create pytorch dataloader from parquet files

.gitignore

@@ -30,3 +30,4 @@ cache
 # SLURM
 jobs
 slurm-*
+.python-version

scripts/debug_structure.py

@@ -0,0 +1,36 @@
+"""
+Debug parquet data structure.
+"""
+
+import pandas as pd
+from pathlib import Path
+import json
+
+def debug_structure():
+    data_path = Path("data")
+    parquet_file = data_path / "train-00000-of-00017.parquet"
+    
+    df = pd.read_parquet(parquet_file)
+    print(f"DataFrame shape: {df.shape}")
+    print(f"Columns: {list(df.columns)}")
+    
+    # Check first sample
+    first_row = df.iloc[0]
+    print(f"\nFirst row data types:")
+    for col in df.columns:
+        print(f"  {col}: {type(first_row[col])}")
+    
+    # Check image column structure
+    image_data = first_row['image']
+    print(f"\nImage data type: {type(image_data)}")
+    if isinstance(image_data, dict):
+        print(f"Image dict keys: {list(image_data.keys())}")
+        for key, value in image_data.items():
+            print(f"  {key}: {type(value)} - {str(value)[:100]}...")
+    elif isinstance(image_data, bytes):
+        print(f"Image bytes length: {len(image_data)}")
+    else:
+        print(f"Image data: {str(image_data)[:200]}...")
+
+if __name__ == "__main__":
+    debug_structure()

scripts/pytorch_dataloader.py

@@ -0,0 +1,74 @@
+import torch
+import pandas as pd
+from torch.utils.data import Dataset, DataLoader
+from PIL import Image
+import io
+from pathlib import Path
+from torchvision import transforms
+
+
+class ImageNet100Parquet(Dataset):
+    def __init__(self, data_dir: str, split: str = "train", transform=None):
+        self.data_dir = Path(data_dir)
+        self.transform = transform
+
+        # Load all parquet files for the split
+        parquet_files = sorted(self.data_dir.glob(f"{split}-*.parquet"))
+        self.data = pd.concat([pd.read_parquet(f)
+                              for f in parquet_files], ignore_index=True)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        row = self.data.iloc[idx]
+
+        # Decode image from bytes (stored in dict)
+        image_bytes = row['image']['bytes']
+        image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+        label = torch.tensor(row['label'], dtype=torch.long)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, label
+
+
+def main():
+    # Define transforms
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+
+    # Create datasets
+    train_dataset = ImageNet100Parquet("data", "train", transform)
+    test_dataset = ImageNet100Parquet("data", "validation", transform)
+
+    # Create dataloaders
+    train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
+    test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)
+
+    print(f"Train dataset size: {len(train_dataset)}")
+    print(f"Test dataset size: {len(test_dataset)}")
+
+    # Test iteration
+    print("\nTesting train loader iteration...")
+    stop_idx = 0
+    for x, y_true in train_loader:
+        print(x.shape, y_true.shape)
+        stop_idx += 1
+        if stop_idx > 10:
+            break
+
+    stop_idx = 0
+    print("\nTesting test loader iteration...")
+    for x, y_true in test_loader:
+        print(x.shape, y_true.shape)
+        stop_idx += 1
+        if stop_idx > 10:
+            break
+
+
+if __name__ == "__main__":
+    main()