Create eval loop and use full train dataset

utils/eval_utils.py

@@ -0,0 +1,65 @@
+import logging
+import time
+
+import torch
+
+
+def evaluate(model, data_loader, device):
+    """Performs evaluation on the dataset for one epoch.
+
+    Args:
+        model (torch.nn.Module): The model to evaluate.
+        data_loader (torch.utils.data.DataLoader): DataLoader for the evaluation data.
+        device (torch.device): The device to run evaluation on.
+
+    Returns:
+        dict: A dictionary containing evaluation metrics (e.g., average loss).
+    """
+    model.eval()  # Set model to evaluation mode
+    total_loss = 0.0
+    num_batches = len(data_loader)
+    eval_start_time = time.time()
+    status_interval = max(1, num_batches // 10)  # Log status roughly 10 times
+
+    logging.info("--- Starting Evaluation --- ")
+
+    with torch.no_grad():  # Disable gradient calculations
+        for i, (images, targets) in enumerate(data_loader):
+            images = list(image.to(device) for image in images)
+            targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+
+            # In eval mode with targets, Mask R-CNN should still return losses
+            # If it returned predictions, logic here would change to process predictions
+            loss_dict = model(images, targets)
+            losses = sum(loss for loss in loss_dict.values())
+            loss_value = losses.item()
+            total_loss += loss_value
+
+            if (i + 1) % status_interval == 0:
+                logging.info(f"  Evaluated batch {i + 1}/{num_batches}")
+
+    avg_loss = total_loss / num_batches if num_batches > 0 else 0
+    eval_duration = time.time() - eval_start_time
+
+    logging.info("--- Evaluation Finished ---")
+    logging.info(f"  Average Evaluation Loss: {avg_loss:.4f}")
+    logging.info(f"  Evaluation Duration: {eval_duration:.2f}s")
+
+    # Return metrics (currently just average loss)
+    metrics = {"average_loss": avg_loss}
+    return metrics
+
+
+# Example usage (can be removed or kept for testing):
+if __name__ == "__main__":
+    # This is a dummy test and requires a model, dataloader, device
+    print(
+        "This script contains the evaluate function and cannot be run directly for testing without setup."
+    )
+    # Example:
+    # device = torch.device('cpu')
+    # # Create dummy model and dataloader
+    # model = ...
+    # data_loader = ...
+    # metrics = evaluate(model, data_loader, device)
+    # print(f"Dummy evaluation metrics: {metrics}")