From f9b51f152e94896a122325fa2c48b0ce10c881c5 Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Wed, 10 Aug 2022 15:17:06 +0800
Subject: Add default schedulers

---
 libs/utils.py | 24 +++++++++++++++++++++---
 1 file changed, 21 insertions(+), 3 deletions(-)

(limited to 'libs')

diff --git a/libs/utils.py b/libs/utils.py
index 5e8529b..c019ba9 100644
--- a/libs/utils.py
+++ b/libs/utils.py
@@ -12,6 +12,7 @@ from torch.utils.tensorboard import SummaryWriter
 
 from libs.logging import CSV_EPOCH_LOGGER, CSV_BATCH_LOGGER, BaseBatchLogRecord, BaseEpochLogRecord, Loggers, \
     init_csv_logger, csv_logger, tensorboard_logger
+from libs.schedulers import LinearWarmupAndCosineAnneal, LinearLR
 
 
 @dataclass
@@ -33,6 +34,7 @@ class BaseConfig:
     @dataclass
     class SchedConfig:
         sched: None
+        warmup_iters: int
 
     dataset_config: DatasetConfig
     dataloader_config: DataLoaderConfig
@@ -221,15 +223,31 @@ class Trainer(ABC):
         return last_metrics
 
     @staticmethod
-    @abstractmethod
     def _configure_scheduler(
             optims: Iterable[tuple[str, torch.optim.Optimizer]],
             last_iter: int, num_iters: int, sched_config: BaseConfig.SchedConfig,
     ) -> Iterable[tuple[str, torch.optim.lr_scheduler._LRScheduler]
                   | tuple[str, None]]:
         for optim_name, optim in optims:
-            sched = torch.optim.lr_scheduler._LRScheduler(optim, -1)
-            yield f"{optim_name}_sched", sched
+            if sched_config.sched == 'warmup-anneal':
+                scheduler = LinearWarmupAndCosineAnneal(
+                    optim,
+                    warm_up=sched_config.warmup_iters / num_iters,
+                    T_max=num_iters,
+                    last_epoch=last_iter,
+                )
+            elif sched_config.sched == 'linear':
+                scheduler = LinearLR(
+                    optim,
+                    num_epochs=num_iters,
+                    last_epoch=last_iter
+                )
+            elif sched_config.sched in {None, '', 'const'}:
+                scheduler = None
+            else:
+                raise NotImplementedError(f"Unimplemented scheduler: '{sched_config.sched}'")
+
+            yield f"{optim_name}_sched", scheduler
 
     def _custom_init_fn(self, config: BaseConfig):
         pass
-- 
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