⚡️ Speed up method DPTImageProcessor._preprocess_segmentation_map by 12%

src/transformers/image_utils.py

@@ -14,34 +14,23 @@
 
 import base64
 import os
+from collections import deque
 from collections.abc import Iterable
 from dataclasses import dataclass
 from io import BytesIO
 from typing import Optional, Union
 
 import httpx
 import numpy as np
+import PIL.Image
 
-from .utils import (
-    ExplicitEnum,
-    is_numpy_array,
-    is_torch_available,
-    is_torch_tensor,
-    is_torchvision_available,
-    is_vision_available,
-    logging,
-    requires_backends,
-    to_numpy,
-)
-from .utils.constants import (  # noqa: F401
-    IMAGENET_DEFAULT_MEAN,
-    IMAGENET_DEFAULT_STD,
-    IMAGENET_STANDARD_MEAN,
-    IMAGENET_STANDARD_STD,
-    OPENAI_CLIP_MEAN,
-    OPENAI_CLIP_STD,
-)
-
+from .utils import (ExplicitEnum, is_numpy_array, is_torch_available,
+                    is_torch_tensor, is_torchvision_available,
+                    is_vision_available, logging, requires_backends, to_numpy)
+from .utils.constants import (IMAGENET_DEFAULT_MEAN,  # noqa: F401
+                              IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN,
+                              IMAGENET_STANDARD_STD, OPENAI_CLIP_MEAN,
+                              OPENAI_CLIP_STD)
 
 if is_vision_available():
     import PIL.Image
@@ -132,14 +121,14 @@ def concatenate_list(input_list):
 
 
 def valid_images(imgs):
-    # If we have an list of images, make sure every image is valid
-    if isinstance(imgs, (list, tuple)):
-        for img in imgs:
-            if not valid_images(img):
-                return False
-    # If not a list of tuple, we have been given a single image or batched tensor of images
-    elif not is_valid_image(imgs):
-        return False
+    # Iteratively validate images/batches/lists for improved performance (no recursion)
+    queue = deque([imgs])
+    while queue:
+        img = queue.pop()
+        if isinstance(img, (list, tuple)):
+            queue.extend(img)
+        elif not is_valid_image(img):
+            return False
     return True
 
 
@@ -213,6 +202,11 @@ def make_flat_list_of_images(
     Returns:
         list: A list of images or a 4d array of images.
     """
+    # If the input is a nested list of images, we flatten it
+    # Fast path for None or empty input
+    if not images or (isinstance(images, (list, tuple)) and len(images) == 0):
+        raise ValueError(f"Could not make a flat list of images from {images}")
+
     # If the input is a nested list of images, we flatten it
     if (
         isinstance(images, (list, tuple))
@@ -222,15 +216,16 @@ def make_flat_list_of_images(
         return [img for img_list in images for img in img_list]
 
     if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
-        if is_pil_image(images[0]) or images[0].ndim == expected_ndims:
+        first_img = images[0]
+        if is_pil_image(first_img) or getattr(first_img, "ndim", None) == expected_ndims:
             return images
-        if images[0].ndim == expected_ndims + 1:
+        if getattr(first_img, "ndim", None) == expected_ndims + 1:
             return [img for img_list in images for img in img_list]
 
     if is_valid_image(images):
-        if is_pil_image(images) or images.ndim == expected_ndims:
+        if is_pil_image(images) or getattr(images, "ndim", None) == expected_ndims:
             return [images]
-        if images.ndim == expected_ndims + 1:
+        if getattr(images, "ndim", None) == expected_ndims + 1:
             return list(images)
 
     raise ValueError(f"Could not make a flat list of images from {images}")
@@ -276,6 +271,8 @@ def make_nested_list_of_images(
 
 
 def to_numpy_array(img) -> np.ndarray:
+    if isinstance(img, np.ndarray) and is_valid_image(img):
+        return img
     if not is_valid_image(img):
         raise ValueError(f"Invalid image type: {type(img)}")