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Reference Software/UFV1.0-Pruning/src/utils/schedulers.py

+
+# See https://github.com/MingSun-Tse/Regularization-Pruning/blob/master/utils.py#L286
+class PresetLRScheduler(object):
+    """Using a manually designed learning rate schedule rules.
+    """
+    def __init__(self, decay_schedule):
+        # decay_schedule is a dictionary
+        # which is for specifying iteration -> lr
+        self.decay_schedule = decay_schedule
+        print('=> Using a preset learning rate schedule:')
+        print(decay_schedule)
+        self.for_once = True
+
+    def __call__(self, optimizer, iteration):
+        for param_group in optimizer.param_groups:
+            lr = self.decay_schedule.get(iteration, param_group['lr'])
+            param_group['lr'] = lr
+
+    @staticmethod
+    def get_lr(optimizer):
+        for param_group in optimizer.param_groups:
+            lr = param_group['lr']
+            return lr
+        
+        
+class PresetLRScheduler(object):
+    """
+    Custom Implementation of 
+    See: https://openaccess.thecvf.com/content/CVPR2022W/ECV/papers/Srinivas_Cyclical_Pruning_for_Sparse_Neural_Networks_CVPRW_2022_paper.pdf
+    """
+    def __init__(self, decay_schedule):
+        # decay_schedule is a dictionary
+        # which is for specifying iteration -> lr
+        self.decay_schedule = decay_schedule
+        print('=> Using a preset learning rate schedule:')
+        pprint(decay_schedule)
+        self.for_once = True
+
+    def __call__(self, optimizer, iteration):
+        for param_group in optimizer.param_groups:
+            lr = self.decay_schedule.get(iteration, param_group['lr'])
+            param_group['lr'] = lr
+
+    @staticmethod
+    def get_lr(optimizer):
+        for param_group in optimizer.param_groups:
+            lr = param_group['lr']
+            return lr
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