Reformat test for CER

1 files changed, 21 insertions, 9 deletions

diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index e86b478..0c70625 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -6,6 +6,7 @@ import pytorch_lightning as pl
 import torch
 from torch import nn
 from torch import Tensor
+import torchmetrics
 
 from text_recognizer import networks
 
@@ -13,7 +14,14 @@ from text_recognizer import networks
 class BaseModel(pl.LightningModule):
     """Abstract PyTorch Lightning class."""
 
-    def __init__(self, network_args: Dict, optimizer_args: Dict, lr_scheduler_args: Dict, criterion_args: Dict, monitor: str = "val_loss") -> None:
+    def __init__(
+        self,
+        network_args: Dict,
+        optimizer_args: Dict,
+        lr_scheduler_args: Dict,
+        criterion_args: Dict,
+        monitor: str = "val_loss",
+    ) -> None:
         super().__init__()
         self.monitor = monitor
         self.network = getattr(networks, network_args["type"])(**network_args["args"])
@@ -22,9 +30,9 @@ class BaseModel(pl.LightningModule):
         self.loss_fn = self.configure_criterion(criterion_args)
 
         # Accuracy metric
-        self.train_acc = pl.metrics.Accuracy()
-        self.val_acc = pl.metrics.Accuracy()
-        self.test_acc = pl.metrics.Accuracy()
+        self.train_acc = torchmetrics.Accuracy()
+        self.val_acc = torchmetrics.Accuracy()
+        self.test_acc = torchmetrics.Accuracy()
 
     @staticmethod
     def configure_criterion(criterion_args: Dict) -> Type[nn.Module]:
@@ -41,8 +49,14 @@ class BaseModel(pl.LightningModule):
             optimizer = getattr(torch.optim, self.optimizer_args["type"])(**args)
 
         args = {} or self.lr_scheduler_args["args"]
-        scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_args["type"])(**args)
-        return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": self.monitor}
+        scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_args["type"])(
+            **args
+        )
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": scheduler,
+            "monitor": self.monitor,
+        }
 
     def forward(self, data: Tensor) -> Tensor:
         """Feedforward pass."""
@@ -55,7 +69,7 @@ class BaseModel(pl.LightningModule):
         loss = self.loss_fn(logits, targets)
         self.log("train_loss", loss)
         self.train_acc(logits, targets)
-        self.log("train_acc": self.train_acc, on_step=False, on_epoch=True)
+        self.log("train_acc", self.train_acc, on_step=False, on_epoch=True)
         return loss
 
     def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
@@ -73,5 +87,3 @@ class BaseModel(pl.LightningModule):
         logits = self(data)
         self.test_acc(logits, targets)
         self.log("test_acc", self.test_acc, on_step=False, on_epoch=True)
-
-