Leave data_format as None in keras.ops.images and add data_format support to pad_images and crop_images (keras-team#19774)

* Fix `data_format`

* Add `data_format` to `pad_images` and `crop_images`

* Fix CI

Author: james77777778

keras/src/backend/jax/image.py

@@ -3,6 +3,7 @@
 import jax
 import jax.numpy as jnp
 
+from keras.src import backend
 from keras.src.backend.jax.core import convert_to_tensor
 
 RESIZE_INTERPOLATIONS = (
@@ -14,7 +15,8 @@
 )
 
 
-def rgb_to_grayscale(image, data_format="channels_last"):
+def rgb_to_grayscale(image, data_format=None):
+    data_format = backend.standardize_data_format(data_format)
     if data_format == "channels_first":
         if len(image.shape) == 4:
             image = jnp.transpose(image, (0, 2, 3, 1))
@@ -46,8 +48,9 @@ def resize(
     pad_to_aspect_ratio=False,
     fill_mode="constant",
     fill_value=0.0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     if interpolation not in RESIZE_INTERPOLATIONS:
         raise ValueError(
             "Invalid value for argument `interpolation`. Expected of one "
@@ -297,8 +300,9 @@ def affine_transform(
     interpolation="bilinear",
     fill_mode="constant",
     fill_value=0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     if interpolation not in AFFINE_TRANSFORM_INTERPOLATIONS.keys():
         raise ValueError(
             "Invalid value for argument `interpolation`. Expected of one "

keras/src/backend/numpy/image.py

@@ -1,6 +1,7 @@
 import jax
 import numpy as np
 
+from keras.src import backend
 from keras.src.backend.numpy.core import convert_to_tensor
 from keras.src.utils.module_utils import scipy
 
@@ -13,7 +14,8 @@
 )
 
 
-def rgb_to_grayscale(image, data_format="channels_last"):
+def rgb_to_grayscale(image, data_format=None):
+    data_format = backend.standardize_data_format(data_format)
     if data_format == "channels_first":
         if len(image.shape) == 4:
             image = np.transpose(image, (0, 2, 3, 1))
@@ -45,8 +47,9 @@ def resize(
     pad_to_aspect_ratio=False,
     fill_mode="constant",
     fill_value=0.0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     if interpolation not in RESIZE_INTERPOLATIONS:
         raise ValueError(
             "Invalid value for argument `interpolation`. Expected of one "
@@ -231,8 +234,9 @@ def affine_transform(
     interpolation="bilinear",
     fill_mode="constant",
     fill_value=0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     if interpolation not in AFFINE_TRANSFORM_INTERPOLATIONS.keys():
         raise ValueError(
             "Invalid value for argument `interpolation`. Expected of one "

keras/src/backend/tensorflow/image.py

@@ -4,6 +4,7 @@
 
 import tensorflow as tf
 
+from keras.src import backend
 from keras.src.backend.tensorflow.core import convert_to_tensor
 
 RESIZE_INTERPOLATIONS = (
@@ -15,7 +16,8 @@
 )
 
 
-def rgb_to_grayscale(image, data_format="channels_last"):
+def rgb_to_grayscale(image, data_format=None):
+    data_format = backend.standardize_data_format(data_format)
     if data_format == "channels_first":
         if len(image.shape) == 4:
             image = tf.transpose(image, (0, 2, 3, 1))
@@ -45,8 +47,9 @@ def resize(
     pad_to_aspect_ratio=False,
     fill_mode="constant",
     fill_value=0.0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     if interpolation not in RESIZE_INTERPOLATIONS:
         raise ValueError(
             "Invalid value for argument `interpolation`. Expected of one "
@@ -255,8 +258,9 @@ def affine_transform(
     interpolation="bilinear",
     fill_mode="constant",
     fill_value=0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     if interpolation not in AFFINE_TRANSFORM_INTERPOLATIONS:
         raise ValueError(
             "Invalid value for argument `interpolation`. Expected of one "

keras/src/backend/torch/image.py

@@ -4,6 +4,7 @@
 
 import torch
 
+from keras.src import backend
 from keras.src.backend.torch.core import convert_to_tensor
 
 RESIZE_INTERPOLATIONS = {}  # populated after torchvision import
@@ -14,7 +15,8 @@
 )
 
 
-def rgb_to_grayscale(image, data_format="channel_last"):
+def rgb_to_grayscale(image, data_format=None):
+    data_format = backend.standardize_data_format(data_format)
     try:
         import torchvision
     except:
@@ -55,8 +57,9 @@ def resize(
     pad_to_aspect_ratio=False,
     fill_mode="constant",
     fill_value=0.0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     try:
         import torchvision
         from torchvision.transforms import InterpolationMode as im
@@ -216,8 +219,9 @@ def affine_transform(
     interpolation="bilinear",
     fill_mode="constant",
     fill_value=0,
-    data_format="channels_last",
+    data_format=None,
 ):
+    data_format = backend.standardize_data_format(data_format)
     if interpolation not in AFFINE_TRANSFORM_INTERPOLATIONS.keys():
         raise ValueError(
             "Invalid value for argument `interpolation`. Expected of one "