Normalize only if needed (#26049)

* Normalize only if needed

* Update examples/pytorch/image-classification/run_image_classification.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* if else in one line

* within block

* one more place, sorry for mess

* import order

* Update examples/pytorch/image-classification/run_image_classification.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update examples/pytorch/image-classification/run_image_classification_no_trainer.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

examples/pytorch/image-classification/run_image_classification.py

@@ -28,6 +28,7 @@ from PIL import Image
 from torchvision.transforms import (
     CenterCrop,
     Compose,
+    Lambda,
     Normalize,
     RandomHorizontalFlip,
     RandomResizedCrop,
@@ -325,7 +326,11 @@ def main():
         size = image_processor.size["shortest_edge"]
     else:
         size = (image_processor.size["height"], image_processor.size["width"])
-    normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+    normalize = (
+        Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+        if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std")
+        else Lambda(lambda x: x)
+    )
     _train_transforms = Compose(
         [
             RandomResizedCrop(size),

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -32,6 +32,7 @@ from torch.utils.data import DataLoader
 from torchvision.transforms import (
     CenterCrop,
     Compose,
+    Lambda,
     Normalize,
     RandomHorizontalFlip,
     RandomResizedCrop,
@@ -331,7 +332,11 @@ def main():
         size = image_processor.size["shortest_edge"]
     else:
         size = (image_processor.size["height"], image_processor.size["width"])
-    normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+    normalize = (
+        Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+        if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std")
+        else Lambda(lambda x: x)
+    )
     train_transforms = Compose(
         [
             RandomResizedCrop(size),