Cleanup collator

olmocr/train/train.py

@@ -4,6 +4,7 @@ Simple script to test OlmOCR dataset loading with YAML configuration.
 
 import argparse
 import logging
+import numpy as np
 
 from transformers import (
     AutoProcessor,
@@ -28,9 +29,10 @@ logging.basicConfig(
 logger = logging.getLogger(__name__)
 
 
-def create_data_collator():
-    """Create a data collator for vision-language models."""
-    def collate_fn(examples):
+class QwenDataCollator:
+    """Data collator for vision-language models that handles numpy arrays."""
+    
+    def __call__(self, examples):
         # Filter out None values and extract the fields we need
         batch = {
             'input_ids': [],
@@ -42,11 +44,22 @@ def create_data_collator():
         
         for example in examples:
             if example is not None:
-                batch['input_ids'].append(example['input_ids'])
-                batch['attention_mask'].append(example['attention_mask'])
-                batch['labels'].append(example['labels'])
-                batch['pixel_values'].append(example['pixel_values'])
-                batch['image_grid_thw'].append(example['image_grid_thw'])
+                # Convert numpy arrays to tensors
+                batch['input_ids'].append(torch.from_numpy(example['input_ids']) if isinstance(example['input_ids'], np.ndarray) else example['input_ids'])
+                batch['attention_mask'].append(torch.from_numpy(example['attention_mask']) if isinstance(example['attention_mask'], np.ndarray) else example['attention_mask'])
+                batch['labels'].append(torch.from_numpy(example['labels']) if isinstance(example['labels'], np.ndarray) else example['labels'])
+                
+                # Handle pixel_values which might be numpy array or already a tensor
+                pixel_values = example['pixel_values']
+                if isinstance(pixel_values, np.ndarray):
+                    pixel_values = torch.from_numpy(pixel_values)
+                batch['pixel_values'].append(pixel_values)
+                
+                # Handle image_grid_thw
+                image_grid_thw = example['image_grid_thw']
+                if isinstance(image_grid_thw, np.ndarray):
+                    image_grid_thw = torch.from_numpy(image_grid_thw)
+                batch['image_grid_thw'].append(image_grid_thw)
         
         # Convert lists to tensors with proper padding
         # Note: For Qwen2-VL, we typically handle variable length sequences
@@ -59,8 +72,6 @@ def create_data_collator():
             'image_grid_thw': torch.stack(batch['image_grid_thw'])
         }
 
-    return collate_fn
-
 
 def main():
     parser = argparse.ArgumentParser(description="Train OlmOCR model")
@@ -215,7 +226,7 @@ def main():
         args=training_args,
         train_dataset=train_dataset,
         eval_dataset=eval_datasets,
-        data_collator=create_data_collator(),
+        data_collator=QwenDataCollator(),
         callbacks=callbacks,
     )