Add --max-size flag for image resizing

- Preserves aspect ratio using PIL thumbnail
- Significantly improves performance for high-res images
- Optional flag - no resizing by default

vlm-classify.py

@@ -63,8 +63,14 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
-def image_to_data_uri(image: Union[Image.Image, Dict[str, Any]]) -> str:
-    """Convert image to base64 data URI for VLM processing."""
+def image_to_data_uri(image: Union[Image.Image, Dict[str, Any]], max_size: Optional[int] = None) -> str:
+    """Convert image to base64 data URI for VLM processing.
+    
+    Args:
+        image: PIL Image or dict with image bytes
+        max_size: Optional maximum dimension (width or height) to resize to.
+                 Preserves aspect ratio using thumbnail method.
+    """
     if isinstance(image, Image.Image):
         pil_img = image
     elif isinstance(image, dict) and "bytes" in image:
@@ -72,6 +78,12 @@ def image_to_data_uri(image: Union[Image.Image, Dict[str, Any]]) -> str:
     else:
         raise ValueError(f"Unsupported image type: {type(image)}")
     
+    # Resize if max_size is specified and image exceeds it
+    if max_size and (pil_img.width > max_size or pil_img.height > max_size):
+        # Use thumbnail to preserve aspect ratio
+        pil_img = pil_img.copy()  # Don't modify original
+        pil_img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
+    
     # Convert to RGB if necessary (handle RGBA, grayscale, etc.)
     if pil_img.mode not in ("RGB", "L"):
         pil_img = pil_img.convert("RGB")
@@ -86,9 +98,10 @@ def image_to_data_uri(image: Union[Image.Image, Dict[str, Any]]) -> str:
 def create_classification_messages(
     image: Union[Image.Image, Dict[str, Any]],
     prompt: str,
+    max_size: Optional[int] = None,
 ) -> List[Dict]:
     """Create chat messages for VLM classification."""
-    image_uri = image_to_data_uri(image)
+    image_uri = image_to_data_uri(image, max_size=max_size)
     
     return [
         {
@@ -110,6 +123,7 @@ def main(
     model: str = "Qwen/Qwen2-VL-7B-Instruct",
     batch_size: int = 8,
     max_samples: Optional[int] = None,
+    max_size: Optional[int] = None,
     gpu_memory_utilization: float = 0.9,
     max_model_len: Optional[int] = None,
     tensor_parallel_size: Optional[int] = None,
@@ -153,6 +167,10 @@ def main(
         dataset = dataset.select(range(min(max_samples, len(dataset))))
         logger.info(f"Limited to {len(dataset)} samples")
     
+    # Log resizing configuration
+    if max_size:
+        logger.info(f"Image resizing enabled: max dimension = {max_size}px")
+    
     # Auto-detect tensor parallel size if not specified
     if tensor_parallel_size is None:
         tensor_parallel_size = torch.cuda.device_count()
@@ -212,7 +230,7 @@ def main(
         try:
             # Create messages for just this batch
             batch_messages = [
-                create_classification_messages(img, prompt) 
+                create_classification_messages(img, prompt, max_size=max_size) 
                 for img in batch_images
             ]
             
@@ -332,6 +350,12 @@ Examples:
         default=None,
         help="Maximum number of samples to process (for testing)",
     )
+    parser.add_argument(
+        "--max-size",
+        type=int,
+        default=None,
+        help="Maximum image dimension in pixels. Images larger than this will be resized while preserving aspect ratio (e.g., 768, 1024)",
+    )
     parser.add_argument(
         "--gpu-memory-utilization",
         type=float,
@@ -382,6 +406,7 @@ hf jobs uv run \\
     davanstrien/sloane-index-cards \\
     username/classified-cards \\
     --classes "index-card,manuscript,title-page,other" \\
+    --max-size 768 \\
     --max-samples 100
         """)
         sys.exit(0)
@@ -395,6 +420,7 @@ hf jobs uv run \\
         model=args.model,
         batch_size=args.batch_size,
         max_samples=args.max_samples,
+        max_size=args.max_size,
         gpu_memory_utilization=args.gpu_memory_utilization,
         max_model_len=args.max_model_len,
         tensor_parallel_size=args.tensor_parallel_size,