Add OmegaConf for configs

1 files changed, 33 insertions, 22 deletions

diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 2d6e435..1004f48 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -1,33 +1,32 @@
 """Base PyTorch Lightning model."""
-from typing import Any, Dict, List, Tuple, Type
+from typing import Any, Dict, List, Union, Tuple, Type
 
 import madgrad
+from omegaconf import OmegaConf
 import pytorch_lightning as pl
 import torch
 from torch import nn
 from torch import Tensor
 import torchmetrics
 
-from text_recognizer import networks
-
 
 class LitBaseModel(pl.LightningModule):
     """Abstract PyTorch Lightning class."""
 
     def __init__(
         self,
-        network_args: Dict,
-        optimizer_args: Dict,
-        lr_scheduler_args: Dict,
-        criterion_args: Dict,
+        network: Type[nn,Module],
+        optimizer: Union[OmegaConf, Dict],
+        lr_scheduler: Union[OmegaConf, Dict],
+        criterion: Union[OmegaConf, Dict],
         monitor: str = "val_loss",
     ) -> None:
         super().__init__()
         self.monitor = monitor
-        self.network = getattr(networks, network_args["type"])(**network_args["args"])
-        self.optimizer_args = optimizer_args
-        self.lr_scheduler_args = lr_scheduler_args
-        self.loss_fn = self.configure_criterion(criterion_args)
+        self.network = network
+        self._optimizer = OmegaConf.create(optimizer)
+        self._lr_scheduler = OmegaConf.create(lr_scheduler)
+        self.loss_fn = self.configure_criterion(criterion)
 
         # Accuracy metric
         self.train_acc = torchmetrics.Accuracy()
@@ -35,27 +34,39 @@ class LitBaseModel(pl.LightningModule):
         self.test_acc = torchmetrics.Accuracy()
 
     @staticmethod
-    def configure_criterion(criterion_args: Dict) -> Type[nn.Module]:
+    def configure_criterion(criterion: Union[OmegaConf, Dict]) -> Type[nn.Module]:
         """Returns a loss functions."""
-        args = {} or criterion_args["args"]
-        return getattr(nn, criterion_args["type"])(**args)
+        criterion = OmegaConf.create(criterion)
+        args = {} or criterion.args
+        return getattr(nn, criterion.type)(**args)
 
-    def configure_optimizer(self) -> Tuple[List[type], List[Dict[str, Any]]]:
-        """Configures optimizer and lr scheduler."""
-        args = {} or self.optimizer_args["args"]
-        if self.optimizer_args["type"] == "MADGRAD":
-            optimizer = getattr(madgrad, self.optimizer_args["type"])(**args)
+    def _configure_optimizer(self) -> type:
+        """Configures the optimizer."""
+        args = {} or self._optimizer.args
+        if self._optimizer.type == "MADGRAD":
+            optimizer_class = madgrad.MADGRAD
         else:
-            optimizer = getattr(torch.optim, self.optimizer_args["type"])(**args)
+            optimizer_class = getattr(torch.optim, self._optimizer.type)
+        return optimizer_class(parameters=self.parameters(), **args)
 
+    def _configure_lr_scheduler(self) -> Dict[str, Any]:
+        """Configures the lr scheduler."""
         scheduler = {"monitor": self.monitor}
-        args = {} or self.lr_scheduler_args["args"]
+        args = {} or self._lr_scheduler.args
+
         if "interval" in args:
             scheduler["interval"] = args.pop("interval")
 
         scheduler["scheduler"] = getattr(
-            torch.optim.lr_scheduler, self.lr_scheduler_args["type"]
+            torch.optim.lr_scheduler, self._lr_scheduler.type
         )(**args)
+        return scheduler
+
+    def configure_optimizers(self) -> Tuple[List[type], List[Dict[str, Any]]]:
+        """Configures optimizer and lr scheduler."""
+        optimizer = self._configure_optimizer()
+        scheduler = self._configure_lr_scheduler()
+
         return [optimizer], [scheduler]
 
     def forward(self, data: Tensor) -> Tensor: