Trying to get the CNNTransformer to work, but it is hard.

5 files changed, 14 insertions, 4 deletions

diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/src/training/trainer/callbacks/lr_schedulers.py
index 907e292..630c434 100644
--- a/src/training/trainer/callbacks/lr_schedulers.py
+++ b/src/training/trainer/callbacks/lr_schedulers.py
@@ -22,7 +22,10 @@ class LRScheduler(Callback):
     def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
         """Takes a step at the end of every epoch."""
         if self.interval == "epoch":
-            self.lr_scheduler.step()
+            if "ReduceLROnPlateau" in self.lr_scheduler.__class__.__name__:
+                self.lr_scheduler.step(logs["val_loss"])
+            else:
+                self.lr_scheduler.step()
 
     def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
         """Takes a step at the end of every training batch."""
diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py
index f24e5cc..1627f17 100644
--- a/src/training/trainer/callbacks/wandb_callbacks.py
+++ b/src/training/trainer/callbacks/wandb_callbacks.py
@@ -111,7 +111,7 @@ class WandbImageLogger(Callback):
                     ]
                 ).rstrip("_")
             else:
-                ground_truth = self.targets[i]
+                ground_truth = self.model.mapper(int(self.targets[i]))
             caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}"
             images.append(wandb.Image(image, caption=caption))
 
diff --git a/src/training/trainer/population_based_training/__init__.py b/src/training/trainer/population_based_training/__init__.py
deleted file mode 100644
index 868d739..0000000
--- a/src/training/trainer/population_based_training/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""TBC."""
diff --git a/src/training/trainer/population_based_training/population_based_training.py b/src/training/trainer/population_based_training/population_based_training.py
deleted file mode 100644
index 868d739..0000000
--- a/src/training/trainer/population_based_training/population_based_training.py
+++ /dev/null
@@ -1 +0,0 @@
-"""TBC."""
diff --git a/src/training/trainer/train.py b/src/training/trainer/train.py
index fb49103..223d9c6 100644
--- a/src/training/trainer/train.py
+++ b/src/training/trainer/train.py
@@ -33,6 +33,7 @@ class Trainer:
         max_epochs: int,
         callbacks: List[Type[Callback]],
         transformer_model: bool = False,
+        max_norm: float = 0.0,
     ) -> None:
         """Initialization of the Trainer.
 
@@ -40,6 +41,7 @@ class Trainer:
             max_epochs (int): The maximum number of epochs in the training loop.
             callbacks (CallbackList): List of callbacks to be called.
             transformer_model (bool): Transformer model flag, modifies the input to the model. Default is False.
+            max_norm (float): Max norm for gradient clipping. Defaults to 0.0.
 
         """
         # Training arguments.
@@ -52,6 +54,8 @@ class Trainer:
 
         self.transformer_model = transformer_model
 
+        self.max_norm = max_norm
+
         # Model placeholders
         self.model = None
 
@@ -124,6 +128,11 @@ class Trainer:
         # Compute the gradients.
         loss.backward()
 
+        if self.max_norm > 0:
+            torch.nn.utils.clip_grad_norm_(
+                self.model.network.parameters(), self.max_norm
+            )
+
         # Perform updates using calculated gradients.
         self.model.optimizer.step()