = initial.get_uniform_copy(0) 
-        # self.m.fill(np.zeros_like(initial.as_array()))
-
-        self.m_hat = initial.copy() 
-        self.m_hat.fill(np.zeros_like(initial.as_array()))
-
-        self.v = initial.copy() 
-        self.v.fill(np.zeros_like(initial.as_array()))
-
-        self.v_hat = initial.copy() 
-        self.v_hat.fill(np.zeros_like(initial.as_array()))
+        self.m_hat = initial.get_uniform_copy(0) 
+        self.v = initial.get_uniform_copy(0) 
+        self.v_hat = initial.get_uniform_copy(0) 
 
 
     def subset_sensitivity(self, subset_num):
@@ -81,6 +74,9 @@ def subset_gradient(self, x, subset_num):
     def epoch(self):
         return self.iteration // self.num_subsets
 
+    def step_size(self):
+        return self.initial_step_size / (1 + self.relaxation_eta * self.epoch())
+
     def update(self):
         g = self.subset_gradient(self.x, self.subset)
         #g = (self.x + self.eps) * g / self.average_sensitivity
@@ -92,7 +88,7 @@ def update(self):
         self.v_hat = self.v.clone() / (1 - self.beta2 ** (self.iteration+1))
         self.v_hat.sqrt(out=self.v_hat)
 
-        self.x_update = self.alpha * self.m_hat / (self.v_hat + self.eps_adam)
+        self.x_update = self.step_size() * self.m_hat / (self.v_hat + self.eps_adam)
         if self.update_filter is not None:
             self.update_filter.apply(self.x_update)
 

main_ADAM.py

@@ -32,11 +32,13 @@ def __call__(self, algorithm: Algorithm):
 
 class Submission(Adam1):
     # note that `issubclass(BSREM1, Algorithm) == True`
-    def __init__(self, data: Dataset, num_subsets: int = 7, update_objective_interval: int = 10):
+    def __init__(self, data: Dataset, 
+                 num_subsets: int = 7, 
+                 update_objective_interval: int = 10,
+                 **kwargs):
         """
         Initialisation function, setting up data & (hyper)parameters.
         NB: in practice, `num_subsets` should likely be determined from the data.
-        This is just an example. Try to modify and improve it!
         """
         data_sub, acq_models, obj_funs = partitioner.data_partition(data.acquired_data, data.additive_term,
                                                                     data.mult_factors, num_subsets,
@@ -47,8 +49,15 @@ def __init__(self, data: Dataset, num_subsets: int = 7, update_objective_interva
         for f in obj_funs: # add prior evenly to every objective function
             f.set_prior(data.prior)
 
-        super().__init__(data_sub, obj_funs, initial=data.OSEM_image, initial_step_size=.3, relaxation_eta=.01,
+        initial_step_size = kwargs.get("initial_step_size", 0.1)
+        relaxation_eta = kwargs.get("relaxation_eta", 0.01)
+
+        super().__init__(data_sub, 
+                         obj_funs, 
+                         initial=data.OSEM_image, 
+                         initial_step_size=initial_step_size, 
+                         relaxation_eta=relaxation_eta,
                          update_objective_interval=update_objective_interval)
 
 
-submission_callbacks = [MaxIteration(660)]
+submission_callbacks = [] #[MaxIteration(660)]

main_BSREM.py

@@ -10,7 +10,7 @@
 from cil.optimisation.algorithms import Algorithm
 from cil.optimisation.utilities import callbacks
 from petric import Dataset
-from sirf.contrib.BSREM.BSREM import BSREM1
+from BSREM import BSREM1
 from sirf.contrib.partitioner import partitioner
 
 assert issubclass(BSREM1, Algorithm)
@@ -32,23 +32,36 @@ def __call__(self, algorithm: Algorithm):
 
 class Submission(BSREM1):
     # note that `issubclass(BSREM1, Algorithm) == True`
-    def __init__(self, data: Dataset, num_subsets: int = 7, update_objective_interval: int = 10):
+    def __init__(self, data: Dataset, 
+                       num_subsets: int = 7, 
+                       update_objective_interval: int = 10,
+                       **kwargs):
         """
         Initialisation function, setting up data & (hyper)parameters.
         NB: in practice, `num_subsets` should likely be determined from the data.
         This is just an example. Try to modify and improve it!
         """
+        mode = kwargs.get("mode", "sequential")
+
         data_sub, acq_models, obj_funs = partitioner.data_partition(data.acquired_data, data.additive_term,
                                                                     data.mult_factors, num_subsets,
-                                                                    initial_image=data.OSEM_image)
+                                                                    initial_image=data.OSEM_image,
+                                                                    mode = mode)
         # WARNING: modifies prior strength with 1/num_subsets (as currently needed for BSREM implementations)
         data.prior.set_penalisation_factor(data.prior.get_penalisation_factor() / len(obj_funs))
         data.prior.set_up(data.OSEM_image)
         for f in obj_funs: # add prior evenly to every objective function
             f.set_prior(data.prior)
 
-        super().__init__(data_sub, obj_funs, initial=data.OSEM_image, initial_step_size=.3, relaxation_eta=.01,
+        initial_step_size = kwargs.get("initial_step_size", 0.3)
+        relaxation_eta = kwargs.get("relaxation_eta", 0.01)
+
+        super().__init__(data_sub, 
+                         obj_funs, 
+                         initial=data.OSEM_image, 
+                         initial_step_size=initial_step_size, 
+                         relaxation_eta=relaxation_eta,
                          update_objective_interval=update_objective_interval)
 
 
-submission_callbacks = [MaxIteration(660)]
+submission_callbacks = [] #[MaxIteration(660)]

main_EWS.py

@@ -13,26 +13,15 @@
 from sirf.contrib.partitioner import partitioner
 
 from ews import EWS
-
 assert issubclass(EWS, Algorithm)
 
-
-class MaxIteration(callbacks.Callback):
-    """
-    The organisers try to `Submission(data).run(inf)` i.e. for infinite iterations (until timeout).
-    This callback forces stopping after `max_iteration` instead.
-    """
-    def __init__(self, max_iteration: int, verbose: int = 1):
-        super().__init__(verbose)
-        self.max_iteration = max_iteration
-
-    def __call__(self, algorithm: Algorithm):
-        if algorithm.iteration >= self.max_iteration:
-            raise StopIteration
-
 class Submission(EWS):
     # note that `issubclass(BSREM1, Algorithm) == True`
-    def __init__(self, data: Dataset, num_subsets: int = 7, update_objective_interval: int = 10):
+    def __init__(self, 
+                 data: Dataset, 
+                 num_subsets: int = 7, 
+                 update_objective_interval: int = 10,
+                 **kwargs):
         """
         Initialisation function, setting up data & (hyper)parameters.
         NB: in practice, `num_subsets` should likely be determined from the data.
@@ -50,5 +39,4 @@ def __init__(self, data: Dataset, num_subsets: int = 7, update_objective_interva
         super().__init__(data_sub, obj_funs, initial=data.OSEM_image, initial_step_size=.3, relaxation_eta=.01,
                          update_objective_interval=update_objective_interval)
 
-
-submission_callbacks = [MaxIteration(600)]
+submission_callbacks = []