train shrink

sCOCA_ML/train/train_gravpot.py

@@ -12,7 +12,8 @@ def train_model(model,
                 device='cuda', 
                 print_timers=False,
                 save_model_path=None,
-                scheduler=None):
+                scheduler=None,
+                shrink:bool = False):
     """
     Train a model with the given dataloader and optimizer.
 
@@ -25,6 +26,7 @@ def train_model(model,
     - print_timers: If True, print timing information for each epoch (default is False).
     - save_model_path: If provided, the model will be saved to this path after each epoch.
     - scheduler: Learning rate scheduler (optional).
+    - shrink: If True, the model will output only the center region of size N//2 of the target
 
     Returns:
     - train_loss_log: List of training losses for each batch.
@@ -64,6 +66,11 @@ def train_model(model,
             # Forward pass
             t1 = time.time()
             output = model(input, style)
+            
+            if shrink:
+                # Shrink the target to the center region of size N//2
+                N = target.shape[-1]
+                target = target[...,  N//4:N*3//4, N//4:N*3//4, N//4:N*3//4]
             loss = loss_fn(output, target)
             forward_time += time.time() - t1
 
@@ -80,7 +87,7 @@ def train_model(model,
 
         # End of epoch, validate the model
         t3 = time.time()
-        val_loss, style_bins_means, style_bins = validate(model, dataloader['val'], loss_fn, device)
+        val_loss, style_bins_means, style_bins = validate(model, dataloader['val'], loss_fn, device, shrink=shrink)
         val_loss_log.append(val_loss)
         validation_time += time.time() - t3
 
@@ -112,7 +119,7 @@ def train_model(model,
         if print_timers:
             total_time = time.time() - epoch_start_time
             print(f"Epoch {epoch+1} Timings:    {total_time:9.0f} s")
-            print(f"  I/O time (train):  {io_time:8.0f} s\t | {100 * io_time / total_time:2.2f}%")
+            print(f"  Sync time + I/O:   {io_time:8.0f} s\t | {100 * io_time / total_time:2.2f}%")
             print(f"  Forward time:      {forward_time:8.0f} s\t | {100 * forward_time / total_time:2.2f}%")
             print(f"  Backward time:     {backward_time:8.0f} s\t | {100 * backward_time / total_time:2.2f}%")
             print(f"  Validation time:   {validation_time:8.0f} s\t | {100 * validation_time / total_time:2.2f}%")
@@ -121,7 +128,7 @@ def train_model(model,
     return train_loss_log, val_loss_log
 
 
-def validate(model, val_loader, loss_fn, device='cuda'):
+def validate(model, val_loader, loss_fn, device='cuda', shrink:bool = False):
     model.eval()
     losses = []
     styles = []
@@ -133,6 +140,11 @@ def validate(model, val_loader, loss_fn, device='cuda'):
             input = batch['input'].to(device)
             target = batch['target'].to(device)
             style = batch['style'].to(device)
+            
+            if shrink:
+                # Shrink the target to the center region of size N//2
+                N = target.shape[-1]
+                target = target[...,  N//4:N*3//4, N//4:N*3//4, N//4:N*3//4]
 
             output = model(input, style)
             loss = loss_fn(output, target)