Remove requirement that all images within a gRPC batch to be the same…

… dimensions -- shift responsibility to NimClient (#435)

src/nv_ingest/util/nim/cached.py

@@ -123,11 +123,6 @@ def chunk_list(lst: list, chunk_size: int) -> List[list]:
             if not batched_images:
                 raise ValueError("No valid images found for gRPC formatting.")
 
-            # Ensure all images have the same shape (excluding batch dimension)
-            shapes = [img.shape[1:] for img in batched_images]  # each is (H, W, C)
-            if any(s != shapes[0] for s in shapes[1:]):
-                raise ValueError(f"All images must have the same dimensions for gRPC batching. Found: {shapes}")
-
             # Chunk the images into groups of size up to max_batch_size
             batched_image_chunks = chunk_list(batched_images, max_batch_size)
             batched_inputs = []

src/nv_ingest/util/nim/deplot.py

@@ -115,11 +115,6 @@ def chunk_list(lst: list, chunk_size: int) -> List[list]:
             if not processed:
                 raise ValueError("No valid images found for gRPC formatting.")
 
-            # Ensure all images have the same dimensions (excluding the batch dimension)
-            shapes = [p.shape[1:] for p in processed]
-            if any(s != shapes[0] for s in shapes[1:]):
-                raise ValueError(f"All images must have the same dimensions for gRPC batching. Found: {shapes}")
-
             # Split processed images into chunks of size at most max_batch_size
             batched_inputs = []
             for chunk in chunk_list(processed, max_batch_size):

src/nv_ingest/util/nim/helpers.py

@@ -183,7 +183,7 @@ def _fetch_max_batch_size(self, model_name, model_version: str = "") -> int:
                 client = self.client if self.client else grpcclient.InferenceServerClient(url=self._grpc_endpoint)
                 model_config = client.get_model_config(model_name=model_name, model_version=model_version)
                 self._max_batch_sizes[model_name] = model_config.config.max_batch_size
-                logger.info(f"Max batch size for model '{model_name}': {self._max_batch_sizes[model_name]}")
+                logger.debug(f"Max batch size for model '{model_name}': {self._max_batch_sizes[model_name]}")
             except Exception as e:
                 self._max_batch_sizes[model_name] = 1
                 logger.warning(f"Failed to retrieve max batch size: {e}, defaulting to 1")

src/nv_ingest/util/nim/paddle.py

@@ -141,11 +141,6 @@ def chunk_list(lst, chunk_size):
                 arr = np.expand_dims(arr, axis=0)  # => shape (1, H, W, C)
                 processed.append(arr)
 
-            # Check that all images have the same shape (excluding batch dimension)
-            shapes = [p.shape[1:] for p in processed]  # List of (H, W, C) shapes
-            if not all(s == shapes[0] for s in shapes[1:]):
-                raise ValueError(f"All images must have the same dimensions for gRPC batching. Found: {shapes}")
-
             batches = []
             for chunk in chunk_list(processed, max_batch_size):
                 # Concatenate arrays in the chunk along the batch dimension => shape (B, H, W, C)