Clean up code

resnext.py

@@ -23,7 +23,6 @@
 from keras.applications.imagenet_utils import _obtain_input_shape
 import keras.backend as K
 
-
 CIFAR_TH_WEIGHTS_PATH = ''
 CIFAR_TF_WEIGHTS_PATH = ''
 CIFAR_TH_WEIGHTS_PATH_NO_TOP = ''
@@ -235,7 +234,7 @@ def ResNextImageNet(input_shape=None, depth=[3, 4, 6, 3], cardinality=32, width=
                                       default_size=224,
                                       min_size=112,
                                       data_format=K.image_data_format(),
-                                      include_top=include_top)
+                                      require_flatten=include_top)
 
     if input_tensor is None:
         img_input = Input(shape=input_shape)
@@ -319,7 +318,7 @@ def __initial_conv_block(input, weight_decay=5e-4):
     return x
 
 
-def __initial_conv_block_inception(input, weight_decay=5e-4):
+def __initial_conv_block_imagenet(input, weight_decay=5e-4):
     ''' Adds an initial conv block, with batch norm and relu for the inception resnext
     Args:
         input: input tensor
@@ -363,8 +362,8 @@ def __grouped_convolution_block(input, grouped_channels, cardinality, strides, w
 
     for c in range(cardinality):
         x = Lambda(lambda z: z[:, :, :, c * grouped_channels:(c + 1) * grouped_channels]
-                   if K.image_data_format() == 'channels_last' else
-                   lambda z: z[:, c * grouped_channels:(c + 1) * grouped_channels, :, :])(input)
+        if K.image_data_format() == 'channels_last' else
+        lambda z: z[:, c * grouped_channels:(c + 1) * grouped_channels, :, :])(input)
 
         x = Conv2D(grouped_channels, (3, 3), padding='same', use_bias=False, strides=(strides, strides),
                    kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(x)
@@ -537,7 +536,7 @@ def __create_res_next_imagenet(nb_classes, img_input, include_top, depth, cardin
         filters_list.append(filters)
         filters *= 2  # double the size of the filters
 
-    x = __initial_conv_block_inception(img_input, weight_decay)
+    x = __initial_conv_block_imagenet(img_input, weight_decay)
 
     # block 1 (no pooling)
     for i in range(N[0]):
@@ -567,3 +566,8 @@ def __create_res_next_imagenet(nb_classes, img_input, include_top, depth, cardin
             x = GlobalMaxPooling2D()(x)
 
     return x
+
+
+if __name__ == '__main__':
+    model = ResNext((32, 32, 3), depth=29, cardinality=8, width=64)
+    model.summary()