Apply python coding style to res (#5816)

Apply python coding style to python scripts in res/

Signed-off-by: Hyeongseok Oh <hseok82.oh@samsung.com>

diff --git a/infra/command/format b/infra/command/format
index cce295a..e4ee9d2 100644 (file)
--- a/infra/command/format
+++ b/infra/command/format
@@ -88,7 +88,7 @@ check_python_files() {
     DIRECTORIES_NOT_TO_BE_TESTED=$1
 
     # Check python files
-    PYTHON_FILES_TO_CHECK=$(git ls-files '*.py' ':!:compiler/*' ':!:res/*')
+    PYTHON_FILES_TO_CHECK=$(git ls-files '*.py' ':!:compiler/*')
     ARR=($PYTHON_FILES_TO_CHECK)
     for s in ${DIRECTORIES_NOT_TO_BE_TESTED[@]}; do
         skip=${s#'.'/}/

diff --git a/res/TensorFlowTests/TF_SMALL_NET_0003/test.py b/res/TensorFlowTests/TF_SMALL_NET_0003/test.py
index 8a8e9c8..4a194fb 100644 (file)
--- a/res/TensorFlowTests/TF_SMALL_NET_0003/test.py
+++ b/res/TensorFlowTests/TF_SMALL_NET_0003/test.py
@@ -1,7 +1,7 @@
 # TF_SMALL_NET_0003/test.pbtxt is create with below script
 
 # Version info
-#  - Tensorflow : 1.13.1 
+#  - Tensorflow : 1.13.1
 #  - Python : 3.5.2
 
 import tensorflow as tf
@@ -9,6 +9,7 @@ import tensorflow as tf
 input = tf.placeholder(tf.float32, [1, 3, 3, 5])
 filter = tf.constant(1.0, shape=[2, 2, 5, 1])
 conv = tf.nn.conv2d(input, filter=filter, strides=[1, 1, 1, 1], padding='SAME')
-fbn = tf.nn.fused_batch_norm(conv, scale=[1.0], offset=[0.0], mean=[0.0], variance=[1.0], is_training=False)
+fbn = tf.nn.fused_batch_norm(
+    conv, scale=[1.0], offset=[0.0], mean=[0.0], variance=[1.0], is_training=False)
 
 print(tf.get_default_graph().as_graph_def())

diff --git a/res/TensorFlowTests/TF_SMALL_NET_0004/test.py b/res/TensorFlowTests/TF_SMALL_NET_0004/test.py
index 00ffb2b..4fa924a 100644 (file)
--- a/res/TensorFlowTests/TF_SMALL_NET_0004/test.py
+++ b/res/TensorFlowTests/TF_SMALL_NET_0004/test.py
@@ -1,15 +1,16 @@
 # TF_SMALL_NET_0004/test.pbtxt is create with below script
 
 # Version info
-#  - Tensorflow : 1.13.1 
+#  - Tensorflow : 1.13.1
 #  - Python : 3.5.2
 
 import tensorflow as tf
 
 input = tf.placeholder(tf.float32, [1, 3, 3, 5])
 filter = tf.constant(1.0, shape=[2, 2, 5, 2])
-dconv = tf.nn.depthwise_conv2d(input, filter, [1,1,1,1], 'SAME')
-const = tf.constant(2.0,shape=[10])
-fbn = tf.nn.fused_batch_norm(x=dconv,scale=const,offset=const,mean=const,variance=const,is_training=False)
+dconv = tf.nn.depthwise_conv2d(input, filter, [1, 1, 1, 1], 'SAME')
+const = tf.constant(2.0, shape=[10])
+fbn = tf.nn.fused_batch_norm(
+    x=dconv, scale=const, offset=const, mean=const, variance=const, is_training=False)
 
 print(tf.get_default_graph().as_graph_def())