update input_image_shape -> image_shape

keras_nlp/src/models/csp_darknet/csp_darknet_backbone.py

@@ -38,7 +38,7 @@ class CSPDarkNetBackbone(Backbone):
             Use `"depthwise_block"` for depthwise conv block
             `"basic_block"` for basic conv block.
             Defaults to "basic_block".
-        input_image_shape: tuple. The input shape without the batch size.
+        image_shape: tuple. The input shape without the batch size.
             Defaults to `(None, None, 3)`.
 
     Examples:
@@ -67,7 +67,7 @@ def __init__(
         stackwise_depth,
         include_rescaling,
         block_type="basic_block",
-        input_image_shape=(224, 224, 3),
+        image_shape=(224, 224, 3),
         **kwargs,
     ):
         # === Functional Model ===
@@ -78,7 +78,7 @@ def __init__(
         )
         base_channels = stackwise_num_filters[0] // 2
 
-        image_input = layers.Input(shape=input_image_shape)
+        image_input = layers.Input(shape=image_shape)
         x = image_input
         if include_rescaling:
             x = layers.Rescaling(scale=1 / 255.0)(x)
@@ -119,7 +119,7 @@ def __init__(
         self.stackwise_depth = stackwise_depth
         self.include_rescaling = include_rescaling
         self.block_type = block_type
-        self.input_image_shape = input_image_shape
+        self.image_shape = image_shape
 
     def get_config(self):
         config = super().get_config()
@@ -129,7 +129,7 @@ def get_config(self):
                 "stackwise_depth": self.stackwise_depth,
                 "include_rescaling": self.include_rescaling,
                 "block_type": self.block_type,
-                "input_image_shape": self.input_image_shape,
+                "image_shape": self.image_shape,
             }
         )
         return config

keras_nlp/src/models/csp_darknet/csp_darknet_backbone_test.py

@@ -28,7 +28,7 @@ def setUp(self):
             "stackwise_depth": [1, 3, 3, 1],
             "include_rescaling": False,
             "block_type": "basic_block",
-            "input_image_shape": (224, 224, 3),
+            "image_shape": (224, 224, 3),
         }
         self.input_data = np.ones((2, 224, 224, 3), dtype="float32")
 

keras_nlp/src/models/csp_darknet/csp_darknet_image_classifier.py

@@ -78,7 +78,7 @@ class CSPDarkNetImageClassifier(ImageClassifier):
         stackwise_depth=[3, 9, 9, 3],
         include_rescaling=False,
         block_type="basic_block",
-        input_image_shape = (224, 224, 3),
+        image_shape = (224, 224, 3),
     )
     classifier = keras_nlp.models.CSPDarkNetImageClassifier(
         backbone=backbone,

keras_nlp/src/models/csp_darknet/csp_darknet_image_classifier_test.py

@@ -33,7 +33,7 @@ def setUp(self):
             stackwise_depth=[1, 3, 3, 1],
             include_rescaling=False,
             block_type="basic_block",
-            input_image_shape=(16, 16, 3),
+            image_shape=(16, 16, 3),
         )
         self.init_kwargs = {
             "backbone": self.backbone,

keras_nlp/src/models/densenet/densenet_backbone.py

@@ -35,7 +35,7 @@ class DenseNetBackbone(Backbone):
         include_rescaling: bool, whether to rescale the inputs. If set
             to `True`, inputs will be passed through a `Rescaling(1/255.0)`
             layer. Defaults to `True`.
-        input_image_shape: optional shape tuple, defaults to (224, 224, 3).
+        image_shape: optional shape tuple, defaults to (224, 224, 3).
         compression_ratio: float, compression rate at transition layers,
             defaults to 0.5.
         growth_rate: int, number of filters added by each dense block,
@@ -62,13 +62,13 @@ def __init__(
         self,
         stackwise_num_repeats,
         include_rescaling=True,
-        input_image_shape=(224, 224, 3),
+        image_shape=(224, 224, 3),
         compression_ratio=0.5,
         growth_rate=32,
         **kwargs,
     ):
         # === Functional Model ===
-        image_input = keras.layers.Input(shape=input_image_shape)
+        image_input = keras.layers.Input(shape=image_shape)
 
         x = image_input
         if include_rescaling:
@@ -116,7 +116,7 @@ def __init__(
         self.include_rescaling = include_rescaling
         self.compression_ratio = compression_ratio
         self.growth_rate = growth_rate
-        self.input_image_shape = input_image_shape
+        self.image_shape = image_shape
 
