added first dataloader conzept

src/data/DataLoader.py

@@ -0,0 +1,68 @@
+import os
+from PIL import Image
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms
+import random
+import numpy as np
+
+class AIIADataLoader:
+    def __init__(self, data_dir, batch_size=32, val_split=0.2, seed=42):
+        self.data_dir = data_dir
+        self.batch_size = batch_size
+        self.val_split = val_split
+        self.seed = seed
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Set random seeds for reproducibility
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(seed)
+        
+        # Load and split dataset
+        self.image_paths = self._load_image_paths()
+        self.train_paths, self.val_paths = self._split_data(self.image_paths)
+        
+        # Define transformations
+        self.transform = transforms.Compose([
+            transforms.ToTensor()
+        ])
+        
+        # Create datasets and dataloaders
+        self.train_dataset = AIIADataset(self.train_paths, transform=self.transform)
+        self.val_dataset = AIIADataset(self.val_paths, transform=self.transform)
+        
+        self.train_loader = DataLoader(self.train_dataset, batch_size=batch_size, shuffle=True)
+        self.val_loader = DataLoader(self.val_dataset, batch_size=batch_size, shuffle=False)
+    
+    def _load_image_paths(self):
+        images = []
+        for filename in os.listdir(self.data_dir):
+            if any(filename.endswith(ext) for ext in ['.png', '.jpg', '.jpeg']):
+                images.append(os.path.join(self.data_dir, filename))
+        return images
+    
+    def _split_data(self, paths):
+        n_val = int(len(paths) * self.val_split)
+        train_paths = paths[n_val:]
+        val_paths = paths[:n_val]
+        return train_paths, val_paths
+
+class AIIADataset(Dataset):
+    def __init__(self, image_paths, transform=None):
+        self.image_paths = image_paths
+        self.transform = transform
+    
+    def __len__(self):
+        return len(self.image_paths)
+    
+    def __getitem__(self, idx):
+        img_path = self.image_paths[idx]
+        image = Image.open(img_path).convert('RGB')
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        return image