""" 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()