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torchreid/optim/lr_scheduler.py

@@ -11,7 +11,8 @@
 from torch import optim
 from torch.optim.lr_scheduler import _LRScheduler
 
-AVAI_SCH = {'single_step', 'multi_step', 'cosine', 'warmup', 'cosine_cycle', 'reduce_on_plateau', 'onecycle'}
+AVAI_SCH = {'single_step', 'multi_step', 'cosine', 'warmup', 'cosine_cycle',
+            'reduce_on_plateau_delayed', 'reduce_on_plateau', 'onecycle'}
 
 def build_lr_scheduler(optimizer, lr_scheduler, base_scheduler, **kwargs):
     if lr_scheduler == 'warmup':
@@ -37,7 +38,8 @@ def _build_scheduler(optimizer,
                      max_lr=0.1,
                      patience=5,
                      lr_decay_factor=100,
-                     pct_start=0.3):
+                     pct_start=0.3,
+		     epoch_delay=0):
 
     init_learning_rate = [param_group['lr'] for param_group in optimizer.param_groups]
     if lr_scheduler not in AVAI_SCH:
@@ -96,7 +98,21 @@ def _build_scheduler(optimizer,
         lb_lr = [lr / lr_decay_factor for lr in init_learning_rate]
         epoch_treshold = max(int(max_epoch * 0.75) - warmup, 1) # 75% of the training - warmup epochs
         scheduler = ReduceLROnPlateauV2(optimizer, epoch_treshold, factor=gamma, patience=patience,
-                                        threshold=2e-4, verbose=True, min_lr=lb_lr )
+                                        threshold=2e-4, verbose=True, min_lr=min_lr)
+    elif lr_scheduler == 'reduce_on_plateau_delayed':
+        if epoch_delay < 0:
+            raise ValueError(f'epoch_delay = {epoch_delay} should be greater than zero')
+
+        if max_epoch < epoch_delay:
+            raise ValueError(f'max_epoch param = {max_epoch} should be greater than'
+                             f' epoch_delay param = {epoch_delay}')
+
+        if epoch_delay < warmup:
+            raise ValueError(f'warmap param = {warmup} should be less than'
+                             f' epoch_delay param = {epoch_delay}')
+        epoch_treshold = max(int(max_epoch * 0.75) - epoch_delay, 1) # 75% of the training - skipped epochs
+        scheduler = ReduceLROnPlateauV2Delayed(optimizer, epoch_treshold, epoch_delay, factor=gamma,
+                                               patience=patience, threshold=2e-4, verbose=True, min_lr=min_lr)
     else:
         raise ValueError('Unknown scheduler: {}'.format(lr_scheduler))
 
@@ -275,3 +291,30 @@ class OneCycleLR(optim.lr_scheduler.OneCycleLR):
     @property
     def warmup_finished(self):
         return self.last_epoch >= self._schedule_phases[0]['end_step']
+
+
+class ReduceLROnPlateauV2Delayed(ReduceLROnPlateauV2):
+    """
+    ReduceOnPlateuV2 scheduler which starts working only
+    after certain amount of epochs specified by epoch delay param.
+    Useful when compression algorithms is applying to prevent
+    lr drop before full model compression. Warmup included into epoch_delay.
+    """
+    def __init__(self,
+                 optimizer: optim.Optimizer,
+                 epoch_treshold: int,
+                 epoch_delay: int,
+                 **kwargs) -> None:
+
+        super().__init__(optimizer, epoch_treshold, **kwargs)
+        self._epoch_delay = epoch_delay
+
+    def step(self, metrics, epoch=None):
+        # If there was less than self._epoch_delay epochs
+        # just update epochs counter
+        if self.last_epoch <= self._epoch_delay:
+            if epoch is None:
+                epoch = self.last_epoch + 1
+            self.last_epoch = epoch
+        else:
+            super().step(metrics, epoch=epoch)