Fix different predictions over all the backends

Author: taehoonlee

keras/applications/xception.py

@@ -32,8 +32,10 @@
 from ..layers import MaxPooling2D
 from ..layers import GlobalAveragePooling2D
 from ..layers import GlobalMaxPooling2D
+from ..layers import ZeroPadding2D
 from ..engine.topology import get_source_inputs
 from ..utils.data_utils import get_file
+from ..utils import layer_utils
 from .. import backend as K
 from . import imagenet_utils
 from .imagenet_utils import decode_predictions
@@ -136,7 +138,8 @@ def Xception(include_top=True, weights='imagenet',
         else:
             img_input = input_tensor
 
-    x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input)
+    x = ZeroPadding2D(padding=(1, 1))(img_input)
+    x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(x)
     x = BatchNormalization(name='block1_conv1_bn')(x)
     x = Activation('relu', name='block1_conv1_act')(x)
     x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
@@ -153,7 +156,8 @@ def Xception(include_top=True, weights='imagenet',
     x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
     x = BatchNormalization(name='block2_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x)
+    x = ZeroPadding2D(padding=(1, 1))(x)
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='valid', name='block2_pool')(x)
     x = layers.add([x, residual])
 
     residual = Conv2D(256, (1, 1), strides=(2, 2),
@@ -167,7 +171,8 @@ def Xception(include_top=True, weights='imagenet',
     x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x)
     x = BatchNormalization(name='block3_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x)
+    x = ZeroPadding2D(padding=(1, 1))(x)
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='valid', name='block3_pool')(x)
     x = layers.add([x, residual])
 
     residual = Conv2D(728, (1, 1), strides=(2, 2),
@@ -181,7 +186,8 @@ def Xception(include_top=True, weights='imagenet',
     x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x)
     x = BatchNormalization(name='block4_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x)
+    x = ZeroPadding2D(padding=(1, 1))(x)
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='valid', name='block4_pool')(x)
     x = layers.add([x, residual])
 
     for i in range(8):
@@ -211,7 +217,8 @@ def Xception(include_top=True, weights='imagenet',
     x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x)
     x = BatchNormalization(name='block13_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x)
+    x = ZeroPadding2D(padding=(1, 1))(x)
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='valid', name='block13_pool')(x)
     x = layers.add([x, residual])
 
     x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
@@ -256,6 +263,9 @@ def Xception(include_top=True, weights='imagenet',
     elif weights is not None:
         model.load_weights(weights)
 
+    if K.backend() == 'theano':
+        layer_utils.convert_all_kernels_in_model(model)
+
     if old_data_format:
         K.set_image_data_format(old_data_format)
     return model