Refactor with attr, working on cnn+transformer network

4 files changed, 27 insertions, 22 deletions

diff --git a/text_recognizer/models/__init__.py b/text_recognizer/models/__init__.py
index 5ac2510..1982daf 100644
--- a/text_recognizer/models/__init__.py
+++ b/text_recognizer/models/__init__.py
@@ -1,3 +1 @@
 """PyTorch Lightning models modules."""
-from .transformer import LitTransformerModel
-from .vqvae import LitVQVAEModel
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 4e803eb..8dc7a36 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -1,5 +1,5 @@
 """Base PyTorch Lightning model."""
-from typing import Any, Dict, List, Union, Tuple, Type
+from typing import Any, Dict, List, Tuple, Type
 
 import attr
 import hydra
@@ -13,7 +13,7 @@ import torchmetrics
 
 
 @attr.s
-class LitBaseModel(pl.LightningModule):
+class BaseLitModel(pl.LightningModule):
     """Abstract PyTorch Lightning class."""
 
     network: Type[nn.Module] = attr.ib()
@@ -30,18 +30,17 @@ class LitBaseModel(pl.LightningModule):
     val_acc = attr.ib(init=False)
     test_acc = attr.ib(init=False)
 
-    def __attrs_pre_init__(self):
+    def __attrs_pre_init__(self) -> None:
         super().__init__()
 
-    def __attrs_post_init__(self):
-        self.loss_fn = self.configure_criterion()
+    def __attrs_post_init__(self) -> None:
+        self.loss_fn = self._configure_criterion()
 
         # Accuracy metric
         self.train_acc = torchmetrics.Accuracy()
         self.val_acc = torchmetrics.Accuracy()
         self.test_acc = torchmetrics.Accuracy()
 
-    @staticmethod
     def configure_criterion(self) -> Type[nn.Module]:
         """Returns a loss functions."""
         log.info(f"Instantiating criterion <{self.criterion_config._target_}>")
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index 6be0ac5..ea54d83 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -1,27 +1,35 @@
 """PyTorch Lightning model for base Transformers."""
 from typing import Dict, List, Optional, Union, Tuple, Type
 
+import attr
 from omegaconf import DictConfig
 from torch import nn, Tensor
 
 from text_recognizer.data.emnist import emnist_mapping
+from text_recognizer.data.mappings import AbstractMapping
 from text_recognizer.models.metrics import CharacterErrorRate
 from text_recognizer.models.base import LitBaseModel
 
 
-class LitTransformerModel(LitBaseModel):
+@attr.s
+class TransformerLitModel(LitBaseModel):
     """A PyTorch Lightning model for transformer networks."""
 
-    def __init__(
-        self,
-        network: Type[nn.Module],
-        optimizer: Union[DictConfig, Dict],
-        lr_scheduler: Union[DictConfig, Dict],
-        criterion: Union[DictConfig, Dict],
-        monitor: str = "val_loss",
-        mapping: Optional[List[str]] = None,
-    ) -> None:
-        super().__init__(network, optimizer, lr_scheduler, criterion, monitor)
+    network: Type[nn.Module] = attr.ib()
+    criterion_config: DictConfig = attr.ib(converter=DictConfig)
+    optimizer_config: DictConfig = attr.ib(converter=DictConfig)
+    lr_scheduler_config: DictConfig = attr.ib(converter=DictConfig)
+    monitor: str = attr.ib()
+    mapping: Type[AbstractMapping] = attr.ib()
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__(
+            network=self.network,
+            optimizer_config=self.optimizer_config,
+            lr_scheduler_config=self.lr_scheduler_config,
+            criterion_config=self.criterion_config,
+            monitor=self.monitor,
+        )
         self.mapping, ignore_tokens = self.configure_mapping(mapping)
         self.val_cer = CharacterErrorRate(ignore_tokens)
         self.test_cer = CharacterErrorRate(ignore_tokens)
diff --git a/text_recognizer/models/vqvae.py b/text_recognizer/models/vqvae.py
index 18e8691..7dc950f 100644
--- a/text_recognizer/models/vqvae.py
+++ b/text_recognizer/models/vqvae.py
@@ -18,7 +18,7 @@ class LitVQVAEModel(LitBaseModel):
         optimizer: Union[DictConfig, Dict],
         lr_scheduler: Union[DictConfig, Dict],
         criterion: Union[DictConfig, Dict],
-        monitor: str = "val_loss",
+        monitor: str = "val/loss",
         *args: Any,
         **kwargs: Dict,
     ) -> None:
@@ -50,7 +50,7 @@ class LitVQVAEModel(LitBaseModel):
         reconstructions, vq_loss = self.network(data)
         loss = self.loss_fn(reconstructions, data)
         loss += vq_loss
-        self.log("train_loss", loss)
+        self.log("train/loss", loss)
         return loss
 
     def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
@@ -59,7 +59,7 @@ class LitVQVAEModel(LitBaseModel):
         reconstructions, vq_loss = self.network(data)
         loss = self.loss_fn(reconstructions, data)
         loss += vq_loss
-        self.log("val_loss", loss, prog_bar=True)
+        self.log("val/loss", loss, prog_bar=True)
         title = "val_pred_examples"
         self._log_prediction(data, reconstructions, title)