Implement full training loop

train.py

@@ -4,6 +4,7 @@ import logging
 import os
 import random
 import sys
+import time  # Import time for timing
 
 import numpy as np
 import torch
@@ -172,58 +173,107 @@ def main(args):
         logging.error(f"Error creating optimizer: {e}", exc_info=True)
         sys.exit(1)
 
-    # --- LR Scheduler (Placeholder for Prompt 10) ---
-    # lr_scheduler = torch.optim.lr_scheduler.StepLR(
-    #     optimizer,
-    #     step_size=config.get('lr_step_size', 3),
-    #     gamma=config.get('lr_gamma', 0.1)
-    # )
-
-    # --- Minimal Training Step (Prompt 8 / Updated for Prompt 9) ---
-    logging.info("--- Starting Minimal Training Step --- ")
-    model.train()  # Set model to training mode
-
+    # --- LR Scheduler (Prompt 10) ---
     try:
-        # Fetch one batch
-        images, targets = next(iter(data_loader_train))
-
-        # Move data to the device
-        images = list(image.to(device) for image in images)
-        # Targets is a list of dicts. Move each tensor in the dict to the device.
-        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
-
-        # Perform forward pass (model returns loss dict in train mode)
-        loss_dict = model(images, targets)
-
-        # Calculate total loss
-        losses = sum(loss for loss in loss_dict.values())
-        loss_value = losses.item()  # Get scalar value
-
-        # Perform backward pass
-        optimizer.zero_grad()  # Clear previous gradients
-        losses.backward()  # Compute gradients
-        optimizer.step()  # Update weights
-
-        # Convert loss_dict tensors to scalar values for logging
-        loss_dict_log = {k: v.item() for k, v in loss_dict.items()}
-        logging.info(f"Single step loss dict: {loss_dict_log}")
-        logging.info(f"Single step total loss: {loss_value:.4f}")
-        logging.info("--- Minimal Training Step Completed Successfully --- ")
-
+        lr_scheduler = torch.optim.lr_scheduler.StepLR(
+            optimizer,
+            step_size=config.get("lr_step_size", 3),
+            gamma=config.get("lr_gamma", 0.1),
+        )
+        logging.info(
+            f"LR scheduler StepLR configured with step_size={config.get('lr_step_size', 3)}, gamma={config.get('lr_gamma', 0.1)}"
+        )
     except Exception as e:
-        logging.error(f"Error during minimal training step: {e}", exc_info=True)
-        import traceback
-
-        # traceback.print_exc() # Already logged with exc_info=True
+        logging.error(f"Error creating LR scheduler: {e}", exc_info=True)
         sys.exit(1)
 
-    # Temporarily exit after the single step (as per Prompt 8)
-    logging.info("Exiting after single training step.")
-    sys.exit(0)
+    # --- Training Loop (Prompt 10) ---
+    logging.info("--- Starting Training Loop --- ")
+    start_time = time.time()
+    num_epochs = config.get("num_epochs", 10)
 
-    # --- Full Training Loop (Placeholder for Prompt 10) ---
-    # print("Basic setup complete. Full training loop implementation pending.")
-    # ... loop implementation ...
+    for epoch in range(num_epochs):
+        model.train()  # Set model to training mode for each epoch
+        epoch_start_time = time.time()
+        logging.info(f"--- Epoch {epoch + 1}/{num_epochs} --- ")
+
+        # Variables for tracking epoch progress (optional)
+        epoch_loss_sum = 0.0
+        num_batches = len(data_loader_train)
+
+        for i, (images, targets) in enumerate(data_loader_train):
+            batch_start_time = time.time()  # Optional: time each batch
+            try:
+                # Move data to the device
+                images = list(image.to(device) for image in images)
+                targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+
+                # Perform forward pass
+                loss_dict = model(images, targets)
+                losses = sum(loss for loss in loss_dict.values())
+                loss_value = losses.item()
+                epoch_loss_sum += loss_value
+
+                # Perform backward pass
+                optimizer.zero_grad()
+                losses.backward()
+                optimizer.step()
+
+                # Log batch loss periodically
+                if (i + 1) % config.get("log_freq", 10) == 0:
+                    batch_time = time.time() - batch_start_time
+                    # Include individual losses if desired
+                    loss_dict_items = {
+                        k: f"{v.item():.4f}" for k, v in loss_dict.items()
+                    }
+                    logging.info(
+                        f"  Epoch {epoch + 1}, Iter {i + 1}/{num_batches}, Loss: {loss_value:.4f}, Batch Time: {batch_time:.2f}s"
+                    )
+                    logging.debug(
+                        f"  Loss Dict: {loss_dict_items}"
+                    )  # Log individual losses at DEBUG level
+
+            except Exception as e:
+                logging.error(
+                    f"Error during training epoch {epoch+1}, batch {i+1}: {e}",
+                    exc_info=True,
+                )
+                # Decide if you want to stop training or continue to next batch/epoch
+                logging.warning("Skipping rest of epoch due to error.")
+                break  # Exit the inner loop for this epoch
+
+        # --- End of Epoch --- #
+        # Step the learning rate scheduler
+        lr_scheduler.step()
+
+        # Log epoch summary
+        epoch_end_time = time.time()
+        epoch_duration = epoch_end_time - epoch_start_time
+        avg_epoch_loss = epoch_loss_sum / num_batches if num_batches > 0 else 0
+        current_lr = optimizer.param_groups[0]["lr"]  # Get current learning rate
+        logging.info(f"--- Epoch {epoch + 1} Summary --- ")
+        logging.info(f"  Average Loss: {avg_epoch_loss:.4f}")
+        logging.info(f"  Learning Rate: {current_lr:.6f}")
+        logging.info(f"  Epoch Duration: {epoch_duration:.2f}s")
+
+        # --- Checkpointing (Placeholder for Prompt 11) --- #
+        # Add checkpoint saving logic here, e.g.:
+        # if (epoch + 1) % config.get('checkpoint_freq', 1) == 0 or (epoch + 1) == num_epochs:
+        #     # ... save checkpoint ...
+        #     logging.info(f"Saved checkpoint for epoch {epoch + 1}")
+
+        # --- Evaluation (Placeholder for Prompt 12) --- #
+        # Add evaluation logic here, e.g.:
+        # if data_loader_val:
+        #     evaluate(model, data_loader_val, device)
+        #     logging.info(f"Ran evaluation for epoch {epoch + 1}")
+
+    # --- End of Training --- #
+    total_training_time = time.time() - start_time
+    logging.info("--- Training Finished --- ")
+    logging.info(
+        f"Total Training Time: {total_training_time:.2f}s ({total_training_time / 3600:.2f} hours)"
+    )
 
 
 if __name__ == "__main__":