     def get_config(self):
         config = super().get_config()
@@ -126,7 +126,7 @@ def get_config(self):
                 "include_rescaling": self.include_rescaling,
                 "compression_ratio": self.compression_ratio,
                 "growth_rate": self.growth_rate,
-                "input_image_shape": self.input_image_shape,
+                "image_shape": self.image_shape,
             }
         )
         return config

keras_nlp/src/models/densenet/densenet_backbone_test.py

@@ -26,7 +26,7 @@ def setUp(self):
             "include_rescaling": True,
             "compression_ratio": 0.5,
             "growth_rate": 32,
-            "input_image_shape": (224, 224, 3),
+            "image_shape": (224, 224, 3),
         }
         self.input_data = np.ones((2, 224, 224, 3), dtype="float32")
 

keras_nlp/src/models/densenet/densenet_image_classifier.py

@@ -76,7 +76,7 @@ class DenseNetImageClassifier(ImageClassifier):
         stackwise_depth=[3, 9, 9, 3],
         include_rescaling=False,
         block_type="basic_block",
-        input_image_shape = (224, 224, 3),
+        image_shape = (224, 224, 3),
     )
     classifier = keras_nlp.models.DenseNetImageClassifier(
         backbone=backbone,

keras_nlp/src/models/densenet/densenet_image_classifier_test.py

@@ -31,7 +31,7 @@ def setUp(self):
             include_rescaling=True,
             compression_ratio=0.5,
             growth_rate=32,
-            input_image_shape=(224, 224, 3),
+            image_shape=(224, 224, 3),
         )
         self.init_kwargs = {
             "backbone": self.backbone,

keras_nlp/src/models/resnet/resnet_backbone.py

@@ -54,7 +54,7 @@ class ResNetBackbone(FeaturePyramidBackbone):
         include_rescaling: boolean. If `True`, rescale the input using
             `Rescaling` and `Normalization` layers. If `False`, do nothing.
             Defaults to `True`.
-        input_image_shape: tuple. The input shape without the batch size.
+        image_shape: tuple. The input shape without the batch size.
             Defaults to `(None, None, 3)`.
         pooling: `None` or str. Pooling mode for feature extraction. Defaults
             to `"avg"`.
@@ -107,7 +107,7 @@ def __init__(
         block_type,
         use_pre_activation=False,
         include_rescaling=True,
-        input_image_shape=(None, None, 3),
+        image_shape=(None, None, 3),
         pooling="avg",
         data_format=None,
         dtype=None,
@@ -139,7 +139,7 @@ def __init__(
         num_stacks = len(stackwise_num_filters)
 
         # === Functional Model ===
-        image_input = layers.Input(shape=input_image_shape)
+        image_input = layers.Input(shape=image_shape)
         if include_rescaling:
             x = layers.Rescaling(scale=1 / 255.0, dtype=dtype)(image_input)
             x = layers.Normalization(
@@ -254,7 +254,7 @@ def __init__(
         self.block_type = block_type
         self.use_pre_activation = use_pre_activation
         self.include_rescaling = include_rescaling
-        self.input_image_shape = input_image_shape
+        self.image_shape = image_shape
         self.pooling = pooling
         self.pyramid_outputs = pyramid_outputs
 
@@ -268,7 +268,7 @@ def get_config(self):
                 "block_type": self.block_type,
                 "use_pre_activation": self.use_pre_activation,
                 "include_rescaling": self.include_rescaling,
-                "input_image_shape": self.input_image_shape,
+                "image_shape": self.image_shape,
                 "pooling": self.pooling,
             }
         )

keras_nlp/src/models/resnet/resnet_backbone_test.py

@@ -27,7 +27,7 @@ def setUp(self):
             "stackwise_num_filters": [64, 64, 64],
             "stackwise_num_blocks": [2, 2, 2],
             "stackwise_num_strides": [1, 2, 2],
-            "input_image_shape": (None, None, 3),
+            "image_shape": (None, None, 3),
             "pooling": "avg",
         }
         self.input_size = 64
@@ -84,7 +84,7 @@ def test_saved_model(self, use_pre_activation, block_type):
             {
                 "block_type": block_type,
                 "use_pre_activation": use_pre_activation,
-                "input_image_shape": (None, None, 3),
+                "image_shape": (None, None, 3),
             }
         )
         self.run_model_saving_test(

keras_nlp/src/models/resnet/resnet_image_classifier_test.py

@@ -31,7 +31,7 @@ def setUp(self):
             stackwise_num_strides=[1, 2, 2],
             block_type="basic_block",
             use_pre_activation=True,
-            input_image_shape=(16, 16, 3),
+            image_shape=(16, 16, 3),
             include_rescaling=False,
             pooling="avg",
         )

keras_nlp/src/models/vgg/vgg_backbone.py

@@ -20,8 +20,7 @@
 
 @keras_nlp_export("keras_nlp.models.VGGBackbone")
 class VGGBackbone(Backbone):
-    """
-    This class represents Keras Backbone of VGG model.
+    """This class represents Keras Backbone of VGG model.
 
