From 263f2b7158d76bc0adad45309625910c0fa7b1fe Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Fri, 6 Aug 2021 14:19:37 +0200
Subject: Remove lr args from model, add Cosine lr, fix to vqvae stack

---
 text_recognizer/models/base.py  | 12 ++++++++----
 text_recognizer/models/vqvae.py |  3 +++
 2 files changed, 11 insertions(+), 4 deletions(-)

(limited to 'text_recognizer/models')

diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 57c5964..ab3fa35 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -26,8 +26,6 @@ class BaseLitModel(LightningModule):
     loss_fn: Type[nn.Module] = attr.ib()
     optimizer_config: DictConfig = attr.ib()
     lr_scheduler_config: DictConfig = attr.ib()
-    interval: str = attr.ib()
-    monitor: str = attr.ib(default="val/loss")
     train_acc: torchmetrics.Accuracy = attr.ib(
         init=False, default=torchmetrics.Accuracy()
     )
@@ -58,12 +56,18 @@ class BaseLitModel(LightningModule):
         self, optimizer: Type[torch.optim.Optimizer]
     ) -> 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": self.monitor,
-            "interval": self.interval,
+            "monitor": monitor,
+            "interval": interval,
             "scheduler": hydra.utils.instantiate(
                 self.lr_scheduler_config, optimizer=optimizer
             ),
diff --git a/text_recognizer/models/vqvae.py b/text_recognizer/models/vqvae.py
index 7f79b78..76b7ba6 100644
--- a/text_recognizer/models/vqvae.py
+++ b/text_recognizer/models/vqvae.py
@@ -23,6 +23,7 @@ class VQVAELitModel(BaseLitModel):
         reconstructions, vq_loss = self(data)
         loss = self.loss_fn(reconstructions, data)
         loss = loss + self.latent_loss_weight * vq_loss
+        self.log("train/vq_loss", vq_loss)
         self.log("train/loss", loss)
         return loss
 
@@ -32,6 +33,7 @@ class VQVAELitModel(BaseLitModel):
         reconstructions, vq_loss = self(data)
         loss = self.loss_fn(reconstructions, data)
         loss = loss + self.latent_loss_weight * vq_loss
+        self.log("val/vq_loss", vq_loss)
         self.log("val/loss", loss, prog_bar=True)
 
     def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
@@ -40,4 +42,5 @@ class VQVAELitModel(BaseLitModel):
         reconstructions, vq_loss = self(data)
         loss = self.loss_fn(reconstructions, data)
         loss = loss + self.latent_loss_weight * vq_loss
+        self.log("test/vq_loss", vq_loss)
         self.log("test/loss", loss)
-- 
cgit v1.2.3-70-g09d2