Update mobilenetv2_cifar10.py

llvm/projects/keras/src/mobilenetv2_cifar10.py

@@ -44,9 +44,9 @@ def _make_divisible(v, divisor, min_value=None):
     return new_v
 
 def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
-    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+    channel_axis = 1
     
-    in_channels = inputs.shape[1] if K.image_data_format() == 'channels_first' else inputs.shape[-1]
+    in_channels = inputs.shape[1]
     pointwise_conv_filters = int(filters * alpha)
     pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
     x = inputs
@@ -57,9 +57,9 @@ def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
         x = Activation('relu')(x)
 
     if stride == 2:
-        x = ZeroPadding2D(padding=((0, 1), (0, 1)))(x)
+        x = ZeroPadding2D(padding=(1, 1))(x)
     else:
-        x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
+        x = ZeroPadding2D(padding=(1, 1))(x)
         
     x = DepthwiseConv2D(kernel_size=3, strides=stride, use_bias=False, padding='valid')(x)
     x = BatchNormalization(axis=channel_axis)(x)
@@ -75,12 +75,12 @@ def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
 
 def get_mobilenetv2(alpha=1.0, depth_multiplier=1):
 
-    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+    channel_axis = 1
     
     first_block_filters = _make_divisible(32 * alpha, 8)
     img_input = Input(shape=(3, 32, 32))
 
-    x = ZeroPadding2D(padding=((0, 2), (0, 2)))(img_input)
+    x = ZeroPadding2D(padding=(1, 1))(img_input)
     x = Conv2D(first_block_filters, kernel_size=3, strides=1, padding='valid', use_bias=False)(x)
     #x = BatchNormalization(axis=channel_axis)(x)
     #x = Activation('relu')(x)