Unify preprocess_input implementation in applications

keras/applications/imagenet_utils.py

@@ -8,12 +8,19 @@
 CLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json'
 
 
-def preprocess_input(x, data_format=None):
+def preprocess_input(x, data_format=None, mode='caffe'):
     """Preprocesses a tensor encoding a batch of images.
 
     # Arguments
         x: input Numpy tensor, 4D.
         data_format: data format of the image tensor.
+        mode: One of "caffe", "tf".
+            - caffe: will convert the images from RGB to BGR,
+                then will zero-center each color channel with
+                respect to the ImageNet dataset,
+                without scaling.
+            - tf: will scale pixels between -1 and 1,
+                sample-wise.
 
     # Returns
         Preprocessed tensor.
@@ -22,6 +29,12 @@ def preprocess_input(x, data_format=None):
         data_format = K.image_data_format()
     assert data_format in {'channels_last', 'channels_first'}
 
+    if mode == 'tf':
+        x /= 255.
+        x -= 0.5
+        x *= 2.
+        return x
+
     if data_format == 'channels_first':
         if x.ndim == 3:
             # 'RGB'->'BGR'

keras/applications/inception_resnet_v2.py

@@ -32,8 +32,9 @@
 from ..layers import MaxPooling2D
 from ..utils.data_utils import get_file
 from ..engine.topology import get_source_inputs
-from ..applications.imagenet_utils import _obtain_input_shape
-from ..applications.imagenet_utils import decode_predictions
+from . import imagenet_utils
+from .imagenet_utils import _obtain_input_shape
+from .imagenet_utils import decode_predictions
 from .. import backend as K
 
 
@@ -43,20 +44,13 @@
 def preprocess_input(x):
     """Preprocesses a numpy array encoding a batch of images.
 
-    This function applies the "Inception" preprocessing which converts
-    the RGB values from [0, 255] to [-1, 1]. Note that this preprocessing
-    function is different from `imagenet_utils.preprocess_input()`.
-
     # Arguments
         x: a 4D numpy array consists of RGB values within [0, 255].
 
     # Returns
         Preprocessed array.
     """
-    x /= 255.
-    x -= 0.5
-    x *= 2.
-    return x
+    return imagenet_utils.preprocess_input(x, mode='tf')
 
 
 def conv2d_bn(x,

keras/applications/inception_v3.py

@@ -29,6 +29,7 @@
 from ..engine.topology import get_source_inputs
 from ..utils.data_utils import get_file
 from .. import backend as K
+from . import imagenet_utils
 from .imagenet_utils import decode_predictions
 from .imagenet_utils import _obtain_input_shape
 
@@ -388,7 +389,12 @@ def InceptionV3(include_top=True,
 
 
 def preprocess_input(x):
-    x /= 255.
-    x -= 0.5
-    x *= 2.
-    return x
+    """Preprocesses a numpy array encoding a batch of images.
+
+    # Arguments
+        x: a 4D numpy array consists of RGB values within [0, 255].
+
+    # Returns
+        Preprocessed array.
+    """
+    return imagenet_utils.preprocess_input(x, mode='tf')

keras/applications/mobilenet.py

@@ -71,8 +71,9 @@
 from ..utils.data_utils import get_file
 from ..engine.topology import get_source_inputs
 from ..engine import InputSpec
-from ..applications.imagenet_utils import _obtain_input_shape
-from ..applications.imagenet_utils import decode_predictions
+from . import imagenet_utils
+from .imagenet_utils import _obtain_input_shape
+from .imagenet_utils import decode_predictions
 from .. import backend as K
 
 
@@ -84,10 +85,15 @@ def relu6(x):
 
 
 def preprocess_input(x):
-    x /= 255.
-    x -= 0.5
-    x *= 2.
-    return x
+    """Preprocesses a numpy array encoding a batch of images.
+
+    # Arguments
+        x: a 4D numpy array consists of RGB values within [0, 255].
+
+    # Returns
+        Preprocessed array.
+    """
+    return imagenet_utils.preprocess_input(x, mode='tf')
 
 
 class DepthwiseConv2D(Conv2D):

keras/applications/xception.py

@@ -36,6 +36,7 @@
 from ..engine.topology import get_source_inputs
 from ..utils.data_utils import get_file
 from .. import backend as K
+from . import imagenet_utils
 from .imagenet_utils import decode_predictions
 from .imagenet_utils import _obtain_input_shape
 
@@ -261,7 +262,12 @@ def Xception(include_top=True, weights='imagenet',
 
 
 def preprocess_input(x):
-    x /= 255.
-    x -= 0.5
-    x *= 2.
-    return x
+    """Preprocesses a numpy array encoding a batch of images.
+
+    # Arguments
+        x: a 4D numpy array consists of RGB values within [0, 255].
+
+    # Returns
+        Preprocessed array.
+    """
+    return imagenet_utils.preprocess_input(x, mode='tf')