src/aiunn/finetune.py

@@ -1,13 +1,13 @@
 import torch
 import pandas as pd
-import numpy as np
-import cv2
-import os
 from albumentations import (
     Compose, Resize, Normalize, RandomBrightnessContrast,
     HorizontalFlip, VerticalFlip, Rotate, GaussianBlur
 )
 from albumentations.pytorch import ToTensorV2
+from PIL import Image, ImageFile
+import io
+import base64
 from torch import nn
 # Import the model and config from your existing code
 from aiia import AIIA, AIIAConfig, AIIABase, AIIABaseShared, AIIAmoe, AIIAchunked, AIIArecursive
@@ -19,12 +19,12 @@ class aiuNNDataset(torch.utils.data.Dataset):
 
         # Data augmentation pipeline
         self.augmentation = Compose([
-            Resize(height=512, width=512),
+            Resize((512, 512)),
             RandomBrightnessContrast(),
             HorizontalFlip(p=0.5),
             VerticalFlip(p=0.5),
-            Rotate(limit=45),
-            GaussianBlur(p=0.3),
+            Rotate(degrees=45),
+            GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),
             Normalize(mean=[0.5], std=[0.5]),
             ToTensorV2()
         ])
@@ -32,17 +32,56 @@ class aiuNNDataset(torch.utils.data.Dataset):
     def __len__(self):
         return len(self.df)
 
-    def __getitem__(self, idx):
-        # Get the byte strings
-        low_res_bytes = self.df.iloc[idx]['image_512']
-        high_res_bytes = self.df.iloc[idx]['image_1024']
+    def load_image(self, image_data):
+        try:
+            # Handle both bytes and base64 encoded strings
+            if isinstance(image_data, str):
+                # Decode base64 string to bytes
+                image_data = base64.b64decode(image_data)
 
-        # Convert bytes to numpy arrays
-        low_res = cv2.imdecode(np.frombuffer(low_res_bytes, np.uint8), -1)
-        high_res = cv2.imdecode(np.frombuffer(high_res_bytes, np.uint8), -1)
+            # Verify data is valid before creating BytesIO
+            if not isinstance(image_data, bytes):
+                raise ValueError("Invalid image data format")
+
+            # Create image stream
+            image_stream = io.BytesIO(image_data)
+
+            # Enable loading of truncated images
+            ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+            # Load and convert image to RGB
+            image = Image.open(image_stream).convert('RGB')
+
+            # Create fresh copy for verify() since it modifies the image object
+            image_verify = image.copy()
+
+            # Verify image is valid
+            try:
+                image_verify.verify()
+            except Exception as e:
+                raise ValueError(f"Image verification failed: {str(e)}")
+            finally:
+                image_verify.close()
+
+            return image
+
+        except Exception as e:
+            raise RuntimeError(f"Error loading image: {str(e)}")
+
+        finally:
+            # Ensure stream is closed
+            if 'image_stream' in locals():
+                image_stream.close()
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+
+        # Load images using the new method
+        low_res_image = self.load_image(row['image_512'])
+        high_res_image = self.load_image(row['image_1024'])
 
         # Apply augmentation and normalization
-        augmented = self.augmentation(image=low_res, mask=high_res)
+        augmented = self.augmentation(image=low_res_image, mask=high_res_image)
         low_res = augmented['image']
         high_res = augmented['mask']
 
@@ -51,8 +90,7 @@ class aiuNNDataset(torch.utils.data.Dataset):
             'high_res': high_res
         }
 
-
-def finetune_model(model: AIIA, train_parquet_path, val_parquet_path, batch_size=8, epochs = 10):
+def finetune_model(model: AIIA, train_parquet_path, val_parquet_path, batch_size=2, epochs = 10):
     # Initialize dataset and dataloader
     train_dataset = aiuNNDataset(train_parquet_path)
     val_dataset = aiuNNDataset(val_parquet_path)