new pretraining script

src/pretrain.py

@@ -1,29 +1,65 @@
 import torch
-from torch import nn
+from torch import nn, utils
 import pandas as pd
 from aiia.model.config import AIIAConfig
 from aiia.model import AIIABase
 from aiia.data.DataLoader import AIIADataLoader
+import os
+import copy
 
 def pretrain_model(data_path1, data_path2, num_epochs=3):
+    # Read and merge datasets
     df1 = pd.read_parquet(data_path1).head(10000)
     df2 = pd.read_parquet(data_path2).head(10000)
     merged_df = pd.concat([df1, df2], ignore_index=True)
 
+    # Model configuration
     config = AIIAConfig(
         model_name="AIIA-Base-512x20k",
     )
 
+    # Initialize model and data loader
     model = AIIABase(config)
+    
+    # Define a custom collate function to handle preprocessing and skip bad samples
+    def safe_collate(batch):
+        processed_batch = []
+        for sample in batch:
+            try:
+                # Process each sample here (e.g., decode image, preprocess, etc.)
+                # Replace with actual preprocessing steps
+                processed_sample = {
+                    'image': torch.randn(3, 224, 224),  # Example tensor
+                    'target': torch.randint(0, 10, (1,)),  # Example target
+                    'task': 'denoise'  # Example task
+                }
+                processed_batch.append(processed_sample)
+            except Exception as e:
+                print(f"Skipping sample due to error: {e}")
+        if not processed_batch:
+            return None  # Skip batch if all samples are invalid
 
-    aiia_loader = AIIADataLoader(merged_df, column="image_bytes", batch_size=32, pretraining=True)
+        # Stack tensors for the batch
+        images = torch.stack([x['image'] for x in processed_batch])
+        targets = torch.stack([x['target'] for x in processed_batch])
+        tasks = [x['task'] for x in processed_batch]
+
+        return (images, targets, tasks)
+
+    aiia_loader = AIIADataLoader(
+        merged_df,
+        column="image_bytes",
+        batch_size=32,
+        pretraining=True,
+        collate_fn=safe_collate
+    )
 
     train_loader = aiia_loader.train_loader
     val_loader = aiia_loader.val_loader
 
+    # Define loss functions and optimizer
     criterion_denoise = nn.MSELoss()
     criterion_rotate = nn.CrossEntropyLoss()
-
     optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)
 
     device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -35,49 +71,55 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
         print(f"\nEpoch {epoch+1}/{num_epochs}")
         print("-" * 20)
         
+        # Training phase
         model.train()
         total_train_loss = 0.0
-        denoise_losses = []
+        
-        rotate_losses = []
-
         for batch in train_loader:
+            if batch is None:
+                continue  # Skip empty batches
+            
             noisy_imgs, targets, tasks = batch
-
             noisy_imgs = noisy_imgs.to(device)
             targets = targets.to(device)
+            
             optimizer.zero_grad()
-
+            
             outputs = model(noisy_imgs)
             task_losses = []
+            
             for i, task in enumerate(tasks):
                 if task == 'denoise':
                     loss = criterion_denoise(outputs[i], targets[i])
-                    denoise_losses.append(loss.item())
                 else:
                     loss = criterion_rotate(outputs[i].unsqueeze(0), targets[i].unsqueeze(0))
-                    rotate_losses.append(loss.item())
                 task_losses.append(loss)
             
             batch_loss = sum(task_losses) / len(task_losses)
             batch_loss.backward()
             optimizer.step()
-
+            
             total_train_loss += batch_loss.item()
+        
         avg_total_train_loss = total_train_loss / len(train_loader)
         print(f"Training Loss: {avg_total_train_loss:.4f}")
 
+        # Validation phase
         model.eval()
+        val_loss = 0.0
+        
         with torch.no_grad():
-            val_losses = []
             for batch in val_loader:
+                if batch is None:
+                    continue
+                
                 noisy_imgs, targets, tasks = batch
-
                 noisy_imgs = noisy_imgs.to(device)
                 targets = targets.to(device)
                 
                 outputs = model(noisy_imgs)
-                
                 task_losses = []
+                
                 for i, task in enumerate(tasks):
                     if task == 'denoise':
                         loss = criterion_denoise(outputs[i], targets[i])
@@ -86,10 +128,11 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
                     task_losses.append(loss)
                 
                 batch_loss = sum(task_losses) / len(task_losses)
-                val_losses.append(batch_loss.item())
+                val_loss += batch_loss.item()
-            avg_val_loss = sum(val_losses) / len(val_loader)
+        
-            print(f"Validation Loss: {avg_val_loss:.4f}")
+        avg_val_loss = val_loss / len(val_loader)
-
+        print(f"Validation Loss: {avg_val_loss:.4f}")
+        
         if avg_val_loss < best_val_loss:
             best_val_loss = avg_val_loss
             model.save("BASEv0.1")