data_pipeline.py

from nexus.data.inputs import InputProcessor, InputConfig
from nexus.data.augmentation import AugmentationPipeline, MixupAugmentation
from nexus.data.cache import DataCache
from nexus.data.streaming import StreamingDataset
import torch
from typing import Dict, Optional

# Setup input processing
input_config = InputConfig(
    input_type="image",
    image_size=(224, 224),
    normalize=True,
    augment=True
)

processor = InputProcessor(input_config)
augmentation = AugmentationPipeline(
    image_size=(224, 224),
    augmentation_strength=0.8
)
mixup = MixupAugmentation(alpha=0.2)
cache = DataCache()

# Create data pipeline
def data_generator(batch_size: int = 32):
    while True:
        try:
            # Get data from source
            data = get_next_batch(batch_size)
            
            # Process inputs
            processed = processor.process(data)
            
            # Apply augmentation
            if input_config.augment:
                processed["image"] = augmentation(processed["image"])
                
            # Cache processed data
            cache_key = cache._get_cache_key(processed)
            cache.save(cache_key, processed)
            
            yield processed
            
        except Exception as e:
            print(f"Error in data generation: {str(e)}")
            continue

# Create streaming dataset
dataset = StreamingDataset(
    data_generator(),
    buffer_size=1000
)

def train_step(batch: Dict[str, torch.Tensor], training: bool = True) -> Dict[str, torch.Tensor]:
    """
    Performs a single training step with optional mixup augmentation.
    
    Args:
        batch: Dictionary containing image and label tensors
        training: Whether in training mode (enables mixup)
        
    Returns:
        Dictionary containing processed batch data
    """
    if training:
        # Apply mixup augmentation
        mixed_images, labels_a, labels_b, lam = mixup(
            batch["image"],
            batch["labels"]
        )
        return {
            "image": mixed_images,
            "labels_a": labels_a,
            "labels_b": labels_b,
            "lam": lam
        }
    return batch

def get_next_batch(batch_size: int = 32) -> Dict[str, torch.Tensor]:
    """
    Fetches the next batch of data for processing.
    
    Args:
        batch_size: Number of samples to fetch in this batch
        
    Returns:
        Dictionary containing:
            - "image": Tensor of shape (batch_size, channels, height, width)
            - "labels": Tensor of shape (batch_size,) containing class labels
    """
    # This is a placeholder implementation
    # In a real application, you would:
    # 1. Load data from your dataset
    # 2. Convert to tensors
    # 3. Apply any basic preprocessing
    
    # Simulate loading image data
    images = torch.randn(batch_size, 3, 224, 224)  # Random RGB images
    labels = torch.randint(0, 1000, (batch_size,))  # Random class labels
    
    return {
        "image": images,
        "labels": labels
    }

# Example usage
if __name__ == "__main__":
    # Training loop
    for batch in dataset:
        # Process batch with mixup during training
        processed_batch = train_step(batch, training=True)
        
        # Use processed batch for model training
        # Your training code here...