src/datasets/multi_dataloader.py

"""
Multi-DataLoader for combining multiple dataloaders with weighted batch sizes.

This module provides a way to train on multiple datasets simultaneously by
creating separate dataloaders for each dataset and combining their batches.
Batch sizes directly control the sampling weight for each dataset.
"""

import torch
from typing import List, Optional
from torch.utils.data import DataLoader


def collate_objaverse_batch(batch):
    """Collate function for Objaverse datasets (SimplifiedViewpointDataset).

    Converts tuple format to dict format compatible with CombinedDataset.

    Args:
        batch: List of tuples (image, rotation, translation, relative_rotation, spherical_angular)

    Returns:
        Dict with batched tensors and dataset_type
    """
    images = torch.stack([item[0] for item in batch])
    rotations = torch.stack([item[1] for item in batch])
    translations = torch.stack([item[2] for item in batch])
    relative_rotations = torch.stack([item[3] for item in batch])
    spherical_angular = torch.stack([item[4] for item in batch])

    return {
        'image': images,
        'rotation': rotations,
        'translation': translations,
        'relative_rotation': relative_rotations,
        'spherical_angular': spherical_angular,
        'dataset_type': torch.tensor([0] * len(batch), dtype=torch.long),  # 0 = objaverse
    }


def collate_compass_batch(batch):
    """Collate function for Compass datasets (CompassDataset).

    Converts tuple format to dict format compatible with CombinedDataset.

    Args:
        batch: List of tuples (image, azimuth, category)
        Note: category is ignored (3rd element not used)

    Returns:
        Dict with batched tensors and dataset_type
    """
    images = torch.stack([item[0] for item in batch])
    azimuths = torch.stack([item[1] for item in batch])
    # Note: item[2] is category but we don't use it

    # Build spherical_angular with only azimuth (rest are zeros)
    # Format: [sin(az), cos(az), sin(el), cos(el), norm_radius, norm_yaw, norm_pitch]
    sin_az = torch.sin(azimuths)
    cos_az = torch.cos(azimuths)
    zeros = torch.zeros_like(azimuths)
    spherical_angular = torch.stack([
        sin_az, cos_az, zeros, zeros, zeros, zeros, zeros
    ], dim=1)  # (B, 7)

    return {
        'image': images,
        'spherical_angular': spherical_angular,
        'dataset_type': torch.tensor([1] * len(batch), dtype=torch.long),  # 1 = compass
    }


def combine_batches(batches: List[dict]) -> dict:
    """Combine multiple batched dicts into a single batch.

    Args:
        batches: List of batch dicts from different dataloaders

    Returns:
        Combined batch dict with concatenated tensors
    """
    if len(batches) == 1:
        return batches[0]

    combined = {}

    # Get all keys
    all_keys = set()
    for batch in batches:
        all_keys.update(batch.keys())

    for key in all_keys:
        values = [batch[key] for batch in batches if key in batch]

        # Concatenate tensors, extend lists
        if torch.is_tensor(values[0]):
            combined[key] = torch.cat(values, dim=0)
        elif isinstance(values[0], list):
            combined[key] = sum(values, [])  # Flatten lists
        else:
            combined[key] = values

    return combined


class MultiDataLoader:
    """Combines multiple dataloaders with specified batch sizes.

    This dataloader iterates multiple dataloaders in parallel, combining their
    batches into a single batch. The epoch length is determined by the longest
    dataloader, and shorter dataloaders are restarted when exhausted (cyclic).

    Args:
        dataloaders: List of DataLoader instances to combine
        batch_sizes: List of batch sizes for each dataloader (must sum to total batch size)
        collate_fn: Optional collate function to combine batches from different dataloaders.
                   If None, batches are returned as a list.

    Example:
        >>> dl1 = DataLoader(dataset1, batch_size=28, num_workers=4, shuffle=True)
        >>> dl2 = DataLoader(dataset2, batch_size=4, num_workers=4, shuffle=True)
        >>> multi_dl = MultiDataLoader([dl1, dl2], batch_sizes=[28, 4])
        >>> for batch in multi_dl:
        ...     # batch contains 32 samples (28 from dl1 + 4 from dl2)
        ...     outputs = model(batch)
    """

    def __init__(
        self,
        dataloaders: List[DataLoader],
        batch_sizes: List[int],
        collate_fn: Optional[callable] = None,
    ):
        assert len(dataloaders) > 0, "Must provide at least one dataloader"
        assert len(dataloaders) == len(batch_sizes), \
            f"Number of dataloaders ({len(dataloaders)}) must match number of batch_sizes ({len(batch_sizes)})"

