Update scripts/trainer.py

scripts/trainer.py

@@ -185,6 +185,8 @@ class TorchTrainer(BaseTrainer):
         The weight decay for the optimizer.
     device : str = "cpu"
         The device to use for training and evaluation.
+    num_workers: int = 8
+        Number of workers.
     optimizer: Callable = Adam
         The optimizer class to use.
     train_step: Callable = slide_level_train_step
@@ -205,6 +207,7 @@ class TorchTrainer(BaseTrainer):
         learning_rate: float = 1.0e-3,
         weight_decay: float = 0.0,
         device: str = "cpu",
+        num_workers: int = 8,
         optimizer: Callable = Adam,
         train_step: Callable = slide_level_train_step,
         val_step: Callable = slide_level_val_step,
@@ -226,6 +229,7 @@ class TorchTrainer(BaseTrainer):
 
         self.collator = collator
         self.device = device
+        self.num_workers = num_workers
 
         self.train_losses: List[float]
         self.val_losses: List[float]
@@ -260,6 +264,7 @@ class TorchTrainer(BaseTrainer):
             pin_memory=True,
             collate_fn=self.collator,
             drop_last=True,
+            num_workers=self.num_workers,
         )
         val_dataloader = DataLoader(
             dataset=val_set,
@@ -268,6 +273,7 @@ class TorchTrainer(BaseTrainer):
             pin_memory=True,
             collate_fn=self.collator,
             drop_last=False,
+            num_workers=self.num_workers,
         )
 
         # Prepare modules.
@@ -355,6 +361,7 @@ class TorchTrainer(BaseTrainer):
             pin_memory=True,
             collate_fn=self.collator,
             drop_last=False,
+            num_workers=self.num_workers,
         )
 
         # Prepare modules.
@@ -401,6 +408,7 @@ class TorchTrainer(BaseTrainer):
             pin_memory=True,
             collate_fn=self.collator,
             drop_last=False,
+            num_workers=self.num_workers,
         )
 
         # Prepare modules
@@ -415,26 +423,4 @@ class TorchTrainer(BaseTrainer):
             device=self.device,
         )
 
-        return test_epoch_labels, test_epoch_logits
-
-    def compute_metrics(
-        self, labels: np.array, logits: np.array
-    ) -> Dict[str, float]:
-        """Compute metrics using the provided labels and logits.
-
-        Parameters
-        ----------
-        labels: np.ndarray
-            The ground truth labels.
-        logits: np.ndarray
-            The predicted logits.
-
-        Returns:
-        Dict[str, float]
-            A dictionary containing the computed metrics.
-        """
-        test_metrics = {
-            k: metric(labels, logits) for k, metric in self.metrics.items()
-        }
-        return test_metrics
-
+        return test_epoch_labels, test_epoch_logits