removed rint statemetns and added csv saving

src/pretrain.py

@@ -1,5 +1,6 @@
 import torch
-from torch import nn, utils
+from torch import nn
+import csv
 import pandas as pd
 from aiia.model.config import AIIAConfig
 from aiia.model import AIIABase
@@ -101,7 +102,8 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
     )
 
     best_val_loss = float('inf')
-
+    train_losses = []
+    val_losses = []
     for epoch in range(num_epochs):
         print(f"\nEpoch {epoch+1}/{num_epochs}")
         print("-" * 20)
@@ -128,14 +130,7 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
                 # Get features from base model
                 features = model(noisy_imgs)
                 # Project features back to image space
-                outputs = projection_head(features, task='denoise')
-                
-                print(f"\nDenoising task shapes:")
-                print(f"Input shape: {noisy_imgs.shape}")
-                print(f"Target shape: {targets.shape}")
-                print(f"Features shape: {features.shape}")
-                print(f"Output shape: {outputs.shape}")
-                
+                outputs = projection_head(features, task='denoise')               
                 loss = criterion_denoise(outputs, targets)
                 batch_loss += loss
             
@@ -150,12 +145,6 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
                 # Project features to rotation predictions
                 outputs = projection_head(features, task='rotate')
                 
-                print(f"\nRotation task shapes:")
-                print(f"Input shape: {imgs.shape}")
-                print(f"Target shape: {targets.shape}")
-                print(f"Features shape: {features.shape}")
-                print(f"Output shape: {outputs.shape}")
-                
                 loss = criterion_rotate(outputs, targets)
                 batch_loss += loss
             
@@ -166,6 +155,7 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
                 batch_count += 1
         
         avg_train_loss = total_train_loss / max(batch_count, 1)
+        train_losses.append(avg_train_loss)
         print(f"Training Loss: {avg_train_loss:.4f}")
 
         # Validation phase
@@ -206,6 +196,7 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
                     val_batch_count += 1
         
         avg_val_loss = val_loss / max(val_batch_count, 1)
+        val_losses.append(avg_val_loss)
         print(f"Validation Loss: {avg_val_loss:.4f}")
         
         if avg_val_loss < best_val_loss:
@@ -214,6 +205,21 @@ def pretrain_model(data_path1, data_path2, num_epochs=3):
             model.save("AIIA-base-512")
             print("Best model saved!")
 
+    # Prepare the data to be written to the CSV file
+    data = list(zip(range(1, len(train_losses) + 1), train_losses, val_losses))
+
+    # Specify the CSV file name
+    csv_file = 'losses.csv'
+
+    # Write the data to the CSV file
+    with open(csv_file, mode='w', newline='') as file:
+        writer = csv.writer(file)
+        # Write the header
+        writer.writerow(['Epoch', 'Train Loss', 'Validation Loss'])
+        # Write the data
+        writer.writerows(data)
+    print(f"Data has been written to {csv_file}")
+
 if __name__ == "__main__":
     data_path1 = "/root/training_data/vision-dataset/images_checkpoint.parquet"
     data_path2 = "/root/training_data/vision-dataset/vec_images_dataset.parquet"