        # Verify batch sizes match dataloader configurations
        for i, (dl, expected_bs) in enumerate(zip(dataloaders, batch_sizes)):
            actual_bs = dl.batch_size
            if actual_bs != expected_bs:
                print(f"Warning: DataLoader {i} has batch_size={actual_bs} but expected {expected_bs}")

        self.dataloaders = dataloaders
        self.batch_sizes = batch_sizes
        self.collate_fn = collate_fn

        # Calculate total batch size
        self.total_batch_size = sum(batch_sizes)

        # Calculate epoch length (longest dataloader)
        self._length = max(len(dl) for dl in dataloaders)

        print(f"MultiDataLoader initialized with {len(dataloaders)} dataloaders:")
        for i, (dl, bs) in enumerate(zip(dataloaders, batch_sizes)):
            print(f"  [{i}] batch_size={bs}, length={len(dl)} iterations")
        print(f"Total batch size: {self.total_batch_size}")
        print(f"Epoch length: {self._length} iterations (determined by longest dataloader)")

    def __len__(self) -> int:
        """Return the number of iterations per epoch (length of longest dataloader)."""
        return self._length

    def __iter__(self):
        """Iterate through all dataloaders in parallel, combining their batches."""
        # Create iterators for all dataloaders
        iterators = [iter(dl) for dl in self.dataloaders]

        # Iterate for the length of the longest dataloader
        for iteration_idx in range(len(self)):
            batches = []

            # Get one batch from each dataloader
            for i, iterator in enumerate(iterators):
                try:
                    batch = next(iterator)
                except StopIteration:
                    # This dataloader is exhausted, restart it (cyclic behavior)
                    iterators[i] = iter(self.dataloaders[i])
                    batch = next(iterators[i])

                batches.append(batch)

            # Combine batches
            if self.collate_fn is not None:
                # Use custom collate function
                combined_batch = self.collate_fn(batches)
            elif len(batches) > 1:
                # Default: return batches as-is (no combination)
                # This is for the default PyTorch collate which returns lists/tuples
                combined_batch = batches
            else:
                # Single batch, return as-is
                combined_batch = batches[0]

            yield combined_batch


def test_multi_dataloader():
    """Test MultiDataLoader with dummy datasets."""
    import torch
    from torch.utils.data import TensorDataset, DataLoader

    print("Testing MultiDataLoader...")

    # Create dummy datasets with different sizes
    data1 = torch.randn(100, 3, 224, 224)
    labels1 = torch.randint(0, 10, (100,))
    dataset1 = TensorDataset(data1, labels1)

    data2 = torch.randn(30, 3, 224, 224)
    labels2 = torch.randint(0, 10, (30,))
    dataset2 = TensorDataset(data2, labels2)

    # Create dataloaders with different batch sizes
    dl1 = DataLoader(dataset1, batch_size=8, shuffle=True)
    dl2 = DataLoader(dataset2, batch_size=2, shuffle=True)

    print(f"\nDataLoader 1: {len(dataset1)} samples, batch_size=8 → {len(dl1)} iterations")
    print(f"DataLoader 2: {len(dataset2)} samples, batch_size=2 → {len(dl2)} iterations")

    # Create multi-dataloader
    multi_dl = MultiDataLoader([dl1, dl2], batch_sizes=[8, 2])

    print(f"\nMultiDataLoader length: {len(multi_dl)} iterations")
    print(f"Expected samples per iteration: 10 (8+2)")

    # Test iteration
    print("\nTesting first 5 iterations:")
    for i, batches in enumerate(multi_dl):
        if i >= 5:
            break

        batch1, batch2 = batches
        img1, lbl1 = batch1
        img2, lbl2 = batch2

        print(f"  Iteration {i}: batch1={img1.shape}, batch2={img2.shape}")

        # Verify batch sizes
        assert img1.shape[0] == 8, f"Expected batch size 8, got {img1.shape[0]}"
        assert img2.shape[0] == 2, f"Expected batch size 2, got {img2.shape[0]}"

    # Test full epoch
    print("\nTesting full epoch...")
    total_iterations = 0
    for _ in multi_dl:
        total_iterations += 1

    print(f"Total iterations in one epoch: {total_iterations}")
    assert total_iterations == len(multi_dl), \
        f"Expected {len(multi_dl)} iterations, got {total_iterations}"

    print("\nTest passed!")


if __name__ == "__main__":
    test_multi_dataloader()