     This class implements a VGG backbone as described in [Very Deep
     Convolutional Networks for Large-Scale Image Recognition](
@@ -36,7 +35,7 @@ class VGGBackbone(Backbone):
             64, 128, 256, 512, 512].
       include_rescaling: bool, whether to rescale the inputs. If set to
         True, inputs will be passed through a `Rescaling(1/255.0)` layer.
-      input_shape: tuple, optional shape tuple, defaults to (224, 224, 3).
+      image_shape: tuple, optional shape tuple, defaults to (224, 224, 3).
       pooling: bool, Optional pooling mode for feature extraction
         when `include_top` is `False`.
         - `None` means that the output of the model will be
@@ -61,7 +60,7 @@ class VGGBackbone(Backbone):
     model = keras_nlp.models.VGGBackbone(
         stackwise_num_repeats = [2, 2, 3, 3, 3],
         stackwise_num_filters = [64, 128, 256, 512, 512],
-        input_shape = (224, 224, 3),
+        image_shape = (224, 224, 3),
         include_rescaling = False,
         pooling = "avg",
     )
@@ -74,13 +73,13 @@ def __init__(
         stackwise_num_repeats,
         stackwise_num_filters,
         include_rescaling,
-        input_image_shape=(224, 224, 3),
+        image_shape=(224, 224, 3),
         pooling="avg",
         **kwargs,
     ):
 
         # === Functional Model ===
-        img_input = keras.layers.Input(shape=input_image_shape)
+        img_input = keras.layers.Input(shape=image_shape)
         x = img_input
 
         if include_rescaling:
@@ -107,15 +106,15 @@ def __init__(
         self.stackwise_num_repeats = stackwise_num_repeats
         self.stackwise_num_filters = stackwise_num_filters
         self.include_rescaling = include_rescaling
-        self.input_image_shape = input_image_shape
+        self.image_shape = image_shape
         self.pooling = pooling
 
     def get_config(self):
         return {
             "stackwise_num_repeats": self.stackwise_num_repeats,
             "stackwise_num_filters": self.stackwise_num_filters,
             "include_rescaling": self.include_rescaling,
-            "input_image_shape": self.input_image_shape,
+            "image_shape": self.image_shape,
             "pooling": self.pooling,
         }
 

keras_nlp/src/models/vgg/vgg_backbone_test.py

@@ -24,7 +24,7 @@ def setUp(self):
         self.init_kwargs = {
             "stackwise_num_repeats": [2, 3, 3],
             "stackwise_num_filters": [8, 64, 64],
-            "input_image_shape": (16, 16, 3),
+            "image_shape": (16, 16, 3),
             "include_rescaling": False,
             "pooling": "avg",
         }

keras_nlp/src/models/vgg/vgg_image_classifier.py

@@ -65,7 +65,7 @@ class VGGImageClassifier(ImageClassifier):
     backbone = keras_nlp.models.VGGBackbone(
         stackwise_num_repeats = [2, 2, 3, 3, 3],
         stackwise_num_filters = [64, 128, 256, 512, 512],
-        input_shape = (224, 224, 3),
+        image_shape = (224, 224, 3),
         include_rescaling = False,
         pooling = "avg",
     )

keras_nlp/src/models/vgg/vgg_image_classifier_test.py

@@ -27,7 +27,7 @@ def setUp(self):
         self.backbone = VGGBackbone(
             stackwise_num_repeats=[2, 4, 4],
             stackwise_num_filters=[2, 16, 16],
-            input_image_shape=(4, 4, 3),
+            image_shape=(4, 4, 3),
             include_rescaling=False,
             pooling="max",
         )

keras_nlp/src/tests/test_case.py

@@ -501,10 +501,10 @@ def run_vision_backbone_test(
                 input_data = ops.transpose(input_data, axes=(2, 0, 1))
             elif len(input_data_shape) == 4:
                 input_data = ops.transpose(input_data, axes=(0, 3, 1, 2))
-            if "input_image_shape" in init_kwargs:
+            if "image_shape" in init_kwargs:
                 init_kwargs = init_kwargs.copy()
-                init_kwargs["input_image_shape"] = tuple(
-                    reversed(init_kwargs["input_image_shape"])
+                init_kwargs["image_shape"] = tuple(
+                    reversed(init_kwargs["image_shape"])
                 )
             self.run_backbone_test(
                 cls=cls,