1 files changed, 48 insertions, 33 deletions
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index ab3fa35..8b68ed9 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -24,8 +24,8 @@ class BaseLitModel(LightningModule):
     network: Type[nn.Module] = attr.ib()
     mapping: Type[AbstractMapping] = attr.ib()
     loss_fn: Type[nn.Module] = attr.ib()
-    optimizer_config: DictConfig = attr.ib()
-    lr_scheduler_config: DictConfig = attr.ib()
+    optimizer_configs: DictConfig = attr.ib()
+    lr_scheduler_configs: DictConfig = attr.ib()
     train_acc: torchmetrics.Accuracy = attr.ib(
         init=False, default=torchmetrics.Accuracy()
     )
@@ -45,40 +45,55 @@ class BaseLitModel(LightningModule):
     ) -> None:
         optimizer.zero_grad(set_to_none=True)
 
-    def _configure_optimizer(self) -> Type[torch.optim.Optimizer]:
+    def _configure_optimizer(self) -> List[Type[torch.optim.Optimizer]]:
         """Configures the optimizer."""
-        log.info(f"Instantiating optimizer <{self.optimizer_config._target_}>")
-        return hydra.utils.instantiate(
-            self.optimizer_config, params=self.network.parameters()
-        )
-
-    def _configure_lr_scheduler(
-        self, optimizer: Type[torch.optim.Optimizer]
-    ) -> Dict[str, Any]:
+        optimizers = []
+        for optimizer_config in self.optimizer_configs.values():
+            network = getattr(self, optimizer_config.parameters)
+            del optimizer_config.parameters
+            log.info(f"Instantiating optimizer <{optimizer_config._target_}>")
+            optimizers.append(
+                hydra.utils.instantiate(
+                    self.optimizer_config, params=network.parameters()
+                )
+            )
+        return optimizers
+
+    def _configure_lr_schedulers(
+        self, optimizers: List[Type[torch.optim.Optimizer]]
+    ) -> List[Dict[str, Any]]:
         """Configures the lr scheduler."""
-        # Extract non-class arguments.
-        monitor = self.lr_scheduler_config.monitor
-        interval = self.lr_scheduler_config.interval
-        del self.lr_scheduler_config.monitor
-        del self.lr_scheduler_config.interval
-
-        log.info(
-            f"Instantiating learning rate scheduler <{self.lr_scheduler_config._target_}>"
-        )
-        scheduler = {
-            "monitor": monitor,
-            "interval": interval,
-            "scheduler": hydra.utils.instantiate(
-                self.lr_scheduler_config, optimizer=optimizer
-            ),
-        }
-        return scheduler
-
-    def configure_optimizers(self) -> Tuple[List[type], List[Dict[str, Any]]]:
+        schedulers = []
+        for optimizer, lr_scheduler_config in zip(
+            optimizers, self.lr_scheduler_configs.values()
+        ):
+            # Extract non-class arguments.
+            monitor = lr_scheduler_config.monitor
+            interval = lr_scheduler_config.interval
+            del lr_scheduler_config.monitor
+            del lr_scheduler_config.interval
+
+            log.info(
+                f"Instantiating learning rate scheduler <{lr_scheduler_config._target_}>"
+            )
+            scheduler = {
+                "monitor": monitor,
+                "interval": interval,
+                "scheduler": hydra.utils.instantiate(
+                    lr_scheduler_config, optimizer=optimizer
+                ),
+            }
+            schedulers.append(scheduler)
+
+        return schedulers
+
+    def configure_optimizers(
+        self,
+    ) -> Tuple[List[Type[torch.optim.Optimizer]], List[Dict[str, Any]]]:
         """Configures optimizer and lr scheduler."""
-        optimizer = self._configure_optimizer()
-        scheduler = self._configure_lr_scheduler(optimizer)
-        return [optimizer], [scheduler]
+        optimizers = self._configure_optimizer()
+        schedulers = self._configure_lr_scheduler(optimizers)
+        return optimizers, schedulers
 
     def forward(self, data: Tensor) -> Tensor:
         """Feedforward pass."""